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The dataset generation failed
Error code: DatasetGenerationError
Exception: DatasetGenerationError
Message: An error occurred while generating the dataset
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2116, in cast_array_to_feature
return array_cast(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1962, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type timestamp[s] to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Open a discussion for direct support.
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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 | 2,059,198,575 | I_kwDODunzps56vOBv | 6,542 | Datasets : wikipedia 20220301.en error | {
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} | [] | open | false | null | [] | null | [
"Hi ! We now recommend using the `wikimedia/wikipedia` dataset, can you try loading this one instead ?\r\n\r\n```python\r\nwiki_dataset = load_dataset(\"wikimedia/wikipedia\", \"20231101.en\")\r\n```"
] | 1,703,838,891,000 | 1,703,838,891,000 | null | NONE | null | ### 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
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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 | 2,058,983,826 | I_kwDODunzps56uZmS | 6,541 | Dataset not loading successfully. | {
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} | [] | open | false | null | [] | null | [] | 1,703,813,747,000 | 1,703,813,747,000 | null | NONE | null | ### 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
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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 | 2,058,965,157 | I_kwDODunzps56uVCl | 6,540 | Extreme inefficiency for `save_to_disk` when merging datasets | {
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"Concatenating datasets doesn't create any indices mapping - so flattening indices is not needed (unless you shuffle the dataset).\r\nCan you share the snippet of code you are using to merge your datasets and save them to disk ?"
] | 1,703,810,675,000 | 1,703,810,675,000 | null | NONE | null | ### 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 | {
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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 | 2,058,493,960 | I_kwDODunzps56siAI | 6,539 | 'Repo card metadata block was not found' when loading a pragmeval dataset | {
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} | [] | open | false | null | [] | null | [] | 1,703,773,105,000 | 1,703,773,117,000 | null | NONE | null | ### 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 | {
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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 | 2,057,377,630 | I_kwDODunzps56oRde | 6,538 | ImportError: cannot import name 'SchemaInferenceError' from 'datasets.arrow_writer' (/opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py) | {
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} | [] | open | false | null | [] | null | [
"Hi ! Are you sure you have `datasets` 2.16 ? I just checked and on 2.16 I can run `from datasets.arrow_writer import SchemaInferenceError` without error",
"I have the same issue - using with datasets version 2.16.1. Also this is on a kaggle notebook - other people with the same issue also seem to be having it on kaggle?"
] | 1,703,683,876,000 | 1,703,683,956,000 | null | NONE | null | ### 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: 2.16.0
### 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 | {
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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 | 2,057,132,173 | I_kwDODunzps56nViN | 6,537 | Adding support for netCDF (*.nc) files | {
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"description": "New feature or request",
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"name": "enhancement",
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] | open | false | null | [] | null | [
"Related to #3113 ",
"Conceptually, we can use xarray to load the netCDF file, then xarray -> pandas -> pyarrow.",
"I'd still need to verify that such a conversion would be lossless, especially for multi-dimensional data."
] | 1,703,669,249,000 | 1,703,710,013,000 | null | NONE | null | ### 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. | {
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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 | 2,056,863,239 | I_kwDODunzps56mT4H | 6,536 | datasets.load_dataset raises FileNotFoundError for datasets==2.16.0 | {
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} | [] | open | false | null | [] | null | [
"Hi ! Thanks for reporting\r\n\r\nThis is a bug in 2.16.0 for some datasets when `cache_dir` is a relative path. I opened https://github.com/huggingface/datasets/pull/6543 to fix this",
"We just released 2.16.1 with a fix:\r\n\r\n```\r\npip install -U datasets\r\n```"
] | 1,703,646,948,000 | 1,703,647,275,000 | null | NONE | null | ### 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 | {
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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 | 2,056,264,339 | I_kwDODunzps56kBqT | 6,535 | IndexError: Invalid key: 47682 is out of bounds for size 0 while using PEFT | {
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} | [] | open | false | null | [] | null | [
"@sabman @pvl @kashif @vigsterkr "
] | 1,703,585,673,000 | 1,703,585,756,000 | null | NONE | null | ### 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 | {
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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 | 2,056,002,548 | I_kwDODunzps56jBv0 | 6,534 | How to configure multiple folders in the same zip package | {
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} | [] | open | false | null | [] | null | [
"@albertvillanova"
] | 1,703,562,980,000 | 1,703,572,276,000 | null | CONTRIBUTOR | null | 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 | {
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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 | 2,055,929,101 | I_kwDODunzps56iv0N | 6,533 | ted_talks_iwslt | Error: Config name is missing | {
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"Hi ! Thanks for reporting. I opened https://github.com/huggingface/datasets/pull/6544 to fix this",
"We just released 2.16.1 with a fix:\r\n\r\n```\r\npip install -U datasets\r\n```"
] | 1,703,551,098,000 | 1,703,551,098,000 | null | NONE | null | ### 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
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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 | 2,055,631,201 | I_kwDODunzps56hnFh | 6,532 | [Feature request] Indexing datasets by a customly-defined id field to enable random access dataset items via the id | {
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"name": "enhancement",
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] | open | false | null | [] | null | [] | 1,703,504,230,000 | 1,703,504,847,000 | null | NONE | null | ### 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. | {
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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 | 2,055,201,605 | PR_kwDODunzps5it5Sm | 6,531 | Add polars compatibility | {
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} | [] | open | false | null | [] | null | [] | 1,703,448,203,000 | 1,703,448,203,000 | null | NONE | null | 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. | {
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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 | 2,054,817,609 | I_kwDODunzps56egdJ | 6,530 | Impossible to save a mapped dataset to disk | {
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"I solved it with `train_dataset.with_format(None)`\r\nBut then faced some more issues (which i later solved too).\r\n\r\nHuggingface does not seem to care, so I do. Here is an updated training script which saves a pre-processed (mapped) dataset to your local directory if you specify `--save_precomputed_data_dir=DIR_NAME`. Then use `--train_precomputed_data_dir` with the same dir to load it instead of `--dataset_name`.\r\n\r\n[Proper SDXL trainer code](https://github.com/kopyl/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py)\r\n[Notebook for pre-computing a dataset and saving locally](https://colab.research.google.com/drive/17Yo08hePx-NlHs99RecdeiNc8CQg4O7l?usp=sharing)\r\n\r\nExample:\r\n\r\n1st run (nothing is pre-computed yet):\r\n```\r\naccelerate launch train_text_to_image_sdxl.py \\\r\n --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \\\r\n --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \\\r\n --dataset_name=lambdalabs/pokemon-blip-captions \\\r\n --save_precomputed_data_dir=\"test-5\"\r\n```\r\n\r\n2nd run (the pre-computed dataset is saved to `test-5` directory):\r\n```\r\naccelerate launch train_text_to_image_sdxl.py \\\r\n --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \\\r\n --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \\\r\n --train_precomputed_data_dir test-5\r\n```\r\n\r\nThis way when you're gonna be using your pre-computed dataset you don't need to worry about re-mapping your dataset when you change an argument for your trainer script"
] | 1,703,344,707,000 | 1,703,410,830,000 | null | NONE | null | ### 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) | {
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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 | 2,054,209,449 | I_kwDODunzps56cL-p | 6,529 | Impossible to only download a test split | {
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"The only way right now is to load with streaming=True"
] | 1,703,264,192,000 | 1,703,277,988,000 | null | NONE | null | 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, | {
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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 | 2,053,996,494 | PR_kwDODunzps5ip9JH | 6,528 | set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6528). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004875 / 0.011353 (-0.006478) | 0.003501 / 0.011008 (-0.007507) | 0.062604 / 0.038508 (0.024096) | 0.031916 / 0.023109 (0.008806) | 0.256138 / 0.275898 (-0.019760) | 0.278514 / 0.323480 (-0.044966) | 0.002917 / 0.007986 (-0.005069) | 0.002636 / 0.004328 (-0.001693) | 0.049154 / 0.004250 (0.044904) | 0.041985 / 0.037052 (0.004933) | 0.256857 / 0.258489 (-0.001632) | 0.282628 / 0.293841 (-0.011213) | 0.027506 / 0.128546 (-0.101041) | 0.010736 / 0.075646 (-0.064910) | 0.207268 / 0.419271 (-0.212003) | 0.035312 / 0.043533 (-0.008221) | 0.259274 / 0.255139 (0.004135) | 0.281463 / 0.283200 (-0.001737) | 0.019905 / 0.141683 (-0.121778) | 1.108719 / 1.452155 (-0.343435) | 1.177871 / 1.492716 (-0.314845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004435 / 0.018006 (-0.013571) | 0.310643 / 0.000490 (0.310153) | 0.000243 / 0.000200 (0.000043) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018013 / 0.037411 (-0.019398) | 0.060702 / 0.014526 (0.046176) | 0.073243 / 0.176557 (-0.103314) | 0.119523 / 0.737135 (-0.617613) | 0.074204 / 0.296338 (-0.222134) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281075 / 0.215209 (0.065866) | 2.722154 / 2.077655 (0.644499) | 1.441052 / 1.504120 (-0.063068) | 1.305940 / 1.541195 (-0.235255) | 1.356752 / 1.468490 (-0.111738) | 0.570399 / 4.584777 (-4.014378) | 2.329158 / 3.745712 (-1.416554) | 2.749093 / 5.269862 (-2.520768) | 1.717752 / 4.565676 (-2.847925) | 0.063228 / 0.424275 (-0.361047) | 0.004981 / 0.007607 (-0.002626) | 0.330601 / 0.226044 (0.104557) | 3.300987 / 2.268929 (1.032059) | 1.778673 / 55.444624 (-53.665951) | 1.507841 / 6.876477 (-5.368636) | 1.520454 / 2.142072 (-0.621619) | 0.650816 / 4.805227 (-4.154412) | 0.118606 / 6.500664 (-6.382058) | 0.042199 / 0.075469 (-0.033271) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.919668 / 1.841788 (-0.922119) | 11.293437 / 8.074308 (3.219129) | 9.928525 / 10.191392 (-0.262867) | 0.127142 / 0.680424 (-0.553282) | 0.013470 / 0.534201 (-0.520731) | 0.284648 / 0.579283 (-0.294636) | 0.264942 / 0.434364 (-0.169422) | 0.321866 / 0.540337 (-0.218471) | 0.414513 / 1.386936 (-0.972423) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005052 / 0.011353 (-0.006301) | 0.003204 / 0.011008 (-0.007804) | 0.051102 / 0.038508 (0.012594) | 0.032105 / 0.023109 (0.008996) | 0.273923 / 0.275898 (-0.001976) | 0.297031 / 0.323480 (-0.026449) | 0.004002 / 0.007986 (-0.003984) | 0.002636 / 0.004328 (-0.001693) | 0.047696 / 0.004250 (0.043445) | 0.044086 / 0.037052 (0.007034) | 0.277779 / 0.258489 (0.019289) | 0.306678 / 0.293841 (0.012837) | 0.028557 / 0.128546 (-0.099989) | 0.010631 / 0.075646 (-0.065015) | 0.056419 / 0.419271 (-0.362852) | 0.054285 / 0.043533 (0.010752) | 0.276506 / 0.255139 (0.021367) | 0.296315 / 0.283200 (0.013116) | 0.018642 / 0.141683 (-0.123040) | 1.146926 / 1.452155 (-0.305229) | 1.257625 / 1.492716 (-0.235092) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094231 / 0.018006 (0.076225) | 0.302805 / 0.000490 (0.302315) | 0.000229 / 0.000200 (0.000029) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022510 / 0.037411 (-0.014901) | 0.076092 / 0.014526 (0.061566) | 0.090642 / 0.176557 (-0.085915) | 0.127016 / 0.737135 (-0.610120) | 0.089169 / 0.296338 (-0.207169) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290812 / 0.215209 (0.075603) | 2.858528 / 2.077655 (0.780873) | 1.577555 / 1.504120 (0.073436) | 1.447810 / 1.541195 (-0.093384) | 1.447546 / 1.468490 (-0.020944) | 0.559118 / 4.584777 (-4.025659) | 2.408930 / 3.745712 (-1.336782) | 2.733761 / 5.269862 (-2.536101) | 1.700107 / 4.565676 (-2.865570) | 0.062447 / 0.424275 (-0.361828) | 0.004999 / 0.007607 (-0.002608) | 0.340207 / 0.226044 (0.114162) | 3.344131 / 2.268929 (1.075203) | 1.902289 / 55.444624 (-53.542335) | 1.628226 / 6.876477 (-5.248251) | 1.629435 / 2.142072 (-0.512637) | 0.625011 / 4.805227 (-4.180216) | 0.119929 / 6.500664 (-6.380735) | 0.041097 / 0.075469 (-0.034372) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977461 / 1.841788 (-0.864327) | 12.303189 / 8.074308 (4.228881) | 11.008743 / 10.191392 (0.817351) | 0.128578 / 0.680424 (-0.551845) | 0.015305 / 0.534201 (-0.518896) | 0.286468 / 0.579283 (-0.292816) | 0.275824 / 0.434364 (-0.158540) | 0.321487 / 0.540337 (-0.218851) | 0.420591 / 1.386936 (-0.966345) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ff3670c18ed34fa8ddfa70a9aa403ae6cc9ad54 \"CML watermark\")\n"
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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 | 2,053,966,748 | PR_kwDODunzps5ip2vd | 6,527 | Release: 2.16.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6527). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004870 / 0.011353 (-0.006483) | 0.003606 / 0.011008 (-0.007402) | 0.062719 / 0.038508 (0.024211) | 0.031785 / 0.023109 (0.008676) | 0.238809 / 0.275898 (-0.037089) | 0.263000 / 0.323480 (-0.060480) | 0.002844 / 0.007986 (-0.005142) | 0.002698 / 0.004328 (-0.001631) | 0.048070 / 0.004250 (0.043819) | 0.042333 / 0.037052 (0.005280) | 0.243032 / 0.258489 (-0.015457) | 0.273197 / 0.293841 (-0.020644) | 0.027498 / 0.128546 (-0.101048) | 0.010592 / 0.075646 (-0.065055) | 0.204770 / 0.419271 (-0.214502) | 0.034837 / 0.043533 (-0.008696) | 0.242518 / 0.255139 (-0.012621) | 0.267461 / 0.283200 (-0.015739) | 0.018479 / 0.141683 (-0.123204) | 1.105444 / 1.452155 (-0.346710) | 1.163659 / 1.492716 (-0.329057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004717 / 0.018006 (-0.013289) | 0.303338 / 0.000490 (0.302849) | 0.000221 / 0.000200 (0.000021) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018298 / 0.037411 (-0.019113) | 0.061225 / 0.014526 (0.046699) | 0.073514 / 0.176557 (-0.103043) | 0.120230 / 0.737135 (-0.616905) | 0.076195 / 0.296338 (-0.220144) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284731 / 0.215209 (0.069522) | 2.773463 / 2.077655 (0.695809) | 1.498239 / 1.504120 (-0.005881) | 1.372143 / 1.541195 (-0.169052) | 1.448949 / 1.468490 (-0.019542) | 0.572516 / 4.584777 (-4.012261) | 2.404041 / 3.745712 (-1.341671) | 2.763047 / 5.269862 (-2.506814) | 1.722419 / 4.565676 (-2.843257) | 0.063104 / 0.424275 (-0.361172) | 0.004989 / 0.007607 (-0.002618) | 0.341864 / 0.226044 (0.115820) | 3.391635 / 2.268929 (1.122707) | 1.872694 / 55.444624 (-53.571931) | 1.594490 / 6.876477 (-5.281987) | 1.596940 / 2.142072 (-0.545132) | 0.645265 / 4.805227 (-4.159962) | 0.117408 / 6.500664 (-6.383256) | 0.042405 / 0.075469 (-0.033064) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963207 / 1.841788 (-0.878580) | 11.676551 / 8.074308 (3.602243) | 10.194287 / 10.191392 (0.002895) | 0.130329 / 0.680424 (-0.550094) | 0.015381 / 0.534201 (-0.518820) | 0.288848 / 0.579283 (-0.290435) | 0.264781 / 0.434364 (-0.169583) | 0.321212 / 0.540337 (-0.219126) | 0.418308 / 1.386936 (-0.968628) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005533 / 0.011353 (-0.005819) | 0.003733 / 0.011008 (-0.007276) | 0.048877 / 0.038508 (0.010369) | 0.030263 / 0.023109 (0.007154) | 0.281161 / 0.275898 (0.005263) | 0.302971 / 0.323480 (-0.020509) | 0.003943 / 0.007986 (-0.004043) | 0.002717 / 0.004328 (-0.001612) | 0.047845 / 0.004250 (0.043594) | 0.045809 / 0.037052 (0.008757) | 0.283337 / 0.258489 (0.024848) | 0.312914 / 0.293841 (0.019073) | 0.029074 / 0.128546 (-0.099472) | 0.010775 / 0.075646 (-0.064871) | 0.057461 / 0.419271 (-0.361810) | 0.053756 / 0.043533 (0.010223) | 0.281809 / 0.255139 (0.026670) | 0.298339 / 0.283200 (0.015139) | 0.019270 / 0.141683 (-0.122413) | 1.117575 / 1.452155 (-0.334580) | 1.191703 / 1.492716 (-0.301013) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093513 / 0.018006 (0.075507) | 0.301267 / 0.000490 (0.300777) | 0.000211 / 0.000200 (0.000012) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022278 / 0.037411 (-0.015133) | 0.076805 / 0.014526 (0.062279) | 0.088820 / 0.176557 (-0.087736) | 0.127903 / 0.737135 (-0.609233) | 0.092988 / 0.296338 (-0.203350) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297787 / 0.215209 (0.082578) | 2.899652 / 2.077655 (0.821997) | 1.598830 / 1.504120 (0.094710) | 1.469398 / 1.541195 (-0.071797) | 1.511099 / 1.468490 (0.042609) | 0.559785 / 4.584777 (-4.024992) | 2.426448 / 3.745712 (-1.319264) | 2.798811 / 5.269862 (-2.471051) | 1.737790 / 4.565676 (-2.827887) | 0.062219 / 0.424275 (-0.362056) | 0.005120 / 0.007607 (-0.002487) | 0.351051 / 0.226044 (0.125007) | 3.492063 / 2.268929 (1.223134) | 1.965674 / 55.444624 (-53.478950) | 1.672874 / 6.876477 (-5.203603) | 1.709700 / 2.142072 (-0.432373) | 0.639347 / 4.805227 (-4.165880) | 0.126383 / 6.500664 (-6.374281) | 0.042731 / 0.075469 (-0.032738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968619 / 1.841788 (-0.873168) | 12.671030 / 8.074308 (4.596722) | 11.125347 / 10.191392 (0.933955) | 0.142983 / 0.680424 (-0.537441) | 0.015726 / 0.534201 (-0.518475) | 0.288610 / 0.579283 (-0.290673) | 0.276473 / 0.434364 (-0.157891) | 0.326590 / 0.540337 (-0.213748) | 0.423832 / 1.386936 (-0.963104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a85fb52fc8ddb41307e61cbf6a5189f3bba27829 \"CML watermark\")\n"
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6526). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005101 / 0.011353 (-0.006252) | 0.003471 / 0.011008 (-0.007537) | 0.062293 / 0.038508 (0.023785) | 0.032650 / 0.023109 (0.009541) | 0.249241 / 0.275898 (-0.026657) | 0.277079 / 0.323480 (-0.046400) | 0.002971 / 0.007986 (-0.005015) | 0.002637 / 0.004328 (-0.001691) | 0.048415 / 0.004250 (0.044165) | 0.042832 / 0.037052 (0.005779) | 0.247840 / 0.258489 (-0.010649) | 0.283994 / 0.293841 (-0.009847) | 0.027764 / 0.128546 (-0.100782) | 0.010544 / 0.075646 (-0.065102) | 0.208810 / 0.419271 (-0.210462) | 0.035744 / 0.043533 (-0.007789) | 0.252811 / 0.255139 (-0.002328) | 0.276163 / 0.283200 (-0.007036) | 0.018581 / 0.141683 (-0.123102) | 1.130043 / 1.452155 (-0.322112) | 1.194298 / 1.492716 (-0.298418) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004488 / 0.018006 (-0.013518) | 0.302072 / 0.000490 (0.301582) | 0.000211 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017799 / 0.037411 (-0.019613) | 0.061146 / 0.014526 (0.046620) | 0.081796 / 0.176557 (-0.094761) | 0.120407 / 0.737135 (-0.616729) | 0.075211 / 0.296338 (-0.221127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295349 / 0.215209 (0.080140) | 2.953511 / 2.077655 (0.875857) | 1.495332 / 1.504120 (-0.008788) | 1.364144 / 1.541195 (-0.177051) | 1.429562 / 1.468490 (-0.038928) | 0.574325 / 4.584777 (-4.010452) | 2.384352 / 3.745712 (-1.361360) | 2.843625 / 5.269862 (-2.426236) | 1.806802 / 4.565676 (-2.758875) | 0.065076 / 0.424275 (-0.359199) | 0.004970 / 0.007607 (-0.002638) | 0.339935 / 0.226044 (0.113891) | 3.375103 / 2.268929 (1.106175) | 1.822921 / 55.444624 (-53.621703) | 1.546126 / 6.876477 (-5.330350) | 1.573630 / 2.142072 (-0.568442) | 0.655081 / 4.805227 (-4.150146) | 0.122446 / 6.500664 (-6.378218) | 0.042220 / 0.075469 (-0.033249) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942127 / 1.841788 (-0.899661) | 11.470401 / 8.074308 (3.396093) | 10.025961 / 10.191392 (-0.165431) | 0.129087 / 0.680424 (-0.551337) | 0.014141 / 0.534201 (-0.520060) | 0.285470 / 0.579283 (-0.293813) | 0.266755 / 0.434364 (-0.167608) | 0.323391 / 0.540337 (-0.216947) | 0.427645 / 1.386936 (-0.959291) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005578 / 0.011353 (-0.005775) | 0.003734 / 0.011008 (-0.007274) | 0.049200 / 0.038508 (0.010692) | 0.030981 / 0.023109 (0.007872) | 0.281195 / 0.275898 (0.005297) | 0.309950 / 0.323480 (-0.013530) | 0.004046 / 0.007986 (-0.003939) | 0.002709 / 0.004328 (-0.001620) | 0.048505 / 0.004250 (0.044254) | 0.046245 / 0.037052 (0.009193) | 0.280130 / 0.258489 (0.021641) | 0.313739 / 0.293841 (0.019898) | 0.029828 / 0.128546 (-0.098718) | 0.011152 / 0.075646 (-0.064495) | 0.057753 / 0.419271 (-0.361518) | 0.055112 / 0.043533 (0.011580) | 0.281861 / 0.255139 (0.026722) | 0.304402 / 0.283200 (0.021203) | 0.019931 / 0.141683 (-0.121752) | 1.150585 / 1.452155 (-0.301570) | 1.217850 / 1.492716 (-0.274866) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091552 / 0.018006 (0.073546) | 0.301772 / 0.000490 (0.301282) | 0.000225 / 0.000200 (0.000025) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023189 / 0.037411 (-0.014223) | 0.078741 / 0.014526 (0.064216) | 0.092320 / 0.176557 (-0.084236) | 0.129636 / 0.737135 (-0.607500) | 0.091673 / 0.296338 (-0.204665) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298542 / 0.215209 (0.083333) | 2.899358 / 2.077655 (0.821703) | 1.673896 / 1.504120 (0.169776) | 1.489518 / 1.541195 (-0.051677) | 1.542853 / 1.468490 (0.074363) | 0.559843 / 4.584777 (-4.024934) | 2.422101 / 3.745712 (-1.323611) | 2.844592 / 5.269862 (-2.425270) | 1.794527 / 4.565676 (-2.771150) | 0.064615 / 0.424275 (-0.359660) | 0.005078 / 0.007607 (-0.002530) | 0.355112 / 0.226044 (0.129068) | 3.462129 / 2.268929 (1.193200) | 1.975393 / 55.444624 (-53.469231) | 1.706513 / 6.876477 (-5.169963) | 1.716954 / 2.142072 (-0.425118) | 0.642094 / 4.805227 (-4.163133) | 0.119215 / 6.500664 (-6.381449) | 0.041941 / 0.075469 (-0.033528) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986774 / 1.841788 (-0.855014) | 12.702049 / 8.074308 (4.627741) | 11.727663 / 10.191392 (1.536271) | 0.135008 / 0.680424 (-0.545416) | 0.016055 / 0.534201 (-0.518146) | 0.293564 / 0.579283 (-0.285719) | 0.284884 / 0.434364 (-0.149480) | 0.332524 / 0.540337 (-0.207814) | 0.425392 / 1.386936 (-0.961544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7b5fc585fcaf77b92839e82d0ce2c2fbf0d9ea95 \"CML watermark\")\n"
] | 1,703,241,356,000 | 1,703,245,342,000 | null | MEMBER | null | Preserve order of configs and splits, as defined in dataset infos.
Fix #6521. | {
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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 | 2,053,119,357 | PR_kwDODunzps5im-lL | 6,525 | BBox type | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6525). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"closing in favor of other ideas that would not involve any typing"
] | 1,703,196,807,000 | 1,703,198,368,000 | null | MEMBER | null | 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. | {
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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 | 2,053,076,311 | I_kwDODunzps56X3VX | 6,524 | Streaming the Pile: Missing Files | {
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"Hello @FelixLabelle,\r\n\r\nAs you can see in the Community tab of the corresponding dataset, it is a known issue: https://huggingface.co/datasets/EleutherAI/pile/discussions/15\r\n\r\nThe data has been taken down due to reported copyright infringement.\r\n\r\nFeel free to continue the discussion there."
] | 1,703,193,909,000 | 1,703,236,625,000 | null | NONE | null | ### 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 | {
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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 | 2,052,643,484 | PR_kwDODunzps5ilV6d | 6,523 | fix tests | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6523). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005160 / 0.011353 (-0.006192) | 0.003962 / 0.011008 (-0.007046) | 0.064952 / 0.038508 (0.026444) | 0.053291 / 0.023109 (0.030182) | 0.237182 / 0.275898 (-0.038716) | 0.263855 / 0.323480 (-0.059625) | 0.004157 / 0.007986 (-0.003829) | 0.002901 / 0.004328 (-0.001428) | 0.050679 / 0.004250 (0.046428) | 0.044885 / 0.037052 (0.007832) | 0.243806 / 0.258489 (-0.014683) | 0.273828 / 0.293841 (-0.020013) | 0.028681 / 0.128546 (-0.099866) | 0.011086 / 0.075646 (-0.064560) | 0.211987 / 0.419271 (-0.207285) | 0.035881 / 0.043533 (-0.007652) | 0.249618 / 0.255139 (-0.005521) | 0.262880 / 0.283200 (-0.020319) | 0.017788 / 0.141683 (-0.123895) | 1.209060 / 1.452155 (-0.243094) | 1.272143 / 1.492716 (-0.220574) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004594 / 0.018006 (-0.013412) | 0.305188 / 0.000490 (0.304698) | 0.000213 / 0.000200 (0.000013) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019526 / 0.037411 (-0.017886) | 0.062280 / 0.014526 (0.047754) | 0.074983 / 0.176557 (-0.101573) | 0.123466 / 0.737135 (-0.613670) | 0.076240 / 0.296338 (-0.220099) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276001 / 0.215209 (0.060792) | 2.689614 / 2.077655 (0.611959) | 1.441092 / 1.504120 (-0.063028) | 1.319775 / 1.541195 (-0.221419) | 1.386904 / 1.468490 (-0.081587) | 0.561388 / 4.584777 (-4.023389) | 2.386718 / 3.745712 (-1.358994) | 2.813959 / 5.269862 (-2.455903) | 1.727447 / 4.565676 (-2.838230) | 0.061965 / 0.424275 (-0.362310) | 0.004977 / 0.007607 (-0.002630) | 0.335077 / 0.226044 (0.109032) | 3.313860 / 2.268929 (1.044932) | 1.814018 / 55.444624 (-53.630606) | 1.542840 / 6.876477 (-5.333637) | 1.586887 / 2.142072 (-0.555185) | 0.643225 / 4.805227 (-4.162002) | 0.117834 / 6.500664 (-6.382830) | 0.044024 / 0.075469 (-0.031445) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.952804 / 1.841788 (-0.888984) | 12.447378 / 8.074308 (4.373070) | 11.281734 / 10.191392 (1.090342) | 0.143407 / 0.680424 (-0.537017) | 0.014749 / 0.534201 (-0.519452) | 0.289298 / 0.579283 (-0.289985) | 0.268217 / 0.434364 (-0.166146) | 0.327995 / 0.540337 (-0.212343) | 0.430302 / 1.386936 (-0.956634) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005683 / 0.011353 (-0.005670) | 0.003813 / 0.011008 (-0.007195) | 0.048943 / 0.038508 (0.010435) | 0.060730 / 0.023109 (0.037621) | 0.266925 / 0.275898 (-0.008973) | 0.292553 / 0.323480 (-0.030927) | 0.004236 / 0.007986 (-0.003750) | 0.002790 / 0.004328 (-0.001538) | 0.048962 / 0.004250 (0.044711) | 0.040354 / 0.037052 (0.003302) | 0.266353 / 0.258489 (0.007864) | 0.298397 / 0.293841 (0.004556) | 0.029977 / 0.128546 (-0.098570) | 0.010788 / 0.075646 (-0.064858) | 0.057529 / 0.419271 (-0.361743) | 0.032896 / 0.043533 (-0.010636) | 0.266696 / 0.255139 (0.011557) | 0.283422 / 0.283200 (0.000223) | 0.020939 / 0.141683 (-0.120744) | 1.169867 / 1.452155 (-0.282287) | 1.213586 / 1.492716 (-0.279130) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097035 / 0.018006 (0.079029) | 0.306968 / 0.000490 (0.306478) | 0.000234 / 0.000200 (0.000034) | 0.000046 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023343 / 0.037411 (-0.014068) | 0.078238 / 0.014526 (0.063712) | 0.091083 / 0.176557 (-0.085474) | 0.131487 / 0.737135 (-0.605649) | 0.092614 / 0.296338 (-0.203724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294454 / 0.215209 (0.079245) | 2.881053 / 2.077655 (0.803398) | 1.623934 / 1.504120 (0.119814) | 1.509001 / 1.541195 (-0.032194) | 1.567541 / 1.468490 (0.099051) | 0.574326 / 4.584777 (-4.010451) | 2.476826 / 3.745712 (-1.268886) | 2.826183 / 5.269862 (-2.443678) | 1.771949 / 4.565676 (-2.793727) | 0.063663 / 0.424275 (-0.360613) | 0.005039 / 0.007607 (-0.002568) | 0.354861 / 0.226044 (0.128816) | 3.397655 / 2.268929 (1.128727) | 1.961958 / 55.444624 (-53.482666) | 1.694795 / 6.876477 (-5.181682) | 1.719459 / 2.142072 (-0.422614) | 0.654512 / 4.805227 (-4.150715) | 0.119285 / 6.500664 (-6.381379) | 0.042146 / 0.075469 (-0.033323) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982187 / 1.841788 (-0.859601) | 12.944627 / 8.074308 (4.870319) | 11.370381 / 10.191392 (1.178989) | 0.142759 / 0.680424 (-0.537665) | 0.016319 / 0.534201 (-0.517882) | 0.291339 / 0.579283 (-0.287944) | 0.276842 / 0.434364 (-0.157522) | 0.324285 / 0.540337 (-0.216052) | 0.426234 / 1.386936 (-0.960702) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e1b82eaa75d2c610e59b463a67d685ec858c0838 \"CML watermark\")\n"
] | 1,703,172,981,000 | 1,703,174,214,000 | null | MEMBER | null | null | {
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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 | 2,052,332,528 | I_kwDODunzps56VBvw | 6,522 | Loading HF Hub Dataset (private org repo) fails to load all features | {
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} | [] | open | false | null | [] | null | [] | 1,703,161,595,000 | 1,703,165,071,000 | null | NONE | null | ### 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 | {
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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 | 2,052,229,538 | I_kwDODunzps56Uomi | 6,521 | The order of the splits is not preserved | {
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] | null | [
"After investigation, I think the issue was introduced by the use of the Parquet export:\r\n- #6448\r\n\r\nI am proposing a fix.\r\n\r\nCC: @lhoestq "
] | 1,703,157,447,000 | 1,703,244,975,000 | null | MEMBER | null | 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 | {
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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 | 2,052,059,078 | PR_kwDODunzps5ijUiw | 6,520 | Support commit_description parameter in push_to_hub | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6520). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005484 / 0.011353 (-0.005869) | 0.003537 / 0.011008 (-0.007471) | 0.062631 / 0.038508 (0.024123) | 0.048037 / 0.023109 (0.024927) | 0.240342 / 0.275898 (-0.035556) | 0.268103 / 0.323480 (-0.055377) | 0.002927 / 0.007986 (-0.005059) | 0.002609 / 0.004328 (-0.001719) | 0.048112 / 0.004250 (0.043862) | 0.046111 / 0.037052 (0.009058) | 0.249249 / 0.258489 (-0.009240) | 0.277723 / 0.293841 (-0.016118) | 0.028374 / 0.128546 (-0.100172) | 0.010900 / 0.075646 (-0.064746) | 0.206252 / 0.419271 (-0.213019) | 0.035262 / 0.043533 (-0.008271) | 0.247438 / 0.255139 (-0.007701) | 0.270003 / 0.283200 (-0.013197) | 0.019157 / 0.141683 (-0.122526) | 1.116833 / 1.452155 (-0.335322) | 1.174495 / 1.492716 (-0.318221) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092490 / 0.018006 (0.074484) | 0.302794 / 0.000490 (0.302304) | 0.000213 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018669 / 0.037411 (-0.018743) | 0.061902 / 0.014526 (0.047376) | 0.073612 / 0.176557 (-0.102945) | 0.121196 / 0.737135 (-0.615940) | 0.075960 / 0.296338 (-0.220378) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286983 / 0.215209 (0.071774) | 2.836819 / 2.077655 (0.759165) | 1.506635 / 1.504120 (0.002515) | 1.387134 / 1.541195 (-0.154061) | 1.442310 / 1.468490 (-0.026180) | 0.571281 / 4.584777 (-4.013496) | 2.440220 / 3.745712 (-1.305492) | 2.775306 / 5.269862 (-2.494555) | 1.727047 / 4.565676 (-2.838630) | 0.064955 / 0.424275 (-0.359320) | 0.004982 / 0.007607 (-0.002625) | 0.343153 / 0.226044 (0.117108) | 3.388745 / 2.268929 (1.119817) | 1.878983 / 55.444624 (-53.565641) | 1.592642 / 6.876477 (-5.283835) | 1.601037 / 2.142072 (-0.541035) | 0.636882 / 4.805227 (-4.168345) | 0.117804 / 6.500664 (-6.382861) | 0.042467 / 0.075469 (-0.033002) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941534 / 1.841788 (-0.900254) | 12.093230 / 8.074308 (4.018922) | 10.590854 / 10.191392 (0.399462) | 0.136636 / 0.680424 (-0.543788) | 0.015244 / 0.534201 (-0.518957) | 0.300216 / 0.579283 (-0.279067) | 0.267622 / 0.434364 (-0.166742) | 0.337526 / 0.540337 (-0.202811) | 0.426856 / 1.386936 (-0.960080) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005282 / 0.011353 (-0.006071) | 0.003595 / 0.011008 (-0.007413) | 0.049237 / 0.038508 (0.010729) | 0.054057 / 0.023109 (0.030948) | 0.269781 / 0.275898 (-0.006117) | 0.293544 / 0.323480 (-0.029936) | 0.003991 / 0.007986 (-0.003995) | 0.002705 / 0.004328 (-0.001623) | 0.048755 / 0.004250 (0.044505) | 0.040425 / 0.037052 (0.003373) | 0.264753 / 0.258489 (0.006264) | 0.312773 / 0.293841 (0.018932) | 0.030011 / 0.128546 (-0.098535) | 0.010707 / 0.075646 (-0.064939) | 0.058164 / 0.419271 (-0.361107) | 0.033365 / 0.043533 (-0.010168) | 0.268854 / 0.255139 (0.013715) | 0.283618 / 0.283200 (0.000418) | 0.019571 / 0.141683 (-0.122111) | 1.114738 / 1.452155 (-0.337417) | 1.178990 / 1.492716 (-0.313726) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092183 / 0.018006 (0.074177) | 0.303797 / 0.000490 (0.303307) | 0.000218 / 0.000200 (0.000018) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023088 / 0.037411 (-0.014323) | 0.079813 / 0.014526 (0.065287) | 0.089593 / 0.176557 (-0.086964) | 0.128127 / 0.737135 (-0.609008) | 0.091578 / 0.296338 (-0.204761) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300153 / 0.215209 (0.084944) | 2.919532 / 2.077655 (0.841877) | 1.587870 / 1.504120 (0.083750) | 1.459031 / 1.541195 (-0.082164) | 1.483305 / 1.468490 (0.014815) | 0.555865 / 4.584777 (-4.028912) | 2.388350 / 3.745712 (-1.357362) | 2.817947 / 5.269862 (-2.451914) | 1.764446 / 4.565676 (-2.801230) | 0.067142 / 0.424275 (-0.357133) | 0.005148 / 0.007607 (-0.002460) | 0.347998 / 0.226044 (0.121953) | 3.431208 / 2.268929 (1.162280) | 1.942175 / 55.444624 (-53.502450) | 1.676606 / 6.876477 (-5.199871) | 1.692431 / 2.142072 (-0.449641) | 0.645974 / 4.805227 (-4.159253) | 0.117729 / 6.500664 (-6.382935) | 0.041670 / 0.075469 (-0.033799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981554 / 1.841788 (-0.860234) | 12.671959 / 8.074308 (4.597650) | 11.230694 / 10.191392 (1.039302) | 0.132694 / 0.680424 (-0.547730) | 0.015694 / 0.534201 (-0.518507) | 0.290271 / 0.579283 (-0.289013) | 0.279358 / 0.434364 (-0.155006) | 0.326515 / 0.540337 (-0.213823) | 0.421755 / 1.386936 (-0.965181) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b2147ac644596b66886f398012351641672ee54 \"CML watermark\")\n"
] | 1,703,151,371,000 | 1,703,170,187,000 | null | MEMBER | null | Support `commit_description` parameter in `push_to_hub`.
CC: @Wauplin | {
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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 | 2,050,759,824 | PR_kwDODunzps5ie4MA | 6,519 | Support push_to_hub canonical datasets | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6519). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"nice catch @albertvillanova ",
"@huggingface/datasets this PR is ready for review.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005306 / 0.011353 (-0.006047) | 0.003454 / 0.011008 (-0.007555) | 0.062157 / 0.038508 (0.023649) | 0.051945 / 0.023109 (0.028835) | 0.241834 / 0.275898 (-0.034064) | 0.265590 / 0.323480 (-0.057890) | 0.003149 / 0.007986 (-0.004837) | 0.002695 / 0.004328 (-0.001633) | 0.049197 / 0.004250 (0.044947) | 0.045576 / 0.037052 (0.008524) | 0.242866 / 0.258489 (-0.015623) | 0.280963 / 0.293841 (-0.012878) | 0.028466 / 0.128546 (-0.100080) | 0.010670 / 0.075646 (-0.064976) | 0.206501 / 0.419271 (-0.212771) | 0.035314 / 0.043533 (-0.008219) | 0.240893 / 0.255139 (-0.014246) | 0.264762 / 0.283200 (-0.018438) | 0.019988 / 0.141683 (-0.121695) | 1.095222 / 1.452155 (-0.356933) | 1.144051 / 1.492716 (-0.348666) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098034 / 0.018006 (0.080028) | 0.308541 / 0.000490 (0.308051) | 0.000261 / 0.000200 (0.000061) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018646 / 0.037411 (-0.018766) | 0.062881 / 0.014526 (0.048355) | 0.074062 / 0.176557 (-0.102494) | 0.120860 / 0.737135 (-0.616276) | 0.075388 / 0.296338 (-0.220951) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282974 / 0.215209 (0.067765) | 2.755589 / 2.077655 (0.677934) | 1.459536 / 1.504120 (-0.044584) | 1.364543 / 1.541195 (-0.176652) | 1.429860 / 1.468490 (-0.038630) | 0.573277 / 4.584777 (-4.011500) | 2.422983 / 3.745712 (-1.322730) | 3.257258 / 5.269862 (-2.012603) | 1.930053 / 4.565676 (-2.635623) | 0.067476 / 0.424275 (-0.356799) | 0.005612 / 0.007607 (-0.001995) | 0.351538 / 0.226044 (0.125494) | 3.380356 / 2.268929 (1.111427) | 1.837887 / 55.444624 (-53.606738) | 1.537994 / 6.876477 (-5.338483) | 1.623630 / 2.142072 (-0.518442) | 0.662652 / 4.805227 (-4.142576) | 0.127074 / 6.500664 (-6.373590) | 0.049311 / 0.075469 (-0.026158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.151273 / 1.841788 (-0.690515) | 12.766622 / 8.074308 (4.692314) | 10.967610 / 10.191392 (0.776218) | 0.131305 / 0.680424 (-0.549119) | 0.014227 / 0.534201 (-0.519974) | 0.292054 / 0.579283 (-0.287229) | 0.262737 / 0.434364 (-0.171627) | 0.334360 / 0.540337 (-0.205978) | 0.446711 / 1.386936 (-0.940225) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005194 / 0.011353 (-0.006159) | 0.003508 / 0.011008 (-0.007500) | 0.049287 / 0.038508 (0.010779) | 0.052109 / 0.023109 (0.029000) | 0.271501 / 0.275898 (-0.004397) | 0.290959 / 0.323480 (-0.032521) | 0.004347 / 0.007986 (-0.003638) | 0.002659 / 0.004328 (-0.001669) | 0.048769 / 0.004250 (0.044518) | 0.039388 / 0.037052 (0.002336) | 0.272811 / 0.258489 (0.014322) | 0.305632 / 0.293841 (0.011791) | 0.028419 / 0.128546 (-0.100127) | 0.010617 / 0.075646 (-0.065029) | 0.057433 / 0.419271 (-0.361838) | 0.032383 / 0.043533 (-0.011149) | 0.266566 / 0.255139 (0.011427) | 0.290993 / 0.283200 (0.007794) | 0.019939 / 0.141683 (-0.121743) | 1.157623 / 1.452155 (-0.294532) | 1.183298 / 1.492716 (-0.309419) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099074 / 0.018006 (0.081068) | 0.315282 / 0.000490 (0.314792) | 0.000235 / 0.000200 (0.000035) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022692 / 0.037411 (-0.014719) | 0.076455 / 0.014526 (0.061929) | 0.089094 / 0.176557 (-0.087462) | 0.126407 / 0.737135 (-0.610728) | 0.089588 / 0.296338 (-0.206750) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.338853 / 0.215209 (0.123644) | 2.809843 / 2.077655 (0.732188) | 1.538262 / 1.504120 (0.034143) | 1.418290 / 1.541195 (-0.122905) | 1.435145 / 1.468490 (-0.033345) | 0.565763 / 4.584777 (-4.019014) | 2.491525 / 3.745712 (-1.254187) | 2.944879 / 5.269862 (-2.324983) | 1.835840 / 4.565676 (-2.729837) | 0.065101 / 0.424275 (-0.359174) | 0.005196 / 0.007607 (-0.002412) | 0.345291 / 0.226044 (0.119247) | 3.399658 / 2.268929 (1.130729) | 1.892321 / 55.444624 (-53.552303) | 1.608293 / 6.876477 (-5.268184) | 1.651188 / 2.142072 (-0.490884) | 0.647806 / 4.805227 (-4.157421) | 0.119318 / 6.500664 (-6.381346) | 0.043058 / 0.075469 (-0.032412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983956 / 1.841788 (-0.857831) | 13.516125 / 8.074308 (5.441817) | 11.712571 / 10.191392 (1.521179) | 0.134253 / 0.680424 (-0.546171) | 0.015844 / 0.534201 (-0.518357) | 0.292444 / 0.579283 (-0.286839) | 0.282182 / 0.434364 (-0.152182) | 0.329327 / 0.540337 (-0.211010) | 0.419960 / 1.386936 (-0.966976) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a887ee78835573f5d80f9e414e8443b4caff3541 \"CML watermark\")\n"
] | 1,703,085,405,000 | 1,703,170,100,000 | null | MEMBER | null | 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 | {
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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 | 2,050,137,038 | PR_kwDODunzps5icu-W | 6,518 | fix get_metadata_patterns function args error | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6518). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"hello!\r\n@albertvillanova \r\nThank you very much for your recognition。\r\nWhen can this PR be merged?",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005205 / 0.011353 (-0.006148) | 0.003730 / 0.011008 (-0.007278) | 0.063195 / 0.038508 (0.024687) | 0.052329 / 0.023109 (0.029219) | 0.247299 / 0.275898 (-0.028599) | 0.269600 / 0.323480 (-0.053880) | 0.004801 / 0.007986 (-0.003185) | 0.002728 / 0.004328 (-0.001600) | 0.049195 / 0.004250 (0.044944) | 0.044859 / 0.037052 (0.007807) | 0.253047 / 0.258489 (-0.005442) | 0.277253 / 0.293841 (-0.016588) | 0.028370 / 0.128546 (-0.100176) | 0.011095 / 0.075646 (-0.064551) | 0.211090 / 0.419271 (-0.208182) | 0.035944 / 0.043533 (-0.007589) | 0.252755 / 0.255139 (-0.002384) | 0.269466 / 0.283200 (-0.013733) | 0.017514 / 0.141683 (-0.124169) | 1.107815 / 1.452155 (-0.344339) | 1.154989 / 1.492716 (-0.337728) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093925 / 0.018006 (0.075919) | 0.300923 / 0.000490 (0.300433) | 0.000219 / 0.000200 (0.000019) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018268 / 0.037411 (-0.019143) | 0.060508 / 0.014526 (0.045983) | 0.074564 / 0.176557 (-0.101992) | 0.121523 / 0.737135 (-0.615612) | 0.077394 / 0.296338 (-0.218945) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275859 / 0.215209 (0.060650) | 2.707593 / 2.077655 (0.629938) | 1.419178 / 1.504120 (-0.084942) | 1.286737 / 1.541195 (-0.254458) | 1.350504 / 1.468490 (-0.117986) | 0.570461 / 4.584777 (-4.014316) | 2.400795 / 3.745712 (-1.344917) | 2.840876 / 5.269862 (-2.428986) | 1.724044 / 4.565676 (-2.841633) | 0.063819 / 0.424275 (-0.360456) | 0.004961 / 0.007607 (-0.002647) | 0.342537 / 0.226044 (0.116492) | 3.370942 / 2.268929 (1.102013) | 1.788659 / 55.444624 (-53.655966) | 1.501921 / 6.876477 (-5.374556) | 1.535352 / 2.142072 (-0.606721) | 0.651838 / 4.805227 (-4.153390) | 0.118979 / 6.500664 (-6.381685) | 0.047796 / 0.075469 (-0.027673) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949850 / 1.841788 (-0.891937) | 11.581988 / 8.074308 (3.507680) | 10.462837 / 10.191392 (0.271445) | 0.133298 / 0.680424 (-0.547125) | 0.015008 / 0.534201 (-0.519193) | 0.299265 / 0.579283 (-0.280018) | 0.268864 / 0.434364 (-0.165500) | 0.332888 / 0.540337 (-0.207450) | 0.420423 / 1.386936 (-0.966513) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005309 / 0.011353 (-0.006044) | 0.003628 / 0.011008 (-0.007380) | 0.049545 / 0.038508 (0.011036) | 0.054095 / 0.023109 (0.030985) | 0.270679 / 0.275898 (-0.005219) | 0.295744 / 0.323480 (-0.027736) | 0.004131 / 0.007986 (-0.003855) | 0.002732 / 0.004328 (-0.001596) | 0.048714 / 0.004250 (0.044464) | 0.039916 / 0.037052 (0.002863) | 0.272354 / 0.258489 (0.013865) | 0.310553 / 0.293841 (0.016712) | 0.029525 / 0.128546 (-0.099021) | 0.011322 / 0.075646 (-0.064324) | 0.058007 / 0.419271 (-0.361265) | 0.032883 / 0.043533 (-0.010650) | 0.273609 / 0.255139 (0.018470) | 0.291780 / 0.283200 (0.008581) | 0.020538 / 0.141683 (-0.121145) | 1.118031 / 1.452155 (-0.334123) | 1.160777 / 1.492716 (-0.331940) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092966 / 0.018006 (0.074959) | 0.301432 / 0.000490 (0.300943) | 0.000225 / 0.000200 (0.000025) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022736 / 0.037411 (-0.014676) | 0.077655 / 0.014526 (0.063129) | 0.093386 / 0.176557 (-0.083171) | 0.129694 / 0.737135 (-0.607441) | 0.092790 / 0.296338 (-0.203548) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299161 / 0.215209 (0.083952) | 2.923300 / 2.077655 (0.845645) | 1.629661 / 1.504120 (0.125541) | 1.510797 / 1.541195 (-0.030398) | 1.507269 / 1.468490 (0.038778) | 0.574346 / 4.584777 (-4.010431) | 2.454396 / 3.745712 (-1.291316) | 2.843402 / 5.269862 (-2.426460) | 1.774815 / 4.565676 (-2.790861) | 0.063601 / 0.424275 (-0.360674) | 0.004977 / 0.007607 (-0.002630) | 0.347693 / 0.226044 (0.121649) | 3.430054 / 2.268929 (1.161126) | 1.987308 / 55.444624 (-53.457316) | 1.682756 / 6.876477 (-5.193721) | 1.688463 / 2.142072 (-0.453609) | 0.646449 / 4.805227 (-4.158778) | 0.117860 / 6.500664 (-6.382804) | 0.041305 / 0.075469 (-0.034164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.987355 / 1.841788 (-0.854433) | 12.398721 / 8.074308 (4.324412) | 11.070442 / 10.191392 (0.879050) | 0.134946 / 0.680424 (-0.545477) | 0.016172 / 0.534201 (-0.518029) | 0.293359 / 0.579283 (-0.285924) | 0.282271 / 0.434364 (-0.152093) | 0.331919 / 0.540337 (-0.208418) | 0.432137 / 1.386936 (-0.954799) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2246d3187222ef939aa8e69cd1aa476cf9526945 \"CML watermark\")\n"
] | 1,703,063,182,000 | 1,703,171,657,000 | null | CONTRIBUTOR | null | Bug get_metadata_patterns arg error https://github.com/huggingface/datasets/issues/6517 | {
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} | [] | closed | false | null | [] | null | [] | 1,703,062,604,000 | 1,703,204,663,000 | null | CONTRIBUTOR | null | 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) | {
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