Datasets:

amartyobanerjee

/

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like 0

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[]

[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @caush, thanks for reporting:\r\n\r\nIn order to load local CSV files, you can use our \"csv\" loading script: https://huggingface.co/docs/datasets/loading#csv\r\n```python\r\ndataset = load_dataset(\"csv\", data_files=[\"data/file1.csv\", \"data/file2.csv\"])\r\n```\r\nOR:\r\n```python\r\ndataset = load_dataset(\"csv\", data_dir=\"data\")\r\n```\r\n\r\nAlternatively, you may also use:\r\n```python\r\ndataset = load_dataset(\"data\")" ]

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[ "Hi @Ierezell, thanks for reporting.\r\n\r\nI'm making a PR to suppress those logs from the terminal. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "What do you think @albertvillanova ?" ]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3926). All of your documentation changes will be reflected on that endpoint." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hmm it's still not good \r\n\r\n\r\nany idea what could cause this ?", "Ok fixed :)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3924). All of your documentation changes will be reflected on that endpoint.", "Yay!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3923). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3922). All of your documentation changes will be reflected on that endpoint.", "Unrelated CI test failure. This PR can be merged." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3921). All of your documentation changes will be reflected on that endpoint.", "Unrelated test failure. This PR can be merged." ]

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[ "Hi @Arij-Aladel,\r\n\r\nYou are using a very old version of our library `datasets`: 1.8.0\r\nCurrent version is 2.0.0 (and the previous one was 1.18.4)\r\n\r\nPlease, try to update `datasets` library and check if the problem persists:\r\n```shell\r\n/env/bin/pip install -U datasets", "The problem I can no I have build my project on this version and old version on transformers. I have preprocessed the data again to use it. Thank for your reply" ]

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[ "You are likely trying to get the `features` from a `DatasetDict`, a dictionary containing `Datasets`. You probably first want to index into a particular split from your `DatasetDict` i.e. `dataset['train'].features`. \r\n\r\nFor example \r\n\r\n```python \r\nds = load_dataset('mnist')\r\nds.features\r\n```\r\nReturns \r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError Traceback (most recent call last)\r\n\r\n[<ipython-input-39-791c1f9df6c2>](https://localhost:8080/#) in <module>()\r\n----> 1 ds.features\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nIf we look at the dataset variable, we see it is a `DatasetDict`:\r\n\r\n```python \r\nprint(ds)\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['image', 'label'],\r\n num_rows: 60000\r\n })\r\n test: Dataset({\r\n features: ['image', 'label'],\r\n num_rows: 10000\r\n })\r\n})\r\n```\r\n\r\nWe can grab the features from a split by indexing into `train`:\r\n```python\r\nds['train'].features\r\n{'image': Image(decode=True, id=None),\r\n 'label': ClassLabel(num_classes=10, names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], id=None)}\r\n```\r\n\r\nHope that helps ", "Yes, Thanks for that clarification," ]

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[ "Hi @willowdong! These issues were fixed on master. We will have a new release of `datasets` later today. In the meantime, you can avoid these issues by installing `datasets` from master as follows:\r\n```bash\r\npip install git+https://github.com/huggingface/datasets.git\r\n```", "You should force redownload:\r\n```python\r\ndataset = load_dataset(\"multi_news\", download_mode=\"force_redownload\")\r\ndataset_2 = load_dataset(\"reddit_tifu\", \"long\", download_mode=\"force_redownload\")", "Fixed by:\r\n- #3787 \r\n- #3843" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3917). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3916). All of your documentation changes will be reflected on that endpoint." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Looks like a great start! I have a general comment and a few specific comments.\r\n\r\nMy general comment is I wonder if we need a post for this template and the data and model card templates (or a combined one?) explaining why this documentation is needed and how it serves both the writer and the audience.\r\n\r\nSpecific comments:\r\n- Maybe we can add some more desiderata to the overview instructions like: what task was the metric originally developed for, what tasks is it used for now, what is the range of possible outputs?\r\n- In the data card, we call the data instances inputs `fields`. It might be good to synchronize on that across the templates and change `input_name` to `input_field`? Also are the instructions for the `input_name` complete? It ends with 'In the *' and I'm not sure what that refers to.\r\n- 'Values' seems ambiguous to me, maybe 'scores' would be more explicit? Also could add a request for the range of possible outputs.\r\n- We could add a reference in the examples section to the overview section if that's where further explanation should go. Suggestion to add: 'Provide a range of examples that show both typical and atypical results' or something similar.\r\n- I'm not sure if we'd want to add this to the example section or make a new section, but it would be good to prompt somewhere for links to specific use cases in HF\r\n- In the limitations and bias section, add 'with links'\r\n", "Looks like a great start! I have a general comment and a few specific comments.\r\n\r\nMy general comment is I wonder if we need a post for this template and the data and model card templates (or a combined one?) explaining why this documentation is needed and how it serves both the writer and the audience.\r\n\r\nSpecific comments:\r\n- Maybe we can add some more desiderata to the overview instructions like: what task was the metric originally developed for, what tasks is it used for now, what is the range of possible outputs?\r\n- In the data card, we call the data instances `fields`. It might be good to synchronize on that across the templates and change `input_name` to `input_field`? Also are the instructions for the `input_name` complete? It ends with 'In the *' and I'm not sure what that refers to.\r\n- 'Values' seems ambiguous to me, maybe 'scores' would be more explicit? Also could add a request for the range of possible outputs.\r\n- We could add a reference to the examples section to the overview section if that's where further explanation should go. Suggestion to add: 'Provide a range of examples that show both typical and atypical results' or something similar.\r\n- I'm not sure if we'd want to add this to the example section or make a new section, but it would be good to prompt somewhere for links to specific use cases in HF\r\n- In the limitations and bias section, add 'with links'\r\n", "Thanks for your feedback, @mcmillanmajora ! I totally agree that we should write a post -- we were going to write one up when we are done with a good chunk of the metric cards, but we can also do that earlier :smile: \r\n\r\nWith regards to your more specific comments:\r\n\r\n- It is our intention to put what the metric was developed for (whether it is a specific task or dataset, for example). You can see the [WER](https://github.com/huggingface/datasets/tree/master/metrics/wer) metric card for that.\r\n- `input_field` works for me!\r\n- the values aren't always scores, it's more like the values the metric can take. And it does include the range of possible values, including the max and min, that are outputted.\r\n- I like the suggestion to add: 'Provide a range of examples that show both typical and atypical results' :hugs: \r\n- I have been putting specific use cases in 'Further references', just because there isn't always something to put there, especially for less popular metrics", "Oh cool! I was just looking at the template, it definitely helps seeing an example metric card. Based on just the instructions, I had assumed that examples meant research papers where the metric was used to evaluate a model, but I like the explicit coding examples! ", "Oh cool! I was just looking at the template, it definitely helps seeing an example metric card. Based on just the instructions, I had assumed that examples meant research papers where the metric was used to evaluate a model, but I like the explicit coding examples! " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3914). All of your documentation changes will be reflected on that endpoint.", "You can ignore the CI failures btw, they're unrelated to this PR" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3913). All of your documentation changes will be reflected on that endpoint.", "I'm surprised this is needed because the order of the `dict` keys is deterministic as of Python 3.6 (documented in 3.7). Is there a reproducer for this behavior? I wouldn't make this change unless it's absolutely needed because `sorted` modifies the initial order of the keys.", "Indeed this doesn't fix the issue apparently. Actually this is probably because the tokenizer used to process the second split is in a state that has been modified by the first split.\r\n\r\nTherefore after reloading the first split from the cache, then the second split can't be reloaded since the tokenizer hasn't seen the first split (and therefore is considered a different tokenizer)." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3912). All of your documentation changes will be reflected on that endpoint.", "That's cool ! Though I would expect such an integration to only require `huggingface_hub`, not the full `datasets` library. \r\n Indeed if users want to use the `datasets` lib they could just to `Dataset.from_pandas(df).push_to_hub()` already. Therefore I would explore something that doesn't not necessarily requires `datasets`.\r\n\r\nFor other could storage solutions (S3, GCS, etc.), pandas allows users to pass URIs like `s3://bucket-name/path/data.csv` to the `read_xxx` and `to_xxx` (for csv, parquet, json, etc). It also support passing the **root directory** like `s3://bucket-name/dataset-dir` instead of a single file name.\r\n\r\nIn the Hugging Face Hub case, we have one dataset = one repository. We can enter pandas' paradigm by saying one dataset = one repository = one root directory. Here is what we could have:\r\n\r\n### push to Hub:\r\n```python\r\n\"\"\"\r\nDemo script for writing a pandas data frame to a CSV file on HF using fsspec-supported pandas APIs\r\n\"\"\"\r\nimport pandas as pd\r\n\r\nHF_USER = os.getenv(\"HF_USER\")\r\nHF_TOKEN = os.getenv(\"HF_TOKEN\")\r\n\r\nbooks_df = pd.DataFrame(\r\n data={\"Title\": [\"Book I\", \"Book II\", \"Book III\"], \"Price\": [56.6, 59.87, 74.54]},\r\n columns=[\"Title\", \"Price\"],\r\n)\r\n\r\ndataset_name = \"books1\"\r\n\r\nbooks_df.to_csv(\r\n f\"hf://{HF_USER}/{dataset_name}\",\r\n index=False,\r\n storage_options={\r\n \"repo_type\": \"dataset\",\r\n \"token\": HF_TOKEN,\r\n },\r\n)\r\n\r\n```\r\n\r\n### load from Hub:\r\n```python\r\n\"\"\"\r\nDemo script for reading a CSV file from HF into a pandas data frame using fsspec-supported pandas\r\nAPIs\r\n\"\"\"\r\nimport pandas as pd\r\n\r\nHF_USER = os.getenv(\"HF_USER\")\r\nHF_TOKEN = os.getenv(\"HF_TOKEN\")\r\n\r\ndataset_name = \"books1\"\r\n\r\nbooks_df = pd.read_csv(\r\n f\"hf://{HF_USER}/{dataset_name}\",\r\n storage_options={\r\n \"repo_type\": \"dataset\",\r\n \"token\": HF_TOKEN,\r\n },\r\n)\r\n\r\nprint(books_df)\r\n```\r\n\r\nAnd you could do the same with Parquet data using `read/to_parquet` or other formats. Formats like CSV, Parquet or JSON Lines would work out of the box with `datasets`. This API would also allow anyone to use Dask with the Hugging Face Hub for example.\r\n\r\nWhat do you think ?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3910). All of your documentation changes will be reflected on that endpoint.", "Looks like the test needs to be updated for windows ^^'", "I don't think this is the same issue as in https://github.com/oscar-corpus/corpus/issues/18, where the OSCAR metadata has line offsets that use only `\\n` as the newline marker to count lines, not `\\r\\n` or `\\r`.\r\n\r\nIt looks like the OSCAR data loader is opening the data files with `gzip.open` directly and I don't think this text loader is used, but I'm not familiar with a lot of `datasets` internals so I could be mistaken?", "You are right @adrianeboyd.\r\n\r\nThis PR fixes #3729.\r\n\r\nAdditionally, this PR is somehow related to the OSCAR issue. However, the OSCAR issue have multiple root causes: one is the offset initialization (as you pointed out); other is similar to this case: Unicode newlines are not properly handled.\r\n\r\nI will make a change proposal for OSCAR this afternoon.", "@lhoestq I'm working on fixing the Windows tests on my Windows machine...", "I finally changed the approach in order to avoid having \"\\r\\n\" and \"\\r\" line breaks in Python `str` read from files on Windows/old Macintosh machines." ]

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[ "Hi ! It could an issue with torchaudio, which version of torchaudio are you using ? Can you also try updating `datasets` to 2.0.0 and see if it works ?", "I _might_ have a similar issue. I'm trying to use the librispeech_asr dataset and read it with soundfile.\r\n\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor\r\nimport soundfile as sf\r\n\r\nlibrispeech_eval = load_dataset(\"librispeech_asr\", \"clean\", split=\"test\") # change to \"other\" for other test dataset\r\nwer = load_metric(\"wer\")\r\n\r\nmodel = Speech2TextForConditionalGeneration.from_pretrained(\"facebook/s2t-small-librispeech-asr\").to(\"cuda\")\r\nprocessor = Speech2TextProcessor.from_pretrained(\"facebook/s2t-small-librispeech-asr\", do_upper_case=True)\r\n\r\ndef map_to_array(batch):\r\n speech, _ = sf.read(batch[\"file\"])\r\n batch[\"speech\"] = speech\r\n return batch\r\n\r\nlibrispeech_eval = librispeech_eval.map(map_to_array)\r\n\r\ndef map_to_pred(batch):\r\n features = processor(batch[\"speech\"], sampling_rate=16000, padding=True, return_tensors=\"pt\")\r\n input_features = features.input_features.to(\"cuda\")\r\n attention_mask = features.attention_mask.to(\"cuda\")\r\n\r\n gen_tokens = model.generate(input_ids=input_features, attention_mask=attention_mask)\r\n batch[\"transcription\"] = processor.batch_decode(gen_tokens, skip_special_tokens=True)\r\n return batch\r\n\r\nresult = librispeech_eval.map(map_to_pred, batched=True, batch_size=8, remove_columns=[\"speech\"])\r\n\r\nprint(\"WER:\", wer(predictions=result[\"transcription\"], references=result[\"text\"]))\r\n```\r\n\r\nThe code is taken directly from \"https://huggingface.co/facebook/s2t-small-librispeech-asr\".\r\n\r\nThe short error code is \"RuntimeError: Error opening '6930-75918-0000.flac': System error.\" (it can't find the first file), and I agree, I can't find the file either. The dataset has downloaded correctly (it says), but on the location, there are only \".arrow\" files, no \".flac\" files.\r\n\r\n**Error message:**\r\n\r\n```python\r\nRuntimeError Traceback (most recent call last)\r\nInput In [15], in <cell line: 16>()\r\n 13 batch[\"speech\"] = speech\r\n 14 return batch\r\n---> 16 librispeech_eval = librispeech_eval.map(map_to_array)\r\n 18 def map_to_pred(batch):\r\n 19 features = processor(batch[\"speech\"], sampling_rate=16000, padding=True, return_tensors=\"pt\")\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\datasets\\arrow_dataset.py:1953, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n 1950 disable_tqdm = not logging.is_progress_bar_enabled()\r\n 1952 if num_proc is None or num_proc == 1:\r\n-> 1953 return self._map_single(\r\n 1954 function=function,\r\n 1955 with_indices=with_indices,\r\n 1956 with_rank=with_rank,\r\n 1957 input_columns=input_columns,\r\n 1958 batched=batched,\r\n 1959 batch_size=batch_size,\r\n 1960 drop_last_batch=drop_last_batch,\r\n 1961 remove_columns=remove_columns,\r\n 1962 keep_in_memory=keep_in_memory,\r\n 1963 load_from_cache_file=load_from_cache_file,\r\n 1964 cache_file_name=cache_file_name,\r\n 1965 writer_batch_size=writer_batch_size,\r\n 1966 features=features,\r\n 1967 disable_nullable=disable_nullable,\r\n 1968 fn_kwargs=fn_kwargs,\r\n 1969 new_fingerprint=new_fingerprint,\r\n 1970 disable_tqdm=disable_tqdm,\r\n 1971 desc=desc,\r\n 1972 )\r\n 1973 else:\r\n 1975 def format_cache_file_name(cache_file_name, rank):\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\datasets\\arrow_dataset.py:519, in transmit_tasks.<locals>.wrapper(*args, **kwargs)\r\n 517 self: \"Dataset\" = kwargs.pop(\"self\")\r\n 518 # apply actual function\r\n--> 519 out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n 520 datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n 521 for dataset in datasets:\r\n 522 # Remove task templates if a column mapping of the template is no longer valid\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\datasets\\arrow_dataset.py:486, in transmit_format.<locals>.wrapper(*args, **kwargs)\r\n 479 self_format = {\r\n 480 \"type\": self._format_type,\r\n 481 \"format_kwargs\": self._format_kwargs,\r\n 482 \"columns\": self._format_columns,\r\n 483 \"output_all_columns\": self._output_all_columns,\r\n 484 }\r\n 485 # apply actual function\r\n--> 486 out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n 487 datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n 488 # re-apply format to the output\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\datasets\\fingerprint.py:458, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)\r\n 452 kwargs[fingerprint_name] = update_fingerprint(\r\n 453 self._fingerprint, transform, kwargs_for_fingerprint\r\n 454 )\r\n 456 # Call actual function\r\n--> 458 out = func(self, *args, **kwargs)\r\n 460 # Update fingerprint of in-place transforms + update in-place history of transforms\r\n 462 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\datasets\\arrow_dataset.py:2318, in Dataset._map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n 2316 if not batched:\r\n 2317 for i, example in enumerate(pbar):\r\n-> 2318 example = apply_function_on_filtered_inputs(example, i, offset=offset)\r\n 2319 if update_data:\r\n 2320 if i == 0:\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\datasets\\arrow_dataset.py:2218, in Dataset._map_single.<locals>.apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n 2216 if with_rank:\r\n 2217 additional_args += (rank,)\r\n-> 2218 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)\r\n 2219 if update_data is None:\r\n 2220 # Check if the function returns updated examples\r\n 2221 update_data = isinstance(processed_inputs, (Mapping, pa.Table))\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\datasets\\arrow_dataset.py:1913, in Dataset.map.<locals>.decorate.<locals>.decorated(item, *args, **kwargs)\r\n 1909 decorated_item = (\r\n 1910 Example(item, features=self.features) if not batched else Batch(item, features=self.features)\r\n 1911 )\r\n 1912 # Use the LazyDict internally, while mapping the function\r\n-> 1913 result = f(decorated_item, *args, **kwargs)\r\n 1914 # Return a standard dict\r\n 1915 return result.data if isinstance(result, LazyDict) else result\r\n\r\nInput In [15], in map_to_array(batch)\r\n 11 def map_to_array(batch):\r\n---> 12 speech, _ = sf.read(batch[\"file\"])\r\n 13 batch[\"speech\"] = speech\r\n 14 return batch\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\soundfile.py:256, in read(file, frames, start, stop, dtype, always_2d, fill_value, out, samplerate, channels, format, subtype, endian, closefd)\r\n 170 def read(file, frames=-1, start=0, stop=None, dtype='float64', always_2d=False,\r\n 171 fill_value=None, out=None, samplerate=None, channels=None,\r\n 172 format=None, subtype=None, endian=None, closefd=True):\r\n 173 \"\"\"Provide audio data from a sound file as NumPy array.\r\n 174 \r\n 175 By default, the whole file is read from the beginning, but the\r\n (...)\r\n 254 \r\n 255 \"\"\"\r\n--> 256 with SoundFile(file, 'r', samplerate, channels,\r\n 257 subtype, endian, format, closefd) as f:\r\n 258 frames = f._prepare_read(start, stop, frames)\r\n 259 data = f.read(frames, dtype, always_2d, fill_value, out)\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\soundfile.py:629, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)\r\n 626 self._mode = mode\r\n 627 self._info = _create_info_struct(file, mode, samplerate, channels,\r\n 628 format, subtype, endian)\r\n--> 629 self._file = self._open(file, mode_int, closefd)\r\n 630 if set(mode).issuperset('r+') and self.seekable():\r\n 631 # Move write position to 0 (like in Python file objects)\r\n 632 self.seek(0)\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\soundfile.py:1183, in SoundFile._open(self, file, mode_int, closefd)\r\n 1181 else:\r\n 1182 raise TypeError(\"Invalid file: {0!r}\".format(self.name))\r\n-> 1183 _error_check(_snd.sf_error(file_ptr),\r\n 1184 \"Error opening {0!r}: \".format(self.name))\r\n 1185 if mode_int == _snd.SFM_WRITE:\r\n 1186 # Due to a bug in libsndfile version <= 1.0.25, frames != 0\r\n 1187 # when opening a named pipe in SFM_WRITE mode.\r\n 1188 # See http://github.com/erikd/libsndfile/issues/77.\r\n 1189 self._info.frames = 0\r\n\r\nFile C:\\ProgramData\\Miniconda3\\envs\\noise_cancel\\lib\\site-packages\\soundfile.py:1357, in _error_check(err, prefix)\r\n 1355 if err != 0:\r\n 1356 err_str = _snd.sf_error_number(err)\r\n-> 1357 raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace'))\r\n\r\nRuntimeError: Error opening '6930-75918-0000.flac': System error.\r\n```\r\n\r\n**Package versions:**\r\n```python\r\npython: 3.9\r\ntransformers: 4.17.0\r\ndatasets: 2.0.0\r\nSoundFile: 0.10.3.post1\r\n```\r\n", "Hi ! In `datasets` 2.0 can access the audio array with `librispeech_eval[0][\"audio\"][\"array\"]` already, no need to use `map_to_array`. See our documentation on [how to process audio data](https://huggingface.co/docs/datasets/audio_process) :)\r\n\r\ncc @patrickvonplaten we will need to update the readme at [facebook/s2t-small-librispeech-asr](https://huggingface.co/facebook/s2t-small-librispeech-asr) as well as https://huggingface.co/docs/transformers/model_doc/speech_to_text", "Thanks!\r\n\r\nAnd sorry for posting this problem in what turned on to be an unrelated thread.\r\n\r\nI rewrote the code, and the model works. The WER is 0.137 however, so I'm not sure if I have missed a step. I will look further into that at a later point. The transcriptions look good through manual inspection.\r\n\r\nThe rewritten code:\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor, Wav2Vec2Processor\r\n\r\nlibrispeech_eval = load_dataset(\"librispeech_asr\", \"clean\", split=\"test\") # change to \"other\" for other test dataset\r\nwer = load_metric(\"wer\")\r\n\r\nmodel = Speech2TextForConditionalGeneration.from_pretrained(\"facebook/s2t-small-librispeech-asr\").to(\"cuda\")\r\nprocessor = Speech2TextProcessor.from_pretrained(\"facebook/s2t-small-librispeech-asr\", do_upper_case=True)\r\n\r\ndef map_to_pred(batch):\r\n audio = batch[\"audio\"]\r\n features = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"], padding=True, return_tensors=\"pt\")\r\n input_features = features.input_features.to(\"cuda\")\r\n attention_mask = features.attention_mask.to(\"cuda\")\r\n\r\n gen_tokens = model.generate(input_features=input_features, attention_mask=attention_mask)\r\n batch[\"transcription\"] = processor.batch_decode(gen_tokens, skip_special_tokens=True)\r\n return batch\r\n\r\nresult = librispeech_eval.map(map_to_pred)#, batched=True, batch_size=8)\r\n\r\nprint(\"WER:\", wer.compute(predictions=result[\"transcription\"], references=result[\"text\"]))\r\n```", "I think the issue comes from the fact that you set `batched=False` while `map_to_pred` still returns a list of strings for \"transcription\". You can fix it by adding `[0]` at the end of this line to get the string:\r\n```python\r\nbatch[\"transcription\"] = processor.batch_decode(gen_tokens, skip_special_tokens=True)[0]\r\n```", "Updating as many model cards now as I can find", "https://github.com/huggingface/transformers/pull/16611" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3908). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3907). All of your documentation changes will be reflected on that endpoint." ]

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[ "Hi @kolk, thanks for reporting.\r\n\r\nIndeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change:\r\n- #3787 \r\n\r\nWe just made patch release last week: 1.18.4 https://github.com/huggingface/datasets/releases/tag/1.18.4\r\n\r\nPlease, feel free to update your local `datasets` version, so that you get the fix:\r\n```shell\r\npip install -U datasets\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3905). All of your documentation changes will be reflected on that endpoint.", "I'm wondering if we should add that perplexity can be used for analyzing datasets as well", "Otherwise, looks good! Good job, @emibaylor !" ]

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[ "Thanks for reporting, @omarespejel.\r\n\r\nI'm sorry but I can't reproduce the issue: the loading of the dataset works perfecto for me and I can reach the data URL: https://data.deepai.org/conll2003.zip\r\n\r\nMight it be due to a temporary problem in the data owner site (https://data.deepai.org/) that is fixed now?\r\nCould you please try loading the dataset again and tell if the problem persists?", "@omarespejel I'm closing this issue. Feel free to reopen it if the problem persists." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the detailed explanation of the structure!\r\n\r\n1. IMO it makes the most sense to yield one example for each person (so the total of 24 examples), so the features dict should be similar to this:\r\n \r\n ```python\r\n features = Features({\r\n \"rgb\": Sequence(Image()), # for the png frames\r\n \"rgb_cal\": {\"intrisic_mat\": Array2D(shape=(3, 3), dtype=\"float32\"), \"extrinsic_mat\": {\"rotation\": Array2D(shape=(3, 3), dtype=\"float32\"), \"translation\": Sequence(Value(\"float32\", length=3)}},\r\n \"depth\": Sequence(Value(\"string\")), # for the depth frames\r\n \"depth_cal\": the same as \"rgb_cal\",\r\n \"head_pose_gt\": Sequence({\"center\": Sequence(Value(\"float32\", length=3), \"rotation\": Array2D(shape=(3, 3), dtype=\"float32\")}),\r\n \"head_template\": Value(\"string\"), # for the person's obj file\r\n\r\n })\r\n ```\r\n We can add a \"Data Processing\" section to the card to explain how to parse the files.\r\n\r\n\r\n2. Yes, it's ok to parse the files as long as it doesn't take too much time/memory (e.g., it's ok to parse the `*_pose.txt` or `*.cal` files, but it's better to leave the `*_depth.bin` or `*.obj` files unprocessed and yield the paths to them)", "Thanks for the suggestions @mariosasko, yielding one example for each person would make things much easier.\r\nOkay. I'll look at parsing the files and then displaying the information.", "Added the following : \r\n- Features, I have included sequence_number and subject_id along with the features you had suggested.\r\n- Tested loading of the dataset along with dummy_data and full_data tests.\r\n- Created the dataset_infos.json file.\r\n\r\nTo-Do :\r\n- [x] Update Dataset Cards with more details.\r\n- [x] \"Data Processing\" section\r\n\r\nAny inputs on what to include in the \"Data Processing\" section ?\r\n", "@mariosasko Please could you review this when you get time. Thank you.", "In the Data Processing section, I've added example code for a compressed binary depth image file. Updated the Readme as well. ", "@mariosasko / @lhoestq , Please could you review this when you get time. Thank you.", "Created an issue here: https://github.com/huggingface/datasets/issues/4152", "Got it. Thanks for the comments. I've collapsed the C++ code in the readme and added the suggestions.", "Hi ! The `AttributeError ` bug has been fixed, feel free to merge `master` into your branch ;)", "I haven't been able to figure out why CI is failing, the error shown is : \r\n\r\n```\r\nE ValueError: The following issues have been found in the dataset cards:\r\nE README Parsing:\r\nE list index out of range\r\nE The following issues have been found in the dataset cards:\r\nE README Validation:\r\nE list index out of range\r\n```\r\n\r\nAny inputs would be helpful.", "I think it's because there are tabulations in the c++ code, can you replace them with regular spaces please ?\r\n\r\n(then in another PR we can maybe fix the Readme parser to support text indented with tabulations)", "@lhoestq , initially the idea was to have one example = one image with an additional field mentioning the frame_number. But each subject, we had a head template, calibration information for the depth and the color camera which was common to all the examples for that subject. Also, the images were continuous frames.\r\n@mariosasko suggested this structure and it made sense to group the images together for a particular subject.", "> Don't you think it would be more practical to have one example = one image in this dataset ?\r\n\r\nHaving one example = one image would be good but since we have a head template, calibration information for the depth and the color camera which is common to all the images for that subject and the images being continuous frames, I think it makes sense to group the images together for each subject. This will make the feature representation easier.\r\n\r\n", "Ok I see, sounds good then. Users can still separate the images if they want to", "The CI fails are unrelated to this PR and fixed on master, merging !", "Great. Thanks @lhoestq , I think we can close this issue now. ( #3822 )" ]

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[ "Update: `\"python3 -c \"from from datasets import Dataset, DatasetDict\"` works, but not if I import without the `python3 -c`", "Hi @arunasank, thanks for reporting.\r\n\r\nIt seems that this can be caused because you are using an old version of `fsspec`: the reason why it works if you run `python3` seems to be that `python3` runs in a Python virtual env (with an updated version of `fsspec`); whereas the error arises when you run the import from other Python virtual env (with an old version of `fsspec`).\r\n\r\nIn order to fix this, you should update `fsspec` from within the \"problematic\" Python virtual env:\r\n```\r\npip install -U \"fsspec[http]>=2021.05.0\"", "I'm closing this issue, @arunasank.\r\n\r\nFeel free to re-open it if the problem persists. " ]

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[ "It seems to have been fixed:\r\n\r\n<img width=\"1534\" alt=\"Capture d’écran 2022-04-12 à 14 10 07\" src=\"https://user-images.githubusercontent.com/1676121/162959599-6b7fef7c-8411-4e03-8f00-90040a658079.png\">\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq Please could you review this when you get time. Thank you.", "Thanks a lot for your inputs @mariosasko .\r\n> Maybe we can add the generated meta-graphs to the card as images (with attributions)?\r\n\r\nYes. We can do this for the default set of classes. Will add this.\r\n\r\n> Would be cool if we could have them as additional configs. Also, maybe we could have configs that expose [image metadata](https://github.com/Weixin-Liang/MetaShift/tree/main/dataset/meta_data) from the https://nlp.stanford.edu/data/gqa/sceneGraphs.zip file (this file is downloaded in the script but not used).\r\n\r\nI'll try adding the bonus section as additional config. \r\nRegarding exposing the image metadata with a config parameter, how will we showcase/display this information ?\r\n", "> Regarding exposing the image metadata with a config parameter, how will we showcase/display this information ?\r\n\r\nOh, I forgot to mention that. Let's add a `Dataset Usage` section to the card to document the params (similar to this: https://huggingface.co/datasets/electricity_load_diagrams#dataset-usage). Also, feel free to add the constants that can be tuned as config params (e.g. `IMAGE_SUBSET_SIZE_THRESHOLD` or the `5` in `len(subject_data) <= 5`).", "Okay. Got it. Will add these and constants as config parameters.\r\n\r\nThe image metadata from scene graphs looks like this : \r\n```json\r\n{\r\n \"2407890\": {\r\n \"width\": 640,\r\n \"height\": 480,\r\n \"location\": \"living room\",\r\n \"weather\": none,\r\n \"objects\": {\r\n \"271881\": {\r\n \"name\": \"chair\",\r\n \"x\": 220,\r\n \"y\": 310,\r\n \"w\": 50,\r\n \"h\": 80,\r\n \"attributes\": [\"brown\", \"wooden\", \"small\"],\r\n \"relations\": {\r\n \"32452\": {\r\n \"name\": \"on\",\r\n \"object\": \"275312\"\r\n },\r\n \"32452\": {\r\n \"name\": \"near\",\r\n \"object\": \"279472\"\r\n } \r\n }\r\n }\r\n }\r\n }\r\n}\r\n```\r\n``load_dataset(\"metashift\", selected_classes=[\"cat\", \"dog\", ...], image_metadata=True)``\r\nHow do we showcase/display the image metadata(json) information ?\r\n", "> How do we showcase/display the image metadata(json) information ?\r\n\r\nWe can add the JSON fields as keys to the features dict:\r\n```python\r\n if self.config.image_metadata:\r\n features.update({\"width\": Value(\"int\"), \"height\": Value(\"int\"), \"location\": Value(\"string\"), ...}) \r\n```\r\n\r\nP.S. Would rename `image_metadata` to `with_image_metadata` ", "I have added the following : \r\n- Added the meta-graphs to the card as images under the Section \"Dataset Meta-Graphs\".\r\n- Generate the Attributes-Dataset using config parameter. [ [Link](https://github.com/Weixin-Liang/MetaShift#bonus-generate-the-metashift-attributes-dataset-subsets-defined-by-subject-attributes) ]\r\n- Expose image metadata using config parameter.\r\nFormat of the image metadata is as follows : [Link](https://cs.stanford.edu/people/dorarad/gqa/download.html)\r\nI have modified the \"Objects\" which is dict to a list of dicts with an additional parameter named object_id. \r\nI have defined the structure as follows : \r\n```\r\n{\r\n \"width\": datasets.Value(\"int64\"),\r\n \"height\": datasets.Value(\"int64\"),\r\n \"location\": datasets.Value(\"string\"),\r\n \"weather\": datasets.Value(\"string\"),\r\n \"objects\": datasets.Sequence(\r\n {\r\n \"object_id\": datasets.Value(\"string\"),\r\n \"name\": datasets.Value(\"string\"),\r\n \"x\": datasets.Value(\"int64\"),\r\n \"y\": datasets.Value(\"int64\"),\r\n \"w\": datasets.Value(\"int64\"),\r\n \"h\": datasets.Value(\"int64\"),\r\n \"attributes\": datasets.Sequence(datasets.Value(\"string\")),\r\n \"relations\": datasets.Sequence(\r\n {\r\n \"name\": datasets.Value(\"string\"),\r\n \"object\": datasets.Value(\"string\"),\r\n }\r\n ),\r\n }\r\n ),\r\n}\r\n```\r\nProblem is that objects is not being shown as list of dicts. The output looks as follows : \r\n\r\n> metashift_dataset['train'][0]\r\n\r\n```json \r\n{'image_id': '2338755', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x281 at 0x7F066C5A49D0>, 'label': 0, 'context': 'ground', 'width': 500, 'height': 281, 'location': None, 'weather': None, 'objects': {'object_id': ['3070704', '3070705', '3070706', '2416713', '3070702', '2790660', '3063157', '2354960', '2037127', '2392939', '2912743', '2125407', '2735257', '3260906', '2351018', '3288269', '3699852', '2734378', '3421201', '2863115'], 'name': ['bicycle', 'bicycle', 'bicycle', 'boot', 'bicycle', 'motorcycle', 'pepperoni', 'head', 'building', 'wall', 'shorts', 'people', 'wheel', 'bricks', 'man', 'cat', 'boot', 'door', 'ground', 'building'], 'x': [137, 371, 458, 215, 468, 399, 368, 245, 0, 140, 260, 284, 138, 451, 339, 187, 210, 26, 0, 313], 'y': [116, 86, 94, 150, 91, 80, 107, 22, 0, 44, 109, 69, 145, 226, 69, 22, 230, 0, 119, 0], 'w': [197, 27, 15, 73, 24, 53, 9, 37, 289, 46, 43, 30, 74, 28, 35, 116, 53, 107, 500, 55], 'h': [126, 25, 38, 128, 43, 50, 16, 44, 158, 73, 51, 52, 97, 15, 73, 252, 46, 147, 162, 77], 'attributes': [[], [], [], ['white'], [], [], [], [], [], [], [], [], [], [], [], ['white'], ['white'], ['large', 'black'], ['brick'], []], 'relations': [{'name': ['to the left of'], 'object': ['3260906']}, {'name': ['to the left of', 'to the right of', 'to the right of', 'to the left of', 'to the right of', 'to the left of', 'to the right of'], 'object': ['3070706', '2351018', '2125407', '2790660', '2037127', '3070702', '3288269']}, {'name': ['to the right of', 'to the right of', 'to the left of', 'to the right of', 'to the right of'], 'object': ['2351018', '3070705', '3070702', '2790660', '3063157']}, {'name': ['to the right of'], 'object': ['2735257']}, {'name': ['to the right of', 'to the right of', 'to the right of', 'to the right of', 'to the right of'], 'object': ['2351018', '2790660', '3070706', '3070705', '3063157']}, {'name': ['to the right of', 'to the right of', 'to the left of', 'to the left of', 'to the right of', 'to the right of', 'to the right of', 'to the right of'], 'object': ['3070705', '2351018', '3070702', '3070706', '3063157', '2125407', '2037127', '3288269']}, {'name': ['to the right of', 'to the left of', 'to the left of', 'to the right of', 'to the right of', 'to the left of', 'to the right of'], 'object': ['2037127', '3070706', '3070702', '2912743', '3288269', '2790660', '2125407']}, {'name': ['to the left of', 'to the right of'], 'object': ['2863115', '2734378']}, {'name': ['to the left of', 'to the left of', 'to the left of', 'to the left of', 'to the left of', 'to the left of'], 'object': ['3070705', '2351018', '3063157', '2125407', '2790660', '2863115']}, {'name': ['to the left of', 'to the right of', 'to the left of'], 'object': ['2125407', '2734378', '3288269']}, {'name': ['to the left of', 'on', 'to the left of'], 'object': ['2351018', '3288269', '3063157']}, {'name': ['to the left of', 'to the left of', 'to the right of', 'to the left of', 'to the right of', 'to the left of'], 'object': ['3063157', '2351018', '2037127', '3070705', '2392939', '2790660']}, {'name': ['to the left of', 'to the left of'], 'object': ['2416713', '3288269']}, {'name': ['to the right of'], 'object': ['3070704']}, {'name': ['to the right of', 'to the left of', 'to the right of', 'to the left of', 'to the left of', 'to the right of', 'to the left of', 'to the right of', 'walking down'], 'object': ['2037127', '2790660', '2125407', '3070705', '3070706', '2912743', '3070702', '3288269', '3421201']}, {'name': ['to the right of', 'to the right of', 'to the left of', 'to the right of', 'to the left of', 'to the left of', 'to the left of', 'to the left of'], 'object': ['2392939', '2734378', '2790660', '2735257', '3063157', '3070705', '2351018', '2863115']}, {'name': [], 'object': []}, {'name': ['of', 'to the left of', 'to the left of', 'to the left of'], 'object': ['2037127', '2354960', '3288269', '2392939']}, {'name': [], 'object': []}, {'name': ['to the right of', 'to the right of', 'to the right of'], 'object': ['2037127', '3288269', '2354960']}]}}\r\n```\r\nExpected output of image_metadata would be : \r\n```\r\n{'height': 281,\r\n 'location': None,\r\n 'objects': [{'attributes': [],\r\n 'h': 126,\r\n 'name': 'bicycle',\r\n 'object_id': '3070704',\r\n 'relations': [{'name': 'to the left of', 'object': '3260906'}],\r\n 'w': 197,\r\n 'x': 137,\r\n 'y': 116},\r\n {'attributes': [],\r\n 'h': 25,\r\n 'name': 'bicycle',\r\n 'object_id': '3070705',\r\n 'relations': [{'name': 'to the left of', 'object': '3070706'},\r\n {'name': 'to the right of', 'object': '2351018'},\r\n {'name': 'to the right of', 'object': '2125407'},\r\n {'name': 'to the left of', 'object': '2790660'},\r\n {'name': 'to the right of', 'object': '2037127'},\r\n {'name': 'to the left of', 'object': '3070702'},\r\n {'name': 'to the right of', 'object': '3288269'}],\r\n 'w': 27,\r\n 'x': 371,\r\n 'y': 86},\r\n {'attributes': ['white'],\r\n 'h': 252,\r\n 'name': 'cat',\r\n 'object_id': '3288269',\r\n 'relations': [{'name': 'to the right of', 'object': '2392939'},\r\n {'name': 'to the right of', 'object': '2734378'},\r\n {'name': 'to the left of', 'object': '2790660'},\r\n {'name': 'to the right of', 'object': '2735257'},\r\n {'name': 'to the left of', 'object': '3063157'},\r\n {'name': 'to the left of', 'object': '3070705'},\r\n {'name': 'to the left of', 'object': '2351018'},\r\n {'name': 'to the left of', 'object': '2863115'}],\r\n 'w': 116,\r\n 'x': 187,\r\n 'y': 22},\r\n {'attributes': ['white'],\r\n 'h': 46,\r\n 'name': 'boot',\r\n 'object_id': '3699852',\r\n 'relations': [],\r\n 'w': 53,\r\n 'x': 210,\r\n 'y': 230},\r\n .\r\n .\r\n .\r\n {'attributes': ['large', 'black'],\r\n 'h': 147,\r\n 'name': 'door',\r\n 'object_id': '2734378',\r\n 'relations': [{'name': 'of', 'object': '2037127'},\r\n {'name': 'to the left of', 'object': '2354960'},\r\n {'name': 'to the left of', 'object': '3288269'},\r\n {'name': 'to the left of', 'object': '2392939'}],\r\n 'w': 107,\r\n 'x': 26,\r\n 'y': 0},\r\n {'attributes': ['brick'],\r\n 'h': 162,\r\n 'name': 'ground',\r\n 'object_id': '3421201',\r\n 'relations': [],\r\n 'w': 500,\r\n 'x': 0,\r\n 'y': 119},\r\n {'attributes': [],\r\n 'h': 77,\r\n 'name': 'building',\r\n 'object_id': '2863115',\r\n 'relations': [{'name': 'to the right of', 'object': '2037127'},\r\n {'name': 'to the right of', 'object': '3288269'},\r\n {'name': 'to the right of', 'object': '2354960'}],\r\n 'w': 55,\r\n 'x': 313,\r\n 'y': 0}],\r\n 'weather': None,\r\n 'width': 500}\r\n\r\n```\r\n\r\nMay I know how to get the list of dicts representation correctly ?\r\n\r\n---\r\nTo-Do : \r\n\r\n- [x] Generate dataset_infos.json file.\r\n- [x] Add “Dataset Usage” section in the cards and write about the config parameters. \r\n- [x] Add the constants that can be tuned as config params.\r\n", "> Problem is that objects is not being shown as list of dicts. The output looks as follows :\r\n\r\nThat's expected. We convert a sequence of dictionaries to a dictionary of sequences to keep the formatting aligned with Tensorflow Datasets. You could disable this behavior by replacing `\"objects\": datasets.Sequence(object_fields_dict)` with `\"objects\": [object_fields_dict]`, but that's not what we usually do, so let's keep it like that. \r\n\r\nAlso, to limit the size of the dataset repo, please remove the pushed images and pass URLs to the images instead under the `src` attribute (and specify `alt` in case the URLs go down).\r\n\r\nI'll do a proper review again after you are finished with the dummy data.", "> That's expected.\r\n\r\nOkay. Got it. Thanks. I thought I was doing something wrong.\r\n\r\n> Also, to limit the size of the dataset repo, please remove the pushed images and pass URLs to the images instead under the src attribute (and specify alt in case the URLs go down).\r\n\r\nSure. Where do we host these images ? Can I upload them to any free image hosting platform or is there any particular website you use ?\r\n\r\n> I'll do a proper review again after you are finished with the dummy data.\r\n\r\nSure. Thanks. I'm working on this part. Will update you.\r\n", "Update : \r\n- I have generated the dataset_infos.json file.\r\n\r\n> I suggest you try to generate the dataset_infos.json file first, and then I can help with the dummy data.\r\n\r\nI am having issues creating the dummy data. I get the following which I use the command : \r\n\r\n`datasets-cli dummy_data datasets/metashift`\r\n\r\n```\r\nDataset metashift with config MetashiftConfig(name='metashift', version=1.0.0, data_dir=None, data_files=None, description=None) seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has to be created with less guidance. Make sure you create the file dummy_data/full-candidate-subsets.pkl.\r\nTraceback (most recent call last):\r\n File \"datasets-cli\", line 33, in <module>\r\n sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())\r\n File \"/datasets/commands/datasets_cli.py\", line 33, in main\r\n service.run()\r\n File \"/datasets/commands/dummy_data.py\", line 324, in run\r\n dataset_builder=dataset_builder, mock_dl_manager=mock_dl_manager\r\n File \"/datasets/commands/dummy_data.py\", line 407, in _print_dummy_data_instructions\r\n for split in generator_splits:\r\nUnboundLocalError: local variable 'generator_splits' referenced before assignment\r\n```", "> Feel free to host the images online (on imgur for example) :)\r\n\r\nSure. Will do that.\r\n\r\nThanks for the explanation regarding the dummy data zip files. I will try it out and let you know.", "Instead of uploading the images to a hosting service, you can directly reference their GitHub URLs (open the image in the MetaShift repo -> click Download -> copy the image URL). For instance, this is the URL of one of the images:`https://raw.githubusercontent.com/Weixin-Liang/MetaShift/main/docs/figures/Cat-MetaGraph.jpg`. Also, feel free to replace `main` with the most recent commit hash in the copied URLs to make them more robust.", "@mariosasko I've actually created metagraphs for all the default classes other than those present in the GitHub Repo and included all of them. :) The Repo has them only for two classes.\r\n\r\nIn case we want to limit the no.of meta graphs included, we can stick to the github URLs from the repo itself.\r\n", "Update : \r\n- I could add the dummy data and get the dummy data test to work. Since we have a preprocessing step on the dataset, one of the .pkl file size is on the higher side. This was done for the tests to pass. I hope that is okay. The dummy.zip file size is about 273K.\r\n\r\nTo-Do :\r\n- [x] Update Dataset Structure in the data cards to include Data Instances when config parameters are used.\r\n\r\nPlease could you review when you get time. Thank you.", "Thanks a lot for your suggestions, Mario. The thing I learnt from the review is that I need to make better sentence formations. I will keep this in mind. :) ", "Thanks a lot for your support. @mariosasko and @lhoestq .\r\n\r\n> Super impressed by your work on this, congrats :)\r\n\r\nIts my first dataset contribution to the 🤗 Datasets library, I'm super excited. Thank you. :)\r\n\r\nAlso, I think we can close this request issue now, [#3813](https://github.com/huggingface/datasets/issues/3813)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3898). All of your documentation changes will be reflected on that endpoint.", "For ASR you can probably ping @patrickvonplaten ", "Ah only noticed now that ` # Values from popular papers` is from a template. @lhoestq @sashavor - not really sure if this section is useful in general really. \r\n\r\nIMO, it's more confusing/misleading than it helps. E.g. a value of 0.03 WER on a fake read-out audio dataset is not better than a WER of 0.3 on a real-world noisy, conversational audio dataset. I think the same holds true for other metrics no? I can think of very little metrics where a metric value is not dataset dependent. E.g. perplexity is super dataset dependent, summarization metrics like ROUGE as well, ...\r\n\r\nAlso, I don't really see what this section tries to achieve - is the idea here to give the reader some papers that use this metric to better understand in which context it is used? Should we maybe rename the section to `Popular papers making use of this metric` or something? \r\n\r\n", "I put \"Values from popular papers\" as a subsection of \"Output values\" -- I hope that's a compromise that works for everyone :hugs: " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3897). All of your documentation changes will be reflected on that endpoint." ]

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[ "reported by @abidlabs ", "related to https://github.com/huggingface/datasets/pull/3843?", "`datasets` 1.18.4 fixes the issue when you load the dataset with `load_dataset`.\r\n\r\nWhen loading in streaming mode, the fix is indeed on https://github.com/huggingface/datasets/pull/3843 which will be merged soon :)", "That is. The PR #3843 was just opened a bit later we had made our 1.18.4 patch release...\r\nOnce merged, that will fix this issue. ", "OK. Should fix the viewer for 50 datasets\r\n\r\n<img width=\"148\" alt=\"Capture d’écran 2022-03-14 à 11 51 02\" src=\"https://user-images.githubusercontent.com/1676121/158157853-6c544a47-2d6d-4ac4-964a-6f10951ec36b.png\">\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3895). All of your documentation changes will be reflected on that endpoint.", "Still not rendered properly: https://moon-ci-docs.huggingface.co/docs/datasets/pr_3895/en/package_reference/main_classes#datasets.Dataset.align_labels_with_mapping", "My last commit should have fixed it, I don't know why the dev doc build is not showing my last changes", "Let me merge this and we can see on `master` how it renders, until the dev doc build is fixed" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3894). All of your documentation changes will be reflected on that endpoint.", "The dev doc build rendering doesn't seem to be updated with my last commit for some reason", "Merging it anyway since I'd like to share this page with users 🙃 " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3893). All of your documentation changes will be reflected on that endpoint.", "Yes! And when we discovered on the Transformers side that this check fails on the GitHub actions, we added a config attribute to have a default. Setting in Transformers fixed the issue of the doc being deployed to main, so porting the fix here too :-)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3892). All of your documentation changes will be reflected on that endpoint.", "Feel free to merge if it's good for you :)", "I've added a test @lhoestq. Once all green, I'll merge. ", "Last failing tests do not have nothing to do with this PR." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3891). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3890). All of your documentation changes will be reflected on that endpoint.", "@albertvillanova Thanks for investigating and fixing that issue. I regenerated the `dataset_infos.json` file." ]

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[ "Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3887). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3886). All of your documentation changes will be reflected on that endpoint.", "I made it more robust by increasing the wait time, and I also added some logs when a request is retried. Let me know if it's ok for you", "At the end you did not set the agreed max value of 60s. \r\n\r\nMoreover, with the new numbers, there is a slight contradiction: although you set max_retries=5, we will only make 4 retries at most because of the combined values of `base_wait_time` and `max_wait_time`.", "Yea I thought that in total we could wait 1min, but if we have a max_wait_time of 20sec between each request it's fine IMO\r\n\r\n> Moreover, with the new numbers, there is a slight contradiction: although you set max_retries=5, we will only make 4 retries at most because of the combined values of base_wait_time and max_wait_time.\r\n\r\nWhat makes you think this ? If the exponential wait time becomes bigger than `max_wait_time` then it still does the retry, but after a wait time of `max_wait_time`", "Sorry, I meant 4 retries **with exponential backoff**; the fifth one is with constant backoff.", "OK, and one question: do you think that the retries do not affect the time the server needs to be operational again and able to process the request? I guess that if does not affect, then the cause are other users' requests, or others; not our specific request.\r\n\r\nJust to be sure: \r\n- Then 20s at most between consecutive requests do not impact the server.\r\n- And we expect after a total of 5 retries (within a total 50s of wait time + request processing/uploading time), the server should be able to come back to normality.", "> do you think that the retries do not affect the time the server needs for being able to process the request (I guess in this case the cause are other users' requests, or other causes; not our specific request).\r\n\r\nYes I don't think the retries would affect the server, I think the cause of the 504 errors is elsewhere\r\n\r\n> Just to be sure:\r\n>\r\n> Then 20s at most between consecutive requests do not impact the server.\r\n> And we expect after a total of 5 retries (within a total 50s of wait time + request processing/uploading time), the server should be able to come back to normality.\r\n\r\nYes I think it's fine for now, we can still adapt this later if needed", "Will be curious to see the impact of this in terms of upload reliability! Don't forget to let us know when you have more data. cc @huggingface/moon-landing-back " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3885). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3884). All of your documentation changes will be reflected on that endpoint." ]

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[ "Hi @zhaowei-wang98, thanks for reporting.\r\n\r\nWe are fixing it... " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3882). All of your documentation changes will be reflected on that endpoint." ]

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[ "Even this from docs throw same error\r\n```\r\ndataset = load_dataset(\"imagefolder\", data_files=\"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\", split=\"train\")\r\n\r\n```", "Hi @INF800,\r\n\r\nPlease note that the `imagefolder` feature enhancement was just recently merged to our master branch (https://github.com/huggingface/datasets/commit/207be676bffe9d164740a41a883af6125edef135), but has not yet been released.\r\n\r\nWe are planning to make the 2.0 release of our library in the coming days and then that feature will be available by updating your `datasets` library from PyPI.\r\n\r\nIn the meantime, you can incorporate that feature if you install our library from our GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\n\r\nThen:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ds = load_dataset(\"imagefolder\", data_files=\"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\", split=\"train\")\r\nUsing custom data configuration default-7eb4e80d960deb18\r\nDownloading and preparing dataset image_folder/default to .../.cache/huggingface/datasets/image_folder/default-7eb4e80d960deb18/0.0.0/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60...\r\nDownloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 690.19it/s]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 852.85it/s]\r\nDataset image_folder downloaded and prepared to .../.cache/huggingface/datasets/image_folder/default-7eb4e80d960deb18/0.0.0/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60. Subsequent calls will reuse this data.\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDataset({\r\n features: ['image', 'label'],\r\n num_rows: 25000\r\n})\r\n```", "Hey @albertvillanova. Does this load entire dataset in memory? Because I am facing huge trouble with loading very big datasets (OOM errors)", "Can you provide the error stack trace? The loader only stores the `data_files` dict, which can get big after globbing. Then, the OOM error would mean you don't have enough memory to keep all the paths to the image files. You can circumvent this by generating an archive and loading the dataset from there. Maybe we can optimize the globbing part in our data files resolution at some point, cc @lhoestq for visibility.", "Hey, memory error is resolved. It was fluke.\r\n\r\nBut there is another issue. Currently `load_dataset(\"imagefolder\", data_dir=\"./path/to/train\",)` takes only `train` as arg to `split` parameter.\r\n\r\nI am creating vaildation dataset using\r\n\r\n```\r\nds_valid = datasets.DatasetDict(valid=load_dataset(\"imagefolder\", data_dir=\"./path/to/valid\",)['train'])\r\n```", "`data_dir=\"path/to/folder\"` is a shorthand syntax fox `data_files={\"train\": \"path/to/folder/**\"}`, so use `data_files` in that case instead:\r\n```python\r\nds = load_dataset(\"imagefolder\", data_files={\"train\": \"path/to/train/**\", \"test\": \"path/to/test/**\", \"valid\": \"path/to/valid/**\"})\r\n```", "And there was another issue. I loaded black and white images (jpeg file). Using load dataset. It reads it as PIL jpeg data format. But instead of converting it into 3 channel tensor, input to collator function is coming as a single channel tensor.", "We don't apply any additional preprocessing on top of `PIL.Image.open(image_file)`, so you need to do the conversion yourself:\r\n\r\n```python\r\ndef to_rgb(batch):\r\n batch[\"image\"] = [img.convert(\"RGB\") for img in batch[\"image\"]]\r\n return batch\r\n\r\nds_rgb = ds.map(to_rgb, batched=True)\r\n```\r\n\r\nPlease use our Forum for questions of this kind in the future." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3880). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3880). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3879). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3878). All of your documentation changes will be reflected on that endpoint.", "Maybe `NonMatchingSplitsSizesError` errors should also tell the user to try using a more recent version of the dataset to get the fixes ?", "@lhoestq Good idea. Will open a new PR to improve the error messages of NonMatchingSplitsSizesError, NonMatchingChecksumsError, ..." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3876). All of your documentation changes will be reflected on that endpoint." ]

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[ "will it solve https://github.com/huggingface/datasets-preview-backend/blob/4c542a74244045929615640ccbba5a902c344c5a/pyproject.toml#L85-L89?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3875). All of your documentation changes will be reflected on that endpoint.", "@severo No, this PR doesn't fix that issue in the current state. We can fix it by adding `__all__` to `datasets/__init__.py` and `datasets/formatting/__init__.py`. However, this would require updating `__all__` for each new function/class definition, which could become cumbersome, and we can't do this dynamically because `mypy` is a static type checker.\r\n\r\n@lhoestq @albertvillanova WDYT?", "Feel free to merge this one if it's good for you :)" ]

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[ "@mariosasko New PR here. I'm not sure how to add you as a co-author here. Also I see flake8 tests are failing, any inputs on how to resolve this ?\r\nAlso, let me know if any other changes are required. Thank you.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3874). All of your documentation changes will be reflected on that endpoint.", "Great. Thank you.", "Thanks so much for this 🙏 💯 " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3873). All of your documentation changes will be reflected on that endpoint.", "Oh one last thing I almost forgot, I think I would add a section \"Examples\" with examples of inputs and outputs and in particular: an example giving maximal values, an examples giving minimal values and maybe a standard examples from SQuAD. What do you think?" ]

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[ "is pushing directly with git (and git-lfs) an option for you?", "I have installed git-lfs and doing this push with that\r\n", "yes but is there any way you could try pushing with `git` command line directly instead of `push_to_hub`?", "Okay. I didnt saved the dataset to my local machine. So, I processed the dataset and pushed it directly to the hub. I think I should try saving those dataset to my local machine by `save_to_disk` and then push it with git command line", "cc @lhoestq @albertvillanova @LysandreJik because maybe I'm giving dumb advice here 😅 ", "`push_to_hub` is the preferred way of uploading a dataset to the Hub, which can then be reloaded with `load_dataset`. Feel free to try again and see if the server is working as expected now. Maybe we can add a retry mechanism in the meantime to workaround 504 errors.\r\n\r\nRegarding `save_to_disk`, this must only be used for local serialization (because it's uncompressed and compatible with memory-mapping). If you upload a dataset saved with `save_to_disk` to the Hub, then to reload it you will have to download/clone the repository locally by yourself and use `load_from_disk`." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3871). All of your documentation changes will be reflected on that endpoint.", "Think failures are unrelated - feel free to merge whenever you want :-)" ]

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[ "@lhoestq Would you mind reviewing it when you're available? Thanks!\r\n", "> Awesome thanks for adding this dataset ! :) The dataset script and dataset cards look pretty good\r\n> \r\n> It looks like your `dummy_data.zip` files are quite big though (>1MB each), do you think we can reduce their sizes ? This way this git repository doesn't become too big\r\n\r\nI have manually reduced the `dummy_data.zip` and its current size is about 54KB. Hope it is fine for you!", "@lhoestq I think the dataset is ready to merge now. Any follow-up question is welcome :-D", "> Thanks ! It looks all good now :)\r\n\r\nAwesome! Thanks for your quick response!" ]

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[ "Hi @omarespejel! I think the philosophy for `datasets` issues is to create concrete issues with proposals to add a specific, individual dataset rather than umbrella issues for things such as datasets for a language, since we could end up with hundreds of issues (one per language). I see NILC - USP has many datasets, I would suggest to either create an issue for their datasets, or even better, we are trying to push to upload datasets as community datasets instead of adding them to the core library as guided in https://huggingface.co/docs/datasets/share. That would have the additional benefit that the dataset would live under the NILC organization.\r\n\r\n@lhoestq correct me if I'm wrong please 😄 " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3868). All of your documentation changes will be reflected on that endpoint.", "Cool thanks ! Could you add a test please ?" ]

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[ "why utils? it's a builder no?", "~~@julien-c there was a vote 🙂 https://huggingface.slack.com/archives/C021H1P1HKR/p1646405136644739~~\r\n\r\noh I see you already commeented in the thread as well", "Thanks ! It looks all good to me (provided `hf-doc-utils` is the name we keep in the end). I'm fine with this name, and `hf-doc-builder` is also fine IMHO", "ok, this is definitely not a hill I'll die on =) @mishig25 @sgugger " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3866). All of your documentation changes will be reflected on that endpoint.", "I think we just need to keep `datasets_logo_name.jpg` and `course_banner.png` because they appear in the README.md of the forks of `datasets`. The other images can be removed", "Force pushed those two imgs only" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3865). All of your documentation changes will be reflected on that endpoint.", "Superceded by https://github.com/huggingface/datasets/pull/3866" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3864). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3863). All of your documentation changes will be reflected on that endpoint." ]

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