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I_kwDODunzps5L_eCw

Try to reduce the number of datasets that require manual download

open

CONTRIBUTOR

> Currently, 41 canonical datasets require manual download. I checked their scripts and I'm pretty sure this number can be reduced to ≈ 30 by not relying on bash scripts to download data, hosting data directly on the Hub when the license permits, etc. Then, we will mostly be left with datasets with restricted access, which we can ignore from https://github.com/huggingface/datasets-server/issues/12#issuecomment-1026920432

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I_kwDODunzps5L_boN

Datasets method `.map` not hashing

closed

CONTRIBUTOR

## Describe the bug Datasets method `.map` not hashing, even with an empty no-op function ## Steps to reproduce the bug ```python from datasets import load_dataset # download 9MB dummy dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") def prepare_dataset(batch): return(batch) ds = ds.map( prepare_dataset, num_proc=1, desc="preprocess train dataset", ) ``` ## Expected results Hashed and cached dataset preprocessing ## Actual results Does not hash properly: ``` Parameter 'function'=<function prepare_dataset at 0x7fccb68e9280> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 cc @lhoestq

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I_kwDODunzps5L_RzM

Failure to hash `dataclasses` - results in functions that cannot be hashed or cached in `.map`

closed

CONTRIBUTOR

Dataclasses cannot be hashed. As a result, they cannot be hashed or cached if used in the `.map` method. Dataclasses are used extensively in Transformers examples scripts: (c.f. [CTC example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py)). Since dataclasses cannot be hashed, one has to define separate variables prior to passing dataclass attributes to the `.map` method: ```python phoneme_language = data_args.phoneme_language ``` in the example https://github.com/huggingface/transformers/blob/3c7e56fbb11f401de2528c1dcf0e282febc031cd/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L603-L630 ## Steps to reproduce the bug ```python from dataclasses import dataclass, field from datasets.fingerprint import Hasher @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ phoneme_language: str = field( default=None, metadata={"help": "The name of the phoneme language to use."} ) data_args = DataTrainingArguments(phoneme_language ="foo") Hasher.hash(data_args) phoneme_language = data_args.phoneme_language Hasher.hash(phoneme_language) ``` ## Expected results A hash. ## Actual results <details> <summary> Traceback </summary> ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Input In [1], in <cell line: 16>() 10 phoneme_language: str = field( 11 default=None, metadata={"help": "The name of the phoneme language to use."} 12 ) 14 data_args = DataTrainingArguments(phoneme_language ="foo") ---> 16 Hasher.hash(data_args) 18 phoneme_language = data_args. phoneme_language 20 Hasher.hash(phoneme_language) File ~/datasets/src/datasets/fingerprint.py:237, in Hasher.hash(cls, value) 235 return cls.dispatch[type(value)](cls, value) 236 else: --> 237 return cls.hash_default(value) File ~/datasets/src/datasets/fingerprint.py:230, in Hasher.hash_default(cls, value) 228 @classmethod 229 def hash_default(cls, value: Any) -> str: --> 230 return cls.hash_bytes(dumps(value)) File ~/datasets/src/datasets/utils/py_utils.py:564, in dumps(obj) 562 file = StringIO() 563 with _no_cache_fields(obj): --> 564 dump(obj, file) 565 return file.getvalue() File ~/datasets/src/datasets/utils/py_utils.py:539, in dump(obj, file) 537 def dump(obj, file): 538 """pickle an object to a file""" --> 539 Pickler(file, recurse=True).dump(obj) 540 return File ~/hf/lib/python3.8/site-packages/dill/_dill.py:620, in Pickler.dump(self, obj) 618 raise PicklingError(msg) 619 else: --> 620 StockPickler.dump(self, obj) 621 return File /usr/lib/python3.8/pickle.py:487, in _Pickler.dump(self, obj) 485 if self.proto >= 4: 486 self.framer.start_framing() --> 487 self.save(obj) 488 self.write(STOP) 489 self.framer.end_framing() File /usr/lib/python3.8/pickle.py:603, in _Pickler.save(self, obj, save_persistent_id) 599 raise PicklingError("Tuple returned by %s must have " 600 "two to six elements" % reduce) 602 # Save the reduce() output and finally memoize the object --> 603 self.save_reduce(obj=obj, *rv) File /usr/lib/python3.8/pickle.py:687, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj) 684 raise PicklingError( 685 "args[0] from __newobj__ args has the wrong class") 686 args = args[1:] --> 687 save(cls) 688 save(args) 689 write(NEWOBJ) File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1838, in save_type(pickler, obj, postproc_list) 1836 postproc_list = [] 1837 postproc_list.append((setattr, (obj, '__qualname__', obj_name))) -> 1838 _save_with_postproc(pickler, (_create_type, ( 1839 type(obj), obj.__name__, obj.__bases__, _dict 1840 )), obj=obj, postproc_list=postproc_list) 1841 log.info("# %s" % _t) 1842 else: File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1140, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list) 1137 pickler._postproc[id(obj)] = postproc_list 1139 # TODO: Use state_setter in Python 3.8 to allow for faster cPickle implementations -> 1140 pickler.save_reduce(*reduction, obj=obj) 1142 if is_pickler_dill: 1143 # pickler.x -= 1 1144 # print(pickler.x*' ', 'pop', obj, id(obj)) 1145 postproc = pickler._postproc.pop(id(obj)) File /usr/lib/python3.8/pickle.py:692, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj) 690 else: 691 save(func) --> 692 save(args) 693 write(REDUCE) 695 if obj is not None: 696 # If the object is already in the memo, this means it is 697 # recursive. In this case, throw away everything we put on the 698 # stack, and fetch the object back from the memo. File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File /usr/lib/python3.8/pickle.py:901, in _Pickler.save_tuple(self, obj) 899 write(MARK) 900 for element in obj: --> 901 save(element) 903 if id(obj) in memo: 904 # Subtle. d was not in memo when we entered save_tuple(), so 905 # the process of saving the tuple's elements must have saved (...) 909 # could have been done in the "for element" loop instead, but 910 # recursive tuples are a rare thing. 911 get = self.get(memo[id(obj)][0]) File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1251, in save_module_dict(pickler, obj) 1248 if is_dill(pickler, child=False) and pickler._session: 1249 # we only care about session the first pass thru 1250 pickler._first_pass = False -> 1251 StockPickler.save_dict(pickler, obj) 1252 log.info("# D2") 1253 return File /usr/lib/python3.8/pickle.py:971, in _Pickler.save_dict(self, obj) 968 self.write(MARK + DICT) 970 self.memoize(obj) --> 971 self._batch_setitems(obj.items()) File /usr/lib/python3.8/pickle.py:997, in _Pickler._batch_setitems(self, items) 995 for k, v in tmp: 996 save(k) --> 997 save(v) 998 write(SETITEMS) 999 elif n: File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File ~/datasets/src/datasets/utils/py_utils.py:862, in save_function(pickler, obj) 859 if state_dict: 860 state = state, state_dict --> 862 dill._dill._save_with_postproc( 863 pickler, 864 ( 865 dill._dill._create_function, 866 (obj.__code__, globs, obj.__name__, obj.__defaults__, closure), 867 state, 868 ), 869 obj=obj, 870 postproc_list=postproc_list, 871 ) 872 else: 873 closure = obj.func_closure File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1153, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list) 1151 dest, source = reduction[1] 1152 if source: -> 1153 pickler.write(pickler.get(pickler.memo[id(dest)][0])) 1154 pickler._batch_setitems(iter(source.items())) 1155 else: 1156 # Updating with an empty dictionary. Same as doing nothing. KeyError: 140434581781568 ``` </details> ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 cc @lhoestq

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https://github.com/huggingface/datasets/pull/4519

PR_kwDODunzps45zhqa

Create new sections for audio and vision in guides

closed

MEMBER

This PR creates separate sections in the guides for audio, vision, text, and general usage so it is easier for users to find loading, processing, or sharing guides specific to the dataset type they're working with. It'll also allow us to scale the docs to additional dataset types - like time series, tabular, etc. - while keeping our docs information architecture. Some other changes include: - ~Experimented with decorating text with some CSS to highlight guides specific to each modality. Hopefully, it'll be easier for users to find and realize that these different docs exist!~ Will experiment with this in a different PR. - Added deprecation warning for Metrics and redirect to Evaluate. - Updated `set_format` section to recommend using the new `to_tf_dataset` function if you need to convert to a TensorFlow dataset. - Reorganized `toctree` to nest general usage, audio, vision, and text sections under the how-to guides. - A quick review and edit to the Load and Process docs for clarity.

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PR_kwDODunzps45zMnB

Patch tests for hfh v0.8.0

closed

MEMBER

This PR patches testing utilities that would otherwise fail with hfh v0.8.0.

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PR_kwDODunzps45zBl0

Add tags for task_ids:summarization-* and task_categories:summarization*

closed

CONTRIBUTOR

yaml header at top of README.md file was edited to add task tags because I couldn't find the existing tags in the json separate Pull Request will modify dataset_infos.json to add these tags The Enron dataset (dataset id aeslc) is only tagged with: arxiv:1906.03497' languages:en pretty_name:AESLC Using the email subject_line field as a label or target variable it possible to create models for the following task_ids (in order of relevance): 'task_ids:summarization' 'task_ids:summarization-other-conversations-summarization' "task_ids:other-other-query-based-multi-document-summarization" 'task_ids:summarization-other-aspect-based-summarization' 'task_ids:summarization--other-headline-generation' The subject might also be used for the task_category "task_categories:summarization" E-mail chains might be used for the task category "task_categories:dialogue-system"

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PR_kwDODunzps45ykYX

Fix hashing for python 3.9

closed

MEMBER

In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function. Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9 To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic. Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now. Fix https://github.com/huggingface/datasets/issues/4506

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PR_kwDODunzps45x5mB

Add uppercased versions of image file extensions for automatic module inference

closed

CONTRIBUTOR

Adds the uppercased versions of the image file extensions to the supported extensions. Another approach would be to call `.lower()` on extensions while resolving data files, but uppercased extensions are not something we want to encourage out of the box IMO unless they are commonly used (as they are in the vision domain) Note that there is a slight discrepancy between the image file resolution and `imagefolder` as the latter calls `.lower()` on file extensions leading to some image file extensions being ignored by the resolution but not by the loader (e.g. `pNg`). Such extensions should also be discouraged, so I'm ignoring that case too. Fix #4514.

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I_kwDODunzps5L6CXO

Allow .JPEG as a file extension

closed

NONE

## Describe the bug When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed. ## Steps to reproduce the bug ```python # use bash to create 2 sham datasets with jpeg and JPEG ext !mkdir dataset_a !mkdir dataset_b !wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg !cp example_img.jpeg ./dataset_a/ !mv example_img.jpeg ./dataset_b/example_img.JPEG from datasets import load_dataset # working df1 = load_dataset("./dataset_a", ignore_verifications=True) #not working df2 = load_dataset("./dataset_b", ignore_verifications=True) # show print(df1, df2) ``` ## Expected results ``` DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 1 }) }) DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 1 }) }) ``` ## Actual results ``` FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome.

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PR_kwDODunzps45xTqv

Update Google Cloud Storage documentation and add Azure Blob Storage example

closed

CONTRIBUTOR

While I was going through the 🤗 Datasets documentation of the Cloud storage filesystems at https://huggingface.co/docs/datasets/filesystems, I realized that the Google Cloud Storage documentation could be improved e.g. bullet point says "Load your dataset" when the actual call was to "Save your dataset", in-line code comment was mentioning "s3 bucket" instead of "gcs bucket", and some more in-line comments could be included. Also, I think that mixing Google Cloud Storage documentation with AWS S3's one was a little bit confusing, so I moved all those to the end of the document under an h2 tab named "Other filesystems", with an h3 for "Google Cloud Storage". Besides that, I was currently working with Azure Blob Storage and found out that the URL to [adlfs](https://github.com/fsspec/adlfs) was common for both filesystems Azure Blob Storage and Azure DataLake Storage, as well as the URL, which was updated even though the redirect was working fine, so I decided to group those under the same row in the column of supported filesystems. And took also the change to add a small documentation entry as for Google Cloud Storage but for Azure Blob Storage, as I assume that AWS S3, GCP Cloud Storage, and Azure Blob Storage, are the most used cloud storage providers. Let me know if you're OK with these changes, or whether you want me to roll back some of those! :hugs:

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PR_kwDODunzps45xEDN

Add links to vision tasks scripts in ADD_NEW_DATASET template

closed

CONTRIBUTOR

Add links to vision dataset scripts in the ADD_NEW_DATASET template.

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PR_kwDODunzps45w7RN

Support all negative values in ClassLabel

closed

MEMBER

We usually use -1 to represent a missing label, but we should also support any negative values (some users use -100 for example). This is a regression from `datasets` 2.3 Fix https://github.com/huggingface/datasets/issues/4508

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https://github.com/huggingface/datasets/pull/4510

PR_kwDODunzps45wq6o

Add regression test for `ArrowWriter.write_batch` when batch is empty

closed

CONTRIBUTOR

As spotted by @cccntu in #4502, there's a logic bug in `ArrowWriter.write_batch` as the if-statement to handle the empty batches as detailed in the docstrings of the function ("Ignores the batch if it appears to be empty, preventing a potential schema update of unknown types."), the current if-statement is not handling properly `writer.write_batch({})` as an error is triggered. Also, if we add a regression test in `test_arrow_writer.py::test_write_batch` before applying the fix, the test will fail as when trying to write an empty batch as follows: ``` =================================================================================== short test summary info =================================================================================== FAILED tests/test_arrow_writer.py::test_write_batch[None-None] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[None-1] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[None-10] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields1-None] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields1-1] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields1-10] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields2-None] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields2-1] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields2-10] - ValueError: Schema and number of arrays unequal ======================================================================== 9 failed, 73 deselected, 7 warnings in 0.81s ========================================================================= ``` So the batch is not ignored when empty, as `batch_examples={}` won't match the condition `if batch_examples: ...`.

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PR_kwDODunzps45wkDl

Support skipping Parquet to Arrow conversion when using Beam

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I_kwDODunzps5L3CZJ

cast_storage method from datasets.features

closed

NONE

## Describe the bug A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets. ## Steps to reproduce the bug Steps are: - load whatever datset - write a preprocessing function such as "tokenize_and_align_labels" written in https://huggingface.co/docs/transformers/tasks/token_classification - map the function on dataset and get "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features # Sample code to reproduce the bug def tokenize_and_align_labels(examples): tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True, max_length=38,padding="max_length") labels = [] for i, label in enumerate(examples[f"labels"]): word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word. previous_word_idx = None label_ids = [] for word_idx in word_ids: # Set the special tokens to -100. if word_idx is None: label_ids.append(-100) elif word_idx != previous_word_idx: # Only label the first token of a given word. label_ids.append(label[word_idx]) else: label_ids.append(-100) previous_word_idx = word_idx labels.append(label_ids) tokenized_inputs["labels"] = labels return tokenized_inputs tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") dt = dataset.map(tokenize_and_align_labels, batched=True) ## Expected results New dataset objects should load and do on older versions. ## Actual results "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features ## Environment info everything works fine on older installations of datasets/transformers Issue arises when installing datasets on google collab under python3.7 I can't manage to find the exact output you're requirering but version printed is datasets-2.3.2

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I_kwDODunzps5L2pP8

How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script

closed

NONE

If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair. Or I can paraphrase the question in the following way: how to skip `_split_generators` step in `DatasetBuilder` to let `as_dataset` gives a single `Dataset` rather than a list`[Dataset]`? Many thanks for any help.

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I_kwDODunzps5L2REf

Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results

closed

NONE

## Describe the bug Sometimes I get messages about not being able to hash a method: `Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset. _map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` Whilst the function looks like this: ```python @staticmethod def _separate_speaker_id_from_dialogue(example: arrow_dataset.Example): speaker_id, dialogue = tuple(zip(*(example["dialogue"]))) example["speaker_id"] = speaker_id example["dialogue"] = dialogue return example ``` This is the first step in my preprocessing pipeline, but sometimes the message about failure to hash is not appearing on the first step, but then appears on a later step. This error is sometimes causing a failure to use cached data, instead of re-running all steps again. ## Steps to reproduce the bug ```python import copy import datasets from datasets import arrow_dataset def main(): dataset = datasets.load_dataset("blended_skill_talk") res = dataset.map(method) print(res) def method(example: arrow_dataset.Example): example['previous_utterance_copy'] = copy.deepcopy(example['previous_utterance']) return example if __name__ == '__main__': main() ``` Run with: ``` python -m reproduce_error ``` ## Expected results Dataset is mapped and cached correctly. ## Actual results The code outputs this at some point: `Parameter 'function'=<function method at 0x7faa83d2a160> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Ubuntu 20.04.3 - Python version: 3.9.12 - PyArrow version: 8.0.0 - Datasets version: 2.3.1

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PR_kwDODunzps45uH-o

Fix double dots in data files

closed

MEMBER

As mentioned in https://github.com/huggingface/transformers/pull/17715 `data_files` can't find a file if the path contains double dots `/../`. This has been introduced in https://github.com/huggingface/datasets/pull/4412, by trying to ignore hidden files and directories (i.e. if they start with a dot) I fixed this and added a test cc @sgugger @ydshieh

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I_kwDODunzps5L15Cw

Can you please add the Stanford dog dataset?

open

NONE

## Adding a Dataset - **Name:** *Stanford dog dataset* - **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/* - **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/* - **Data:** *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/ImageNetDogs/)* - **Motivation:** *The dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It is useful for fine-grain purpose * Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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PR_kwDODunzps45twLR

Refactor and add metadata to fever dataset

closed

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Related to: #4452 and #3792.

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I_kwDODunzps5L1pOk

Logic bug in arrow_writer?

closed

CONTRIBUTOR

https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488 I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows: ``` - if batch_examples and len(next(iter(batch_examples.values()))) == 0: + if not batch_examples or len(next(iter(batch_examples.values()))) == 0: return ``` @lhoestq

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PR_kwDODunzps45th2M

Corrected broken links in doc

closed

CONTRIBUTOR

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PR_kwDODunzps45tdxk

Add `concatenate_datasets` for iterable datasets

closed

MEMBER

`concatenate_datasets` currently only supports lists of `datasets.Dataset`, not lists of `datasets.IterableDataset` like `interleave_datasets` Fix https://github.com/huggingface/datasets/issues/2564 I also moved `_interleave_map_style_datasets` from combine.py to arrow_dataset.py, since the logic depends a lot on the `Dataset` object internals And I moved `concatenate_datasets` from arrow_dataset.py to combine.py to have it with `interleave_datasets` (though it's also copied in arrow_dataset module for backward compatibility for now)

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PR_kwDODunzps45s6Jh

fix ETT m1/m2 test/val dataset

closed

CONTRIBUTOR

https://huggingface.co/datasets/ett/discussions/1

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I_kwDODunzps5L0rbF

WER and CER > 1

closed

NONE

## Describe the bug It seems that in some cases in which the `prediction` is longer than the `reference` we may have word/character error rate higher than 1 which is a bit odd. If it's a real bug I think I can solve it with a PR changing [this](https://github.com/huggingface/datasets/blob/master/metrics/wer/wer.py#L105) line to ```python return min(incorrect / total, 1.0) ``` ## Steps to reproduce the bug ```python from datasets import load_metric wer = load_metric("wer") wer_value = wer.compute(predictions=["Hi World vka"], references=["Hello"]) print(wer_value) ``` ## Expected results ``` 1.0 ``` ## Actual results ``` 3.0 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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PR_kwDODunzps45sYns

Re-add download_manager module in utils

closed

MEMBER

https://github.com/huggingface/datasets/pull/4384 moved `datasets.utils.download_manager` to `datasets.download.download_manager` This breaks `evaluate` which imports `DownloadMode` from `datasets.utils.download_manager` This PR re-adds `datasets.utils.download_manager` without circular imports. We could also show a message that says that accessing it is deprecated, but I think we can do this in a subsequent PR, and just focus on doing a patch release for now

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PR_kwDODunzps45sUnW

Replace `assertEqual` with `assertTupleEqual` in unit tests for verbosity

closed

CONTRIBUTOR

As detailed in #4419 and as suggested by @mariosasko, we could replace the `assertEqual` assertions with `assertTupleEqual` when the assertion is between Tuples, in order to make the tests more verbose.

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PR_kwDODunzps45sAgO

Fix patching module that doesn't exist

closed

MEMBER

Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true When trying to patch `scipy.io.loadmat`: ```python ModuleNotFoundError: No module named 'scipy' ``` Instead it shouldn't raise an error and do nothing Bug introduced by #4375 Fix https://github.com/huggingface/datasets/issues/4494

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I_kwDODunzps5LzuZn

Patching fails for modules that are not installed or don't exist

closed

MEMBER

Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true When trying to patch `scipy.io.loadmat`: ```python ModuleNotFoundError: No module named 'scipy' ``` Instead it shouldn't raise an error and do nothing We use patching to extend such functions to support remote URLs and work in streaming mode

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PR_kwDODunzps45qL7J

Add `@transmit_format` in `flatten`

open

CONTRIBUTOR

As suggested by @mariosasko in https://github.com/huggingface/datasets/pull/4411, we should include the `@transmit_format` decorator to `flatten`, `rename_column`, and `rename_columns` so as to ensure that the value of `_format_columns` in an `ArrowDataset` is properly updated. **Edit**: according to @mariosasko comment below, the decorator `@transmit_format` doesn't handle column renaming, so it's done manually for those instead.

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PR_kwDODunzps45pktu

Pin the revision in imagenet download links

closed

MEMBER

Use the commit sha in the data files URLs of the imagenet-1k download script, in case we want to restructure the data files in the future. For example we may split it into many more shards for better paralellism. cc @mariosasko

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I_kwDODunzps5Lvu1u

Dataset Viewer issue for Pavithree/test

closed

NONE

### Link https://huggingface.co/datasets/Pavithree/test ### Description I have extracted the subset of original eli5 dataset found at hugging face. However, while loading the dataset It throws ArrowNotImplementedError: Unsupported cast from string to null using function cast_null error. Is there anything missing from my end? Kindly help. ### Owner _No response_

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I_kwDODunzps5LvaJi

Use `torch.nested_tensor` for arrays of varying length in torch formatter

open

CONTRIBUTOR

Use `torch.nested_tensor` for arrays of varying length in `TorchFormatter`. The PyTorch API of nested tensors is in the prototype stage, so wait for it to become more mature.

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PR_kwDODunzps45oONF

Add SV-Ident dataset

closed

NONE

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PR_kwDODunzps45n6Ja

Update PASS dataset version

closed

CONTRIBUTOR

Update the PASS dataset to version v3 (the newest one) from the [version history](https://github.com/yukimasano/PASS/blob/main/version_history.txt). PS: The older versions are not exposed as configs in the script because v1 was removed from Zenodo, and the same thing will probably happen to v2.

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PR_kwDODunzps45nm5J

Support streaming UDHR dataset

closed

MEMBER

This PR: - Adds support for streaming UDHR dataset - Adds the BCP 47 language code as feature

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PR_kwDODunzps45kP88

Add CCAgT dataset

closed

NONE

As described in #4075 I could not generate the dummy data. Also, on the data repository isn't provided the split IDs, but I copy the functions that provide the correct data split. In summary, to have a better distribution, the data in this dataset should be separated based on the amount of NORs in each image.

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PR_kwDODunzps45kD7A

Fix cast to null

closed

MEMBER

It currently fails with `ArrowNotImplementedError` instead of `TypeError` when one tries to cast integer to null type. Because if this, type inference breaks when one replaces null values with integers in `map` (it first tries to cast to the previous type before inferring the new type). Fix https://github.com/huggingface/datasets/issues/4483

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PR_kwDODunzps45jywZ

Better ImportError message when a dataset script dependency is missing

closed

MEMBER

When a depenency is missing for a dataset script, an ImportError message is shown, with a tip to install the missing dependencies. This message is not ideal at the moment: it may show duplicate dependencies, and is not very readable. I improved it from ``` ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance' ``` to ``` ImportError: To be able to use bigbench, you need to install the following dependency: bigbench. Please install it using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"' for instance' ```

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I_kwDODunzps5Lp0bQ

Dataset.map throws pyarrow.lib.ArrowNotImplementedError when converting from list of empty lists

closed

NONE

## Describe the bug Dataset.map throws pyarrow.lib.ArrowNotImplementedError: Unsupported cast from int64 to null using function cast_null when converting from a type of 'empty lists' to 'lists with some type'. This appears to be due to the interaction of arrow internals and some assumptions made by datasets. The bug appeared when binarizing some labels, and then adding a dataset which had all these labels absent (to force the model to not label empty strings such with anything) Particularly the fact that this only happens in batched mode is strange. ## Steps to reproduce the bug ```python import numpy as np ds = Dataset.from_dict( { "text": ["the lazy dog jumps over the quick fox", "another sentence"], "label": [[], []], } ) def mapper(features): features['label'] = [ [0,0,0] for l in features['label'] ] return features ds_mapped = ds.map(mapper,batched=True) ``` ## Expected results Not crashing ## Actual results ``` ../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2346: in map return self._map_single( ../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:532: in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:499: in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/fingerprint.py:458: in wrapper out = func(self, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2751: in _map_single writer.write_batch(batch) ../.venv/lib/python3.8/site-packages/datasets/arrow_writer.py:503: in write_batch arrays.append(pa.array(typed_sequence)) pyarrow/array.pxi:230: in pyarrow.lib.array ??? pyarrow/array.pxi:110: in pyarrow.lib._handle_arrow_array_protocol ??? ../.venv/lib/python3.8/site-packages/datasets/arrow_writer.py:198: in __arrow_array__ out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) ../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper return func(array, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/table.py:1812: in cast_array_to_feature casted_values = _c(array.values, feature.feature) ../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper return func(array, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/table.py:1843: in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper return func(array, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/table.py:1752: in array_cast return array.cast(pa_type) pyarrow/array.pxi:915: in pyarrow.lib.Array.cast ??? ../.venv/lib/python3.8/site-packages/pyarrow/compute.py:376: in cast return call_function("cast", [arr], options) pyarrow/_compute.pyx:542: in pyarrow._compute.call_function ??? pyarrow/_compute.pyx:341: in pyarrow._compute.Function.call ??? pyarrow/error.pxi:144: in pyarrow.lib.pyarrow_internal_check_status ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ > ??? E pyarrow.lib.ArrowNotImplementedError: Unsupported cast from int64 to null using function cast_null pyarrow/error.pxi:121: ArrowNotImplementedError ``` ## Workarounds * Not using batched=True * Using an np.array([],dtype=float) or similar instead of [] in the input * Naming the output column differently from the input column ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.2 - Platform: Ubuntu - Python version: 3.8 - PyArrow version: 8.0.0

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PR_kwDODunzps45jS_c

Test that TensorFlow is not imported on startup

open

MEMBER

TF takes some time to be imported, and also uses some GPU memory. I just added a test to make sure that in the future it's never imported by default when ```python import datasets ``` is called. Right now this fails because `huggingface_hub` does import tensorflow (though this is fixed now on their `main` branch) I'll mark this PR as ready for review once `huggingface_hub` has a new release

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PR_kwDODunzps45jIRi

Fix iwslt2017

closed

MEMBER

The files were moved to google drive, I hosted them on the Hub instead (ok according to the license) I also updated the `datasets_infos.json`

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I_kwDODunzps5LojTf

Bigbench tensorflow GPU dependency

closed

CONTRIBUTOR

## Describe the bug Loading bigbech ```py from datasets import load_dataset dataset = load_dataset("bigbench","swedish_to_german_proverbs") ``` tries to use gpu and fails with OOM with the following error ``` Downloading and preparing dataset bigbench/swedish_to_german_proverbs (download: Unknown size, generated: 68.92 KiB, post-processed: Unknown size, total: 68.92 KiB) to /home/ceyda/.cache/huggingface/datasets/bigbench/swedish_to_german_proverbs/1.0.0/7d2f6e537fa937dfaac8b1c1df782f2055071d3fd8e4f4ae93d28012a354ced0... Generating default split: 0%| | 0/72 [00:00<?, ? examples/s]2022-06-13 14:11:04.154469: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-06-13 14:11:05.133600: F tensorflow/core/platform/statusor.cc:33] Attempting to fetch value instead of handling error INTERNAL: failed initializing StreamExecutor for CUDA device ordinal 3: INTERNAL: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 25396838400 Aborted (core dumped) ``` I think this is because bigbench dependency (below) installs tensorflow (GPU version) and dataloading tries to use GPU as default. `pip install bigbench@https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz` while just doing 'pip install bigbench' results in following error ``` File "/home/ceyda/.local/lib/python3.7/site-packages/datasets/load.py", line 109, in import_main_class module = importlib.import_module(module_path) File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1006, in _gcd_import File "<frozen importlib._bootstrap>", line 983, in _find_and_load File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 677, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 728, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/ceyda/.cache/huggingface/modules/datasets_modules/datasets/bigbench/7d2f6e537fa937dfaac8b1c1df782f2055071d3fd8e4f4ae93d28012a354ced0/bigbench.py", line 118, in <module> class Bigbench(datasets.GeneratorBasedBuilder): File "/home/ceyda/.cache/huggingface/modules/datasets_modules/datasets/bigbench/7d2f6e537fa937dfaac8b1c1df782f2055071d3fd8e4f4ae93d28012a354ced0/bigbench.py", line 127, in Bigbench BigBenchConfig(name=name, version=datasets.Version("1.0.0")) for name in bb_utils.get_all_json_task_names() AttributeError: module 'bigbench.api.util' has no attribute 'get_all_json_task_names' ``` ## Steps to avoid the bug Not ideal but can solve with (since I don't really use tensorflow elsewhere) `pip uninstall tensorflow` `pip install tensorflow-cpu` ## Environment info - datasets @ master - Python version: 3.7

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PR_kwDODunzps45hHtZ

Include entity positions as feature in ReCoRD

open

CONTRIBUTOR

https://huggingface.co/datasets/super_glue/viewer/record/validation TLDR: We need to record entity positions, which are included in the source data but excluded by the loading script, to enable efficient and effective training for ReCoRD. Currently, the loading script ignores the entity positions ("entity_start", "entity_end") and only records entity text. This might be because the training method of the official baseline is to make n training instance from a datapoint by replacing \"\@ placeholder\" in query with each entity individually. But it increases the already heavy computation by multiple folds. So DeBERTa uses a method that take entity embeddings by their positions in the passage, and thus makes one training instance from one data point. It is way more efficient and proved effective for the ReCoRD task. Can anybody help me with the dataset card rendering error? Maybe @lhoestq ?

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I_kwDODunzps5LmZxF

Dataset slow during model training

open

NONE

## Describe the bug While migrating towards 🤗 Datasets, I encountered an odd performance degradation: training suddenly slows down dramatically. I train with an image dataset using Keras and execute a `to_tf_dataset` just before training. First, I have optimized my dataset following https://discuss.huggingface.co/t/solved-image-dataset-seems-slow-for-larger-image-size/10960/6, which actually improved the situation from what I had before but did not completely solve it. Second, I saved and loaded my dataset using `tf.data.experimental.save` and `tf.data.experimental.load` before training (for which I would have expected no performance change). However, I ended up with the performance I had before tinkering with 🤗 Datasets. Any idea what's the reason for this and how to speed-up training with 🤗 Datasets? ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset import os dataset_dir = "./dataset" prep_dataset_dir = "./prepdataset" model_dir = "./model" # Load Data dataset = load_dataset("Lehrig/Monkey-Species-Collection", "downsized") def read_image_file(example): with open(example["image"].filename, "rb") as f: example["image"] = {"bytes": f.read()} return example dataset = dataset.map(read_image_file) dataset.save_to_disk(dataset_dir) # Preprocess from datasets import ( Array3D, DatasetDict, Features, load_from_disk, Sequence, Value ) import numpy as np from transformers import ImageFeatureExtractionMixin dataset = load_from_disk(dataset_dir) num_classes = dataset["train"].features["label"].num_classes one_hot_matrix = np.eye(num_classes) feature_extractor = ImageFeatureExtractionMixin() def to_pixels(image): image = feature_extractor.resize(image, size=size) image = feature_extractor.to_numpy_array(image, channel_first=False) image = image / 255.0 return image def process(examples): examples["pixel_values"] = [ to_pixels(image) for image in examples["image"] ] examples["label"] = [ one_hot_matrix[label] for label in examples["label"] ] return examples features = Features({ "pixel_values": Array3D(dtype="float32", shape=(size, size, 3)), "label": Sequence(feature=Value(dtype="int32"), length=num_classes) }) prep_dataset = dataset.map( process, remove_columns=["image"], batched=True, batch_size=batch_size, num_proc=2, features=features, ) prep_dataset = prep_dataset.with_format("numpy") # Split train_dev_dataset = prep_dataset['test'].train_test_split( test_size=test_size, shuffle=True, seed=seed ) train_dev_test_dataset = DatasetDict({ 'train': train_dev_dataset['train'], 'dev': train_dev_dataset['test'], 'test': prep_dataset['test'], }) train_dev_test_dataset.save_to_disk(prep_dataset_dir) # Train Model import datetime import tensorflow as tf from tensorflow.keras import Sequential from tensorflow.keras.applications import InceptionV3 from tensorflow.keras.layers import Dense, Dropout, GlobalAveragePooling2D, BatchNormalization from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping from transformers import DefaultDataCollator dataset = load_from_disk(prep_data_dir) data_collator = DefaultDataCollator(return_tensors="tf") train_dataset = dataset["train"].to_tf_dataset( columns=['pixel_values'], label_cols=['label'], shuffle=True, batch_size=batch_size, collate_fn=data_collator ) validation_dataset = dataset["dev"].to_tf_dataset( columns=['pixel_values'], label_cols=['label'], shuffle=False, batch_size=batch_size, collate_fn=data_collator ) print(f'{datetime.datetime.now()} - Saving Data') tf.data.experimental.save(train_dataset, model_dir+"/train") tf.data.experimental.save(validation_dataset, model_dir+"/val") print(f'{datetime.datetime.now()} - Loading Data') train_dataset = tf.data.experimental.load(model_dir+"/train") validation_dataset = tf.data.experimental.load(model_dir+"/val") shape = np.shape(dataset["train"][0]["pixel_values"]) backbone = InceptionV3( include_top=False, weights='imagenet', input_shape=shape ) for layer in backbone.layers: layer.trainable = False model = Sequential() model.add(backbone) model.add(GlobalAveragePooling2D()) model.add(Dense(128, activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.3)) model.add(Dense(64, activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.3)) model.add(Dense(10, activation='softmax')) model.compile( optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'] ) print(model.summary()) earlyStopping = EarlyStopping( monitor='val_loss', patience=10, verbose=0, mode='min' ) mcp_save = ModelCheckpoint( f'{model_dir}/best_model.hdf5', save_best_only=True, monitor='val_loss', mode='min' ) reduce_lr_loss = ReduceLROnPlateau( monitor='val_loss', factor=0.1, patience=7, verbose=1, min_delta=0.0001, mode='min' ) hist = model.fit( train_dataset, epochs=epochs, validation_data=validation_dataset, callbacks=[earlyStopping, mcp_save, reduce_lr_loss] ) ``` ## Expected results Same performance when training without my "save/load hack" or a good explanation/recommendation about the issue. ## Actual results Performance slower without my "save/load hack". **Epoch Breakdown (without my "save/load hack"):** - Epoch 1/10 41s 2s/step - loss: 1.6302 - accuracy: 0.5048 - val_loss: 1.4713 - val_accuracy: 0.3273 - lr: 0.0010 - Epoch 2/10 32s 2s/step - loss: 0.5357 - accuracy: 0.8510 - val_loss: 1.0447 - val_accuracy: 0.5818 - lr: 0.0010 - Epoch 3/10 36s 3s/step - loss: 0.3547 - accuracy: 0.9231 - val_loss: 0.6245 - val_accuracy: 0.7091 - lr: 0.0010 - Epoch 4/10 36s 3s/step - loss: 0.2721 - accuracy: 0.9231 - val_loss: 0.3395 - val_accuracy: 0.9091 - lr: 0.0010 - Epoch 5/10 32s 2s/step - loss: 0.1676 - accuracy: 0.9856 - val_loss: 0.2187 - val_accuracy: 0.9636 - lr: 0.0010 - Epoch 6/10 42s 3s/step - loss: 0.2066 - accuracy: 0.9615 - val_loss: 0.1635 - val_accuracy: 0.9636 - lr: 0.0010 - Epoch 7/10 32s 2s/step - loss: 0.1814 - accuracy: 0.9423 - val_loss: 0.1418 - val_accuracy: 0.9636 - lr: 0.0010 - Epoch 8/10 32s 2s/step - loss: 0.1301 - accuracy: 0.9856 - val_loss: 0.1388 - val_accuracy: 0.9818 - lr: 0.0010 - Epoch 9/10 loss: 0.1102 - accuracy: 0.9856 - val_loss: 0.1185 - val_accuracy: 0.9818 - lr: 0.0010 - Epoch 10/10 32s 2s/step - loss: 0.1013 - accuracy: 0.9808 - val_loss: 0.0978 - val_accuracy: 0.9818 - lr: 0.0010 **Epoch Breakdown (with my "save/load hack"):** - Epoch 1/10 13s 625ms/step - loss: 3.0478 - accuracy: 0.1146 - val_loss: 2.3061 - val_accuracy: 0.0727 - lr: 0.0010 - Epoch 2/10 0s 80ms/step - loss: 2.3105 - accuracy: 0.2656 - val_loss: 2.3085 - val_accuracy: 0.0909 - lr: 0.0010 - Epoch 3/10 0s 77ms/step - loss: 1.8608 - accuracy: 0.3542 - val_loss: 2.3130 - val_accuracy: 0.0909 - lr: 0.0010 - Epoch 4/10 1s 98ms/step - loss: 1.8677 - accuracy: 0.3750 - val_loss: 2.3157 - val_accuracy: 0.0909 - lr: 0.0010 - Epoch 5/10 1s 204ms/step - loss: 1.5561 - accuracy: 0.4583 - val_loss: 2.3049 - val_accuracy: 0.0909 - lr: 0.0010 - Epoch 6/10 1s 210ms/step - loss: 1.4657 - accuracy: 0.4896 - val_loss: 2.2944 - val_accuracy: 0.0909 - lr: 0.0010 - Epoch 7/10 1s 205ms/step - loss: 1.4018 - accuracy: 0.5312 - val_loss: 2.2917 - val_accuracy: 0.0909 - lr: 0.0010 - Epoch 8/10 1s 207ms/step - loss: 1.2370 - accuracy: 0.5729 - val_loss: 2.2814 - val_accuracy: 0.0909 - lr: 0.0010 - Epoch 9/10 1s 214ms/step - loss: 1.1190 - accuracy: 0.6250 - val_loss: 2.2733 - val_accuracy: 0.0909 - lr: 0.0010 - Epoch 10/10 1s 207ms/step - loss: 1.1484 - accuracy: 0.6302 - val_loss: 2.2624 - val_accuracy: 0.0909 - lr: 0.0010 ## Environment info - `datasets` version: 2.2.2 - Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 - TensorFlow: 2.8.0 - GPU (used during training): Tesla V100-SXM2-32GB

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I_kwDODunzps5LmNv6

Dataset Viewer issue for fgrezes/WIESP2022-NER

closed

NONE

### Link _No response_ ### Description _No response_ ### Owner _No response_

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I_kwDODunzps5Lk_Qr

`to_pandas` doesn't take into account format.

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** I have a large dataset that I need to convert part of to pandas to do some further analysis. Calling `to_pandas` directly on it is expensive. So I thought I could simply select the columns that I want and then call `to_pandas`. **Describe the solution you'd like** ```python from datasets import Dataset ds = Dataset.from_dict({'a': [1,2,3], 'b': [5,6,7], 'c': [8,9,10]}) pandas_df = ds.with_format(columns=['a', 'b']).to_pandas() # I would expect `pandas_df` to only include a,b as column. ``` **Describe alternatives you've considered** I could remove all columns that I don't want? But I don't know all of them in advance. **Additional context** I can probably make a PR with some pointers.

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PR_kwDODunzps45eufw

Improve error message for missing pacakges from inside dataset script

closed

CONTRIBUTOR

Improve the error message for missing packages from inside a dataset script: With this change, the error message for missing packages for `bigbench` looks as follows: ``` ImportError: To be able to use bigbench, you need to install the following dependencies: - 'bigbench' using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"' ``` And this is how it looked before: ``` ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance' ```

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PR_kwDODunzps45en98

[Docs] How to use with PyTorch page

closed

MEMBER

Currently the docs about PyTorch are scattered around different pages, and we were missing a place to explain more in depth how to use and optimize a dataset for PyTorch. This PR is related to #4457 which is the TF counterpart :) cc @Rocketknight1 we can try to align both documentations contents now I think cc @stevhliu let me know what you think !

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PR_kwDODunzps45d5-R

Add SST-2 dataset

closed

MEMBER

Add SST-2 dataset. Currently it is part of GLUE benchmark. This PR adds it as a standalone dataset. CC: @julien-c

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PR_kwDODunzps45drcb

Fix 401 error for unauthticated requests to non-existing repos

closed

MEMBER

The hub now returns 401 instead of 404 for unauthenticated requests to non-existing repos. This PR add support for the 401 error and fixes the CI fails on `master`

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I_kwDODunzps5LjCNE

CI error with repo lhoestq/_dummy

closed

MEMBER

## Describe the bug CI is failing because of repo "lhoestq/_dummy". See: https://app.circleci.com/pipelines/github/huggingface/datasets/12461/workflows/1b040b45-9578-4ab9-8c44-c643c4eb8691/jobs/74269 ``` requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/api/datasets/lhoestq/_dummy?full=true ``` The repo seems to no longer exist: https://huggingface.co/api/datasets/lhoestq/_dummy ``` error: "Repository not found" ``` CC: @lhoestq

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PR_kwDODunzps45dnYw

Reorder returned validation/test splits in script template

closed

MEMBER

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PR_kwDODunzps45cweQ

Replace data URLs in wider_face dataset once hosted on the Hub

closed

MEMBER

This PR replaces the URLs of data files in Google Drive with our Hub ones, once the data owners have approved to host their data on the Hub. They also informed us that their dataset is licensed under CC BY-NC-ND.

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PR_kwDODunzps45bERK

Generalize tutorials for audio and vision

closed

MEMBER

This PR updates the tutorials to be more generalizable to all modalities. After reading the tutorials, a user should be able to load any type of dataset, know how to index into and slice a dataset, and do the most basic/common type of preprocessing (tokenization, resampling, applying transforms) depending on their dataset. Other changes include: - Removed the sections about a dataset's metadata, features, and columns because we cover this in an earlier tutorial about inspecting the `DatasetInfo` through the dataset builder. - Separate the sharing dataset tutorial into two sections: (1) uploading via the web interface and (2) using the `huggingface_hub` library. - Renamed some tutorials in the TOC to be more clear and specific. - Added more text to nudge users towards joining the community and asking questions on the forums. - If it's okay with everyone, I'd also like to remove the section about loading and using metrics since we have the `evaluate` docs now.

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I_kwDODunzps5LePV2

Transcript string 'null' converted to [None] by load_dataset()

closed

NONE

## Issue I am training a luxembourgish speech-recognition model in Colab with a custom dataset, including a dictionary of luxembourgish words, for example the speaken numbers 0 to 9. When preparing the dataset with the script `ds_train1 = mydataset.map(prepare_dataset)` the following error was issued: ``` ValueError Traceback (most recent call last) <ipython-input-69-1e8f2b37f5bc> in <module>() ----> 1 ds_train = mydataset_train.map(prepare_dataset) 11 frames /usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs) 2450 if not _is_valid_text_input(text): 2451 raise ValueError( -> 2452 "text input must of type str (single example), List[str] (batch or single pretokenized example) " 2453 "or List[List[str]] (batch of pretokenized examples)." 2454 ) ValueError: text input must of type str (single example), List[str] (batch or single pretokenized example) or List[List[str]] (batch of pretokenized examples). ``` Debugging this problem was not easy, all transcriptions in the dataset are correct strings. Finally I discovered that the transcription string 'null' is interpreted as [None] by the `load_dataset()` script. By deleting this row in the dataset the training worked fine. ## Expected result: transcription 'null' interpreted as 'str' instead of 'None'. ## Reproduction Here is the code to reproduce the error with a one-row-dataset. ``` with open("null-test.csv") as f: reader = csv.reader(f) for row in reader: print(row) ``` ['wav_filename', 'wav_filesize', 'transcript'] ['wavs/female/NULL1.wav', '17530', 'null'] ``` dataset = load_dataset('csv', data_files={'train': 'null-test.csv'}) ``` Using custom data configuration default-81ac0c0e27af3514 Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-81ac0c0e27af3514/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 1/1 [00:00<00:00, 29.55it/s] Extracting data files: 100% 1/1 [00:00<00:00, 23.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-81ac0c0e27af3514/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 1/1 [00:00<00:00, 25.84it/s] ``` print(dataset['train']['transcript']) ``` [None] ## Environment info ``` !pip install datasets==2.2.2 !pip install transformers==4.19.2 ```

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PR_kwDODunzps45ZLsd

Optimize contiguous shard and select

closed

MEMBER

Currently `.shard()` and `.select()` always create an indices mapping. However if the requested data are contiguous, it's much more optimized to simply slice the Arrow table instead of building an indices mapping. In particular: - the shard/select operation will be much faster - reading speed will be much faster in the resulting dataset, since it won't have to do a lookup step in the indices mapping Since `.shard()` is also used for `.map()` with `num_proc>1`, it will also significantly improve the reading speed of multiprocessed `.map()` operations Here is an example of speed-up: ```python >>> import io >>> import numpy as np >>> from datasets import Dataset >>> ds = Dataset.from_dict({"a": np.random.rand(10_000_000)}) >>> shard = ds.shard(num_shards=4, index=0, contiguous=True) # this calls `.select(range(2_500_000))` >>> buf = io.BytesIO() >>> %time dd.to_json(buf) Creating json from Arrow format: 100%|██████████████████| 100/100 [00:00<00:00, 376.17ba/s] CPU times: user 258 ms, sys: 9.06 ms, total: 267 ms Wall time: 266 ms ``` while previously it was ```python Creating json from Arrow format: 100%|███████████████████| 100/100 [00:03<00:00, 29.41ba/s] CPU times: user 3.33 s, sys: 69.1 ms, total: 3.39 s Wall time: 3.4 s ``` In this simple case the speed-up is x10, but @sayakpaul experienced a x100 speed-up on its data when exporting to JSON. ## Implementation details I mostly improved `.select()`: it now checks if the input corresponds to a contiguous chunk of data and then it slices the main Arrow table (or the indices mapping table if it exists). To check if the input indices are contiguous it checks two possibilities: - if the indices is of type `range`, it checks that start >= 0 and step = 1 - otherwise in the general case, it iterates over the indices. If all the indices are contiguous then we're good, otherwise we have to build an indices mapping. Having to iterate over the indices doesn't cause performance issues IMO because: - either they are contiguous and in this case the cost of iterating over the indices is much less than the cost of creating an indices mapping - or they are not contiguous, and then iterating generally stops quickly when it first encounters the first indice that is not contiguous.

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PR_kwDODunzps45X0XY

Fix bigbench config names

closed

MEMBER

Fix https://github.com/huggingface/datasets/issues/4462 in the case of bigbench

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PR_kwDODunzps45XlWW

Extend support for streaming datasets that use xml.dom.minidom.parse

closed

MEMBER

This PR extends the support in streaming mode for datasets that use `xml.dom.minidom.parse`, by patching that function. This PR adds support for streaming datasets like "Yaxin/SemEval2015". Fix #4453.

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PR_kwDODunzps45Vnzu

Use config_id to check split sizes instead of config name

closed

MEMBER

Fix https://github.com/huggingface/datasets/issues/4462

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I_kwDODunzps5LZ5Qz

BigBench: NonMatchingSplitsSizesError when passing a dataset configuration parameter

open

MEMBER

As noticed in https://github.com/huggingface/datasets/pull/4125 when a dataset config class has a parameter that reduces the number of examples (e.g. named `max_examples`), then loading the dataset and passing `max_examples` raises `NonMatchingSplitsSizesError`. This is because it will check for expected the number of examples of the config with the same name without taking into account the `max_examples` parameter. This can be fixed by checking the expected number of examples using the **config id** instead of name. Indeed the config id corresponds to the config name + an optional suffix that depends on the config parameters

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I_kwDODunzps5LY1LD

AttributeError: module 'datasets' has no attribute 'load_dataset'

closed

NONE

## Describe the bug I have piped install datasets, but this package doesn't have these attributes: load_dataset, load_metric. ## Environment info - `datasets` version: 1.9.0 - Platform: Linux-5.13.0-44-generic-x86_64-with-debian-bullseye-sid - Python version: 3.6.13 - PyArrow version: 6.0.1

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PR_kwDODunzps45UHIs

Drop Python 3.6 support

closed

CONTRIBUTOR

Remove the fallback imports/checks in the code needed for Python 3.6 and update the requirements/CI files. Also, use Python types for the NumPy dtype wherever possible to avoid deprecation warnings in newer NumPy versions.

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PR_kwDODunzps45UFc8

Add and fix language tags for udhr dataset

closed

MEMBER

Related to #4362.

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PR_kwDODunzps45QZCU

First draft of the docs for TF + Datasets

closed

MEMBER

I might cc a few of the other TF people to take a look when this is closer to being finished, but it's still a draft for now.

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I_kwDODunzps5LS4jp

Workflow for Tabular data

open

MEMBER

Tabular data are treated very differently than data for NLP, audio, vision, etc. and therefore the worflow for tabular data in `datasets` is not ideal. For example for tabular data, it is common to use pandas/spark/dask to process the data, and then load the data into X and y (X is an array of features and y an array of labels), then train_test_split and finally feed the data to a machine learning model. In `datasets` the workflow is different: we use load_dataset, then map, then train_test_split (if we only have a train split) and we end up with columnar dataset splits, not formatted as X and y. Right now, it is already possible to convert a dataset from and to pandas, but there are still many things that could improve the workflow for tabular data: - be able to load the data into X and y - be able to load a dataset from the output of spark or dask (as far as I know it's usually csv or parquet files on S3/GCS/HDFS etc.) - support "unsplit" datasets explicitly, instead of putting everything in "train" by default cc @adrinjalali @merveenoyan feel free to complete/correct this :) Feel free to also share ideas of APIs that would be super intuitive in your opinion !

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PR_kwDODunzps45O5F9

Update data URLs in fever dataset

closed

MEMBER

As stated in their website, data owners updated their URLs on 28/04/2022. This PR updates the data URLs. Fix #4452.

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I_kwDODunzps5LQuQd

Dataset Viewer issue for Yaxin/SemEval2015

closed

NONE

### Link _No response_ ### Description the link could not visit ### Owner _No response_

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I_kwDODunzps5LQuC5

Dataset Viewer issue for Yaxin/SemEval2015

closed

NONE

### Link _No response_ ### Description _No response_ ### Owner _No response_

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I_kwDODunzps5LQKx2

Trying to load FEVER dataset results in NonMatchingChecksumError

closed

NONE

## Describe the bug Trying to load the `fever` dataset fails with `datasets.utils.info_utils.NonMatchingChecksumError`. I tried with `download_mode="force_redownload"` but that did not fix the error. I also tried with `ignore_verification=True` but then that raised a `json.decoder.JSONDecodeError`. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('fever', 'v1.0') # Fails with NonMatchingChecksumError dataset = load_dataset('fever', 'v1.0', download_mode="force_redownload") # Fails with NonMatchingChecksumError dataset = load_dataset('fever', 'v1.0', ignore_verification=True)` # Fails with JSONDecodeError ``` ## Expected results I expect this call to return with no error raised. ## Actual results With `ignore_verification=False`: ``` *** datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://s3-eu-west-1.amazonaws.com/fever.public/train.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev_public.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_test.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_test.jsonl'] ``` With `ignore_verification=True`: ``` *** json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.3.dev0 - Platform: Linux-4.15.0-50-generic-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps45LkGc

Use newer version of multi-news with fixes

closed

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Closes #4430.

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PR_kwDODunzps45Kzwh

Update README.md of fquad

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I_kwDODunzps5LLVX2

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import android.content.DialogInterface; import android.database.Cursor; import android.os.Bundle; import android.view.View; import android.widget.ArrayAdapter; import android.widget.Button; import android.widget.EditText; import android.widget.Toast; import androidx.appcompat.app.AlertDialog; import androidx.appcompat.app.AppCompatActivity; public class MainActivity extends AppCompatActivity { private EditText editTextID; private EditText editTextName; private EditText editTextNum; private String name; private int number; private String ID; private dbHelper db; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); db = new dbHelper(this); editTextID = findViewById(R.id.editText1); editTextName = findViewById(R.id.editText2); editTextNum = findViewById(R.id.editText3); Button buttonSave = findViewById(R.id.button); Button buttonRead = findViewById(R.id.button2); Button buttonUpdate = findViewById(R.id.button3); Button buttonDelete = findViewById(R.id.button4); Button buttonSearch = findViewById(R.id.button5); Button buttonDeleteAll = findViewById(R.id.button6); buttonSave.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { name = editTextName.getText().toString(); String num = editTextNum.getText().toString(); if (name.isEmpty() || num.isEmpty()) { Toast.makeText(MainActivity.this, "Cannot Submit Empty Fields", Toast.LENGTH_SHORT).show(); } else { number = Integer.parseInt(num); try { // Insert Data db.insertData(name, number); // Clear the fields editTextID.getText().clear(); editTextName.getText().clear(); editTextNum.getText().clear(); } catch (Exception e) { e.printStackTrace(); } } } }); buttonRead.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { final ArrayAdapter<String> adapter = new ArrayAdapter<>(MainActivity.this, android.R.layout.simple_list_item_1); String name; String num; String id; try { Cursor cursor = db.readData(); if (cursor != null && cursor.getCount() > 0) { while (cursor.moveToNext()) { id = cursor.getString(0); // get data in column index 0 name = cursor.getString(1); // get data in column index 1 num = cursor.getString(2); // get data in column index 2 // Add SQLite data to listView adapter.add("ID :- " + id + "\n" + "Name :- " + name + "\n" + "Number :- " + num + "\n\n"); } } else { adapter.add("No Data"); } cursor.close(); } catch (Exception e) { e.printStackTrace(); } // show the saved data in alertDialog AlertDialog.Builder builder = new AlertDialog.Builder(MainActivity.this); builder.setTitle("SQLite saved data"); builder.setIcon(R.mipmap.app_icon_foreground); builder.setAdapter(adapter, new DialogInterface.OnClickListener() { @Override public void onClick(DialogInterface dialog, int which) { } }); builder.setPositiveButton("OK", new DialogInterface.OnClickListener() { @Override public void onClick(DialogInterface dialog, int which) { dialog.cancel(); } }); AlertDialog dialog = builder.create(); dialog.show(); } }); buttonUpdate.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { name = editTextName.getText().toString(); String num = editTextNum.getText().toString(); ID = editTextID.getText().toString(); if (name.isEmpty() || num.isEmpty() || ID.isEmpty()) { Toast.makeText(MainActivity.this, "Cannot Submit Empty Fields", Toast.LENGTH_SHORT).show(); } else { number = Integer.parseInt(num); try { // Update Data db.updateData(ID, name, number); // Clear the fields editTextID.getText().clear(); editTextName.getText().clear(); editTextNum.getText().clear(); } catch (Exception e) { e.printStackTrace(); } } } }); buttonDelete.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { ID = editTextID.getText().toString(); if (ID.isEmpty()) { Toast.makeText(MainActivity.this, "Please enter the ID", Toast.LENGTH_SHORT).show(); } else { try { // Delete Data db.deleteData(ID); // Clear the fields editTextID.getText().clear(); editTextName.getText().clear(); editTextNum.getText().clear(); } catch (Exception e) { e.printStackTrace(); } } } }); buttonDeleteAll.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { // Delete all data // You can simply delete all the data by calling this method --> db.deleteAllData(); // You can try this also AlertDialog.Builder builder = new AlertDialog.Builder(MainActivity.this); builder.setIcon(R.mipmap.app_icon_foreground); builder.setTitle("Delete All Data"); builder.setCancelable(false); builder.setMessage("Do you really need to delete your all data ?"); builder.setPositiveButton("Yes", new DialogInterface.OnClickListener() { @Override public void onClick(DialogInterface dialog, int which) { // User confirmed , now you can delete the data db.deleteAllData(); // Clear the fields editTextID.getText().clear(); editTextName.getText().clear(); editTextNum.getText().clear(); } }); builder.setNegativeButton("No", new DialogInterface.OnClickListener() { @Override public void onClick(DialogInterface dialog, int which) { // user not confirmed dialog.cancel(); } }); AlertDialog dialog = builder.create(); dialog.show(); } }); buttonSearch.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { ID = editTextID.getText().toString(); if (ID.isEmpty()) { Toast.makeText(MainActivity.this, "Please enter the ID", Toast.LENGTH_SHORT).show(); } else { try { // Search data Cursor cursor = db.searchData(ID); if (cursor.moveToFirst()) { editTextName.setText(cursor.getString(1)); editTextNum.setText(cursor.getString(2)); Toast.makeText(MainActivity.this, "Data successfully searched", Toast.LENGTH_SHORT).show(); } else { Toast.makeText(MainActivity.this, "ID not found", Toast.LENGTH_SHORT).show(); editTextNum.setText("ID Not found"); editTextName.setText("ID not found"); } cursor.close(); } catch (Exception e) { e.printStackTrace(); } } } }); } }

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