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PR_kwDODunzps5nTxGu

Fix imagefolder dataset url

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PR_kwDODunzps5nRME6

Update GitHub Actions to Node 20

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Update GitHub Actions to Node 20. Fix #6679.

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PR_kwDODunzps5nRItQ

Update release instructions

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Update release instructions.

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PR_kwDODunzps5nRHcz

Set dev version

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I_kwDODunzps5_q5-9

Node.js 16 GitHub Actions are deprecated

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`Node.js` 16 GitHub Actions are deprecated. See: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/ We should update them to Node 20. See warnings in our CI, e.g.: https://github.com/huggingface/datasets/actions/runs/7957295009?pr=6678 ``` Node.js 16 actions are deprecated. Please update the following actions to use Node.js 20: actions/checkout@v3, actions/setup-python@v4. For more information see: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/. ```

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PR_kwDODunzps5nQ2ZO

Release: 2.17.1

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PR_kwDODunzps5nOmo_

Pass through information about location of cache directory.

open

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If cache directory is set, information is not passed through. Pass download config in as an arg too.

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I_kwDODunzps5_l7Sr

Can't Read List of JSON Files Properly

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### Describe the bug Trying to read a bunch of JSON files into Dataset class but default approach doesn't work. I don't get why it works when I read it one by one but not when I pass as a list :man_shrugging: The code fails with ``` ArrowInvalid: JSON parse error: Invalid value. in row 0 UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug This doesn't work ``` from datasets import Dataset # dir contains 100 json files. Dataset.from_json("/PUT SOME PATH HERE/*") ``` This works: ``` from datasets import concatenate_datasets ls_ds = [] for file in list_of_json_files: ls_ds.append(Dataset.from_json(file)) ds = concatenate_datasets(ls_ds) ``` ### Expected behavior I expect this to read json files properly as error is not clear ### Environment info - `datasets` version: 2.17.0 - Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

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I_kwDODunzps5_iFI9

Allow image model (color conversion) to be specified as part of datasets Image() decode

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### Feature request Typical torchvision / torch Datasets in image applications apply color conversion in the Dataset portion of the code as part of image decode, separately from the image transform stack. This is true for PIL.Image where convert is usually called in dataset, for native torchvision https://pytorch.org/vision/main/generated/torchvision.io.decode_jpeg.html, and similarly in tensorflow.data pipelines decode_jpeg or https://www.tensorflow.org/api_docs/python/tf/io/decode_and_crop_jpeg have a channels arg that allows controlling the image mode in the decode step. datasets currently requires this pattern (from [examples](https://huggingface.co/docs/datasets/main/en/image_process)): ``` from torchvision.transforms import Compose, ColorJitter, ToTensor jitter = Compose( [ ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.7), ToTensor(), ] ) def transforms(examples): examples["pixel_values"] = [jitter(image.convert("RGB")) for image in examples["image"]] return examples ``` ### Motivation It would be nice to be able to handle `image.convert("RGB")` (or other modes) in the decode step, before applying torchvision transforms, this would reduce differences in code when handling pipelines that can handle torchvision, webdatset, or hf datasets with fewer code differences and without needing to handle image mode argument passing in two different stages of the pipelines... ### Your contribution Can do a PR with guidance on how mode should be passed / set on the dataset.

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I_kwDODunzps5_h6M4

Depprcated Overview.ipynb Link to new Quickstart Notebook invalid

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NONE

### Describe the bug For the dreprecated notebook found [here](https://github.com/huggingface/datasets/blob/main/notebooks/Overview.ipynb). The link to the new notebook is broken. ### Steps to reproduce the bug Click the [Quickstart notebook](https://github.com/huggingface/notebooks/blob/main/datasets_doc/quickstart.ipynb) link in the notebook. ### Expected behavior I believe is it suposed to link [here](https://github.com/huggingface/notebooks/blob/main/datasets_doc/en/quickstart.ipynb). That is mentioned in the readme. ### Environment info Colab

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I_kwDODunzps5_hocL

IterableDataset `set_epoch` is ignored when DataLoader `persistent_workers=True`

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### Describe the bug When persistent workers are enabled, the epoch that's set via the IterableDataset instance held by the training process is ignored by the workers as they are disconnected across processes. PyTorch samplers for non-iterable datasets have a mechanism to sync this, datasets.IterableDataset does not. In my own use of IterableDatasets I usually track the epoch count which crosses process boundaries in a multiprocessing.Value ### Steps to reproduce the bug Use a streaming dataset (Iterable) w/ the recommended pattern below and `persistent_workers=True` in the torch DataLoader. ``` for epoch in range(epochs): shuffled_dataset.set_epoch(epoch) for example in shuffled_dataset: ... ``` ### Expected behavior When the canonical bit of code above is used with `num_workers > 0` and `persistent_workers=True`, the epoch set via `set_epoch()` is propagated to the IterableDataset instances in the worker processes ### Environment info N/A

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PR_kwDODunzps5nGAlw

Remove deprecated verbose parameter from CSV builder

closed

MEMBER

Remove deprecated `verbose` parameter from CSV builder. Note that the `verbose` parameter is deprecated since pandas 2.2.0. See: - https://github.com/pandas-dev/pandas/pull/56556 - https://github.com/pandas-dev/pandas/pull/57450 Fix #6671.

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I_kwDODunzps5_emW-

CSV builder raises deprecation warning on verbose parameter

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MEMBER

CSV builder raises a deprecation warning on `verbose` parameter: ``` FutureWarning: The 'verbose' keyword in pd.read_csv is deprecated and will be removed in a future version. ``` See: - https://github.com/pandas-dev/pandas/pull/56556 - https://github.com/pandas-dev/pandas/pull/57450

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I_kwDODunzps5_dPte

ValueError

closed

NONE

### Describe the bug ValueError Traceback (most recent call last) [<ipython-input-11-9b99bc80ec23>](https://localhost:8080/#) in <cell line: 11>() 9 import numpy as np 10 import matplotlib.pyplot as plt ---> 11 from datasets import DatasetDict, Dataset 12 from transformers import AutoTokenizer, AutoModelForSequenceClassification 13 from transformers import Trainer, TrainingArguments 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 16 __version__ = "2.17.0" 17 ---> 18 from .arrow_dataset import Dataset 19 from .arrow_reader import ReadInstruction 20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 65 66 from . import config ---> 67 from .arrow_reader import ArrowReader 68 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 69 from .data_files import sanitize_patterns [/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module> 27 28 import pyarrow as pa ---> 29 import pyarrow.parquet as pq 30 from tqdm.contrib.concurrent import thread_map 31 [/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/__init__.py](https://localhost:8080/#) in <module> 18 # flake8: noqa 19 ---> 20 from .core import * [/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py](https://localhost:8080/#) in <module> 34 import pyarrow as pa 35 import pyarrow.lib as lib ---> 36 import pyarrow._parquet as _parquet 37 38 from pyarrow._parquet import (ParquetReader, Statistics, # noqa /usr/local/lib/python3.10/dist-packages/pyarrow/_parquet.pyx in init pyarrow._parquet() ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Steps to reproduce the bug ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Expected behavior Resolve the binary incompatibility ### Environment info Google Colab Note book

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I_kwDODunzps5_dDbm

attribute error when writing trainer.train()

open

NONE

### Describe the bug AttributeError Traceback (most recent call last) Cell In[39], line 2 1 # Start the training process ----> 2 trainer.train() File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs) 1537 hf_hub_utils.enable_progress_bars() 1538 else: -> 1539 return inner_training_loop( 1540 args=args, 1541 resume_from_checkpoint=resume_from_checkpoint, 1542 trial=trial, 1543 ignore_keys_for_eval=ignore_keys_for_eval, 1544 ) File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 1833 rng_to_sync = True 1835 step = -1 -> 1836 for step, inputs in enumerate(epoch_iterator): 1837 total_batched_samples += 1 1839 if self.args.include_num_input_tokens_seen: File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self) 449 # We iterate one batch ahead to check when we are at the end 450 try: --> 451 current_batch = next(dataloader_iter) 452 except StopIteration: 453 yield File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self) 627 if self._sampler_iter is None: 628 # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E) 629 self._reset() # type: ignore[call-arg] --> 630 data = self._next_data() 631 self._num_yielded += 1 632 if self._dataset_kind == _DatasetKind.Iterable and \ 633 self._IterableDataset_len_called is not None and \ 634 self._num_yielded > self._IterableDataset_len_called: File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self) 672 def _next_data(self): 673 index = self._next_index() # may raise StopIteration --> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 675 if self._pin_memory: 676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key) 1762 def __getitem__(self, key): # noqa: F811 1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1764 return self._getitem( 1765 key, 1766 ) File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs) 1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs) 1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1749 formatted_output = format_table( 1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1751 ) 1752 return formatted_output File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns) 538 else: 539 pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns) --> 540 formatted_output = formatter(pa_table_to_format, query_type=query_type) 541 if output_all_columns: 542 if isinstance(formatted_output, MutableMapping): File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table) 56 def format_row(self, pa_table: pa.Table) -> dict: ---> 57 row = self.numpy_arrow_extractor().extract_row(pa_table) 58 return self.recursive_tensorize(row) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table) 153 def extract_row(self, pa_table: pa.Table) -> dict: --> 154 return _unnest(self.extract_batch(pa_table)) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: --> 196 if any( 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, **self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: 196 if any( --> 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, **self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr) 319 warnings.warn( 320 f"In the future `np.{attr}` will be defined as the " 321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2) 323 if attr in __former_attrs__: --> 324 raise AttributeError(__former_attrs__[attr]) 326 if attr == 'testing': 327 import numpy.testing as testing AttributeError: module 'numpy' has no attribute 'object'. `np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecationsAttributeError Traceback (most recent call last) Cell In[39], line 2 1 # Start the training process ----> 2 trainer.train() File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs) 1537 hf_hub_utils.enable_progress_bars() 1538 else: -> 1539 return inner_training_loop( 1540 args=args, 1541 resume_from_checkpoint=resume_from_checkpoint, 1542 trial=trial, 1543 ignore_keys_for_eval=ignore_keys_for_eval, 1544 ) File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 1833 rng_to_sync = True 1835 step = -1 -> 1836 for step, inputs in enumerate(epoch_iterator): 1837 total_batched_samples += 1 1839 if self.args.include_num_input_tokens_seen: File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self) 449 # We iterate one batch ahead to check when we are at the end 450 try: --> 451 current_batch = next(dataloader_iter) 452 except StopIteration: 453 yield File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self) 627 if self._sampler_iter is None: 628 # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E) 629 self._reset() # type: ignore[call-arg] --> 630 data = self._next_data() 631 self._num_yielded += 1 632 if self._dataset_kind == _DatasetKind.Iterable and \ 633 self._IterableDataset_len_called is not None and \ 634 self._num_yielded > self._IterableDataset_len_called: File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self) 672 def _next_data(self): 673 index = self._next_index() # may raise StopIteration --> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 675 if self._pin_memory: 676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key) 1762 def __getitem__(self, key): # noqa: F811 1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1764 return self._getitem( 1765 key, 1766 ) File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs) 1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs) 1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1749 formatted_output = format_table( 1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1751 ) 1752 return formatted_output File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns) 538 else: 539 pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns) --> 540 formatted_output = formatter(pa_table_to_format, query_type=query_type) 541 if output_all_columns: 542 if isinstance(formatted_output, MutableMapping): File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table) 56 def format_row(self, pa_table: pa.Table) -> dict: ---> 57 row = self.numpy_arrow_extractor().extract_row(pa_table) 58 return self.recursive_tensorize(row) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table) 153 def extract_row(self, pa_table: pa.Table) -> dict: --> 154 return _unnest(self.extract_batch(pa_table)) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: --> 196 if any( 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, **self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: 196 if any( --> 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, **self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr) 319 warnings.warn( 320 f"In the future `np.{attr}` will be defined as the " 321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2) 323 if attr in __former_attrs__: --> 324 raise AttributeError(__former_attrs__[attr]) 326 if attr == 'testing': 327 import numpy.testing as testing AttributeError: module 'numpy' has no attribute 'object'. `np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations Please help me to resolve the above error ### Steps to reproduce the bug Please resolve the issue of deprecated function np.object to object in the numpy ### Expected behavior np.object should be written as object only ### Environment info kaggle notebook

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I_kwDODunzps5_bSdf

Chapter 6 - Issue Loading `cnn_dailymail` dataset

open

NONE

### Describe the bug So I am getting this bug when I try to run cell 4 of the Chapter 6 notebook code: `dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0")` Error Message: ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[4], line 4 1 #hide_output 2 from datasets import load_dataset ----> 4 dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0") 7 # dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0", trust_remote_code=True) 8 print(f"Features: {dataset['train'].column_names}") File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\load.py:2587, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2583 # Build dataset for splits 2584 keep_in_memory = ( 2585 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2586 ) -> 2587 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2588 # Rename and cast features to match task schema 2589 if task is not None: 2590 # To avoid issuing the same warning twice File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1244, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1241 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1243 # Create a dataset for each of the given splits -> 1244 datasets = map_nested( 1245 partial( 1246 self._build_single_dataset, 1247 run_post_process=run_post_process, 1248 verification_mode=verification_mode, 1249 in_memory=in_memory, 1250 ), 1251 split, 1252 map_tuple=True, 1253 disable_tqdm=True, 1254 ) 1255 if isinstance(datasets, dict): 1256 datasets = DatasetDict(datasets) File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:477, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc) 466 mapped = [ 467 map_nested( 468 function=function, (...) 474 for obj in iterable 475 ] 476 elif num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: --> 477 mapped = [ 478 _single_map_nested((function, obj, types, None, True, None)) 479 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc) 480 ] 481 else: 482 with warnings.catch_warnings(): File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:478, in <listcomp>(.0) 466 mapped = [ 467 map_nested( 468 function=function, (...) 474 for obj in iterable 475 ] 476 elif num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: 477 mapped = [ --> 478 _single_map_nested((function, obj, types, None, True, None)) 479 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc) 480 ] 481 else: 482 with warnings.catch_warnings(): File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:370, in _single_map_nested(args) 368 # Singleton first to spare some computation 369 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 370 return function(data_struct) 372 # Reduce logging to keep things readable in multiprocessing with tqdm 373 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1274, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory) 1271 split = Split(split) 1273 # Build base dataset -> 1274 ds = self._as_dataset( 1275 split=split, 1276 in_memory=in_memory, 1277 ) 1278 if run_post_process: 1279 for resource_file_name in self._post_processing_resources(split).values(): File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1348, in DatasetBuilder._as_dataset(self, split, in_memory) 1346 if self._check_legacy_cache(): 1347 dataset_name = self.name -> 1348 dataset_kwargs = ArrowReader(cache_dir, self.info).read( 1349 name=dataset_name, 1350 instructions=split, 1351 split_infos=self.info.splits.values(), 1352 in_memory=in_memory, 1353 ) 1354 fingerprint = self._get_dataset_fingerprint(split) 1355 return Dataset(fingerprint=fingerprint, **dataset_kwargs) File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\arrow_reader.py:254, in BaseReader.read(self, name, instructions, split_infos, in_memory) 252 if not files: 253 msg = f'Instruction "{instructions}" corresponds to no data!' --> 254 raise ValueError(msg) 255 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) **ValueError: Instruction "validation" corresponds to no data!** ```` Looks like the data is not being loaded. Any advice would be appreciated. Thanks! ### Steps to reproduce the bug Run all cells of Chapter 6 notebook. ### Expected behavior Data should load correctly without any errors. ### Environment info - `datasets` version: 2.17.0 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.9.18 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

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I_kwDODunzps5_a8ZQ

Default config for squad is incorrect

open

NONE

### Describe the bug If you download Squad, it will download the plain_text version, but the config still specifies "default", so if you set the offline mode the cache will try to look it up according to the config_id which is "default" and this will say; ValueError: Couldn't find cache for squad for config 'default' Available configs in the cache: ['plain_text'] ### Steps to reproduce the bug 1. export HF_DATASETS_OFFLINE=0 2. load_dataset("squad") 3. export HF_DATASETS_OFFLINE=1 4. load_dataset("squad") ### Expected behavior We should change the config_name I guess? ### Environment info linux, latest version of datasets

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PR_kwDODunzps5m9JgW

Allow SplitDict setitem to replace existing SplitInfo

open

MEMBER

Fix this code provided by @clefourrier ```python import datasets import os token = os.getenv("TOKEN") results = datasets.load_dataset("gaia-benchmark/results_public", "2023", token=token, download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD) results["test"] = datasets.Dataset.from_list([row for row in results["test"] if row["model"] != "StateFlow"]) results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test") ``` ``` ValueError Traceback (most recent call last) Cell In[43], line 1 ----> 1 results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test") File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/arrow_dataset.py:5498, in Dataset.push_to_hub(self, repo_id, config_name, split, private, token, branch, max_shard_size, num_shards, embed_external_files) 5496 repo_info.dataset_size = (repo_info.dataset_size or 0) + dataset_nbytes 5497 repo_info.size_in_bytes = repo_info.download_size + repo_info.dataset_size -> 5498 repo_info.splits[split] = SplitInfo( 5499 split, num_bytes=dataset_nbytes, num_examples=len(self), dataset_name=dataset_name 5500 ) 5501 info_to_dump = repo_info 5502 # create the metadata configs if it was uploaded with push_to_hub before metadata configs existed File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/splits.py:541, in SplitDict.__setitem__(self, key, value) 539 raise ValueError(f"Cannot add elem. (key mismatch: '{key}' != '{value.name}')") 540 if key in self: --> 541 raise ValueError(f"Split {key} already present") 542 super().__setitem__(key, value) ValueError: Split test already present ```

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PR_kwDODunzps5m67g0

Revert the changes in `arrow_writer.py` from #6636

closed

CONTRIBUTOR

#6636 broke `write_examples_on_file` and `write_batch` from the class `ArrowWriter`. I'm undoing these changes. See #6663. Note the current implementation doesn't keep the order of the columns and the schema, thus setting a wrong schema for each column.

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I_kwDODunzps5_SNnr

`write_examples_on_file` and `write_batch` are broken in `ArrowWriter`

closed

CONTRIBUTOR

### Describe the bug `write_examples_on_file` and `write_batch` are broken in `ArrowWriter` since #6636. The order between the columns and the schema is not preserved anymore. So these functions don't work anymore unless the order happens to align well. ### Steps to reproduce the bug Try to do `write_batch` with anything that has many columns, and it's likely to break. ### Expected behavior I expect these functions to work, instead of it trying to cast a column to its incorrect type. ### Environment info - `datasets` version: 2.17.0 - Platform: Linux-5.15.0-1040-aws-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.19.4 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5mwgKP

fix: show correct package name to install biopython

open

NONE

When you try to download a dataset that uses [biopython](https://github.com/biopython/biopython), like `load_dataset("InstaDeepAI/multi_species_genomes")`, you get the error: ``` >>> from datasets import load_dataset >>> dataset = load_dataset("InstaDeepAI/multi_species_genomes") /home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py:1454: FutureWarning: The repository for InstaDeepAI/multi_species_genomes contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/InstaDeepAI/multi_species_genomes You can avoid this message in future by passing the argument `trust_remote_code=True`. Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`. warnings.warn( Downloading builder script: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.51k/7.51k [00:00<00:00, 7.67MB/s] Downloading readme: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17.2k/17.2k [00:00<00:00, 11.0MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2548, in load_dataset builder_instance = load_dataset_builder( File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2220, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1871, in dataset_module_factory raise e1 from None File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1844, in dataset_module_factory ).get_module() File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1466, in get_module local_imports = _download_additional_modules( File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 346, in _download_additional_modules raise ImportError( ImportError: To be able to use InstaDeepAI/multi_species_genomes, you need to install the following dependency: Bio. Please install it using 'pip install Bio' for instance. >>> ``` `Bio` comes from the `biopython` package that can be installed with `pip install biopython`, not with `pip install Bio` as suggested. This PR adds special logic to show the correct package name in the error message of ` _download_additional_modules`, similarly as is done for `sklearn` / `scikit-learn` already. There are more packages where importable module name differs from the PyPI package name, so this could be made more generic, like: ``` # Mapping of importable module names to their PyPI package names package_map = { "sklearn": "scikit-learn", "Bio": "biopython", "PIL": "Pillow", "bs4": "beautifulsoup4" } for module_name, pypi_name in package_map.items(): if module_name in needs_to_be_installed.keys(): needs_to_be_installed[module_name] = pypi_name ```

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I_kwDODunzps5_GEJL

Import error on Google Colab

closed

NONE

### Describe the bug Cannot be imported on Google Colab, the import throws the following error: ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Steps to reproduce the bug 1. `! pip install -U datasets` 2. `import datasets` ### Expected behavior Should be possible to use the library ### Environment info - `datasets` version: 2.17.0 - Platform: Linux-6.1.58+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0

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PR_kwDODunzps5mu9wU

Automatic Conversion for uint16/uint32 to Compatible PyTorch Dtypes

open

NONE

This PR addresses an issue encountered when utilizing uint16 or uint32 datatypes with datasets, followed by attempting to convert these datasets into PyTorch-compatible formats. Currently, doing so results in a TypeError due to incompatible datatype conversion, as illustrated by the following example: ```python from datasets import Dataset, Sequence, Value, Features def gen(): for i in range(100): yield {'seq': list(range(i, i + 20))} ds = Dataset.from_generator(gen, features=Features({'seq': Sequence(feature=Value(dtype='uint16'), length=-1)})) ds.set_format('torch') print(ds[0]) ``` This code snippet triggers the following error due to the inability to convert numpy.uint16 arrays to a PyTorch-supported format: ``` TypeError: can't convert np.ndarray of type numpy.uint16. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool. ``` This PR introduces an automatic mechanism to convert np.uint16 and np.uint32 datatypes to np.int64 for seamless compatibility with PyTorch formats, simplifying workflows and improving developer experience by eliminating the need for manual conversion handling.

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PR_kwDODunzps5mlmmo

Change default compression argument for JsonDatasetWriter

open

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Change default compression type from `None` to "infer", to align with pandas' defaults. Documentation asks the user to supply `to_json_kwargs` with arguments suitable for pandas' `to_json` method. At the same time, while pandas' by default uses ["infer"](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) for compression, datasets enforce `None` as default. This, likely, confuses user, as they expect the same behaviour, i.e they expect that if they name their output file as "dataset.jsonl.zst" then the compression would be inferred as "zstd" and file will be compressed before writing. Moreover, while it is probably outside of the scope of this pull request, `compression` argument needs to be capable of taking `dict` as input (along with `str`), as it does in pandas, in order to allow user to specify compression parameters. Current implementation will likely fail with `NotImplementedError`, as it expects either `None` or `str` specifying compression algo.

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PR_kwDODunzps5mlZyb

[Resumable IterableDataset] Add IterableDataset state_dict

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A simple implementation of a mechanism to resume an IterableDataset. This is WIP and untested. Example: ```python from datasets import Dataset, concatenate_datasets ds = Dataset.from_dict({"a": range(5)}).to_iterable_dataset(num_shards=3) ds = concatenate_datasets([ds] * 2) print(f"{ds.state_dict()=}") for i, example in enumerate(ds): print(example) if i == 6: state_dict = ds.state_dict() ds.load_state_dict(state_dict) print(f"{ds.state_dict()=}") for example in ds: print(example) ``` returns ``` ds.state_dict()={'ex_iterable_idx': 0, 'ex_iterables': [{'shard_idx': 0, 'shard_example_idx': 0}, {'shard_idx': 0, 'shard_example_idx': 0}]} {'a': 0} {'a': 1} {'a': 2} {'a': 3} {'a': 4} {'a': 0} {'a': 1} {'a': 2} {'a': 3} {'a': 4} ds.state_dict()={'ex_iterable_idx': 1, 'ex_iterables': [{'shard_idx': 3, 'shard_example_idx': 0}, {'shard_idx': 0, 'shard_example_idx': 2}]} {'a': 2} {'a': 3} {'a': 4} ```

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I_kwDODunzps5-6DTN

Release not pushed to conda channel

open

NONE

### Describe the bug The github actions step to publish the release 2.17.0 to conda channel has failed due to expired token. Can some one please update the anaconda token rerun the failed action? @albertvillanova ?  ### Steps to reproduce the bug Please see this actions [link](https://github.com/huggingface/datasets/actions/runs/7842473662) ### Expected behavior The action runs successfully and the latest release is pushed to HuggingFace conda channel ### Environment info Not applicable.

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I_kwDODunzps5-zJuJ

Error when loading a big local json file

open

NONE

### Describe the bug When trying to load big json files from a local directory, `load_dataset` throws the following error ``` Traceback (most recent call last): File "/miniconda3/envs/conda-env/lib/python3.10/site-packages/datasets/builder.py", line 1989, in _prepare_split_single writer.write_table(table) File "miniconda3/envs/conda-env/lib/python3.10/site-packages/datasets/arrow_writer.py", line 573, in write_table pa_table = pa_table.combine_chunks() File "pyarrow/table.pxi", line 3638, in pyarrow.lib.Table.combine_chunks File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays ``` ### Steps to reproduce the bug 1. Download a big file, e.g. `https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz` 2. Load it like `data = load_dataset("json", data_files=["nq-train.json"], split="train")` ```python from datasets import load_dataset data = load_dataset("json", data_files=["nq-train.json"], split="train") ``` A similarly formatted but smaller file, e.g. e.g. `https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz` is loaded without issues ```python from datasets import load_dataset data = load_dataset("json", data_files=["nq-dev.json"], split="train") ``` ### Expected behavior It should load normally ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.31 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

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I_kwDODunzps5-x8AK

Cannot load the dataset go_emotions

open

NONE

### Describe the bug When I run the following code I get an exception; `go_emotions = load_dataset("go_emotions")` > AttributeError Traceback (most recent call last) Cell In[6], [line 1](vscode-notebook-cell:?execution_count=6&line=1) ----> [1](vscode-notebook-cell:?execution_count=6&line=1) go_emotions = load_dataset("go_emotions") [2](vscode-notebook-cell:?execution_count=6&line=2) data = go_emotions.data File [c:\Users\hijik\anaconda3\Lib\site-packages\datasets\load.py:2523](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2523), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) [2518](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2518) verification_mode = VerificationMode( [2519](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2519) (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS [2520](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2520) ) [2522](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2522) # Create a dataset builder -> [2523](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2523) builder_instance = load_dataset_builder( [2524](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2524) path=path, [2525](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2525) name=name, [2526](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2526) data_dir=data_dir, [2527](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2527) data_files=data_files, [2528](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2528) cache_dir=cache_dir, [2529](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2529) features=features, [2530](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2530) download_config=download_config, [2531](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2531) download_mode=download_mode, [2532](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2532) revision=revision, [2533](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2533) token=token, [2534](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2534) storage_options=storage_options, [2535](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2535) trust_remote_code=trust_remote_code, [2536](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2536) _require_default_config_name=name is None, ... ---> [63](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:63) if issubclass(obj_type, transformers.PreTrainedTokenizerBase): [64](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:64) pklregister(obj_type)(_save_transformersPreTrainedTokenizerBase) [66](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:66) # Unwrap `torch.compile`-ed functions AttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase' Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?10bc0728-6947-456e-9a3e-f056872b04c6) or open in a [text editor](command:workbench.action.openLargeOutput?10bc0728-6947-456e-9a3e-f056872b04c6). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)... ### Steps to reproduce the bug ``` from datasets import load_dataset go_emotions = load_dataset("go_emotions") ``` ### Expected behavior Should simply load the variable with the data from the file ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.16.1 - Platform: Windows-10-10.0.22631-SP0 - Python version: 3.11.4 - `huggingface_hub` version: 0.20.3 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.10.0

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I_kwDODunzps5-xoTe

Batched dataset map throws exception that cannot cast fixed length array to Sequence

closed

NONE

### Describe the bug I encountered a TypeError when batch processing a dataset with Sequence features in datasets package version 2.16.1. The error arises from a mismatch in handling fixed-size list arrays during the map function execution. Debugging pinpoints the issue to an if-statement in datasets/table.py, line 2093, failing to correctly process sequence lengths. ### Steps to reproduce the bug Create virtual environment and activate ``` virtualenv venv source venv/bin/activate ``` Then install the datasets package (I'm using the latest version) ``` pip install datasets==2.16.1 ``` Then run ```python # bug.py from datasets import Dataset from datasets.features import Features, Sequence, Value data = { "num": [[1, 2], [3, 4]], } features = Features({'num': Sequence(feature=Value(dtype='int32'), length=2)}) dataset = Dataset.from_dict(data, features=features) dataset.map(lambda x: x, batched=True, batch_size=1) ``` ### Expected behavior I get the following stack trace ``` Map: 50%|█████ | 1/2 [00:00<00:00, 423.92 examples/s] Traceback (most recent call last): File "/PATH/TO/BUG_PORT/bug.py", line 9, in <module> dataset.map(lambda x: x, batched=True, batch_size=1) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3093, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3489, in _map_single writer.write_batch(batch) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 551, in write_batch array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type fixed_size_list<item: int32>[2] to Sequence(feature=Value(dtype='int32', id=None), length=2, id=None) ``` After some debugging, I found that the if-statement that is actually failing is line 2093 in `datasets/table.py` ```python # datasets/table.py ... 2093 if feature.length * len(array) == len(array_values): 2094 return pa.FixedSizeListArray.from_arrays(_c(array_values, feature.feature), feature.length) ... ``` ### Environment info Platform: MacOS Datasets version: datasets==2.16.1 Python version: 3.9.6

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PR_kwDODunzps5mdv5S

Set dev version

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PR_kwDODunzps5mdgcv

Release: 2.17.0

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I_kwDODunzps5-whka

Slice splits support for datasets.load_from_disk

open

NONE

### Feature request Support for slice splits in `datasets.load_from_disk`, similar to how it's already supported for `datasets.load_dataset`. See https://www.nature.com/articles/s41551-023-01093-3. ### Motivation Slice splits are convienient in a numer of cases - adding support to `datasets.load_from_disk` would make working with local datasets easier and homogenize the APIs of load_from_disk and load_dataset. ### Your contribution Sure, if the devs think the feature request is sensible.

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I_kwDODunzps5-s1Ff

AttributeError: 'InMemoryTable' object has no attribute '_batches'

open

NONE

### Describe the bug ``` Traceback (most recent call last): File "finetune.py", line 103, in <module> main(args) File "finetune.py", line 45, in main data_tokenized = data.map(partial(funcs.tokenize_function, tokenizer, File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/dataset_dict.py", line 868, in map { File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/dataset_dict.py", line 869, in <dictcomp> k: dataset.map( File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3093, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3432, in _map_single arrow_formatted_shard = shard.with_format("arrow") File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2667, in with_format dataset = copy.deepcopy(self) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 270, in _reconstruct state = deepcopy(state, memo) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 146, in deepcopy y = copier(x, memo) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 230, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 153, in deepcopy y = copier(memo) File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/table.py", line 176, in __deepcopy__ memo[id(self._batches)] = list(self._batches) AttributeError: 'InMemoryTable' object has no attribute '_batches' ``` ### Steps to reproduce the bug I'm running an MLOps flow using AzureML. The error appears when I run the following function in my training script: ```python data_tokenized = data.map(partial(funcs.tokenize_function, tokenizer, seq_length), batched=True, batch_size=batch_size, remove_columns=['col1', 'col2']) ``` ```python def tokenize_function(tok, seq_length, example) # Pad so that each batch has the same sequence length inp = tok(example['col1'], padding=True, truncation=True) outp = tok(example['col2'], padding="max_length", max_length=seq_length) res = { 'input_ids': inp['input_ids'], 'attention_mask': inp['attention_mask'], 'decoder_input_ids': outp['input_ids'], 'labels': outp['input_ids'], 'decoder_attention_mask': outp['attention_mask'] } return res ``` ### Expected behavior Processing proceeds without errors. I ran this same workflow 2 weeks ago without a problem. I recreated the environment since then but it doesn't appear that datasets versions have changed since Dec. '23. ### Environment info datasets 2.16.1 transformers 4.35.2 pyarrow 15.0.0 pyarrow-hotfix 0.6 torch 2.0.1 I'm not using the latest transformers version because there was an error due to a conflict with Azure mlflow when I tried the last time.

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PR_kwDODunzps5mXRo8

Minor multi gpu doc improvement

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just added torch.no_grad and eval()

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PR_kwDODunzps5mW1MA

Document usage of hfh cli instead of git

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(basically the same content as the hfh upload docs, but adapted for datasets)

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PR_kwDODunzps5mSB2B

Update loading.mdx to include "jsonl" file loading.

open

NONE

* A small update to the documentation, noting the ability to load jsonl files.

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PR_kwDODunzps5mRIma

Better multi-gpu example

closed

MEMBER

Use Qwen1.5-0.5B-Chat as an easy example for multi-GPU the previous example was using a model for translation and the way it was setup was not really the right way to use the model.

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I_kwDODunzps5-icAS

Support fsspec 2024.2

open

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Support fsspec 2024.2. First, we should address: - #6644

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I_kwDODunzps5-iboS

Support fsspec 2023.12

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Support fsspec 2023.12 by handling previous and new glob behavior.

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I_kwDODunzps5-b4n_

Faiss GPU index cannot be serialised when passed to trainer

open

NONE

### Describe the bug I am working on a retrieval project and encountering I have encountered two issues in the hugging face faiss integration: 1. I am trying to pass in a dataset with a faiss index to the Huggingface trainer. The code works for a cpu faiss index, but doesn't for a gpu one, getting error: ``` File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1543, in train return inner_training_loop( File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1555, in _inner_training_loop train_dataloader = self.get_train_dataloader() File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 831, in get_train_dataloader train_dataset = self._remove_unused_columns(train_dataset, description="training") File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 725, in _remove_unused_columns return dataset.remove_columns(ignored_columns) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/fingerprint.py", line 481, in wrapper out = func(dataset, *args, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2146, in remove_columns dataset = copy.deepcopy(self) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct state = deepcopy(state, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy y = copier(x, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy y = copier(x, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct state = deepcopy(state, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy y = copier(x, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 161, in deepcopy rv = reductor(4) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 556, in index_getstate return {"this": serialize_index(self).tobytes()} File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 1607, in serialize_index write_index(index, writer) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/swigfaiss.py", line 9843, in write_index return _swigfaiss.write_index(*args) RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /project/faiss/faiss/impl/index_write.cpp:590: don't know how to serialize this type of index ``` The index was created with the add_faiss_index method ``` train_dataset.add_faiss_index( column='embeddings', index_name='embeddings', string_factory=faiss_index_string, train_size=config.faiss_train_size, device=0, # Use -1 for CPU, or specify GPU device ID faiss_verbose=True ) ``` 2. Athough faiss is written to be compatible on the gpu for searching [https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU) I am getting error when trying to use the hugggingface code to do the search on gpu. This seems to be caused by this line https://github.com/huggingface/datasets/blob/f9975f636542df7f95c27065ea93147440d690b7/src/datasets/search.py#L376 producing error ``` total_scores, total_examples = self.dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 773, in get_nearest_examples_batch total_scores, total_indices = self.search_batch(index_name, queries, k, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 727, in search_batch return self._indexes[index_name].search_batch(queries, k, **kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 376, in search_batch if not queries.flags.c_contiguous: AttributeError: 'Tensor' object has no attribute 'flags' ``` ### Steps to reproduce the bug ``` train_dataset.add_faiss_index( column='embeddings', index_name='embeddings', string_factory=faiss_index_string, train_size=config.faiss_train_size, device=0, # Use -1 for CPU, or specify GPU device ID faiss_verbose=True ) Trainer( model=model, args=args, train_dataset=train_dataset, eval_dataset=eval_dataset, data_collator=data_collator, tokenizer=tokenizer ) train_dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k) ``` ### Expected behavior I would expect the faiss database code to be gpu compatible ### Environment info huggingface Version: 2.16.1

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I_kwDODunzps5-Tq7G

Differently dataset object saved than it is loaded.

closed

NONE

### Describe the bug Differently sized object is saved than it is loaded. ### Steps to reproduce the bug Hi, I save dataset in a following way: ``` dataset = load_dataset("json", data_files={ "train": os.path.join(input_folder, f"{task_meta_type}_{task_type}_train.jsonl"), "test": os.path.join(input_folder, f"{task_meta_type}_{task_type}_test.jsonl")}) print(os.path.join(output_folder, f"{task_meta_type}_{task_type}")) print(f"Length of train dataset: {len(dataset['train'])}") print(f"Length of test dataset: {len(dataset['test'])}") dataset.save_to_disk(os.path.join(output_folder, f"{task_meta_type}_{task_type}")) ``` this yields output ``` .data/hf_dataset/propaganda_zanr Length of train dataset: 7642 Length of test dataset: 1000 ``` Everything looks fine. Then I load the dataset ```python from datasets import load_dataset dataset_path = ".data/hf_dataset/propaganda_zanr" dataset = load_dataset(dataset_path) print(f"Length of train dataset: {len(dataset['train'])}") print(f"Length of test dataset: {len(dataset['test'])}") ``` this prints ``` Generating train split: 1 examples [00:00, 72.10 examples/s] Generating test split: 1 examples [00:00, 100.69 examples/s] Length of train dataset: 1 Length of test dataset: 1 ``` I dont' understand :( ### Expected behavior same object is loaded ### Environment info datasets==2.16.1

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I_kwDODunzps5-Lks8

unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte

closed

NONE

### Describe the bug unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte ### Steps to reproduce the bug ``` import sys sys.getdefaultencoding() 'utf-8' from datasets import load_dataset print(f"Train dataset size: {len(dataset['train'])}") print(f"Test dataset size: {len(dataset['test'])}") Resolving data files: 100% 159/159 [00:00<00:00, 9909.28it/s] Using custom data configuration samsum-0b1209637541c9e6 Downloading and preparing dataset json/samsum to C:/Users/Administrator/.cache/huggingface/datasets/json/samsum-0b1209637541c9e6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51... Downloading data files: 100% 3/3 [00:00<00:00, 119.99it/s] Extracting data files: 100% 3/3 [00:00<00:00, 9.54it/s] Generating train split: 88392/0 [00:15<00:00, 86848.17 examples/s] Generating test split: 0/0 [00:00<?, ? examples/s] --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:132, in Json._generate_tables(self, files) 131 try: --> 132 pa_table = paj.read_json( 133 io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) 134 ) 135 break File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\_json.pyx:290, in pyarrow._json.read_json() File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status() File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:100, in pyarrow.lib.check_status() ArrowInvalid: JSON parse error: Invalid value. in row 0 During handling of the above exception, another exception occurred: UnicodeDecodeError Traceback (most recent call last) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1819, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1818 _time = time.time() -> 1819 for _, table in generator: 1820 if max_shard_size is not None and writer._num_bytes > max_shard_size: File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:153, in Json._generate_tables(self, files) 152 with open(file, encoding="utf-8") as f: --> 153 dataset = json.load(f) 154 except json.JSONDecodeError: File ~\AppData\Local\Programs\Python\Python310\lib\json\__init__.py:293, in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 276 """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing 277 a JSON document) to a Python object. 278 (...) 291 kwarg; otherwise ``JSONDecoder`` is used. 292 """ --> 293 return loads(fp.read(), 294 cls=cls, object_hook=object_hook, 295 parse_float=parse_float, parse_int=parse_int, 296 parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) File ~\AppData\Local\Programs\Python\Python310\lib\codecs.py:322, in BufferedIncrementalDecoder.decode(self, input, final) 321 data = self.buffer + input --> 322 (result, consumed) = self._buffer_decode(data, self.errors, final) 323 # keep undecoded input until the next call UnicodeDecodeError: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[81], line 5 1 from datasets import load_dataset 3 # Load dataset from the hub 4 #dataset = load_dataset("json",data_files="C:/Users/Administrator/Desktop/samsum/samsum/data/corpus/train.json",field="data") ----> 5 dataset = load_dataset('json',"samsum") 6 #dataset = load_dataset("samsum") 7 print(f"Train dataset size: {len(dataset['train'])}") File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py:1758, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs) 1755 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1757 # Download and prepare data -> 1758 builder_instance.download_and_prepare( 1759 download_config=download_config, 1760 download_mode=download_mode, 1761 ignore_verifications=ignore_verifications, 1762 try_from_hf_gcs=try_from_hf_gcs, 1763 num_proc=num_proc, 1764 ) 1766 # Build dataset for splits 1767 keep_in_memory = ( 1768 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1769 ) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:860, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 858 if num_proc is not None: 859 prepare_split_kwargs["num_proc"] = num_proc --> 860 self._download_and_prepare( 861 dl_manager=dl_manager, 862 verify_infos=verify_infos, 863 **prepare_split_kwargs, 864 **download_and_prepare_kwargs, 865 ) 866 # Sync info 867 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:953, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 949 split_dict.add(split_generator.split_info) 951 try: 952 # Prepare split will record examples associated to the split --> 953 self._prepare_split(split_generator, **prepare_split_kwargs) 954 except OSError as e: 955 raise OSError( 956 "Cannot find data file. " 957 + (self.manual_download_instructions or "") 958 + "\nOriginal error:\n" 959 + str(e) 960 ) from None File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1708, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1706 gen_kwargs = split_generator.gen_kwargs 1707 job_id = 0 -> 1708 for job_id, done, content in self._prepare_split_single( 1709 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1710 ): 1711 if done: 1712 result = content File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1851, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1849 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1850 e = e.__context__ -> 1851 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1853 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior can't load dataset ### Environment info dataset:samsum system :win10 gpu:m40 24G

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I_kwDODunzps5-HYfT

Sign Language Support

open

NONE

### Feature request Currently, there are only several Sign Language labels, I would like to propose adding all the Signed Languages as new labels which are described in this ISO standard: https://www.evertype.com/standards/iso639/sign-language.html ### Motivation Datasets currently only have labels for several signed languages. There are more signed languages in the world. Furthermore, some signed languages that have a lot of online data cannot be found because of this reason (for instance, German Sign Language, and there is no German Sign Language label on huggingface datasets even though there are a lot of readily available sign language datasets exist for German Sign Language, which are used very frequently in Sign Language Processing papers, and models.) ### Your contribution I can submit a PR for this as well, adding the ISO codes and languages to the labels in datasets.

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PR_kwDODunzps5l0KPG

Run download_and_prepare if missing splits

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A first step towards https://github.com/huggingface/datasets/issues/6529

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I_kwDODunzps599thp

Cannot download wmt16 dataset

closed

NONE

### Describe the bug As of this morning (PST) 2/1/2024, seeing the wmt16 dataset is missing from opus , could you suggest an alternative? ``` Downloading data files: 0%| | 0/4 [00:00<?, ?it/s]Traceback (most recent call last): File "test.py", line 2, in <module> raw_datasets = load_dataset("wmt16","ro-en",split="train") File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1027, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/746749a11d25c02058042da7502d973ff410e73457f3d305fc1177dc0e8c4227/wmt_utils.py", line 754, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 565, in download_and_extract return self.extract(self.download(url_or_urls)) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 428, in download downloaded_path_or_paths = map_nested( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 464, in map_nested mapped = [ File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 465, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 367, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 454, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 182, in cached_path output_path = get_from_cache( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 596, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz ``` ### Steps to reproduce the bug ``` from datasets import load_dataset raw_datasets = load_dataset("wmt16","ro-en",split="train") ``` ### Expected behavior Expect the dataset to be downloaded/ at least a clean exit with error explaining dataset is missing and a suggestion for next steps ### Environment info - `datasets` version: 2.14.7 - Platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.17.3 - PyArrow version: 15.0.0 - Pandas version: 2.0.1

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I_kwDODunzps598je3

'with_format' is extremely slow when used together with 'interleave_datasets' or 'shuffle' on IterableDatasets

open

NONE

### Describe the bug If you: 1. Interleave two iterable datasets together with the interleave_datasets function, or shuffle an iterable dataset 2. Set the output format to torch tensors with .with_format('torch') Then iterating through the dataset becomes over 100x slower than it is if you don't apply the torch formatting. ### Steps to reproduce the bug ```python import datasets import torch from tqdm import tqdm rand_a = torch.randn(3,224,224) rand_b = torch.randn(3,224,224) a = torch.stack([rand_a] * 1000) b = torch.stack([rand_b] * 1000) features = datasets.Features({"tensor": datasets.Array3D(shape=(3,224,224), dtype="float32")}) ds_a = datasets.Dataset.from_dict({"tensor": a}, features=features).to_iterable_dataset() ds_b = datasets.Dataset.from_dict({"tensor": b}, features=features).to_iterable_dataset() # Iterating through either dataset with torch formatting is really fast (2000it/s on my machine) for example in tqdm(ds_a.with_format('torch')): pass # Iterating through either dataset shuffled is also pretty fast (100it/s on my machine) for example in tqdm(ds_a.shuffle()): pass # Iterating through this interleaved dataset is pretty fast (200it/s on my machine) ds_fast = datasets.interleave_datasets([ds_a, ds_b]) for example in tqdm(ds_fast): pass # Iterating through either dataset with torch formatting *after shuffling* is really slow... (<2it/s on my machine) for example in tqdm(ds_a.shuffle().with_format('torch')): pass # Iterating through this torch formatted interleaved dataset is also really slow (<2it/s on my machine)... ds_slow = datasets.interleave_datasets([ds_a, ds_b]).with_format('torch') for example in tqdm(ds_slow): pass # Even doing this is way faster!! (70it/s on my machine) for example in tqdm(ds_fast): test = torch.tensor(example['tensor']) ``` ### Expected behavior Applying torch formatting to the interleaved dataset shouldn't increase the time taken to iterate through the dataset by very much, since even explicitly converting every example is over 70x faster than calling .with_format('torch'). ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.38 - Python version: 3.11.6 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

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https://github.com/huggingface/datasets/pull/6636

PR_kwDODunzps5lm4zI

Faster column validation and reordering

closed

CONTRIBUTOR

I work with bioinformatics data and often these tables have thousands and even tens of thousands of features. These tables are also accompanied by metadata that I do not want to pass in the model. When I perform `set_format('pt', columns=large_column_list)` , it can take several minutes before it finishes. The culprit is when the following check is performed: `any(col not in self._data.column_names for col in columns)`. Replacing this by `set(columns) - (self._data.column_names)` is more efficient.

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PR_kwDODunzps5lmeNO

Fix missing info when loading some datasets from Parquet export

closed

MEMBER

Fix getting the info for script-based datasets with Parquet export with a single config not named "default". E.g. ```python from datasets import load_dataset_builder b = load_dataset_builder("bookcorpus") print(b.info.features) # should print {'text': Value(dtype='string', id=None)} ``` I fixed this by setting the default config name when there is only one config.

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https://github.com/huggingface/datasets/pull/6634

PR_kwDODunzps5llB9a

Support data_dir parameter in push_to_hub

closed

MEMBER

Support `data_dir` parameter in `push_to_hub`. This allows users to organize the data files according to their specific needs. For example, "wikimedia/wikipedia" files could be organized by year and/or date, e.g. "2024/20240101/20240101.en".

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PR_kwDODunzps5lknz9

dataset viewer requires no-script

closed

CONTRIBUTOR

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PR_kwDODunzps5lfPuk

Fix reload cache with data dir

closed

MEMBER

The cache used to only check for the latest cache directory with a given config_name, but it was wrong (e.g. `default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` instead of `default-data_dir=data%2Ffortran`) I fixed this by not passing the `config_kwargs` to the parent Builder `__init__`, and passing the config_id forged from the `config_kwargs` directly close https://github.com/huggingface/datasets/issues/6609

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PR_kwDODunzps5lcu9A

Fix filelock: use current umask for filelock >= 3.10

closed

MEMBER

reported in https://github.com/huggingface/evaluate/issues/542 cc @stas00 @williamberrios close https://github.com/huggingface/datasets/issues/6589

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PR_kwDODunzps5lYPi3

Bump max range of dill to 0.3.8

closed

CONTRIBUTOR

Release on Jan 27, 2024: https://pypi.org/project/dill/0.3.8/#history

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PR_kwDODunzps5lV0aF

Support push_to_hub without org/user to default to logged-in user

closed

MEMBER

This behavior is aligned with: - the behavior of `datasets` before merging #6519 - the behavior described in the corresponding docstring - the behavior of `huggingface_hub.create_repo` Revert "Support push_to_hub canonical datasets (#6519)" - This reverts commit a887ee78835573f5d80f9e414e8443b4caff3541. Fix #6597.

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PR_kwDODunzps5lVxXU

Make CLI test support multi-processing

closed

MEMBER

Support passing `--num_proc` to CLI test. This was really useful recently to run the command on `pubmed`: https://huggingface.co/datasets/pubmed/discussions/11

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PR_kwDODunzps5lVr-t

Disable `tqdm` bars in non-interactive environments

closed

CONTRIBUTOR

Replace `disable=False` with `disable=None` in the `tqdm` bars to disable them in non-interactive environments (by default). For more info, see a [similar PR](https://github.com/huggingface/huggingface_hub/pull/2000) in `huggingface_hub`.

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PR_kwDODunzps5lU0I2

Raise error on bad split name

closed

MEMBER

e.g. dashes '-' are not allowed in split names This should add an error message on datasets with unsupported split names like https://huggingface.co/datasets/open-source-metrics/test cc @AndreaFrancis

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I_kwDODunzps59Z71-

How to download the laion-coco dataset

closed

NONE

The laion coco dataset is not available now. How to download it https://huggingface.co/datasets/laion/laion-coco

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I_kwDODunzps59ZoKr

streaming datasets doesn't work properly with multi-node

open

NONE

### Feature request Let’s say I have a dataset with 5 samples with values [1, 2, 3, 4, 5], with 2 GPUs (for DDP) and batch size of 2. This dataset is an `IterableDataset` since I am streaming it. Now I split the dataset using `split_dataset_by_node` to ensure it doesn’t get repeated. And since it’s already splitted, I don’t have to use `DistributedSampler` (also they don't work with iterable datasets anyway)? But in this case I noticed that the: First iteraton: first GPU will get → [1, 2] first GPU will get → [3, 4] Second iteraton: first GPU will get → [5] first GPU will get → Nothing which actually creates an issue since in case of `DistributedSampler`, the samples are repeated internally to ensure non of the GPUs at any iteration is missing any data for gradient sync. So my questions are: 1. Here since splitting is happening before hand, how to make sure each GPU get’s a batch at each iteration to avoid gradient sync issues? 2. Do we need to use `DistributedSampler`? If yes, how? 3. in the docstrings of `split_dataset_by_node`, this is mentioned: *"If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples."* Can you explain the last part here? 4. If `dataset.n_shards % world_size != 0`, is it possible to shard the streaming dataset on the fly to avoid the case where data is missing? ### Motivation Somehow streaming datasets should work with DDP since for big LLMs a lot of data is required and DDP/multi-node is mostly used to train such models and streaming can actually help solve the data part of it. ### Your contribution Yes, I can help in submitting the PR once we get mutual understanding on how it should behave.

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I_kwDODunzps59ZSVZ

multi-GPU map does not work

closed

NONE

### Describe the bug Here is the code for single-GPU processing: https://pastebin.com/bfmEeK2y Here is the code for multi-GPU processing: https://pastebin.com/gQ7i5AQy Here is the video showing that the multi-GPU mapping does not work as expected (there are so many things wrong here, it's better to watch the 3-minute video than explain here): https://youtu.be/RNbdPkSppc4 ### Steps to reproduce the bug - ### Expected behavior - ### Environment info x2 RTX A4000

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I_kwDODunzps59Y4me

deleted

closed

NONE

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I_kwDODunzps59WuuI

wiki_dpr.py error (ID mismatch between lines {id} and vector {vec_id}

closed

NONE

### Describe the bug I'm trying to run a rag example, and the dataset is wiki_dpr. wiki_dpr download and extracting have been completed successfully. However, at the generating train split stage, an error from wiki_dpr.py keeps popping up. Especially in "_generate_examples" : 1. The following error occurs in the line **id, text, title = line.strip().split("\t")** ValueError: not enough values ​​to unpack (expected 3, got 2) -> This part handles exceptions so that even if an error occurs, it passes. 2. **ID mismatch between lines {id} and vector {vec_id}** This error seems to occur at the line " assert int(id) == int(vec_id),". After I handled the exception in the split error, generating train split progressed to 80%, but an id mismatch error occurred at about the 16200000th vector id. Debugging is even more difficult because it takes a long time to download and split wiki_dpr. I need help. thank you in advance!! ### Steps to reproduce the bug Occurs in the generating train split step when running the rag example in the transformers repository. Specifically, it is an error in wiki_dpr.py. ### Expected behavior . ### Environment info python 3.8

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PR_kwDODunzps5lK2VY

Migrate from `setup.cfg` to `pyproject.toml`

closed

CONTRIBUTOR

Based on https://github.com/huggingface/huggingface_hub/pull/1971 in `hfh`

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I_kwDODunzps59R_am

While importing load_dataset from datasets

closed

NONE

### Describe the bug cannot import name 'DEFAULT_CIPHERS' from 'urllib3.util.ssl_' this is the error i received ### Steps to reproduce the bug from datasets import load_dataset ### Expected behavior No errors ### Environment info python 3.11.5

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PR_kwDODunzps5lEagV

Fix CI: pyarrow 15, pandas 2.2 and sqlachemy

closed

MEMBER

this should fix the CI failures on `main` close https://github.com/huggingface/datasets/issues/5477

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PR_kwDODunzps5lDSEL

Use schema metadata only if it matches features

closed

MEMBER

e.g. if we use `map` in arrow format and transform the table, the returned table might have new columns but the metadata might be wrong

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I_kwDODunzps59G3Tx

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I_kwDODunzps59Gm-o

`datasets/downloads` cleanup tool

open