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I_kwDODunzps49br0b

Loading Data from HDF files

open

NONE

**Is your feature request related to a problem? Please describe.** More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset. **Describe the solution you'd like** I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc). **Describe alternatives you've considered** Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible. **Additional context** HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.

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I_kwDODunzps49behb

OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB

open

NONE

## Describe the bug Despite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error : > OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB Note that I always run `batch_size=writer_batch_size` : ## Steps to reproduce the bug ```python datasets.map(lambda example : {"column_name" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc="blablabla") ``` ## Introspecting CUDA memory during bug Placed within `function(arguments)` the following statement to introspect memory usage, merely a little over 1/4 of 2Gb `print(torch.cuda.memory_summary(device=device, abbreviated=False))` > |===========================================================================| | PyTorch CUDA memory summary, device ID 0 | |---------------------------------------------------------------------------| | CUDA OOMs: 0 | cudaMalloc retries: 0 | |===========================================================================| | Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed | |---------------------------------------------------------------------------| | Allocated memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB | | from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB | | from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB | |---------------------------------------------------------------------------| | Active memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB | | from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB | | from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB | |---------------------------------------------------------------------------| | GPU reserved memory | 598016 KB | 598016 KB | 598016 KB | 0 B | | from large pool | 595968 KB | 595968 KB | 595968 KB | 0 B | | from small pool | 2048 KB | 2048 KB | 2048 KB | 0 B | |---------------------------------------------------------------------------| | Non-releasable memory | 36117 KB | 52292 KB | 274275 KB | 238158 KB | | from large pool | 34816 KB | 51537 KB | 261713 KB | 226897 KB | | from small pool | 1301 KB | 2045 KB | 12562 KB | 11261 KB | |---------------------------------------------------------------------------| | Allocations | 198 | 224 | 478 | 280 | | from large pool | 74 | 75 | 75 | 1 | | from small pool | 124 | 150 | 403 | 279 | |---------------------------------------------------------------------------| | Active allocs | 198 | 224 | 478 | 280 | | from large pool | 74 | 75 | 75 | 1 | | from small pool | 124 | 150 | 403 | 279 | |---------------------------------------------------------------------------| | GPU reserved segments | 21 | 21 | 21 | 0 | | from large pool | 20 | 20 | 20 | 0 | | from small pool | 1 | 1 | 1 | 0 | |---------------------------------------------------------------------------| | Non-releasable allocs | 18 | 23 | 166 | 148 | | from large pool | 17 | 18 | 19 | 2 | | from small pool | 1 | 6 | 147 | 146 | |===========================================================================| ## Expected results Efficiently process the datasets and write it down to disk. ## Actual results -------------------------------------------------------------------------- OverflowError Traceback (most recent call last) ~\anaconda3\envs\xxx\lib\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, disable_tqdm, desc, cache_only) 2390 else: -> 2391 writer.write(example) 2392 else: ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write(self, example, key, writer_batch_size) 367 --> 368 self.write_examples_on_file() 369 ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self) 316 if not isinstance(pa_array[0], pa.lib.FloatScalar): --> 317 raise OverflowError( 318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format( OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB During handling of the above exception, another exception occurred: OverflowError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_16268/2456940807.py in <module> 3 #tracker = OfflineEmissionsTracker(country_iso_code="FRA", project_name='xxx'+time_stamp,output_dir='./codecarbon') 4 #tracker.start() ----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True, 6 clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False, 7 max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) # ~\xxx\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc) 481 for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False): 482 if "xxx_" in column: --> 483 dataset = dataset.map(lambda example : 484 {"embeddings_"+str(column).replace("translated_",""):function(input_ids=example[column], 485 token_type_ids=example[column.replace("input_ids","token_type_ids")], ~\anaconda3\envs\xxx\lib\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) 2034 2035 if num_proc is None or num_proc == 1: -> 2036 return self._map_single( 2037 function=function, 2038 with_indices=with_indices, ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs) 501 self: "Dataset" = kwargs.pop("self") 502 # apply actual function --> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 505 for dataset in datasets: ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs) 468 } 469 # apply actual function --> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 472 # re-apply format to the output ~\anaconda3\envs\xxx\lib\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms + update in-place history of transforms ~\anaconda3\envs\xxx\lib\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, disable_tqdm, desc, cache_only) 2425 if update_data: 2426 if writer is not None: -> 2427 writer.finalize() 2428 if tmp_file is not None: 2429 tmp_file.close() ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in finalize(self, close_stream) 440 # Re-intializing to empty list for next batch 441 self.hkey_record = [] --> 442 self.write_examples_on_file() 443 if self.pa_writer is None: 444 if self._schema is not None: ~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self) 315 # This check fails with FloatArrays with nans, which is not what we want, so account for that: 316 if not isinstance(pa_array[0], pa.lib.FloatScalar): --> 317 raise OverflowError( 318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format( 319 type(pa_array) OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.13.3 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.8.11 - PyArrow version: 3.0.0 ##Next steps Testing on Linux. @albertvillanova

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I_kwDODunzps49bbFH

concatenate_datasets removes ClassLabel typing.

closed

CONTRIBUTOR

## Describe the bug When concatenating two datasets, we lose typing of ClassLabel columns. I can work on this if this is a legitimate bug, ## Steps to reproduce the bug ```python import datasets from datasets import Dataset, ClassLabel, Value, concatenate_datasets DS_LEN = 100 my_dataset = Dataset.from_dict( { "sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)], "label": [i % 2 for i in range(DS_LEN)] } ) my_predictions = Dataset.from_dict( { "pred": [(i + 1) % 2 for i in range(DS_LEN)] } ) my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])})) print("Original") print(my_dataset) print(my_dataset.features) concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1) print("Concatenated") print(concat_ds) print(concat_ds.features) ``` ## Expected results The features of `concat_ds` should contain ClassLabel. ## Actual results On master, I get: ``` {'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)} ``` ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.11 - PyArrow version: 4.0.1

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I_kwDODunzps49Vs1b

download_mode=`force_redownload` does not work on removed datasets

open

CONTRIBUTOR

## Describe the bug If a cached dataset is removed from the library, I don't see how to delete it programmatically. I thought that using `force_redownload` would try to refresh the cache, then raise an exception, but it reuses the cache instead. ## Steps to reproduce the bug _requires to already have `wit` in the cache_: see https://github.com/huggingface/datasets/pull/2981 ```python import datasets as ds dataset = ds.load_dataset("wit", split="train", download_mode='force_redownload') ``` ## Expected results It should raise an exception, since the dataset does not exist anymore. ## Actual results It uses the cached result ``` Using the latest cached version of the module from /home/slesage/.cache/huggingface/modules/datasets_modules/datasets/wit/107afbffd48e058b19101bddc47fbee25fa68eb6d50a733e262875f1285a5171 (last modified on Wed Sep 29 08:21:10 2021) since it couldn't be found locally at wit, or remotely on the Hugging Face Hub. ``` ## Environment info - `datasets` version: 1.13.4.dev0 - Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 4.0.1

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I_kwDODunzps49VoVc

Missing Zenodo 1.13.3 release

closed

MEMBER

After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305 TODO: - [x] Contact Zenodo support - [x] Check it is fixed

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I_kwDODunzps49VlE2

Unsuitable project description in PyPI

closed

MEMBER

Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/

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I_kwDODunzps49SzGe

AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'

closed

NONE

## Describe the bug When using `pip install datasets` or use `conda install -c huggingface -c conda-forge datasets` cannot install datasets ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("sst", "default") ``` ## Actual results --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-5-fbe7981e6e21> in <module> 1 import torch 2 import transformers ----> 3 from datasets import load_dataset 4 5 dataset = load_dataset("sst", "default") ~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/__init__.py in <module> 35 from .arrow_reader import ArrowReader, ReadInstruction 36 from .arrow_writer import ArrowWriter ---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder 38 from .combine import interleave_datasets 39 from .dataset_dict import DatasetDict, IterableDatasetDict ~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/builder.py in <module> 42 ) 43 from .arrow_writer import ArrowWriter, BeamWriter ---> 44 from .data_files import DataFilesDict, _sanitize_patterns 45 from .dataset_dict import DatasetDict, IterableDatasetDict 46 from .fingerprint import Hasher ~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/data_files.py in <module> 118 119 def _exec_patterns_in_dataset_repository( --> 120 dataset_info: huggingface_hub.hf_api.DatasetInfo, 121 patterns: List[str], 122 allowed_extensions: Optional[list] = None, AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.13.3 - Platform: macOS-11.3.1-arm64-arm-64bit - Python version: 3.8.10 - PyArrow version: 5.0.0

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I_kwDODunzps49Qjub

`ModuleNotFoundError: No module named 'fsspec.exceptions'`

closed

MEMBER

## Describe the bug I keep runnig into a fsspec ModuleNotFound error ## Steps to reproduce the bug ```python >>> from datasets import get_dataset_infos 2021-10-15 15:25:37.863206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2021-10-15 15:25:37.863252: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/__init__.py", line 37, in <module> from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 56, in <module> from .utils.streaming_download_manager import StreamingDownloadManager File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 11, in <module> from fsspec.exceptions import FSTimeoutError ModuleNotFoundError: No module named 'fsspec.exceptions' ``` Yet, I do have `fsspec`: ```bash hf@victor-scale:~/dev/promptsource$ pip show fsspec Name: fsspec Version: 2021.5.0 Summary: File-system specification Home-page: http://github.com/intake/filesystem_spec Author: None Author-email: None License: BSD Location: /home/hf/dev/promptsource/.venv/lib/python3.7/site-packages Requires: Required-by: datasets ``` With the same version of fsspec and `datasets==1.9.0`, I don't see this problem.... ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> I can't even run `datasets-cli env` actually.., but here's my env: - `datasets` version: 1.13.3 - Platform: Ubuntu 18.04 - Python version: 3.7.10 - PyArrow version: 3.0.0

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I_kwDODunzps49PbDa

`cast_column` makes audio decoding fail

closed

MEMBER

## Describe the bug After changing the sampling rate automatic decoding fails. ## Steps to reproduce the bug ```python from datasets import load_dataset import datasets ds = load_dataset("common_voice", "ab", split="train") ds = ds.cast_column("audio", datasets.features.Audio(sampling_rate=16_000)) print(ds[0]["audio"]) # <- this fails currently ``` yields: ``` TypeError: forward() takes 2 positional arguments but 4 were given ``` ## Expected results no failure ## 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. --> Copy-and-paste the text below in your GitHub issue. - `datasets` version: 1.13.2 (master) - Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 5.0.0

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I_kwDODunzps49O8p5

Support loading a dataset from SQLite files

open

MEMBER

As requested by @julien-c, we could eventually support loading a dataset from SQLite files, like it is the case for JSON/CSV files.

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I_kwDODunzps49Osas

Error loading json dataset with multiple splits if keys in nested dicts have a different order

open

NONE

## Describe the bug Loading a json dataset with multiple splits that have nested dicts with keys in different order results in the error below. If the keys in the nested dicts always have the same order or even if you just load a single split in which the nested dicts don't have the same order, everything works fine. ## Steps to reproduce the bug Create two json files: train.json ``` {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} ``` test.json ``` {"a": {"b": 1, "c": 2}} {"a": {"b": 3, "c": 4}} ``` ```python from datasets import load_dataset # Loading the files individually works (even though the keys in train.json don't have the same order) load_dataset('json', data_files={"test": "test.json"}) load_dataset('json', data_files={"train": "train.json"}) # Loading both splits fails load_dataset('json', data_files={"train": "train.json", "test": "test.json"}) ``` ## Expected results Loading both splits should not give an error whether the nested dicts are have the same order or not. ## Actual results ``` >>> load_dataset('json', data_files={"train": "train.json", "test": "test.json"}) Using custom data configuration default-f1bc76fd07398c4c Downloading and preparing dataset json/default to /home/dthulke/.cache/huggingface/datasets/json/default-f1bc76fd07398c4c/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426... 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 8839.42it/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 477.82it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/load.py", line 1632, in load_dataset use_auth_token=use_auth_token, File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 608, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 1159, in _prepare_split writer.write_table(table) File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/arrow_writer.py", line 428, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1596, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 592, in pyarrow.lib._sanitize_arrays File "pyarrow/array.pxi", line 329, in pyarrow.lib.asarray File "pyarrow/table.pxi", line 277, in pyarrow.lib.ChunkedArray.cast File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/pyarrow/compute.py", line 297, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 527, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 337, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct ``` ## Environment info - `datasets` version: 1.13.2 - Platform: Linux-4.15.0-147-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyArrow version: 5.0.0

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I_kwDODunzps49Op1K

`blog_authorship_corpus` is broken

closed

NONE

## Describe the bug The dataset `blog_authorship_corpus` is broken. By bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty. I suspect it is because the data download url is broken (http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip). ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("blog_authorship_corpus", split="train", download_mode='force_redownload') ``` ## Expected results No error. ## Actual results ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) /tmp/ipykernel_5237/1729238701.py in <module> 2 ds = load_dataset( 3 "blog_authorship_corpus", split="train", ----> 4 download_mode='force_redownload' 5 ) /opt/conda/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, streaming, **config_kwargs) 1115 ignore_verifications=ignore_verifications, 1116 try_from_hf_gcs=try_from_hf_gcs, -> 1117 use_auth_token=use_auth_token, 1118 ) 1119 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 635 if not downloaded_from_gcs: 636 self._download_and_prepare( --> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 638 ) 639 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 707 if verify_infos: 708 verify_checksums( --> 709 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" 710 ) 711 /opt/conda/lib/python3.7/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip'] ``` ## Environment info - `datasets` version: 1.13.2 - Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11 - Python version: 3.7.10 - PyArrow version: 5.0.0

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I_kwDODunzps49Nl6w

JNLPBA Dataset

closed

NONE

## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` ## Expected results The dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L81-L83) are: O, B, and I. The correct entities from the original data file are: ['O', 'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-cell_line', 'I-cell_line', 'B-cell_type', 'I-cell_type', 'B-protein', 'I-protein'] ## Actual results The dataset loader script needs to include the following NER names: ['O', 'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-cell_line', 'I-cell_line', 'B-cell_type', 'I-cell_type', 'B-protein', 'I-protein'] And the [data](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:

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I_kwDODunzps49M201

Removing label column in a text classification dataset yields to errors

closed

MEMBER

## Describe the bug This looks like #3059 but it's not linked to the cache this time. Removing the `label` column from a text classification dataset and then performing any processing will result in an error. To reproduce: ```py from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("imdb") raw_datasets = raw_datasets.remove_columns("label") model_checkpoint = "distilbert-base-cased" tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) context_length = 128 def tokenize_pad_and_truncate(texts): return tokenizer(texts["text"], truncation=True, padding="max_length", max_length=context_length) tokenized_datasets = raw_datasets.map(tokenize_pad_and_truncate, batched=True) ``` Traceback: ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-1-ba61bb32f786> in <module> 12 return tokenizer(texts["text"], truncation=True, padding="max_length", max_length=context_length) 13 ---> 14 tokenized_datasets = raw_datasets.map(tokenize_pad_and_truncate, batched=True) ~/git/datasets/src/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) 500 desc=desc, 501 ) --> 502 for k, dataset in self.items() 503 } 504 ) ~/git/datasets/src/datasets/dataset_dict.py in <dictcomp>(.0) 500 desc=desc, 501 ) --> 502 for k, dataset in self.items() 503 } 504 ) ~/git/datasets/src/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) 2051 new_fingerprint=new_fingerprint, 2052 disable_tqdm=disable_tqdm, -> 2053 desc=desc, 2054 ) 2055 else: ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 501 self: "Dataset" = kwargs.pop("self") 502 # apply actual function --> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 505 for dataset in datasets: ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 468 } 469 # apply actual function --> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 472 # re-apply format to the output ~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms + update in-place history of transforms ~/git/datasets/src/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, disable_tqdm, desc, cache_only) 2243 if os.path.exists(cache_file_name) and load_from_cache_file: 2244 logger.warning("Loading cached processed dataset at %s", cache_file_name) -> 2245 info = self.info.copy() 2246 info.features = features 2247 info.task_templates = None ~/git/datasets/src/datasets/info.py in copy(self) 278 279 def copy(self) -> "DatasetInfo": --> 280 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) 281 282 ~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes) ~/git/datasets/src/datasets/info.py in __post_init__(self) 177 for idx, template in enumerate(self.task_templates): 178 if isinstance(template, TextClassification): --> 179 labels = self.features[template.label_column].names 180 self.task_templates[idx] = TextClassification( 181 text_column=template.text_column, label_column=template.label_column, labels=labels KeyError: 'label' ```

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I_kwDODunzps49LhBA

VisibleDeprecationWarning when using `set_format("numpy")`

closed

CONTRIBUTOR

Code to reproduce: ``` from datasets import load_dataset dataset = load_dataset("glue", "mnli") from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('distilbert-base-cased') def tokenize_function(dataset): return tokenizer(dataset['premise']) tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset['train'].features) tokenized_datasets.set_format("numpy") tokenized_datasets['train'][5:8] ``` Outputs: ``` python3.9/site-packages/datasets/formatting/formatting.py:167: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray return np.array(array, copy=False, **self.np_array_kwargs) ```

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I_kwDODunzps49LZOG

Datasets with Audio feature raise error when loaded from cache due to _resampler parameter

closed

MEMBER

## Describe the bug As reported by @patrickvonplaten, when loaded from the cache, datasets containing the Audio feature raise TypeError. ## Steps to reproduce the bug ```python from datasets import load_dataset # load first time works ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") # load from cache breaks ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") ``` ## Actual results ``` TypeError: __init__() got an unexpected keyword argument '_resampler' ```

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I_kwDODunzps49LVNS

Error related to timeout keyword argument

closed

MEMBER

## Describe the bug As reported by @patrickvonplaten, a TypeError is raised when trying to load a dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") ``` ## Actual results ``` TypeError: dataset_info() got an unexpected keyword argument 'timeout' ```

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I_kwDODunzps49KT_M

Error when loading a metric

closed

MEMBER

## Describe the bug As reported by @sgugger, after last release, exception is thrown when loading a metric. ## Steps to reproduce the bug ```python from datasets import load_metric metric = load_metric("squad_v2") ``` ## Actual results ``` FileNotFoundError Traceback (most recent call last) <ipython-input-1-e612a8cab787> in <module> 1 from datasets import load_metric ----> 2 metric = load_metric("squad_v2") d:\projects\huggingface\datasets\src\datasets\load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, script_version, **metric_init_kwargs) 1336 ) 1337 revision = script_version -> 1338 metric_module = metric_module_factory( 1339 path, revision=revision, download_config=download_config, download_mode=download_mode 1340 ).module_path d:\projects\huggingface\datasets\src\datasets\load.py in metric_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, **download_kwargs) 1237 if not isinstance(e1, FileNotFoundError): 1238 raise e1 from None -> 1239 raise FileNotFoundError( 1240 f"Couldn't find a metric script at {relative_to_absolute_path(combined_path)}. " 1241 f"Metric '{path}' doesn't exist on the Hugging Face Hub either." FileNotFoundError: Couldn't find a metric script at D:\projects\huggingface\datasets\squad_v2\squad_v2.py. Metric 'squad_v2' doesn't exist on the Hugging Face Hub either. ```

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I_kwDODunzps49IzjF

Import error installing with ppc64le

closed

NONE

## Describe the bug Installing the datasets library with a computer running with ppc64le seems to cause an issue when importing the datasets library. ``` python Python 3.6.13 | packaged by conda-forge | (default, Sep 23 2021, 07:37:44) [GCC 9.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import datasets Illegal instruction (core dumped) ``` Error when importing `Illegal instruction (core dumped)` ## Steps to reproduce the bug I get this error when installing the library by using conda. I can't install with pip I believe because pyarrow only has the ppc64le library on conda forge ``` conda create --name transformers_py36_v2 python=3.6 conda activate transformers_py36_v2 conda install datasets ``` ## Tracebacks conda create --name transformers_py36_v2 python=3.6 ``` Collecting package metadata (current_repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.9.2 latest version: 4.10.3 Please update conda by running $ conda update -n base -c defaults conda ## Package Plan ## environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2 added / updated specs: - python=3.6 The following NEW packages will be INSTALLED: _libgcc_mutex conda-forge/linux-ppc64le::_libgcc_mutex-0.1-conda_forge _openmp_mutex conda-forge/linux-ppc64le::_openmp_mutex-4.5-1_gnu ca-certificates conda-forge/linux-ppc64le::ca-certificates-2021.10.8-h1084571_0 certifi pkgs/main/linux-ppc64le::certifi-2020.12.5-py36h6ffa863_0 ld_impl_linux-ppc~ conda-forge/linux-ppc64le::ld_impl_linux-ppc64le-2.36.1-ha35d02b_2 libffi conda-forge/linux-ppc64le::libffi-3.4.2-h3b9df90_4 libgcc-ng conda-forge/linux-ppc64le::libgcc-ng-11.2.0-h7698a5e_11 libgomp conda-forge/linux-ppc64le::libgomp-11.2.0-h7698a5e_11 libstdcxx-ng conda-forge/linux-ppc64le::libstdcxx-ng-11.2.0-habdf983_11 libzlib conda-forge/linux-ppc64le::libzlib-1.2.11-h339bb43_1013 ncurses conda-forge/linux-ppc64le::ncurses-6.2-hea85c5d_4 openssl conda-forge/linux-ppc64le::openssl-1.1.1l-h4e0d66e_0 pip conda-forge/noarch::pip-21.3-pyhd8ed1ab_0 python conda-forge/linux-ppc64le::python-3.6.13-h57873ef_2_cpython readline conda-forge/linux-ppc64le::readline-8.1-h5c45dff_0 setuptools pkgs/main/linux-ppc64le::setuptools-58.0.4-py36h6ffa863_0 sqlite conda-forge/linux-ppc64le::sqlite-3.36.0-h4e2196e_2 tk conda-forge/linux-ppc64le::tk-8.6.11-h41c6715_1 wheel conda-forge/noarch::wheel-0.37.0-pyhd8ed1ab_1 xz conda-forge/linux-ppc64le::xz-5.2.5-h6eb9509_1 zlib conda-forge/linux-ppc64le::zlib-1.2.11-h339bb43_1013 Proceed ([y]/n)? y Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate transformers_py36_v2 # # To deactivate an active environment, use # # $ conda deactivate ``` conda activate transformers_py36_v2 conda install datasets ``` Collecting package metadata (current_repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source. Collecting package metadata (repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.9.2 latest version: 4.10.3 Please update conda by running $ conda update -n base -c defaults conda ## Package Plan ## environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2 added / updated specs: - datasets The following NEW packages will be INSTALLED: abseil-cpp conda-forge/linux-ppc64le::abseil-cpp-20210324.2-h3b9df90_0 aiohttp conda-forge/linux-ppc64le::aiohttp-3.7.4.post0-py36hc33305d_0 arrow-cpp conda-forge/linux-ppc64le::arrow-cpp-5.0.0-py36hf9cf308_8_cpu async-timeout conda-forge/noarch::async-timeout-3.0.1-py_1000 attrs conda-forge/noarch::attrs-21.2.0-pyhd8ed1ab_0 aws-c-cal conda-forge/linux-ppc64le::aws-c-cal-0.5.11-hb3fac3d_0 aws-c-common conda-forge/linux-ppc64le::aws-c-common-0.6.2-h4e0d66e_0 aws-c-event-stream conda-forge/linux-ppc64le::aws-c-event-stream-0.2.7-h76da5f2_13 aws-c-io conda-forge/linux-ppc64le::aws-c-io-0.10.5-hf6a6c7c_0 aws-checksums conda-forge/linux-ppc64le::aws-checksums-0.1.11-hfe76d68_7 aws-sdk-cpp conda-forge/linux-ppc64le::aws-sdk-cpp-1.8.186-h90855e8_3 brotlipy conda-forge/linux-ppc64le::brotlipy-0.7.0-py36hc33305d_1001 bzip2 conda-forge/linux-ppc64le::bzip2-1.0.8-h4e0d66e_4 c-ares conda-forge/linux-ppc64le::c-ares-1.17.2-h4e0d66e_0 cffi conda-forge/linux-ppc64le::cffi-1.14.6-py36h021ab3c_1 chardet conda-forge/linux-ppc64le::chardet-4.0.0-py36h270354c_1 colorama conda-forge/noarch::colorama-0.4.4-pyh9f0ad1d_0 cryptography conda-forge/linux-ppc64le::cryptography-3.4.7-py36hc71b123_0 dataclasses conda-forge/noarch::dataclasses-0.8-pyh787bdff_2 datasets conda-forge/noarch::datasets-1.12.1-pyhd8ed1ab_1 dill conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0 filelock conda-forge/noarch::filelock-3.3.0-pyhd8ed1ab_0 fsspec conda-forge/noarch::fsspec-2021.10.0-pyhd8ed1ab_0 gflags conda-forge/linux-ppc64le::gflags-2.2.2-hb209c28_1004 glog conda-forge/linux-ppc64le::glog-0.5.0-h4040248_0 grpc-cpp conda-forge/linux-ppc64le::grpc-cpp-1.40.0-h2bf711c_2 huggingface_hub conda-forge/noarch::huggingface_hub-0.0.19-pyhd8ed1ab_0 idna conda-forge/noarch::idna-2.10-pyh9f0ad1d_0 idna_ssl conda-forge/noarch::idna_ssl-1.0.0-0 importlib-metadata conda-forge/linux-ppc64le::importlib-metadata-4.8.1-py36h270354c_0 importlib_metadata conda-forge/noarch::importlib_metadata-4.8.1-hd8ed1ab_0 krb5 conda-forge/linux-ppc64le::krb5-1.19.2-haf43566_2 libblas conda-forge/linux-ppc64le::libblas-3.9.0-11_linuxppc64le_openblas libbrotlicommon conda-forge/linux-ppc64le::libbrotlicommon-1.0.9-h4e0d66e_5 libbrotlidec conda-forge/linux-ppc64le::libbrotlidec-1.0.9-h4e0d66e_5 libbrotlienc conda-forge/linux-ppc64le::libbrotlienc-1.0.9-h4e0d66e_5 libcblas conda-forge/linux-ppc64le::libcblas-3.9.0-11_linuxppc64le_openblas libcurl conda-forge/linux-ppc64le::libcurl-7.79.1-he415e40_1 libedit conda-forge/linux-ppc64le::libedit-3.1.20191231-h41a240f_2 libev conda-forge/linux-ppc64le::libev-4.33-h6eb9509_1 libevent conda-forge/linux-ppc64le::libevent-2.1.10-h97db324_4 libgfortran-ng conda-forge/linux-ppc64le::libgfortran-ng-11.2.0-hfdc3801_11 libgfortran5 conda-forge/linux-ppc64le::libgfortran5-11.2.0-he58fbb4_11 liblapack conda-forge/linux-ppc64le::liblapack-3.9.0-11_linuxppc64le_openblas libnghttp2 conda-forge/linux-ppc64le::libnghttp2-1.43.0-h42039ad_1 libopenblas conda-forge/linux-ppc64le::libopenblas-0.3.17-pthreads_h486567c_1 libprotobuf conda-forge/linux-ppc64le::libprotobuf-3.18.1-h690f14c_0 libssh2 conda-forge/linux-ppc64le::libssh2-1.10.0-ha5a9321_2 libthrift conda-forge/linux-ppc64le::libthrift-0.15.0-h54f692e_1 libutf8proc conda-forge/linux-ppc64le::libutf8proc-2.6.1-h4e0d66e_0 lz4-c conda-forge/linux-ppc64le::lz4-c-1.9.3-h3b9df90_1 multidict conda-forge/linux-ppc64le::multidict-5.2.0-py36hc33305d_0 multiprocess conda-forge/linux-ppc64le::multiprocess-0.70.12.2-py36hc33305d_0 numpy conda-forge/linux-ppc64le::numpy-1.19.5-py36h86665d4_1 orc conda-forge/linux-ppc64le::orc-1.7.0-hae6b4bd_0 packaging conda-forge/noarch::packaging-21.0-pyhd8ed1ab_0 pandas conda-forge/linux-ppc64le::pandas-1.1.5-py36hab1a6e6_0 parquet-cpp conda-forge/noarch::parquet-cpp-1.5.1-2 pyarrow conda-forge/linux-ppc64le::pyarrow-5.0.0-py36h7a46c7e_8_cpu pycparser conda-forge/noarch::pycparser-2.20-pyh9f0ad1d_2 pyopenssl conda-forge/noarch::pyopenssl-21.0.0-pyhd8ed1ab_0 pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0 pysocks conda-forge/linux-ppc64le::pysocks-1.7.1-py36h270354c_3 python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0 python-xxhash conda-forge/linux-ppc64le::python-xxhash-2.0.2-py36hc33305d_0 python_abi conda-forge/linux-ppc64le::python_abi-3.6-2_cp36m pytz conda-forge/noarch::pytz-2021.3-pyhd8ed1ab_0 pyyaml conda-forge/linux-ppc64le::pyyaml-5.4.1-py36hc33305d_1 re2 conda-forge/linux-ppc64le::re2-2021.09.01-h3b9df90_0 requests conda-forge/noarch::requests-2.25.1-pyhd3deb0d_0 s2n conda-forge/linux-ppc64le::s2n-1.0.10-h97db324_0 six conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 snappy conda-forge/linux-ppc64le::snappy-1.1.8-hb209c28_3 tqdm conda-forge/noarch::tqdm-4.62.3-pyhd8ed1ab_0 typing-extensions conda-forge/noarch::typing-extensions-3.10.0.2-hd8ed1ab_0 typing_extensions conda-forge/noarch::typing_extensions-3.10.0.2-pyha770c72_0 urllib3 conda-forge/noarch::urllib3-1.26.7-pyhd8ed1ab_0 xxhash conda-forge/linux-ppc64le::xxhash-0.8.0-h4e0d66e_3 yaml conda-forge/linux-ppc64le::yaml-0.2.5-h6eb9509_0 yarl conda-forge/linux-ppc64le::yarl-1.6.3-py36hc33305d_2 zipp conda-forge/noarch::zipp-3.6.0-pyhd8ed1ab_0 zstd conda-forge/linux-ppc64le::zstd-1.5.0-h65c4b1a_0 The following packages will be UPDATED: certifi pkgs/main::certifi-2020.12.5-py36h6ff~ --> conda-forge::certifi-2021.5.30-py36h270354c_0 Proceed ([y]/n)? y Preparing transaction: done Verifying transaction: done Executing transaction: done ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Red Hat Enterprise Linux 8.2 (Ootpa) - Python version: 3.6 - PyArrow version: pyarrow - 5.0.0 - py36h7a46c7e_8_cpu - conda-forge Any help would be appreciated! I've been struggling on installing datasets on this machine.

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I_kwDODunzps49FqI1

Custom plain text dataset, plain json dataset and plain csv dataset are remove from datasets template folder

closed

NONE

## Adding a Dataset - **Name:** text, json, csv - **Description:** I am developing a customized dataset loading script. The problem is mainly about my custom dataset is seperate into many files and I only find a dataset loading template in [https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py](https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py) that can handle my circumstance. I'm afraid these templates are too old to use. Could you re-add these three templates to current master branch?

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I_kwDODunzps49FX34

CI fails on Windows with FileNotFoundError when stting up s3_base fixture

closed

MEMBER

## Describe the bug After commit 9353fc863d0c99ab0427f83cc5a4f04fcf52f1df, the CI fails on Windows with FileNotFoundError when stting up s3_base fixture. See: https://app.circleci.com/pipelines/github/huggingface/datasets/8151/workflows/5db8d154-badd-4d3d-b202-ca7a318997a2/jobs/50321 Error summary: ``` ERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3 - FileNotF... ERROR tests/test_dataset_dict.py::test_dummy_dataset_serialize_s3 - FileNotFo... ``` Stack trace: ``` ______________ ERROR at setup of test_dummy_dataset_serialize_s3 ______________ [gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe @pytest.fixture() def s3_base(): # writable local S3 system import shlex import subprocess # Mocked AWS Credentials for moto. old_environ = os.environ.copy() os.environ.update(S3_FAKE_ENV_VARS) > proc = subprocess.Popen(shlex.split("moto_server s3 -p %s" % s3_port)) tests\s3_fixtures.py:32: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ C:\tools\miniconda3\lib\subprocess.py:729: in __init__ restore_signals, start_new_session) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <subprocess.Popen object at 0x0000012BB8A4B908> args = 'moto_server s3 -p 5555', executable = None, preexec_fn = None close_fds = True, pass_fds = (), cwd = None, env = None startupinfo = <subprocess.STARTUPINFO object at 0x0000012BB8177630> creationflags = 0, shell = False, p2cread = -1, p2cwrite = -1, c2pread = -1 c2pwrite = -1, errread = -1, errwrite = -1, unused_restore_signals = True unused_start_new_session = False def _execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session): """Execute program (MS Windows version)""" assert not pass_fds, "pass_fds not supported on Windows." if not isinstance(args, str): args = list2cmdline(args) # Process startup details if startupinfo is None: startupinfo = STARTUPINFO() if -1 not in (p2cread, c2pwrite, errwrite): startupinfo.dwFlags |= _winapi.STARTF_USESTDHANDLES startupinfo.hStdInput = p2cread startupinfo.hStdOutput = c2pwrite startupinfo.hStdError = errwrite if shell: startupinfo.dwFlags |= _winapi.STARTF_USESHOWWINDOW startupinfo.wShowWindow = _winapi.SW_HIDE comspec = os.environ.get("COMSPEC", "cmd.exe") args = '{} /c "{}"'.format (comspec, args) # Start the process try: hp, ht, pid, tid = _winapi.CreateProcess(executable, args, # no special security None, None, int(not close_fds), creationflags, env, os.fspath(cwd) if cwd is not None else None, > startupinfo) E FileNotFoundError: [WinError 2] The system cannot find the file specified C:\tools\miniconda3\lib\subprocess.py:1017: FileNotFoundError ```

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I_kwDODunzps49B3mr

Make `interleave_datasets` more robust

open

NONE

**Is your feature request related to a problem? Please describe.** Right now there are few hiccups using `interleave_datasets`. Interleaved dataset iterates until the smallest dataset completes it's iterator. In this way larger datasets may not complete full epoch of iteration. It creates new problems in calculation of epoch since there are no way to track which dataset from `interleave_datasets` completes how many epoch. **Describe the solution you'd like** For `interleave_datasets` module, - [ ] Add a boolean argument `--stop-iter` in `interleave_datasets` that enables dataset to either iterate infinite amount of time or not. That means it should not return `StopIterator` exception in case `--stop-iter=False`. - [ ] Internal list variable `iter_cnt` that explains how many times (in steps/epochs) each dataset iterates at a given point. - [ ] Add an argument `--max-iter` (list type) that explain maximum amount of time each of the dataset can iterate. After complete `--max-iter` of one dataset, other dataset should continue sampling and when all the dataset finish their respective `--max-iter`, only then return `StopIterator` Note: I'm new to `datasets` api. May be these features are already there in the datasets. Since multitask training is the latest trends, I believe this feature would make the `datasets` api more popular. @lhoestq

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I_kwDODunzps49ArfJ

Windows CI is unable to test streaming properly because of SSL issues

open

MEMBER

In https://github.com/huggingface/datasets/pull/3041 the windows tests were skipped because of SSL issues with moon-staging.huggingface.co:443 The issue appears only on windows with asyncio. On Linux it works. With requests it works as well. And with the production environment huggingface.co it also works. to reproduce on windows: ```python import fsspec # use any URL to a file in a dataset repo url = "https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes" fsspec.open(url).open() ``` raises ```python FileNotFoundError: https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes ``` because of ```python aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')] ```

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I_kwDODunzps48-1CP

Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?)

open

NONE

**A clear and concise description of what you want to happen.** It would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions. **Describe alternatives you've considered** By the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish? **Additional context** Furthering tqdm integration #2374 and huggingface/transformers#11797 solutioned by huggingface/transformers#12226 provided with tqdm description as `desc=` @sgugger @bhavitvyamalik

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I_kwDODunzps48-MVM

load_dataset('openwebtext') yields "Compressed file ended before the end-of-stream marker was reached"

closed

NONE

## Describe the bug When I try `load_dataset('openwebtext')`, I receive a "EOFError: Compressed file ended before the end-of-stream marker was reached" error. ## Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset('openwebtext') ``` ## Expected results I expect the `dataset` variable to be properly constructed. ## Actual results ``` File "/home/rschaef/CoCoSci-Language-Distillation/distillation_v2/ratchet_learning/tasks/base.py", line 37, in create_dataset dataset_str, File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/load.py", line 1117, in load_dataset use_auth_token=use_auth_token, File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 637, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 704, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/rschaef/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1/openwebtext.py", line 61, in _split_generators dl_dir = dl_manager.download_and_extract(_URL) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 261, in extract partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 197, in map_nested return function(data_struct) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 316, in cached_path output_path, force_extract=download_config.force_extract File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 40, in extract self.extractor.extract(input_path, output_path, extractor=extractor) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 179, in extract return extractor.extract(input_path, output_path) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 53, in extract tar_file.extractall(output_path) File "/usr/lib/python3.6/tarfile.py", line 2010, in extractall numeric_owner=numeric_owner) File "/usr/lib/python3.6/tarfile.py", line 2052, in extract numeric_owner=numeric_owner) File "/usr/lib/python3.6/tarfile.py", line 2122, in _extract_member self.makefile(tarinfo, targetpath) File "/usr/lib/python3.6/tarfile.py", line 2171, in makefile copyfileobj(source, target, tarinfo.size, ReadError, bufsize) File "/usr/lib/python3.6/tarfile.py", line 249, in copyfileobj buf = src.read(bufsize) File "/usr/lib/python3.6/lzma.py", line 200, in read return self._buffer.read(size) File "/usr/lib/python3.6/_compression.py", line 68, in readinto data = self.read(len(byte_view)) File "/usr/lib/python3.6/_compression.py", line 99, in read raise EOFError("Compressed file ended before the " python-BaseException EOFError: Compressed file ended before the end-of-stream marker was reached ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial - Python version: 3.6.10 - PyArrow version: 5.0.0

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I_kwDODunzps4889S4

Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader.

open

NONE

## Describe the bug I have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`. ## Steps to reproduce the bug Run the `run_mlm_no_trainer.py` and the given script on this [link](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3. ## Expected results The data batchs are fetched from the data loader and train. ## Actual results The first time to fetch data batch occurs error. `Traceback (most recent call last): File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors tensor = as_tensor(value) ValueError: too many dimensions 'str' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "src/original_run_mlm_no_trainer.py", line 528, in <module> main() File "src/original_run_mlm_no_trainer.py", line 488, in main for step, batch in enumerate(train_dataloader): File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/accelerate/data_loader.py", line 303, in __iter__ for batch in super().__iter__(): File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__ data = self._next_data() File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 557, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch return self.collate_fn(data) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 41, in __call__ return self.torch_call(features) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 671, in torch_call batch = self.tokenizer.pad(examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2774, in pad return BatchEncoding(batch_outputs, tensor_type=return_tensors) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 210, in __init__ self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 722, in convert_to_tensors "Unable to create tensor, you should probably activate truncation and/or padding " ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. ` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.6 - PyArrow version: 5.0.0

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I_kwDODunzps488j0b

Error in per class precision computation

closed

NONE

## Describe the bug When trying to get the per class precision values by providing `average=None`, following error is thrown `ValueError: can only convert an array of size 1 to a Python scalar` ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric precision_metric = load_metric("precision") predictions = [0, 2, 1, 0, 0, 1] references = [0, 1, 2, 0, 1, 2] results = precision_metric.compute(predictions=predictions, references=references, average=None) ``` ## Expected results ` {'precision': array([0.66666667, 0. , 0. ])}` as per https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py ## Actual results ``` output = self._compute(predictions=predictions, references=references, **kwargs) File "~/.cache/huggingface/modules/datasets_modules/metrics/precision/94709a71c6fe37171ef49d3466fec24dee9a79846c9f176dff66a649e9811690/precision.py", line 110, in _compute sample_weight=sample_weight, ValueError: can only convert an array of size 1 to a Python scalar ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: linux - Python version: 3.6.9 - PyArrow version: 5.0.0

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I_kwDODunzps4871qG

CI test suite fails after meteor metric update

closed

MEMBER

## Describe the bug CI test suite fails: https://app.circleci.com/pipelines/github/huggingface/datasets/8110/workflows/f059ba43-9154-4632-bebb-82318447ddc9/jobs/50010 Stack trace: ``` ___________________ LocalMetricTest.test_load_metric_meteor ____________________ [gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 self = <tests.test_metric_common.LocalMetricTest testMethod=test_load_metric_meteor> metric_name = 'meteor' def test_load_metric(self, metric_name): doctest.ELLIPSIS_MARKER = "[...]" metric_module = importlib.import_module(datasets.load.prepare_module(os.path.join("metrics", metric_name))[0]) metric = datasets.load.import_main_class(metric_module.__name__, dataset=False) # check parameters parameters = inspect.signature(metric._compute).parameters self.assertTrue("predictions" in parameters) self.assertTrue("references" in parameters) self.assertTrue(all([p.kind != p.VAR_KEYWORD for p in parameters.values()])) # no **kwargs # run doctest with self.patch_intensive_calls(metric_name, metric_module.__name__): with self.use_local_metrics(): > results = doctest.testmod(metric_module, verbose=True, raise_on_error=True) tests/test_metric_common.py:75: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1951: in testmod runner.run(test) ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1839: in run r = DocTestRunner.run(self, test, compileflags, out, False) ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1476: in run return self.__run(test, compileflags, out) ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1382: in __run exception) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <doctest.DebugRunner object at 0x7f4c26bd3da0> out = <built-in method write of _io.TextIOWrapper object at 0x7f51a21852d0> test = <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Mete...ets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)> example = <doctest.Example object at 0x7f4c26bd3eb8> exc_info = (<class 'TypeError'>, TypeError('"hypothesis" expects pre-tokenized hypothesis (Iterable[str]): It is a guide to action which ensures that the military always obeys the commands of the party',), <traceback object at 0x7f4cd01afec8>) def report_unexpected_exception(self, out, test, example, exc_info): > raise UnexpectedException(test, example, exc_info) E doctest.UnexpectedException: <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Meteor from /tmp/pytest-of-circleci/pytest-0/popen-gw1/cache/modules/datasets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)> ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1845: UnexpectedException ```

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I_kwDODunzps4866fp

load_dataset('the_pile_openwebtext2') produces ArrowInvalid, value too large to fit in C integer type

open

NONE

## Describe the bug When loading `the_pile_openwebtext2`, we get the error `pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type` ## Steps to reproduce the bug ```python import datasets ds = datasets.load_dataset('the_pile_openwebtext2') ``` ## Expected results Should download the dataset, convert it to an arrow file, and return a working Dataset object. ## Actual results The download works, but conversion to the arrow file fails as follows: ``` >>> ds = datasets.load_dataset('the_pile_openwebtext2') Downloading and preparing dataset openwebtext2/plain_text (download: 27.33 GiB, generated: 63.86 GiB , post-processed: Unknown size, total: 91.19 GiB) to /home/davidbau/.cache/huggingface/datasets/open webtext2/plain_text/1.0.0/c48ec73ba3483bac673463f48f67e9a4fd8cb49a9d6ec4fb957f0b424b97cf25... Traceback (most recent call last): File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/builder.py", line 1133, in _prepare_split writer.write(example, key) File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 366, in write self.write_examples_on_file() File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 311, in write_examples_on_file pa_array = pa.array(typed_sequence) File "pyarrow/array.pxi", line 222, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 115, in __arrow_array__ out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type) File "pyarrow/array.pxi", line 305, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: ``` - Platform: Ubuntu 20.04 - Python version: python 3.9 - PyArrow version: 3.0.0

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I_kwDODunzps486aJz

load_dataset cannot download the data and hangs on forever if cache dir specified

closed

NONE

## Describe the bug After updating datasets, a code that ran just fine for ages began to fail. Specifying _datasets.load_dataset_'s _cache_dir_ optional argument on Windows 10 machine results in data download to hang on forever. Same call without cache_dir works just fine. Surprisingly exact same code just runs perfectly fine on Linux docker instance running in cloud. Unfortunately I updated Windows also at the same time and I can't remember which version of datasets was running in my conda environment prior to the update otherwise I would have tried both to check this out. :( ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` cache_dir = 'c:/data/datasets' dataset = load_dataset('wikipedia', '20200501.en', split='train',cache_dir=cache_dir) ``` Note that exact same code without specifying _cache_dir_ argument works perfectly fine. ``` cache_dir = 'c:/data/datasets' dataset = load_dataset('wikipedia', '20200501.en', split='train') ``` ## Expected results Downloads the dataset and cache is handled in the _cache_dir_ directory ## Actual results Data download keeps hanging on forever, **NO TRACEBACK**! ## Environment info - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.8.11 - PyArrow version: 3.0.0

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I_kwDODunzps486DpK

Non-Matching Checksum Error with crd3 dataset

open

NONE

## Describe the bug When I try loading the crd3 dataset (https://huggingface.co/datasets/crd3), an error is thrown. ## Steps to reproduce the bug ```python dataset = load_dataset('crd3', split='train') ``` ## Expected results I expect no error to be thrown. ## Actual results A non-matching checksum error is thrown. ``` datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/RevanthRameshkumar/CRD3/archive/master.zip'] ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial - Python version: 3.6.10 - PyArrow version: 5.0.0

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I_kwDODunzps485vkY

TimeoutError during streaming

closed

CONTRIBUTOR

## Describe the bug I got a TimeoutError after streaming for about 10h. ## Steps to reproduce the bug Very long code but we could do a test of streaming indefinitely data, though error may take a while to appear. ## Expected results This error was not expected in the code which considers only `ClientError` but not `TimeoutError`. See [this line](https://github.com/huggingface/datasets/blob/2814fbd0e18150be409f10804670e98d9ecb87d4/src/datasets/utils/streaming_download_manager.py#L129). Based on the traceback, it looks like the `TimeoutError` was not captured. ## Actual results ``` File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 25, in _runner result[0] = await coro File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/implementations/http.py", line 614, in async_fetch_range out = await r.read() File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1032, in read self._body = await self.content.read() File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 370, in read block = await self.readany() File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 392, in readany await self._wait("readany") File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 306, in _wait await waiter File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/helpers.py", line 656, in __exit__ raise asyncio.TimeoutError from None asyncio.exceptions.TimeoutError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 1027, in <module> main() File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 991, in main for batch in tqdm( File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/tqdm/std.py", line 1180, in __iter__ for obj in iterable: File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 376, in data_loader_streaming for item in dataset: File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 179, in __iter__ key_examples_list = [(key, example)] + [ File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 179, in <listcomp> key_examples_list = [(key, example)] + [ File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 176, in __iter__ for key, example in iterator: File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 225, in __iter__ for x in self.ex_iterable: File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 99, in __iter__ for key, example in self.generate_examples_fn(**kwargs_with_shuffled_shards): File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 287, in wrapper for key, table in generate_tables_fn(**kwargs): File "/home/koush/datasets/src/datasets/packaged_modules/json/json.py", line 107, in _generate_tables batch = f.read(self.config.chunksize) File "/home/koush/datasets/src/datasets/utils/streaming_download_manager.py", line 126, in read_with_retries out = read(*args, **kwargs) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/implementations/http.py", line 572, in read return super().read(length) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/spec.py", line 1533, in read out = self.cache._fetch(self.loc, self.loc + length) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/caching.py", line 390, in _fetch self.cache = self.fetcher(start, bend) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 91, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 69, in sync raise FSTimeoutError from return_result fsspec.exceptions.FSTimeoutError ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.2.dev0 - Platform: Linux-5.4.0-1043-gcp-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps485ugd

Identify which shard data belongs to

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** I'm training on a large dataset made of multiple sub-datasets. During training I can observe some jumps in loss which may correspond to different shards.  My suspicion is that either: * some of the sub-datasets are harder for the model than others * some of the sub-datasets are not formatted properly I'd like to identify which shards correspond to those jumps. **Describe the solution you'd like** It would be nice to have a key associated to each data sample or data batch containing details on where the data comes from (shard idx + item idx within the shard). This should be supported both in local and streaming mode. **Describe alternatives you've considered** A fix would be for me to add myself details (shard id, sample id) as part of each data sample. The inconvenient is that it requires users to process/reupload every dataset when they need this feature.

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I_kwDODunzps484Lno

Loading from cache a dataset for LM built from a text classification dataset sometimes errors

closed

MEMBER

## Describe the bug Yes, I know, that description sucks. So the problem is arising in the course when we build a masked language modeling dataset using the IMDB dataset. To reproduce (or try since it's a bit fickle). Create a dataset for masled-language modeling from the IMDB dataset. ```python from datasets import load_dataset from transformers import Autotokenizer tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased) imdb_dataset = load_dataset("imdb", split="train") def tokenize_function(examples): return tokenizer(examples["text"]) tokenized_dataset = imdb_dataset.map( tokenize_function, batched=True, remove_columns=["text", "label"] ) chunk_size = 128 def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} # Compute length of concatenated texts total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the last chunk if it's smaller than chunk_size total_length = (total_length // chunk_size) * chunk_size # Split by chunks of max_len. result = { k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)] for k, t in concatenated_examples.items() } # Create a new labels column result["labels"] = result["input_ids"].copy() return result lm_dataset = tokenized_dataset.map(group_texts, batched=True) ``` Until now, all is well. The problem comes when you re-execute that code, more specifically: ```python tokenized_dataset = imdb_dataset.map( tokenize_function, batched=True, remove_columns=["text", "label"] ) lm_dataset = tokenized_dataset.map(group_texts, batched=True) ``` Try several times if the bug doesn't appear instantly, or just each line at a time, ideally in a notebook/Colab and you should get at some point: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-40-357a56ee3d53> in <module> ----> 1 lm_dataset = tokenized_dataset.map(group_texts, batched=True) ~/git/datasets/src/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) 1947 new_fingerprint=new_fingerprint, 1948 disable_tqdm=disable_tqdm, -> 1949 desc=desc, 1950 ) 1951 else: ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 424 } 425 # apply actual function --> 426 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 427 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 428 # re-apply format to the output ~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms + update in-place history of transforms ~/git/datasets/src/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, disable_tqdm, desc, cache_only) 2138 if os.path.exists(cache_file_name) and load_from_cache_file: 2139 logger.warning("Loading cached processed dataset at %s", cache_file_name) -> 2140 info = self.info.copy() 2141 info.features = features 2142 return Dataset.from_file(cache_file_name, info=info, split=self.split) ~/git/datasets/src/datasets/info.py in copy(self) 278 279 def copy(self) -> "DatasetInfo": --> 280 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) 281 282 ~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes) ~/git/datasets/src/datasets/info.py in __post_init__(self) 177 for idx, template in enumerate(self.task_templates): 178 if isinstance(template, TextClassification): --> 179 labels = self.features[template.label_column].names 180 self.task_templates[idx] = TextClassification( 181 text_column=template.text_column, label_column=template.label_column, labels=labels KeyError: 'label' ``` It seems that when loading the cache, the dataset tries to access some kind of text classification template (which I imagine comes from the original dataset) and to look at a key that has since been removed.

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I_kwDODunzps482TyS

Inconsistent caching behaviour when using `Dataset.map()` with a `new_fingerprint` and `num_proc>1`

open

NONE

## Describe the bug Caching does not work when using `Dataset.map()` with: 1. a function that cannot be deterministically fingerprinted 2. `num_proc>1` 3. using a custom fingerprint set with the argument `new_fingerprint`. This means that the dataset will be mapped with the function for each and every call, which does not happen if `num_proc==1`. In that case (`num_proc==1`) subsequent calls will load the transformed dataset from the cache, which is the expected behaviour. The example can easily be translated into a unit test. I have a fix and will submit a pull request asap. ## Steps to reproduce the bug ```python import hashlib import json import os from typing import Dict, Any import numpy as np from datasets import load_dataset, Dataset Batch = Dict[str, Any] filename = 'example.json' class Transformation(): """A transformation with a random state that cannot be fingerprinted""" def __init__(self): self.state = np.random.random() def __call__(self, batch: Batch) -> Batch: batch['x'] = [np.random.random() for _ in batch['x']] return batch def generate_dataset(): """generate a simple dataset""" rgn = np.random.RandomState(24) data = { 'data': [{'x': float(y), 'y': -float(y)} for y in rgn.random(size=(1000,))]} if not os.path.exists(filename): with open(filename, 'w') as f: f.write(json.dumps(data)) return filename def process_dataset_with_cache(num_proc=1, remove_cache=False, cache_expected_to_exist=False): # load the generated dataset dset: Dataset = next( iter(load_dataset('json', data_files=filename, field='data').values())) new_fingerprint = hashlib.md5("static-id".encode("utf8")).hexdigest() # get the expected cached path cache_path = dset._get_cache_file_path(new_fingerprint) if remove_cache and os.path.exists(cache_path): os.remove(cache_path) # check that the cache exists, and print a statement # if was actually expected to exist cache_exist = os.path.exists(cache_path) print(f"> cache file exists={cache_exist}") if cache_expected_to_exist and not cache_exist: print("=== Cache does not exist! ====") # apply the transformation with the new fingerprint dset = dset.map( Transformation(), batched=True, num_proc=num_proc, new_fingerprint=new_fingerprint, desc="mapping dataset with transformation") generate_dataset() for num_proc in [1, 2]: print(f"# num_proc={num_proc}, first pass") # first pass to generate the cache (always create a new cache here) process_dataset_with_cache(remove_cache=True, num_proc=num_proc, cache_expected_to_exist=False) print(f"# num_proc={num_proc}, second pass") # second pass, expects the cache to exist process_dataset_with_cache(remove_cache=False, num_proc=num_proc, cache_expected_to_exist=True) os.remove(filename) ``` ## Expected results In the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache` ("=== Cache does not exist! ====" should not be printed). When the cache is successfully created, `map()` is called only one time. ## Actual results In the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing "=== Cache does not exist! ===="). Because the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache. ## Environment info - `datasets` version: 1.12.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.8 - PyArrow version: 5.0.0

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I_kwDODunzps48z8_S

Add PASS dataset

open

NONE

## Adding a Dataset - **Name:** PASS - **Description:** An ImageNet replacement for self-supervised pretraining without humans - **Data:** https://www.robots.ox.ac.uk/~vgg/research/pass/ https://github.com/yukimasano/PASS Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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I_kwDODunzps48uWML

[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset

closed

MEMBER

## Describe the bug When only keeping a dummy size of a dataset (say the first 100 samples), and then saving it to disk to upload it in the following to the hub for easy demo/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big. ## Steps to reproduce the bug E.g. run the following: ```python from datasets import load_dataset, save_to_disk nlp = load_dataset("glue", "mnli", split="train") nlp.save_to_disk("full") nlp = nlp.select(range(100)) nlp.save_to_disk("dummy") ``` Now one can see that both `"dummy"` and `"full"` have the same size. This shouldn't be the case IMO. ## Expected results IMO `"dummy"` should be much smaller so that one can easily play around with the dataset on the hub. ## 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: 1.12.2.dev0 - Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 5.0.0

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I_kwDODunzps48qK-I

Protect master branch to force contributions via Pull Requests

closed

MEMBER

In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed. - The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution - The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context/discussions) Note that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests. Please note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release. The only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.

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I_kwDODunzps48mq4X

`load_dataset` does not work with uploaded arrow file

open

MEMBER

## Describe the bug I've preprocessed and uploaded a dataset here: https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed . The dataset is in `.arrow` format. The dataset can correctly be loaded when doing: ```bash git lfs install git clone https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed ``` followed by ```python from datasets import load_from_disk ds = load_from_disk("./ami_headset_single_preprocessed") ``` However when I try to directly download the dataset as follows: ```python from datasets import load_dataset ds = load_dataset("ami-wav2vec2/ami_headset_single_preprocessed") ``` the following error occurs: ```bash /usr/local/lib/python3.7/dist-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, streaming, **config_kwargs) 1115 ignore_verifications=ignore_verifications, 1116 try_from_hf_gcs=try_from_hf_gcs, -> 1117 use_auth_token=use_auth_token, 1118 ) 1119 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 635 if not downloaded_from_gcs: 636 self._download_and_prepare( --> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 638 ) 639 # Sync info /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 724 try: 725 # Prepare split will record examples associated to the split --> 726 self._prepare_split(split_generator, **prepare_split_kwargs) 727 except OSError as e: 728 raise OSError( /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator) 1186 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET) 1187 ): -> 1188 writer.write_table(table) 1189 num_examples, num_bytes = writer.finalize() 1190 /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_table(self, pa_table, writer_batch_size) 424 # reorder the arrays if necessary + cast to self._schema 425 # we can't simply use .cast here because we may need to change the order of the columns --> 426 pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) 427 batches: List[pa.RecordBatch] = pa_table.to_batches(max_chunksize=writer_batch_size) 428 self._num_bytes += sum(batch.nbytes for batch in batches) /usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays() /usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib._sanitize_arrays() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray() /usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast() /usr/local/lib/python3.7/dist-packages/pyarrow/compute.py in cast(arr, target_type, safe) 279 else: 280 options = CastOptions.unsafe(target_type) --> 281 return call_function("cast", [arr], options) 282 283 /usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function() /usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from struct<train: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, validation: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, test: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>> to list using function cast_list ``` ## Expected results The dataset should be correctly loaded with `load_dataset` IMO. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.2.dev0 - Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 5.0.0

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I_kwDODunzps48moOi

Errors loading dataset using fs = a gcsfs.GCSFileSystem

open

NONE

## Describe the bug Cannot load dataset using a `gcsfs.GCSFileSystem`. I'm not sure if this should be a bug in `gcsfs` or here... Basically what seems to be happening is that since datasets saves datasets as folders and folders aren't "real objects" in gcs, gcsfs raises a 404 error. There are workarounds if you use gcsfs directly to download the file, but as is I can't get `load_from_disk` to work. ## Steps to reproduce the bug ```python from datasets import load_dataset # load some dataset dataset = load_dataset("squad", split="train") # save it to gcs import gcsfs fs = gcsfs.GCSFileSystem(project="my-gs-project") dataset.save_to_disk("gs://my-bucket/squad", fs=fs) # try to load it from gcs from datasets import load_from_disk dataset2 = load_from_disk("my-bucket/squad", fs=fs) ``` ## Expected results `dataset2` would be a copy of `dataset` but loaded from my bucket. ## Actual results Long traceback but essentially it's a 404 error from gcsfs saying the object `my-bucket/squad` doesn't exist when this is called: https://github.com/huggingface/datasets/blob/9c81b7d2e6d9feae69a084a3abda265a4ca07fb5/src/datasets/arrow_dataset.py#L977 This is because there is no actual object called `my-bucket/squad`, there are objects called `my-bucket/squad/dataset.arrow`, etc. Note that *this* works fine, since it's explicitly saying "download all the objects with this prefix": ```python fs.download(src_dataset_path + "/*", dataset_path.as_posix(), recursive=True) ``` For example, I can do a workaround this way: ```python import tempfile with tempfile.TemporaryDirectory() as temppath: fs.download("gs://my-bucket/squad/*", temppath) dataset2 = load_from_disk(temppath) ``` It's unclear to me if it's `gcsfs`'s responsibility to say "hey that's folder not a file, I should try to get objects inside of it not the object itself", or if that's `datasets`'s responsibility... I'm leaning towards the latter since you're never loading a dataset from one file using this function/method, only a dataset folder? Another minor thing that should maybe should be rolled into this bug... https://github.com/huggingface/datasets/blob/9c81b7d2e6d9feae69a084a3abda265a4ca07fb5/src/datasets/arrow_dataset.py#L968 These fail if you pass in a `gs://` path, e.g. ```python dataset2 = load_from_disk("gs://my-bucket/squad", fs=fs) ``` Because at this point, `dataset_info_path` is `gs:/my-bucket/squad/dataset_info.json`, gcsfs throws a: ``` Invalid bucket name: 'gs:' ``` error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: macOS Big Sur 11.6 - Python version: 3.7.12 - PyArrow version: 5.0.0

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I_kwDODunzps48lmIb

Error when loading private dataset with "data_files" arg

closed

CONTRIBUTOR

## Describe the bug A clear and concise description of what the bug is. Private datasets with no loading script can't be loaded using `data_files` parameter. ## Steps to reproduce the bug ```python from datasets import load_dataset data_files = {"train": "**/train/*/*.jsonl", "valid": "**/valid/*/*.jsonl"} dataset = load_dataset('dalle-mini/encoded', data_files=data_files, use_auth_token=True, streaming=True) ``` Same error happens in non-streaming mode. ## Expected results Files should be loaded (whether in streaming or not). ## Actual results Error: ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /usr/local/lib/python3.7/dist-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, return_associated_base_path, data_files, **download_kwargs) 539 try: --> 540 local_path = cached_path(file_path, download_config=download_config) 541 except FileNotFoundError: 8 frames FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/dalle-mini/encoded/resolve/main/encoded.py During handling of the above exception, another exception occurred: HTTPError Traceback (most recent call last) HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/datasets/dalle-mini/encoded?full=true During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) /usr/local/lib/python3.7/dist-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, return_associated_base_path, data_files, **download_kwargs) 547 except Exception: 548 raise FileNotFoundError( --> 549 f"Couldn't find a directory or a {resource_type} named '{path}'. " 550 f"It doesn't exist locally at {expected_dir_for_combined_path_abs} or remotely on {hf_api.endpoint}/datasets" 551 ) FileNotFoundError: Couldn't find a directory or a dataset named 'dalle-mini/encoded'. It doesn't exist locally at /content/dalle-mini/encoded or remotely on https://huggingface.co/datasets ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0 @lhoestq

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I_kwDODunzps48kThl

Resolve data_files by split name

closed

MEMBER

This issue is about discussing the default behavior when someone loads a dataset that consists in data files. For example: ```python load_dataset("lhoestq/demo1") ``` should return two splits "train" and "test" since the dataset repostiory is like ``` data/ ├── train.csv └── test.csv ``` Currently it returns only one split "train" which contains the data of both files I started playing with this idea on this branch btw: `resolve-data_files-by-split-name` Basically the idea is that if you named you data files after split names then the default pattern is ```python { "train": ["*train*"], "test": ["*test*"], "validation": ["*dev*", "valid"], } ``` otherwise it's ```python { "train": ["*"] } ``` Let me know what you think ! cc @albertvillanova @LysandreJik @vblagoje

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I_kwDODunzps48j7yv

Windows test suite fails

closed

MEMBER

## Describe the bug There is an error during installation of tests dependencies for Windows: https://app.circleci.com/pipelines/github/huggingface/datasets/7981/workflows/9b6a0114-2b8e-4069-94e5-e844dbbdba4e/jobs/49206 ``` ERROR: Cannot uninstall 'ruamel-yaml'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall. ```

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I_kwDODunzps48hG4F

Support multiple zipped CSV data files

open

MEMBER

As requested by @lewtun, support loading multiple zipped CSV data files. ```python from datasets import load_dataset url = "https://domain.org/filename.zip" data_files = {"train": "train_filename.csv", "test": "test_filename.csv"} dataset = load_dataset("csv", data_dir=url, data_files=data_files) ```

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I_kwDODunzps48fxEj

Improve `get_dataset_infos`?

open

CONTRIBUTOR

Using the dedicated function `get_dataset_infos` on a dataset that has no dataset-info.json file returns an empty info: ``` >>> from datasets import get_dataset_infos >>> get_dataset_infos('wit') {} ``` While it's totally possible to get it (regenerate it) with: ``` >>> from datasets import load_dataset_builder >>> builder = load_dataset_builder('wit') >>> builder.info DatasetInfo(description='Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set\n of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its\n size enables WIT to be used as a pretraining dataset for multimodal machine learning models.\n', citation='@article{srinivasan2021wit,\n title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},\n author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},\n journal={arXiv preprint arXiv:2103.01913},\n year={2021}\n}\n', homepage='https://github.com/google-research-datasets/wit', license='', features={'b64_bytes': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'image_url': Value(dtype='string', id=None), 'metadata_url': Value(dtype='string', id=None), 'original_height': Value(dtype='int32', id=None), 'original_width': Value(dtype='int32', id=None), 'mime_type': Value(dtype='string', id=None), 'caption_attribution_description': Value(dtype='string', id=None), 'wit_features': Sequence(feature={'language': Value(dtype='string', id=None), 'page_url': Value(dtype='string', id=None), 'attribution_passes_lang_id': Value(dtype='string', id=None), 'caption_alt_text_description': Value(dtype='string', id=None), 'caption_reference_description': Value(dtype='string', id=None), 'caption_title_and_reference_description': Value(dtype='string', id=None), 'context_page_description': Value(dtype='string', id=None), 'context_section_description': Value(dtype='string', id=None), 'hierarchical_section_title': Value(dtype='string', id=None), 'is_main_image': Value(dtype='string', id=None), 'page_changed_recently': Value(dtype='string', id=None), 'page_title': Value(dtype='string', id=None), 'section_title': Value(dtype='string', id=None)}, length=-1, id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='wit', config_name='default', version=0.0.0, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None) ``` Should we test if info is empty, and in that case regenerate it? Or always generate it?

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I_kwDODunzps48frCh

load_dataset_builder should error if "name" does not exist?

open

CONTRIBUTOR

``` import datasets as ds builder = ds.load_dataset_builder('sent_comp', name="doesnotexist") builder.info.config_name ``` returns ``` 'doesnotexist' ``` Shouldn't it raise an error instead? For this dataset, the only valid values for `name` should be: `"default"` or `None` (ie. argument not passed)

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I_kwDODunzps48fm1G

Chain filtering is leaking

open

NONE

## Describe the bug As there's no support for lists within dataset fields, I convert my lists to json-string format. However, the bug described is occurring even when the data format is 'string'. These samples show that filtering behavior diverges from what's expected when chaining filterings. On sample 2 the second filtering leads to "leaking" of data that should've been filtered on the first filtering into the results. ## Steps to reproduce the bug Sample 1: ```python import datasets import json items = [[1, 2], [3], [4]] jsoned_items = map(json.dumps, [[1, 2], [3], [4]]) ds = datasets.Dataset.from_dict({'a': jsoned_items}) print(list(ds)) # > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] as expected filtered = ds # get all lists that are shorter than 2 filtered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False) print(list(filtered)) # > Prints: [{'a': '[3]'}, {'a': '[4]'}] as expected # get all lists, which have a value bigger than 3 on its zero index filtered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False) print(list(filtered)) # > Should be: [{'a': [4]}] # > Prints: [{'a': [3]}] ``` Sample 2: ```python import datasets import json items = [[1, 2], [3], [4]] jsoned_items = map(json.dumps, [[1, 2], [3], [4]]) ds = datasets.Dataset.from_dict({'a': jsoned_items}) print(list(ds)) # > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] filtered = ds # get all lists, which have a value bigger than 3 on its zero index filtered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False) print(list(filtered)) # > Prints: [{'a': '[4]'}] as expected # get all lists that are shorter than 2 filtered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False) print(list(filtered)) # > Prints: [{'a': '[1, 2]'}] # > Should be: [{'a': '[4]'}] (remain intact) ``` ## Expected results Expected and actual results are attached to the code snippets. ## Actual results Expected and actual results are attached to the code snippets. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps48ec18

DatasetDict.filter and Dataset.filter crashes with any "fn_kwargs" argument

closed

NONE

## Describe the bug The ".filter" method of DatasetDict or Dataset objects fails when passing any "fn_kwargs" argument ## Steps to reproduce the bug ```python import datasets example_dataset = datasets.Dataset.from_dict({"a": {1, 2, 3, 4}}) def filter_value(example, value): return example['a'] == value filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3}) ``` ## Expected results `filtered` is a dataset containing {"a": {3}} ## Actual results > Traceback (most recent call last): > File "C:\Users\qsemi\Documents\git\nlp_experiments\gpt_celebrity\src\test_faulty_filter.py", line 8, in <module> > filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3}) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper > out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper > out = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2169, in filter > indices = self.map( > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1686, in map > return self._map_single( > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper > out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper > out = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2048, in _map_single > batch = apply_function_on_filtered_inputs( > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1939, in apply_function_on_filtered_inputs > function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) > TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'value' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps48ZtfH

cannot shuffle dataset loaded from disk

open

NONE

## Describe the bug dataset loaded from disk cannot be shuffled. ## Steps to reproduce the bug ``` my_dataset = load_from_disk('s3://my_file/validate', fs=s3) sample = my_dataset.select(range(100)).shuffle(seed=1234) ``` ## Actual results ``` sample = my_dataset .select(range(100)).shuffle(seed=1234) File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper out = func(self, *args, **kwargs) File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2494, in shuffle new_fingerprint=new_fingerprint, File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper out = func(self, *args, **kwargs) File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2303, in select tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False) File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 547, in NamedTemporaryFile (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type) File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 258, in _mkstemp_inner fd = _os.open(file, flags, 0o600) FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpnnu5uhnx/my_file/validate/tmpy76d70g4' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Python version: 3.7 - PyArrow version: 5.0.0

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I_kwDODunzps48ZUY1

Dataset has incorrect labels

closed

NONE

The dataset https://huggingface.co/datasets/turkish_product_reviews has incorrect labels - all reviews are labelled with "1" (positive sentiment). None of the reviews is labelled with "0". See screenshot attached: 

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I_kwDODunzps48XJ3J

Can't download `trivia_qa/unfiltered`

closed

MEMBER

## Describe the bug For some reason, I can't download `trivia_qa/unfilted`. A file seems to be missing... I am able to see it fine though the viewer tough... ## Steps to reproduce the bug ```python >>> from datasets import load_dataset >>> load_dataset("trivia_qa", "unfiltered") Downloading and preparing dataset trivia_qa/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to /gpfsscratch/rech/six/commun/datasets/trivia_qa/unfiltered/1.1.0/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6... Traceback (most recent call last): File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 251, in _add_context with open(os.path.join(file_dir, fname), encoding="utf-8") as f: FileNotFoundError: [Errno 2] No such file or directory: '/gpfsscratch/rech/six/commun/datasets/downloads/extracted/9fcb7eddc6afd46fd074af3c5128931dfe4b548f933c925a23847faf4c1995ad/evidence/wikipedia/Peanuts.txt' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/load.py", line 852, in load_dataset use_auth_token=use_auth_token, File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 616, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split disable=bool(logging.get_verbosity() == logging.NOTSET), File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 303, in _generate_examples example = parse_example(article) File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 274, in parse_example _add_context(article.get("EntityPages", []), "WikiContext", wiki_dir), File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 253, in _add_context except (IOError, datasets.Value("errors").NotFoundError): File "<string>", line 5, in __init__ File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/features.py", line 265, in __post_init__ self.pa_type = string_to_arrow(self.dtype) File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/features.py", line 134, in string_to_arrow f"Neither {datasets_dtype} nor {datasets_dtype + '_'} seems to be a pyarrow data type. " ValueError: Neither errors nor errors_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions ``` ## Expected results I am able to load another subset (`rc`), but unable to load. I am not sure why the try/except doesn't catch it... https://github.com/huggingface/datasets/blob/9675a5a1e7b99a86f9c250f6ea5fa5d1e6d5cc7d/datasets/trivia_qa/trivia_qa.py#L253 ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.11.0 - Platform: Linux-4.18.0-147.51.2.el8_1.x86_64-x86_64-with-redhat-8.1-Ootpa - Python version: 3.7.10 - PyArrow version: 3.0.0

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I_kwDODunzps48VI_n

add docmentation for the `Unix style pattern` matching feature that can be leverage for `data_files` into `load_dataset`

open

NONE

Unless I'm mistaken, it seems that in the new documentation it is no longer mentioned that you can use Unix style pattern matching in the `data_files` argument of the `load_dataset` method. This feature was mentioned [here](https://huggingface.co/docs/datasets/loading_datasets.html#from-a-community-dataset-on-the-hugging-face-hub) in the previous documentation. I'd love to hear your opinion @lhoestq , @albertvillanova and @stevhliu

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I_kwDODunzps48ROTx

IndexError: Invalid key: 14 is out of bounds for size 0

open

NONE

## Describe the bug A clear and concise description of what the bug is. Hi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error, since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq ## Steps to reproduce the bug ``` Traceback (most recent call last): File "run_clm.py", line 723, in <module> main() File "run_clm.py", line 669, in main train_result = trainer.train(resume_from_checkpoint=checkpoint) File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/transformers/trainer.py", line 1258, in train for step, inputs in enumerate(epoch_iterator): File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ data = self._next_data() File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1530, in __getitem__ format_kwargs=self._format_kwargs, File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1517, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 368, in query_table _check_valid_index_key(key, size) File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 311, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 14 is out of bounds for size 0 ``` ## Expected results not getting the index error ## Actual results Please see the above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: datasets 1.12.1 - Platform: linux - Python version: 3.7.11 - PyArrow version: 5.0.0

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I_kwDODunzps48Qwjd

ArrowInvalid: Can only convert 1-dimensional array values

closed

NONE

## Describe the bug For the ViT and LayoutLMv2 demo notebooks in my [Transformers-Tutorials repo](https://github.com/NielsRogge/Transformers-Tutorials), people reported an ArrowInvalid issue after applying the following function to a Dataset: ``` def preprocess_data(examples): images = [Image.open(path).convert("RGB") for path in examples['image_path']] words = examples['words'] boxes = examples['bboxes'] word_labels = examples['ner_tags'] encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels, padding="max_length", truncation=True) return encoded_inputs ``` ``` Full trace: --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) <ipython-input-8-0fc3efc6f0c2> in <module>() 27 28 train_dataset = datasets['train'].map(preprocess_data, batched=True, remove_columns=datasets['train'].column_names, ---> 29 features=features) 30 test_dataset = datasets['test'].map(preprocess_data, batched=True, remove_columns=datasets['test'].column_names, 31 features=features) 13 frames /usr/local/lib/python3.7/dist-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) 1701 new_fingerprint=new_fingerprint, 1702 disable_tqdm=disable_tqdm, -> 1703 desc=desc, 1704 ) 1705 else: /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 183 } 184 # apply actual function --> 185 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 186 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 187 # re-apply format to the output /usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 396 # Call actual function 397 --> 398 out = func(self, *args, **kwargs) 399 400 # Update fingerprint of in-place transforms + update in-place history of transforms /usr/local/lib/python3.7/dist-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, disable_tqdm, desc, cache_only) 2063 writer.write_table(batch) 2064 else: -> 2065 writer.write_batch(batch) 2066 if update_data and writer is not None: 2067 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 409 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) 410 typed_sequence_examples[col] = typed_sequence --> 411 pa_table = pa.Table.from_pydict(typed_sequence_examples) 412 self.write_table(pa_table, writer_batch_size) 413 /usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 106 storage = numpy_to_pyarrow_listarray(self.data, type=type.value_type) 107 else: --> 108 storage = pa.array(self.data, type.storage_dtype) 109 out = pa.ExtensionArray.from_storage(type, storage) 110 elif isinstance(self.data, np.ndarray): /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Can only convert 1-dimensional array values ``` It can be fixed by adding the following line: ```diff def preprocess_data(examples): images = [Image.open(path).convert("RGB") for path in examples['image_path']] words = examples['words'] boxes = examples['bboxes'] word_labels = examples['ner_tags'] encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels, padding="max_length", truncation=True) + encoded_inputs["image"] = np.array(encoded_inputs["image"]) return encoded_inputs ``` However, would be great if this can be fixed within Datasets itself.

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I_kwDODunzps48OsRm

Exceeded maximum rows when reading large files

closed

NONE

## Describe the bug A clear and concise description of what the bug is. When using `load_dataset` with json files, if the files are too large, there will be "Exceeded maximum rows" error. ## Steps to reproduce the bug ```python dataset = load_dataset('json', data_files=data_files) # data files have 3M rows in a single file ``` ## Expected results No error ## Actual results ``` ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 134 with open(file, encoding="utf-8") as f: --> 135 dataset = json.load(f) 136 except json.JSONDecodeError: ~/anaconda3/envs/python/lib/python3.9/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 292 """ --> 293 return loads(fp.read(), 294 cls=cls, object_hook=object_hook, ~/anaconda3/envs/python/lib/python3.9/json/__init__.py in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 345 parse_constant is None and object_pairs_hook is None and not kw): --> 346 return _default_decoder.decode(s) 347 if cls is None: ~/anaconda3/envs/python/lib/python3.9/json/decoder.py in decode(self, s, _w) 339 if end != len(s): --> 340 raise JSONDecodeError("Extra data", s, end) 341 return obj JSONDecodeError: Extra data: line 2 column 1 (char 20321) During handling of the above exception, another exception occurred: ArrowInvalid Traceback (most recent call last) <ipython-input-20-ab3718a6482f> in <module> ----> 1 dataset = load_dataset('json', data_files=data_files) ~/anaconda3/envs/python/lib/python3.9/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, streaming, **config_kwargs) 841 842 # Download and prepare data --> 843 builder_instance.download_and_prepare( 844 download_config=download_config, 845 download_mode=download_mode, ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 606 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 607 if not downloaded_from_gcs: --> 608 self._download_and_prepare( 609 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 610 ) ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 684 try: 685 # Prepare split will record examples associated to the split --> 686 self._prepare_split(split_generator, **prepare_split_kwargs) 687 except OSError as e: 688 raise OSError( ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1153 generator = self._generate_tables(**split_generator.gen_kwargs) 1154 with ArrowWriter(features=self.info.features, path=fpath) as writer: -> 1155 for key, table in utils.tqdm( 1156 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET) 1157 ): ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 135 dataset = json.load(f) 136 except json.JSONDecodeError: --> 137 raise e 138 raise ValueError( 139 f"Not able to read records in the JSON file at {file}. " ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 114 while True: 115 try: --> 116 pa_table = paj.read_json( 117 BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) 118 ) ~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json() ~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Exceeded maximum rows ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Linux - Python version: 3.9 - PyArrow version: 4.0.1

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I_kwDODunzps48MXJK

OpenSLR 25: ASR data for Amharic, Swahili and Wolof

open

CONTRIBUTOR

## Adding a Dataset - **Name:** *SLR25* - **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https://huggingface.co/datasets/openslr, 25 covers Amharic, Swahili and Wolof data* - **Paper:** *https://www.openslr.org/25/ has citations for each of the three subsubsets. * - **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps://www.openslr.org/25/* - **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.* https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do.

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I_kwDODunzps48Lctj

ValueError when computing f1 metric with average None

closed

NONE

## Describe the bug When I try to compute the f1 score for each class in a multiclass classification problem, I get a ValueError. The same happens with recall and precision. I traced the error to the `.item()` in these scripts, which is probably there for the other averages. E.g. from f1.py: ```python return { "f1": f1_score( references, predictions, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight, ).item(), } ``` Since the result is an array with more than one item, the `.item()` throws the error. I didn't submit a PR because this might be needed for the other averages, I'm not very familiar with the library ## Steps to reproduce the bug ```python from datasets import load_metric metric = load_metric("f1") metric.add_batch(predictions=[2,34,1,34,1,2,3], references=[23,52,1,3,523,5,8]) metric.compute(average=None) ``` ## Expected results `array([0.66666667, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ])` ## Actual results ValueError: can only convert an array of size 1 to a Python scalar ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.5 - PyArrow version: 5.0.0

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I_kwDODunzps48LBML

Run CI tests against non-production server

open

MEMBER

Currently, the CI test suite performs requests to the HF production server. As discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`.

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I_kwDODunzps48KeWE

Impossible to load compressed csv

closed

CONTRIBUTOR

## Describe the bug It is not possible to load from a compressed csv anymore. ## Steps to reproduce the bug ```python load_dataset('csv', data_files=['/path/to/csv.bz2']) ``` ## Problem and possible solution This used to work, but the commit that broke it is [this one](https://github.com/huggingface/datasets/commit/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782). `pandas` usually gets the compression information from the filename itself (which was previously directly passed). Now, since it gets a file descriptor, it might be good to auto-infer the compression or let the user pass the `compression` kwarg to `load_dataset` (or maybe warn the user if the file ends with a commonly known compression scheme?). ## Environment info - `datasets` version: 1.10.0 (and over) - Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 3.0.0

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I_kwDODunzps48Hr7R

Can't load dataset

closed

NONE

I'm trying to load a wikitext dataset ``` from datasets import load_dataset raw_datasets = load_dataset("wikitext") ``` ValueError: Config name is missing. Please pick one among the available configs: ['wikitext-103-raw-v1', 'wikitext-2-raw-v1', 'wikitext-103-v1', 'wikitext-2-v1'] Example of usage: `load_dataset('wikitext', 'wikitext-103-raw-v1')`. If I try ``` from datasets import load_dataset raw_datasets = load_dataset("wikitext-2-v1") ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.12.1/datasets/wikitext-2-v1/wikitext-2-v1.py #### Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic (colab) - Python version: 3.7.12 - PyArrow version: 3.0.0

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I_kwDODunzps48EfDK

OSError: Not enough disk space.

closed

CONTRIBUTOR

## Describe the bug I'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `/` disk having no enough space. The file system structure is like below. The root `/` has `115G` disk space available, and the `sda1` is mounted to `/mnt`, which has `1.2T` disk space available: ``` / /mnt/sda1/path/to/args.dataset_cache_dir ``` ## Steps to reproduce the bug ```python dataset_config = DownloadConfig( cache_dir=os.path.abspath(args.dataset_cache_dir), resume_download=True, ) dataset = load_dataset("natural_questions", download_config=dataset_config) ``` ## Expected results Can download the dataset without an error. ## Actual results The following error raised: ``` OSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Ubuntu 18.04 - Python version: 3.8.10 - PyArrow version:

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I_kwDODunzps48EDqK

masakhaner dataset load problem

closed

CONTRIBUTOR

## Describe the bug Masakhaner dataset is not loading ## Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("masakhaner",'amh') ``` ## Expected results Expected the return of a dataset ## Actual results ``` NonMatchingSplitsSizesError Traceback (most recent call last) <ipython-input-3-a6abc1161d4c> in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("masakhaner",'amh') 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_splits(expected_splits, recorded_splits) 72 ] 73 if len(bad_splits) > 0: ---> 74 raise NonMatchingSplitsSizesError(str(bad_splits)) 75 logger.info("All the splits matched successfully.") 76 NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=639927, num_examples=1751, dataset_name='masakhaner'), 'recorded': SplitInfo(name='train', num_bytes=639911, num_examples=1750, dataset_name='masakhaner')}, {'expected': SplitInfo(name='validation', num_bytes=92768, num_examples=251, dataset_name='masakhaner'), 'recorded': SplitInfo(name='validation', num_bytes=92753, num_examples=250, dataset_name='masakhaner')}, {'expected': SplitInfo(name='test', num_bytes=184286, num_examples=501, dataset_name='masakhaner'), 'recorded': SplitInfo(name='test', num_bytes=184271, num_examples=500, dataset_name='masakhaner')}] ``` ## Environment info Google Colab

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I_kwDODunzps48Cso5

Magnet’s

closed

NONE

## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have 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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I_kwDODunzps48COzL

medical-dialog error

closed

NONE

## Describe the bug A clear and concise description of what the bug is. When I attempt to download the huggingface datatset medical_dialog it errors out midway through ## Steps to reproduce the bug ```python raw_datasets = load_dataset("medical_dialog", "en", split="train", download_mode="force_redownload", data_dir="./Medical-Dialogue-Dataset-English") ``` ## Expected results A clear and concise description of the expected results. No error ## Actual results ``` 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_splits(expected_splits, recorded_splits) 72 ] 73 if len(bad_splits) > 0: ---> 74 raise NonMatchingSplitsSizesError(str(bad_splits)) 75 logger.info("All the splits matched successfully.") 76 NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=295097913, num_examples=229674, dataset_name='medical_dialog')}] ``` 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: 1.21.1 - Platform: colab - Python version: colab 3.7 - PyArrow version: N/A

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I_kwDODunzps48CMwQ

`DatasetDict` cannot be exported to parquet if the splits have different features

closed

MEMBER

## Describe the bug I'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. For `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file. ## Steps to reproduce the bug The following works as expected: ```python from datasets import load_dataset ds = load_dataset("lhoestq/custom_squad") ds['train'].to_parquet("./ds/train/split.parquet") ds['validation'].to_parquet("./ds/validation/split.parquet") brand_new_dataset = load_dataset("ds") ``` Modifying a single split to add a new feature ends up in a crash: ```python from datasets import load_dataset ds = load_dataset("lhoestq/custom_squad") def identical_answers(e): e['identical_answers'] = len(set(e['answers']['text'])) == 1 return e ds['validation'] = ds['validation'].map(identical_answers) ds['train'].to_parquet("./ds/train/split.parquet") ds['validation'].to_parquet("./ds/validation/split.parquet") brand_new_dataset = load_dataset("ds") ``` ``` File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 26, in <module> brand_new_dataset = load_dataset("ds") File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1151, in load_dataset builder_instance.download_and_prepare( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 642, in download_and_prepare self._download_and_prepare( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 732, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 1194, in _prepare_split writer.write_table(table) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in <listcomp> pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1257, in pyarrow.lib.Table.__getitem__ File "pyarrow/table.pxi", line 1833, in pyarrow.lib.Table.column File "pyarrow/table.pxi", line 1808, in pyarrow.lib.Table._ensure_integer_index KeyError: 'Field "identical_answers" does not exist in table schema' ``` It does work, however, to use the `save_to_disk` and `load_from_disk` methods: ```py from datasets import load_from_disk ds = load_dataset("lhoestq/custom_squad") def identical_answers(e): e['identical_answers'] = len(set(e['answers']['text'])) == 1 return e ds['validation'] = ds['validation'].map(identical_answers) ds.save_to_disk("local_path") brand_new_dataset = load_from_disk("local_path") ``` ## Expected results The saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files. If it's helpful, I've traced a possible patch to the `write_table` method here: https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L424-L425 The writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features: https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L254-L255 but the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`: https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/builder.py#L1190 Updating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise: ``` File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 27, in <module> brand_new_dataset = load_dataset("ds") File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1163, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 819, in as_dataset datasets = utils.map_nested( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 207, in map_nested mapped = [ File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 208, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 143, in _single_map_nested return function(data_struct) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 850, in _build_single_dataset ds = self._as_dataset( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 920, in _as_dataset dataset_kwargs = ArrowReader(self._cache_dir, self.info).read( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 217, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 238, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 173, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 308, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 327, in read_table return table_cls.from_file(filename) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 458, in from_file table = _memory_mapped_arrow_table_from_file(filename) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 45, in _memory_mapped_arrow_table_from_file pa_table = opened_stream.read_all() File "pyarrow/ipc.pxi", line 563, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 114, in pyarrow.lib.check_status OSError: Header-type of flatbuffer-encoded Message is not RecordBatch. ``` ## Environment info - `datasets` version: 1.12.2.dev0 - Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps48CJLV

Adding vision-and-language datasets (e.g., VQA, VCR) to Datasets

closed

NONE

**Is your feature request related to a problem? Please describe.** Would you like to add any vision-and-language datasets (e.g., VQA, VCR) to Huggingface Datasets? **Describe the solution you'd like** N/A **Describe alternatives you've considered** N/A **Additional context** This is Da Yin at UCLA. Recently, we have published an EMNLP 2021 paper about geo-diverse visual commonsense reasoning (https://arxiv.org/abs/2109.06860). We propose a new dataset called GD-VCR, a vision-and-language dataset to evaluate how well V&L models perform on scenarios involving geo-location-specific commonsense. We hope to have our V&L dataset incorporated into Huggingface to further promote our project, but I haven't seen much V&L datasets in the current package. Is it possible to add V&L datasets, and if so, how should we prepare for the loading? Thank you very much!

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I_kwDODunzps48BuJ5

Invalid download URL of WMT17 `zh-en` data

open

NONE

## Describe the bug Partial data (wmt17 zh-en) cannot be downloaded due to an invalid URL. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('wmt17','zh-en') ``` ## Expected results ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip

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I_kwDODunzps47_5ZQ

Error when calculating Matthews Correlation Coefficient loaded with `load_metric`

closed

NONE

## Describe the bug After loading the metric named "[Matthews Correlation Coefficient](https://huggingface.co/metrics/matthews_correlation)" from `🤗datasets`, the `.compute` method fails with the following exception `AttributeError: 'float' object has no attribute 'item'` (complete stack trace can be provided if required). ## Steps to reproduce the bug ```python import torch predictions = torch.ones((10,)) references = torch.zeros((10,)) from datasets import load_metric METRIC = load_metric("matthews_correlation") result = METRIC.compute(predictions=predictions, references=references) ``` ## Expected results We should expect a Python `dict` as it follows: ``` { "matthews_correlation": float() } ``` as defined in https://github.com/huggingface/datasets/blob/master/metrics/matthews_correlation/matthews_correlation.py, so the fix will imply removing `.item()`, since the value returned by the `scikit-learn` function is not a `torch.Tensor` but a `float`, which means that the `.item()` will fail. ## Actual results ``` Traceback (most recent call last): File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 59, in main app() File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 214, in __call__ return get_command(self)(*args, **kwargs) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1137, in __call__ return self.main(*args, **kwargs) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1062, in main rv = self.invoke(ctx) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1668, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 763, in invoke return __callback(*args, **kwargs) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 500, in wrapper return callback(**use_params) # type: ignore File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 43, in train metrics = trainer.evaluate() File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2051, in evaluate output = eval_loop( File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2292, in evaluation_loop metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels)) File "/home/alvaro.bartolome/XXX/xxx/metrics.py", line 20, in compute_metrics res = METRIC.compute(predictions=predictions, references=eval_preds.label_ids) File "/home/alvaro.bartolome/miniconda3/envs/lang/lib/python3.9/site-packages/datasets/metric.py", line 402, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/alvaro.bartolome/.cache/huggingface/modules/datasets_modules/metrics/matthews_correlation/0275f1e9a4d318e3ea8cdd87547ee0d58d894966616052e3d18444ac8ddd2357/matthews_correlation.py", line 88, in _compute "matthews_correlation": matthews_corrcoef(references, predictions, sample_weight=sample_weight).item(), AttributeError: 'float' object has no attribute 'item' ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-4.15.0-1113-azure-x86_64-with-glibc2.23 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps47_1K9

raise TypeError( TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'list'>. Make sure provided `function` returns a variable of type `dict` to update the dataset or `None` if you are only interested in side effects.

closed

NONE

## Describe the bug A clear and concise description of what the bug is. I am trying to use Dataset to load my file in order to use Bert embeddings model baut when I finished loading using dataset and I want to pass to the tokenizer using the function map; I get the following error : raise TypeError( TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'list'>. Make sure provided `function` returns a variable of type `dict` to update the dataset or `None` if you are only interested in side effects. I was able to load my file using dataset before but since this morning , I keep getting this erreor. ## Steps to reproduce the bug ```python # Xtrain, ytrain, filename, len_labels = read_file_2(fic) # Xtrain, lge_size = get_flaubert_layer(Xtrain, path_to_model_lge) data_preprocessed = make_new_traindata(Xtrain) my_dict = {"verbatim": data_preprocessed[1], "label": ytrain} # lemme avec conjonction dataset = Dataset.from_dict(my_dict) ``` ## Expected results A clear and concise description of the expected results. ## 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: - Platform: - Python version: - PyArrow version:

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I_kwDODunzps47_tni

Enable splits during streaming the dataset

open

CONTRIBUTOR

## Describe the Problem I'd like to stream only a specific percentage or part of the dataset. I want to do splitting when I'm streaming dataset as well. ## Solution Enabling splits when `streaming = True` as well. `e.g. dataset = load_dataset('dataset', split='train[:100]', streaming = True)` ## Alternatives Below is the alternative of doing it. `dataset = load_dataset("dataset", split='train', streaming = True).take(100)`

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I_kwDODunzps475RLx

MultiWOZ Dataset NonMatchingChecksumError

open

NONE

## Describe the bug The checksums for the downloaded MultiWOZ dataset and source MultiWOZ dataset aren't matching. ## Steps to reproduce the bug Both of the below dataset versions yield the checksum error: ```python from datasets import load_dataset dataset = load_dataset('multi_woz_v22', 'v2.2') dataset = load_dataset('multi_woz_v22', 'v2.2_active_only') ``` ## Expected results For the above calls to `load_dataset` to work. ## Actual results NonMatchingChecksumError. Traceback: > Traceback (most recent call last): File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-15-4e91280e112e>", line 1, in <module> dataset = load_dataset('multi_woz_v22', 'v2.2') File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/load.py", line 847, in load_dataset builder_instance.download_and_prepare( File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/builder.py", line 615, in download_and_prepare self._download_and_prepare( File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare verify_checksums( File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json'] ## Environment info - `datasets` version: 1.11.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.10 - PyArrow version: 5.0.0

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I_kwDODunzps473H-_

Cache problem in the `load_dataset` method for local compressed file(s)

open

NONE

## Describe the bug Cache problem in the `load_dataset` method: when modifying a compressed file in a local folder `load_dataset` doesn't detect the change and load the previous version. ## Steps to reproduce the bug To test it directly, I have prepared a [Google Colaboratory notebook](https://colab.research.google.com/drive/11Em_Amoc-aPGhSBIkSHU2AvEh24nVayy?usp=sharing) that shows this behavior. For this example, I have created a toy dataset at: https://huggingface.co/datasets/SaulLu/toy_struc_dataset This dataset is composed of two versions: - v1 on commit `a6beb46` which has a single example `{'id': 1, 'value': {'tag': 'a', 'value': 1}}` in file `train.jsonl.gz` - v2 on commit `e7935f4` (`main` head) which has a single example `{'attr': 1, 'id': 1, 'value': 'a'}` in file `train.jsonl.gz` With a terminal, we can start to get the v1 version of the dataset ```bash git lfs install git clone https://huggingface.co/datasets/SaulLu/toy_struc_dataset cd toy_struc_dataset git checkout a6beb46 ``` Then we can load it with python and look at the content: ```python from datasets import load_dataset path = "/content/toy_struc_dataset" dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"}) print(dataset["train"][0]) ``` Output ``` {'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1 ``` With a terminal, we can now start to get the v1 version of the dataset ```bash git checkout main ``` Then we can load it with python and look at the content: ```python from datasets import load_dataset path = "/content/toy_struc_dataset" dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"}) print(dataset["train"][0]) ``` Output ``` {'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1 (not v2) ``` ## Expected results The last output should have been ``` {"id":1, "value": "a", "attr": 1} # This is the example in v2 ``` ## Ideas As discussed offline with Quentin, if the cache hash was ever sensitive to changes in a compressed file we would probably not have the problem anymore. This situation leads me to suggest 2 other features: - to also have an `load_from_cache_file` argument in the "load_dataset" method - to reorganize the cache so that we can delete the caches related to a dataset (cf issue #ToBeFilledSoon) And thanks again for this great library :hugs: ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0

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I_kwDODunzps47xQC1

Trying to get in touch regarding a security issue

closed

NONE

Hey there! I'd like to report a security issue but cannot find contact instructions on your repository. If not a hassle, might you kindly add a `SECURITY.md` file with an email, or another contact method? GitHub [recommends](https://docs.github.com/en/code-security/getting-started/adding-a-security-policy-to-your-repository) this best practice to ensure security issues are responsibly disclosed, and it would serve as a simple instruction for security researchers in the future. Thank you for your consideration, and I look forward to hearing from you! (cc @huntr-helper)

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I_kwDODunzps47pFLz

Protect master branch

closed

MEMBER

After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.: - 00cc036fea7c7745cfe722360036ed306796a3f2 - 13ae8c98602bbad8197de3b9b425f4c78f582af1 - ... I propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future: - [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch - Currently, simple merge commits are already disabled - I propose to disable rebase merging as well - ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~ - ~~This protection would reject direct pushes to master branch~~ - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~ - [x] Protect the master branch only from direct pushing of **merge commits** - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch). - No need to disable/re-enable this protection on each release This purpose of this Issue is to open a discussion about this problem and to agree in a solution.

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https://github.com/huggingface/datasets/issues/2944

I_kwDODunzps47oxhy

Add `remove_columns` to `IterableDataset `

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. ```python from datasets import load_dataset dataset = load_dataset("c4", 'realnewslike', streaming =True, split='train') dataset = dataset.remove_columns('url') ``` ``` AttributeError: 'IterableDataset' object has no attribute 'remove_columns' ``` **Describe the solution you'd like** It would be nice to have `.remove_columns()` to match the `Datasets` api. **Describe alternatives you've considered** This can be done with a single call to `.map()`, I can try to help add this. 🤗

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