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MDU6SXNzdWU5NDQ0ODQ1MjI=

Add web_split dataset for Paraphase and Rephrase benchmark

open

CONTRIBUTOR

## Describe: For getting simple sentences from complex sentence there are dataset and task like wiki_split that is available in hugging face datasets. This web_split is a very similar dataset. There some research paper which states that by combining these two datasets we if we train the model it will yield better results on both tests data. This dataset is made from web NLG data. All the dataset related details are provided in the below repository Github link: https://github.com/shashiongithub/Split-and-Rephrase

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MDU6SXNzdWU5NDQzNzk5NTQ=

downloading of yahoo_answers_topics dataset failed

open

NONE

## Describe the bug I get an error datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files when I try to download the yahoo_answers_topics dataset ## Steps to reproduce the bug self.dataset = load_dataset( 'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]') # Sample code to reproduce the bug self.dataset = load_dataset( 'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]') ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files

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MDU6SXNzdWU5NDQzNzQyODQ=

load_dataset processing failed with OS error after downloading a dataset

closed

NONE

## Describe the bug After downloading a dataset like opus100, there is a bug that OSError: Cannot find data file. Original error: dlopen: cannot load any more object with static TLS ## Steps to reproduce the bug ```python from datasets import load_dataset this_dataset = load_dataset('opus100', 'af-en') ``` ## Expected results there is no error when running load_dataset. ## Actual results Specify the actual results or traceback. Traceback (most recent call last): File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split example = self.info.features.encode_example(record) File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example example = cast_to_python_objects(example) File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob return _cast_to_python_objects(obj)[0] File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o import torch File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module> _load_global_deps() File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL) File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__ self._handle = _dlopen(self._name, mode) OSError: dlopen: cannot load any more object with static TLS During handling of the above exception, another exception occurred: Traceback (most recent call last): File "download_hub_opus100.py", line 9, in <module> this_dataset = load_dataset('opus100', language_pair) File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset use_auth_token=use_auth_token, File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep + str(e) OSError: Cannot find data file. Original error: dlopen: cannot load any more object with static TLS ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid - Python version: 3.6.6 - PyArrow version: 3.0.0

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MDU6SXNzdWU5NDQyNTQ3NDg=

Batched `map` not allowed to return 0 items

closed

CONTRIBUTOR

## Describe the bug I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`. However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case. For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs. ## Steps to reproduce the bug ```python def select_rows(examples): # `key` is a column name that exists in the original dataset # The following line simulates no matches found, so we return an empty batch result = {'key': []} return result filtered_dataset = dataset.map( select_rows, remove_columns = dataset.column_names, batched = True, num_proc = 1, desc = "Selecting rows with images that exist" ) ``` The code above immediately triggers the exception. If we use the following instead: ```python def select_rows(examples): # `key` is a column name that exists in the original dataset result = {'key': []} # or defaultdict or whatever # code to check for condition and append elements to result # some_items_found will be set to True if there were any matching elements in the batch return result if some_items_found else {} ``` Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it. In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items. ## Expected results The dataset would be filtered and only the matching fields would be returned. ## Actual results An exception is encountered, as described. Using a workaround makes it fail further along the line. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.1.dev0 - Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 4.0.1

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MDU6SXNzdWU5NDQyMjAyNzM=

Enum used in map functions will raise a RecursionError with dill.

open

NONE

## Describe the bug Enums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https://github.com/uqfoundation/dill/issues/250#issuecomment-852566284 In my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above. ## Steps to reproduce the bug ```python from datasets import load_dataset from enum import Enum class A(Enum): a = 'a' def main(): a = A.a def f(x): return {} if a == a.a else x ds = load_dataset('cnn_dailymail', '3.0.0')['test'] ds = ds.map(f, num_proc=15) if __name__ == "__main__": main() ``` ## Expected results The known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood. ## Actual results ```python File "/home/xxxx/miniconda3/lib/python3.8/site-packages/dill/_dill.py", line 1373, in save_type pickler.save_reduce(_create_type, (type(obj), obj.__name__, File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 690, in save_reduce save(args) File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 558, in save f(self, obj) # Call unbound method with explicit self File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 899, in save_tuple save(element) File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 534, in save self.framer.commit_frame() File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 220, in commit_frame if f.tell() >= self._FRAME_SIZE_TARGET or force: RecursionError: maximum recursion depth exceeded while calling a Python object ``` ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 3.0.0

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MDU6SXNzdWU5NDQxNzU2OTc=

Support multi-worker with streaming dataset (IterableDataset).

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** The current `.map` does not support multi-process, CPU can become bottleneck if the pre-processing is complex (e.g. t5 span masking). **Describe the solution you'd like** Ideally `.map` should support multi-worker like tfds, with `AUTOTUNE`. **Describe alternatives you've considered** A simpler solution is to shard the dataset and process it in parallel with pytorch dataloader. The shard does not need to be of equal size. * https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset **Additional context**

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MDU6SXNzdWU5NDM4MzgwODU=

load_dataset("financial_phrasebank") NonMatchingChecksumError

open

NONE

## Describe the bug Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("financial_phrasebank", 'sentences_allagree') ``` ## Expected results I expect to see the financial_phrasebank dataset downloaded successfully ## Actual results NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip'] ## Environment info - `datasets` version: 1.9.0 - Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6 - Python version: 3.7.10 - PyArrow version: 4.0.1

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MDU6SXNzdWU5NDMyOTA3MzY=

Add the CIDEr metric?

open

NONE

Hi, I find the api in https://huggingface.co/metrics quite useful. I am playing around with video/image captioning task, where CIDEr is a popular metric. Do you plan to add this into the HF ```datasets``` library? Thanks.

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MDU6SXNzdWU5NDIxMDI5NTY=

Progress bars are not properly rendered in Jupyter notebook

open

MEMBER

## Describe the bug The progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal). ## Steps to reproduce the bug ```python ds.map(tokenize, num_proc=10) ``` ## Expected results Jupyter widgets displaying the progress bars. ## Actual results Simple plane progress bars. cc: Reported by @thomwolf

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MDU6SXNzdWU5NDE4MTkyMDU=

Load datasets from the Hub without requiring a dataset script

closed

MEMBER

As a user I would like to be able to upload my csv/json/text/parquet/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script. Moreover I would like to be able to specify which file goes into which split using the `data_files` argument. This feature should be compatible with private repositories and dataset streaming. This can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv/json/text/parquet/etc.)

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MDU6SXNzdWU5NDE0Mzk5MjI=

⚛️😇⚙️🔑

closed

NONE

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MDU6SXNzdWU5NDEzMTgyNDc=

can't set verbosity for `metric.py`

closed

NONE

## Describe the bug ``` [2021-07-10 20:13:11,528][datasets.utils.filelock][INFO] - Lock 139705371374976 acquired on /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.lock [2021-07-10 20:13:11,529][datasets.arrow_writer][INFO] - Done writing 32 examples in 6100 bytes /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow. [2021-07-10 20:13:11,531][datasets.arrow_dataset][INFO] - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns. [2021-07-10 20:13:11,543][/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric.py][INFO] - Removing /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow ``` As you can see, `datasets` logging come from different places. `filelock`, `arrow_writer` & `arrow_dataset` comes from `datasets.*` which are expected However, `metric.py` logging comes from `/conda/envs/myenv/lib/python3.8/site-packages/datasets/` So when setting `datasets.utils.logging.set_verbosity_error()`, it still logs the last message which is annoying during evaluation. I had to do ``` logging.getLogger("/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric").setLevel(logging.ERROR) ``` to fully mute these messages ## Expected results it shouldn't log these messages when setting `datasets.utils.logging.set_verbosity_error()` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: tried both 1.8.0 & 1.9.0 - Platform: Ubuntu 18.04.5 LTS - Python version: 3.8.10 - PyArrow version: 3.0.0

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MDU6SXNzdWU5NDExMjc3ODU=

Integration with AugLy

open

NONE

**Is your feature request related to a problem? Please describe.** Facebook recently launched a library, [AugLy](https://github.com/facebookresearch/AugLy) , that has a unified API for augmentations for image, video and text. It would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial. **Describe the solution you'd like** The biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader. **Describe alternatives you've considered** One possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.

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MDU6SXNzdWU5NDA4NTI2NDA=

`filelock.py` Error

open

NONE

## Describe the bug It seems that the `filelock.py` went error. ``` >>> ds=load_dataset('xsum') ^CTraceback (most recent call last): File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) OSError: [Errno 37] No locks available ``` According to error log, it is OSError, but there is an `except` in the `_acquire` function. ``` def _acquire(self): open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC try: fd = os.open(self._lock_file, open_mode) except (IOError, OSError): pass else: self._lock_file_fd = fd return None ``` I don't know why it stucked rather than `pass` directly. I am not quite familiar with filelock operation, so any help is highly appriciated. ## Steps to reproduce the bug ```python ds = load_dataset('xsum') ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` >>> ds=load_dataset('xsum') ^CTraceback (most recent call last): File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) OSError: [Errno 37] No locks available During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 818, in load_dataset use_auth_token=use_auth_token, File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 470, in prepare_module with FileLock(lock_path): File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 323, in __enter__ self.acquire() File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 272, in acquire self._acquire() File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) KeyboardInterrupt ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid - Python version: 3.6.13 - PyArrow version: 4.0.1

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MDU6SXNzdWU5NDA3OTQzMzk=

Jsonlines export error

closed

MEMBER

## Describe the bug When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default ## Steps to reproduce the bug This what I'm running: in python: ``` from datasets import load_dataset ptb = load_dataset("ptb_text_only") ptb["train"].to_json("ptb.jsonl") ``` then out of python: ``` head -10000 ptb.jsonl ``` ## Expected results Properly separated lines ## Actual results The last line is a concatenation of two lines ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.1.dev0 - Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyArrow version: 4.0.1

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MDU6SXNzdWU5Mzg3OTY5MDI=

Streaming local gzip compressed JSON line files is not working

closed

MEMBER

## Describe the bug Using streaming to iterate on local gzip compressed JSON files raise a file not exist error ## Steps to reproduce the bug ```python from datasets import load_dataset streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True) next(iter(streamed_dataset)) ``` ## Actual results ``` FileNotFoundError Traceback (most recent call last) <ipython-input-6-27a664e29784> in <module> ----> 1 next(iter(streamed_dataset)) ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self) 336 337 def __iter__(self): --> 338 for key, example in self._iter(): 339 if self.features: 340 # we encode the example for ClassLabel feature types for example ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in _iter(self) 333 else: 334 ex_iterable = self._ex_iterable --> 335 yield from ex_iterable 336 337 def __iter__(self): ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self) 76 77 def __iter__(self): ---> 78 for key, example in self.generate_examples_fn(**self.kwargs): 79 yield key, example 80 ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in wrapper(**kwargs) 282 def wrapper(**kwargs): 283 python_formatter = PythonFormatter() --> 284 for key, table in generate_tables_fn(**kwargs): 285 batch = python_formatter.format_batch(table) 286 for i, example in enumerate(_batch_to_examples(batch)): ~/Documents/GitHub/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files, original_files) 85 file, 86 read_options=self.config.pa_read_options, ---> 87 parse_options=self.config.pa_parse_options, 88 ) 89 except pa.ArrowInvalid as err: ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json._get_reader() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_input_stream() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_native_file() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile.__cinit__() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile._open_readable() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() FileNotFoundError: [Errno 2] Failed to open local file 'gzip://file-000000000000.json::/Users/thomwolf/github-dataset/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory ``` ## Environment info - `datasets` version: 1.9.1.dev0 - Platform: Darwin-19.6.0-x86_64-i386-64bit - Python version: 3.7.7 - PyArrow version: 1.0.0

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MDU6SXNzdWU5Mzg3NjM2ODQ=

[Metrics] addition of wiki_split metrics

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** While training the model on sentence split the task in English we require to evaluate the trained model on `Exact Match`, `SARI` and `BLEU` score like this  While training we require metrics which can give all the output Currently, we don't have an exact match for text normalized data **Describe the solution you'd like** A custom metrics for wiki_split that can calculate these three values and provide it in the form of a single dictionary For exact match, we can refer to [this](https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py) **Describe alternatives you've considered** Two metrics are already present one more can be added for an exact match then we can run all three metrics in training script #self-assign

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MDU6SXNzdWU5Mzg2MDIyMzc=

Add option to delete temporary files (e.g. extracted files) when loading dataset

closed

MEMBER

I'm loading a dataset constituted of 44 GB of compressed JSON files. When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter. I can maybe tackle this one in the JSON script unless you want a more general solution.

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MDU6SXNzdWU5MzgwODY3NDU=

Crash when using multiprocessing (`num_proc` > 1) on `filter` and all samples are discarded

closed

CONTRIBUTOR

## Describe the bug If `filter` is applied to a dataset using multiprocessing (`num_proc` > 1) and all sharded datasets are empty afterwards (due to all samples being discarded), the program crashes. ## Steps to reproduce the bug ```python from datasets import Dataset data = Dataset.from_dict({'id': [0,1]}) data.filter(lambda x: False, num_proc=2) ``` ## Expected results An empty table should be returned without crashing. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/user/venv/lib/python3.8/site-packages/datasets/fingerprint.py", line 397, in wrapper out = func(self, *args, **kwargs) File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2143, in filter return self.map( File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1738, in map result = concatenate_datasets(transformed_shards) File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3267, in concatenate_datasets table = concat_tables(tables_to_concat, axis=axis) File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 853, in concat_tables return ConcatenationTable.from_tables(tables, axis=axis) File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 713, in from_tables blocks = to_blocks(tables[0]) IndexError: list index out of range ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-5.12.11-300.fc34.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.8.10 - PyArrow version: 3.0.0

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MDU6SXNzdWU5Mzc5MzA2MzI=

Unable to download omp dataset

closed

NONE

## Describe the bug The omp dataset cannot be downloaded because of a DuplicatedKeysError ## Steps to reproduce the bug from datasets import load_dataset omp = load_dataset('omp', 'posts_labeled') print(omp) ## Expected results This code should download the omp dataset and print the dictionary ## Actual results Downloading and preparing dataset omp/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to /home/erika_distefano/.cache/huggingface/datasets/omp/posts_labeled/1.1.0/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b... 0 examples [00:00, ? examples/s]2021-07-06 09:43:55.868815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0 Traceback (most recent call last): File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 990, in _prepare_split writer.write(example, key) File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 338, in write self.check_duplicate_keys() File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 3326 Keys should be unique and deterministic in nature During handling of the above exception, another exception occurred: Traceback (most recent call last): File "hf_datasets.py", line 32, in <module> omp = load_dataset('omp', 'posts_labeled') File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset use_auth_token=use_auth_token, File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 992, in _prepare_split num_examples, num_bytes = writer.finalize() File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 409, in finalize self.check_duplicate_keys() File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 3326 Keys should be unique and deterministic in nature ## Environment info - `datasets` version: 1.8.0 - Platform: Ubuntu 18.04.4 LTS - Python version: 3.6.9 - PyArrow version: 3.0.0

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MDU6SXNzdWU5Mzc1OTg5MTQ=

Transformer Class on dataset

open

NONE

Just wondering if you have intenttion to create TransformerClass : dataset --> dataset and make determnistic transformation (ie not fit).

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ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets

closed

NONE

Error traceback: --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-8-a7b592d3bca0> in <module>() 1 from datasets import load_dataset, load_metric 2 ----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation") 4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test") 9 frames /root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>() 19 20 import datasets ---> 21 from datasets.tasks import AutomaticSpeechRecognition 22 23 ModuleNotFoundError: No module named 'datasets.tasks'

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MDU6SXNzdWU5MzY5NTc5NzU=

Cached dataset overflowing disk space

closed

CONTRIBUTOR

I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb). The cache folder is 500gb (and now my disk space is full). Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files). This might not technically be a bug, but I was unsure and I felt that the bug was the closest one. Traceback (most recent call last): File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker result = (True, func(*args, **kwds)) File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper out = func(self, *args, **kwargs) File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single writer.finalize() File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize self.pa_writer.close() File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device """ The above exception was the direct cause of the following exception:

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MDU6SXNzdWU5MzY0ODQ0MTk=

sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index

closed

NONE

## Describe the bug The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start']. For example: id = '56d1f453e7d4791d009025bd' answers = {'text': ['Pure Land'], 'answer_start': [146]} However the actual text in context at location 146 is 'ure Land,' Which is an off-by-one error from the correct answer. ## Steps to reproduce the bug ```python import datasets def check_context_answer_alignment(example): for a_idx in range(len(example['answers']['text'])): # check raw dataset for answer consistency between context and answer answer_text = example['answers']['text'][a_idx] a_st_idx = example['answers']['answer_start'][a_idx] a_end_idx = a_st_idx + len(example['answers']['text'][a_idx]) answer_text_from_context = example['context'][a_st_idx:a_end_idx] if answer_text != answer_text_from_context: #print(example['id']) return False return True dataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True) start_len = len(dataset) dataset = dataset.filter(check_context_answer_alignment, num_proc=1, keep_in_memory=True) end_len = len(dataset) print('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len)) ``` ## Expected results This code should result in 0 rows being filtered out from the dataset. ## Actual results This filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location. This code will reproduce the problem and produce the following count: "258 instances contain mis-alignment between the answer text and answer index." ## Environment info Steps to rebuilt the Conda environment: ``` # create a virtual environment to stuff all these packages into conda create -n round8 python=3.8 -y # activate the virtual environment conda activate round8 # install pytorch (best done through conda to handle cuda dependencies) conda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia pip install jsonpickle transformers datasets matplotlib ``` OS: Ubuntu 20.04 Python 3.8 Result of `conda env export`: ``` name: round8 channels: - pytorch-lts - nvidia - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=4.5=1_gnu - blas=1.0=mkl - brotlipy=0.7.0=py38h27cfd23_1003 - bzip2=1.0.8=h7b6447c_0 - ca-certificates=2021.5.25=h06a4308_1 - certifi=2021.5.30=py38h06a4308_0 - cffi=1.14.5=py38h261ae71_0 - chardet=4.0.0=py38h06a4308_1003 - cryptography=3.4.7=py38hd23ed53_0 - cudatoolkit=11.1.74=h6bb024c_0 - ffmpeg=4.2.2=h20bf706_0 - freetype=2.10.4=h5ab3b9f_0 - gmp=6.2.1=h2531618_2 - gnutls=3.6.15=he1e5248_0 - idna=2.10=pyhd3eb1b0_0 - intel-openmp=2021.2.0=h06a4308_610 - jpeg=9b=h024ee3a_2 - lame=3.100=h7b6447c_0 - lcms2=2.12=h3be6417_0 - ld_impl_linux-64=2.35.1=h7274673_9 - libffi=3.3=he6710b0_2 - libgcc-ng=9.3.0=h5101ec6_17 - libgomp=9.3.0=h5101ec6_17 - libidn2=2.3.1=h27cfd23_0 - libopus=1.3.1=h7b6447c_0 - libpng=1.6.37=hbc83047_0 - libstdcxx-ng=9.3.0=hd4cf53a_17 - libtasn1=4.16.0=h27cfd23_0 - libtiff=4.2.0=h85742a9_0 - libunistring=0.9.10=h27cfd23_0 - libuv=1.40.0=h7b6447c_0 - libvpx=1.7.0=h439df22_0 - libwebp-base=1.2.0=h27cfd23_0 - lz4-c=1.9.3=h2531618_0 - mkl=2021.2.0=h06a4308_296 - mkl-service=2.3.0=py38h27cfd23_1 - mkl_fft=1.3.0=py38h42c9631_2 - mkl_random=1.2.1=py38ha9443f7_2 - ncurses=6.2=he6710b0_1 - nettle=3.7.3=hbbd107a_1 - ninja=1.10.2=hff7bd54_1 - numpy=1.20.2=py38h2d18471_0 - numpy-base=1.20.2=py38hfae3a4d_0 - olefile=0.46=py_0 - openh264=2.1.0=hd408876_0 - openssl=1.1.1k=h27cfd23_0 - pillow=8.2.0=py38he98fc37_0 - pip=21.1.2=py38h06a4308_0 - pycparser=2.20=py_2 - pyopenssl=20.0.1=pyhd3eb1b0_1 - pysocks=1.7.1=py38h06a4308_0 - python=3.8.10=h12debd9_8 - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0 - readline=8.1=h27cfd23_0 - requests=2.25.1=pyhd3eb1b0_0 - setuptools=52.0.0=py38h06a4308_0 - six=1.16.0=pyhd3eb1b0_0 - sqlite=3.35.4=hdfb4753_0 - tk=8.6.10=hbc83047_0 - torchtext=0.9.1=py38 - torchvision=0.9.1=py38_cu111 - typing_extensions=3.7.4.3=pyha847dfd_0 - urllib3=1.26.4=pyhd3eb1b0_0 - wheel=0.36.2=pyhd3eb1b0_0 - x264=1!157.20191217=h7b6447c_0 - xz=5.2.5=h7b6447c_0 - zlib=1.2.11=h7b6447c_3 - zstd=1.4.9=haebb681_0 - pip: - click==8.0.1 - cycler==0.10.0 - datasets==1.8.0 - dill==0.3.4 - filelock==3.0.12 - fsspec==2021.6.0 - huggingface-hub==0.0.8 - joblib==1.0.1 - jsonpickle==2.0.0 - kiwisolver==1.3.1 - matplotlib==3.4.2 - multiprocess==0.70.12.2 - packaging==20.9 - pandas==1.2.4 - pyarrow==3.0.0 - pyparsing==2.4.7 - python-dateutil==2.8.1 - pytz==2021.1 - regex==2021.4.4 - sacremoses==0.0.45 - tokenizers==0.10.3 - tqdm==4.49.0 - transformers==4.6.1 - xxhash==2.0.2 prefix: /home/mmajurski/anaconda3/envs/round8 ```

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MDU6SXNzdWU5MzYwMzQ5NzY=

Error iteration over IterableDataset using Torch DataLoader

closed

NONE

## Describe the bug I have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler. I am not sure if this is how it is meant to be used, but that's what seemed reasonable to me. ## Steps to reproduce the bug 1. Does not work. ```python >>> from datasets import load_dataset >>> dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True) >>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4) >>> dataloader.sampler <torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208> >>> for batch in dataloader: ... print(batch) ``` 2. Works. ```python import torch from torch.utils.data import Dataset, IterableDataset, DataLoader class CustomIterableDataset(IterableDataset): 'Characterizes a dataset for PyTorch' def __init__(self, data): 'Initialization' self.data = data def __iter__(self): return iter(self.data) data = list(range(12)) dataset = CustomIterableDataset(data) dataloader = DataLoader(dataset, batch_size=4) print("dataloader: ", dataloader.sampler) for batch in dataloader: print(batch) ``` ## Expected results To get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different. dataloader: <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50> tensor([0, 1, 2, 3]) tensor([4, 5, 6, 7]) tensor([ 8, 9, 10, 11]) ## Actual results <torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208> ... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ data = self._next_data() File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 474, in _next_data index = self._next_index() # may raise StopIteration File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index return next(self._sampler_iter) # may raise StopIteration File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 227, in __iter__ for idx in self.sampler: File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 67, in __iter__ return iter(range(len(self.data_source))) TypeError: object of type 'IterableDataset' has no len() ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: '1.8.1.dev0' - Platform: Linux - Python version: Python 3.6.8 - PyArrow version: '3.0.0'

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MDU6SXNzdWU5MzQ1ODQ3NDU=

Finding right block-size with JSON loading difficult for user

open

MEMBER

As reported by @thomwolf, while loading a JSON Lines file with "json" loading script, he gets > json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)

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MDU6SXNzdWU5MzQ1NzM3Njc=

Support Zstandard compressed files

closed

MEMBER

Add support for Zstandard compressed files: https://facebook.github.io/zstd/

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MDU6SXNzdWU5MzMwMTU3OTc=

Weights of model checkpoint not initialized for RobertaModel for Bertscore

closed

NONE

When applying bertscore out of the box, ```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']``` Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html ``` from datasets import load_metric metric = load_metric('bertscore') # Example of typical usage for batch in dataset: inputs, references = batch predictions = model(inputs) metric.add_batch(predictions=predictions, references=references) score = metric.compute(lang="en") #score = metric.compute(model_type="roberta-large") # gives the same error ``` I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo.... ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27 - Python version: 3.9.5 - PyArrow version: 3.0.0

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MDU6SXNzdWU5MzIzODk2Mzk=

concatenate_datasets for iterable datasets

open

MEMBER

Currently `concatenate_datasets` only works for map-style `Dataset`. It would be nice to have it work for `IterableDataset` objects as well. It would simply chain the iterables of the iterable datasets.

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MDU6SXNzdWU5MzIzODc2Mzk=

interleave_datasets for map-style datasets

closed

MEMBER

Currently the `interleave_datasets` functions only works for `IterableDataset`. Let's make it work for map-style `Dataset` objects as well. It would work the same way: either alternate between the datasets in order or randomly given probabilities specified by the user.

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MDU6SXNzdWU5MzIzMjE3MjU=

Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`

open

NONE

## Describe the bug If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets. ## Steps to reproduce the bug - Create a local dataset builder class - load the local builder class file using `load_dataset` and let the cache build - update the file's content - The cache should rebuilt. ## Expected results With `ignore_verifications=True`, `load_dataset` should pick up existing cache. ## Actual results Creates new cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.7 - PyArrow version: 3.0.0

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MDU6SXNzdWU5MzE4NDk3MjQ=

Memory usage consistently increases when processing a dataset with `.map`

open

NONE

## Describe the bug I have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help. ## Steps to reproduce the bug Providing code as it is would be hard. I can provide a MVP if that helps. ## Expected results Memory usage should become consistent after some time following the launch of processing. ## Actual results Memory usage keeps on increasing. ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.7 - PyArrow version: 3.0.0

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MDU6SXNzdWU5MzE1OTU4NzI=

Better DuplicateKeysError error to help the user debug the issue

open

MEMBER

As mentioned in https://github.com/huggingface/datasets/issues/2552 it would be nice to improve the error message when a dataset fails to build because there are duplicate example keys. The current one is ```python datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 48 Keys should be unique and deterministic in nature ``` and we could have something that guides the user to debugging the issue: ```python DuplicateKeysError: both 42th and 1337th examples have the same keys `48`. Please fix the dataset script at <path/to/the/dataset/script> ```

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MDU6SXNzdWU5MzE0NTM4NTU=

Multilabel metrics not supported

closed

NONE

When I try to use a metric like F1 macro I get the following error: ``` TypeError: int() argument must be a string, a bytes-like object or a number, not 'list' ``` There is an explicit casting here: https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/src/datasets/features.py#L274 And looks like this is because here https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/metrics/f1/f1.py#L88 the features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work: ```python class F1(datasets.Metric): def _info(self): return datasets.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features( { "predictions": datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")), } ), reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"], ) def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None): return { "f1": f1_score( references, predictions, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight, ), } ```

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load_dataset("web_nlg") NonMatchingChecksumError

closed

NONE

Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('web_nlg', name="release_v3.0_en", split="dev") ``` Gives ``` NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip'] ``` ## Environment info - `datasets` version: 1.8.0 - Platform: macOS-11.3.1-x86_64-i386-64bit - Python version: 3.9.4 - PyArrow version: 3.0.0 Also tested on Linux, with python 3.6.8

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Keys should be unique error on code_search_net

closed

MEMBER

## Describe the bug Loading `code_search_net` seems not possible at the moment. ## Steps to reproduce the bug ```python >>> load_dataset('code_search_net') Downloading: 8.50kB [00:00, 3.09MB/s] Downloading: 19.1kB [00:00, 10.1MB/s] No config specified, defaulting to: code_search_net/all Downloading and preparing dataset code_search_net/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to /Users/thomwolf/.cache/huggingface/datasets/code_search_net/all/1.0.0/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a... Traceback (most recent call last): File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/builder.py", line 1067, in _prepare_split writer.write(example, key) File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 343, in write self.check_duplicate_keys() File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 354, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 48 Keys should be unique and deterministic in nature ``` ## Environment info - `datasets` version: 1.8.1.dev0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 2.0.0

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MDU6SXNzdWU5MzA5NTEyODc=

Allow for incremental cumulative metric updates in a distributed setup

closed

CONTRIBUTOR

Currently, using a metric allows for one of the following: - Per example/batch metrics - Cumulative metrics over the whole data What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation. Since most metrics are just an average of per-example metrics (which aren't?), an efficient calculation can be done as follows: `((score_cumulative * n_cumulative) + (score_new * n_new)) / (n_cumulative+ n_new)` where `n` and `score` refer to number of examples and metric score, `cumulative` refers to the cumulative metric and `new` refers to the addition of new examples. If you don't want to add this capability in the library, a simple solution exists so users can do it themselves: It is easy to implement for a single process setup, but in a distributed one there is no way to get the correct `n_new`. The solution for this is to return the number of examples that was used to compute the metrics in `.compute()` by adding the following line here: https://github.com/huggingface/datasets/blob/5a3221785311d0ce86c2785b765e86bd6997d516/src/datasets/metric.py#L402-L403 ``` output["number_of_examples"] = len(predictions) ``` and also remove the log message here so it won't spam: https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/src/datasets/metric.py#L411 If this change is ok with you, I'll open a pull request.

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MDU6SXNzdWU5Mjk4MTkwOTM=

Handling unlabeled datasets

closed

NONE

Hi! Is there a way for datasets to produce unlabeled instances (e.g., the `ClassLabel` can be nullable). For example, I want to use the MNLI dataset reader ( https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi_nli.py ) on a file that doesn't have the `gold_label` field. I tried setting `"label": data.get("gold_label")`, but got the following error: ``` File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset use_auth_token=use_auth_token, File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 989, in _prepare_split example = self.info.features.encode_example(record) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 953, in encode_example return encode_nested_example(self, example) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in encode_nested_example k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in <dictcomp> k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 875, in encode_nested_example return schema.encode_example(obj) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 653, in encode_example if not -1 <= example_data < self.num_classes: TypeError: '<=' not supported between instances of 'int' and 'NoneType' ``` What's the proper way to handle reading unlabeled datasets, especially for downstream usage with Transformers?

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MDU6SXNzdWU5MjkyMzI4MzE=

Field order issue in loading json

closed

NONE

## Describe the bug The `load_dataset` function expects columns in alphabetical order when loading json files. Similar bug was previously reported for csv in #623 and fixed in #684. ## Steps to reproduce the bug For a json file `j.json`, ``` {"c":321, "a": 1, "b": 2} ``` Running the following, ``` f= datasets.Features({'a': Value('int32'), 'b': Value('int32'), 'c': Value('int32')}) json_data = datasets.load_dataset('json', data_files='j.json', features=f) ``` ## Expected results A successful load. ## Actual results ``` File "pyarrow/table.pxi", line 1409, in pyarrow.lib.Table.cast ValueError: Target schema's field names are not matching the table's field names: ['c', 'a', 'b'], ['a', 'b', 'c'] ``` ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyArrow version: 3.0.0

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MDU6SXNzdWU5MjkxOTIzMjk=

Dataset load_from_disk is too slow

open

NONE

@lhoestq ## Describe the bug It's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example). ## Steps to reproduce the bug Just get the oscar in spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset. ## Expected results I expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Ubuntu 18 - Python version: 3.8 I've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model.

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MDU6SXNzdWU5Mjg1NzE5MTU=

switching some low-level log.info's to log.debug?

closed

CONTRIBUTOR

In https://github.com/huggingface/transformers/pull/12276 we are now changing the examples to have `datasets` on the same log level as `transformers`, so that one setting can do a consistent logging across all involved components. The trouble is that now we get a ton of these: ``` 06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 acquired on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock 06/23/2021 12:15:31 - INFO - datasets.arrow_writer - Done writing 50 examples in 12280 bytes /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow. 06/23/2021 12:15:31 - INFO - datasets.arrow_dataset - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns. 06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 released on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock ``` May I suggest that these can be `log.debug` as it's no informative to the user. More examples: these are not informative - too much information: ``` 06/23/2021 12:14:26 - INFO - datasets.load - Checking /home/stas/.cache/huggingface/datasets/downloads/459933f1fe47711fad2f6ff8110014ff189120b45ad159ef5b8e90ea43a174fa.e23e7d1259a8c6274a82a42a8936dd1b87225302c6dc9b7261beb3bc2daac640.py for additional imports. 06/23/2021 12:14:27 - INFO - datasets.builder - Constructing Dataset for split train, validation, test, from /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a ``` While these are: ``` 06/23/2021 12:14:27 - INFO - datasets.info - Loading Dataset Infos from /home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt16/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a 06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a) ``` I also realize that `transformers` examples don't have do use `info` for `datasets` to let the default `warning` keep logging to less noisy. But I think currently the log levels are slightly misused and skewed by 1 level. Many `warnings` will better be `info`s and most `info`s be `debug`. e.g.: ``` 06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a) ``` why is this a warning? it is informing me that the cache is used, there is nothing to be worried about. I'd have it as `info`. Warnings are typically something that's bordering error or the first thing to check when things don't work as expected. infrequent info is there to inform of the different stages or important events. Everything else is debug. At least the way I understand things.

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MDU6SXNzdWU5Mjg1NDAzODI=

`datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa/adversarialQA`

closed

MEMBER

## Describe the bug Failure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("drop") load_dataset("adversarial_qa", "adversarialQA") ``` ## Expected results The examples keys should be unique. ## Actual results ```bash >>> load_dataset("drop") Using custom data configuration default Downloading and preparing dataset drop/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to /home/hf/.cache/huggingface/datasets/drop/default/0.1.0/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset use_auth_token=use_auth_token, File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 992, in _prepare_split num_examples, num_bytes = writer.finalize() File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 409, in finalize self.check_duplicate_keys() File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 28553293-d719-441b-8f00-ce3dc6df5398 Keys should be unique and deterministic in nature ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.7.0 - Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.10 - PyArrow version: 3.0.0

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MDU6SXNzdWU5Mjc5NDA2OTE=

Loading partial dataset when debugging

open

NONE

I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits). Every time I execute load_dataset for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues. Is there a way to only load part of the dataset on load_dataset? This would really speed up my workflow. Something like a debug mode would really help. Thanks!

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MDU6SXNzdWU5MjczMzg2Mzk=

Use `Audio` features for `AutomaticSpeechRecognition` task template

open

MEMBER

In #2533 we added a task template for speech recognition that relies on the file paths to the audio files. As pointed out by @SBrandeis this is brittle as it doesn't port easily across different OS'. The solution is to use dedicated `Audio` features when casting the dataset. These features are not yet available in `datasets`, but should be included in the `AutomaticSpeechRecognition` template once they are.

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MDU6SXNzdWU5MjcwNjMxOTY=

Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task

closed

CONTRIBUTOR

[This colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https://huggingface.co/transformers/custom_datasets.html#tok-ner). The pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https://en.wikipedia.org/wiki/Kana_ligature) break the alignment of `return_offsets_mapping`:  Without the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https://colab.research.google.com/drive/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing) It is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('ヿ')` return 'コト'. One workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`. I guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this. p.s. **I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https://github.com/huggingface/datasets/pull/2466)** `get_dataset `is just a simple wrapping for `load_dataset` and the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained("xlm-roberta-large")`

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MDU6SXNzdWU5MjYzMTQ2NTY=

Logging cannot be set to NOTSET similar to transformers

closed

NONE

## Describe the bug In the transformers library you can set the verbosity level to logging.NOTSET to work around the usage of tqdm and IPywidgets, however in Datasets this is no longer possible. This is because transformers set the verbosity level of tqdm with [this](https://github.com/huggingface/transformers/blob/b53bc55ba9bb10d5ee279eab51a2f0acc5af2a6b/src/transformers/file_utils.py#L1449) `disable=bool(logging.get_verbosity() == logging.NOTSET)` and datasets accomplishes this like [so](https://github.com/huggingface/datasets/blob/83554e410e1ab8c6f705cfbb2df7953638ad3ac1/src/datasets/utils/file_utils.py#L493) `not_verbose = bool(logger.getEffectiveLevel() > WARNING)` ## Steps to reproduce the bug ```python import datasets import logging datasets.logging.get_verbosity = lambda : logging.NOTSET datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy") ``` ## Expected results The code should download and load the dataset as normal without displaying progress bars ## Actual results ```ImportError Traceback (most recent call last) <ipython-input-4-aec65c0509c6> in <module> ----> 1 datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy") ~/venv/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs) 713 dataset=True, 714 return_resolved_file_path=True, --> 715 use_auth_token=use_auth_token, 716 ) 717 # Set the base path for downloads as the parent of the script location ~/venv/lib/python3.7/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs) 350 file_path = hf_bucket_url(path, filename=name, dataset=False) 351 try: --> 352 local_path = cached_path(file_path, download_config=download_config) 353 except FileNotFoundError: 354 raise FileNotFoundError( ~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 289 use_etag=download_config.use_etag, 290 max_retries=download_config.max_retries, --> 291 use_auth_token=download_config.use_auth_token, 292 ) 293 elif os.path.exists(url_or_filename): ~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) 668 headers=headers, 669 cookies=cookies, --> 670 max_retries=max_retries, 671 ) 672 ~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries) 493 initial=resume_size, 494 desc="Downloading", --> 495 disable=not_verbose, 496 ) 497 for chunk in response.iter_content(chunk_size=1024): ~/venv/lib/python3.7/site-packages/tqdm/notebook.py in __init__(self, *args, **kwargs) 217 total = self.total * unit_scale if self.total else self.total 218 self.container = self.status_printer( --> 219 self.fp, total, self.desc, self.ncols) 220 self.sp = self.display 221 ~/venv/lib/python3.7/site-packages/tqdm/notebook.py in status_printer(_, total, desc, ncols) 95 if IProgress is None: # #187 #451 #558 #872 96 raise ImportError( ---> 97 "IProgress not found. Please update jupyter and ipywidgets." 98 " See https://ipywidgets.readthedocs.io/en/stable" 99 "/user_install.html") ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Linux-5.4.95-42.163.amzn2.x86_64-x86_64-with-debian-10.8 - Python version: 3.7.10 - PyArrow version: 3.0.0 I am running this code on Deepnote and which important to this issue **does not** support IPywidgets

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MDU6SXNzdWU5MjU5MjkyMjg=

Add COCO datasets

open

NONE

## Adding a Dataset - **Name:** COCO - **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset. - **Paper + website:** https://cocodataset.org/#home - **Data:** https://cocodataset.org/#download - **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API. 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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MDU6SXNzdWU5MjU0MjEwMDg=

Fr

closed

NONE

__Originally posted by @lewtun in https://github.com/huggingface/datasets/pull/2469__

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MDU6SXNzdWU5MjUzMzQzNzk=

Documentation Mistakes in Dataset: emotion

open

NONE

As per documentation, Dataset: emotion Homepage: https://github.com/dair-ai/emotion_dataset Dataset: https://github.com/huggingface/datasets/blob/master/datasets/emotion/emotion.py Permalink: https://huggingface.co/datasets/viewer/?dataset=emotion Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper. But when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust.

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MDU6SXNzdWU5MjUwMTUwMDQ=

Datasets with tricky task templates

open

MEMBER

I'm collecting a list of datasets here that don't follow the "standard" taxonomy and require further investigation to implement task templates for. ## Text classification * [hatexplain](https://huggingface.co/datasets/hatexplain): ostensibly a form of text classification, but not in the standard `(text, target)` format and each sample appears to be tokenized. * [muchocine](https://huggingface.co/datasets/muchocine): contains two candidate text columns (long-form and summary) which in principle requires two `TextClassification` templates which is not currently supported

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MDU6SXNzdWU5MjQ1OTc0NzA=

datasets.map pickle issue resulting in invalid mapping function

open

NONE

I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` / `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts. The following reproduces the issue - most likely I'm missing something A simulated tokeniser which can be pickled ``` class CustomTokenizer: def __init__(self): self.state = "init" def __getstate__(self): print("__getstate__ called") out = self.__dict__.copy() self.state = "pickled" return out def __setstate__(self, d): print("__setstate__ called") self.__dict__ = d self.state = "restored" tokenizer = CustomTokenizer() ``` Test that it actually works - prints "__getstate__ called" and "__setstate__ called" ``` import pickle serialized = pickle.dumps(tokenizer) restored = pickle.loads(serialized) assert restored.state == "restored" ``` Simulate a function that tokenises examples, when dataset.map is called, this function ``` def tokenize_function(examples): assert tokenizer.state == "restored" # this shouldn't fail but it does output = tokenizer(examples) # this will fail as tokenizer isn't really a tokenizer return output ``` Use map to simulate tokenization ``` import glob from datasets import load_dataset assert tokenizer.state == "restored" train_files = glob.glob('train*.csv') validation_files = glob.glob('validation*.csv') datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files)) tokenized_datasets = datasets.map( tokenize_function, batched=True, ) ``` What's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ / __setstate__. I'm not sure if there's another hook I'm supposed to implement as well? --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-22-a2aef4f74aaa> in <module> 8 tokenized_datasets = datasets.map( 9 tokenize_function, ---> 10 batched=True, 11 ) ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc) 487 desc=desc, 488 ) --> 489 for k, dataset in self.items() 490 } 491 ) ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0) 487 desc=desc, 488 ) --> 489 for k, dataset in self.items() 490 } 491 ) ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, 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) 1633 fn_kwargs=fn_kwargs, 1634 new_fingerprint=new_fingerprint, -> 1635 desc=desc, 1636 ) 1637 else: ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 184 } 185 # apply actual function --> 186 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 187 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 188 # re-apply format to the output ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 395 # Call actual function 396 --> 397 out = func(self, *args, **kwargs) 398 399 # Update fingerprint of in-place transforms + update in-place history of transforms ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, 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, desc) 1961 indices, 1962 check_same_num_examples=len(input_dataset.list_indexes()) > 0, -> 1963 offset=offset, 1964 ) 1965 except NumExamplesMismatch: ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 1853 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset 1854 processed_inputs = ( -> 1855 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) 1856 ) 1857 if update_data is None: <ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples) 1 def tokenize_function(examples): ----> 2 assert tokenizer.state == "restored" 3 tokenizer(examples) 4 return examples

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MDU6SXNzdWU5MjQ0MTcxNzI=

Can datasets remove duplicated rows?

open

NONE

**Is your feature request related to a problem? Please describe.** i find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that.. **Describe the solution you'd like** have a functionality of " remove duplicated rows" **Describe alternatives you've considered** convert dataset to pandas, remove duplicate, and convert back... **Additional context** no

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MDU6SXNzdWU5MjQxNzQ0MTM=

Corelation should be Correlation

closed

NONE

https://github.com/huggingface/datasets/blob/0e87e1d053220e8ecddfa679bcd89a4c7bc5af62/metrics/matthews_correlation/matthews_correlation.py#L66

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MDU6SXNzdWU5MjQwNjkzNTM=

seqeval metric does not work with a recent version of sklearn: classification_report() got an unexpected keyword argument 'output_dict'

closed

NONE

## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric seqeval = load_metric("seqeval") seqeval.compute(predictions=[['A']], references=[['A']]) ``` ## Expected results The function computes a dict with metrics ## Actual results ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-39-69a57f5cf06f> in <module> 1 from datasets import load_dataset, load_metric 2 seqeval = load_metric("seqeval") ----> 3 seqeval.compute(predictions=[['A']], references=[['A']]) ~/p3/lib/python3.7/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/81eda1ff004361d4fa48754a446ec69bb7aa9cf4d14c7215f407d1475941c5ff/seqeval.py in _compute(self, predictions, references, suffix) 95 96 def _compute(self, predictions, references, suffix=False): ---> 97 report = classification_report(y_true=references, y_pred=predictions, suffix=suffix, output_dict=True) 98 report.pop("macro avg") 99 report.pop("weighted avg") TypeError: classification_report() got an unexpected keyword argument 'output_dict' ``` ## Environment info sklearn=0.24 datasets=1.1.3

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MDU6SXNzdWU5MjM3NjIxMzM=

Add C4

closed

MEMBER

## Adding a Dataset - **Name:** *C4* - **Description:** *https://github.com/allenai/allennlp/discussions/5056* - **Paper:** *https://arxiv.org/abs/1910.10683* - **Data:** *https://huggingface.co/datasets/allenai/c4* - **Motivation:** *Used a lot for pretraining* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Should fix https://github.com/huggingface/datasets/issues/1710

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MDU6SXNzdWU5MjE4NjMxNzM=

Load Image Classification Dataset from Local

open

NONE

**Is your feature request related to a problem? Please describe.** Yes - we would like to load an image classification dataset with datasets without having to write a custom data loader. **Describe the solution you'd like** Given a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like "cifar10". **Describe alternatives you've considered** Implement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path) Write custom data loader logic **Additional context** We're training ViT on custom dataset

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MDU6SXNzdWU5MjA2MzYxODY=

SubjQA wrong boolean values in entries

open

NONE

## Describe the bug SubjQA seems to have a boolean that's consistently wrong. It defines: - question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective). - is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective) However, `is_ques_subjective` seems to have wrong values in the entire dataset. For instance, in the example in the dataset card, we have: - "question_subj_level": 2 - "is_ques_subjective": false However, according to the description, the question should be subjective since the `question_subj_level` is below 4

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Python Programming Puzzles

open

MEMBER

## Adding a Dataset - **Name:** Python Programming Puzzles - **Description:** Programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis - **Paper:** https://arxiv.org/pdf/2106.05784.pdf - **Data:** https://github.com/microsoft/PythonProgrammingPuzzles ([Scrolling through the data](https://github.com/microsoft/PythonProgrammingPuzzles/blob/main/problems/README.md)) - **Motivation:** Spans a large range of difficulty, problems, and domains. A useful resource for evaluation as we don't have a clear understanding of the abilities and skills of extremely large LMs. Note: it's a growing dataset (contributions are welcome), so we'll need careful versioning for this dataset. 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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Improve torch formatting performance

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** It would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. A bit more background. I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs. The current performance is about 30% slower than NVidia optimized BERT [examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. **Describe the solution you'd like** Using profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.  As you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. Digging a bit deeper into format_batch we can see the following profiler data:  Once again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. **Describe alternatives you've considered** I am not familiar with pyarrow and have not yet considered the alternatives to the current approach. Most of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement. Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%.

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MDU6SXNzdWU5MjAyMTYzMTQ=

Dataset fingerprint changes after moving the cache directory, which prevent cache reload when using `map`

closed

MEMBER

`Dataset.map` uses the dataset fingerprint (a hash) for caching. However the fingerprint seems to change when someone moves the cache directory of the dataset. This is because it uses the default fingerprint generation: 1. the dataset path is used to get the fingerprint 2. the modification times of the arrow file is also used to get the fingerprint To fix that we could set the fingerprint of the dataset to be a hash of (<dataset_name>, <config_name>, <version>, <script_hash>), i.e. a hash of the the cache path relative to the cache directory.

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JAX formatting

closed

MEMBER

We already support pytorch, tensorflow, numpy, pandas and arrow dataset formatting. Let's add jax as well

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Improve docs on Enhancing performance

open

MEMBER

In the ["Enhancing performance"](https://huggingface.co/docs/datasets/loading_datasets.html#enhancing-performance) section of docs, add specific use cases: - How to make datasets the fastest - How to make datasets take the less RAM - How to make datasets take the less hard drive mem cc: @thomwolf

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Allow latest pyarrow version once segfault bug is fixed

closed

MEMBER

As pointed out by @symeneses (see https://github.com/huggingface/datasets/pull/2268#issuecomment-860048613), pyarrow has fixed the segfault bug present in version 4.0.0 (see https://issues.apache.org/jira/browse/ARROW-12568): - it was fixed on 3 May 2021 - version 4.0.1 was released on 19 May 2021 with the bug fix

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Implement layered building

open

MEMBER

As discussed with @stas00 and @lhoestq (see also here https://github.com/huggingface/datasets/issues/2481#issuecomment-859712190): > My suggestion for this would be to have this enabled by default. > > Plus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is: > > 1. uncompress a handful of files via a generator enough to generate one arrow file > 2. process arrow file 1 > 3. delete all the files that went in and aren't needed anymore. > > rinse and repeat. > > 1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project > 2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing > 3. It would already include deleting temp files this issue is talking about > > I wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders.

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Implement loading a dataset builder

closed

MEMBER

As discussed with @stas00 and @lhoestq, this would allow things like: ```python from datasets import load_dataset_builder dataset_name = "openwebtext" builder = load_dataset_builder(dataset_name) print(builder.cache_dir) ```

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Delete extracted files to save disk space

closed

MEMBER

As discussed with @stas00 and @lhoestq, allowing the deletion of extracted files would save a great amount of disk space to typical user.

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Set download/extracted paths configurable

open

MEMBER

As discussed with @stas00 and @lhoestq, setting these paths configurable may allow to overcome disk space limitation on different partitions/drives. TODO: - [x] Set configurable extracted datasets path: #2487 - [x] Set configurable downloaded datasets path: #2488 - [ ] Set configurable "incomplete" datasets path?

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Create release script

open

MEMBER

Create a script so that releases can be done automatically (as done in `transformers`).

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MDU6SXNzdWU5MTc2NTA4ODI=

Issue in timit_asr database

closed

NONE

## Describe the bug I am trying to load the timit_asr dataset however only the first record is shown (duplicated over all the rows). I am using the next code line dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10)) The above code result with the same sentence duplicated ten times. It also happens when I use the dataset viewer at Streamlit . ## Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10)) data = dataset.to_pandas() # Sample code to reproduce the bug ``` ## Expected results table with different row information ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.4.1 (also occur in the latest version) - Platform: Linux-4.15.0-143-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyTorch version (GPU?): 1.8.1+cu102 (False) - Tensorflow version (GPU?): 1.15.3 (False) - Using GPU in script?: No - Using distributed or parallel set-up in script?: No - `datasets` version: - Platform: - Python version: - PyArrow version:

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cache_dir parameter for load_from_disk ?

open

NONE

**Is your feature request related to a problem? Please describe.** When using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk: ` from datasets import load_from_disk myPreprocessedData = load_from_disk("/content/gdrive/MyDrive/ASR_data/myPreprocessedData") ` I know that chaching on google drive could slow down learning. But at least it would run. **Describe the solution you'd like** Add cache_Dir parameter to the load_from_disk function. **Describe alternatives you've considered** It looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?

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MDU6SXNzdWU5MTc0NjM4MjE=

Fix automatic generation of Zenodo DOI

closed

MEMBER

After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as "Received", instead of in green as "Published". I have contacted Zenodo support to fix this issue. TODO: - [x] Check with Zenodo to fix the issue - [x] Check BibTeX entry is right

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MDU6SXNzdWU5MTcwNjcxNjU=

Fix PermissionError on Windows when using tqdm >=4.50.0

closed

MEMBER

See: https://app.circleci.com/pipelines/github/huggingface/datasets/235/workflows/cfb6a39f-68eb-4802-8b17-2cd5e8ea7369/jobs/1111 ``` PermissionError: [WinError 32] The process cannot access the file because it is being used by another process ```

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MDU6SXNzdWU5MTY3MjQyNjA=

Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`.

closed