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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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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3862). All of your documentation changes will be reflected on that endpoint.", "> IIUC we check if columns are present/not present directly in the yielded examples and not in info.features because info.features can be None (after map, for instance)?\r\n\r\nYes exactly\r\n\r\n> We should develop a solution that ensures info.features is never None. For example, one approach would be to infer them from examples in map and make them promotable from Value(\"null\") to a specific type, in case of None values.\r\n\r\nI agree this would be useful. Though inferring the type requires to start streaming some data, which takes a few seconds (compared to being instantaneous right now).\r\n\r\nLet's discuss this in a new issue maybe ?" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3860). All of your documentation changes will be reflected on that endpoint.", "There are still some `.. code-block:: python` (e.g. see [this](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#datasets.Dataset.align_labels_with_mapping)) directives in our codebase, so maybe we can remove those as well as part of this PR." ]

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[ "Hi @slvcsl, thanks for reporting.\r\n\r\nYesterday we just made a patch release of our `datasets` library that fixes this issue: version 1.18.4.\r\nhttps://pypi.org/project/datasets/#history\r\n\r\nPlease, feel free to update `datasets` library to the latest version: \r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then you should force redownload of the data file to update your local cache: \r\n```python\r\nds = load_dataset(\"big_patent\", \"g\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\n- Note that before the fix, you just downloaded and cached the Google Drive virus scan warning page, instead of the data file\r\n\r\nThis issue was already reported \r\n- #3784\r\n\r\nand its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe already fixed it. See:\r\n- #3787 \r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3858). All of your documentation changes will be reflected on that endpoint." ]

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[ "I agree using `glob.glob` alone is bad practice because it's not deterministic. Using `sorted` is a nice solution.\r\n\r\nNote that the `xglob` function you are referring to in the `streaming_download_manager.py` code just extends `glob.glob` for URLs - we don't change its behavior. That's why it has no `sorted()`" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3856). All of your documentation changes will be reflected on that endpoint." ]

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[ "We raise the error “ FileNotFoundError: can’t find the dataset” mainly to follow best practice in security (otherwise users could be able to guess what private repositories users/orgs may have)\r\n\r\nWe can indeed reformulate this and add the \"If this is a private repository,...\" part !", "Resolved via https://github.com/huggingface/datasets/pull/4536" ]

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[ "Hi @amanjaiswal777,\r\n\r\nFirst note that the dataset you are trying to load is deprecated: it was the Common Voice dataset release as of Dec 2020.\r\n\r\nCurrently, Common Voice dataset releases are directly hosted on the Hub, under the Mozilla Foundation organization: https://huggingface.co/mozilla-foundation\r\n\r\nFor example, to get their latest Common Voice relase (8.0):\r\n- Go to the dataset page and request access permission (Mozilla Foundation requires this for people willing to use their datasets): https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0\r\n- Looking at the dataset card, you can check that data instances have, among other fields, the ones you are interested in: \"accent\", \"age\",... \r\n- Then you can load their \"en\" language dataset as usual, besides passing your authentication token (more info on auth token here: https://huggingface.co/docs/hub/security)\r\n ```python\r\n from datasets import load_dataset\r\n ds_en = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"en\", use_auth_token=True)\r\n ```\r\n- Finally, you can filter only the data instances you are interested in (more info on `filter` here: https://huggingface.co/docs/datasets/process#select-and-filter):\r\n ```python\r\n ds_england_en = ds_en.filter(lambda item: item[\"accent\"] == \"England English\")\r\n ```\r\n\r\nFeel free to reopen this issue if you need further assistance." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3853). All of your documentation changes will be reflected on that endpoint.", "The CI fail is unrelated to this dataset, merging :)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3852). All of your documentation changes will be reflected on that endpoint." ]

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[ "Hi @lemoner20, thanks for reporting.\r\n\r\nI'm sorry but I cannot reproduce your problem:\r\n```python\r\nIn [1]: from datasets import load_dataset, load_metric, Audio\r\n ...: raw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\n ...: print(raw_datasets[0][\"audio\"])\r\nDownloading builder script: 30.2kB [00:00, 13.0MB/s] \r\nDownloading metadata: 38.0kB [00:00, 16.6MB/s] \r\nDownloading and preparing dataset superb/ks (download: 1.45 GiB, generated: 9.64 MiB, post-processed: Unknown size, total: 1.46 GiB) to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.49G/1.49G [00:37<00:00, 39.3MB/s]\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71.3M/71.3M [00:01<00:00, 36.1MB/s]\r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:41<00:00, 20.67s/it]\r\nExtracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:28<00:00, 14.24s/it]\r\nDataset superb downloaded and prepared to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9. Subsequent calls will reuse this data.\r\n{'path': '.../.cache/huggingface/datasets/downloads/extracted/8571921d3088b48f58f75b2e514815033e1ffbd06aa63fd4603691ac9f1c119f/_background_noise_/doing_the_dishes.wav', 'array': array([ 0. , 0. , 0. , ..., -0.00592041,\r\n -0.00405884, -0.00253296], dtype=float32), 'sampling_rate': 16000}\r\n``` \r\n\r\nWhich version of `datasets` are you using? Could you please fill in the environment info requested in the bug report template? You can run the command `datasets-cli env` and copy-and-paste its output below\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:", "@albertvillanova Thanks for your reply. The environment info below\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-4.19.91-007.ali4000.alios7.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.12\r\n- PyArrow version: 6.0.1", "Thanks @lemoner20,\r\n\r\nI cannot reproduce your issue in datasets version 1.18.3 either.\r\n\r\nMaybe redownloading the data file may work if you had already cached this dataset previously. Could you please try passing \"force_redownload\"?\r\n```python\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\", download_mode=\"force_redownload\")", "Thanks, @albertvillanova,\r\n\r\nI install the python package of **librosa=0.9.1** again, it works now!\r\n\r\n\r\n", "Cool!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq can you review when you have some time?", "Hi @lhoestq -- thanks so much for your review! I just added the stuff you requested to the README.md, including an example from the dataset, the table of contents, and lots of section headers with \"More Information Needed\" below. Let me know if there's anything else I need to do!", "Feel free to also merge `master` into your branch to get the latest updates for the tests ;)", "thanks @lhoestq - just made all the updates you requested!" ]

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[ "Hi @jxmorris12, thanks for reporting.\r\n\r\nThe objective of `verify_checksums` is to check that both checksums are equal. Therefore if one is None and the other is non-None, they are not equal, and the function accordingly raises a NonMatchingChecksumError. That behavior is expected.\r\n\r\nThe question is: how did you generate the expected checksum? Normally, it should not be None. To properly generate it (it is contained in the `dataset_infos.json` file), you should have runned: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nOn the other hand, you should take into account that the generation of this file is NOT mandatory for personal/community datasets (we only require it for \"canonical\" datasets, i.e., datasets added to our library GitHub repository: https://github.com/huggingface/datasets/tree/master/datasets). Therefore, other option would be just to delete the `dataset_infos.json` file. If that file is not present, the function `verify_checksums` is not executed.\r\n\r\nFinally, you can circumvent the `verify_checksums` function by passing `ignore_verifications=True` to `load_dataset`:\r\n```python\r\nload_dataset(..., ignore_verifications=True)\r\n``` ", "Thanks @albertvillanova!\r\n\r\nThat's fine. I did run that command when I was adding a new dataset. Maybe because the command crashed in the middle, the checksum wasn't stored properly. I don't know where the bug is happening. But either (i) `verify_checksums` should properly handle this edge case, where the passed checksum is None or (ii) the `datasets-cli test` shouldn't generate a corrupted dataset_infos.json file.\r\n\r\nJust a more high-level thing, I was trying to follow the instructions for adding a dataset in the CONTRIBUTING.md, so if running that command isn't even necessary, that should probably be mentioned in the document, right? But that's somewhat of a moot point, since something isn't working quite right internally if I was able to get into this corrupted state in the first place, just by following those instructions.", "Hi @jxmorris12,\r\n\r\nDefinitely, your `dataset_infos.json` was corrupted (and wrongly contains expected None checksum). \r\n\r\nWhile we further investigate how this can happen and fix it, feel free to delete your `dataset_infos.json` file and recreate it with:\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nAlso note that `verify_checksum` is working as expected: if it receives a None and and a non-None checksums as input pair, it must raise an exception: they are not equal. That is not a bug.", "At a higher level, also note that we are preparing the release of `datasets` version 2.0, and some docs are being updated...\r\n\r\nIn order to add a dataset, I think the most updated instructions are in our official documentation pages: https://huggingface.co/docs/datasets/share", "Thanks for the info. Maybe you can update the contributing.md if it's not up-to-date.", "Hi @jxmorris12, we have discovered the bug why `None` checksums wrongly appeared when generating the `dataset_infos.json` file:\r\n- #3892\r\n\r\nThe fix will be accessible once this PR merged. And we are planning to do our 2.0 release today.\r\n\r\nWe are also working on updating all our docs for our release today.", "Thanks @albertvillanova - congrats on the release!" ]

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[ "<s>I think this is because the tokenizer is stateful and because the order in which the splits are processed is not deterministic. Because of that, the hash of the tokenizer may change for certain splits, which causes issues with caching.\r\n\r\nTo fix this we can try making the order of the splits deterministic for map.</s>", "Actually this is not because of the order of the splits, but most likely 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).\r\n\r\nThis is a bit trickier to fix, we can explore fixing this next week maybe", "Sorry didn't have the bandwidth to take care of this yet - will re-assign when I'm diving into it again !", "I had this issue with `run_speech_recognition_ctc.py` for wa2vec2.0 fine-tuning. I made a small change and the hash for the function (which includes tokenisation) is now the same before and after pre-porocessing. With the hash being the same, the caching works as intended.\r\n\r\nBefore:\r\n```\r\n def prepare_dataset(batch):\r\n # load audio\r\n sample = batch[audio_column_name]\r\n\r\n inputs = feature_extractor(sample[\"array\"], sampling_rate=sample[\"sampling_rate\"])\r\n batch[\"input_values\"] = inputs.input_values[0]\r\n batch[\"input_length\"] = len(batch[\"input_values\"])\r\n\r\n # encode targets\r\n additional_kwargs = {}\r\n if phoneme_language is not None:\r\n additional_kwargs[\"phonemizer_lang\"] = phoneme_language\r\n\r\n batch[\"labels\"] = tokenizer(batch[\"target_text\"], **additional_kwargs).input_ids\r\n\r\n return batch\r\n\r\n with training_args.main_process_first(desc=\"dataset map preprocessing\"):\r\n vectorized_datasets = raw_datasets.map(\r\n prepare_dataset,\r\n remove_columns=next(iter(raw_datasets.values())).column_names,\r\n num_proc=num_workers,\r\n desc=\"preprocess datasets\",\r\n )\r\n```\r\nAfter:\r\n```\r\n def prepare_dataset(batch, feature_extractor, tokenizer):\r\n # load audio\r\n sample = batch[audio_column_name]\r\n\r\n inputs = feature_extractor(sample[\"array\"], sampling_rate=sample[\"sampling_rate\"])\r\n batch[\"input_values\"] = inputs.input_values[0]\r\n batch[\"input_length\"] = len(batch[\"input_values\"])\r\n\r\n # encode targets\r\n additional_kwargs = {}\r\n if phoneme_language is not None:\r\n additional_kwargs[\"phonemizer_lang\"] = phoneme_language\r\n\r\n batch[\"labels\"] = tokenizer(batch[\"target_text\"], **additional_kwargs).input_ids\r\n\r\n return batch\r\n\r\n pd = lambda batch: prepare_dataset(batch, feature_extractor, tokenizer)\r\n\r\n with training_args.main_process_first(desc=\"dataset map preprocessing\"):\r\n vectorized_datasets = raw_datasets.map(\r\n pd,\r\n remove_columns=next(iter(raw_datasets.values())).column_names,\r\n num_proc=num_workers,\r\n desc=\"preprocess datasets\",\r\n )\r\n```", "Not sure why the second one would work and not the first one - they're basically the same with respect to hashing. In both cases the function is hashed recursively, and therefore the feature_extractor and the tokenizer are hashed the same way.\r\n\r\nWith which tokenizer or feature extractor are you experiencing this behavior ?\r\n\r\nDo you also experience this ?\r\n> Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.", "Thanks ! Hopefully this can be useful to others, and also to better understand and improve hashing/caching ", "`tokenizer.save_pretrained(training_args.output_dir)` produces a different tokenizer hash when loaded on restart of the script. When I was debugging before I was terminating the script prior to this command, then rerunning. \r\n\r\nI compared the tokenizer items on the first and second runs, there are two different items:\r\n1st:\r\n```\r\n('_additional_special_tokens', [AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True)])\r\n\r\n...\r\n\r\n('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7f4d6d0ddb38>)\r\n```\r\n\r\n2nd:\r\n```\r\n('_additional_special_tokens', [AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True)])\r\n\r\n...\r\n\r\n('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7efc23dcce80>)\r\n```\r\n\r\n On every run of this the special tokens are being added on, and the hash is different on the `tokens_trie`. The increase in the special tokens category could be cleaned, but not sure about the hash for the `tokens_trie`. What might work is that the call for the tokenizer encoding can be translated into a function that strips any unnecessary information out, but that's a guess.\r\n", "Thanks for investigating ! Does that mean that `save_pretrained`() produces non-deterministic tokenizers on disk ? Or is it `from_pretrained()` which is not deterministic given the same files on disk ?\r\n\r\nI think one way to fix this would be to make save/from_pretrained deterministic, or make the pickling of `transformers.tokenization_utils.Trie` objects deterministic (this could be implemented in `transformers`, but maybe let's discuss in an issue in `transformers` before opening a PR)", "Late to the party but everything should be deterministic (afaik at least).\r\n\r\nBut `Trie` is a simple class object, so afaik it's hash function is linked to its `id(self)` so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ?", "> But Trie is a simple class object, so afaik it's hash function is linked to its id(self) so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ?\r\n\r\nWe're computing the hash of the pickle dump of the class so it should be fine, as long as the pickle dump is deterministic", "I've ported wav2vec2.0 fine-tuning into Optimum-Graphcore which is where I found the issue. The majority of the script was copied from the Transformers version to keep it similar, [here is the tokenizer loading section from the source](https://github.com/huggingface/transformers/blob/f0982682bd6fd0b438dda79ec45f3a8fac83a985/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L531).\r\n\r\nIn the last comment I have two loaded tokenizers, one from run 'N' of the script and one from 'N+1'. I think what's happening is that when you add special tokens (e.g. PAD and UNK) another AddedToken object is appended when tokenizer is saved regardless of whether special tokens are there already. \r\n\r\nIf there is a AddedTokens cleanup at load/save this could solve the issue, but then is Trie going to cause hash to be different? I'm not sure. ", "Which Python version are you using ?\r\n\r\nThe trie is basically a big dict of dics, so deterministic nature depends on python version:\r\nhttps://stackoverflow.com/questions/2053021/is-the-order-of-a-python-dictionary-guaranteed-over-iterations\r\n\r\nMaybe the investigation is actually not finding the right culprit though (the memory id is changed, but `datasets` is not using that to compare, so maybe we need to be looking within `datasets` so see where the comparison fails)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3846). 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_3845). All of your documentation changes will be reflected on that endpoint.", "@mariosasko I've reopened it here. Please suggest any changes if required. Thank you.", "Thanks for suggestions. :) I have added update the KWARGS_DESCRIPTION for the missing params and also changed RMSE to MSE.\r\nWhile testing, I noticed that when the input is a list of lists, we get an error :\r\n`TypeError: float() argument must be a string or a number, not 'list'`\r\nCould you suggest the datasets.Value() attribute to support both list of floats and list of lists containing floats ?\r\n", "Just add a new config to cover that case. You can do this by replacing the current `features` dict with:\r\n```python\r\nfeatures=datasets.Features(\r\n {\r\n \"predictions\": datasets.Sequence(datasets.Value(\"float\")),\r\n \"references\": datasets.Sequence(datasets.Value(\"float\")),\r\n }\r\n if self.config_name == \"multioutput\"\r\n else {\r\n \"predictions\": datasets.Value(\"float\"),\r\n \"references\": datasets.Value(\"float\"),\r\n }\r\n),\r\n```\r\nFeel free to suggest a better name for the config than `multioutput`", "Also, could you please move the changes to a new branch and open a PR from there (for the 3rd time 😄) because the diff shows changes from unrelated PRs (maybe due to rebasing?).", "Thanks for the input, I have added new config to support multi-dimensional lists and updated the examples as well.\r\n\r\nSure. Will do that and open a new PR for these changes." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3844). All of your documentation changes will be reflected on that endpoint.", "@dnaveenr This PR is in pretty good shape, so feel free to reopen it." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3843). All of your documentation changes will be reflected on that endpoint.", "Cool ! Looks like it breaks `test_streaming_gg_drive_gzipped` for some reason..." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3842). All of your documentation changes will be reflected on that endpoint.", "We should also add `generator` as a param to `shuffle` to fully align the APIs, no?", "I added the `generator` argument.\r\n\r\nI had to make a few other adjustments to make it work. In particular when you call `set_epoch()` on a streaming dataset, it updates the underlying random generator by using a new effective seed. The effective seed is generated using the previous generator and the epoch number." ]

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