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https://github.com/huggingface/datasets/issues/4291
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message
Hi @leondz, thanks for reporting. Indeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions. In particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`. Let me know if you need some help: I could push a commit to your repo with the fix.
### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes
103
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message ### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes Hi @leondz, thanks for reporting. Indeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions. In particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`. Let me know if you need some help: I could push a commit to your repo with the fix.
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https://github.com/huggingface/datasets/issues/4291
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message
Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)
### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes
37
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message ### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)
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https://github.com/huggingface/datasets/issues/4287
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None
So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above. As it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered. So on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU. I'll add the code in a PR linked to this issue in case you want to merge it!
## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
102
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None ## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above. As it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered. So on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU. I'll add the code in a PR linked to this issue in case you want to merge it!
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https://github.com/huggingface/datasets/issues/4287
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None
Adding here the complete error traceback! ``` Traceback (most recent call last): File "/home/alvarobartt/lol.py", line 12, in <module> ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3656, in add_faiss_index super().add_faiss_index( File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 478, in add_faiss_index faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 281, in add_vectors self.faiss_index = self._faiss_index_to_device(index, self.device) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 327, in _faiss_index_to_device faiss_res = faiss.StandardGpuResources() NameError: name 'faiss' is not defined ```
## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
66
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None ## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 Adding here the complete error traceback! ``` Traceback (most recent call last): File "/home/alvarobartt/lol.py", line 12, in <module> ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3656, in add_faiss_index super().add_faiss_index( File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 478, in add_faiss_index faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 281, in add_vectors self.faiss_index = self._faiss_index_to_device(index, self.device) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 327, in _faiss_index_to_device faiss_res = faiss.StandardGpuResources() NameError: name 'faiss' is not defined ```
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
Thanks for reporting, @vblagoje. Indeed, I noticed some of these issues while reviewing this PR: - #4259 This is in my TODO list.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 Thanks for reporting, @vblagoje. Indeed, I noticed some of these issues while reviewing this PR: - #4259 This is in my TODO list.
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency. For example, other datasets also flatten "question.stem" into "question": - ai2_arc: ```python question = data["question"]["stem"] choices = data["question"]["choices"] text_choices = [choice["text"] for choice in choices] label_choices = [choice["label"] for choice in choices] yield id_, { "id": id_, "answerKey": answerkey, "question": question, "choices": {"text": text_choices, "label": label_choices}, } ``` - commonsense_qa: ```python question = data["question"] stem = question["stem"] yield id_, { "answerKey": answerkey, "question": stem, "choices": {"label": labels, "text": texts}, } ``` - cos_e: ```python "question": cqa["question"]["stem"], ``` - qasc - quartz - wiqa Exceptions: - exams I think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency. For example, other datasets also flatten "question.stem" into "question": - ai2_arc: ```python question = data["question"]["stem"] choices = data["question"]["choices"] text_choices = [choice["text"] for choice in choices] label_choices = [choice["label"] for choice in choices] yield id_, { "id": id_, "answerKey": answerkey, "question": question, "choices": {"text": text_choices, "label": label_choices}, } ``` - commonsense_qa: ```python question = data["question"] stem = question["stem"] yield id_, { "answerKey": answerkey, "question": stem, "choices": {"label": labels, "text": texts}, } ``` - cos_e: ```python "question": cqa["question"]["stem"], ``` - qasc - quartz - wiqa Exceptions: - exams I think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
@albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 @albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
I'm opening a PR that adds the missing fields. Let's agree on the feature structure: @lhoestq @mariosasko @polinaeterna
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 I'm opening a PR that adds the missing fields. Let's agree on the feature structure: @lhoestq @mariosasko @polinaeterna
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-0.1471597403, 0.1740896553, 0.0317044854, 0.8797950745, 0.2091995627, -0.10173475, 0.0753023103, 0.1399415135 ]
https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new.
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets. There is always the tension between: - preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset), - and on the other hand performing some king of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain "standard" structure). - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model Recently, in the BigScience BioMedical hackathon, they adopted a different approach: - they implement a "source" config, respecting the original structure as much as possible - they implement additional config for each task, with a "standard" nested structure per task, which is most useful for users.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets. There is always the tension between: - preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset), - and on the other hand performing some king of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain "standard" structure). - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model Recently, in the BigScience BioMedical hackathon, they adopted a different approach: - they implement a "source" config, respecting the original structure as much as possible - they implement additional config for each task, with a "standard" nested structure per task, which is most useful for users.
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
@albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 @albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best.
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https://github.com/huggingface/datasets/issues/4271
A typo in docs of datasets.disable_progress_bar
Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)
## Describe the bug in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable".
17
A typo in docs of datasets.disable_progress_bar ## Describe the bug in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable". Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for ["word"](https://en.wiktionary.org/wiki/word), Pronunciation ([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/ ([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
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error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for ["word"](https://en.wiktionary.org/wiki/word), Pronunciation ([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/ ([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Hi @i-am-neo, thanks for reporting. Normally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public. Also note that last commit "Add metadata" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well. @i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
100
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Hi @i-am-neo, thanks for reporting. Normally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public. Also note that last commit "Add metadata" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well. @i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
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error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
All datasets are private now. Re:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
18
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 All datasets are private now. Re:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Thanks a lot, @cakiki. @i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets.
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
32
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Thanks a lot, @cakiki. @i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets.
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Thanks for letting me know, @albertvillanova @cakiki. Any chance of having a subset alpha version in the meantime? I only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes. Would like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. (Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
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error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Thanks for letting me know, @albertvillanova @cakiki. Any chance of having a subset alpha version in the meantime? I only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes. Would like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. (Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Hey @i-am-neo, Cool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
19
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Hey @i-am-neo, Cool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
@i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/) You're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
19
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 @i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/) You're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
34
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!
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https://github.com/huggingface/datasets/issues/4261
data leakage in `webis/conclugen` dataset
Hi @xflashxx, thanks for reporting. Please note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis We are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.
## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi @xflashxx, thanks for reporting. Please note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis We are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.
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https://github.com/huggingface/datasets/issues/4261
data leakage in `webis/conclugen` dataset
Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.
## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.
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https://github.com/huggingface/datasets/issues/4261
data leakage in `webis/conclugen` dataset
Hi @xflashxx and @albertvillanova, I have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to "new" items. The length of `ids_validation` and `ids_testing` is zero. Regarding impact on scores: 1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. 2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository. Hope this helps!
## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
164
data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi @xflashxx and @albertvillanova, I have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to "new" items. The length of `ids_validation` and `ids_testing` is zero. Regarding impact on scores: 1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. 2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository. Hope this helps!
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https://github.com/huggingface/datasets/issues/4261
data leakage in `webis/conclugen` dataset
Thanks @shahbazsyed for your fast fix. As a side note: - Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de - Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings.
## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Thanks @shahbazsyed for your fast fix. As a side note: - Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de - Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings.
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https://github.com/huggingface/datasets/issues/4248
conll2003 dataset loads original data.
Thanks for reporting @sue99. Unfortunately. I'm not able to reproduce your problem: ```python In [1]: import datasets ...: from datasets import load_dataset ...: dataset = load_dataset("conll2003") In [2]: dataset Out[2]: DatasetDict({ train: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 14042 }) validation: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3251 }) test: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3454 }) }) In [3]: dataset["train"][0] Out[3]: {'id': '0', 'tokens': ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7], 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0], 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]} ``` Just guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub.
## Describe the bug I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text. Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ? ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset dataset = load_dataset("conll2003") ``` ## Expected results { "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0], "id": "0", "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7], "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."] } ## Actual results ```python print(dataset) DatasetDict({ train: Dataset({ features: ['text'], num_rows: 219554 }) test: Dataset({ features: ['text'], num_rows: 50350 }) validation: Dataset({ features: ['text'], num_rows: 55044 }) }) ``` ```python for i in range(20): print(dataset['train'][i]) {'text': '-DOCSTART- -X- -X- O'} {'text': ''} {'text': 'EU NNP B-NP B-ORG'} {'text': 'rejects VBZ B-VP O'} {'text': 'German JJ B-NP B-MISC'} {'text': 'call NN I-NP O'} {'text': 'to TO B-VP O'} {'text': 'boycott VB I-VP O'} {'text': 'British JJ B-NP B-MISC'} {'text': 'lamb NN I-NP O'} {'text': '. . O O'} {'text': ''} {'text': 'Peter NNP B-NP B-PER'} {'text': 'Blackburn NNP I-NP I-PER'} {'text': ''} {'text': 'BRUSSELS NNP B-NP B-LOC'} {'text': '1996-08-22 CD I-NP O'} {'text': ''} {'text': 'The DT B-NP O'} {'text': 'European NNP I-NP B-ORG'} ```
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conll2003 dataset loads original data. ## Describe the bug I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text. Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ? ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset dataset = load_dataset("conll2003") ``` ## Expected results { "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0], "id": "0", "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7], "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."] } ## Actual results ```python print(dataset) DatasetDict({ train: Dataset({ features: ['text'], num_rows: 219554 }) test: Dataset({ features: ['text'], num_rows: 50350 }) validation: Dataset({ features: ['text'], num_rows: 55044 }) }) ``` ```python for i in range(20): print(dataset['train'][i]) {'text': '-DOCSTART- -X- -X- O'} {'text': ''} {'text': 'EU NNP B-NP B-ORG'} {'text': 'rejects VBZ B-VP O'} {'text': 'German JJ B-NP B-MISC'} {'text': 'call NN I-NP O'} {'text': 'to TO B-VP O'} {'text': 'boycott VB I-VP O'} {'text': 'British JJ B-NP B-MISC'} {'text': 'lamb NN I-NP O'} {'text': '. . O O'} {'text': ''} {'text': 'Peter NNP B-NP B-PER'} {'text': 'Blackburn NNP I-NP I-PER'} {'text': ''} {'text': 'BRUSSELS NNP B-NP B-LOC'} {'text': '1996-08-22 CD I-NP O'} {'text': ''} {'text': 'The DT B-NP O'} {'text': 'European NNP I-NP B-ORG'} ``` Thanks for reporting @sue99. Unfortunately. I'm not able to reproduce your problem: ```python In [1]: import datasets ...: from datasets import load_dataset ...: dataset = load_dataset("conll2003") In [2]: dataset Out[2]: DatasetDict({ train: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 14042 }) validation: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3251 }) test: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3454 }) }) In [3]: dataset["train"][0] Out[3]: {'id': '0', 'tokens': ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7], 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0], 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]} ``` Just guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub.
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https://github.com/huggingface/datasets/issues/4247
The data preview of XGLUE
Thanks for reporting @czq1999. Note that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet. That is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming.
It seems that something wrong with the data previvew of XGLUE
43
The data preview of XGLUE It seems that something wrong with the data previvew of XGLUE Thanks for reporting @czq1999. Note that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet. That is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming.
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https://github.com/huggingface/datasets/issues/4241
NonMatchingChecksumError when attempting to download GLUE
Hi :) I think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with: ```py pip install -U datasets ``` Then you can download: ```py from datasets import load_dataset ds = load_dataset("glue", "rte") ```
## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5
51
NonMatchingChecksumError when attempting to download GLUE ## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5 Hi :) I think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with: ```py pip install -U datasets ``` Then you can download: ```py from datasets import load_dataset ds = load_dataset("glue", "rte") ```
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https://github.com/huggingface/datasets/issues/4241
NonMatchingChecksumError when attempting to download GLUE
This appears to work. Thank you! On Wed, Apr 27, 2022, 1:18 PM Steven Liu ***@***.***> wrote: > Hi :) > > I think your issue may be related to the older nlp library. I was able to > download glue with the latest version of datasets. Can you try updating > with: > > pip install -U datasets > > Then you can download: > > from datasets import load_datasetds = load_dataset("glue", "rte") > > β€” > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA> > . > You are receiving this because you authored the thread.Message ID: > ***@***.***> >
## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5
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NonMatchingChecksumError when attempting to download GLUE ## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5 This appears to work. Thank you! On Wed, Apr 27, 2022, 1:18 PM Steven Liu ***@***.***> wrote: > Hi :) > > I think your issue may be related to the older nlp library. I was able to > download glue with the latest version of datasets. Can you try updating > with: > > pip install -U datasets > > Then you can download: > > from datasets import load_datasetds = load_dataset("glue", "rte") > > β€” > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA> > . > You are receiving this because you authored the thread.Message ID: > ***@***.***> >
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https://github.com/huggingface/datasets/issues/4238
Dataset caching policy
Hi @loretoparisi, thanks for reporting. There is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode="force_redownload")`. Please, let me know if this fixes your problem. I can confirm you that your dataset loads without any problem for me: ```python In [2]: ds = load_dataset("loretoparisi/tatoeba-sentences", data_files={"train": "train.csv", "test": "test.csv"}, delimiter="\t", column_names=['label', 'text']) In [3]: ds Out[3]: DatasetDict({ train: Dataset({ features: ['label', 'text'], num_rows: 8256449 }) test: Dataset({ features: ['label', 'text'], num_rows: 2061204 }) }) ```
## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```
87
Dataset caching policy ## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ``` Hi @loretoparisi, thanks for reporting. There is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode="force_redownload")`. Please, let me know if this fixes your problem. I can confirm you that your dataset loads without any problem for me: ```python In [2]: ds = load_dataset("loretoparisi/tatoeba-sentences", data_files={"train": "train.csv", "test": "test.csv"}, delimiter="\t", column_names=['label', 'text']) In [3]: ds Out[3]: DatasetDict({ train: Dataset({ features: ['label', 'text'], num_rows: 8256449 }) test: Dataset({ features: ['label', 'text'], num_rows: 2061204 }) }) ```
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https://github.com/huggingface/datasets/issues/4238
Dataset caching policy
@albertvillanova thank you, it seems it still does not work using: ```python sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], download_mode="force_redownload" ) ``` [This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook! The problem is that the download file's revision for `test.csv` is not correctly parsed ![Schermata 2022-04-27 alle 18 09 41](https://user-images.githubusercontent.com/163333/165563507-0be53eb6-8f61-49b0-b959-306e59281de3.png) If you download that file `test.csv` from the repo, the line `\\N` is not there anymore (it was there at the first file upload). My impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\N`) since are binary files: ``` !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow !head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json ```
## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```
125
Dataset caching policy ## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ``` @albertvillanova thank you, it seems it still does not work using: ```python sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], download_mode="force_redownload" ) ``` [This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook! The problem is that the download file's revision for `test.csv` is not correctly parsed ![Schermata 2022-04-27 alle 18 09 41](https://user-images.githubusercontent.com/163333/165563507-0be53eb6-8f61-49b0-b959-306e59281de3.png) If you download that file `test.csv` from the repo, the line `\\N` is not there anymore (it was there at the first file upload). My impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\N`) since are binary files: ``` !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow !head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json ```
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https://github.com/huggingface/datasets/issues/4238
Dataset caching policy
SOLVED! The problem was the with the file itself, using caching parameter helped indeed. Thanks for helping!
## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```
17
Dataset caching policy ## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ``` SOLVED! The problem was the with the file itself, using caching parameter helped indeed. Thanks for helping!
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https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
Thanks for reporting. I understand it's an error in the dataset script. To reproduce: ```python >>> import datasets as ds >>> split_names = ds.get_dataset_split_names("mozilla-foundation/common_voice_8_0", use_auth_token="**********") Downloading builder script: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 10.9k/10.9k [00:00<00:00, 10.9MB/s] Downloading extra modules: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2.98k/2.98k [00:00<00:00, 3.36MB/s] Downloading extra modules: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 53.1k/53.1k [00:00<00:00, 650kB/s] No config specified, defaulting to: common_voice/en Traceback (most recent call last): File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 280, in get_dataset_config_info for split_generator in builder._split_generators( File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 153, in _split_generators self._log_download(self.config.name, bundle_version, hf_auth_token) File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 139, in _log_download email = HfApi().whoami(auth_token)["email"] KeyError: 'email' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_split_names info = get_dataset_config_info( File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 285, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config. ```
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
151
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Thanks for reporting. I understand it's an error in the dataset script. To reproduce: ```python >>> import datasets as ds >>> split_names = ds.get_dataset_split_names("mozilla-foundation/common_voice_8_0", use_auth_token="**********") Downloading builder script: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 10.9k/10.9k [00:00<00:00, 10.9MB/s] Downloading extra modules: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2.98k/2.98k [00:00<00:00, 3.36MB/s] Downloading extra modules: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 53.1k/53.1k [00:00<00:00, 650kB/s] No config specified, defaulting to: common_voice/en Traceback (most recent call last): File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 280, in get_dataset_config_info for split_generator in builder._split_generators( File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 153, in _split_generators self._log_download(self.config.name, bundle_version, hf_auth_token) File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 139, in _log_download email = HfApi().whoami(auth_token)["email"] KeyError: 'email' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_split_names info = get_dataset_config_info( File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 285, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config. ```
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-0.1224749535, -0.0402533263, 0.4102576971, 0.0025106065, -0.0645008683, 0.1102685407, -0.0553387478 ]
https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
Thanks for reporting @patrickvonplaten and thanks for the investigation @severo. Unfortunately I'm not able to reproduce the error. I think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139 ```python from huggingface_hub import HfApi, HfFolder if isinstance(auth_token, bool): email = HfApi().whoami(auth_token) email = HfApi().whoami(auth_token)["email"] ``` Could you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
70
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Thanks for reporting @patrickvonplaten and thanks for the investigation @severo. Unfortunately I'm not able to reproduce the error. I think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139 ```python from huggingface_hub import HfApi, HfFolder if isinstance(auth_token, bool): email = HfApi().whoami(auth_token) email = HfApi().whoami(auth_token)["email"] ``` Could you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?
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-0.0415402502, -0.0429128632, 0.3550975323, 0.0441450551, 0.0538326129, 0.3761520088, -0.0080621308 ]
https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email! ```python >>> from huggingface_hub import HfApi, HfFolder >>> auth_token = "hf_app_******" >>> t = HfApi().whoami(auth_token) >>> t {'type': 'app', 'name': 'dataset-preview-backend'} >>> t["email"] Traceback (most recent call last): File "<stdin>", line 1, in <module> KeyError: 'email' ``` Note also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key. @SBrandeis @julien-c: do you think the app token should have an email associated, like the users?
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
105
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email! ```python >>> from huggingface_hub import HfApi, HfFolder >>> auth_token = "hf_app_******" >>> t = HfApi().whoami(auth_token) >>> t {'type': 'app', 'name': 'dataset-preview-backend'} >>> t["email"] Traceback (most recent call last): File "<stdin>", line 1, in <module> KeyError: 'email' ``` Note also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key. @SBrandeis @julien-c: do you think the app token should have an email associated, like the users?
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https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
We can workaround this with ```python email = HfApi().whoami(auth_token).get("email", "system@huggingface.co") ``` in the common voice scripts
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
16
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 We can workaround this with ```python email = HfApi().whoami(auth_token).get("email", "system@huggingface.co") ``` in the common voice scripts
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-0.1471394151, 0.4502892494, 0.2141239494, 0.0495649278, -0.0975916684, -0.1163917631 ]
https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
Hmmm, does this mean that any person who downloads the common voice dataset will be logged as "system@huggingface.co"? If so, it would defeat the purpose of sending the user's email to the commonvoice API, right?
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
35
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Hmmm, does this mean that any person who downloads the common voice dataset will be logged as "system@huggingface.co"? If so, it would defeat the purpose of sending the user's email to the commonvoice API, right?
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https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
I agree with @severo: we cannot set our system email as default, allowing anybody not authenticated to by-pass the Common Voice usage policy. Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead. CC: @patrickvonplaten @lhoestq @SBrandeis @julien-c
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
61
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 I agree with @severo: we cannot set our system email as default, allowing anybody not authenticated to by-pass the Common Voice usage policy. Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead. CC: @patrickvonplaten @lhoestq @SBrandeis @julien-c
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-0.162664026, 0.4691651464, 0.2229687124, 0.0681517273, -0.287202239, -0.1066097692 ]
https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
Hmm I don't agree here. Anybody can always just bypass the system by setting whatever email. As soon as someone has access to the downloading script it's trivial to tweak the code to not send the "correct" email but to just whatever and it would work. Note that someone only has visibility on the code after having "signed" the access-mechanism so I think we can expect the users to have agreed to not do anything malicious. I'm fine with both @lhoestq's solution or we find a way that forces the user to be logged in + being able to load the data for the datasets viewer. Wdyt @lhoestq @severo @albertvillanova ?
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
111
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Hmm I don't agree here. Anybody can always just bypass the system by setting whatever email. As soon as someone has access to the downloading script it's trivial to tweak the code to not send the "correct" email but to just whatever and it would work. Note that someone only has visibility on the code after having "signed" the access-mechanism so I think we can expect the users to have agreed to not do anything malicious. I'm fine with both @lhoestq's solution or we find a way that forces the user to be logged in + being able to load the data for the datasets viewer. Wdyt @lhoestq @severo @albertvillanova ?
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https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
> Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead. Yes, I agree we can forget about this @patrickvonplaten. After having had a look at Common Voice website, I've seen they only require sending an email (no auth is inplace on their side, contrary to what I had previously thought). Therefore, currently we impose stronger requirements than them: we require the user having logged in and accepted the access mechanism. Currently the script as it is already requires the user being logged in: ```python HfApi().whoami(auth_token) ``` throws an exception if None/invalid auth_token is passed. On the other hand, we should agree on the way to allow the viewer to stream the data.
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
136
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 > Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead. Yes, I agree we can forget about this @patrickvonplaten. After having had a look at Common Voice website, I've seen they only require sending an email (no auth is inplace on their side, contrary to what I had previously thought). Therefore, currently we impose stronger requirements than them: we require the user having logged in and accepted the access mechanism. Currently the script as it is already requires the user being logged in: ```python HfApi().whoami(auth_token) ``` throws an exception if None/invalid auth_token is passed. On the other hand, we should agree on the way to allow the viewer to stream the data.
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https://github.com/huggingface/datasets/issues/4230
Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data?
Thanks for reporting @beyondguo. Indeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip And that URL only contains the English version.
![image](https://user-images.githubusercontent.com/37113676/165416606-96b5db18-b16c-4b6b-928c-de8620fd943e.png) But on huggingface datasets: ![image](https://user-images.githubusercontent.com/37113676/165416649-8fd77980-ca0d-43f0-935e-f398ba8323a4.png) Where is the German data?
24
Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data? ![image](https://user-images.githubusercontent.com/37113676/165416606-96b5db18-b16c-4b6b-928c-de8620fd943e.png) But on huggingface datasets: ![image](https://user-images.githubusercontent.com/37113676/165416649-8fd77980-ca0d-43f0-935e-f398ba8323a4.png) Where is the German data? Thanks for reporting @beyondguo. Indeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip And that URL only contains the English version.
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https://github.com/huggingface/datasets/issues/4221
Dictionary Feature
Hi @jordiae, Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`: ```python "list_of_dict_feature": [ { "key1_in_dict": datasets.Value("string"), "key2_in_dict": datasets.Value("int32"), ... } ], ``` Feel free to re-open this issue if that does not work for your use case.
Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance.
48
Dictionary Feature Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance. Hi @jordiae, Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`: ```python "list_of_dict_feature": [ { "key1_in_dict": datasets.Value("string"), "key2_in_dict": datasets.Value("int32"), ... } ], ``` Feel free to re-open this issue if that does not work for your use case.
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https://github.com/huggingface/datasets/issues/4221
Dictionary Feature
> Hi @jordiae, > > Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`: > > ```python > "list_of_dict_feature": [ > { > "key1_in_dict": datasets.Value("string"), > "key2_in_dict": datasets.Value("int32"), > ... > } > ], > ``` > > Feel free to re-open this issue if that does not work for your use case. Thank you
Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance.
65
Dictionary Feature Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance. > Hi @jordiae, > > Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`: > > ```python > "list_of_dict_feature": [ > { > "key1_in_dict": datasets.Value("string"), > "key2_in_dict": datasets.Value("int32"), > ... > } > ], > ``` > > Feel free to re-open this issue if that does not work for your use case. Thank you
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https://github.com/huggingface/datasets/issues/4217
Big_Patent dataset broken
Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive. See related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com To quote [@lhoestq](https://github.com/huggingface/datasets/issues/4075#issuecomment-1087362551): > PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.
## Dataset viewer issue for '*big_patent*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)* *Unable to view because it says FileNotFound, also cannot download it through the python API* Am I the one who added this dataset ? No
62
Big_Patent dataset broken ## Dataset viewer issue for '*big_patent*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)* *Unable to view because it says FileNotFound, also cannot download it through the python API* Am I the one who added this dataset ? No Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive. See related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com To quote [@lhoestq](https://github.com/huggingface/datasets/issues/4075#issuecomment-1087362551): > PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.
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https://github.com/huggingface/datasets/issues/4217
Big_Patent dataset broken
We should find out if the dataset license allows redistribution and contact the data owners to propose them to host their data on our Hub.
## Dataset viewer issue for '*big_patent*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)* *Unable to view because it says FileNotFound, also cannot download it through the python API* Am I the one who added this dataset ? No
25
Big_Patent dataset broken ## Dataset viewer issue for '*big_patent*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)* *Unable to view because it says FileNotFound, also cannot download it through the python API* Am I the one who added this dataset ? No We should find out if the dataset license allows redistribution and contact the data owners to propose them to host their data on our Hub.
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https://github.com/huggingface/datasets/issues/4211
DatasetDict containing Datasets with different features when pushed to hub gets remapped features
Hi @pietrolesci, thanks for reporting. Please note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different **split**. To handle sub-datasets with different features, we use another approach: use different **configurations** instead of **splits**. However, for the moment `push_to_hub` does not support specifying different configurations. IMHO, we should implement this.
Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```
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DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` Hi @pietrolesci, thanks for reporting. Please note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different **split**. To handle sub-datasets with different features, we use another approach: use different **configurations** instead of **splits**. However, for the moment `push_to_hub` does not support specifying different configurations. IMHO, we should implement this.
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https://github.com/huggingface/datasets/issues/4211
DatasetDict containing Datasets with different features when pushed to hub gets remapped features
Hi @albertvillanova, Thanks a lot for your reply! I got it now. The strange thing for me was to have it correctly working (i.e., DatasetDict with different features in some datasets) locally and not on the Hub. It would be great to have configuration supported by `push_to_hub`. Personally, this latter functionality allowed me to iterate rather quickly on dataset curation. Again, thanks for your time @albertvillanova! Best, Pietro
Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```
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DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` Hi @albertvillanova, Thanks a lot for your reply! I got it now. The strange thing for me was to have it correctly working (i.e., DatasetDict with different features in some datasets) locally and not on the Hub. It would be great to have configuration supported by `push_to_hub`. Personally, this latter functionality allowed me to iterate rather quickly on dataset curation. Again, thanks for your time @albertvillanova! Best, Pietro
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https://github.com/huggingface/datasets/issues/4211
DatasetDict containing Datasets with different features when pushed to hub gets remapped features
Hi! Yes, we should override `DatasetDict.__setitem__` and throw an error if features dictionaries are different. `DatasetDict` is a subclass of `dict`, so `DatasetDict.{update/setdefault}` need to be overridden as well. We could avoid this by subclassing `UserDict`, but then we would get the name collision - `DatasetDict.data` vs. `UserDict.data`. This makes me think we should rename the `data` attribute of `DatasetDict`/`Dataset` for easier dict subclassing (would also simplify https://github.com/huggingface/datasets/pull/3997) and to follow good Python practices. Another option is to have a custom `UserDict` class in `py_utils`, but it can be hard to keep this class consistent with the built-in `UserDict`. @albertvillanova @lhoestq wdyt?
Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```
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DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` Hi! Yes, we should override `DatasetDict.__setitem__` and throw an error if features dictionaries are different. `DatasetDict` is a subclass of `dict`, so `DatasetDict.{update/setdefault}` need to be overridden as well. We could avoid this by subclassing `UserDict`, but then we would get the name collision - `DatasetDict.data` vs. `UserDict.data`. This makes me think we should rename the `data` attribute of `DatasetDict`/`Dataset` for easier dict subclassing (would also simplify https://github.com/huggingface/datasets/pull/3997) and to follow good Python practices. Another option is to have a custom `UserDict` class in `py_utils`, but it can be hard to keep this class consistent with the built-in `UserDict`. @albertvillanova @lhoestq wdyt?
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https://github.com/huggingface/datasets/issues/4211
DatasetDict containing Datasets with different features when pushed to hub gets remapped features
I would keep things simple and keep subclassing dict. Regarding the features check, I guess this can be done only for `push_to_hub` right ? It is the only function right now that requires the underlying datasets to be splits (e.g. train/test) and have the same features. Note that later you will be able to push datasets with different features as different dataset **configurations** (similarly to the [GLUE subsets](https://huggingface.co/datasets/glue) for example). We will work on this soon
Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```
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DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` I would keep things simple and keep subclassing dict. Regarding the features check, I guess this can be done only for `push_to_hub` right ? It is the only function right now that requires the underlying datasets to be splits (e.g. train/test) and have the same features. Note that later you will be able to push datasets with different features as different dataset **configurations** (similarly to the [GLUE subsets](https://huggingface.co/datasets/glue) for example). We will work on this soon
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https://github.com/huggingface/datasets/issues/4210
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
Hi! Casting class labels from strings is currently not supported in the CSV loader, but you can get the same result with an additional map as follows: ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: features["label"].str2int(ex["label"]) if ex["label"] is not None else None, features=features) ``` @lhoestq IIRC, I suggested adding `cast_to_storage` to `ClassLabel` + `table_cast` to the packaged loaders if the `ClassLabel`/`Image`/`Audio` type is present in `features` to avoid this kind of error, but your concern was speed. IMO shouldn't be a problem if we do `table_cast` only when these features are present.
### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vΔ›dΔ›t, co je tam uvnitΕ™. ces Kdo o tom chce slyΕ‘et? deu Tom sagte, er fΓΌhle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rΓ‘ rΓΆgtΓΆn. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hΓΆren. ``` ### Expected behavior ```shell correctly load train and test files. ```
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TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' ### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vΔ›dΔ›t, co je tam uvnitΕ™. ces Kdo o tom chce slyΕ‘et? deu Tom sagte, er fΓΌhle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rΓ‘ rΓΆgtΓΆn. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hΓΆren. ``` ### Expected behavior ```shell correctly load train and test files. ``` Hi! Casting class labels from strings is currently not supported in the CSV loader, but you can get the same result with an additional map as follows: ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: features["label"].str2int(ex["label"]) if ex["label"] is not None else None, features=features) ``` @lhoestq IIRC, I suggested adding `cast_to_storage` to `ClassLabel` + `table_cast` to the packaged loaders if the `ClassLabel`/`Image`/`Audio` type is present in `features` to avoid this kind of error, but your concern was speed. IMO shouldn't be a problem if we do `table_cast` only when these features are present.
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0.0947931334, -0.2111215293, -0.1357300431, 0.2142552882, 0.0796013325, -0.091796957, 0.1080016941, -0.2276802808, 0.164167136, -0.0471278019, -0.0558544621, -0.2296596617, -0.000068264, -0.2843836248, 0.1208273768, -0.4259039164, 0.2273452282, 0.1863930523, 0.105484508, -0.3405477405, -0.2014196664, 0.0592442229, 0.0019962532, -0.043080423, 0.164234221, -0.0863453448, 0.4284532666, 0.0224234816, 0.1440993547, -0.0185547303, -0.3307959139, 0.165139541, 0.4618390799, -0.0608106144, -0.2200914621, 0.2863713503, 0.4150553346, 0.2264840752, -0.2363422513, 0.2629530728, 0.0683605075, -0.3372965157, -0.0744847059, 0.2843070328, 0.3062033057, 0.3336184919, 0.2868262231, 0.2640824318, -0.2991374135, -0.1666556299, 0.1528553963, 0.008310861, 0.2482055426, 0.5052460432, -0.0418701544, -0.4077041745, -0.0686031654, -0.1974014342, -0.5350337029, -0.2064782679, 0.1075426489, -0.2314076871, 0.26383394, 0.1656094193, 0.1325246692, -0.0257342663, 0.5575472116, -0.0234561283, 0.1785559803, -0.4049807787, 0.1025912613, -0.222391516, 0.2665467858, 0.3310826719, 0.0687109306, 0.1985699683, 0.2189537585, 0.1238905936, 0.1835174263, -0.0276710317, -0.2438267767, 0.156933099, 0.2294225693, -0.0579549484, 0.0035186182, 0.0164830405, 0.0092698243, 0.2666772008, -0.4339877069, 0.2716411352, 0.0088166054, 0.2757121325, -0.3280782998, 0.0776313022, -0.0038259185, -0.142139852, 0.0882014707, -0.0796647295, 0.5117201805, -0.1819565594, -0.1123145819, 0.2242079377, -0.2020687312, -0.1495587975, 0.1795028448, 0.2430046499, 0.3789004087, 0.1096352041, 0.0341851003, -0.2756944001, 0.1485710442, -0.0860385075, -0.1794837266, -0.1554077715, 0.3504404426, -0.2647797763, 0.104934141, -0.0201003645, 0.1318541467, 0.1608710289, 0.2491208166, -0.3544086218, -0.4123487175, 0.6665608287, 0.058869198, -0.3451695144, -0.0857329741, 0.0318886675, -0.1323498487, 0.0408525951, -0.7296362519, 0.2549422681, -0.0165216532, -0.2539709806, -0.1664779186, 0.2007682025, -0.0108907139, -0.0075598387, -0.0239946414, 0.4769183695, 0.2388567179, -0.1805017889, -0.183375001, -0.1403684765 ]
https://github.com/huggingface/datasets/issues/4210
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
@albertvillanova @mariosasko thank you, with that change now I get ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-9-eeb68eeb9bec>](https://localhost:8080/#) in <module>() 11 ) 12 # You can make this part faster with num_proc=<some int> ---> 13 sentences = sentences.map(lambda ex: features["label"].str2int(ex["label"]) if ex["label"] is not None else None, features=features) 14 sentences = sentences.shuffle() 8 frames [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices) 2193 if processed_inputs is not None and not isinstance(processed_inputs, (Mapping, pa.Table)): 2194 raise TypeError( -> 2195 f"Provided `function` which is applied to all elements of table returns a variable of type {type(processed_inputs)}. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects." 2196 ) 2197 elif isinstance(indices, list) and isinstance(processed_inputs, Mapping): TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'int'>. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects. ``` the error is raised by [this](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L2221) ``` [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices) ```
### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vΔ›dΔ›t, co je tam uvnitΕ™. ces Kdo o tom chce slyΕ‘et? deu Tom sagte, er fΓΌhle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rΓ‘ rΓΆgtΓΆn. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hΓΆren. ``` ### Expected behavior ```shell correctly load train and test files. ```
187
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' ### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vΔ›dΔ›t, co je tam uvnitΕ™. ces Kdo o tom chce slyΕ‘et? deu Tom sagte, er fΓΌhle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rΓ‘ rΓΆgtΓΆn. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hΓΆren. ``` ### Expected behavior ```shell correctly load train and test files. ``` @albertvillanova @mariosasko thank you, with that change now I get ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-9-eeb68eeb9bec>](https://localhost:8080/#) in <module>() 11 ) 12 # You can make this part faster with num_proc=<some int> ---> 13 sentences = sentences.map(lambda ex: features["label"].str2int(ex["label"]) if ex["label"] is not None else None, features=features) 14 sentences = sentences.shuffle() 8 frames [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices) 2193 if processed_inputs is not None and not isinstance(processed_inputs, (Mapping, pa.Table)): 2194 raise TypeError( -> 2195 f"Provided `function` which is applied to all elements of table returns a variable of type {type(processed_inputs)}. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects." 2196 ) 2197 elif isinstance(indices, list) and isinstance(processed_inputs, Mapping): TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'int'>. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects. ``` the error is raised by [this](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L2221) ``` [/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices) ```
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-0.0239946414, 0.4769183695, 0.2388567179, -0.1805017889, -0.183375001, -0.1403684765 ]
https://github.com/huggingface/datasets/issues/4210
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
@mariosasko changed it like ```python sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) ``` to avoid the above errorr.
### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vΔ›dΔ›t, co je tam uvnitΕ™. ces Kdo o tom chce slyΕ‘et? deu Tom sagte, er fΓΌhle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rΓ‘ rΓΆgtΓΆn. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hΓΆren. ``` ### Expected behavior ```shell correctly load train and test files. ```
26
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' ### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vΔ›dΔ›t, co je tam uvnitΕ™. ces Kdo o tom chce slyΕ‘et? deu Tom sagte, er fΓΌhle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rΓ‘ rΓΆgtΓΆn. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hΓΆren. ``` ### Expected behavior ```shell correctly load train and test files. ``` @mariosasko changed it like ```python sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) ``` to avoid the above errorr.
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-0.4049807787, 0.1025912613, -0.222391516, 0.2665467858, 0.3310826719, 0.0687109306, 0.1985699683, 0.2189537585, 0.1238905936, 0.1835174263, -0.0276710317, -0.2438267767, 0.156933099, 0.2294225693, -0.0579549484, 0.0035186182, 0.0164830405, 0.0092698243, 0.2666772008, -0.4339877069, 0.2716411352, 0.0088166054, 0.2757121325, -0.3280782998, 0.0776313022, -0.0038259185, -0.142139852, 0.0882014707, -0.0796647295, 0.5117201805, -0.1819565594, -0.1123145819, 0.2242079377, -0.2020687312, -0.1495587975, 0.1795028448, 0.2430046499, 0.3789004087, 0.1096352041, 0.0341851003, -0.2756944001, 0.1485710442, -0.0860385075, -0.1794837266, -0.1554077715, 0.3504404426, -0.2647797763, 0.104934141, -0.0201003645, 0.1318541467, 0.1608710289, 0.2491208166, -0.3544086218, -0.4123487175, 0.6665608287, 0.058869198, -0.3451695144, -0.0857329741, 0.0318886675, -0.1323498487, 0.0408525951, -0.7296362519, 0.2549422681, -0.0165216532, -0.2539709806, -0.1664779186, 0.2007682025, -0.0108907139, -0.0075598387, -0.0239946414, 0.4769183695, 0.2388567179, -0.1805017889, -0.183375001, -0.1403684765 ]
https://github.com/huggingface/datasets/issues/4210
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
Any update on this? Is this correct ? > @mariosasko changed it like > > ```python > sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) > ``` > > to avoid the above errorr.
### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vΔ›dΔ›t, co je tam uvnitΕ™. ces Kdo o tom chce slyΕ‘et? deu Tom sagte, er fΓΌhle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rΓ‘ rΓΆgtΓΆn. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hΓΆren. ``` ### Expected behavior ```shell correctly load train and test files. ```
41
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' ### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vΔ›dΔ›t, co je tam uvnitΕ™. ces Kdo o tom chce slyΕ‘et? deu Tom sagte, er fΓΌhle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rΓ‘ rΓΆgtΓΆn. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hΓΆren. ``` ### Expected behavior ```shell correctly load train and test files. ``` Any update on this? Is this correct ? > @mariosasko changed it like > > ```python > sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) > ``` > > to avoid the above errorr.
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https://github.com/huggingface/datasets/issues/4199
Cache miss during reload for datasets using image fetch utilities through map
Hi ! Maybe one of the objects in the function is not deterministic across sessions ? You can read more about it and how to investigate here: https://huggingface.co/docs/datasets/about_cache
## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1
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Cache miss during reload for datasets using image fetch utilities through map ## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Hi ! Maybe one of the objects in the function is not deterministic across sessions ? You can read more about it and how to investigate here: https://huggingface.co/docs/datasets/about_cache
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https://github.com/huggingface/datasets/issues/4199
Cache miss during reload for datasets using image fetch utilities through map
Hi @apsdehal! Can you verify that replacing ```python def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image ``` with ```python USER_AGENT = get_datasets_user_agent() def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": USER_AGENT}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image ``` fixes the issue?
## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1
88
Cache miss during reload for datasets using image fetch utilities through map ## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Hi @apsdehal! Can you verify that replacing ```python def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image ``` with ```python USER_AGENT = get_datasets_user_agent() def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": USER_AGENT}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image ``` fixes the issue?
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https://github.com/huggingface/datasets/issues/4199
Cache miss during reload for datasets using image fetch utilities through map
Thanks @mariosasko. That does fix the issue. In general, I think these image downloading utilities since they are being used by a lot of image dataset should be provided as a part of `datasets` library right to keep the logic consistent and READMEs smaller? If they already exists, that is also great, please point me to those. I saw that `http_get` does exist.
## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1
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Cache miss during reload for datasets using image fetch utilities through map ## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Thanks @mariosasko. That does fix the issue. In general, I think these image downloading utilities since they are being used by a lot of image dataset should be provided as a part of `datasets` library right to keep the logic consistent and READMEs smaller? If they already exists, that is also great, please point me to those. I saw that `http_get` does exist.
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https://github.com/huggingface/datasets/issues/4199
Cache miss during reload for datasets using image fetch utilities through map
You can find my rationale (and a proposed solution) for why these utilities are not a part of `datasets` here: https://github.com/huggingface/datasets/pull/4100#issuecomment-1097994003.
## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1
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Cache miss during reload for datasets using image fetch utilities through map ## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 You can find my rationale (and a proposed solution) for why these utilities are not a part of `datasets` here: https://github.com/huggingface/datasets/pull/4100#issuecomment-1097994003.
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https://github.com/huggingface/datasets/issues/4199
Cache miss during reload for datasets using image fetch utilities through map
Makes sense. But, I think as the number of image datasets as grow, more people are copying pasting original code from docs to work as it is while we make fixes to them later. I think we do need a central place for these to avoid that confusion as well as more easier access to image datasets. Should we restart that discussion, possible on slack?
## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1
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Cache miss during reload for datasets using image fetch utilities through map ## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Makes sense. But, I think as the number of image datasets as grow, more people are copying pasting original code from docs to work as it is while we make fixes to them later. I think we do need a central place for these to avoid that confusion as well as more easier access to image datasets. Should we restart that discussion, possible on slack?
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https://github.com/huggingface/datasets/issues/4192
load_dataset can't load local dataset,Unable to find ...
Hi! :) I believe that should work unless `dataset_infos.json` isn't actually a dataset. For Hugging Face datasets, there is usually a file named `dataset_infos.json` which contains metadata about the dataset (eg. the dataset citation, license, description, etc). Can you double-check that `dataset_infos.json` isn't just metadata please?
Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained ![image](https://user-images.githubusercontent.com/33253979/164413285-84ea65ac-9126-408f-9cd2-ce4751a5dd73.png) ![image](https://user-images.githubusercontent.com/33253979/164413338-4735142f-408b-41d9-ab87-8484de2be54f.png) the code is in the model.py,why I can't use the load_dataset function to load my local dataset?
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load_dataset can't load local dataset,Unable to find ... Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained ![image](https://user-images.githubusercontent.com/33253979/164413285-84ea65ac-9126-408f-9cd2-ce4751a5dd73.png) ![image](https://user-images.githubusercontent.com/33253979/164413338-4735142f-408b-41d9-ab87-8484de2be54f.png) the code is in the model.py,why I can't use the load_dataset function to load my local dataset? Hi! :) I believe that should work unless `dataset_infos.json` isn't actually a dataset. For Hugging Face datasets, there is usually a file named `dataset_infos.json` which contains metadata about the dataset (eg. the dataset citation, license, description, etc). Can you double-check that `dataset_infos.json` isn't just metadata please?
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https://github.com/huggingface/datasets/issues/4192
load_dataset can't load local dataset,Unable to find ...
Hi @ahf876828330, As @stevhliu pointed out, the proper way to load a dataset is not trying to load its metadata file. In your case, as the dataset script is local, you should better point to your local loading script: ```python dataset = load_dataset("dataset/opus_books.py") ``` Please, feel free to re-open this issue if the previous code snippet does not work for you.
Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained ![image](https://user-images.githubusercontent.com/33253979/164413285-84ea65ac-9126-408f-9cd2-ce4751a5dd73.png) ![image](https://user-images.githubusercontent.com/33253979/164413338-4735142f-408b-41d9-ab87-8484de2be54f.png) the code is in the model.py,why I can't use the load_dataset function to load my local dataset?
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load_dataset can't load local dataset,Unable to find ... Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained ![image](https://user-images.githubusercontent.com/33253979/164413285-84ea65ac-9126-408f-9cd2-ce4751a5dd73.png) ![image](https://user-images.githubusercontent.com/33253979/164413338-4735142f-408b-41d9-ab87-8484de2be54f.png) the code is in the model.py,why I can't use the load_dataset function to load my local dataset? Hi @ahf876828330, As @stevhliu pointed out, the proper way to load a dataset is not trying to load its metadata file. In your case, as the dataset script is local, you should better point to your local loading script: ```python dataset = load_dataset("dataset/opus_books.py") ``` Please, feel free to re-open this issue if the previous code snippet does not work for you.
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https://github.com/huggingface/datasets/issues/4192
load_dataset can't load local dataset,Unable to find ...
> Hi! :) > > I believe that should work unless `dataset_infos.json` isn't actually a dataset. For Hugging Face datasets, there is usually a file named `dataset_infos.json` which contains metadata about the dataset (eg. the dataset citation, license, description, etc). Can you double-check that `dataset_infos.json` isn't just metadata please? Yes,you are right!So if I have a metadata dataset local,How can I turn it to a dataset that can be used by the load_dataset() function?Are there some examples?
Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained ![image](https://user-images.githubusercontent.com/33253979/164413285-84ea65ac-9126-408f-9cd2-ce4751a5dd73.png) ![image](https://user-images.githubusercontent.com/33253979/164413338-4735142f-408b-41d9-ab87-8484de2be54f.png) the code is in the model.py,why I can't use the load_dataset function to load my local dataset?
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load_dataset can't load local dataset,Unable to find ... Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained ![image](https://user-images.githubusercontent.com/33253979/164413285-84ea65ac-9126-408f-9cd2-ce4751a5dd73.png) ![image](https://user-images.githubusercontent.com/33253979/164413338-4735142f-408b-41d9-ab87-8484de2be54f.png) the code is in the model.py,why I can't use the load_dataset function to load my local dataset? > Hi! :) > > I believe that should work unless `dataset_infos.json` isn't actually a dataset. For Hugging Face datasets, there is usually a file named `dataset_infos.json` which contains metadata about the dataset (eg. the dataset citation, license, description, etc). Can you double-check that `dataset_infos.json` isn't just metadata please? Yes,you are right!So if I have a metadata dataset local,How can I turn it to a dataset that can be used by the load_dataset() function?Are there some examples?
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https://github.com/huggingface/datasets/issues/4192
load_dataset can't load local dataset,Unable to find ...
The metadata file isn't a dataset so you can't turn it into one. You should try @albertvillanova's code snippet above (now merged in the docs [here](https://huggingface.co/docs/datasets/master/en/loading#local-loading-script)), which uses your local loading script `opus_books.py` to: 1. Download the actual dataset. 2. Once the dataset is downloaded, `load_dataset` will load it for you.
Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained ![image](https://user-images.githubusercontent.com/33253979/164413285-84ea65ac-9126-408f-9cd2-ce4751a5dd73.png) ![image](https://user-images.githubusercontent.com/33253979/164413338-4735142f-408b-41d9-ab87-8484de2be54f.png) the code is in the model.py,why I can't use the load_dataset function to load my local dataset?
51
load_dataset can't load local dataset,Unable to find ... Traceback (most recent call last): File "/home/gs603/ahf/pretrained/model.py", line 48, in <module> dataset = load_dataset("json",data_files="dataset/dataset_infos.json") File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset **config_kwargs, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder data_files=data_files, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory download_mode=download_mode, File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained ![image](https://user-images.githubusercontent.com/33253979/164413285-84ea65ac-9126-408f-9cd2-ce4751a5dd73.png) ![image](https://user-images.githubusercontent.com/33253979/164413338-4735142f-408b-41d9-ab87-8484de2be54f.png) the code is in the model.py,why I can't use the load_dataset function to load my local dataset? The metadata file isn't a dataset so you can't turn it into one. You should try @albertvillanova's code snippet above (now merged in the docs [here](https://huggingface.co/docs/datasets/master/en/loading#local-loading-script)), which uses your local loading script `opus_books.py` to: 1. Download the actual dataset. 2. Once the dataset is downloaded, `load_dataset` will load it for you.
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https://github.com/huggingface/datasets/issues/4291
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message
Hi @leondz, thanks for reporting. Indeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions. In particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`. Let me know if you need some help: I could push a commit to your repo with the fix.
### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes
103
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message ### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes Hi @leondz, thanks for reporting. Indeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions. In particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`. Let me know if you need some help: I could push a commit to your repo with the fix.
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https://github.com/huggingface/datasets/issues/4291
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message
Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)
### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes
37
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message ### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)
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https://github.com/huggingface/datasets/issues/4287
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None
So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above. As it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered. So on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU. I'll add the code in a PR linked to this issue in case you want to merge it!
## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
102
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None ## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above. As it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered. So on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU. I'll add the code in a PR linked to this issue in case you want to merge it!
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https://github.com/huggingface/datasets/issues/4287
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None
Adding here the complete error traceback! ``` Traceback (most recent call last): File "/home/alvarobartt/lol.py", line 12, in <module> ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3656, in add_faiss_index super().add_faiss_index( File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 478, in add_faiss_index faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 281, in add_vectors self.faiss_index = self._faiss_index_to_device(index, self.device) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 327, in _faiss_index_to_device faiss_res = faiss.StandardGpuResources() NameError: name 'faiss' is not defined ```
## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
66
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None ## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 Adding here the complete error traceback! ``` Traceback (most recent call last): File "/home/alvarobartt/lol.py", line 12, in <module> ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3656, in add_faiss_index super().add_faiss_index( File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 478, in add_faiss_index faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 281, in add_vectors self.faiss_index = self._faiss_index_to_device(index, self.device) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 327, in _faiss_index_to_device faiss_res = faiss.StandardGpuResources() NameError: name 'faiss' is not defined ```
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
Thanks for reporting, @vblagoje. Indeed, I noticed some of these issues while reviewing this PR: - #4259 This is in my TODO list.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 Thanks for reporting, @vblagoje. Indeed, I noticed some of these issues while reviewing this PR: - #4259 This is in my TODO list.
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency. For example, other datasets also flatten "question.stem" into "question": - ai2_arc: ```python question = data["question"]["stem"] choices = data["question"]["choices"] text_choices = [choice["text"] for choice in choices] label_choices = [choice["label"] for choice in choices] yield id_, { "id": id_, "answerKey": answerkey, "question": question, "choices": {"text": text_choices, "label": label_choices}, } ``` - commonsense_qa: ```python question = data["question"] stem = question["stem"] yield id_, { "answerKey": answerkey, "question": stem, "choices": {"label": labels, "text": texts}, } ``` - cos_e: ```python "question": cqa["question"]["stem"], ``` - qasc - quartz - wiqa Exceptions: - exams I think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency. For example, other datasets also flatten "question.stem" into "question": - ai2_arc: ```python question = data["question"]["stem"] choices = data["question"]["choices"] text_choices = [choice["text"] for choice in choices] label_choices = [choice["label"] for choice in choices] yield id_, { "id": id_, "answerKey": answerkey, "question": question, "choices": {"text": text_choices, "label": label_choices}, } ``` - commonsense_qa: ```python question = data["question"] stem = question["stem"] yield id_, { "answerKey": answerkey, "question": stem, "choices": {"label": labels, "text": texts}, } ``` - cos_e: ```python "question": cqa["question"]["stem"], ``` - qasc - quartz - wiqa Exceptions: - exams I think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
@albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 @albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
I'm opening a PR that adds the missing fields. Let's agree on the feature structure: @lhoestq @mariosasko @polinaeterna
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 I'm opening a PR that adds the missing fields. Let's agree on the feature structure: @lhoestq @mariosasko @polinaeterna
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-0.1471597403, 0.1740896553, 0.0317044854, 0.8797950745, 0.2091995627, -0.10173475, 0.0753023103, 0.1399415135 ]
https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new.
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets. There is always the tension between: - preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset), - and on the other hand performing some king of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain "standard" structure). - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model Recently, in the BigScience BioMedical hackathon, they adopted a different approach: - they implement a "source" config, respecting the original structure as much as possible - they implement additional config for each task, with a "standard" nested structure per task, which is most useful for users.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets. There is always the tension between: - preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset), - and on the other hand performing some king of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain "standard" structure). - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model Recently, in the BigScience BioMedical hackathon, they adopted a different approach: - they implement a "source" config, respecting the original structure as much as possible - they implement additional config for each task, with a "standard" nested structure per task, which is most useful for users.
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https://github.com/huggingface/datasets/issues/4276
OpenBookQA has missing and inconsistent field names
@albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best.
## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 @albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best.
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https://github.com/huggingface/datasets/issues/4271
A typo in docs of datasets.disable_progress_bar
Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)
## Describe the bug in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable".
17
A typo in docs of datasets.disable_progress_bar ## Describe the bug in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable". Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for ["word"](https://en.wiktionary.org/wiki/word), Pronunciation ([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/ ([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
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error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for ["word"](https://en.wiktionary.org/wiki/word), Pronunciation ([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/ ([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Hi @i-am-neo, thanks for reporting. Normally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public. Also note that last commit "Add metadata" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well. @i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
100
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Hi @i-am-neo, thanks for reporting. Normally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public. Also note that last commit "Add metadata" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well. @i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
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error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
All datasets are private now. Re:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
18
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 All datasets are private now. Re:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Thanks a lot, @cakiki. @i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets.
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
32
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Thanks a lot, @cakiki. @i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets.
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Thanks for letting me know, @albertvillanova @cakiki. Any chance of having a subset alpha version in the meantime? I only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes. Would like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. (Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
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error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Thanks for letting me know, @albertvillanova @cakiki. Any chance of having a subset alpha version in the meantime? I only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes. Would like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. (Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
Hey @i-am-neo, Cool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
19
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Hey @i-am-neo, Cool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
@i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/) You're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
19
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 @i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/) You're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file
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https://github.com/huggingface/datasets/issues/4268
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!
## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1
34
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!
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https://github.com/huggingface/datasets/issues/4261
data leakage in `webis/conclugen` dataset
Hi @xflashxx, thanks for reporting. Please note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis We are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.
## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi @xflashxx, thanks for reporting. Please note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis We are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.
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https://github.com/huggingface/datasets/issues/4261
data leakage in `webis/conclugen` dataset
Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.
## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.
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https://github.com/huggingface/datasets/issues/4261
data leakage in `webis/conclugen` dataset
Hi @xflashxx and @albertvillanova, I have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to "new" items. The length of `ids_validation` and `ids_testing` is zero. Regarding impact on scores: 1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. 2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository. Hope this helps!
## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
164
data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi @xflashxx and @albertvillanova, I have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to "new" items. The length of `ids_validation` and `ids_testing` is zero. Regarding impact on scores: 1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. 2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository. Hope this helps!
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https://github.com/huggingface/datasets/issues/4261
data leakage in `webis/conclugen` dataset
Thanks @shahbazsyed for your fast fix. As a side note: - Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de - Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings.
## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the *training* split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Thanks @shahbazsyed for your fast fix. As a side note: - Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de - Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings.
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https://github.com/huggingface/datasets/issues/4248
conll2003 dataset loads original data.
Thanks for reporting @sue99. Unfortunately. I'm not able to reproduce your problem: ```python In [1]: import datasets ...: from datasets import load_dataset ...: dataset = load_dataset("conll2003") In [2]: dataset Out[2]: DatasetDict({ train: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 14042 }) validation: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3251 }) test: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3454 }) }) In [3]: dataset["train"][0] Out[3]: {'id': '0', 'tokens': ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7], 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0], 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]} ``` Just guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub.
## Describe the bug I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text. Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ? ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset dataset = load_dataset("conll2003") ``` ## Expected results { "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0], "id": "0", "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7], "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."] } ## Actual results ```python print(dataset) DatasetDict({ train: Dataset({ features: ['text'], num_rows: 219554 }) test: Dataset({ features: ['text'], num_rows: 50350 }) validation: Dataset({ features: ['text'], num_rows: 55044 }) }) ``` ```python for i in range(20): print(dataset['train'][i]) {'text': '-DOCSTART- -X- -X- O'} {'text': ''} {'text': 'EU NNP B-NP B-ORG'} {'text': 'rejects VBZ B-VP O'} {'text': 'German JJ B-NP B-MISC'} {'text': 'call NN I-NP O'} {'text': 'to TO B-VP O'} {'text': 'boycott VB I-VP O'} {'text': 'British JJ B-NP B-MISC'} {'text': 'lamb NN I-NP O'} {'text': '. . O O'} {'text': ''} {'text': 'Peter NNP B-NP B-PER'} {'text': 'Blackburn NNP I-NP I-PER'} {'text': ''} {'text': 'BRUSSELS NNP B-NP B-LOC'} {'text': '1996-08-22 CD I-NP O'} {'text': ''} {'text': 'The DT B-NP O'} {'text': 'European NNP I-NP B-ORG'} ```
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conll2003 dataset loads original data. ## Describe the bug I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text. Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ? ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset dataset = load_dataset("conll2003") ``` ## Expected results { "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0], "id": "0", "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7], "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."] } ## Actual results ```python print(dataset) DatasetDict({ train: Dataset({ features: ['text'], num_rows: 219554 }) test: Dataset({ features: ['text'], num_rows: 50350 }) validation: Dataset({ features: ['text'], num_rows: 55044 }) }) ``` ```python for i in range(20): print(dataset['train'][i]) {'text': '-DOCSTART- -X- -X- O'} {'text': ''} {'text': 'EU NNP B-NP B-ORG'} {'text': 'rejects VBZ B-VP O'} {'text': 'German JJ B-NP B-MISC'} {'text': 'call NN I-NP O'} {'text': 'to TO B-VP O'} {'text': 'boycott VB I-VP O'} {'text': 'British JJ B-NP B-MISC'} {'text': 'lamb NN I-NP O'} {'text': '. . O O'} {'text': ''} {'text': 'Peter NNP B-NP B-PER'} {'text': 'Blackburn NNP I-NP I-PER'} {'text': ''} {'text': 'BRUSSELS NNP B-NP B-LOC'} {'text': '1996-08-22 CD I-NP O'} {'text': ''} {'text': 'The DT B-NP O'} {'text': 'European NNP I-NP B-ORG'} ``` Thanks for reporting @sue99. Unfortunately. I'm not able to reproduce your problem: ```python In [1]: import datasets ...: from datasets import load_dataset ...: dataset = load_dataset("conll2003") In [2]: dataset Out[2]: DatasetDict({ train: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 14042 }) validation: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3251 }) test: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3454 }) }) In [3]: dataset["train"][0] Out[3]: {'id': '0', 'tokens': ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7], 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0], 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]} ``` Just guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub.
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https://github.com/huggingface/datasets/issues/4247
The data preview of XGLUE
Thanks for reporting @czq1999. Note that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet. That is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming.
It seems that something wrong with the data previvew of XGLUE
43
The data preview of XGLUE It seems that something wrong with the data previvew of XGLUE Thanks for reporting @czq1999. Note that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet. That is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming.
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https://github.com/huggingface/datasets/issues/4241
NonMatchingChecksumError when attempting to download GLUE
Hi :) I think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with: ```py pip install -U datasets ``` Then you can download: ```py from datasets import load_dataset ds = load_dataset("glue", "rte") ```
## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5
51
NonMatchingChecksumError when attempting to download GLUE ## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5 Hi :) I think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with: ```py pip install -U datasets ``` Then you can download: ```py from datasets import load_dataset ds = load_dataset("glue", "rte") ```
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https://github.com/huggingface/datasets/issues/4241
NonMatchingChecksumError when attempting to download GLUE
This appears to work. Thank you! On Wed, Apr 27, 2022, 1:18 PM Steven Liu ***@***.***> wrote: > Hi :) > > I think your issue may be related to the older nlp library. I was able to > download glue with the latest version of datasets. Can you try updating > with: > > pip install -U datasets > > Then you can download: > > from datasets import load_datasetds = load_dataset("glue", "rte") > > β€” > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA> > . > You are receiving this because you authored the thread.Message ID: > ***@***.***> >
## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5
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NonMatchingChecksumError when attempting to download GLUE ## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5 This appears to work. Thank you! On Wed, Apr 27, 2022, 1:18 PM Steven Liu ***@***.***> wrote: > Hi :) > > I think your issue may be related to the older nlp library. I was able to > download glue with the latest version of datasets. Can you try updating > with: > > pip install -U datasets > > Then you can download: > > from datasets import load_datasetds = load_dataset("glue", "rte") > > β€” > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA> > . > You are receiving this because you authored the thread.Message ID: > ***@***.***> >
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https://github.com/huggingface/datasets/issues/4238
Dataset caching policy
Hi @loretoparisi, thanks for reporting. There is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode="force_redownload")`. Please, let me know if this fixes your problem. I can confirm you that your dataset loads without any problem for me: ```python In [2]: ds = load_dataset("loretoparisi/tatoeba-sentences", data_files={"train": "train.csv", "test": "test.csv"}, delimiter="\t", column_names=['label', 'text']) In [3]: ds Out[3]: DatasetDict({ train: Dataset({ features: ['label', 'text'], num_rows: 8256449 }) test: Dataset({ features: ['label', 'text'], num_rows: 2061204 }) }) ```
## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```
87
Dataset caching policy ## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ``` Hi @loretoparisi, thanks for reporting. There is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode="force_redownload")`. Please, let me know if this fixes your problem. I can confirm you that your dataset loads without any problem for me: ```python In [2]: ds = load_dataset("loretoparisi/tatoeba-sentences", data_files={"train": "train.csv", "test": "test.csv"}, delimiter="\t", column_names=['label', 'text']) In [3]: ds Out[3]: DatasetDict({ train: Dataset({ features: ['label', 'text'], num_rows: 8256449 }) test: Dataset({ features: ['label', 'text'], num_rows: 2061204 }) }) ```
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https://github.com/huggingface/datasets/issues/4238
Dataset caching policy
@albertvillanova thank you, it seems it still does not work using: ```python sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], download_mode="force_redownload" ) ``` [This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook! The problem is that the download file's revision for `test.csv` is not correctly parsed ![Schermata 2022-04-27 alle 18 09 41](https://user-images.githubusercontent.com/163333/165563507-0be53eb6-8f61-49b0-b959-306e59281de3.png) If you download that file `test.csv` from the repo, the line `\\N` is not there anymore (it was there at the first file upload). My impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\N`) since are binary files: ``` !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow !head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json ```
## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```
125
Dataset caching policy ## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ``` @albertvillanova thank you, it seems it still does not work using: ```python sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], download_mode="force_redownload" ) ``` [This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook! The problem is that the download file's revision for `test.csv` is not correctly parsed ![Schermata 2022-04-27 alle 18 09 41](https://user-images.githubusercontent.com/163333/165563507-0be53eb6-8f61-49b0-b959-306e59281de3.png) If you download that file `test.csv` from the repo, the line `\\N` is not there anymore (it was there at the first file upload). My impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\N`) since are binary files: ``` !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow !head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json ```
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https://github.com/huggingface/datasets/issues/4238
Dataset caching policy
SOLVED! The problem was the with the file itself, using caching parameter helped indeed. Thanks for helping!
## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```
17
Dataset caching policy ## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ``` SOLVED! The problem was the with the file itself, using caching parameter helped indeed. Thanks for helping!
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https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
Thanks for reporting. I understand it's an error in the dataset script. To reproduce: ```python >>> import datasets as ds >>> split_names = ds.get_dataset_split_names("mozilla-foundation/common_voice_8_0", use_auth_token="**********") Downloading builder script: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 10.9k/10.9k [00:00<00:00, 10.9MB/s] Downloading extra modules: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2.98k/2.98k [00:00<00:00, 3.36MB/s] Downloading extra modules: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 53.1k/53.1k [00:00<00:00, 650kB/s] No config specified, defaulting to: common_voice/en Traceback (most recent call last): File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 280, in get_dataset_config_info for split_generator in builder._split_generators( File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 153, in _split_generators self._log_download(self.config.name, bundle_version, hf_auth_token) File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 139, in _log_download email = HfApi().whoami(auth_token)["email"] KeyError: 'email' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_split_names info = get_dataset_config_info( File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 285, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config. ```
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
151
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Thanks for reporting. I understand it's an error in the dataset script. To reproduce: ```python >>> import datasets as ds >>> split_names = ds.get_dataset_split_names("mozilla-foundation/common_voice_8_0", use_auth_token="**********") Downloading builder script: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 10.9k/10.9k [00:00<00:00, 10.9MB/s] Downloading extra modules: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2.98k/2.98k [00:00<00:00, 3.36MB/s] Downloading extra modules: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 53.1k/53.1k [00:00<00:00, 650kB/s] No config specified, defaulting to: common_voice/en Traceback (most recent call last): File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 280, in get_dataset_config_info for split_generator in builder._split_generators( File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 153, in _split_generators self._log_download(self.config.name, bundle_version, hf_auth_token) File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 139, in _log_download email = HfApi().whoami(auth_token)["email"] KeyError: 'email' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_split_names info = get_dataset_config_info( File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 285, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config. ```
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-0.1224749535, -0.0402533263, 0.4102576971, 0.0025106065, -0.0645008683, 0.1102685407, -0.0553387478 ]
https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
Thanks for reporting @patrickvonplaten and thanks for the investigation @severo. Unfortunately I'm not able to reproduce the error. I think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139 ```python from huggingface_hub import HfApi, HfFolder if isinstance(auth_token, bool): email = HfApi().whoami(auth_token) email = HfApi().whoami(auth_token)["email"] ``` Could you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
70
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Thanks for reporting @patrickvonplaten and thanks for the investigation @severo. Unfortunately I'm not able to reproduce the error. I think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139 ```python from huggingface_hub import HfApi, HfFolder if isinstance(auth_token, bool): email = HfApi().whoami(auth_token) email = HfApi().whoami(auth_token)["email"] ``` Could you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?
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-0.0415402502, -0.0429128632, 0.3550975323, 0.0441450551, 0.0538326129, 0.3761520088, -0.0080621308 ]
https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email! ```python >>> from huggingface_hub import HfApi, HfFolder >>> auth_token = "hf_app_******" >>> t = HfApi().whoami(auth_token) >>> t {'type': 'app', 'name': 'dataset-preview-backend'} >>> t["email"] Traceback (most recent call last): File "<stdin>", line 1, in <module> KeyError: 'email' ``` Note also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key. @SBrandeis @julien-c: do you think the app token should have an email associated, like the users?
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
105
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email! ```python >>> from huggingface_hub import HfApi, HfFolder >>> auth_token = "hf_app_******" >>> t = HfApi().whoami(auth_token) >>> t {'type': 'app', 'name': 'dataset-preview-backend'} >>> t["email"] Traceback (most recent call last): File "<stdin>", line 1, in <module> KeyError: 'email' ``` Note also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key. @SBrandeis @julien-c: do you think the app token should have an email associated, like the users?
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https://github.com/huggingface/datasets/issues/4237
Common Voice 8 doesn't show datasets viewer
We can workaround this with ```python email = HfApi().whoami(auth_token).get("email", "system@huggingface.co") ``` in the common voice scripts
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
16
Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 We can workaround this with ```python email = HfApi().whoami(auth_token).get("email", "system@huggingface.co") ``` in the common voice scripts
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