Datasets:

aidiary

/

github-issues

like 0

https://api.github.com/repos/huggingface/datasets/issues/6546

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6546/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6546/comments

https://api.github.com/repos/huggingface/datasets/issues/6546/events

https://github.com/huggingface/datasets/pull/6546

PR_kwDODunzps5i-Jgv

Release: 2.16.1

closed

MEMBER

https://api.github.com/repos/huggingface/datasets/issues/6546/timeline

https://api.github.com/repos/huggingface/datasets/issues/6545

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6545/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6545/comments

https://api.github.com/repos/huggingface/datasets/issues/6545/events

https://github.com/huggingface/datasets/issues/6545

I_kwDODunzps561ScD

`image` column not automatically inferred if image dataset only contains 1 image

closed

NONE

### Describe the bug By default, the standard Image Dataset maps out `file_name` to `image` when loading an Image Dataset. However, if the dataset contains only 1 image, this does not take place ### Steps to reproduce the bug Input (dataset with one image `multimodalart/repro_1_image`) ```py from datasets import load_dataset dataset = load_dataset("multimodalart/repro_1_image") dataset ``` Output: ```py DatasetDict({ train: Dataset({ features: ['file_name', 'prompt'], num_rows: 1 }) }) ``` Input (dataset with 2+ images `multimodalart/repro_2_image`) ```py from datasets import load_dataset dataset = load_dataset("multimodalart/repro_2_image") dataset ``` Output: ```py DatasetDict({ train: Dataset({ features: ['image', 'prompt'], num_rows: 2 }) }) ``` ### Expected behavior Expected to map `file_name` → `image` for all dataset sizes, including 1. ### Environment info Both latest main and 2.16.0

https://api.github.com/repos/huggingface/datasets/issues/6545/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6544

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6544/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6544/comments

https://api.github.com/repos/huggingface/datasets/issues/6544/events

https://github.com/huggingface/datasets/pull/6544

PR_kwDODunzps5i-G4_

Fix custom configs from script

closed

MEMBER

We should not use the parquet export when the user is passing config_kwargs I also fixed a regression that would disallow creating a custom config when a dataset has multiple predefined configs fix https://github.com/huggingface/datasets/issues/6533

https://api.github.com/repos/huggingface/datasets/issues/6544/timeline

https://api.github.com/repos/huggingface/datasets/issues/6543

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6543/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6543/comments

https://api.github.com/repos/huggingface/datasets/issues/6543/events

https://github.com/huggingface/datasets/pull/6543

PR_kwDODunzps5i-Frx

Fix dl_manager.extract returning FileNotFoundError

closed

MEMBER

The dl_manager base path is remote (e.g. a hf:// path), so local cached paths should be passed as absolute paths. This could happen if users provide a relative path as `cache_dir` fix https://github.com/huggingface/datasets/issues/6536

https://api.github.com/repos/huggingface/datasets/issues/6543/timeline

https://api.github.com/repos/huggingface/datasets/issues/6542

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6542/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6542/comments

https://api.github.com/repos/huggingface/datasets/issues/6542/events

https://github.com/huggingface/datasets/issues/6542

I_kwDODunzps56vOBv

Datasets : wikipedia 20220301.en error

closed

NONE

### Describe the bug When I used load_dataset to download this data set, the following error occurred. The main problem was that the target data did not exist. ### Steps to reproduce the bug 1.I tried downloading directly. ```python wiki_dataset = load_dataset("wikipedia", "20220301.en") ``` An exception occurred ``` MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/ If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). Example of usage: `load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')` ``` 2.I modified the code as prompted. ```python wiki_dataset = load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner') ``` An exception occurred: ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json ``` ### Expected behavior I searched in the parent directory of the corresponding URL, but there was no corresponding "20220301" directory. I really need this data set and hope to provide a download method. ### Environment info python 3.8 datasets 2.16.0 apache-beam 2.52.0 dill 0.3.7

https://api.github.com/repos/huggingface/datasets/issues/6542/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6541

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6541/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6541/comments

https://api.github.com/repos/huggingface/datasets/issues/6541/events

https://github.com/huggingface/datasets/issues/6541

I_kwDODunzps56uZmS

Dataset not loading successfully.

closed

NONE

### Describe the bug When I run down the below code shows this error: AttributeError: module 'numpy' has no attribute '_no_nep50_warning' I also added this issue in transformers library please check out: [link](https://github.com/huggingface/transformers/issues/28099) ### Steps to reproduce the bug ## Reproduction Hi, please check this line of code, when I run Show attribute error. ``` from datasets import load_dataset from transformers import WhisperProcessor, WhisperForConditionalGeneration # Select an audio file and read it: ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = ds[0]["audio"] waveform = audio_sample["array"] sampling_rate = audio_sample["sampling_rate"] # Load the Whisper model in Hugging Face format: processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") # Use the model and processor to transcribe the audio: input_features = processor( waveform, sampling_rate=sampling_rate, return_tensors="pt" ).input_features # Generate token ids predicted_ids = model.generate(input_features) # Decode token ids to text transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) transcription[0] ``` **Attribute Error** ``` AttributeError Traceback (most recent call last) Cell In[9], line 6 4 # Select an audio file and read it: 5 ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") ----> 6 audio_sample = ds[0]["audio"] 7 waveform = audio_sample["array"] 8 sampling_rate = audio_sample["sampling_rate"] File /opt/pytorch/lib/python3.8/site-packages/datasets/arrow_dataset.py:2795, in Dataset.__getitem__(self, key) 2793 def __getitem__(self, key): # noqa: F811 2794 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 2795 return self._getitem(key) File /opt/pytorch/lib/python3.8/site-packages/datasets/arrow_dataset.py:2780, in Dataset._getitem(self, key, **kwargs) 2778 formatter = get_formatter(format_type, features=self._info.features, **format_kwargs) 2779 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 2780 formatted_output = format_table( 2781 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 2782 ) 2783 return formatted_output File /opt/pytorch/lib/python3.8/site-packages/datasets/formatting/formatting.py:629, in format_table(table, key, formatter, format_columns, output_all_columns) 627 python_formatter = PythonFormatter(features=formatter.features) 628 if format_columns is None: --> 629 return formatter(pa_table, query_type=query_type) 630 elif query_type == "column": 631 if key in format_columns: File /opt/pytorch/lib/python3.8/site-packages/datasets/formatting/formatting.py:396, in Formatter.__call__(self, pa_table, query_type) 394 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 395 if query_type == "row": --> 396 return self.format_row(pa_table) 397 elif query_type == "column": 398 return self.format_column(pa_table) File /opt/pytorch/lib/python3.8/site-packages/datasets/formatting/formatting.py:437, in PythonFormatter.format_row(self, pa_table) 435 return LazyRow(pa_table, self) 436 row = self.python_arrow_extractor().extract_row(pa_table) --> 437 row = self.python_features_decoder.decode_row(row) 438 return row File /opt/pytorch/lib/python3.8/site-packages/datasets/formatting/formatting.py:215, in PythonFeaturesDecoder.decode_row(self, row) 214 def decode_row(self, row: dict) -> dict: --> 215 return self.features.decode_example(row) if self.features else row File /opt/pytorch/lib/python3.8/site-packages/datasets/features/features.py:1917, in Features.decode_example(self, example, token_per_repo_id) 1903 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None): 1904 """Decode example with custom feature decoding. 1905 1906 Args: (...) 1914 `dict[str, Any]` 1915 """ -> 1917 return { 1918 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id) 1919 if self._column_requires_decoding[column_name] 1920 else value 1921 for column_name, (feature, value) in zip_dict( 1922 {key: value for key, value in self.items() if key in example}, example 1923 ) 1924 } File /opt/pytorch/lib/python3.8/site-packages/datasets/features/features.py:1918, in <dictcomp>(.0) 1903 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None): 1904 """Decode example with custom feature decoding. 1905 1906 Args: (...) 1914 `dict[str, Any]` 1915 """ 1917 return { -> 1918 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id) 1919 if self._column_requires_decoding[column_name] 1920 else value 1921 for column_name, (feature, value) in zip_dict( 1922 {key: value for key, value in self.items() if key in example}, example 1923 ) 1924 } File /opt/pytorch/lib/python3.8/site-packages/datasets/features/features.py:1339, in decode_nested_example(schema, obj, token_per_repo_id) 1336 elif isinstance(schema, (Audio, Image)): 1337 # we pass the token to read and decode files from private repositories in streaming mode 1338 if obj is not None and schema.decode: -> 1339 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) 1340 return obj File /opt/pytorch/lib/python3.8/site-packages/datasets/features/audio.py:191, in Audio.decode_example(self, value, token_per_repo_id) 189 array = array.T 190 if self.mono: --> 191 array = librosa.to_mono(array) 192 if self.sampling_rate and self.sampling_rate != sampling_rate: 193 array = librosa.resample(array, orig_sr=sampling_rate, target_sr=self.sampling_rate) File /opt/pytorch/lib/python3.8/site-packages/lazy_loader/__init__.py:78, in attach.<locals>.__getattr__(name) 76 submod_path = f"{package_name}.{attr_to_modules[name]}" 77 submod = importlib.import_module(submod_path) ---> 78 attr = getattr(submod, name) 80 # If the attribute lives in a file (module) with the same 81 # name as the attribute, ensure that the attribute and *not* 82 # the module is accessible on the package. 83 if name == attr_to_modules[name]: File /opt/pytorch/lib/python3.8/site-packages/lazy_loader/__init__.py:77, in attach.<locals>.__getattr__(name) 75 elif name in attr_to_modules: 76 submod_path = f"{package_name}.{attr_to_modules[name]}" ---> 77 submod = importlib.import_module(submod_path) 78 attr = getattr(submod, name) 80 # If the attribute lives in a file (module) with the same 81 # name as the attribute, ensure that the attribute and *not* 82 # the module is accessible on the package. File /usr/lib/python3.8/importlib/__init__.py:127, in import_module(name, package) 125 break 126 level += 1 --> 127 return _bootstrap._gcd_import(name[level:], package, level) File <frozen importlib._bootstrap>:1014, in _gcd_import(name, package, level) File <frozen importlib._bootstrap>:991, in _find_and_load(name, import_) File <frozen importlib._bootstrap>:975, in _find_and_load_unlocked(name, import_) File <frozen importlib._bootstrap>:671, in _load_unlocked(spec) File <frozen importlib._bootstrap_external>:848, in exec_module(self, module) File <frozen importlib._bootstrap>:219, in _call_with_frames_removed(f, *args, **kwds) File /opt/pytorch/lib/python3.8/site-packages/librosa/core/audio.py:13 11 import audioread 12 import numpy as np ---> 13 import scipy.signal 14 import soxr 15 import lazy_loader as lazy File /opt/pytorch/lib/python3.8/site-packages/scipy/signal/__init__.py:323 314 from ._spline import ( # noqa: F401 315 cspline2d, 316 qspline2d, (...) 319 symiirorder2, 320 ) 322 from ._bsplines import * --> 323 from ._filter_design import * 324 from ._fir_filter_design import * 325 from ._ltisys import * File /opt/pytorch/lib/python3.8/site-packages/scipy/signal/_filter_design.py:16 13 from numpy.polynomial.polynomial import polyval as npp_polyval 14 from numpy.polynomial.polynomial import polyvalfromroots ---> 16 from scipy import special, optimize, fft as sp_fft 17 from scipy.special import comb 18 from scipy._lib._util import float_factorial File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/__init__.py:405 1 """ 2 ===================================================== 3 Optimization and root finding (:mod:`scipy.optimize`) (...) 401 402 """ 404 from ._optimize import * --> 405 from ._minimize import * 406 from ._root import * 407 from ._root_scalar import * File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/_minimize.py:26 24 from ._trustregion_krylov import _minimize_trust_krylov 25 from ._trustregion_exact import _minimize_trustregion_exact ---> 26 from ._trustregion_constr import _minimize_trustregion_constr 28 # constrained minimization 29 from ._lbfgsb_py import _minimize_lbfgsb File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/_trustregion_constr/__init__.py:4 1 """This module contains the equality constrained SQP solver.""" ----> 4 from .minimize_trustregion_constr import _minimize_trustregion_constr 6 __all__ = ['_minimize_trustregion_constr'] File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py:5 3 from scipy.sparse.linalg import LinearOperator 4 from .._differentiable_functions import VectorFunction ----> 5 from .._constraints import ( 6 NonlinearConstraint, LinearConstraint, PreparedConstraint, strict_bounds) 7 from .._hessian_update_strategy import BFGS 8 from .._optimize import OptimizeResult File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/_constraints.py:8 6 from ._optimize import OptimizeWarning 7 from warnings import warn, catch_warnings, simplefilter ----> 8 from numpy.testing import suppress_warnings 9 from scipy.sparse import issparse 12 def _arr_to_scalar(x): 13 # If x is a numpy array, return x.item(). This will 14 # fail if the array has more than one element. File /opt/pytorch/lib/python3.8/site-packages/numpy/testing/__init__.py:11 8 from unittest import TestCase 10 from . import _private ---> 11 from ._private.utils import * 12 from ._private.utils import (_assert_valid_refcount, _gen_alignment_data) 13 from ._private import extbuild, decorators as dec File /opt/pytorch/lib/python3.8/site-packages/numpy/testing/_private/utils.py:480 476 pprint.pprint(desired, msg) 477 raise AssertionError(msg.getvalue()) --> 480 @np._no_nep50_warning() 481 def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True): 482 """ 483 Raises an AssertionError if two items are not equal up to desired 484 precision. (...) 548 549 """ 550 __tracebackhide__ = True # Hide traceback for py.test File /opt/pytorch/lib/python3.8/site-packages/numpy/__init__.py:313, in __getattr__(attr) 305 raise AttributeError(__former_attrs__[attr]) 307 # Importing Tester requires importing all of UnitTest which is not a 308 # cheap import Since it is mainly used in test suits, we lazy import it 309 # here to save on the order of 10 ms of import time for most users 310 # 311 # The previous way Tester was imported also had a side effect of adding 312 # the full `numpy.testing` namespace --> 313 if attr == 'testing': 314 import numpy.testing as testing 315 return testing AttributeError: module 'numpy' has no attribute '_no_nep50_warning' ``` ### Expected behavior ``` ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.' ``` Also, make sure this script is provided for your official website so please update: [script](https://huggingface.co/docs/transformers/model_doc/whisper) ### Environment info **System Info** * transformers -> 4.36.1 * datasets -> 2.15.0 * huggingface_hub -> 0.19.4 * python -> 3.8.10 * accelerate -> 0.25.0 * pytorch -> 2.0.1+cpu * Using GPU in Script -> No

https://api.github.com/repos/huggingface/datasets/issues/6541/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6540

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6540/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6540/comments

https://api.github.com/repos/huggingface/datasets/issues/6540/events

https://github.com/huggingface/datasets/issues/6540

I_kwDODunzps56uVCl

Extreme inefficiency for `save_to_disk` when merging datasets

open

NONE

### Describe the bug Hi, I tried to merge in total 22M sequences of data, where each sequence is of maximum length 2000. I found that merging these datasets and then `save_to_disk` is extremely slow because of flattening the indices. Wondering if you have any suggestions or guidance on this. Thank you very much! ### Steps to reproduce the bug The source data is too big to demonstrate ### Expected behavior The source data is too big to demonstrate ### Environment info python 3.9.0 datasets 2.7.0 pytorch 2.0.0 tokenizers 0.13.1 transformers 4.31.0

https://api.github.com/repos/huggingface/datasets/issues/6540/timeline

https://api.github.com/repos/huggingface/datasets/issues/6539

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6539/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6539/comments

https://api.github.com/repos/huggingface/datasets/issues/6539/events

https://github.com/huggingface/datasets/issues/6539

I_kwDODunzps56siAI

'Repo card metadata block was not found' when loading a pragmeval dataset

open

NONE

### Describe the bug I can't load dataset subsets of 'pragmeval'. The funny thing is I ran the dataset author's [colab notebook](https://colab.research.google.com/drive/1sg--LF4z7XR1wxAOfp0-3d4J6kQ9nj_A?usp=sharing) and it works just fine. I tried to install exactly the same packages that are installed on colab using poetry, so my environment info only differs from the one from colab in linux version - I still get the same bug outside colab. ### Steps to reproduce the bug Install dependencies with poetry pyproject.toml ``` [tool.poetry] name = "project" version = "0.1.0" description = "" authors = [] [tool.poetry.dependencies] python = "^3.10" datasets = "2.16.0" pandas = "1.5.3" pyarrow = "10.0.1" huggingface-hub = "0.19.4" fsspec = "2023.6.0" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" ``` `poetry run python -c "import datasets; print(datasets.get_dataset_config_names('pragmeval'))` prints ['default'] ### Expected behavior The command should print ``` ['emergent', 'emobank-arousal', 'emobank-dominance', 'emobank-valence', 'gum', 'mrda', 'pdtb', 'persuasiveness-claimtype', 'persuasiveness-eloquence', 'persuasiveness-premisetype', 'persuasiveness-relevance', 'persuasiveness-specificity', 'persuasiveness-strength', 'sarcasm', 'squinky-formality', 'squinky-implicature', 'squinky-informativeness', 'stac', 'switchboard', 'verifiability'] ``` ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 10.0.1 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0

https://api.github.com/repos/huggingface/datasets/issues/6539/timeline

https://api.github.com/repos/huggingface/datasets/issues/6538

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6538/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6538/comments

https://api.github.com/repos/huggingface/datasets/issues/6538/events

https://github.com/huggingface/datasets/issues/6538

I_kwDODunzps56oRde

ImportError: cannot import name 'SchemaInferenceError' from 'datasets.arrow_writer' (/opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py)

closed

NONE

### Describe the bug While importing from packages getting the error Code: ``` import os import torch from datasets import load_dataset, Dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging ) from peft import LoraConfig, PeftModel from trl import SFTTrainer from huggingface_hub import login import pandas as pd ``` Error: ```` --------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[5], line 14 4 from transformers import ( 5 AutoModelForCausalLM, 6 AutoTokenizer, (...) 11 logging 12 ) 13 from peft import LoraConfig, PeftModel ---> 14 from trl import SFTTrainer 15 from huggingface_hub import login 16 import pandas as pd File /opt/conda/lib/python3.10/site-packages/trl/__init__.py:21 8 from .import_utils import ( 9 is_diffusers_available, 10 is_npu_available, (...) 13 is_xpu_available, 14 ) 15 from .models import ( 16 AutoModelForCausalLMWithValueHead, 17 AutoModelForSeq2SeqLMWithValueHead, 18 PreTrainedModelWrapper, 19 create_reference_model, 20 ) ---> 21 from .trainer import ( 22 DataCollatorForCompletionOnlyLM, 23 DPOTrainer, 24 IterativeSFTTrainer, 25 PPOConfig, 26 PPOTrainer, 27 RewardConfig, 28 RewardTrainer, 29 SFTTrainer, 30 ) 33 if is_diffusers_available(): 34 from .models import ( 35 DDPOPipelineOutput, 36 DDPOSchedulerOutput, 37 DDPOStableDiffusionPipeline, 38 DefaultDDPOStableDiffusionPipeline, 39 ) File /opt/conda/lib/python3.10/site-packages/trl/trainer/__init__.py:44 42 from .ppo_trainer import PPOTrainer 43 from .reward_trainer import RewardTrainer, compute_accuracy ---> 44 from .sft_trainer import SFTTrainer 45 from .training_configs import RewardConfig File /opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:23 21 import torch.nn as nn 22 from datasets import Dataset ---> 23 from datasets.arrow_writer import SchemaInferenceError 24 from datasets.builder import DatasetGenerationError 25 from transformers import ( 26 AutoModelForCausalLM, 27 AutoTokenizer, (...) 33 TrainingArguments, 34 ) ImportError: cannot import name 'SchemaInferenceError' from 'datasets.arrow_writer' (/opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py ```` transformers version: 4.36.2 python version: 3.10.12 datasets version: 2.16.1 ### Steps to reproduce the bug 1. Install packages ``` !pip install -U datasets trl accelerate peft bitsandbytes transformers trl huggingface_hub ``` 2. import packages ``` import os import torch from datasets import load_dataset, Dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging ) from peft import LoraConfig, PeftModel from trl import SFTTrainer from huggingface_hub import login import pandas as pd ``` ### Expected behavior No error while importing ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.15.133+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.20.1 - PyArrow version: 11.0.0 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

https://api.github.com/repos/huggingface/datasets/issues/6538/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6537

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6537/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6537/comments

https://api.github.com/repos/huggingface/datasets/issues/6537/events

https://github.com/huggingface/datasets/issues/6537

I_kwDODunzps56nViN

Adding support for netCDF (*.nc) files

open

NONE

### Feature request netCDF (*.nc) is a file format for storing multidimensional scientific data, which is used by packages like `xarray` (labelled multi-dimensional arrays in Python). It would be nice to have native support for netCDF in `datasets`. ### Motivation When uploading *.nc files onto Huggingface Hub through the `datasets` API, I would like to be able to preview the dataset without converting it to another format. ### Your contribution I can submit a PR, provided I have the time.

https://api.github.com/repos/huggingface/datasets/issues/6537/timeline

https://api.github.com/repos/huggingface/datasets/issues/6536

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6536/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6536/comments

https://api.github.com/repos/huggingface/datasets/issues/6536/events

https://github.com/huggingface/datasets/issues/6536

I_kwDODunzps56mT4H

datasets.load_dataset raises FileNotFoundError for datasets==2.16.0

closed

NONE

### Describe the bug Seems `datasets.load_dataset` raises FileNotFoundError for some hub datasets with the latest `datasets==2.16.0` ### Steps to reproduce the bug For example `pip install datasets==2.16.0` then ```python import datasets datasets.load_dataset("wentingzhao/anthropic-hh-first-prompt", cache_dir='cache1')["train"] ``` This will raise: ```bash Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/load.py", line 2545, in load_dataset builder_instance.download_and_prepare( File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/builder.py", line 1003, in download_and_prepare self._download_and_prepare( File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/builder.py", line 1076, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 43, in _split_generators data_files = dl_manager.download_and_extract(self.config.data_files) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/download/download_manager.py", line 566, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/download/download_manager.py", line 539, in extract extracted_paths = map_nested( File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 466, in map_nested mapped = [ File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 467, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 387, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 387, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 370, in _single_map_nested return function(data_struct) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/download/download_manager.py", line 451, in _download out = cached_path(url_or_filename, download_config=download_config) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 188, in cached_path output_path = get_from_cache( File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 570, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/wentingzhao/anthropic-hh-first-prompt/resolve/11b393a5545f706a357ebcd4a5285d93db176715/cache1/downloads/87d66c365626feca116cba323c4856c9aae056e4503f09f23e34aa085eb9de15 ``` However, seems it works fine for some datasets, for example, if works fine for `datasets.load_dataset("ag_news", cache_dir='cache2')["test"]` But the dataset works fine for datasets==2.15.0, for example `pip install datasets==2.15.0`, then ```python import datasets datasets.load_dataset("wentingzhao/anthropic-hh-first-prompt", cache_dir='cache3')["train"] Dataset({ features: ['user', 'system', 'source'], num_rows: 8552 }) ``` ### Expected behavior 2.16.0 should work as same as 2.15.0 for all datasets ### Environment info python3.9 conda env tested on MacOS and Linux

https://api.github.com/repos/huggingface/datasets/issues/6536/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6535

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6535/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6535/comments

https://api.github.com/repos/huggingface/datasets/issues/6535/events

https://github.com/huggingface/datasets/issues/6535

I_kwDODunzps56kBqT

IndexError: Invalid key: 47682 is out of bounds for size 0 while using PEFT

open

NONE

### Describe the bug I am trying to fine-tune the t5 model on the paraphrasing task. While running the same code without- model = get_peft_model(model, config) the model trains without any issues. However, using the model returned from get_peft_model raises the following error due to datasets- IndexError: Invalid key: 47682 is out of bounds for size 0. I had raised this in https://github.com/huggingface/peft/issues/1299#issue-2056173386 and they suggested that I raise it here. Here is the complete error- IndexError Traceback (most recent call last) in <cell line: 1>() ----> 1 trainer.train() 11 frames [/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs) 1553 hf_hub_utils.enable_progress_bars() 1554 else: -> 1555 return inner_training_loop( 1556 args=args, 1557 resume_from_checkpoint=resume_from_checkpoint, [/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in _inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 1836 1837 step = -1 -> 1838 for step, inputs in enumerate(epoch_iterator): 1839 total_batched_samples += 1 1840 if rng_to_sync: [/usr/local/lib/python3.10/dist-packages/accelerate/data_loader.py](https://localhost:8080/#) in iter(self) 446 # We iterate one batch ahead to check when we are at the end 447 try: --> 448 current_batch = next(dataloader_iter) 449 except StopIteration: 450 yield [/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py](https://localhost:8080/#) in next(self) 628 # TODO(https://github.com/pytorch/pytorch/issues/76750) 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 \ [/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py](https://localhost:8080/#) in _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) [/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py](https://localhost:8080/#) in fetch(self, possibly_batched_index) 47 if self.auto_collation: 48 if hasattr(self.dataset, "getitems") and self.dataset.getitems: ---> 49 data = self.dataset.getitems(possibly_batched_index) 50 else: 51 data = [self.dataset[idx] for idx in possibly_batched_index] [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in getitems(self, keys) 2802 def getitems(self, keys: List) -> List: 2803 """Can be used to get a batch using a list of integers indices.""" -> 2804 batch = self.getitem(keys) 2805 n_examples = len(batch[next(iter(batch))]) 2806 return [{col: array[i] for col, array in batch.items()} for i in range(n_examples)] [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in getitem(self, key) 2798 def getitem(self, key): # noqa: F811 2799 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 2800 return self._getitem(key) 2801 2802 def getitems(self, keys: List) -> List: [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in _getitem(self, key, **kwargs) 2782 format_kwargs = format_kwargs if format_kwargs is not None else {} 2783 formatter = get_formatter(format_type, features=self._info.features, **format_kwargs) -> 2784 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 2785 formatted_output = format_table( 2786 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in query_table(table, key, indices) 581 else: 582 size = indices.num_rows if indices is not None else table.num_rows --> 583 _check_valid_index_key(key, size) 584 # Query the main table 585 if indices is None: [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size) 534 elif isinstance(key, Iterable): 535 if len(key) > 0: --> 536 _check_valid_index_key(int(max(key)), size=size) 537 _check_valid_index_key(int(min(key)), size=size) 538 else: [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size) 524 if isinstance(key, int): 525 if (key < 0 and key + size < 0) or (key >= size): --> 526 raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") 527 return 528 elif isinstance(key, slice): IndexError: Invalid key: 47682 is out of bounds for size 0 ### Steps to reproduce the bug device = "cuda:0" if torch.cuda.is_available() else "cpu" #defining model name for tokenizer and model loading model_name= "t5-small" #loading the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) def preprocess_function(data, tokenizer): inputs = [f"Paraphrase this sentence: {doc}" for doc in data["text"]] model_inputs = tokenizer(inputs, max_length=150, truncation=True) labels = [ast.literal_eval(i)[0] for i in data['paraphrases']] labels = tokenizer(labels, max_length=150, truncation=True) model_inputs["labels"] = labels["input_ids"] return model_inputs train_dataset = load_dataset("humarin/chatgpt-paraphrases", split="train").shuffle(seed=42).select(range(50000)) val_dataset = load_dataset("humarin/chatgpt-paraphrases", split="train").shuffle(seed=42).select(range(50000,55000)) tokenized_train = train_dataset.map(lambda batch: preprocess_function(batch, tokenizer), batched=True) tokenized_val = val_dataset.map(lambda batch: preprocess_function(batch, tokenizer), batched=True) def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" ) config = LoraConfig( r=16, #attention heads lora_alpha=32, #alpha scaling lora_dropout=0.05, bias="none", task_type="Seq2Seq" ) #loading the model model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device) model = get_peft_model(model, config) print_trainable_parameters(model) #loading the data collator data_collator = DataCollatorForSeq2Seq( tokenizer=tokenizer, model=model, label_pad_token_id=-100, padding="longest" ) #defining the training arguments training_args = Seq2SeqTrainingArguments( output_dir=os.getcwd(), evaluation_strategy="epoch", save_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, weight_decay=1e-3, save_total_limit=3, load_best_model_at_end=True, num_train_epochs=1, predict_with_generate=True ) def compute_metric_with_extra(tokenizer): def compute_metrics(eval_preds): metric = evaluate.load('rouge') preds, labels = eval_preds # decode preds and labels labels = np.where(labels != -100, labels, tokenizer.pad_token_id) decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) # rougeLSum expects newline after each sentence decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds] decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels] result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True) return result return compute_metrics trainer = Seq2SeqTrainer( model=model, args=training_args, train_dataset=tokenized_train, eval_dataset=tokenized_val, tokenizer=tokenizer, data_collator=data_collator, compute_metrics= compute_metric_with_extra(tokenizer) ) trainer.train() ### Expected behavior I would want the trainer to train normally as it was before I used- model = get_peft_model(model, config) ### Environment info datasets version- 2.16.0 peft version- 0.7.1 transformers version- 4.35.2 accelerate version- 0.25.0 python- 3.10.12 enviroment- google colab

https://api.github.com/repos/huggingface/datasets/issues/6535/timeline

https://api.github.com/repos/huggingface/datasets/issues/6534

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6534/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6534/comments

https://api.github.com/repos/huggingface/datasets/issues/6534/events

https://github.com/huggingface/datasets/issues/6534

I_kwDODunzps56jBv0

How to configure multiple folders in the same zip package

open

CONTRIBUTOR

How should I write "config" in readme when all the data, such as train test, is in a zip file train floder and test floder in data.zip

https://api.github.com/repos/huggingface/datasets/issues/6534/timeline

https://api.github.com/repos/huggingface/datasets/issues/6533

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6533/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6533/comments

https://api.github.com/repos/huggingface/datasets/issues/6533/events

https://github.com/huggingface/datasets/issues/6533

I_kwDODunzps56iv0N

ted_talks_iwslt | Error: Config name is missing

closed

NONE

### Describe the bug Running load_dataset using the newest `datasets` library like below on the ted_talks_iwslt using year pair data will throw an error "Config name is missing" see also: https://huggingface.co/datasets/ted_talks_iwslt/discussions/3 likely caused by #6493, where the `and not config_kwargs` part in the if logic was removed https://github.com/huggingface/datasets/blob/ef3b5dd3633995c95d77f35fb17f89ff44990bc4/src/datasets/builder.py#L512 ### Steps to reproduce the bug run: ```python load_dataset("ted_talks_iwslt", language_pair=("ja", "en"), year="2015") ``` ### Expected behavior Load the data without error ### Environment info datasets 2.16.0

https://api.github.com/repos/huggingface/datasets/issues/6533/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6532

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6532/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6532/comments

https://api.github.com/repos/huggingface/datasets/issues/6532/events

https://github.com/huggingface/datasets/issues/6532

I_kwDODunzps56hnFh

[Feature request] Indexing datasets by a customly-defined id field to enable random access dataset items via the id

open

NONE

### Feature request Some datasets may contain an id-like field, for example the `id` field in [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) and the `_id` field in [BeIR/dbpedia-entity](https://huggingface.co/datasets/BeIR/dbpedia-entity). HF datasets support efficient random access via row, but not via this kinds of id fields. I wonder if it is possible to add support for indexing by a custom "id-like" field to enable random access via such ids. The ids may be numbers or strings. ### Motivation In some cases, especially during inference/evaluation, I may want to find out the item that has a specified id, defined by the dataset itself. For example, in a typical re-ranking setting in information retrieval, the user may want to re-rank the set of candidate documents of each query. The input is usually presented in a TREC-style run file, with the following format: ``` <qid> Q0 <docno> <rank> <score> <tag> ``` The re-ranking program should be able to fetch the queries and documents according to the `<qid>` and `<docno>`, which are the original id defined in the query/document datasets. To accomplish this, I have to iterate over the whole HF dataset to get the mapping from real ids to row ids every time I start the program, which is time-consuming. Thus I want HF dataset to provide options for users to index by a custom id column, not by row. ### Your contribution I'm not an expert in this project and I'm afraid that I'm not able to make contributions on the code.

https://api.github.com/repos/huggingface/datasets/issues/6532/timeline

https://api.github.com/repos/huggingface/datasets/issues/6531

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6531/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6531/comments

https://api.github.com/repos/huggingface/datasets/issues/6531/events

https://github.com/huggingface/datasets/pull/6531

PR_kwDODunzps5it5Sm

Add polars compatibility

open

CONTRIBUTOR

Hey there, I've just finished adding support to convert and format to `polars.DataFrame`. This was in response to the open issue about integrating Polars [#3334](https://github.com/huggingface/datasets/issues/3334). Datasets can be switched to Polars format via `Dataset.set_format("polars")`. I've also included `to_polars` and `from_polars`. All polars functions are checked via config.POLARS_AVAILABLE. A few notes: This only supports `DataFrames` and not `LazyFrames`. This probably could be integrated fairly easily via `is_lazy` args in `set_format`, and `to_polars`. Let me know your feedbacks.

https://api.github.com/repos/huggingface/datasets/issues/6531/timeline

https://api.github.com/repos/huggingface/datasets/issues/6530

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6530/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6530/comments

https://api.github.com/repos/huggingface/datasets/issues/6530/events

https://github.com/huggingface/datasets/issues/6530

I_kwDODunzps56egdJ

Impossible to save a mapped dataset to disk

open

NONE

### Describe the bug I want to play around with different hyperparameters when training but don't want to re-map my dataset with 3 million samples each time for tens of hours when I [fully fine-tune SDXL](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py). After I do the mapping like this: ``` train_dataset = train_dataset.map(compute_embeddings_fn, batched=True) train_dataset = train_dataset.map( compute_vae_encodings_fn, batched=True, batch_size=16, ) ``` and try to save it like this: `train_dataset.save_to_disk("test")` i get this error ([full traceback](https://pastebin.com/kq3vt739)): ``` TypeError: Object of type function is not JSON serializable The format kwargs must be JSON serializable, but key 'transform' isn't. ``` But what is interesting is that pushing to hub works like that: `train_dataset.push_to_hub("kopyl/mapped-833-icons-sdxl-1024-dataset", token=True)` Here is the link of the pushed dataset: https://huggingface.co/datasets/kopyl/mapped-833-icons-sdxl-1024-dataset ### Steps to reproduce the bug Here is the self-contained notebook: https://colab.research.google.com/drive/1RtCsEMVcwWcMwlWURk_cj_9xUBHz065M?usp=sharing ### Expected behavior It should be easily saved to disk ### Environment info NVIDIA A100, Linux (NC24ads A100 v4 from Azure), CUDA 12.2. [pip freeze](https://pastebin.com/QTNb6iru)

https://api.github.com/repos/huggingface/datasets/issues/6530/timeline

https://api.github.com/repos/huggingface/datasets/issues/6529

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6529/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6529/comments

https://api.github.com/repos/huggingface/datasets/issues/6529/events

https://github.com/huggingface/datasets/issues/6529

I_kwDODunzps56cL-p

Impossible to only download a test split

open

NONE

I've spent a significant amount of time trying to locate the split object inside my _split_generators() custom function. Then after diving [in the code](https://github.com/huggingface/datasets/blob/5ff3670c18ed34fa8ddfa70a9aa403ae6cc9ad54/src/datasets/load.py#L2558) I realized that `download_and_prepare` is executed before! split is passed to the dataset builder in `as_dataset`. If I'm not missing something, this seems like bad design, for the following use case: > Imagine there is a huge dataset that has an evaluation test set and you want to just download and run just to compare your method. Is there a current workaround that can help me achieve the same result? Thank you,

https://api.github.com/repos/huggingface/datasets/issues/6529/timeline

https://api.github.com/repos/huggingface/datasets/issues/6528

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6528/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6528/comments

https://api.github.com/repos/huggingface/datasets/issues/6528/events

https://github.com/huggingface/datasets/pull/6528

PR_kwDODunzps5ip9JH

set dev version

closed

MEMBER

https://api.github.com/repos/huggingface/datasets/issues/6528/timeline

https://api.github.com/repos/huggingface/datasets/issues/6527

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6527/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6527/comments

https://api.github.com/repos/huggingface/datasets/issues/6527/events

https://github.com/huggingface/datasets/pull/6527

PR_kwDODunzps5ip2vd

Release: 2.16.0

closed

MEMBER

https://api.github.com/repos/huggingface/datasets/issues/6527/timeline

https://api.github.com/repos/huggingface/datasets/issues/6526

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6526/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6526/comments

https://api.github.com/repos/huggingface/datasets/issues/6526/events

https://github.com/huggingface/datasets/pull/6526

PR_kwDODunzps5ipB5v

Preserve order of configs and splits when using Parquet exports

closed

MEMBER

Preserve order of configs and splits, as defined in dataset infos. Fix #6521.

https://api.github.com/repos/huggingface/datasets/issues/6526/timeline

https://api.github.com/repos/huggingface/datasets/issues/6525

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6525/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6525/comments

https://api.github.com/repos/huggingface/datasets/issues/6525/events

https://github.com/huggingface/datasets/pull/6525

PR_kwDODunzps5im-lL

BBox type

closed

MEMBER

see [internal discussion](https://huggingface.slack.com/archives/C02EK7C3SHW/p1703097195609209) Draft to get some feedback on a possible `BBox` feature type that can be used to get object detection bounding boxes data in one format or another. ```python >>> from datasets import load_dataset, BBox >>> ds = load_dataset("svhn", "full_numbers", split="train") >>> ds[0] { 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x126409BE0>, 'digits': {'bbox': [[38, 1, 21, 40], [57, 3, 16, 40]], 'label': [4, 6]} } >>> ds = ds.rename_column("digits", "annotations").cast_column("annotations", BBox(format="coco")) >>> ds[0] { 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x147730070>, 'annotations': [{'bbox': [38, 1, 21, 40], 'category_id': 4}, {'bbox': [57, 3, 16, 40], 'category_id': 6}] } ``` note that it's a type for a list of bounding boxes, not just one - which would be needed to switch from a format to another using type casting.

https://api.github.com/repos/huggingface/datasets/issues/6525/timeline

https://api.github.com/repos/huggingface/datasets/issues/6524

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6524/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6524/comments

https://api.github.com/repos/huggingface/datasets/issues/6524/events

https://github.com/huggingface/datasets/issues/6524

I_kwDODunzps56X3VX

Streaming the Pile: Missing Files

closed

NONE

### Describe the bug The pile does not stream, a "File not Found error" is returned. It looks like the Pile's files have been moved. ### Steps to reproduce the bug To reproduce run the following code: ``` from datasets import load_dataset dataset = load_dataset('EleutherAI/pile', 'en', split='train', streaming=True) next(iter(dataset)) ``` I get the following error: `FileNotFoundError: https://the-eye.eu/public/AI/pile/train/00.jsonl.zst` ### Expected behavior Return the data in a stream. ### Environment info - `datasets` version: 2.12.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.11.5 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.3

https://api.github.com/repos/huggingface/datasets/issues/6524/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6523

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6523/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6523/comments

https://api.github.com/repos/huggingface/datasets/issues/6523/events

https://github.com/huggingface/datasets/pull/6523

PR_kwDODunzps5ilV6d

fix tests

closed

MEMBER

https://api.github.com/repos/huggingface/datasets/issues/6523/timeline

https://api.github.com/repos/huggingface/datasets/issues/6522

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6522/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6522/comments

https://api.github.com/repos/huggingface/datasets/issues/6522/events

https://github.com/huggingface/datasets/issues/6522

I_kwDODunzps56VBvw

Loading HF Hub Dataset (private org repo) fails to load all features

open

NONE

### Describe the bug When pushing a `Dataset` with multiple `Features` (`input`, `output`, `tags`) to Huggingface Hub (private org repo), and later downloading the `Dataset`, only `input` and `output` load - I believe the expected behavior is for all `Features` to be loaded by default? ### Steps to reproduce the bug Pushing the data. `data_concat` is a `list` of `dict`s. ```python for datum in data_concat: datum_tags = {d["key"]: d["value"] for d in datum["tags"]} split_fraction = # some logic that generates a train/test split number if split_faction < test_fraction: data_test.append(datum) else: data_train.append(datum) dataset = DatasetDict( { "train": Dataset.from_list(data_train), "test": Dataset.from_list(data_test), "full": Dataset.from_list(data_concat), }, ) dataset_shuffled = dataset.shuffle(seed=shuffle_seed) dataset_shuffled.push_to_hub( repo_id=hf_repo_id, private=True, config_name=m, revision=revision, token=hf_token, ) ``` Loading it later: ```python dataset = datasets.load_dataset( path=hf_repo_id, name=name, token=hf_token, ) ``` Produces: ``` DatasetDict({ train: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) test: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) full: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) }) ``` ### Expected behavior The expected result is below: ``` DatasetDict({ train: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) test: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) full: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) }) ``` My workaround is as follows: ```python dsinfo = datasets.get_dataset_config_info( path=data_files, config_name=data_config, token=hf_token, ) allfeatures = dsinfo.features.copy() if "tags" not in allfeatures: allfeatures["tags"] = [{"key": Value(dtype="string", id=None), "value": Value(dtype="string", id=None)}] dataset = datasets.load_dataset( path=data_files, name=data_config, features=allfeatures, token=hf_token, ) ``` Interestingly enough (and perhaps a related bug?), if I don't add the `tags` to `allfeatures` above (i.e. only loading `input` and `output`), it throws an error when executing `load_dataset`: ``` ValueError: Couldn't cast tags: list<element: struct<key: string, value: string>> child 0, element: struct<key: string, value: string> child 0, key: string child 1, value: string input: <obfuscated> output: <obfuscated> -- schema metadata -- huggingface: '{"info": {"features": {"tags": [{"key": {"dtype": "string",' + 532 to {'input': <obfuscated>, 'output': <obfuscated> because column names don't match ``` Traceback for this: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/load.py", line 2152, in load_dataset builder_instance.download_and_prepare( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 948, in download_and_prepare self._download_and_prepare( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1043, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1805, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1950, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info - `datasets` version: 2.15.0 - Platform: macOS-14.0-arm64-arm-64bit - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

https://api.github.com/repos/huggingface/datasets/issues/6522/timeline

https://api.github.com/repos/huggingface/datasets/issues/6521

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6521/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6521/comments

https://api.github.com/repos/huggingface/datasets/issues/6521/events

https://github.com/huggingface/datasets/issues/6521

I_kwDODunzps56Uomi

The order of the splits is not preserved

closed

MEMBER

We had a regression and the order of the splits is not preserved. They are alphabetically sorted, instead of preserving original "train", "validation", "test" order. Check: In branch "main" ```python In [9]: dataset = load_dataset("adversarial_qa", '"adversarialQA") In [10]: dataset Out[10]: DatasetDict({ test: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) train: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 30000 }) validation: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) }) ``` Before (2.15.0) it was: ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 30000 }) validation: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) test: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) }) ``` See issues: - https://huggingface.co/datasets/adversarial_qa/discussions/3 - https://huggingface.co/datasets/beans/discussions/4 This is a regression because it was previously fixed. See: - #6196 - #5728

https://api.github.com/repos/huggingface/datasets/issues/6521/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6520

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6520/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6520/comments

https://api.github.com/repos/huggingface/datasets/issues/6520/events

https://github.com/huggingface/datasets/pull/6520

PR_kwDODunzps5ijUiw

Support commit_description parameter in push_to_hub

closed

MEMBER

Support `commit_description` parameter in `push_to_hub`. CC: @Wauplin

https://api.github.com/repos/huggingface/datasets/issues/6520/timeline

https://api.github.com/repos/huggingface/datasets/issues/6519

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6519/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6519/comments

https://api.github.com/repos/huggingface/datasets/issues/6519/events

https://github.com/huggingface/datasets/pull/6519

PR_kwDODunzps5ie4MA

Support push_to_hub canonical datasets

closed

MEMBER

Support `push_to_hub` canonical datasets. This is necessary in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet Note that before this PR, the `repo_id` "dataset_name" was transformed to "user/dataset_name". This behavior was introduced by: - #6269

https://api.github.com/repos/huggingface/datasets/issues/6519/timeline

https://api.github.com/repos/huggingface/datasets/issues/6518

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6518/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6518/comments

https://api.github.com/repos/huggingface/datasets/issues/6518/events

https://github.com/huggingface/datasets/pull/6518

PR_kwDODunzps5icu-W

fix get_metadata_patterns function args error

closed

CONTRIBUTOR

Bug get_metadata_patterns arg error https://github.com/huggingface/datasets/issues/6517

https://api.github.com/repos/huggingface/datasets/issues/6518/timeline

https://api.github.com/repos/huggingface/datasets/issues/6517

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6517/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6517/comments

https://api.github.com/repos/huggingface/datasets/issues/6517/events

https://github.com/huggingface/datasets/issues/6517

I_kwDODunzps56Ml90

Bug get_metadata_patterns arg error

closed

CONTRIBUTOR

https://github.com/huggingface/datasets/blob/3f149204a2a5948287adcade5e90707aa5207a92/src/datasets/load.py#L1240C1-L1240C69 metadata_patterns = get_metadata_patterns(base_path, download_config=self.download_config)

https://api.github.com/repos/huggingface/datasets/issues/6517/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6516

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6516/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6516/comments

https://api.github.com/repos/huggingface/datasets/issues/6516/events

https://github.com/huggingface/datasets/pull/6516

PR_kwDODunzps5icYX0

Support huggingface-hub pre-releases

closed

MEMBER

Support `huggingface-hub` pre-releases. This way we will have our CI green when testing `huggingface-hub` release candidates. See: https://github.com/huggingface/datasets/tree/ci-test-huggingface-hub-v0.20.0.rc1 Close #6513.

https://api.github.com/repos/huggingface/datasets/issues/6516/timeline

https://api.github.com/repos/huggingface/datasets/issues/6515

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6515/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6515/comments

https://api.github.com/repos/huggingface/datasets/issues/6515/events

https://github.com/huggingface/datasets/issues/6515

I_kwDODunzps56LE9b

Why call http_head() when fsspec_head() succeeds

closed

CONTRIBUTOR

https://github.com/huggingface/datasets/blob/a91582de288d98e94bcb5ab634ca1cfeeff544c5/src/datasets/utils/file_utils.py#L510C1-L523C14

https://api.github.com/repos/huggingface/datasets/issues/6515/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6514

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6514/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6514/comments

https://api.github.com/repos/huggingface/datasets/issues/6514/events

https://github.com/huggingface/datasets/pull/6514

PR_kwDODunzps5ia6Os

Cache backward compatibility with 2.15.0

closed

MEMBER

...for datasets without scripts It takes into account the changes in cache from - https://github.com/huggingface/datasets/pull/6493: switch to `config/version/commit_sha` schema - https://github.com/huggingface/datasets/pull/6454: fix `DataFilesDict` keys ordering when hashing requires https://github.com/huggingface/datasets/pull/6493 to be merged

https://api.github.com/repos/huggingface/datasets/issues/6514/timeline

https://api.github.com/repos/huggingface/datasets/issues/6513

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6513/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6513/comments

https://api.github.com/repos/huggingface/datasets/issues/6513/events

https://github.com/huggingface/datasets/issues/6513

I_kwDODunzps56H0Mf

Support huggingface-hub 0.20.0

closed

MEMBER

CI to test the support of `huggingface-hub` 0.20.0: https://github.com/huggingface/datasets/compare/main...ci-test-huggingface-hub-v0.20.0.rc1 We need to merge: - #6510 - #6512 - #6516

https://api.github.com/repos/huggingface/datasets/issues/6513/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6512

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6512/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6512/comments

https://api.github.com/repos/huggingface/datasets/issues/6512/events

https://github.com/huggingface/datasets/pull/6512

PR_kwDODunzps5iYI5z

Remove deprecated HfFolder

closed

MEMBER

...and use `huggingface_hub.get_token()` instead

https://api.github.com/repos/huggingface/datasets/issues/6512/timeline

https://api.github.com/repos/huggingface/datasets/issues/6511

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6511/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6511/comments

https://api.github.com/repos/huggingface/datasets/issues/6511/events

https://github.com/huggingface/datasets/pull/6511

PR_kwDODunzps5iXAXR

Implement get dataset default config name

closed

MEMBER

Implement `get_dataset_default_config_name`. Now that we support setting a configuration as default in `push_to_hub` (see #6500), we need a programmatically way to know in advance which is the default configuration. This will be used in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet Follow-up of: - #6500 CC: @severo

https://api.github.com/repos/huggingface/datasets/issues/6511/timeline

https://api.github.com/repos/huggingface/datasets/issues/6510

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6510/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6510/comments

https://api.github.com/repos/huggingface/datasets/issues/6510/events

https://github.com/huggingface/datasets/pull/6510

PR_kwDODunzps5iRyiV

Replace `list_files_info` with `list_repo_tree` in `push_to_hub`

closed

CONTRIBUTOR

Starting from `huggingface_hub` 0.20.0, `list_files_info` will be deprecated in favor of `list_repo_tree` (see https://github.com/huggingface/huggingface_hub/pull/1910)

https://api.github.com/repos/huggingface/datasets/issues/6510/timeline

https://api.github.com/repos/huggingface/datasets/issues/6509

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6509/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6509/comments

https://api.github.com/repos/huggingface/datasets/issues/6509/events

https://github.com/huggingface/datasets/pull/6509

PR_kwDODunzps5iREyE

Better cast error when generating dataset

closed

MEMBER

I want to improve the error message for datasets like https://huggingface.co/datasets/m-a-p/COIG-CQIA Cc @albertvillanova @severo is this new error ok ? Or should I use a dedicated error class ? New: ```python Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1920, in _prepare_split_single writer.write_table(table) File "/Users/quentinlhoest/hf/datasets/src/datasets/arrow_writer.py", line 574, in write_table pa_table = table_cast(pa_table, self._schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2322, in table_cast return cast_table_to_schema(table, schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2276, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast instruction: string other: string index: string domain: list<item: string> child 0, item: string output: string task_type: struct<major: list<item: string>, minor: list<item: string>> child 0, major: list<item: string> child 0, item: string child 1, minor: list<item: string> child 0, item: string task_name_in_eng: string input: string to {'answer_from': Value(dtype='string', id=None), 'instruction': Value(dtype='string', id=None), 'human_verified': Value(dtype='bool', id=None), 'domain': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'output': Value(dtype='string', id=None), 'task_type': {'major': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'minor': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'copyright': Value(dtype='string', id=None), 'input': Value(dtype='string', id=None)} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/playground/ttest.py", line 74, in <module> load_dataset("m-a-p/COIG-CQIA") File "/Users/quentinlhoest/hf/datasets/src/datasets/load.py", line 2529, in load_dataset builder_instance.download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 936, in download_and_prepare self._download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1031, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1791, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1922, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 3 new columns (other, index, task_name_in_eng) and 3 missing columns (answer_from, copyright, human_verified). This happened while the json dataset builder was generating data using hf://datasets/m-a-p/COIG-CQIA/coig_pc/coig_pc_core_sample.json (at revision b7b7ecf290f6515036c7c04bd8537228ac2eb474) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) ``` Previously: ```python Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1931, in _prepare_split_single writer.write_table(table) File "/Users/quentinlhoest/hf/datasets/src/datasets/arrow_writer.py", line 574, in write_table pa_table = table_cast(pa_table, self._schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2295, in table_cast return cast_table_to_schema(table, schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2253, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") ValueError: Couldn't cast task_type: struct<major: list<item: string>, minor: list<item: string>> child 0, major: list<item: string> child 0, item: string child 1, minor: list<item: string> child 0, item: string other: string instruction: string task_name_in_eng: string domain: list<item: string> child 0, item: string index: string output: string input: string to {'human_verified': Value(dtype='bool', id=None), 'task_type': {'major': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'minor': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'answer_from': Value(dtype='string', id=None), 'copyright': Value(dtype='string', id=None), 'instruction': Value(dtype='string', id=None), 'domain': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'output': Value(dtype='string', id=None), 'input': Value(dtype='string', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/playground/ttest.py", line 74, in <module> load_dataset("m-a-p/COIG-CQIA") File "/Users/quentinlhoest/hf/datasets/src/datasets/load.py", line 2529, in load_dataset builder_instance.download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 949, in download_and_prepare self._download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1044, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1804, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1949, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ```

https://api.github.com/repos/huggingface/datasets/issues/6509/timeline

https://api.github.com/repos/huggingface/datasets/issues/6508

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6508/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6508/comments

https://api.github.com/repos/huggingface/datasets/issues/6508/events

https://github.com/huggingface/datasets/pull/6508

PR_kwDODunzps5iNvAu

Read GeoParquet files using parquet reader

closed

CONTRIBUTOR

Let GeoParquet files with the file extension `*.geoparquet` or `*.gpq` be readable by the default parquet reader. Those two file extensions are the ones most commonly used for GeoParquet files, and is included in the `gpq` validator tool at https://github.com/planetlabs/gpq/blob/e5576b4ee7306b4d2259d56c879465a9364dab90/cmd/gpq/command/convert.go#L73-L75 Addresses https://github.com/huggingface/datasets/issues/6438

https://api.github.com/repos/huggingface/datasets/issues/6508/timeline

https://api.github.com/repos/huggingface/datasets/issues/6507

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6507/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6507/comments

https://api.github.com/repos/huggingface/datasets/issues/6507/events

https://github.com/huggingface/datasets/issues/6507

I_kwDODunzps555o6g

where is glue_metric.py> @Frankie123421 what was the resolution to this?

closed

NONE

> @Frankie123421 what was the resolution to this? use glue_metric.py instead of glue.py in load_metric _Originally posted by @Frankie123421 in https://github.com/huggingface/datasets/issues/2117#issuecomment-905093763_

https://api.github.com/repos/huggingface/datasets/issues/6507/timeline

not_planned

https://api.github.com/repos/huggingface/datasets/issues/6506

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6506/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6506/comments

https://api.github.com/repos/huggingface/datasets/issues/6506/events

https://github.com/huggingface/datasets/issues/6506

I_kwDODunzps5549e-

Incorrect test set labels for RTE and CoLA datasets via load_dataset

closed

NONE

### Describe the bug The test set labels for the RTE and CoLA datasets when loading via datasets load_dataset are all -1. Edit: It appears this is also the case for every other dataset except for MRPC (stsb, sst2, qqp, mnli (both matched and mismatched), qnli, wnli, ax). Is this intended behavior to safeguard the test set for evaluation purposes? ### Steps to reproduce the bug !pip install datasets from datasets import load_dataset rte_data = load_dataset('glue', 'rte') cola_data = load_dataset('glue', 'cola') print(rte_data['test'][0:30]['label']) print(cola_data['test'][0:30]['label']) Output: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] The non-label test data seems to be fine: e.g. rte_data['test'][1] is: {'sentence1': "Authorities in Brazil say that more than 200 people are being held hostage in a prison in the country's remote, Amazonian-jungle state of Rondonia.", 'sentence2': 'Authorities in Brazil hold 200 people as hostage.', 'label': -1, 'idx': 1} Training and validation data are also fine: e.g. rte_data['train][0] is: {'sentence1': 'No Weapons of Mass Destruction Found in Iraq Yet.', 'sentence2': 'Weapons of Mass Destruction Found in Iraq.', 'label': 1, 'idx': 0} ### Expected behavior Expected the labels to be binary 0/1 values; Got all -1s instead ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-6.1.58+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 10.0.1 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0

https://api.github.com/repos/huggingface/datasets/issues/6506/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6505

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6505/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6505/comments

https://api.github.com/repos/huggingface/datasets/issues/6505/events

https://github.com/huggingface/datasets/issues/6505

I_kwDODunzps553_iI

Got stuck when I trying to load a dataset

open

NONE

### Describe the bug Hello, everyone. I met a problem when I am trying to load a data file using load_dataset method on a Debian 10 system. The data file is not very large, only 1.63MB with 600 records. Here is my code: from datasets import load_dataset dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json') I waited it for 20 minutes. It still no response. I cannot using Ctrl+C to cancel the command. I have to use Ctrl+Z to kill it. I also try it with a txt file, it still no response in a long time. I can load the same file successfully using my laptop (windows 10, python 3.8.5, datasets==2.14.5). I can also make it on another computer (Ubuntu 20.04.5 LTS, python 3.10.13, datasets 2.14.7). It only takes me 1-2 miniutes. Could you give me some suggestions? Thank you. ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json') ### Expected behavior I hope it can load the file successfully. ### Environment info OS: Debian GNU/Linux 10 Python: Python 3.10.13 Pip list: Package Version ------------------------- ------------ accelerate 0.25.0 addict 2.4.0 aiofiles 23.2.1 aiohttp 3.9.1 aiosignal 1.3.1 aliyun-python-sdk-core 2.14.0 aliyun-python-sdk-kms 2.16.2 altair 5.2.0 annotated-types 0.6.0 anyio 3.7.1 async-timeout 4.0.3 attrs 23.1.0 certifi 2023.11.17 cffi 1.16.0 charset-normalizer 3.3.2 click 8.1.7 contourpy 1.2.0 crcmod 1.7 cryptography 41.0.7 cycler 0.12.1 datasets 2.14.7 dill 0.3.7 docstring-parser 0.15 einops 0.7.0 exceptiongroup 1.2.0 fastapi 0.105.0 ffmpy 0.3.1 filelock 3.13.1 fonttools 4.46.0 frozenlist 1.4.1 fsspec 2023.10.0 gast 0.5.4 gradio 3.50.2 gradio_client 0.6.1 h11 0.14.0 httpcore 1.0.2 httpx 0.25.2 huggingface-hub 0.19.4 idna 3.6 importlib-metadata 7.0.0 importlib-resources 6.1.1 jieba 0.42.1 Jinja2 3.1.2 jmespath 0.10.0 joblib 1.3.2 jsonschema 4.20.0 jsonschema-specifications 2023.11.2 kiwisolver 1.4.5 markdown-it-py 3.0.0 MarkupSafe 2.1.3 matplotlib 3.8.2 mdurl 0.1.2 modelscope 1.10.0 mpmath 1.3.0 multidict 6.0.4 multiprocess 0.70.15 networkx 3.2.1 nltk 3.8.1 numpy 1.26.2 nvidia-cublas-cu12 12.1.3.1 nvidia-cuda-cupti-cu12 12.1.105 nvidia-cuda-nvrtc-cu12 12.1.105 nvidia-cuda-runtime-cu12 12.1.105 nvidia-cudnn-cu12 8.9.2.26 nvidia-cufft-cu12 11.0.2.54 nvidia-curand-cu12 10.3.2.106 nvidia-cusolver-cu12 11.4.5.107 nvidia-cusparse-cu12 12.1.0.106 nvidia-nccl-cu12 2.18.1 nvidia-nvjitlink-cu12 12.3.101 nvidia-nvtx-cu12 12.1.105 orjson 3.9.10 oss2 2.18.3 packaging 23.2 pandas 2.1.4 peft 0.7.1 Pillow 10.1.0 pip 23.3.1 platformdirs 4.1.0 protobuf 4.25.1 psutil 5.9.6 pyarrow 14.0.1 pyarrow-hotfix 0.6 pycparser 2.21 pycryptodome 3.19.0 pydantic 2.5.2 pydantic_core 2.14.5 pydub 0.25.1 Pygments 2.17.2 pyparsing 3.1.1 python-dateutil 2.8.2 python-multipart 0.0.6 pytz 2023.3.post1 PyYAML 6.0.1 referencing 0.32.0 regex 2023.10.3 requests 2.31.0 rich 13.7.0 rouge-chinese 1.0.3 rpds-py 0.13.2 safetensors 0.4.1 scipy 1.11.4 semantic-version 2.10.0 sentencepiece 0.1.99 setuptools 68.2.2 shtab 1.6.5 simplejson 3.19.2 six 1.16.0 sniffio 1.3.0 sortedcontainers 2.4.0 sse-starlette 1.8.2 starlette 0.27.0 sympy 1.12 tiktoken 0.5.2 tokenizers 0.15.0 tomli 2.0.1 toolz 0.12.0 torch 2.1.2 tqdm 4.66.1 transformers 4.36.1 triton 2.1.0 trl 0.7.4 typing_extensions 4.9.0 tyro 0.6.0 tzdata 2023.3 urllib3 2.1.0 uvicorn 0.24.0.post1 websockets 11.0.3 wheel 0.41.2 xxhash 3.4.1 yapf 0.40.2 yarl 1.9.4 zipp 3.17.0

https://api.github.com/repos/huggingface/datasets/issues/6505/timeline

https://api.github.com/repos/huggingface/datasets/issues/6504

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6504/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6504/comments

https://api.github.com/repos/huggingface/datasets/issues/6504/events

https://github.com/huggingface/datasets/issues/6504

I_kwDODunzps553Tji

Error Pushing to Hub

closed

NONE

### Describe the bug Error when trying to push a dataset in a special format to hub ### Steps to reproduce the bug ``` import datasets from datasets import Dataset dataset_dict = { "filename": ["apple", "banana"], "token": [[[1,2],[3,4]],[[1,2],[3,4]]], "label": [0, 1], } dataset = Dataset.from_dict(dataset_dict) dataset = dataset.cast_column("token", datasets.features.features.Array2D(shape=(2, 2),dtype="int16")) dataset.push_to_hub("SequenceModel/imagenet_val_256") ``` Error: ``` ... ConstructorError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/tuple' in "<unicode string>", line 8, column 16: shape: !!python/tuple ^ ``` ### Expected behavior Dataset being pushed to hub ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.19.0-1022-gcp-x86_64-with-glibc2.35 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

https://api.github.com/repos/huggingface/datasets/issues/6504/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6503

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6503/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6503/comments

https://api.github.com/repos/huggingface/datasets/issues/6503/events

https://github.com/huggingface/datasets/pull/6503

PR_kwDODunzps5iHgZf

Fix streaming xnli

closed

MEMBER

This code was failing ```python In [1]: from datasets import load_dataset In [2]: ...: ds = load_dataset("xnli", "all_languages", split="test", streaming=True) ...: ...: sample_data = next(iter(ds))["premise"] # pick up one data ...: input_text = list(sample_data.values()) ``` ``` File ~/hf/datasets/src/datasets/features/translation.py:104, in TranslationVariableLanguages.encode_example(self, translation_dict) 102 return translation_dict 103 elif self.languages and set(translation_dict) - lang_set: --> 104 raise ValueError( 105 f'Some languages in example ({", ".join(sorted(set(translation_dict) - lang_set))}) are not in valid set ({", ".join(lang_set)}).' 106 ) 108 # Convert dictionary into tuples, splitting out cases where there are 109 # multiple translations for a single language. 110 translation_tuples = [] ValueError: Some languages in example (language, translation) are not in valid set (ur, fr, hi, sw, vi, el, de, th, en, tr, zh, ar, bg, ru, es). ``` because in streaming mode we expect features encode methods to be no-ops if the example is already encoded. I fixed `TranslationVariableLanguages` to account for that

https://api.github.com/repos/huggingface/datasets/issues/6503/timeline

https://api.github.com/repos/huggingface/datasets/issues/6502

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6502/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6502/comments

https://api.github.com/repos/huggingface/datasets/issues/6502/events

https://github.com/huggingface/datasets/pull/6502

PR_kwDODunzps5iHPt-

Pickle support for `torch.Generator` objects

closed

CONTRIBUTOR

Fix for https://discuss.huggingface.co/t/caching-a-dataset-processed-with-randomness/65616

https://api.github.com/repos/huggingface/datasets/issues/6502/timeline

https://api.github.com/repos/huggingface/datasets/issues/6501

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6501/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6501/comments

https://api.github.com/repos/huggingface/datasets/issues/6501/events

https://github.com/huggingface/datasets/issues/6501

I_kwDODunzps55y3ZY

OverflowError: value too large to convert to int32_t

open

NONE

### Describe the bug  ### Steps to reproduce the bug just loading datasets ### Expected behavior how can I fix it ### Environment info pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl done

https://api.github.com/repos/huggingface/datasets/issues/6501/timeline

https://api.github.com/repos/huggingface/datasets/issues/6500

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6500/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6500/comments

https://api.github.com/repos/huggingface/datasets/issues/6500/events

https://github.com/huggingface/datasets/pull/6500

PR_kwDODunzps5iFc6e

Enable setting config as default when push_to_hub

closed

MEMBER

Fix #6497.

https://api.github.com/repos/huggingface/datasets/issues/6500/timeline

https://api.github.com/repos/huggingface/datasets/issues/6499

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6499/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6499/comments

https://api.github.com/repos/huggingface/datasets/issues/6499/events

https://github.com/huggingface/datasets/pull/6499

PR_kwDODunzps5iFIUF

docs: add reference Git over SSH

closed

CONTRIBUTOR

see https://discuss.huggingface.co/t/update-datasets-getting-started-to-new-git-security/65893

https://api.github.com/repos/huggingface/datasets/issues/6499/timeline

https://api.github.com/repos/huggingface/datasets/issues/6498

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6498/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6498/comments

https://api.github.com/repos/huggingface/datasets/issues/6498/events

https://github.com/huggingface/datasets/pull/6498

PR_kwDODunzps5iBcFj

Fallback on dataset script if user wants to load default config

closed

MEMBER

Right now this code is failing on `main`: ```python load_dataset("openbookqa") ``` This is because it tries to load the dataset from the Parquet export but the dataset has multiple configurations and the Parquet export doesn't know which one is the default one. I fixed this by simply falling back on using the dataset script (which tells the user to pass `trust_remote_code=True`): ```python load_dataset("openbookqa", trust_remote_code=True) ``` Note that if the user happened to specify a config name I don't fall back on the script since we can use the Parquet export in this case (no need to know which config is the default) ```python load_dataset("openbookqa", "main") ```

https://api.github.com/repos/huggingface/datasets/issues/6498/timeline

https://api.github.com/repos/huggingface/datasets/issues/6497

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6497/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6497/comments

https://api.github.com/repos/huggingface/datasets/issues/6497/events

https://github.com/huggingface/datasets/issues/6497

I_kwDODunzps55tlwi

Support setting a default config name in push_to_hub

closed

MEMBER

In order to convert script-datasets to no-script datasets, we need to support setting a default config name for those scripts that set one.

https://api.github.com/repos/huggingface/datasets/issues/6497/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6496

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6496/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6496/comments

https://api.github.com/repos/huggingface/datasets/issues/6496/events

https://github.com/huggingface/datasets/issues/6496

I_kwDODunzps55sC6K

Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.

open

NONE

**Describe the bug** Getting a `412 Client Error: Precondition Failed` when trying to write a dataset to the HF hub. ``` huggingface_hub.utils._errors.HfHubHTTPError: 412 Client Error: Precondition Failed for url: https://huggingface.co/api/datasets/GLorr/test-dask/commit/main (Request ID: Root=1-657ae26f-3bd92bf861bb254b2cc0826c;50a09ab7-9347-406a-ba49-69f98abee9cc) A commit has happened since. Please refresh and try again. ``` **Steps to reproduce the bug** This is a minimal reproducer: ``` import dask.dataframe as dd import pandas as pd import random import os import huggingface_hub import datasets huggingface_hub.login(token=os.getenv("HF_TOKEN")) data = {"number": [random.randint(0,10) for _ in range(1000)]} df = pd.DataFrame.from_dict(data) dataframe = dd.from_pandas(df, npartitions=1) dataframe = dataframe.repartition(npartitions=3) schema = datasets.Features({"number": datasets.Value("int64")}).arrow_schema repo_id = "GLorr/test-dask" repo_path = f"hf://datasets/{repo_id}" huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True) dd.to_parquet(dataframe, path=f"{repo_path}/data", schema=schema) ``` **Expected behavior** Would expect to write to the hub without any problem. **Environment info** ``` datasets==2.15.0 huggingface-hub==0.19.4 ```

https://api.github.com/repos/huggingface/datasets/issues/6496/timeline

https://api.github.com/repos/huggingface/datasets/issues/6494

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6494/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6494/comments

https://api.github.com/repos/huggingface/datasets/issues/6494/events

https://github.com/huggingface/datasets/issues/6494

I_kwDODunzps55kx7n

Image Data loaded Twice

open

NONE

### Describe the bug  When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images ### Steps to reproduce the bug from datasets import Dataset, load_dataset dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False) # print(dataset["train"][0]["image"] == dataset["train"][1]["image"]) print(dataset) print(dataset["train"]["image"]) print(len(dataset["train"]["image"])) ### Expected behavior DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 8 }) }) [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>] 8 ### Environment info - `datasets` version: 2.14.5 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.9.17 - Huggingface_hub version: 0.19.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3

https://api.github.com/repos/huggingface/datasets/issues/6494/timeline

https://api.github.com/repos/huggingface/datasets/issues/6495

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6495/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6495/comments

https://api.github.com/repos/huggingface/datasets/issues/6495/events

https://github.com/huggingface/datasets/issues/6495

I_kwDODunzps55k3tx

Newline characters don't behave as expected when calling dataset.info

open

NONE

### System Info - `transformers` version: 4.32.1 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.11.5 - Huggingface_hub version: 0.15.1 - Safetensors version: 0.3.2 - Accelerate version: not installed - Accelerate config: not found - PyTorch version (GPU?): 2.1.1+cpu (False) - Tensorflow version (GPU?): 2.15.0 (False) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: no - Using distributed or parallel set-up in script?: no ### Who can help? @marios ### Information - [X] The official example scripts - [ ] My own modified scripts ### Tasks - [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction [Source](https://huggingface.co/docs/datasets/v2.2.1/en/access) ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo(description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='glue', dataset_name=None, config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943843, num_examples=3668, shard_lengths=None, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105879, num_examples=408, shard_lengths=None, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442410, num_examples=1725, shard_lengths=None, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': None}}, download_size=1494541, post_processing_size=None, dataset_size=1492132, size_in_bytes=2986673) ### Expected behavior ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo( description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, builder_name='glue', config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943851, num_examples=3668, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105887, num_examples=408, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442418, num_examples=1725, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': '971d7767d81b997fd9060ade0ec23c4fc31cbb226a55d1bd4a1bac474eb81dc7'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': '60a9b09084528f0673eedee2b69cb941920f0b8cd0eeccefc464a98768457f89'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': 'a04e271090879aaba6423d65b94950c089298587d9c084bf9cd7439bd785f784'}}, download_size=1494541, post_processing_size=None, dataset_size=1492156, size_in_bytes=2986697 )

https://api.github.com/repos/huggingface/datasets/issues/6495/timeline

https://api.github.com/repos/huggingface/datasets/issues/6493

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6493/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6493/comments

https://api.github.com/repos/huggingface/datasets/issues/6493/events

https://github.com/huggingface/datasets/pull/6493

PR_kwDODunzps5h0XJK

Lazy data files resolution and offline cache reload

closed

MEMBER

Includes both https://github.com/huggingface/datasets/pull/6458 and https://github.com/huggingface/datasets/pull/6459 This PR should be merged instead of the two individually, since they are conflicting ## Offline cache reload it can reload datasets that were pushed to hub if they exist in the cache. example: ```python >>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp") >>> load_dataset("lhoestq/tmp") DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` and later, without connection: ```python >>> load_dataset("lhoestq/tmp") Using the latest cached version of the dataset since lhoestq/tmp couldn't be found on the Hugging Face Hub Found the latest cached dataset configuration 'default' at /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/default/0.0.0/da0e902a945afeb9 (last modified on Wed Dec 13 14:55:52 2023). DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` - Updated `CachedDatasetModuleFactory` to look for datasets in the cache at `<namespace>___<dataset_name>/<config_id>` - Since the metadata configs parameters are not available in offline mode, we don't know which folder to load (config_id and hash change), so I simply load the latest one - I instantiate a BuilderConfig even if there is no metadata config with the right config_name - Its config_id is equal to the config_name to be able to retrieve it in the cache (no more suffix for configs from metadata configs) - We can reload this config if offline mode by specifying the right config_name (same as online !) - Consequences of this change: - Only when there are user's parameters it creates a custom builder config with config_id = config_name + user parameters hash - the hash used to name the cache folder takes into account the metadata config and the dataset info, so that the right cache can be reloaded when there is internet connection without redownloading the data or resolving the data files. For local directories I hash the builder configs and dataset info, and for datasets on the hub I use the commit sha as hash. - cache directories now look like `config/version/commit_sha` for hub datasets which is clean :) Fix https://github.com/huggingface/datasets/issues/3547 ## Lazy data files resolution this makes this code run in 2sec instead of >10sec ```python from datasets import load_dataset ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False) ``` For some datasets with many configs and files it can be up to 100x faster. This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts. The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict

https://api.github.com/repos/huggingface/datasets/issues/6493/timeline

https://api.github.com/repos/huggingface/datasets/issues/6492

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6492/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6492/comments

https://api.github.com/repos/huggingface/datasets/issues/6492/events

https://github.com/huggingface/datasets/pull/6492

PR_kwDODunzps5hzjhQ

Make push_to_hub return CommitInfo

closed

MEMBER

Make `push_to_hub` return `CommitInfo`. This is useful, for example, if we pass `create_pr=True` and we want to know the created PR ID. CC: @severo for the use case in https://huggingface.co/datasets/jmhessel/newyorker_caption_contest/discussions/4

https://api.github.com/repos/huggingface/datasets/issues/6492/timeline

https://api.github.com/repos/huggingface/datasets/issues/6491

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6491/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6491/comments

https://api.github.com/repos/huggingface/datasets/issues/6491/events

https://github.com/huggingface/datasets/pull/6491

PR_kwDODunzps5hyiTY

Fix metrics dead link

closed

CONTRIBUTOR

https://api.github.com/repos/huggingface/datasets/issues/6491/timeline

https://api.github.com/repos/huggingface/datasets/issues/6490

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6490/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6490/comments

https://api.github.com/repos/huggingface/datasets/issues/6490/events

https://github.com/huggingface/datasets/issues/6490

I_kwDODunzps55bUec

`load_dataset(...,save_infos=True)` not working without loading script

open

NONE

### Describe the bug It seems that saving a dataset infos back into the card file is not working for datasets without a loading script. After tracking the problem a bit it looks like saving the infos uses `Builder.get_imported_module_dir()` as its destination directory. Internally this is a call to `inspect.getfile()` but since the actual builder class used is dynamically created (cf. `datasets.load.configure_builder_class`) this method actually return te path to the parent builder class (e.g. `datasets.packaged_modules.json.JSON`). ### Steps to reproduce the bug 1. Have a local dataset without any loading script 2. Make sure there are no dataset infos in the README.md 3. Load with `save_infos=True` 4. No change in the dataset README.md 5. A new README.md file is created in the directory of the parent builder class (e.g. for json in `.../site-packages/datasets/packaged_modules/json/README.md`) ### Expected behavior The dataset README.md should be updated and no file should be created in the python environment. ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.6.0

https://api.github.com/repos/huggingface/datasets/issues/6490/timeline

https://api.github.com/repos/huggingface/datasets/issues/6489

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6489/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6489/comments

https://api.github.com/repos/huggingface/datasets/issues/6489/events

https://github.com/huggingface/datasets/issues/6489

I_kwDODunzps55Zj5h

load_dataset imageflder for aws s3 path

open

NONE

### Feature request I would like to load a dataset from S3 using the imagefolder option something like `dataset = datasets.load_dataset('imagefolder', data_dir='s3://.../lsun/train/bedroom', fs=S3FileSystem(), streaming=True) ` ### Motivation no need of data_files ### Your contribution no experience with this

https://api.github.com/repos/huggingface/datasets/issues/6489/timeline

https://api.github.com/repos/huggingface/datasets/issues/6488

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6488/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6488/comments

https://api.github.com/repos/huggingface/datasets/issues/6488/events

https://github.com/huggingface/datasets/issues/6488

I_kwDODunzps55WV36

429 Client Error

open

NONE

Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it? Thanks Dataset: https://huggingface.co/datasets/cerebras/SlimPajama-627B Error: `requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`

https://api.github.com/repos/huggingface/datasets/issues/6488/timeline

https://api.github.com/repos/huggingface/datasets/issues/6487

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6487/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6487/comments

https://api.github.com/repos/huggingface/datasets/issues/6487/events

https://github.com/huggingface/datasets/pull/6487

PR_kwDODunzps5hqyfV

Update builder hash with info

closed

MEMBER

Currently if you change the `dataset_info` of a dataset (e.g. in the YAML part of the README.md), the cache ignores this change. This is problematic because you want to regenerate a dataset if you change the features or the split sizes for example (e.g. after push_to_hub) Ideally we should take the resolved files into account as well but this will be for another PR

https://api.github.com/repos/huggingface/datasets/issues/6487/timeline

https://api.github.com/repos/huggingface/datasets/issues/6486

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6486/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6486/comments

https://api.github.com/repos/huggingface/datasets/issues/6486/events

https://github.com/huggingface/datasets/pull/6486

PR_kwDODunzps5hqCSc

Fix docs phrasing about supported formats when sharing a dataset

closed

MEMBER

Fix docs phrasing.

https://api.github.com/repos/huggingface/datasets/issues/6486/timeline

https://api.github.com/repos/huggingface/datasets/issues/6485

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6485/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6485/comments

https://api.github.com/repos/huggingface/datasets/issues/6485/events

https://github.com/huggingface/datasets/issues/6485

I_kwDODunzps55Tcz8

FileNotFoundError: [Errno 2] No such file or directory: 'nul'

closed

NONE

### Describe the bug it seems that sth wrong with my terrible "bug body" life, When i run this code, "import datasets" i meet this error FileNotFoundError: [Errno 2] No such file or directory: 'nul'   ### Steps to reproduce the bug 1.import datasets ### Expected behavior i just run a single line code and stuct in this bug ### Environment info OS: Windows10 Datasets==2.15.0 python=3.10

https://api.github.com/repos/huggingface/datasets/issues/6485/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6483

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6483/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6483/comments

https://api.github.com/repos/huggingface/datasets/issues/6483/events

https://github.com/huggingface/datasets/issues/6483

I_kwDODunzps55LE8l

Iterable Dataset: rename column clashes with remove column

closed

CONTRIBUTOR

### Describe the bug Suppose I have a two iterable datasets, one with the features: * `{"audio", "text", "column_a"}` And the other with the features: * `{"audio", "sentence", "column_b"}` I want to combine both datasets using `interleave_datasets`, which requires me to unify the column names. I would typically do this by: 1. Renaming the common columns to the same name (e.g. `"text"` -> `"sentence"`) 2. Removing the unwanted columns (e.g. `"column_a"`, `"column_b"`) However, the process of renaming and removing columns in an iterable dataset doesn't work, since we need to preserve the original text column, meaning we can't combine the datasets. ### Steps to reproduce the bug ```python from datasets import load_dataset # load LS in streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # check original features dataset_features = dataset.features.keys() print("Original features: ", dataset_features) # rename "text" -> "sentence" dataset = dataset.rename_column("text", "sentence") # remove unwanted columns COLUMNS_TO_KEEP = {"audio", "sentence"} dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) # stream first sample, should return "audio" and "sentence" columns print(next(iter(dataset))) ``` Traceback: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[5], line 17 14 COLUMNS_TO_KEEP = {"audio", "sentence"} 15 dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) ---> 17 print(next(iter(dataset))) File ~/datasets/src/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self) 1350 yield formatter.format_row(pa_table) 1351 return -> 1353 for key, example in ex_iterable: 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) File ~/datasets/src/datasets/iterable_dataset.py:652, in MappedExamplesIterable.__iter__(self) 650 yield from ArrowExamplesIterable(self._iter_arrow, {}) 651 else: --> 652 yield from self._iter() File ~/datasets/src/datasets/iterable_dataset.py:729, in MappedExamplesIterable._iter(self) 727 if self.remove_columns: 728 for c in self.remove_columns: --> 729 del transformed_example[c] 730 yield key, transformed_example 731 current_idx += 1 KeyError: 'text' ``` => we see that `datasets` is looking for the column "text", even though we've renamed this to "sentence" and then removed the un-wanted "text" column from our dataset. ### Expected behavior Should be able to rename and remove columns from iterable dataset. ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: macOS-13.5.1-arm64-arm-64bit - Python version: 3.11.6 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2

https://api.github.com/repos/huggingface/datasets/issues/6483/timeline

completed

https://api.github.com/repos/huggingface/datasets/issues/6484

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6484/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6484/comments

https://api.github.com/repos/huggingface/datasets/issues/6484/events

https://github.com/huggingface/datasets/issues/6484

I_kwDODunzps55MjQu

[Feature Request] Dataset versioning

open

NONE

**Is your feature request related to a problem? Please describe.** I am working on a project, where I would like to test different preprocessing methods for my ML-data. Thus, I would like to work a lot with revisions and compare them. Currently, I was not able to make it work with the revision keyword because it was not redownloading the data, it was reading in some cached data, until I put `download_mode="force_redownload"`, even though the reversion was different. Of course, I may have done something wrong or missed a setting somewhere! **Describe the solution you'd like** The solution would allow me to easily work with revisions: - create a new dataset (by combining things, different preprocessing, ..) and give it a new revision (v.1.2.3), maybe like this: `dataset_audio.push_to_hub('kenfus/xy', revision='v1.0.2')` - then, get the current revision as follows: ``` dataset = load_dataset( 'kenfus/xy', revision='v1.0.2', ) ``` this downloads the new version and does not load in a different revision and all future map, filter, .. operations are done on this dataset and not loaded from cache produced from a different revision. - if I rerun the run, the caching should be smart enough in every step to not reuse a mapping operation on a different revision. **Describe alternatives you've considered** I created my own caching, putting `download_mode="force_redownload"` and `load_from_cache_file=False,` everywhere. **Additional context** Thanks a lot for your great work! Creating NLP datasets and training a model with them is really easy and straightforward with huggingface. This is the data loading in my script: ``` ## CREATE PATHS prepared_dataset_path = os.path.join( DATA_FOLDER, str(DATA_VERSION), "prepared_dataset" ) os.makedirs(os.path.join(DATA_FOLDER, str(DATA_VERSION)), exist_ok=True) ## LOAD DATASET if os.path.exists(prepared_dataset_path): print("Loading prepared dataset from disk...") dataset_prepared = load_from_disk(prepared_dataset_path) else: print("Loading dataset from HuggingFace Datasets...") dataset = load_dataset( PATH_TO_DATASET, revision=DATA_VERSION, download_mode="force_redownload" ) print("Preparing dataset...") dataset_prepared = dataset.map( prepare_dataset, remove_columns=["audio", "transcription"], num_proc=os.cpu_count(), load_from_cache_file=False, ) dataset_prepared.save_to_disk(prepared_dataset_path) del dataset if CHECK_DATASET: ## CHECK DATASET dataset_prepared = dataset_prepared.map( check_dimensions, num_proc=os.cpu_count(), load_from_cache_file=False ) dataset_filtered = dataset_prepared.filter( lambda example: not example["incorrect_dimension"], load_from_cache_file=False, ) for example in dataset_prepared.filter( lambda example: example["incorrect_dimension"], load_from_cache_file=False ): print(example["path"]) print( f"Number of examples with incorrect dimension: {len(dataset_prepared) - len(dataset_filtered)}" ) print("Number of examples train: ", len(dataset_filtered["train"])) print("Number of examples test: ", len(dataset_filtered["test"])) ```

https://api.github.com/repos/huggingface/datasets/issues/6484/timeline

https://api.github.com/repos/huggingface/datasets/issues/6482

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6482/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6482/comments

https://api.github.com/repos/huggingface/datasets/issues/6482/events

https://github.com/huggingface/datasets/pull/6482

PR_kwDODunzps5hhl23

Fix max lock length on unix

closed

MEMBER

reported in https://github.com/huggingface/datasets/pull/6482

https://api.github.com/repos/huggingface/datasets/issues/6482/timeline

https://api.github.com/repos/huggingface/datasets/issues/6481

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6481/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6481/comments

https://api.github.com/repos/huggingface/datasets/issues/6481/events

https://github.com/huggingface/datasets/issues/6481

I_kwDODunzps55J8cT

using torchrun, save_to_disk suddenly shows SIGTERM

open

NONE

### Describe the bug When I run my code using the "torchrun" command, when the code reaches the "save_to_disk" part, suddenly I get the following warning and error messages: Because the dataset is too large, the "save_to_disk" function splits it into 70 parts for saving. However, an error occurs suddenly when it reaches the 14th shard. WARNING: torch.distributed.elastic.multiprocessing.api: Sending process 2224968 closing signal SIGTERM ERROR: torch.distributed.elastic.multiprocessing.api: failed (exitcode: -7). traceback: Signal 7 (SIGBUS) received by PID 2224967. ### Steps to reproduce the bug ds_shard = ds_shard.map(map_fn, *args, **kwargs) ds_shard.save_to_disk(ds_shard_filepaths[rank]) Saving the dataset (14/70 shards): 20%|██ | 875350/4376702 [00:19<01:53, 30863.15 examples/s] WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 2224968 closing signal SIGTERM ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -7) local_rank: 0 (pid: 2224967) of binary: /home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/python Traceback (most recent call last): File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/torchrun", line 8, in <module> sys.exit(main()) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper return f(*args, **kwargs) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 794, in main run(args) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 785, in run elastic_launch( File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ========================================================== run.py FAILED ---------------------------------------------------------- Failures: <NO_OTHER_FAILURES> ---------------------------------------------------------- Root Cause (first observed failure): [0]: time : 2023-12-08_20:09:04 rank : 0 (local_rank: 0) exitcode : -7 (pid: 2224967) error_file: <N/A> traceback : Signal 7 (SIGBUS) received by PID 2224967 ### Expected behavior I hope it can save successfully without any issues, but it seems there is a problem. ### Environment info `datasets` version: 2.14.6 - Platform: Linux-4.19.90-24.4.v2101.ky10.aarch64-aarch64-with-glibc2.28 - Python version: 3.10.11 - Huggingface_hub version: 0.17.3 - PyArrow version: 14.0.0 - Pandas version: 2.1.2

https://api.github.com/repos/huggingface/datasets/issues/6481/timeline

https://api.github.com/repos/huggingface/datasets/issues/6480

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6480/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6480/comments

https://api.github.com/repos/huggingface/datasets/issues/6480/events

https://github.com/huggingface/datasets/pull/6480

PR_kwDODunzps5hcS7P

Add IterableDataset `__repr__`

closed

MEMBER

Example for glue sst2: Dataset ``` DatasetDict({ test: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 1821 }) train: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 67349 }) validation: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 872 }) }) ``` IterableDataset (new) ``` IterableDatasetDict({ test: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) train: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) validation: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) }) ``` IterableDataset (before) ``` {'test': <datasets.iterable_dataset.IterableDataset object at 0x130d421f0>, 'train': <datasets.iterable_dataset.IterableDataset object at 0x136f3aaf0>, 'validation': <datasets.iterable_dataset.IterableDataset object at 0x136f4b100>} {'sentence': 'hide new secretions from the parental units ', 'label': 0, 'idx': 0} ```

https://api.github.com/repos/huggingface/datasets/issues/6480/timeline

https://api.github.com/repos/huggingface/datasets/issues/6479

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6479/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6479/comments

https://api.github.com/repos/huggingface/datasets/issues/6479/events

https://github.com/huggingface/datasets/pull/6479

PR_kwDODunzps5hVLom

More robust preupload retry mechanism

closed

CONTRIBUTOR

https://api.github.com/repos/huggingface/datasets/issues/6479/timeline

https://api.github.com/repos/huggingface/datasets/issues/6478

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6478/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6478/comments

https://api.github.com/repos/huggingface/datasets/issues/6478/events

https://github.com/huggingface/datasets/issues/6478

I_kwDODunzps544eqs

How to load data from lakefs

open

CONTRIBUTOR

My dataset is stored on the company's lakefs server. How can I write code to load the dataset? It would be great if I could provide code examples or provide some references

https://api.github.com/repos/huggingface/datasets/issues/6478/timeline

https://api.github.com/repos/huggingface/datasets/issues/6477

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/6477/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/6477/comments

https://api.github.com/repos/huggingface/datasets/issues/6477/events

https://github.com/huggingface/datasets/pull/6477

PR_kwDODunzps5hRq_N

Fix PermissionError on Windows CI

closed

MEMBER