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https://api.github.com/repos/huggingface/datasets/issues/3651
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Update link in wiki_bio dataset
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[ "> all the tests pass, but I'm still not able to import the dataset\r\n\r\nSince it's not merged on `master` yet, you have to provide the path to your local `wiki_bio.py` to use it.\r\nIndeed the library downloads the dataset files from `master` if you have a dev installation of the library.\r\n\r\nI agree it would be nice to change that, and use the local dataset scripts from the `datasets` directory - it feels definitely more natural.", "Cool, thanks for your help and I agree!" ]
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Fixes #3580 and makes the wiki_bio dataset work again. I changed the link and some documentation, and all the tests pass. Thanks @lhoestq for uploading the dataset to the HuggingFace data bucket. @lhoestq -- all the tests pass, but I'm still not able to import the dataset, as the old Google Drive link is cached somewhere: ```python >>> from datasets import load_dataset load_dataset("wiki_bio>>> load_dataset("wiki_bio") Using custom data configuration default Downloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to /home/jxm3/.cache/huggingface/datasets/wiki_bio/default/1.1.0/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9... Traceback (most recent call last): ... File "/home/jxm3/random/datasets/src/datasets/utils/file_utils.py", line 612, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil ``` what do I have to do to invalidate the cache and actually import the dataset? It's clearly set up correctly, since the data is downloaded and processed by the tests. As an aside, this caching-loading-scripts behavior makes for a really bad developer experience. I just wasted an hour trying to figure out where the caching was happening and how to disable it, and I don't know. All I wanted to do was update the link and submit a pull request! I recommend that you all either change this behavior (i.e. updating the link to a dataset should "just work") or document it, since I couldn't find any information about this in the contributing.md or readme or anywhere else! Thanks!
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Allow 'to_json' to run in unordered fashion in order to lower memory footprint
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[ "Hi @thomasw21, I remember suggesting `imap_unordered` to @lhoestq at that time to speed up `to_json` further but after trying `pool_imap` on multiple datasets (>9GB) , memory utilisation was almost constant and we decided to go ahead with that only. \r\n\r\n1. Did you try this without `gzip`? Because `gzip` feature was introduced recently and I didn't check multi_proc thing with `gzip`. One thing I know is that `gzip` is slow in our implementation than `zip` (it's a WIP #3551) \r\n2. You can try reducing your batch size, this can also help in avoiding OOM errors!", "Thanks @bhavitvyamalik ! I see. I'm not sure this PR actually fixes things for me either (I ended up reducing the num_proc/batch_size to lower it). It does allow the process to run for longer, but I think the reason why it was waiting is that one of the process crashes .... Unfortunately I was working on a setup with a low RAM/cpu core ratio. I'm actually very surprised that it doesn't change memory utilization, otherwise I don't see the purpose of `imap_unordered` existing. I think it's main purpose are when you have high variance in samples (in terms of bytes), which causes unecessary accumulation in `imap`\r\n 1. Did not try without `gzip`\r\n 2. Yeah or `num_proc`", "Can you please try without `gzip` to see how it performs? If it works fine then we can improve `gzip` from our side (I'm already working on it)", "I'll be busy for next few weeks on another project, will do as soon as I have some bandwidth.\r\n" ]
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I'm using `to_json(..., num_proc=num_proc, compressiong='gzip')` with `num_proc>1`. I'm having an issue where things seem to deadlock at some point. Eventually I see OOM. I'm guessing it's an issue where one process starts to take a long time for a specific batch, and so other process keep accumulating their results in memory. In order to flush memory, I propose we use optional `imap_unordered`. This will prevent one process to block the other ones. The logical thinking is that index are rarily relevant, and in one wants to keep an index, one can still create another column and reconstruct from there.
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Add IGLUE dataset
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## Adding a Dataset - **Name:** IGLUE - **Description:** IGLUE brings together 4 vision-and-language tasks across 20 languages (Twitter [thread](https://twitter.com/ebugliarello/status/1487045497583976455?s=20&t=SB4LZGDhhkUW83ugcX_m5w)) - **Paper:** https://arxiv.org/abs/2201.11732 - **Data:** https://github.com/e-bug/iglue - **Motivation:** This dataset would provide a nice example of combining the text and image features of `datasets` together for multimodal applications. Note: the data / code are not yet visible on the GitHub repo, so I've pinged the authors for more information. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Fix Windows CI: bump python to 3.7
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Python>=3.7 is needed to install `tokenizers` 0.11
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Fix `add_column` on datasets with indices mapping
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[ "Sure, let's include this in today's release.", "Cool ! The windows CI should be fixed on master now, feel free to merge :)" ]
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My initial idea was to avoid the `flatten_indices` call and reorder a new column instead, but in the end I decided to follow `concatenate_datasets` and use `flatten_indices` to avoid padding when `dataset._indices.num_rows != dataset._data.num_rows`. Fix #3599
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Fix streaming datasets that are not reset correctly
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[ "Works smoothly with the `transformers.Trainer` class now, thank you!" ]
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Streaming datasets that use `StreamingDownloadManager.iter_archive` and `StreamingDownloadManager.iter_files` had some issues. Indeed if you try to iterate over such dataset twice, then the second time it will be empty. This is because the two methods above are generator functions. I fixed this by making them return iterables that are reset properly instead. Close https://github.com/huggingface/datasets/issues/3645 cc @anton-l
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Streaming dataset based on dl_manager.iter_archive/iter_files are not reset correctly
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Hi ! When iterating over a streaming dataset once, it's not reset correctly because of some issues with `dl_manager.iter_archive` and `dl_manager.iter_files`. Indeed they are generator functions (so the iterator that is returned can be exhausted). They should be iterables instead, and be reset if we do a for loop again: ```python from datasets import load_dataset d = load_dataset("common_voice", "ab", split="test", streaming=True) i = 0 for i, _ in enumerate(d): pass print(i) # 8 # let's do it again i = 0 for i, _ in enumerate(d): pass print(i) # 0 ```
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Add a GROUP BY operator
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[ "Hi ! At the moment you can use `to_pandas()` to get a pandas DataFrame that supports `group_by` operations (make sure your dataset fits in memory though)\r\n\r\nWe use Arrow as a back-end for `datasets` and it doesn't have native group by (see https://github.com/apache/arrow/issues/2189) unfortunately.\r\n\r\nI just drafted what it could look like to have `group_by` in `datasets`:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n\r\ndef group_by(d, col, join): \r\n \"\"\"from: https://github.com/huggingface/datasets/issues/3644\"\"\"\r\n # Get the indices of each group\r\n groups = {key: [] for key in d.unique(col)} \r\n def create_groups_indices(key, i): \r\n groups[key].append(i) \r\n d.map(create_groups_indices, with_indices=True, input_columns=col) \r\n # Get one dataset object per group\r\n groups = {key: d.select(indices) for key, indices in groups.items()} \r\n # Apply join function\r\n groups = {\r\n key: dataset_group.map(join, batched=True, batch_size=len(dataset_group), remove_columns=d.column_names)\r\n for key, dataset_group in groups.items()\r\n } \r\n # Return concatenation of all the joined groups\r\n return concatenate_datasets(groups.values())\r\n```\r\n\r\nexample of usage:\r\n```python\r\n\r\ndef join(batch): \r\n # take the batch of all the examples of a group, and return a batch with one aggregated example\r\n # (we could aggregate examples into several rows instead of one, if you want)\r\n return {\"total\": [batch[\"i\"]]} \r\n\r\nd = Dataset.from_dict({\r\n \"i\": [i for i in range(50)],\r\n \"group_key\": [i % 4 for i in range(50)],\r\n})\r\nprint(group_by(d, \"group_key\", join))\r\n# total\r\n# 0 [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48]\r\n# 1 [1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41, 45, 49]\r\n# 2 [2, 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46]\r\n# 3 [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47]\r\n```\r\n\r\nLet me know if that helps !\r\n\r\ncc @albertvillanova @mariosasko for visibility", "@lhoestq As of PyArrow 7.0.0, `pa.Table` has the [`group_by` method](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.group_by), so we should also consider using that function for grouping. ", "Any update on this?" ]
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**Is your feature request related to a problem? Please describe.** Using batch mapping, we can easily split examples. However, we lack an appropriate option for merging them back together by some key. Consider this example: ```python # features: # { # "example_id": datasets.Value("int32"), # "text": datasets.Value("string") # } ds = datasets.Dataset() def split(examples): sentences = [text.split(".") for text in examples["text"]] return { "example_id": [ example_id for example_id, sents in zip(examples["example_id"], sentences) for _ in sents ], "sentence": [sent for sents in sentences for sent in sents], "sentence_id": [i for sents in sentences for i in range(len(sents))], } split_ds = ds.map(split, batched=True) def process(examples): outputs = some_neural_network_that_works_on_sentences(examples["sentence"]) return {"outputs": outputs} split_ds = split_ds.map(process, batched=True) ``` I have a dataset consisting of texts that I would like to process sentence by sentence in a batched way. Afterwards, I would like to put it back together as it was, merging the outputs together. **Describe the solution you'd like** Ideally, it would look something like this: ```python def join(examples): order = np.argsort(examples["sentence_id"]) text = ".".join(examples["text"][i] for i in order) outputs = [examples["outputs"][i] for i in order] return {"text": text, "outputs": outputs} ds = split_ds.group_by("example_id", join) ``` **Describe alternatives you've considered** Right now, we can do this: ```python def merge(example): meeting_id = example["example_id"] parts = split_ds.filter(lambda x: x["example_id"] == meeting_id).sort("segment_no") return {"outputs": list(parts["outputs"])} ds = ds.map(merge) ``` Of course, we could process the dataset like this: ```python def process(example): outputs = some_neural_network_that_works_on_sentences(example["text"].split(".")) return {"outputs": outputs} ds = ds.map(process, batched=True) ``` However, that does not allow using an arbitrary batch size and may lead to very inefficient use of resources if the batch size is much larger than the number of sentences in one example. I would very much appreciate some kind of group by operator to merge examples based on the value of one column.
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Fix sem_eval_2018_task_1 download location
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[ "I fixed those two things, the two remaining failing checks seem to be due to some dependency missing in the tests." ]
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As discussed with @lhoestq in https://github.com/huggingface/datasets/issues/3549#issuecomment-1020176931_ this is the new pull request to fix the download location.
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Fix dataset slicing with negative bounds when indices mapping is not `None`
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Fix #3611
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Fix numpy rngs when seed is None
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Fixes the NumPy RNG when `seed` is `None`. The problem becomes obvious after reading the NumPy notes on RNG (returned by `np.random.get_state()`): > The MT19937 state vector consists of a 624-element array of 32-bit unsigned integers plus a single integer value between 0 and 624 that indexes the current position within the main array. `The MT19937 state vector`: the seed which we currently index, but this value stays the same for multiple rounds. `plus a single integer value`: the `pos` value in this PR (is 624 if `seed` is set to a fixed value with `np.random.seed`, so we take the first value in the `seed` array returned by `np.random.get_state()`: https://stackoverflow.com/questions/32172054/how-can-i-retrieve-the-current-seed-of-numpys-random-number-generator) NumPy notes: https://numpy.org/doc/stable/reference/random/bit_generators/mt19937.html Fix #3634
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Issues with custom dataset in Wav2Vec2
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[ "Closed and moved to transformers." ]
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We are training Vav2Vec using the run_speech_recognition_ctc_bnb.py-script. This is working fine with Common Voice, however using our custom dataset and data loader at [NbAiLab/NPSC]( https://huggingface.co/datasets/NbAiLab/NPSC) it crashes after roughly 1 epoch with the following stack trace: ![image](https://user-images.githubusercontent.com/9079808/151355893-6d5887cc-ca19-4b12-948a-124eb6dac372.png) We are able to work around the issue, for instance by adding this check in line#222 in transformers/models/wav2vec2/modeling_wav2vec2.py: ```python if input_length - (mask_length - 1) < num_masked_span: num_masked_span = input_length - (mask_length - 1) ``` Interestingly, these are the variable values before the adjustment: ``` input_length=10 mask_length=10 num_masked_span=2 ```` After adjusting num_masked_spin to 1, the training script runs. The issue is also fixed by setting “replace=True” in the same function. Do you have any idea what is causing this, and how to fix this error permanently? If you do not think this is an Datasets issue, feel free to move the issue.
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same value of precision, recall, f1 score at each epoch for classification task.
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[ "Hi @Dhanachandra, \r\n\r\nWe have tests for all our metrics and they work as expected: under the hood, we use scikit-learn implementations.\r\n\r\nMaybe the cause is somewhere else. For example:\r\n- Is it a binary or a multiclass or a multilabel classification? Default computation of these metrics is for binary classification; if you would like multiclass or multilabel, you should pass the corresponding parameters; see their documentation (e.g.: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) or code below:\r\n\r\nhttps://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores\r\n\r\n```python\r\nIn [1]: from datasets import load_metric\r\n\r\nIn [2]: precision = load_metric(\"precision\")\r\n\r\nIn [3]: print(precision.inputs_description)\r\n\r\nArgs:\r\n predictions: Predicted labels, as returned by a model.\r\n references: Ground truth labels.\r\n labels: The set of labels to include when average != 'binary', and\r\n their order if average is None. Labels present in the data can\r\n be excluded, for example to calculate a multiclass average ignoring\r\n a majority negative class, while labels not present in the data will\r\n result in 0 components in a macro average. For multilabel targets,\r\n labels are column indices. By default, all labels in y_true and\r\n y_pred are used in sorted order.\r\n average: This parameter is required for multiclass/multilabel targets.\r\n If None, the scores for each class are returned. Otherwise, this\r\n determines the type of averaging performed on the data:\r\n binary: Only report results for the class specified by pos_label.\r\n This is applicable only if targets (y_{true,pred}) are binary.\r\n micro: Calculate metrics globally by counting the total true positives,\r\n false negatives and false positives.\r\n macro: Calculate metrics for each label, and find their unweighted mean.\r\n This does not take label imbalance into account.\r\n weighted: Calculate metrics for each label, and find their average\r\n weighted by support (the number of true instances for each label).\r\n This alters ‘macro’ to account for label imbalance; it can result\r\n in an F-score that is not between precision and recall.\r\n samples: Calculate metrics for each instance, and find their average\r\n (only meaningful for multilabel classification).\r\n sample_weight: Sample weights.\r\n\r\nReturns:\r\n precision: Precision score.\r\n\r\nExamples:\r\n\r\n >>> precision_metric = datasets.load_metric(\"precision\")\r\n >>> results = precision_metric.compute(references=[0, 1], predictions=[0, 1])\r\n >>> print(results)\r\n {'precision': 1.0}\r\n\r\n >>> predictions = [0, 2, 1, 0, 0, 1]\r\n >>> references = [0, 1, 2, 0, 1, 2]\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')\r\n >>> print(results)\r\n {'precision': 0.2222222222222222}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')\r\n >>> print(results)\r\n {'precision': 0.3333333333333333}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')\r\n >>> print(results)\r\n {'precision': 0.2222222222222222}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\r\n >>> print(results)\r\n {'precision': array([0.66666667, 0. , 0. ])}\r\n```\r\n" ]
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**1st Epoch:** 1/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.59it/s] 01/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:30:49 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7612903225806451} RECALL: {'recall': 0.7612903225806451} F1: {'f1': 0.7612903225806451} {'eval_loss': 1.4658324718475342, 'eval_accuracy': 0.7612903118133545, 'eval_runtime': 30.0054, 'eval_samples_per_second': 46.492, 'eval_steps_per_second': 46.492, 'epoch': 3.0} **4th Epoch:** 1/27/2022 09:56:55 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.92it/s] 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7698924731182796} RECALL: {'recall': 0.7698924731182796} F1: {'f1': 0.7698924731182796} ## Environment info !git clone https://github.com/huggingface/transformers %cd transformers !pip install . !pip install -r /content/transformers/examples/pytorch/token-classification/requirements.txt !pip install datasets
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[ "This issue was original reported at https://github.com/huggingface/transformers/issues/14931 and It seems like this issue also occur with other AutoClass like AutoFeatureExtractor.", "Thanks for moving the issue here !\r\n\r\nI wasn't able to reproduce the issue on my env (the hashes stay the same):\r\n```\r\n- `transformers` version: 1.15.0\r\n- `tokenizers` version: 0.10.3\r\n- `datasets` version: 1.18.1\r\n- `dill` version: 0.3.4\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11\r\n- Python version: 3.7.10\r\n- PyArrow version: 6.0.1\r\n```\r\nHowever I was able to reproduce it on Google Colab (the hashes end up different):\r\n```\r\n- `transformers` version: 1.15.0\r\n- `tokenizers` version: 0.10.3\r\n- `datasets` version: 1.18.1\r\n- `dill` version: 0.3.4\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n```\r\nI'll investigate why it doesn't work properly on Google Colab :)", "I found the issue: the tokenizer has something inside it that changes.\r\n\r\nBefore the call, `tokenizer._tokenizer.truncation` is None, and after the call it changes to this for some reason:\r\n```\r\n{'max_length': 512, 'strategy': 'longest_first', 'stride': 0}\r\n```\r\n\r\nDoes anybody know why calling the tokenizer would change its state this way ? cc @Narsil @SaulLu maybe ?", "`tokenizer.encode(..)` does not accept argument like max_length, strategy or stride.\r\n\r\nIn `tokenizers` you have to modify the tokenizer state by setting various `TruncationParams` (and/or `PaddingParams`).\r\nHowever, since this is modifying the state, you need to mutably borrow the tokenizer (a rust concept). The key principle is that there can ever be only 1 mutable borrow at a time during the span of the tokenizer lifecycle.\r\n\r\nBecause of this, if `transformers` blindly set `TruncationParams` and `PaddingParams` on every call, it would cause the tokenizer to crash (or make the various threads accessing it hang, which is not necessarily better).\r\n\r\nIn order to avoid that, we decided to handle it this way : https://github.com/huggingface/transformers/pull/12550 . \r\n\r\nWhich should explain the state of the tokenizer being modified (hence its hash).\r\n\r\nNow for a temporary solution, simply encoding once with the tokenizer should give it it's proper hash (since by default the tokenizer doesn't have this state, looks at the first encoding call, and creates it).\r\n\r\nWe could try and set these 2 dicts at initialization time, but it wouldn't work if a user modified the tokenizer state later\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(..)\r\ntokenizer.truncation_side = \"left\"\r\n# Now we have a difference between `tokenizer._tokenizer.truncation` and `tokenizer.truncation_side`\r\n```\r\nIf we wanted to fix it correctly it would mean mapping every assignation to it's proper location on `tokenizer.{padding/truncation}`\r\n\r\nI think it's important to note that we cannot guarantee a tokenizer' hash remains the same if *any* of those parameters are modified through the `.map` function.\r\n\r\nEdit: Another option would be to override the default __hash__ function, but I don't know if there's a sound implementation that could fit.", "Thanks a lot for the explanation !\r\nI think if we set these 2 dicts at initialization time it would be amazing already\r\n\r\nShall we open an issue in `transformers` to ask for these dictionaries to be set when the tokenizer is instantiated ?\r\n\r\n> Edit: Another option would be to override the default hash function, but I don't know if there's a sound implementation that could fit.\r\n\r\nIn `datasets` we can easily have custom hashing for objects of the other HF libraries if we want. For example we ignore the cache some tokenizers have. However in this specific case it touches parameters that may change the behavior of the tokenizer itself. I'm not sure the logic that determines how a tokenizer behaves should be in `datasets`", "A hack we could have in the `datasets` lib would be to call the tokenizer before hashing it in order to set all its parameters correctly - but it sounds a lot like a hack and I'm not sure this can work in the long run", "Fully agree with everything you said. \r\n\r\nI think the best course of action is creating an issue in `transformers`. I can start the work on this.\r\nI think the code changes are fairly simple. Making a sound test + not breaking other stuff might be different :D", "It should be noted that this problem also occurs in other AutoClasses, such as AutoFeatureExtractor, so I don't think handling it in Datasets is a long-term practice either.", "> I think the best course of action is creating an issue in `transformers`. I can start the work on this.\r\n\r\n@Narsil Hi, I reopen this issue in `transformers` https://github.com/huggingface/transformers/issues/14931", "Here is @Narsil comment from https://github.com/huggingface/transformers/issues/14931#issuecomment-1074981569\r\n> # TL;DR\r\n> Call the function once on a dummy example beforehand will fix it.\r\n> \r\n> ```python\r\n> tokenizer(\"Some\", \"test\", truncation=True)\r\n> ```\r\n> \r\n> # Long answer\r\n> If I remember the last status, it's hard doing anything, since the call itself\r\n> \r\n> ```python\r\n> tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n> ```\r\n> \r\n> will modify the tokenizer. It's the `truncation=True` that modifies the tokenizer to put it into truncation mode if you will. Calling the tokenizer once with that argument would fix the cache.\r\n> \r\n> Finding a fix that :\r\n> \r\n> * Doesn't imply a huge chunk of work on `tokenizers` (with potential loss of performance, and breaking backward compatibility)\r\n> * Doesn't imply `datasets` running a first pass of the loop\r\n> * Doesn't imply `datasets` looking at the map function itself\r\n> * Uses a sound `hash` for this object in `datasets`.\r\n> \r\n> is IIRC impossible for this use case.\r\n> \r\n> I can explain a bit more why the first option is not desirable.\r\n> \r\n> In order to \"fix\" this for tokenizers, we would need to make `tokenizer(..)` purely without side effects. This means that the \"options\" of tokenization (like `truncation` and `padding` at least) would have\r\n" ]
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## Describe the bug AutoTokenizer hash value got change after datasets.map ## Steps to reproduce the bug 1. trash huggingface datasets cache 2. run the following code: ```python from transformers import AutoTokenizer, BertTokenizer from datasets import load_dataset from datasets.fingerprint import Hasher tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') def tokenize_function(example): return tokenizer(example["sentence1"], example["sentence2"], truncation=True) raw_datasets = load_dataset("glue", "mrpc") print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) ``` got ``` Reusing dataset glue (/home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad) 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1112.35it/s] f4976bb4694ebc51 3fca35a1fd4a1251 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 6.96ba/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15.25ba/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 5.81ba/s] d32837619b7d7d01 5fd925c82edd62b6 ``` 3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache. ## Expected results `AutoTokenizer` work like specific Tokenizer (The hash value don't change after map): ```python from transformers import AutoTokenizer, BertTokenizer from datasets import load_dataset from datasets.fingerprint import Hasher tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') def tokenize_function(example): return tokenizer(example["sentence1"], example["sentence2"], truncation=True) raw_datasets = load_dataset("glue", "mrpc") print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) ``` ``` Reusing dataset glue (/home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad) 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1091.22it/s] 46d4b31f54153fc7 5b8771afd8d43888 Loading cached processed dataset at /home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6b07ff82ae9d5c51.arrow Loading cached processed dataset at /home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-af738a6d84f3864b.arrow Loading cached processed dataset at /home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-531d2a603ba713c1.arrow 46d4b31f54153fc7 5b8771afd8d43888 ``` ## Environment info - `datasets` version: 1.18.0 - Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27 - Python version: 3.9.7 - PyArrow version: 6.0.1
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[ "Hi @lewtun!\r\n \r\nThis one was tricky to debug. Initially, I tought there is a bug in the recently-added (by @lhoestq ) `cast_array_to_feature` function because `git bisect` points to the https://github.com/huggingface/datasets/commit/6ca96c707502e0689f9b58d94f46d871fa5a3c9c commit. Then, I noticed that the feature tpye of the `dialogue` field is `list`, which explains why you didn't get an error in earlier versions. Is there a specific reason why you use `list` instead of `Sequence` in the script? Maybe to avoid turning list of dicts to dicts of lists as it's done by `Sequence` for compatibility with TFDS or for performance reasons? If the field was `Sequence`, you would get an error in `encode_nested_example` because **the scripts yields some additional (nested) columns which are not specified in the `features` dictionary**. Previously, these additional columns would've been ignored by PyArrow (1), but now we have a check for them (2).\r\n(1) See PyArrow behavior:\r\n```\r\n>>> pa.array([{\"a\": 2, \"b\": 3}], type=pa.struct({\"a\": pa.int32()})) # pyarrow ignores the extra column\r\n-- is_valid: all not null\r\n-- child 0 type: int32\r\n [\r\n 2\r\n ]\r\n ```\r\n\r\n(2) Check:\r\nhttps://github.com/huggingface/datasets/blob/4c417d52def6e20359ca16c6723e0a2855e5c3fd/src/datasets/table.py#L1059\r\n\r\nThe fix is very simple: just add the missing columns to the _EMPTY_BELIEF_STATE list:\r\n```python\r\n_EMPTY_BELIEF_STATE.extend(['通用-产品类别', '火车-舱位档次', '通用-系列', '通用-价格区间', '通用-品牌'])\r\n```", "Hey @mariosasko, thank you so much for figuring this one out - it certainly looks like a tricky bug 😱 ! I don't think there's a specific reason to use `list` instead of `Sequence` with the script, but I'll let the dataset creators know to see if your suggestion is acceptable.\r\n\r\nThank you again!", "Thanks, this was indeed the fix! Would it make sense to produce a more informative error message in such cases? \r\n\r\nThe issue can be closed. \r\n\r\n" ]
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## Describe the bug I am trying to load the [`GEM/RiSAWOZ` dataset](https://huggingface.co/datasets/GEM/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too. As far as I can tell, the dataset loading script is correct and the problematic features [here](https://huggingface.co/datasets/GEM/RiSAWOZ/blob/main/RiSAWOZ.py#L237) also look fine to me. ## Steps to reproduce the bug ```python from datasets import load_dataset dset = load_dataset("GEM/RiSAWOZ") ``` ## Expected results I can load the dataset without error. ## Actual results <details><summary>Traceback</summary> ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1083 example = self.info.features.encode_example(record) -> 1084 writer.write(example, key) 1085 finally: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write(self, example, key, writer_batch_size) 445 --> 446 self.write_examples_on_file() 447 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 403 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 404 self.write_batch(batch_examples=batch_examples) 405 self.current_examples = [] ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 497 arrays.append(pa.array(typed_sequence)) 498 inferred_features[col] = typed_sequence.get_inferred_type() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 204 # We only do it if trying_type is False - since this is what the user asks for. --> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 206 return out ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1064 if isinstance(feature, list): -> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0])) 1066 elif isinstance(feature, Sequence): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) -> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") 1088 TypeError: Couldn't cast array of type struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string> to {'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)} During handling of the above exception, another exception occurred: TypeError Traceback (most recent call last) /var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_44306/2896005239.py in <module> ----> 1 dset = load_dataset("GEM/RiSAWOZ") 2 dset ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1692 1693 # Download and prepare data -> 1694 builder_instance.download_and_prepare( 1695 download_config=download_config, 1696 download_mode=download_mode, ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 593 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 594 if not downloaded_from_gcs: --> 595 self._download_and_prepare( 596 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 597 ) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 682 try: 683 # Prepare split will record examples associated to the split --> 684 self._prepare_split(split_generator, **prepare_split_kwargs) 685 except OSError as e: 686 raise OSError( ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1084 writer.write(example, key) 1085 finally: -> 1086 num_examples, num_bytes = writer.finalize() 1087 1088 split_generator.split_info.num_examples = num_examples ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in finalize(self, close_stream) 525 # Re-intializing to empty list for next batch 526 self.hkey_record = [] --> 527 self.write_examples_on_file() 528 if self.pa_writer is None: 529 if self.schema: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 402 # Since current_examples contains (example, key) tuples 403 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 404 self.write_batch(batch_examples=batch_examples) 405 self.current_examples = [] 406 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 495 col_try_type = try_features[col] if try_features is not None and col in try_features else None 496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 497 arrays.append(pa.array(typed_sequence)) 498 inferred_features[col] = typed_sequence.get_inferred_type() 499 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 203 # Also, when trying type "string", we don't want to convert integers or floats to "string". 204 # We only do it if trying_type is False - since this is what the user asks for. --> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 206 return out 207 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1063 # feature must be either [subfeature] or Sequence(subfeature) 1064 if isinstance(feature, list): -> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0])) 1066 elif isinstance(feature, Sequence): 1067 if feature.length > -1: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1085 elif not isinstance(feature, (Sequence, dict, list, tuple)): 1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) -> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") 1088 1089 TypeError: Couldn't cast array of type struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string> to {'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)} ``` </details> ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.10 - PyArrow version: 3.0.0
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Update index.rst
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Make `ted_talks_iwslt` dataset streamable
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[ "Thanks for adding this @mariosasko! It worked for me when running it with a local data file, however, when using the file on Google Drive I get the following error:\r\n```Python\r\nds = load_dataset(\"./ted_talks_iwslt\",\"eu_ca_2014\", streaming=True, split=\"train\", use_auth_token=True)\r\nnext(iter(ds))\r\n```\r\n```\r\n---------------------------------------------------------------------------\r\nClientResponseError Traceback (most recent call last)\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/implementations/http.py:383, in HTTPFileSystem._info(self, url, **kwargs)\r\n 381 try:\r\n 382 info.update(\r\n--> 383 await _file_info(\r\n 384 url,\r\n 385 size_policy=policy,\r\n 386 session=session,\r\n 387 **self.kwargs,\r\n 388 **kwargs,\r\n 389 )\r\n 390 )\r\n 391 if info.get(\"size\") is not None:\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/implementations/http.py:734, in _file_info(url, session, size_policy, **kwargs)\r\n 733 async with r:\r\n--> 734 r.raise_for_status()\r\n 736 # TODO:\r\n 737 # recognise lack of 'Accept-Ranges',\r\n 738 # or 'Accept-Ranges': 'none' (not 'bytes')\r\n 739 # to mean streaming only, no random access => return None\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/aiohttp/client_reqrep.py:1004, in ClientResponse.raise_for_status(self)\r\n 1003 self.release()\r\n-> 1004 raise ClientResponseError(\r\n 1005 self.request_info,\r\n 1006 self.history,\r\n 1007 status=self.status,\r\n 1008 message=self.reason,\r\n 1009 headers=self.headers,\r\n 1010 )\r\n\r\nClientResponseError: 403, message='Forbidden', url=URL('https://drive.google.com/u/0/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download&confirm=1RJz')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nFileNotFoundError Traceback (most recent call last)\r\nInput In [9], in <module>\r\n 1 iterable = iter(ds)\r\n 2 for i in range(10):\r\n----> 3 item = next(iterable)\r\n 4 print(item['text'][:10], item['meta'])\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/iterable_dataset.py:341, in IterableDataset.__iter__(self)\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n 344 encoded_example = self.features.encode_example(example)\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/iterable_dataset.py:338, in IterableDataset._iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/iterable_dataset.py:78, in ExamplesIterable.__iter__(self)\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n\r\nFile ~/.cache/huggingface/modules/datasets_modules/datasets/lm_en_ted_talks_iwslt/756148758e86e64a350f9b320744a2bd5ed5cff74f7df620763a2b5e1a45e6c6/lm_en_ted_talks_iwslt.py:118, in TedTalksIWSLT._generate_examples(self, files)\r\n 116 for _LANG in _LANG_CODES:\r\n 117 source_file_path = _YEAR_FOLDER[year] + \"/ted_\" + _LANG + _YEAR[year] + \".zip\"\r\n--> 118 for path, file in files:\r\n 119 if path.endswith(source_file_path):\r\n 120 source_talks, _ = parse_zip_file(path, file.read())\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py:596, in StreamingDownloadManager.iter_archive(self, urlpath_or_buf)\r\n 594 yield from _iter_archive(urlpath_or_buf)\r\n 595 else:\r\n--> 596 with xopen(urlpath_or_buf, \"rb\", use_auth_token=self.download_config.use_auth_token) as f:\r\n 597 yield from _iter_archive(f)\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py:296, in xopen(file, mode, use_auth_token, *args, **kwargs)\r\n 294 new_kwargs = {}\r\n 295 kwargs = {**kwargs, **new_kwargs}\r\n--> 296 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n 297 _add_retries_to_file_obj_read_method(file_obj)\r\n 298 return file_obj\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/core.py:140, in OpenFile.open(self)\r\n 132 def open(self):\r\n 133 \"\"\"Materialise this as a real open file without context\r\n 134 \r\n 135 The file should be explicitly closed to avoid enclosed file\r\n (...)\r\n 138 been deleted; but a with-context is better style.\r\n 139 \"\"\"\r\n--> 140 out = self.__enter__()\r\n 141 closer = out.close\r\n 142 fobjects = self.fobjects.copy()[:-1]\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self)\r\n 100 def __enter__(self):\r\n 101 mode = self.mode.replace(\"t\", \"\").replace(\"b\", \"\") + \"b\"\r\n--> 103 f = self.fs.open(self.path, mode=mode)\r\n 105 self.fobjects = [f]\r\n 107 if self.compression is not None:\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/spec.py:1009, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1007 else:\r\n 1008 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1009 f = self._open(\r\n 1010 path,\r\n 1011 mode=mode,\r\n 1012 block_size=block_size,\r\n 1013 autocommit=ac,\r\n 1014 cache_options=cache_options,\r\n 1015 **kwargs,\r\n 1016 )\r\n 1017 if compression is not None:\r\n 1018 from fsspec.compression import compr\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/implementations/http.py:343, in HTTPFileSystem._open(self, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\r\n 341 kw[\"asynchronous\"] = self.asynchronous\r\n 342 kw.update(kwargs)\r\n--> 343 size = size or self.info(path, **kwargs)[\"size\"]\r\n 344 session = sync(self.loop, self.set_session)\r\n 345 if block_size and size:\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/asyn.py:91, in sync_wrapper.<locals>.wrapper(*args, **kwargs)\r\n 88 @functools.wraps(func)\r\n 89 def wrapper(*args, **kwargs):\r\n 90 self = obj or args[0]\r\n---> 91 return sync(self.loop, func, *args, **kwargs)\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/asyn.py:71, in sync(loop, func, timeout, *args, **kwargs)\r\n 69 raise FSTimeoutError from return_result\r\n 70 elif isinstance(return_result, BaseException):\r\n---> 71 raise return_result\r\n 72 else:\r\n 73 return return_result\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/asyn.py:25, in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/implementations/http.py:396, in HTTPFileSystem._info(self, url, **kwargs)\r\n 393 except Exception as exc:\r\n 394 if policy == \"get\":\r\n 395 # If get failed, then raise a FileNotFoundError\r\n--> 396 raise FileNotFoundError(url) from exc\r\n 397 logger.debug(str(exc))\r\n 399 return {\"name\": url, \"size\": None, **info, \"type\": \"file\"}\r\n\r\nFileNotFoundError: https://drive.google.com/u/0/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download&confirm=1RJz\r\n```" ]
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Dataset.shuffle(seed=None) gives fixed row permutation
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[ "I'm not sure if this is expected behavior.\r\n\r\nAm I supposed to work with a copy of the dataset, i.e. `shuffled_dataset = data.shuffle(seed=None)`?\r\n\r\n```diff\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+shuffled_data = data.shuffle(seed=None)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+ shuffled_data = shuffled_data.shuffle(seed=None)\r\n+ print(shuffled_data[:])\r\n- print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n df = data.to_pandas().sample(frac=1.0)\r\n print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\nor provide a `generator` instead?\r\n\r\n```diff\r\nimport datasets\r\n+from numpy.random import default_rng\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+rng = default_rng()\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+ print(data.shuffle(generator=rng)[:])\r\n- print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n df = data.to_pandas().sample(frac=1.0)\r\n print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```", "Hi! Thanks for reporting! Yes, this is not expected behavior. I've opened a PR with the fix." ]
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## Describe the bug Repeated attempts to `shuffle` a dataset without specifying a seed give the same results. ## Steps to reproduce the bug ```python import datasets # Some toy example data = datasets.Dataset.from_dict( {"feature": [1, 2, 3, 4, 5], "label": ["a", "b", "c", "d", "e"]} ) # Doesn't work as expected print("Shuffle dataset") for _ in range(3): print(data.shuffle(seed=None)[:]) # This seems to work with pandas print("\nShuffle via pandas") for _ in range(3): df = data.to_pandas().sample(frac=1.0) print(datasets.Dataset.from_pandas(df, preserve_index=False)[:]) ``` ## Expected results I assumed that the default setting would initialize a new/random state of a `np.random.BitGenerator` (see [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)). Wouldn't that reshuffle the rows each time I call `data.shuffle()`? ## Actual results ```bash Shuffle dataset {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} Shuffle via pandas {'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']} {'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']} {'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.0 - Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1
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Mirror canonical datasets in prod
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Push the datasets changes to the Hub in production by setting `HF_USE_PROD=1` I also added a fix that makes the script ignore the json, csv, text, parquet and pandas dataset builders. cc @SBrandeis
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Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid)
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[ "Hi @AnzorGozalishvili,\r\n\r\nMaybe their site was temporarily down, but it seems to work fine now.\r\n\r\nCould you please try again and confirm if the problem persists? ", "Hi @albertvillanova \r\nI checked and it works. \r\nIt seems that it was really temporarily down.\r\nThanks!" ]
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CONTRIBUTOR
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## Describe the bug The dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable. Check out the dataset [homepage](http://data.statmt.org/cc-100/) which isn't accessible. Also the URLs for dataset file per language isn't accessible: http://data.statmt.org/cc-100/<language code here>.txt.xz (language codes: am, sr, ka, etc.) ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("cc100", "ka") ``` It throws 503 error. ## Expected results It should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible. ## Environment info Run from google colab. Just installed the library using pip: ```!pip install -U datasets```
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3,631
Labels conflict when loading a local CSV file.
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null
[ "Hi @pichljan, thanks for reporting.\r\n\r\nThis should be fixed. I'm looking at it. " ]
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## Describe the bug I am trying to load a local CSV file with a separate file containing label names. It is successfully loaded for the first time, but when I try to load it again, there is a conflict between provided labels and the cached dataset info. Disabling caching globally and/or using `download_mode="force_redownload"` did not help. ## Steps to reproduce the bug ```python load_dataset('csv', data_files='data/my_data.csv', features=Features(text=Value(dtype='string'), label=ClassLabel(names_file='data/my_data_labels.txt'))) ``` `my_data.csv` file has the following structure: ``` text,label "example1",0 "example2",1 ... ``` and the `my_data_labels.txt` looks like this: ``` label1 label2 ... ``` ## Expected results Successfully loaded dataset. ## Actual results ```python File "/usr/local/lib/python3.8/site-packages/datasets/load.py", line 1706, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 766, in as_dataset datasets = utils.map_nested( File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 261, in map_nested mapped = [ File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 262, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 797, in _build_single_dataset ds = self._as_dataset( File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 872, in _as_dataset return Dataset(fingerprint=fingerprint, **dataset_kwargs) File "/usr/local/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 638, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1242, in from_arrow_schema return Features.from_dict(metadata["info"]["features"]) File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1271, in from_dict obj = generate_from_dict(dic) File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1083, in generate_from_dict return class_type(**{k: v for k, v in obj.items() if k in field_names}) File "<string>", line 7, in __init__ File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 776, in __post_init__ raise ValueError("Please provide either names or names_file but not both.") ValueError: Please provide either names or names_file but not both. ``` ## Environment info - `datasets` version: 1.18.0 - Python version: 3.8.2
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DuplicatedKeysError of NewsQA dataset
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null
[ "Thanks for reporting, @StevenTang1998.\r\n\r\nI'm fixing it. " ]
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After processing the dataset following official [NewsQA](https://github.com/Maluuba/newsqa), I used datasets to load it: ``` a = load_dataset('newsqa', data_dir='news') ``` and the following error occurred: ``` Using custom data configuration default-data_dir=news Downloading and preparing dataset newsqa/default to /root/.cache/huggingface/datasets/newsqa/default-data_dir=news/1.0.0/b0b23e22d94a3d352ad9d75aff2b71375264a122fae301463079ee8595e05ab9... Traceback (most recent call last): File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1084, in _prepare_split writer.write(example, key) File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 442, in write self.check_duplicate_keys() File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story Keys should be unique and deterministic in nature During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 1694, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 595, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 684, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1086, in _prepare_split num_examples, num_bytes = writer.finalize() File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 524, in finalize self.check_duplicate_keys() File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story Keys should be unique and deterministic in nature ```
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3,629
Fix Hub repos update when there's a new release
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It was not listing the full list of datasets correctly cc @SBrandeis this is why it failed for 1.18.0 We should be good now !
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Dataset Card Creator drops information for "Additional Information" Section
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First of all, the card creator is a great addition and really helpful for streamlining dataset cards! ## Describe the bug I encountered an inconvenient bug when entering "Additional Information" in the react app, which drops already entered text when switching to a previous section, and then back again to "Additional Information". I was able to reproduce the issue in both Firefox and Chrome, so I suspect a problem with the React logic that doesn't expect users to switch back in the final section. Edit: I'm also not sure whether this is the right place to open the bug report on, since it's not clear to me which particular project it belongs to, or where I could find associated source code. ## Steps to reproduce the bug 1. Navigate to the Section "Additional Information" in the [dataset card creator](https://huggingface.co/datasets/card-creator/) 2. Enter text in an arbitrary field, e.g., "Dataset Curators". 3. Switch back to a previous section, like "Dataset Creation". 4. When switching back again to "Additional Information", the text has been deleted. Notably, this behavior can be reproduced again and again, it's not just problematic for the first "switch-back" from Additional Information. ## Expected results For step 4, the previously entered information should still be present in the boxes, similar to the behavior to all other sections (switching back there works as expected) ## Actual results The text boxes are empty again, and previously entered text got deleted. ## Environment info - `datasets` version: N/A - Platform: Firefox 96.0 / Chrome 97.0 - Python version: N/A - PyArrow version: N/A
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Fix host URL in The Pile datasets
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[ "We should also update the `bookcorpusopen` download url (see #3561) , no? ", "For `the_pile_openwebtext2` and `the_pile_stack_exchange` I did not regenerate the JSON files, but instead I just changed the download_checksums URL. " ]
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This PR fixes the host URL in The Pile datasets, once they have mirrored their data in another server. Fix #3626.
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The Pile cannot connect to host
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## Describe the bug The Pile had issues with their previous host server and have mirrored its content to another server. The new URL server should be updated.
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Add a metadata field for when source data was produced
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[ "A question to the datasets maintainers: is there a policy about how the set of allowed metadata fields is maintained and expanded?\r\n\r\nMetadata are very important, but defining the standard is always a struggle between allowing exhaustivity without being too complex. Archivists have Dublin Core, open data has https://frictionlessdata.io/, geo has ISO 19139 and INSPIRE, etc. and it's always a mess! I'm not sure we want to dig too much into it, but I'm curious to know if there has been some work on the metadata standard.", "> Metadata are very important, but defining the standard is always a struggle between allowing exhaustivity without being too complex. Archivists have Dublin Core, open data has [frictionlessdata.io](https://frictionlessdata.io/), geo has ISO 19139 and INSPIRE, etc. and it's always a mess! I'm not sure we want to dig too much into it, but I'm curious to know if there has been some work on the metadata standard.\r\n\r\n\r\nI thought this is a potential issue with adding this field since it might be hard to define what is general enough to be useful for most data vs what becomes very domain-specific. Potentially adding one extra field leads to more and more fields in the future. \r\n\r\nAnother issue is that there are some metadata standards around data i.e. [datacite](https://schema.datacite.org/meta/kernel-4.4/), but not many aimed explicitly at ML data afaik. Some of the discussions around metadata for ML are also more focused on versioning/managing data in production environments. My thinking is that here, some reference to the time of production would also often be tracked/relevant, i.e. for triggering model training, so having this information available in the hub would also help address this use case. ", "Adding a relevant paper related to this topic: [TimeLMs: Diachronic Language Models from Twitter](https://arxiv.org/abs/2202.03829)\r\n\r\n", "Related: https://github.com/huggingface/datasets/issues/3877" ]
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**Is your feature request related to a problem? Please describe.** The current problem is that information about when source data was produced is not easily visible. Though there are a variety of metadata fields available in the dataset viewer, time period information is not included. This feature request suggests making metadata relating to the time that the underlying *source* data was produced more prominent and outlines why this specific information is of particular importance, both in domain-specific historic research and more broadly. **Describe the solution you'd like** There are a variety of metadata fields exposed in the dataset viewer (license, task categories, etc.) These fields make this metadata more prominent both for human users and as potentially machine-actionable information (for example, through the API). I would propose to add a metadata field that says when some underlying data was produced. For example, a dataset would be labelled as being produced between `1800-1900`. **Describe alternatives you've considered** This information is sometimes available in the Datacard or a paper describing the dataset. However, it's often not that easy to identify or extract this information, particularly if you want to use this field as a filter to identify relevant datasets. **Additional context** I believe this feature is relevant for a number of reasons: - Increasingly, there is an interest in using historical data for training language models (for example, https://huggingface.co/dbmdz/bert-base-historic-dutch-cased), and datasets to support this task (for example, https://huggingface.co/datasets/bnl_newspapers). For these datasets, indicating the time periods covered is particularly relevant. - More broadly, time is likely a common source of domain drift. Datasets of movie reviews from the 90s may not work well for recent movie reviews. As the documentation and long-term management of ML data become more of a priority, quickly understanding the time when the underlying text (or other data types) is arguably more important. - time-series data: datasets are adding more support for time series data. Again, the periods covered might be particularly relevant here. **open questions** - I think some of my points above apply not only to the underlying data but also to annotations. As a result, there could also be an argument for encoding this information somewhere. However, I would argue (but could be persuaded otherwise) that this is probably less important for filtering. This type of context is already addressed in the datasheets template and often requires more narrative to discuss. - what level of granularity would make sense for this? e.g. assigning a decade, century or year? - how to encode this information? What formatting makes sense - what specific time to encode; a data range? (mean, modal, min, max value?) This is a slightly amorphous feature request - I would be happy to discuss further/try and propose a more concrete solution if this seems like something that could be worth considering. I realise this might also touch on other parts of the 🤗 hubs ecosystem.
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Extend support for streaming datasets that use os.path.relpath
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This PR extends the support in streaming mode for datasets that use `os.path.relpath`, by patching that function. This feature will also be useful to yield the relative path of audio or image files, within an archive or parent dir. Close #3622.
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Extend support for streaming datasets that use os.path.relpath
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Extend support for streaming datasets that use `os.path.relpath`. This feature will also be useful to yield the relative path of audio or image files.
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Consider adding `ipywidgets` as a dependency.
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[ "Hi! We use `tqdm` to display progress bars, so I suggest you open this issue in their repo.", "It depends on how you use `tqdm`, no? \r\n\r\nDoesn't this library import via; \r\n\r\n```\r\nfrom tqdm.notebook import tqdm\r\n```", "Hi! Sorry for the late reply. We import `tqdm` as `from tqdm.auto import tqdm`, which should be equal to `from tqdm.notebook import tqdm` in Jupyter.", "Any objection if I make a PR that checks if the widgets library is installed beforehand? " ]
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When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. ``` ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html ``` It's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here?
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Add Fon language tag
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Add Fon language tag to resources.
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fix meta in mls
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[ "Feel free to merge @polinaeterna as soon as you got an approval from either @lhoestq , @albertvillanova or @mariosasko" ]
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`monolingual` value of `m ultilinguality` param in yaml meta was changed to `multilingual` :)
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TIMIT Dataset not working with GPU
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[ "Hi ! I think you should avoid calling `timit_train['audio']`. Indeed by doing so you're **loading all the audio column in memory**. This is problematic in your case because the TIMIT dataset is huge.\r\n\r\nIf you want to access the audio data of some samples, you should do this instead `timit_train[:10][\"train\"]` for example.\r\n\r\nOther than that, I'm not sure why you get a `TypeError: string indices must be integers`, do you have a code snippet that reproduces the issue that you can share here ?", "I get the same error when I try to do `timit_train[0]` or really any indexing into the whole thing. \r\n\r\nReally, that IS the code snippet that reproduces the issue. If you index into other fields like 'file' or whatever, it works. As soon as one of the fields you're looking into is 'audio', you get that issue. It's a weird issue and I suspect it's Sagemaker/environment related, maybe the mix of libraries and dependencies are not good. \r\n\r\n\r\nExample code snippet with issue. \r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train[0])\r\n```", "Ok I see ! From the error you got, it looks like the `value` encoded in the arrow file of the TIMIT dataset you loaded is a string instead of a dictionary with keys \"path\" and \"bytes\" but we don't support this since 1.18\r\n\r\nCan you try regenerating the dataset with `load_dataset('timit_asr', download_mode=\"force_redownload\")` please ? I think it should fix the issue." ]
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## Describe the bug I am working trying to use the TIMIT dataset in order to fine-tune Wav2Vec2 model and I am unable to load the "audio" column from the dataset when working with a GPU. I am working on Amazon Sagemaker Studio, on the Python 3 (PyTorch 1.8 Python 3.6 GPU Optimized) environment, with a single ml.g4dn.xlarge instance (corresponds to a Tesla T4 GPU). I don't know if the issue is GPU related or Python environment related because everything works when I work off of the CPU Optimized environment with a non-GPU instance. My code also works on Google Colab with a GPU instance. This issue is blocking because I cannot get the 'audio' column in any way due to this error, which means that I can't pass it to any functions. I later use the dataset.map function and that is where I originally noticed this error. ## Steps to reproduce the bug ```python from datasets import load_dataset timit_train = load_dataset('timit_asr', split='train') print(timit_train['audio']) ``` ## Expected results Expected to see inside the 'audio' column, which contains an 'array' nested field with the array data I actually need. ## Actual results Traceback ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-6-ceeac555e921> in <module> ----> 1 timit_train['audio'] /opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1917 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1918 return self._getitem( -> 1919 key, 1920 ) 1921 /opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1902 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1903 formatted_output = format_table( -> 1904 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1905 ) 1906 return formatted_output /opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 529 python_formatter = PythonFormatter(features=None) 530 if format_columns is None: --> 531 return formatter(pa_table, query_type=query_type) 532 elif query_type == "column": 533 if key in format_columns: /opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 280 return self.format_row(pa_table) 281 elif query_type == "column": --> 282 return self.format_column(pa_table) 283 elif query_type == "batch": 284 return self.format_batch(pa_table) /opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_column(self, pa_table) 315 column = self.python_arrow_extractor().extract_column(pa_table) 316 if self.decoded: --> 317 column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) 318 return column 319 /opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_column(self, column, column_name) 221 222 def decode_column(self, column: list, column_name: str) -> list: --> 223 return self.features.decode_column(column, column_name) if self.features else column 224 225 def decode_batch(self, batch: dict) -> dict: /opt/conda/lib/python3.6/site-packages/datasets/features/features.py in decode_column(self, column, column_name) 1337 return ( 1338 [self[column_name].decode_example(value) if value is not None else None for value in column] -> 1339 if self._column_requires_decoding[column_name] 1340 else column 1341 ) /opt/conda/lib/python3.6/site-packages/datasets/features/features.py in <listcomp>(.0) 1336 """ 1337 return ( -> 1338 [self[column_name].decode_example(value) if value is not None else None for value in column] 1339 if self._column_requires_decoding[column_name] 1340 else column /opt/conda/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 85 dict 86 """ ---> 87 path, file = (value["path"], BytesIO(value["bytes"])) if value["bytes"] is not None else (value["path"], None) 88 if path is None and file is None: 89 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.") TypeError: string indices must be integers ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.0 - Platform: Linux-4.14.256-197.484.amzn2.x86_64-x86_64-with-debian-buster-sid - Python version: 3.6.13 - PyArrow version: 6.0.1
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PR for the CFPB Consumer Complaints dataset
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[ "> Nice ! Thanks for adding this dataset :)\n> \n> \n> \n> I left a few comments:\n\nThanks!\n\nI'd be interested in contributing to the core codebase - I had to go down the custom loading approach because I couldn't pull this dataset in using the load_dataset() method. Using either the json or csv files available for this dataset as it was erroring. \n\nI'll rerun it and share the errors and try debug", "Hey @lhoestq ,\r\n\r\nWhen I use this dataset as part of my project, I'm using this method\r\n\r\n`text_dataset = text_dataset['train'].train_test_split(test_size=0.2)`\r\n\r\nto create a train and test split as this dataset doesn't have one. \r\n\r\nCan I add this directly in the script itself somehow, or is it better to give users the flexibility to slice and split their datasets after loading?", "> I'd be interested in contributing to the core codebase - I had to go down the custom loading approach because I couldn't pull this dataset in using the load_dataset() method. Using either the json or csv files available for this dataset as it was erroring.\r\n>\r\n> I'll rerun it and share the errors and try debug\r\n\r\nCool ! Let me know if you have questions or if I can help :)\r\n\r\n> Can I add this directly in the script itself somehow, or is it better to give users the flexibility to slice and split their datasets after loading?\r\n\r\nUsually we let the users the flexibility to split the datasets themselves (unless the dataset is already split, or if there is already a standard way to split it in the papers that use it)", "Thanks Quentin!\r\nAll okay to merge now?", "Thanks for the feedback Quentin and Mario - implemented all changes :)\r\n![Screenshot 2022-01-31 at 23 11 20](https://user-images.githubusercontent.com/42403093/151889262-30737feb-ac9c-4c5a-9326-9812db1d05bc.png)\r\n", "Hey @lhoestq / @mariosasko \r\nAny other changes required to merge? 🤗", "Hi ! Thanks and sorry for the late response \r\n\r\nIt looks very good ! The CI is still failing because it can't file the dummy_data.zip file, you can fix that by moving `datasets/consumer-finance-complaints/dummy/1.0.0/dummy_data.zip` to `datasets/consumer-finance-complaints/dummy/0.0.0/dummy_data.zip` and it should be all good !", "@lhoestq - hopefully that should do it!\r\n" ]
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Think I followed all the steps but please let me know if anything needs changing or any improvements I can make to the code quality
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Make streamable the BnL Historical Newspapers dataset
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I've refactored the code in order to make the dataset streamable and to avoid it takes too long: - I've used `iter_files` Close #3615
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Dataset BnL Historical Newspapers does not work in streaming mode
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[ "@albertvillanova let me know if there is anything I can do to help with this. I had a quick look at the code again and though I could try the following changes:\r\n- use `download` instead of `download_and_extract`\r\nhttps://github.com/huggingface/datasets/blob/d3d339fb86d378f4cb3c5d1de423315c07a466c6/datasets/bnl_newspapers/bnl_newspapers.py#L136\r\n- swith to using `iter_archive` to loop through downloaded data to replace\r\nhttps://github.com/huggingface/datasets/blob/d3d339fb86d378f4cb3c5d1de423315c07a466c6/datasets/bnl_newspapers/bnl_newspapers.py#L159\r\n\r\nLet me know if it's useful for me to try and make those changes. ", "Thanks @davanstrien.\r\n\r\nI have already been working on it so that it can be used in the BigScience workshop.\r\n\r\nI agree that the `rglob()` is not efficient in this case.\r\n\r\nI tried different solutions without success:\r\n- `iter_archive` cannot be used in this case because it does not support ZIP files yet\r\n\r\nFinally I have used `iter_files()`.", "I see this is fixed now 🙂. I also picked up a few other tips from your redactors so hopefully my next attempts will support streaming from the start. " ]
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## Describe the bug When trying to load in streaming mode, it "hangs"... ## Steps to reproduce the bug ```python ds = load_dataset("bnl_newspapers", split="train", streaming=True) ``` ## Expected results The code should be optimized, so that it works fast in streaming mode. CC: @davanstrien
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Minor fixes
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This PR: * adds "desc" to the `ignore_kwargs` list in `Dataset.filter` * fixes the default value of `id` in `DatasetDict.prepare_for_task`
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Files not updating in dataset viewer
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[ "Yes. The jobs queue is full right now, following an upgrade... Back to normality in the next hours hopefully. I'll look at your datasets to be sure the dataset viewer works as expected on them.", "Should have been fixed now." ]
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## Dataset viewer issue for '*name of the dataset*' **Link:** Some examples: * https://huggingface.co/datasets/abidlabs/crowdsourced-speech4 * https://huggingface.co/datasets/abidlabs/test-audio-13 *short description of the issue* It seems that the dataset viewer is reading a cached version of the dataset and it is not updating to reflect new files that are added to the dataset. I get this error: ![image](https://user-images.githubusercontent.com/1778297/150566660-30dc0dcd-18fd-4471-b70c-7c4bdc6a23c6.png) Am I the one who added this dataset? Yes
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wikifix
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[ "tests fail because of dataset_infos.json isn't updated. Unfortunately, I cannot get the datasets-cli locally to execute without error. Would need to troubleshoot, what's missing. Maybe someone else can pick up the stick. ", "Hi ! If we change the default date to the latest one, users won't be able to load the \"big\" languages like english anymore, because it requires an Apache Beam runtime to process them. On the contrary, the old data 20200501 has been processed by Hugging Face so that users don't need to run Apache Beam stuff.\r\n\r\nTherefore I'm in favor of not changing the default date until we have processed the latest versions of wikipedia.\r\n\r\nUsers that want to load other languages or that can use Apache Beam can still pass the `language` and `date` parameter to `load_dataset` if they want anyway:\r\n```python\r\nload_dataset(\"wikipedia\", language=\"fr\", date=\"20220120\")\r\n```", "in that case you can close the PR", "Ok thanks !\r\n\r\n(oh I I just noticed that the dataset card is missing the documentation regarding the language and date parameters, let me add it)" ]
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This should get the wikipedia dataloading script back up and running - at least I hope so (tested with language ff and ii)
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Indexing bug after dataset.select()
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[ "Hi! Thanks for reporting! I've opened a PR with the fix." ]
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## Describe the bug A clear and concise description of what the bug is. Dataset indexing is not working as expected after `dataset.select(range(100))` ## Steps to reproduce the bug ```python # Sample code to reproduce the bug import datasets task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } task_name = "sst2" raw_datasets = datasets.load_dataset("glue", task_name) train_dataset = raw_datasets["train"] print("before select: ",train_dataset[-2:]) # before select: {'sentence': ['a patient viewer ', 'this new jangle of noise , mayhem and stupidity must be a serious contender for the title . '], 'label': [1, 0], 'idx': [67347, 67348]} train_dataset = train_dataset.select(range(100)) print("after select: ",train_dataset[-2:]) # after select: {'sentence': [], 'label': [], 'idx': []} ``` link to colab: https://colab.research.google.com/drive/1LngeRC9f0jE7eSQ4Kh1cIeb411lRXQD-?usp=sharing ## Expected results A clear and concise description of the expected results. showing 98, 99 index data ## Actual results Specify the actual results or traceback. empty ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0
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Checksum error when trying to load amazon_review dataset
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug I am getting the issue when trying to load dataset using ``` dataset = load_dataset("amazon_polarity") ``` ## Expected results dataset loaded ## Actual results ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-3-b4758ba980ae> in <module>() ----> 1 dataset = load_dataset("amazon_polarity") 2 dataset.set_format(type='pandas') 3 content_series = dataset['train']['content'] 4 label_series = dataset['train']['label'] 5 df = pd.concat([content_series, label_series], axis=1) 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.0 - Platform: Google colab - Python version: 3.7.12
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Fixes to pubmed dataset download function
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[ "Hi ! I think we can simply add a new configuration for the 2022 data instead of replacing them.\r\nYou can add the new configuration here:\r\n```python\r\n BUILDER_CONFIGS = [\r\n datasets.BuilderConfig(name=\"2021\", description=\"The 2021 annual record\", version=datasets.Version(\"1.0.0\")),\r\n datasets.BuilderConfig(name=\"2022\", description=\"The 2022 annual record\", version=datasets.Version(\"1.0.0\")),\r\n ]\r\n```\r\n\r\nAnd we can have the URLs for these two versions this way:\r\n```python\r\n_URLs = {\r\n \"2021\": f\"ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n{i:04d}.xml.gz\" for i in range(1, 1063)],\r\n \"2022\": f\"ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed22n{i:04d}.xml.gz\" for i in range(1, 1114)]\r\n}\r\n```\r\nand depending on the configuration name (you can get it with `self.config.name`) we can pick the URLs of 2021 or the ones of 2022 and pass them to the `dl_manager` in `_split_generators`\r\n\r\nFeel free to ping me if you have questions or if I can help !", "Hi @spacemanidol, thanks for your contribution.\r\n\r\nThe update of the PubMed dataset URL (besides the update of the corresponding metadata and the dummy data) was already merged to master branch in this other PR:\r\n- #3692 \r\n\r\nI'm closing this PR then.\r\n\r\n@lhoestq please take into account that 2021 data is no longer accessible: every year PubMed releases the baseline data (containing all previous data until that year) and from that on, they release daily updates. ", "> @lhoestq please take into account that 2021 data is no longer accessible: every year PubMed releases the baseline data (containing all previous data until that year) and from that on, they release daily updates.\r\n\r\nOh ok I didn't know, thanks" ]
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Pubmed has updated its settings for 2022 and thus existing download script does not work.
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Add support for continuous metrics (RMSE, MAE)
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[ "Hey @ck37 \r\n\r\nYou can always use a custom metric as explained [in this guide from HF](https://huggingface.co/docs/datasets/master/loading_metrics.html#using-a-custom-metric-script).\r\n\r\nIf this issue needs to be contributed to (for enhancing the metric API) I think [this link](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html) would be helpful for the `MAE` metric.", "You can use a local metric script just by providing its path instead of the usual shortcut name ", "#self-assign I have starting working on this issue to enhance the metric API." ]
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**Is your feature request related to a problem? Please describe.** I am uploading our dataset and models for the "Constructing interval measures" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https://huggingface.co/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome. **Describe the solution you'd like** I would like to be able to tag our models on the Hub with the following metrics: - RMSE - MAE **Describe alternatives you've considered** I don't know if there are any alternatives. **Additional context** Our preprint is available here: https://arxiv.org/abs/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https://www.kaggle.com/c/jigsaw-toxic-severity-rating/overview . I have our first model uploaded to the Hub at https://huggingface.co/ucberkeley-dlab/hate-measure-roberta-large Thanks, Chris
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Add MIT Scene Parsing Benchmark
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Add MIT Scene Parsing Benchmark (a subset of ADE20k). TODOs: * [x] add dummy data * [x] add dataset card * [x] generate `dataset_info.json`
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audio column not saved correctly after resampling
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[ "Hi ! We just released a new version of `datasets` that should fix this.\r\n\r\nI tested resampling and using save/load_from_disk afterwards and it seems to be fixed now", "Hi @lhoestq, \r\n\r\nJust tested the latest datasets version, and confirming that this is fixed for me. \r\n\r\nThanks!", "Also, just an FYI, data that I had saved (with save_to_disk) previously from common voice using datasets==1.17.0 now give the error below when loading (with load_from disk) using datasets==1.18.0. \r\n\r\nHowever, when starting fresh using load_dataset, then doing the resampling, the save/load_from disk worked fine. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n 1747 return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n 1748 elif fs.isfile(Path(dest_dataset_path, config.DATASETDICT_JSON_FILENAME).as_posix()):\r\n-> 1749 return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n 1750 else:\r\n 1751 raise FileNotFoundError(\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/dataset_dict.py in load_from_disk(dataset_dict_path, fs, keep_in_memory)\r\n 769 else Path(dest_dataset_dict_path, k).as_posix()\r\n 770 )\r\n--> 771 dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n 772 return dataset_dict\r\n 773 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n 1118 info=dataset_info,\r\n 1119 split=split,\r\n-> 1120 fingerprint=state[\"_fingerprint\"],\r\n 1121 )\r\n 1122 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n 655 if self.info.features.type != inferred_features.type:\r\n 656 raise ValueError(\r\n--> 657 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n 658 )\r\n 659 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=48000, mono=True, id=None), 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<bytes: binary, path: string>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64>\r\n\r\nbut expected something like\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<path: string, bytes: binary>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64> \r\n```" ]
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## Describe the bug After resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type. ## Steps to reproduce the bug - load a subset of common voice dataset (48Khz) - resample audio column to 16Khz - save with save_to_disk() - load with load_from_disk() ## Expected results I expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it) {'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None), 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)} ## Actual results Audio column does not have the right type {'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)}, 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)} ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.0 - Platform: linux - Python version: - PyArrow version:
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Adding Turkic X-WMT evaluation set for machine translation
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[ "hi! Thank you for all the comments! I believe I addressed them all. Let me know if there is anything else", "Hi there! I was wondering if there is anything else to change before this can be merged", "@lhoestq Hi! Just a gentle reminder about the steps to merge this one! ", "Thanks for the heads up ! I think I fixed the last issue with the YAML tags", "The CI failure is unrelated to this PR and fixed on master, let's merge :)\r\n\r\nThanks a lot !" ]
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This dataset is a human-translated evaluation set for MT crowdsourced and provided by the [Turkic Interlingua ](turkic-interlingua.org) community. It contains eval sets for 8 Turkic languages covering 88 language directions. Languages being covered are: Azerbaijani (az) Bashkir (ba) English (en) Karakalpak (kaa) Kazakh (kk) Kirghiz (ky) Russian (ru) Turkish (tr) Sakha (sah) Uzbek (uz) More info about the corpus is here: [https://github.com/turkic-interlingua/til-mt/tree/master/xwmt](https://github.com/turkic-interlingua/til-mt/tree/master/xwmt) A paper describing the test set is here: [https://arxiv.org/abs/2109.04593](https://arxiv.org/abs/2109.04593)
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Dataset Viewer not showing Previews for Private Datasets
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[ "Sure, it's on the roadmap." ]
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## Dataset viewer issue for 'abidlabs/test-audio-13' It seems that the dataset viewer does not show previews for `private` datasets, even for the user who's private dataset it is. See [1] for example. If I change the visibility to public, then it does show, but it would be useful to have the viewer even for private datasets. ![image](https://user-images.githubusercontent.com/1778297/150200515-93ff1545-11fd-4793-be64-6bed3cd895e2.png) **Link:** [1] https://huggingface.co/datasets/abidlabs/test-audio-13 **Am I the one who added this dataset?** Yes
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Add British Library books dataset
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[ "Thanks for all the help and suggestions\r\n\r\n> Since the dataset has a very specific structure it might not be that easy so feel free to ping me if you have questions or if I can help !\r\n\r\nI did get a little stuck here! So far I have created directories for each config i.e:\r\n\r\n`datasets/datasets/blbooks/dummy/1700_1799/1.0.2/dummy_data.zip` \r\n\r\nI have then added two examples of the `jsonl.gz` files that are in the underlying dataset to each dummy_data directory.This fails the test using local files. \r\n\r\nSince \r\n\r\n```python\r\ndef _generate_examples(self, data_dirs):\r\n```\r\n\r\ntakes as input `data_dirs` which is a list of `iter_dirs` do I need to put the dummy files inside another directory? i.e. \r\n\r\n`datasets/datasets/blbooks/dummy/1700_1799/1.0.2/dummy_data/1700/00.jsonl.gz` \r\n\r\n\r\n ", "I think I managed to create the dummy data :)\r\n\r\nI think everything is good now, if you don't have other changes to do, please mark your PR as \"ready for review\" and ping me!", "> I think I managed to create the dummy data :)\r\n\r\nThanks so much for that!\r\n\r\n> I think everything is good now, if you don't have other changes to do, please mark your PR as \"ready for review\" and ping me!\r\n\r\nThink it is ready to merge from my end @lhoestq. ", "The CI failure on windows is unrelated to your PR and fixed on `master`, we can ignore it" ]
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This pull request adds a dataset of text from digitised (primarily 19th Century) books from the British Library. This collection has previously been used for training language models, e.g. https://github.com/dbmdz/clef-hipe/blob/main/hlms.md. It would be nice to make this dataset more accessible for others to use through datasets. This is still a WIP but I wanted to get some initial feedback in particular; I wanted to check: - I am handling the use of `iter_archive` correctly - I intend to ensure that `dl_manager.download` gets the complete list of URLs to download upfront, so the progress bar knows how much is left to download and then to pass through the `gen_kwargs` a list of downloaded zip archives wrapped in `iter_archive`. I am unsure if there is a more elegant approach for this? - the number of configs: I have aimed to keep this limited - there are a lot of URLs covering the entire dataset, but I have tried to base the configs on what I believe the majority of people will want to they are not presented with too many options - I am happy to hear suggestions for changing this If there are other glaring omissions or mistakes, I'd be happy to hear them. If this approach seems sensible in general, I will finish all the remaining TODOs, generate dummy_data, etc.
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Update url for conll2003
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[ "Hi. lhoestq \r\n\r\n![image](https://user-images.githubusercontent.com/21982975/150345097-154f2b1a-bb12-47af-bddf-40eec0a0dadb.png)\r\nWhat is the solution for it?\r\nyou can see it is still doesn't work here.\r\nhttps://colab.research.google.com/drive/1l52FGWuSaOaGYchit4CbmtUSuzNDx_Ok?usp=sharing\r\nThank you.\r\n", "For now you can specify `load_dataset(..., revision=\"master\")` to use the fix on `master`.\r\n\r\nWe'll also do a new release of `datasets` tomorrow I think" ]
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Following https://github.com/huggingface/datasets/issues/3582 I'm changing the download URL of the conll2003 data files, since the previous host doesn't have the authorization to redistribute the data
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Add conll2003 licensing
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Following https://github.com/huggingface/datasets/issues/3582, this PR updates the licensing section of the CoNLL2003 dataset.
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Use old url for conll2003
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As reported in https://github.com/huggingface/datasets/issues/3582 the CoNLL2003 data files are not available in the master branch of the repo that used to host them. For now we can use the URL from an older commit to access the data files
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The `add_column()` method does not work if used on dataset sliced with `select()`
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[ "similar #3611 " ]
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Hello, I posted this as a question on the forums ([here](https://discuss.huggingface.co/t/add-column-does-not-work-if-used-on-dataset-sliced-with-select/13893)): I have a dataset with 2000 entries > dataset = Dataset.from_dict({'colA': list(range(2000))}) and from which I want to extract the first one thousand rows, create a new dataset with these and also add a new column to it: > dataset2 = dataset.select(list(range(1000))) > final_dataset = dataset2.add_column('colB', list(range(1000))) This gives an error >ArrowInvalid: Added column's length must match table's length. Expected length 2000 but got length 1000 So it looks like even though it is a dataset with 1000 rows, it "remembers" the shape of the one it was sliced from. ## Actual results ``` ArrowInvalid Traceback (most recent call last) <ipython-input-138-e806860f3ce3> in <module> ----> 1 final_dataset = dataset2.add_column('colB', list(range(1000))) ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 468 } 469 # apply actual function --> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 472 # re-apply format to the output ~/.local/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms + update in-place history of transforms ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_column(self, name, column, new_fingerprint) 3343 column_table = InMemoryTable.from_pydict({name: column}) 3344 # Concatenate tables horizontally -> 3345 table = ConcatenationTable.from_tables([self._data, column_table], axis=1) 3346 # Update features 3347 info = self.info.copy() ~/.local/lib/python3.8/site-packages/datasets/table.py in from_tables(cls, tables, axis) 729 table_blocks = to_blocks(table) 730 blocks = _extend_blocks(blocks, table_blocks, axis=axis) --> 731 return cls.from_blocks(blocks) 732 733 @property ~/.local/lib/python3.8/site-packages/datasets/table.py in from_blocks(cls, blocks) 668 @classmethod 669 def from_blocks(cls, blocks: TableBlockContainer) -> "ConcatenationTable": --> 670 blocks = cls._consolidate_blocks(blocks) 671 if isinstance(blocks, TableBlock): 672 table = blocks ~/.local/lib/python3.8/site-packages/datasets/table.py in _consolidate_blocks(cls, blocks) 664 return cls._merge_blocks(blocks, axis=0) 665 else: --> 666 return cls._merge_blocks(blocks) 667 668 @classmethod ~/.local/lib/python3.8/site-packages/datasets/table.py in _merge_blocks(cls, blocks, axis) 650 merged_blocks += list(block_group) 651 else: # both --> 652 merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks] 653 if all(len(row_block) == 1 for row_block in merged_blocks): 654 merged_blocks = cls._merge_blocks( ~/.local/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 650 merged_blocks += list(block_group) 651 else: # both --> 652 merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks] 653 if all(len(row_block) == 1 for row_block in merged_blocks): 654 merged_blocks = cls._merge_blocks( ~/.local/lib/python3.8/site-packages/datasets/table.py in _merge_blocks(cls, blocks, axis) 647 for is_in_memory, block_group in groupby(blocks, key=lambda x: isinstance(x, InMemoryTable)): 648 if is_in_memory: --> 649 block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))] 650 merged_blocks += list(block_group) 651 else: # both ~/.local/lib/python3.8/site-packages/datasets/table.py in _concat_blocks(blocks, axis) 626 else: 627 for name, col in zip(table.column_names, table.columns): --> 628 pa_table = pa_table.append_column(name, col) 629 return pa_table 630 else: ~/.local/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.append_column() ~/.local/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.add_column() ~/.local/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/.local/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Added column's length must match table's length. Expected length 2000 but got length 1000 ``` A solution provided by @mariosasko is to use `dataset2.flatten_indices()` after the `select()` and before attempting to add the new column: > dataset = Dataset.from_dict({'colA': list(range(2000))}) > dataset2 = dataset.select(list(range(1000))) > dataset2 = dataset2.flatten_indices() > final_dataset = dataset2.add_column('colB', list(range(1000))) which works. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.13.2 (note: also checked with version 1.17.0, still the same error) - Platform: Ubuntu 20.04.3 - Python version: 3.8.10 - PyArrow version: 6.0.0
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Readme info not being parsed to show on Dataset card page
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[ "i suspect a markdown parsing error, @severo do you want to take a quick look at it when you have some time?", "# Problem\r\nThe issue seems to coming from the front matter of the README\r\n```---\r\nannotations_creators:\r\n- no-annotation\r\nlanguage_creators:\r\n- machine-generated\r\nlanguages:\r\n- 'ca'\r\n- 'de'\r\nlicenses:\r\n- cc-by-4.0\r\nmultilinguality:\r\n- translation\r\npretty_name: Catalan-German aligned corpora to train NMT systems.\r\nsize_categories:\r\n- \"1M<n<10M\" \r\nsource_datasets:\r\n- extended|tilde_model\r\ntask_categories:\r\n- machine-translation\r\ntask_ids:\r\n- machine-translation\r\n---\r\n``` \r\n# Solution\r\nThe fix is to correctly style the README as explained [here](https://huggingface.co/docs/datasets/v1.12.0/dataset_card.html). I have also correctly parsed the font matter as shown below:\r\n```\r\n---\r\nannotations_creators: []\r\nlanguage_creators: [machine-generated]\r\nlanguages: ['ca', 'de']\r\nlicenses: []\r\nmultilinguality:\r\n- multilingual\r\npretty_name: 'Catalan-German aligned corpora to train NMT systems.'\r\nsize_categories: \r\n- 1M<n<10M\r\nsource_datasets: ['extended|tilde_model']\r\ntask_categories: ['machine-translation']\r\ntask_ids: ['machine-translation']\r\n---\r\n```\r\nYou can find the README for a sample dataset [here](https://huggingface.co/datasets/ritwikraha/Test)", "Thank you. It finally worked implementing your changes and leaving a white line between title and text in the description.", "Thanks, if this solves your issue, can you please close it?" ]
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## Describe the bug The info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README. ## Steps to reproduce the bug # Sample code to reproduce the bug The README file is this one: https://huggingface.co/datasets/softcatala/Tilde-MODEL-Catalan/blob/main/README.md ## Expected results README info should appear in the Dataset card page. ## Actual results Nothing is shown. However, labels are parsed and shown successfully.
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ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
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[ "Hi! The `cd` command in Jupyer/Colab needs to start with `%`, so this should work:\r\n```\r\n!git clone https://github.com/huggingface/datasets.git\r\n%cd datasets\r\n!pip install -e \".[streaming]\"\r\n```" ]
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## Bug The install of streaming dataset is giving following error. ## Steps to reproduce the bug ```python ! git clone https://github.com/huggingface/datasets.git ! cd datasets ! pip install -e ".[streaming]" ``` ## Actual results Cloning into 'datasets'... remote: Enumerating objects: 50816, done. remote: Counting objects: 100% (2356/2356), done. remote: Compressing objects: 100% (1606/1606), done. remote: Total 50816 (delta 834), reused 1741 (delta 525), pack-reused 48460 Receiving objects: 100% (50816/50816), 72.47 MiB | 27.68 MiB/s, done. Resolving deltas: 100% (22541/22541), done. Checking out files: 100% (6722/6722), done. ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
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Loss of cast `Image` feature on certain dataset method
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[ "Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`/`Audio` feature.", "> Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`/`Audio` feature.\r\n\r\nThanks, I'll keep an eye out for #3575 getting merged. I managed to use `push_to_hub` sucesfully with images when they were loaded via `map` - something like `ds.map(lambda example: {\"img\": load_image_function(example['fname']})`, this only pushed the images to the hub if the `load_image_function` return a PIL Image without the filename attribute though. I guess this might often be the prefered behaviour though. \r\n", "Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?", "> Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?\r\n\r\nThanks for checking. There is no longer an error when calling `select` but it appears the cast value isn't preserved. Before `select`\r\n\r\n```python\r\ndataset.features\r\n{'url': Image(id=None)}\r\n```\r\n\r\nafter select:\r\n```\r\n{'url': Value(dtype='string', id=None)}\r\n```\r\n\r\nUpdated Colab example [here](https://colab.research.google.com/gist/davanstrien/4e88f55a3675c279b5c2f64299ae5c6f/potential_casting_bug.ipynb) ", "Hmmm, if I re-run your google colab I'm getting the right type at the end:\r\n```\r\nsample.features\r\n# {'url': Image(id=None)}\r\n```", "Appolgies - I've just run again and also got this output. I have also sucesfully used the `push_to_hub` method. I think this is fixed now so will close this issue. ", "Fixed in #3575 " ]
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## Describe the bug When an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`. I suspect this might be related to https://github.com/huggingface/datasets/pull/3556 but I don't believe that pull request fixes this issue. ## Steps to reproduce the bug An example of casting a url to an image followed by using the `select` method: ```python from datasets import Dataset from datasets import features url = "https://cf.ltkcdn.net/cats/images/std-lg/246866-1200x816-grey-white-kitten.webp" data_dict = {"url": [url]*2} dataset = Dataset.from_dict(data_dict) dataset = dataset.cast_column('url',features.Image()) sample = dataset.select([1]) ``` [example notebook](https://gist.github.com/davanstrien/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb) ## Expected results The cast value is maintained when further methods are applied to the dataset. ## Actual results ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-12-47f393bc2d0d> in <module>() ----> 1 sample = dataset.select([1]) 4 frames /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 487 } 488 # apply actual function --> 489 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 490 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 491 # re-apply format to the output /usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 409 # Call actual function 410 --> 411 out = func(self, *args, **kwargs) 412 413 # Update fingerprint of in-place transforms + update in-place history of transforms /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 2772 ) 2773 else: -> 2774 return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint) 2775 2776 @transmit_format /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint) 2688 split=self.split, 2689 indices_table=indices_table, -> 2690 fingerprint=fingerprint, 2691 ) 2692 /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint) 664 if self.info.features.type != inferred_features.type: 665 raise ValueError( --> 666 f"External features info don't match the dataset:\nGot\n{self.info.features}\nwith type\n{self.info.features.type}\n\nbut expected something like\n{inferred_features}\nwith type\n{inferred_features.type}" 667 ) 668 ValueError: External features info don't match the dataset: Got {'url': Image(id=None)} with type struct<url: extension<arrow.py_extension_type<ImageExtensionType>>> but expected something like {'url': Value(dtype='string', id=None)} with type struct<url: string> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.1.dev0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0
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Add ImageNet toy datasets from fastai
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Adds the ImageNet toy datasets from FastAI: Imagenette, Imagewoof and Imagewang. TODOs: * [ ] add dummy data * [ ] add dataset card * [ ] generate `dataset_info.json`
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fix multiple language downloading in mC4
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[ "The CI failure is unrelated to your PR and fixed on master, merging :)" ]
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If we try to access multiple languages of the [mC4 dataset](https://github.com/huggingface/datasets/tree/master/datasets/mc4), it will throw an error. For example, if we do ```python mc4_subset_two_langs = load_dataset("mc4", languages=["st", "su"]) ``` we got ``` FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/multilingual/c4-st+su.tfrecord-00000-of-00002.json.gz ``` Now it should work. Check it (from the root dir of a project): ```python mc4_subset_two_langs = load_dataset("./datasets/mc4/", languages=["st", "su"]) ```
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Update README.md
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Towards license of Tweet Eval parts
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Add QuickDraw dataset
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Add the QuickDraw dataset. TODOs: * [x] add dummy data * [x] add dataset card * [x] generate `dataset_info.json`
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Add support for time, date, duration, and decimal dtypes
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[ "Is there a dataset which uses these four datatypes for tests purposes?\r\n", "@severo Not yet. I'll let you know if that changes." ]
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Add support for the pyarrow time (maps to `datetime.time` in python), date (maps to `datetime.time` in python), duration (maps to `datetime.timedelta` in python), and decimal (maps to `decimal.decimal` in python) dtypes. This should be helpful when writing scripts for time-series datasets.
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Update ANLI README.md
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Update license and little things concerning ANLI
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Pin torchmetrics to fix the COMET test
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Torchmetrics 0.7.0 got released and has issues with `transformers` (see https://github.com/PyTorchLightning/metrics/issues/770) I'm pinning it to 0.6.0 in the CI, since 0.7.0 makes the COMET metric test fail. COMET requires torchmetrics==0.6.0 anyway.
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Update HellaSwag README.md
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Adding information from the git repo and paper that were missing
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No module named 'fsspec.archive'
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## Describe the bug Cannot import datasets after installation. ## Steps to reproduce the bug ```shell $ python Python 3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> import datasets Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/__init__.py", line 34, in <module> from .arrow_dataset import Dataset, concatenate_datasets File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 61, in <module> from .arrow_writer import ArrowWriter, OptimizedTypedSequence File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_writer.py", line 28, in <module> from .features import ( File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/features/__init__.py", line 2, in <module> from .audio import Audio File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/features/audio.py", line 7, in <module> from ..utils.streaming_download_manager import xopen File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py", line 18, in <module> from ..filesystems import COMPRESSION_FILESYSTEMS File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/filesystems/__init__.py", line 6, in <module> from . import compression File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/filesystems/compression.py", line 5, in <module> from fsspec.archive import AbstractArchiveFileSystem ModuleNotFoundError: No module named 'fsspec.archive' ```
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Revisit `enable/disable_` toggle function prefix
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As discussed in https://github.com/huggingface/transformers/pull/15167, we should revisit the `enable/disable_` toggle function prefix, potentially in favor of `set_enabled_`. Concretely, this translates to - De-deprecating `disable_progress_bar()` - Adding `enable_progress_bar()` - On the caching side, adding `enable_caching` and `disable_caching` Additional decisions have to be made with regards to the existing `set_enabled_X` functions; that is, whether to keep them as is or deprecate them in favor of the aforementioned functions. cc @mariosasko @lhoestq
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Datasets streaming + map doesn't work for `Audio`
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[ "This seems related to https://github.com/huggingface/datasets/issues/3505." ]
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## Describe the bug When using audio datasets in streaming mode, applying a `map(...)` before iterating leads to an error as the key `array` does not exist anymore. ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("common_voice", "en", streaming=True, split="train") def map_fn(batch): print("audio keys", batch["audio"].keys()) batch["audio"] = batch["audio"]["array"][:100] return batch ds = ds.map(map_fn) sample = next(iter(ds)) ``` I think the audio is somehow decoded before `.map(...)` is actually called. ## Expected results IMO, the above code snippet should work. ## Actual results ```bash audio keys dict_keys(['path', 'bytes']) Traceback (most recent call last): File "./run_audio.py", line 15, in <module> sample = next(iter(ds)) File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 192, in __iter__ yield key, self.function(example) File "./run_audio.py", line 9, in map_fn batch["input"] = batch["audio"]["array"][:100] KeyError: 'array' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.1.dev0 - Platform: Linux-5.3.0-64-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1
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## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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Add The Medical Segmentation Decathlon Dataset
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[ "Hello! I have recently been involved with a medical image segmentation project myself and was going through the `The Medical Segmentation Decathlon Dataset` as well. \r\nI haven't yet had experience adding datasets to this repository yet but would love to get started. Should I take this issue?\r\nIf yes, I've got two questions -\r\n1. There are 10 different datasets available, so are all datasets to be added in a single PR, or one at a time? \r\n2. Since it's a competition, masks for the test-set are not available. How is that to be tackled? Sorry if it's a silly question, I have recently started exploring `datasets`.", "Hi! Sure, feel free to take this issue. You can self-assign the issue by commenting `#self-assign`.\r\n\r\nTo answer your questions:\r\n1. It makes the most sense to add each one as a separate config, so one dataset script with 10 configs in a single PR.\r\n2. Just set masks in the test set to `None`.\r\n\r\nNote that the images/masks in this dataset are in NIfTI format, which our `Image` feature currently doesn't support, so I think it's best to yield the paths to the images/masks in the script and add a preprocessing section to the card where we explain how to load/process the images/masks with `nibabel` (I can help with that). \r\n\r\n", "> Note that the images/masks in this dataset are in NIfTI format, which our `Image` feature currently doesn't support, so I think it's best to yield the paths to the images/masks in the script and add a preprocessing section to the card where we explain how to load/process the images/masks with `nibabel` (I can help with that).\r\n\r\nGotcha, thanks. Will start working on the issue and let you know in case of any doubt.", "#self-assign", "This is great! There is a first model on the HUb that uses this dataset! https://huggingface.co/MONAI/example_spleen_segmentation" ]
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## Adding a Dataset - **Name:** *The Medical Segmentation Decathlon Dataset* - **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. - **Paper:** [link to the dataset paper if available](https://arxiv.org/abs/2106.05735) - **Data:** http://medicaldecathlon.com/ - **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community. (cc @osanseviero @abidlabs ) Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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[ "I came to open the same issue.", "Thanks for reporting !\r\n\r\nI pushed a temporary fix on `master` that uses an URL from a previous commit to access the dataset for now, until we have a better solution", "I changed the URL again to use another host, the fix is available on `master` and we'll probably do a new release of `datasets` tomorrow.\r\n\r\nIn the meantime, feel free to do `load_dataset(..., revision=\"master\")` to use the fixed script", "We just released a new version of `datasets` with a working URL. Feel free to update `datasets` and try again :)" ]
1,642,287,857,000
1,642,784,252,000
1,642,784,252,000
NONE
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## Describe the bug Loading `conll2003` dataset fails because it was removed (just yesterday 1/14/2022) from the location it is looking for. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("conll2003") ``` ## Expected results The dataset should load. ## Actual results It is looking for the dataset at `https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt` but it was removed from there yesterday (see [commit](https://github.com/davidsbatista/NER-datasets/commit/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https://github.com/davidsbatista/NER-datasets/issues/8)). - We should replace this with an alternate valid location. - this is being referenced in the huggingface course chapter 7 [colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter7/section2_pt.ipynb), which is also broken. ```python FileNotFoundError Traceback (most recent call last) <ipython-input-4-27c956bec93c> in <module>() 1 from datasets import load_dataset 2 ----> 3 raw_datasets = load_dataset("conll2003") 11 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params) 610 ) 611 elif response is not None and response.status_code == 404: --> 612 raise FileNotFoundError(f"Couldn't find file at {url}") 613 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 614 if head_error is not None: FileNotFoundError: Couldn't find file at https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:
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I_kwDODunzps5B2sre
3,581
Unable to create a dataset from a parquet file in S3
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[ "Hi ! Currently it only works with local paths, file-like objects are not supported yet" ]
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## Describe the bug Trying to create a dataset from a parquet file in S3. ## Steps to reproduce the bug ```python import s3fs from datasets import Dataset s3 = s3fs.S3FileSystem(anon=False) with s3.open(PATH_LTR_TOY_CLEAN_DATASET, 'rb') as s3file: dataset = Dataset.from_parquet(s3file) ``` ## Expected results A new Dataset object ## Actual results ```AttributeError: 'S3File' object has no attribute 'decode'``` ``` AttributeError Traceback (most recent call last) <command-2452877612515691> in <module> 5 6 with s3.open(PATH_LTR_TOY_CLEAN_DATASET, 'rb') as s3file: ----> 7 dataset = Dataset.from_parquet(s3file) /databricks/python/lib/python3.8/site-packages/datasets/arrow_dataset.py in from_parquet(path_or_paths, split, features, cache_dir, keep_in_memory, columns, **kwargs) 907 from .io.parquet import ParquetDatasetReader 908 --> 909 return ParquetDatasetReader( 910 path_or_paths, 911 split=split, /databricks/python/lib/python3.8/site-packages/datasets/io/parquet.py in __init__(self, path_or_paths, split, features, cache_dir, keep_in_memory, **kwargs) 28 path_or_paths = path_or_paths if isinstance(path_or_paths, dict) else {self.split: path_or_paths} 29 hash = _PACKAGED_DATASETS_MODULES["parquet"][1] ---> 30 self.builder = Parquet( 31 cache_dir=cache_dir, 32 data_files=path_or_paths, /databricks/python/lib/python3.8/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, base_path, info, features, use_auth_token, namespace, data_files, data_dir, **config_kwargs) 246 247 if data_files is not None and not isinstance(data_files, DataFilesDict): --> 248 data_files = DataFilesDict.from_local_or_remote( 249 sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token 250 ) /databricks/python/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token) 576 for key, patterns_for_key in patterns.items(): 577 out[key] = ( --> 578 DataFilesList.from_local_or_remote( 579 patterns_for_key, 580 base_path=base_path, /databricks/python/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token) 544 ) -> "DataFilesList": 545 base_path = base_path if base_path is not None else str(Path().resolve()) --> 546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) 547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token) 548 return cls(data_files, origin_metadata) /databricks/python/lib/python3.8/site-packages/datasets/data_files.py in resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) 191 data_files = [] 192 for pattern in patterns: --> 193 if is_remote_url(pattern): 194 data_files.append(Url(pattern)) 195 else: /databricks/python/lib/python3.8/site-packages/datasets/utils/file_utils.py in is_remote_url(url_or_filename) 115 116 def is_remote_url(url_or_filename: str) -> bool: --> 117 parsed = urlparse(url_or_filename) 118 return parsed.scheme in ("http", "https", "s3", "gs", "hdfs", "ftp") 119 /usr/lib/python3.8/urllib/parse.py in urlparse(url, scheme, allow_fragments) 370 Note that we don't break the components up in smaller bits 371 (e.g. netloc is a single string) and we don't expand % escapes.""" --> 372 url, scheme, _coerce_result = _coerce_args(url, scheme) 373 splitresult = urlsplit(url, scheme, allow_fragments) 374 scheme, netloc, url, query, fragment = splitresult /usr/lib/python3.8/urllib/parse.py in _coerce_args(*args) 122 if str_input: 123 return args + (_noop,) --> 124 return _decode_args(args) + (_encode_result,) 125 126 # Result objects are more helpful than simple tuples /usr/lib/python3.8/urllib/parse.py in _decode_args(args, encoding, errors) 106 def _decode_args(args, encoding=_implicit_encoding, 107 errors=_implicit_errors): --> 108 return tuple(x.decode(encoding, errors) if x else '' for x in args) 109 110 def _coerce_args(*args): /usr/lib/python3.8/urllib/parse.py in <genexpr>(.0) 106 def _decode_args(args, encoding=_implicit_encoding, 107 errors=_implicit_errors): --> 108 return tuple(x.decode(encoding, errors) if x else '' for x in args) 109 110 def _coerce_args(*args): AttributeError: 'S3File' object has no attribute 'decode' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.0 - Platform: Ubuntu 20.04.3 LTS - Python version: 3.8.10 - PyArrow version: 6.0.1
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3,580
Bug in wiki bio load
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[ "+1, here's the error I got: \r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>>\r\n>>> load_dataset(\"wiki_bio\")\r\nDownloading: 7.58kB [00:00, 4.42MB/s]\r\nDownloading: 2.71kB [00:00, 1.30MB/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to /home/jxm3/.cache/huggingface/datasets/wiki_bio/default/1.1.0/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/load.py\", line 1694, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/builder.py\", line 595, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/builder.py\", line 662, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/jxm3/.cache/huggingface/modules/datasets_modules/datasets/wiki_bio/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9/wiki_bio.py\", line 125, in _split_generators\r\n data_dir = dl_manager.download_and_extract(my_urls)\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 308, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 196, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 251, in map_nested\r\n return function(data_struct)\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 217, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 298, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 612, in get_from_cache\r\n raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil\r\n>>>\r\n```\r\n", "@alejandrocros and @lhoestq - you added the wiki_bio dataset in #1173. It doesn't work anymore. Can you take a look at this?", "And if something is wrong with Google Drive, you could try to download (and collate and unzip) from here: https://github.com/DavidGrangier/wikipedia-biography-dataset", "Hi ! Thanks for reporting. I've downloaded the data and concatenated them into a zip file available here: https://huggingface.co/datasets/wiki_bio/tree/main/data\r\n\r\nI guess we can update the dataset script to use this zip file now :)" ]
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wiki_bio is failing to load because of a failing drive link . Can someone fix this ? ![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https://user-images.githubusercontent.com/3104771/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png) ![653C1C76-C725-4A04-A0D8-084373BA612F](https://user-images.githubusercontent.com/3104771/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png) a
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PR_kwDODunzps4xBmY4
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Add Text2log Dataset
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[ "The CI fails are unrelated to your PR and fixed on master, I think we can merge now !" ]
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Adding the text2log dataset used for training FOL sentence translating models
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I_kwDODunzps5BxJkX
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label information get lost after parquet serialization
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[ "Hi ! We did a release of `datasets` today that may fix this issue. Can you try updating `datasets` and trying again ?\r\n\r\nEDIT: the issue is still there actually\r\n\r\nI think we can fix that by storing the Features in the parquet schema metadata, and then reload them when loading the parquet file" ]
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## Describe the bug In *dataset_info.json* file, information about the label get lost after the dataset serialization. ## Steps to reproduce the bug ```python from datasets import load_dataset # normal save dataset = load_dataset('glue', 'sst2', split='train') dataset.save_to_disk("normal_save") # save after parquet serialization dataset.to_parquet("glue-sst2-train.parquet") dataset = load_dataset("parquet", data_files='glue-sst2-train.parquet') dataset.save_to_disk("save_after_parquet") ``` ## Expected results I expected to keep label information in *dataset_info.json* file even after parquet serialization ## Actual results In the normal serialization i got ```json "label": { "num_classes": 2, "names": [ "negative", "positive" ], "names_file": null, "id": null, "_type": "ClassLabel" }, ``` And after parquet serialization i got ```json "label": { "dtype": "int64", "id": null, "_type": "Value" }, ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.0 - Platform: ubuntu 20.04 - Python version: 3.8.10 - PyArrow version: 6.0.1
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Add The Mexican Emotional Speech Database (MESD)
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## Adding a Dataset - **Name:** *The Mexican Emotional Speech Database (MESD)* - **Description:** *Contains 864 voice recordings with six different prosodies: anger, disgust, fear, happiness, neutral, and sadness. Furthermore, three voice categories are included: female adult, male adult, and child. * - **Paper:** *[Paper](https://ieeexplore.ieee.org/abstract/document/9629934/authors#authors)* - **Data:** *[link to the Github repository or current dataset location](https://data.mendeley.com/datasets/cy34mh68j9/3)* - **Motivation:** *Would add Spanish speech data to the HF datasets :) * Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add PASS dataset
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This PR adds the PASS dataset. Closes #3043
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Add Arrow type casting to struct for Image and Audio + Support nested casting
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[ "Regarding the tests I'm just missing the FixedSizeListType type casting for ListArray objects, will to it tomorrow as well as adding new tests + docstrings\r\n\r\nand also adding soundfile in the CI", "While writing some tests I noticed that the ExtensionArray can't be directly concatenated - maybe we can get rid of the extension types/arrays and only keep their storages in native arrow types.\r\n\r\nIn this case the `cast_storage` functions should be the responsibility of the Image and Audio classes directly. And therefore we would need to never cast to a pyarrow type again but to a HF feature - since they'd end up being the one able to tell what's castable or not. This is fine in my opinion but let me know what you think. I can take care of this on monday I think", "Alright I got rid of all the extension type stuff, I'm writing the new tests now :)", "Tests are done, I'll finish the comments and docstrings tomorrow and set the PR on ready for review once it's done !", "> While writing some tests I noticed that the ExtensionArray can't be directly concatenated - maybe we can get rid of the extension types/arrays and only keep their storages in native arrow types.\r\n>\r\n>In this case the cast_storage functions should be the responsibility of the Image and Audio classes directly. And therefore we would need two never cast to a pyarrow type again but to a HF feature - since they'd end up being the one able to tell what's castable or not. This is fine in my opinion but let me know what you think. I can take care of this on monday I think\r\n\r\nDoes this change affect performance?", "> Does this change affect performance?\r\n\r\nIn general it shouldn't have a significant impact on performance since the structure of the features is rarely complex (in general we have <20 features and <4 levels of nesting)\r\n\r\nRegarding Audio and Image specifically, casting from a StringArray is a little bit more costly since it creates the \"bytes\" BinaryArray with `None` values with the same length as the \"path\" array. From the tests I did locally this is very fast though and shouldn't affect the user experience at the current scale of the audio/image datasets we have. It also requires a little bit of RAM though\r\n", "Alright this is ready for review now ! Let me know if you have comments and/or improvements :)" ]
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## Intro 1. Currently, it's not possible to have nested features containing Audio or Image. 2. Moreover one can keep an Arrow array as a StringArray to store paths to images, but such arrays can't be directly concatenated to another image array if it's stored an another Arrow type (typically, a StructType). 3. Allowing several Arrow types for a single HF feature type also leads to bugs like this one #3497 4. Issues like #3247 are quite frequent and happen when Arrow fails to reorder StructArrays. 5. Casting Audio feature type is blocking preparation for the ASR task template: https://github.com/huggingface/datasets/pull/3364 All those issues are linked together by the fact that: - we are limited by the Arrow type casting which is lacking features for nested types. - and especially for Audio and Image: they are not robust enough for concatenation and feature inference. ## Proposed solution To fix 1 and 4 I implemented nested array type casting (which is missing in PyArrow). To fix 2, 3 and 5 while having a simple implementation for nested array type casting, I changed the storage type of Audio and Image to always be a StructType. Also casting from StringType is directly implemented via a new function `cast_storage` that is defined individually for Audio and Image. I also added nested decoding. ## Implementation details ### I. Better Arrow data type casting for nested data structures I implemented new functions `array_cast` and `table_cast` that do the exact same as `pyarrow.Array.cast` or `pyarrow.Table.cast` but support nested struct casting and array re-ordering. These functions can be used on PyArrow objects, and are already integrated in our own `datasets.table.Table.cast` functions. So one can do `my_dataset.data.cast(pyarrow_schema_with_custom_hf_types)` directly. ### II. New image and audio extension types with custom casting I used PyArrow extension types to be able to define what casting is allowed or not. For example both StringType->ImageExtensionType and StructType->ImageExtensionType are allowed, via the `cast_storage` method. I factorized all the PyArrow + Pandas extension stuff in the `base_extension.py` file. This aims at separating the front-facing API code of `datasets` from the Arrow back-end which requires advanced knowledge. ### III. Nested feature decoding I added a new function `decode_nested_example` to decode image and audio data in nested data structures. For optimization's sake, this function is only called if a column has at least one feature that requires decoding. ## Alternative considered The casting to struct type could have been done directly with python objects using some Audio and Image methods, but bringing arrow data to python objects is expensive. The Audio and Image types could also have been able to convert the arrow data directly, but this is not convenient to use when casting a full Arrow Table with nested fields. Therefore I decided to keep the Arrow data casting logic in Arrow extension types. ## Future work This work can be used to allow the ArrayND feature types to be nested too (see issue #887) ## TODO - [x] fix current tests - [x] add new tests - [x] docstrings/comments
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Fix qa4mre tags
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The YAML tags were invalid. I also fixed the dataset mirroring logging that failed because of this issue [here](https://github.com/huggingface/datasets/actions/runs/1690109581)
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Add Mauve metric
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[ "Hi ! The CI was failing because `mauve-text` wasn't installed. I added it to the CI setup :)\r\n\r\nI also did some minor changes to the script itself, especially to remove `**kwargs` and explicitly mentioned all the supported arguments (this way if someone does a typo with some parameters they get an error)" ]
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Add support for the [Mauve](https://github.com/krishnap25/mauve) metric introduced in this [paper](https://arxiv.org/pdf/2102.01454.pdf) (Neurips, 2021).
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ConnectionError in IndicGLUE dataset
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[ "@sahoodib, thanks for reporting.\r\n\r\nIndeed, none of the data links appearing in the IndicGLUE website are working, e.g.: https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/soham-articles.tar.gz\r\n```\r\n<Error>\r\n<Code>UserProjectAccountProblem</Code>\r\n<Message>User project billing account not in good standing.</Message>\r\n<Details>\r\nThe billing account for the owning project is disabled in state delinquent\r\n</Details>\r\n</Error>\r\n```\r\n\r\nWe have contacted the data owners to inform them about their issue and ask them if they plan to fix it." ]
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While I am trying to load IndicGLUE dataset (https://huggingface.co/datasets/indic_glue) it is giving me with the error: ``` ConnectionError: Couldn't reach https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/wikiann-ner.tar.gz (error 403)
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Add missing tasks to MuchoCine dataset
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Addresses the 2nd bullet point in #2520. I'm also removing the licensing information, because I couldn't verify that it is correct.
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Add the KMWP dataset (extension of #3564)
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[ "Sorry, I'm late to check! I'll send it to you soon!" ]
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New pull request of #3564 (Add the KMWP dataset)
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Add the DKTC dataset (Extension of #3564)
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[ "I reflect your comment! @lhoestq ", "Wait, the format of the data just changed, so I'll take it into consideration and commit it.", "I update the code according to the dataset structure change.", "Thanks ! I think the dummy data are not valid yet - the dummy train.csv file only contains a partial example (the quotes `\"` start but never end).", "> Thanks ! I think the dummy data are not valid yet - the dummy train.csv file only contains a partial example (the quotes `\"` start but never end).\r\n\r\nHi! @lhoestq There is a problem. \r\n<img src=\"https://user-images.githubusercontent.com/42150335/149804142-3800e635-f5a0-44d9-9694-0c2b0c05f16b.png\" width=500>\r\n \r\nAs shown in the picture above, the conversation is divided into \"\\n\" in the \"conversion\" column. \r\nThat's why there's an error in the file path that only saved only five lines like below. \r\n\r\n```\r\n'idx', 'class', 'conversation'\r\n'0', '협박 대화', '\"지금 너 스스로를 죽여달라고 애원하는 것인가?'\r\n아닙니다. 죄송합니다.'\r\n죽을 거면 혼자 죽지 우리까지 사건에 휘말리게 해? 진짜 죽여버리고 싶게.'\r\n정말 잘못했습니다.\r\n```\r\n \r\nIn fact, these five lines are all one line. \r\n \r\n\r\n", "Hi ! I see, in this case ca you make sure that the dummy data has a full sample ?\r\n\r\nFeel free to open the dummy train.csv in the dummy_data.zip file and add the missing lines", "Sorry, I'm late to check! I'll send it to you soon!" ]
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New pull request of #3564. (for DKTC)
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Downloading Hugging Face Medical Dialog Dataset NonMatchingSplitsSizesError
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[ "Hi @fabianslife, thanks for reporting.\r\n\r\nI think you were using an old version of `datasets` because this bug was already fixed in version `1.13.0` (13 Oct 2021):\r\n- Fix: 55fd140a63b8f03a0e72985647e498f1fc799d3f\r\n- PR: #3046\r\n- Issue: #2969 \r\n\r\nPlease, feel free to update the library: `pip install -U datasets`." ]
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I wanted to download the Nedical Dialog Dataset from huggingface, using this github link: https://github.com/huggingface/datasets/tree/master/datasets/medical_dialog After downloading the raw datasets from google drive, i unpacked everything and put it in the same folder as the medical_dialog.py which is: ``` import copy import os import re import datasets _CITATION = """\ @article{chen2020meddiag, title={MedDialog: a large-scale medical dialogue dataset}, author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao}, journal={arXiv preprint arXiv:2004.03329}, year={2020} } """ _DESCRIPTION = """\ The MedDialog dataset (English) contains conversations (in English) between doctors and patients.\ It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. \ The raw dialogues are from healthcaremagic.com and icliniq.com.\ All copyrights of the data belong to healthcaremagic.com and icliniq.com. """ _HOMEPAGE = "https://github.com/UCSD-AI4H/Medical-Dialogue-System" _LICENSE = "" class MedicalDialog(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="en", description="The dataset of medical dialogs in English.", version=VERSION), datasets.BuilderConfig(name="zh", description="The dataset of medical dialogs in Chinese.", version=VERSION), ] @property def manual_download_instructions(self): return """\ \n For English:\nYou need to go to https://drive.google.com/drive/folders/1g29ssimdZ6JzTST6Y8g6h-ogUNReBtJD?usp=sharing,\ and manually download the dataset from Google Drive. Once it is completed, a file named Medical-Dialogue-Dataset-English-<timestamp-info>.zip will appear in your Downloads folder( or whichever folder your browser chooses to save files to). Unzip the folder to obtain a folder named "Medical-Dialogue-Dataset-English" several text files. Now, you can specify the path to this folder for the data_dir argument in the datasets.load_dataset(...) option. The <path/to/folder> can e.g. be "/Downloads/Medical-Dialogue-Dataset-English". The data can then be loaded using the below command:\ datasets.load_dataset("medical_dialog", name="en", data_dir="/Downloads/Medical-Dialogue-Dataset-English")`. \n For Chinese:\nFollow the above process. Change the 'name' to 'zh'.The download link is https://drive.google.com/drive/folders/1r09_i8nJ9c1nliXVGXwSqRYqklcHd9e2 **NOTE** - A caution while downloading from drive. It is better to download single files since creating a zip might not include files <500 MB. This has been observed mutiple times. - After downloading the files and adding them to the appropriate folder, the path of the folder can be given as input tu the data_dir path. """ datasets.load_dataset("medical_dialog", name="en", data_dir="Medical-Dialogue-Dataset-English") def _info(self): if self.config.name == "zh": features = datasets.Features( { "file_name": datasets.Value("string"), "dialogue_id": datasets.Value("int32"), "dialogue_url": datasets.Value("string"), "dialogue_turns": datasets.Sequence( { "speaker": datasets.ClassLabel(names=["病人", "医生"]), "utterance": datasets.Value("string"), } ), } ) if self.config.name == "en": features = datasets.Features( { "file_name": datasets.Value("string"), "dialogue_id": datasets.Value("int32"), "dialogue_url": datasets.Value("string"), "dialogue_turns": datasets.Sequence( { "speaker": datasets.ClassLabel(names=["Patient", "Doctor"]), "utterance": datasets.Value("string"), } ), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, features=features, supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) if not os.path.exists(path_to_manual_file): raise FileNotFoundError( f"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('medical_dialog', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})" ) filepaths = [ os.path.join(path_to_manual_file, txt_file_name) for txt_file_name in sorted(os.listdir(path_to_manual_file)) if txt_file_name.endswith("txt") ] return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": filepaths})] def _generate_examples(self, filepaths): """Yields examples. Iterates over each file and give the creates the corresponding features. NOTE: - The code makes some assumption on the structure of the raw .txt file. - There are some checks to separate different id's. Hopefully, should not cause further issues later when more txt files are added. """ data_lang = self.config.name id_ = -1 for filepath in filepaths: with open(filepath, encoding="utf-8") as f_in: # Parameters to just "sectionize" the raw data last_part = "" last_dialog = {} last_list = [] last_user = "" check_list = [] # These flags are present to have a single function address both chinese and english data # English data is a little hahazard (i.e. the sentences spans multiple different lines), # Chinese is compact with one line for doctor and patient. conv_flag = False des_flag = False while True: line = f_in.readline() if not line: break # Extracting the dialog id if line[:2] == "id": # Hardcode alert! # Handling ID references that may come in the description # These were observed in the Chinese dataset and were not # followed by numbers try: dialogue_id = int(re.findall(r"\d+", line)[0]) except IndexError: continue # Extracting the url if line[:4] == "http": # Hardcode alert! dialogue_url = line.rstrip() # Extracting the patient info from description. if line[:11] == "Description": # Hardcode alert! last_part = "description" last_dialog = {} last_list = [] last_user = "" last_conv = {"speaker": "", "utterance": ""} while True: line = f_in.readline() if (not line) or (line in ["\n", "\n\r"]): break else: if data_lang == "zh": # Condition in chinese if line[:5] == "病情描述:": # Hardcode alert! last_user = "病人" sen = f_in.readline().rstrip() des_flag = True if data_lang == "en": last_user = "Patient" sen = line.rstrip() des_flag = True if des_flag: if sen == "": continue if sen in check_list: last_conv["speaker"] = "" last_conv["utterance"] = "" else: last_conv["speaker"] = last_user last_conv["utterance"] = sen check_list.append(sen) des_flag = False break # Extracting the conversation info from dialogue. elif line[:8] == "Dialogue": # Hardcode alert! if last_part == "description" and len(last_conv["utterance"]) > 0: last_part = "dialogue" if data_lang == "zh": last_user = "病人" if data_lang == "en": last_user = "Patient" while True: line = f_in.readline() if (not line) or (line in ["\n", "\n\r"]): conv_flag = False last_user = "" last_list.append(copy.deepcopy(last_conv)) # To ensure close of conversation, only even number of sentences # are extracted last_turn = len(last_list) if int(last_turn / 2) > 0: temp = int(last_turn / 2) id_ += 1 last_dialog["file_name"] = filepath last_dialog["dialogue_id"] = dialogue_id last_dialog["dialogue_url"] = dialogue_url last_dialog["dialogue_turns"] = last_list[: temp * 2] yield id_, last_dialog break if data_lang == "zh": if line[:3] == "病人:" or line[:3] == "医生:": # Hardcode alert! user = line[:2] # Hardcode alert! line = f_in.readline() conv_flag = True # The elif block is to ensure that multi-line sentences are captured. # This has been observed only in english. if data_lang == "en": if line.strip() == "Patient:" or line.strip() == "Doctor:": # Hardcode alert! user = line.replace(":", "").rstrip() line = f_in.readline() conv_flag = True elif line[:2] != "id": # Hardcode alert! conv_flag = True # Continues till the next ID is parsed if conv_flag: sen = line.rstrip() if sen == "": continue if user == last_user: last_conv["utterance"] = last_conv["utterance"] + sen else: last_user = user last_list.append(copy.deepcopy(last_conv)) last_conv["utterance"] = sen last_conv["speaker"] = user ``` running this code gives me the error: ``` File "C:\Users\Fabia\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\utils\info_utils.py", line 74, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=292801173, num_examples=229674, dataset_name='medical_dialog')}] ```
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Fix push to hub to allow individual split push
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# Description of the issue If one decides to push a split on a datasets repo, he uploads the dataset and overrides the config. However previous config splits end up being lost despite still having the dataset necessary. The new flow is the following: - query the old config from the repo - update into a new config (add/overwrite new split for example) - push the new config # Side fix - `repo_id` in HfFileSystem was wrongly typed. - I've added `indent=2` as it becomes much easier to read now.
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3,566
Add initial electricity time series dataset
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[ "@kashif Some commits on the PR branch are not authored by you, so could you please open a new PR and not use rebase this time :)? You can copy and paste the dataset dir to the new branch. \r\n\r\n", "making a new PR" ]
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Here is an initial prototype time series dataset
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Add parameter `preserve_index` to `from_pandas`
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[ "> \r\n\r\nI did `make style` and it affected over 500 files\r\n\r\n```\r\nAll done! ✨ 🍰 ✨\r\n575 files reformatted, 372 files left unchanged.\r\nisort tests src benchmarks datasets/**/*.py metri\r\n```\r\n\r\n(result)\r\n![image](https://user-images.githubusercontent.com/20703486/149166681-2f9d1bc4-116a-4f53-ad42-e54e3b8bd605.png)\r\n", "Nvm I was using wrong black version" ]
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Added optional parameter, so that user can get rid of useless index preserving. [Issue](https://github.com/huggingface/datasets/issues/3563)
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Add the KMWP & DKTC dataset.
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[ "I reflect your review. cc. @lhoestq ", "Ah sorry, I missed KMWP comment, wait.", "I request 2 new pull requests. #3569 #3570" ]
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Add the DKTC dataset. - https://github.com/tunib-ai/DKTC
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Dataset.from_pandas preserves useless index
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[ "Hi! That makes sense. Sure, feel free to open a PR! Just a small suggestion: let's make `preserve_index` a parameter of `Dataset.from_pandas` (which we then pass to `InMemoryTable.from_pandas`) with `None` as a default value to not have this as a breaking change. " ]
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## Describe the bug Let's say that you want to create a Dataset object from pandas dataframe. Most likely you will write something like this: ``` import pandas as pd from datasets import Dataset df = pd.read_csv('some_dataset.csv') # Some DataFrame preprocessing code... dataset = Dataset.from_pandas(df) ``` If your preprocessing code contain indexing operations like this: ``` df = df[df.col1 == some_value] ``` then your df.index can be changed from (default) ```RangeIndex(start=0, stop=16590, step=1)``` to something like this ```Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ... 83979, 83980, 83981, 83982, 83983, 83984, 83985, 83986, 83987, 83988], dtype='int64', length=16590)``` In this case, PyArrow (by default) will preserve this non-standard index. In the result, your dataset object will have the extra field that you likely don't want to have: '__index_level_0__'. You can easily fix this by just adding extra argument ```preserve_index=False``` to call of ```InMemoryTable.from_pandas``` in ```arrow_dataset.py```. If you approve that this isn't desirable behavior, I can make a PR fixing that. ## Environment info - `datasets` version: 1.16.1 - Platform: Linux-5.11.0-44-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 6.0.1
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Allow multiple task templates of the same type
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Add support for multiple task templates of the same type. Fixes (partially) #2520. CC: @lewtun
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Cannot load ‘bookcorpusopen’
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[ "The host of this copy of the dataset (https://the-eye.eu) is down and has been down for a good amount of time ([potentially months](https://www.reddit.com/r/Roms/comments/q82s15/theeye_downdied/))\r\n\r\nFinding this dataset is a little esoteric, as the original authors took down the official BookCorpus dataset some time ago.\r\n\r\nThere are community-created versions of BookCorpus, such as the files hosted in the link below.\r\nhttps://battle.shawwn.com/sdb/bookcorpus/\r\n\r\nAnd more discussion here:\r\nhttps://github.com/soskek/bookcorpus\r\n\r\nDo we want to remove this dataset entirely? There's a fair argument for this, given that the official BookCorpus dataset was taken down by the authors. If not, perhaps can open a PR with the link to the community-created tar above and updated dataset description.", "Hi! The `bookcorpusopen` dataset is not working for the same reason as explained in this comment: https://github.com/huggingface/datasets/issues/3504#issuecomment-1004564980", "Hi @HUIYINXUE, it should work now that the data owners created a mirror server with all data, and we updated the URL in our library." ]
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## Describe the bug Cannot load 'bookcorpusopen' ## Steps to reproduce the bug ```python dataset = load_dataset('bookcorpusopen') ``` or ```python dataset = load_dataset('bookcorpusopen',script_version='master') ``` ## Actual results ConnectionError: Couldn't reach https://the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz ## Environment info - `datasets` version: 1.9.0 - Platform: Linux version 3.10.0-1160.45.1.el7.x86_64 - Python version: 3.6.13 - PyArrow version: 6.0.1
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Run pyupgrade for Python 3.6+
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[ "Hi ! Thanks for the change :)\r\nCould it be possible to only run it for the code in `src/` ? We try to not change the code in the `datasets/` directory too often since it refreshes the users cache when they upgrade `datasets`.", "> Hi ! Thanks for the change :)\r\n> Could it be possible to only run it for the code in `src/` ? We try to not change the code in the `datasets/` directory too often since it refreshes the users cache when they upgrade `datasets`.\r\n\r\nI reverted the changes in `datasets/` instead of changing only `src/`. Does it sound good?", "I just resolved some conflicts with the master branch. If the CI is green we can merge :)" ]
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Run the command: ```bash pyupgrade $(find . -name "*.py" -type f) --py36-plus ``` Which mainly avoids unnecessary lists creations and also removes unnecessary code for Python 3.6+. It was originally part of #3489. Tip for reviewing faster: use the CLI (`git diff`) and scroll.
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Fix `DuplicatedKeysError` and improve card in `tweet_qa`
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Fix #3555
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Integrate Milvus (pymilvus) library
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[ "Hi @mariosasko,Just search randomly and I found this issue~ I'm the tech lead of Milvus and we are looking forward to integrate milvus together with huggingface datasets.\r\n\r\nAny suggestion on how we could start?\r\n", "Feel free to assign to me and we probably need some guide on it", "@mariosasko any updates my man?\r\n", "Hi! For starters, I suggest you take a look at this file: https://github.com/huggingface/datasets/blob/master/src/datasets/search.py, which contains all the code for Faiss/ElasticSearch support. We could set up a Slack channel for additional guidance. Let me know what you prefer.", "> Hi! For starters, I suggest you take a look at this file: https://github.com/huggingface/datasets/blob/master/src/datasets/search.py, which contains all the code for Faiss/ElasticSearch support. We could set up a Slack channel for additional guidance. Let me know what you prefer.\r\n\r\nSure, we take a look and do some research" ]
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Milvus is a popular open-source vector database. We should add a new vector index to support this project.
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Fix bug in `ImageClassifcation` task template
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[ "The CI failures are unrelated to the changes in this PR.", "> The CI failures are unrelated to the changes in this PR.\r\n\r\nIt seems that some of the failures are due to the tests on the dataset cards (e.g. CIFAR, MNIST, FASHION_MNIST). Perhaps it's worth addressing those in this PR to avoid confusing downstream developers who branch off `master` and suddenly have a failing CI?", "@lewtun We only run these tests against the modified datasets on the PR branch, so this will not lead to errors after merging." ]
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Fixes a bug in the `ImageClassification` task template which requires specifying class labels twice in dataset scripts. Additionally, this PR refactors the API around the classification task templates for cleaner `labels` handling. CC: @lewtun @nateraw
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Preserve encoding/decoding with features in `Iterable.map` call
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As described in https://github.com/huggingface/datasets/issues/3505#issuecomment-1004755657, this PR uses a generator expression to encode/decode examples with `features` (which are set to None in `map`) before applying a map transform. Fix #3505
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[ "Hi, we've just merged the PR with the fix. The fixed version of the dataset can be downloaded as follows:\r\n```python\r\nimport datasets\r\ndset = datasets.load_dataset(\"tweet_qa\", revision=\"master\")\r\n```" ]
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When loading the tweet_qa dataset with `load_dataset('tweet_qa')`, the following error occurs: `DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 2a167f9e016ba338e1813fed275a6a1e Keys should be unique and deterministic in nature ` Might be related to issues #2433 and #2333 - `datasets` version: 1.17.0 - Python version: 3.8.5
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ImportError: cannot import name 'is_valid_waiter_error'
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[ "Hi! I can't reproduce this error in Colab, but I'm assuming you are using Amazon SageMaker Studio Notebooks (you mention the `conda_pytorch_p36` kernel), so maybe @philschmid knows more about what might be causing this issue? ", "Hey @mariosasko. Yes, I am using **Amazon SageMaker Studio Jupyter Labs**. However, I no longer need this notebook; but it would be nice to have this problem solved for others. So don't stress too much if you two can't reproduce error.", "Hey @danielbellhv, \r\n\r\nThis issue might be related to Studio probably not having an up to date `botocore` and `boto3` version. I ran into this as well a while back. My workaround was \r\n```python\r\n# using older dataset due to incompatibility of sagemaker notebook & aws-cli with > s3fs and fsspec to >= 2021.10\r\n!pip install \"datasets==1.13\" --upgrade\r\n```\r\n\r\nIn `datasets` we use the latest `s3fs` and `fsspec` but aws-cli and notebook is not supporting this. You could also update the `aws-cli` and associated packages to get the latest `datasets` version\r\n" ]
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Based on [SO post](https://stackoverflow.com/q/70606147/17840900). I'm following along to this [Notebook][1], cell "**Loading the dataset**". Kernel: `conda_pytorch_p36`. I run: ``` ! pip install datasets transformers optimum[intel] ``` Output: ``` Requirement already satisfied: datasets in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (1.17.0) Requirement already satisfied: transformers in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (4.15.0) Requirement already satisfied: optimum[intel] in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (0.1.3) Requirement already satisfied: numpy>=1.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (1.19.5) Requirement already satisfied: dill in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.3.4) Requirement already satisfied: tqdm>=4.62.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (4.62.3) Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.2.1) Requirement already satisfied: packaging in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (21.3) Requirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (6.0.1) Requirement already satisfied: pandas in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (1.1.5) Requirement already satisfied: xxhash in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2.0.2) Requirement already satisfied: aiohttp in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (3.8.1) Requirement already satisfied: fsspec[http]>=2021.05.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2021.11.1) Requirement already satisfied: dataclasses in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.8) Requirement already satisfied: multiprocess in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.70.12.2) Requirement already satisfied: importlib-metadata in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (4.5.0) Requirement already satisfied: requests>=2.19.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2.25.1) Requirement already satisfied: pyyaml>=5.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (5.4.1) Requirement already satisfied: regex!=2019.12.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (2021.4.4) Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (0.10.3) Requirement already satisfied: filelock in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (3.0.12) Requirement already satisfied: sacremoses in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (0.0.46) Requirement already satisfied: torch>=1.9 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.10.1) Requirement already satisfied: sympy in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.8) Requirement already satisfied: coloredlogs in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (15.0.1) Requirement already satisfied: pycocotools in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (2.0.3) Requirement already satisfied: neural-compressor>=1.7 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.9) Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.10.0.0) Requirement already satisfied: sigopt in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.2.0) Requirement already satisfied: opencv-python in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (4.5.1.48) Requirement already satisfied: cryptography in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.4.7) Requirement already satisfied: py-cpuinfo in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.0.0) Requirement already satisfied: gevent in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (21.1.2) Requirement already satisfied: schema in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.7.5) Requirement already satisfied: psutil in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.8.0) Requirement already satisfied: gevent-websocket in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.10.1) Requirement already satisfied: hyperopt in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.2.7) Requirement already satisfied: Flask in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.0.1) Requirement already satisfied: prettytable in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.5.0) Requirement already satisfied: Flask-SocketIO in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.1.1) Requirement already satisfied: scikit-learn in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.24.2) Requirement already satisfied: Pillow in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.4.0) Requirement already satisfied: Flask-Cors in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.0.10) Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from packaging->datasets) (2.4.7) Requirement already satisfied: chardet<5,>=3.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (4.0.0) Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (2021.5.30) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (1.26.5) Requirement already satisfied: idna<3,>=2.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (2.10) Requirement already satisfied: yarl<2.0,>=1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.6.3) Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (2.0.9) Requirement already satisfied: attrs>=17.3.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (21.2.0) Requirement already satisfied: asynctest==0.13.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (0.13.0) Requirement already satisfied: idna-ssl>=1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.1.0) Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (4.0.1) Requirement already satisfied: aiosignal>=1.1.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.2.0) Requirement already satisfied: frozenlist>=1.1.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.2.0) Requirement already satisfied: multidict<7.0,>=4.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (5.1.0) Requirement already satisfied: humanfriendly>=9.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from coloredlogs->optimum[intel]) (10.0) Requirement already satisfied: zipp>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from importlib-metadata->datasets) (3.4.1) Requirement already satisfied: python-dateutil>=2.7.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pandas->datasets) (2.8.1) Requirement already satisfied: pytz>=2017.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pandas->datasets) (2021.1) Requirement already satisfied: matplotlib>=2.1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (3.3.4) Requirement already satisfied: cython>=0.27.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (0.29.23) Requirement already satisfied: setuptools>=18.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (52.0.0.post20210125) Requirement already satisfied: joblib in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (1.0.1) Requirement already satisfied: click in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (8.0.1) Requirement already satisfied: six in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (1.16.0) Requirement already satisfied: mpmath>=0.19 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sympy->optimum[intel]) (1.2.1) Requirement already satisfied: kiwisolver>=1.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from matplotlib>=2.1.0->pycocotools->optimum[intel]) (1.3.1) Requirement already satisfied: cycler>=0.10 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib>=2.1.0->pycocotools->optimum[intel]) (0.10.0) Requirement already satisfied: cffi>=1.12 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from cryptography->neural-compressor>=1.7->optimum[intel]) (1.14.5) Requirement already satisfied: Werkzeug>=2.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from Flask->neural-compressor>=1.7->optimum[intel]) (2.0.2) Requirement already satisfied: Jinja2>=3.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from Flask->neural-compressor>=1.7->optimum[intel]) (3.0.1) Requirement already satisfied: itsdangerous>=2.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from Flask->neural-compressor>=1.7->optimum[intel]) (2.0.1) Requirement already satisfied: python-socketio>=5.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (5.5.0) Requirement already satisfied: zope.event in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from gevent->neural-compressor>=1.7->optimum[intel]) (4.5.0) Requirement already satisfied: greenlet<2.0,>=0.4.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from gevent->neural-compressor>=1.7->optimum[intel]) (1.1.0) Requirement already satisfied: zope.interface in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from gevent->neural-compressor>=1.7->optimum[intel]) (5.4.0) Requirement already satisfied: future in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (0.18.2) Requirement already satisfied: cloudpickle in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (1.6.0) Requirement already satisfied: networkx>=2.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (2.5) Requirement already satisfied: scipy in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (1.5.3) Requirement already satisfied: py4j in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (0.10.7) Requirement already satisfied: wcwidth in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from prettytable->neural-compressor>=1.7->optimum[intel]) (0.2.5) Requirement already satisfied: contextlib2>=0.5.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from schema->neural-compressor>=1.7->optimum[intel]) (0.6.0.post1) Requirement already satisfied: threadpoolctl>=2.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from scikit-learn->neural-compressor>=1.7->optimum[intel]) (2.1.0) Requirement already satisfied: pyOpenSSL>=20.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (20.0.1) Requirement already satisfied: pypng>=0.0.20 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (0.0.21) Requirement already satisfied: kubernetes<13.0.0,>=12.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (12.0.1) Requirement already satisfied: rsa<5.0.0,>=4.7 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (4.7.2) 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cachetools<5.0,>=2.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (4.2.2) Requirement already satisfied: parso<0.8.0,>=0.7.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from jedi>=0.10->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.1) Requirement already satisfied: ipython-genutils in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from traitlets>=4.2->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0) Requirement already satisfied: ptyprocess>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pexpect->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.0) Requirement already satisfied: oauthlib>=3.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests-oauthlib->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.1) ``` --- **Cell:** ```python from datasets import load_dataset, load_metric ``` OR ```python import datasets ``` **Traceback:** ``` --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-7-34fb7ba3338d> in <module> ----> 1 from datasets import load_dataset, load_metric ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/__init__.py in <module> 32 ) 33 ---> 34 from .arrow_dataset import Dataset, concatenate_datasets 35 from .arrow_reader import ArrowReader, ReadInstruction 36 from .arrow_writer import ArrowWriter ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/arrow_dataset.py in <module> 59 from . import config, utils 60 from .arrow_reader import ArrowReader ---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper 63 from .filesystems import extract_path_from_uri, is_remote_filesystem ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/arrow_writer.py in <module> 26 27 from . import config, utils ---> 28 from .features import ( 29 Features, 30 ImageExtensionType, ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/features/__init__.py in <module> 1 # flake8: noqa ----> 2 from .audio import Audio 3 from .features import * 4 from .features import ( 5 _ArrayXD, ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/features/audio.py in <module> 5 import pyarrow as pa 6 ----> 7 from ..utils.streaming_download_manager import xopen 8 9 ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/utils/streaming_download_manager.py in <module> 16 17 from .. import config ---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS 19 from .download_manager import DownloadConfig, map_nested 20 from .file_utils import ( ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/filesystems/__init__.py in <module> 11 12 if _has_s3fs: ---> 13 from .s3filesystem import S3FileSystem # noqa: F401 14 15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [ ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/filesystems/s3filesystem.py in <module> ----> 1 import s3fs 2 3 4 class S3FileSystem(s3fs.S3FileSystem): 5 """ ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/s3fs/__init__.py in <module> ----> 1 from .core import S3FileSystem, S3File 2 from .mapping import S3Map 3 4 from ._version import get_versions 5 ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/s3fs/core.py in <module> 12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper 13 ---> 14 import aiobotocore 15 import botocore 16 import aiobotocore.session ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/__init__.py in <module> ----> 1 from .session import get_session, AioSession 2 3 __all__ = ['get_session', 'AioSession'] 4 __version__ = '1.3.0' ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/session.py in <module> 4 from botocore import retryhandler, translate 5 from botocore.exceptions import PartialCredentialsError ----> 6 from .client import AioClientCreator, AioBaseClient 7 from .hooks import AioHierarchicalEmitter 8 from .parsers import AioResponseParserFactory ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/client.py in <module> 11 from .args import AioClientArgsCreator 12 from .utils import AioS3RegionRedirector ---> 13 from . import waiter 14 15 history_recorder = get_global_history_recorder() ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/waiter.py in <module> 4 from botocore.exceptions import ClientError 5 from botocore.waiter import WaiterModel # noqa: F401, lgtm[py/unused-import] ----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \ 7 NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error 8 from botocore.docs.docstring import WaiterDocstring ImportError: cannot import name 'is_valid_waiter_error' ``` Please let me know if there's anything else I can add to post. [1]: https://github.com/huggingface/notebooks/blob/master/examples/text_classification_quantization_inc.ipynb
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I_kwDODunzps5BZr2z
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set_format("np") no longer works for Image data
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[ "A quick fix for now is doing this:\r\n\r\n```python\r\nX_train = np.stack(dataset[\"train\"][\"image\"])[..., None]", "This error also propagates to jax and is even trickier to fix, since `.with_format(type='jax')` will use numpy conversion internally (and fail). For a three line failure:\r\n\r\n```python\r\ndataset = datasets.load_dataset(\"mnist\")\r\ndataset.set_format(\"jax\")\r\nX_train = dataset[\"train\"][\"image\"]\r\n```", "Hi! We've recently introduced a new Image feature that yields PIL Images (and caches transforms on them) instead of arrays.\r\n\r\nHowever, this feature requires a custom transform to yield np arrays directly:\r\n```python\r\nddict = datasets.load_dataset(\"mnist\")\r\n\r\ndef pil_image_to_array(batch):\r\n return {\"image\": [np.array(img) for img in batch[\"image\"]]} # or jnp.array(img) for Jax\r\n\r\nddict.set_transform(pil_image_to_array, columns=\"image\", output_all_columns=True)\r\n```\r\n\r\n[Docs](https://huggingface.co/docs/datasets/master/process.html#format-transform) on `set_transform`.\r\n\r\nAlso, the approach proposed by @cgarciae is not the best because it loads the entire column in memory.\r\n\r\n@albertvillanova @lhoestq WDYT? The Audio and the Image feature currently don't support the TF/Jax/PT Formatters, but for the Numpy Formatter maybe it makes more sense to return np arrays (and not a dict in the case of the Audio feature or a PIL Image object in the case of the Image feature).", "Yes I agree it should return arrays and not a PIL image (and possible an array instead of a dict for audio data).\r\nI'm currently finishing some code refactoring of the image and audio and opening a PR today. Maybe we can look into that after the refactoring" ]
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## Describe the bug `dataset.set_format("np")` no longer works for image data, previously you could load the MNIST like this: ```python dataset = load_dataset("mnist") dataset.set_format("np") X_train = dataset["train"]["image"][..., None] # <== No longer a numpy array ``` but now it doesn't work, `set_format("np")` seems to have no effect and the dataset just returns a list/array of PIL images instead of numpy arrays as requested.
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PR_kwDODunzps4wsM29
3,552
Add the KMWP & DKTC dataset.
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Add the KMWP & DKTC dataset. Additional notes: - Both datasets will be released on January 10 through the GitHub link below. - https://github.com/tunib-ai/DKTC - https://github.com/tunib-ai/KMWP - So it doesn't work as a link at the moment, but the code will work soon (after it is released on January 10).
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3,551
Add more compression types for `to_json`
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[ "@lhoestq, I looked into how to compress with `zipfile` for which few methods exist, let me know which one looks good:\r\n1. create the file in normal `wb` mode and then zip it separately\r\n2. use `ZipFile.write_str` to write file into the archive. For this we'll need to change how we're writing files from `_write` method \r\n\r\nHow `pandas` handles it is that they have created a wrapper for standard library class `ZipFile` and allow the returned file-like handle to accept byte strings via `write` method instead of `write_str` (purpose was to change the name of function by creating that wrapper)", "1. sounds not ideal since it creates an intermediary file.\r\nI like pandas' approach. Is it possible to implement 2. using the pandas class ? Or maybe we can have something similar ?", "Definitely, @lhoestq! I've adapted that from original code and turns out it is faster than `gz` compression. Apart from that I've also added `infer` option to automatically infer compression type from `path_or_buf` given", "One small thing, currently I'm assuming that user will provide compression extension in `path_or_buf`. Is it this also possible?\r\n`dataset.to_json(\"from_dataset.json\", compression=\"zip\")`? \r\nShould I put an `assert` to ensure the file name provided always has a compression extension?", "Thanks !\r\n\r\n> One small thing, currently I'm assuming that user will provide compression extension in path_or_buf. Is it this also possible?\r\n>dataset.to_json(\"from_dataset.json\", compression=\"zip\")?\r\n>Should I put an assert to ensure the file name provided always has a compression extension?\r\n\r\nI think it's fine as it is right now :) No need to check the extension of the filename passed to `path_or_buf`.\r\n", "> turns out it is faster than gz compression\r\n\r\nI think the default compression level of `gzip` is 9 in python, which is very slow. Maybe we can switch to compression level 6 instead which is faster, like the `gzip` command on unix", "I found that `fsspec` has something that may interest you: [fsspec.open(..., compression=...)](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.open). I don't remember if we've already mentioned it or not\r\n\r\nIt also has `zip` if I understand correctly ! see https://github.com/fsspec/filesystem_spec/blob/master/fsspec/compression.py#L70\r\n\r\nSince `fsspec` is a dependency of `datasets` we can use all this :)\r\n\r\nLet me know if you prefer using `fsspec` instead (I haven't tested this yet to write compressed files). IMO it sounds pretty easy to use and it would make the code base simpler", "Just tried `fsspec` but I'm not able to write compressed `zip` files :/\r\n`gzip`, `xz`, `bz2` are all working fine and it's really simple (no need for `FileWriteHandler` now!)" ]
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This PR adds `bz2`, `xz`, and `zip` (WIP) for `to_json`. I also plan to add `infer` like how `pandas` does it
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