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] | open | false | null | [] | null | [] | 2023-03-22T11:05:17 | 2023-03-22T11:05:17 | null | NONE | null | ### Feature request
Wouldn't it be great if the various class balancing operations from imbalanced-learn were available as part of datasets?
### Motivation
I'm trying to use imbalanced-learn to balance a dataset, but it's not clear how to get the two to interoperate - what would be great would be some examples. I've looked online, asked gpt-4, but so far not making much progress.
### Your contribution
If I can get this working myself I can submit a PR with example code to go in the docs | {
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"cc @polinaeterna @lhoestq ",
"@sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). \r\nRequired `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume it **might not** be the case for some non standard Linux distributions. \r\nThe only solution for using `soundfile` here is to build [`libsndfile`](https://github.com/libsndfile/libsndfile) from source:\r\n\r\n```bash\r\ngit clone https://github.com/libsndfile/libsndfile.git\r\ncd libsndfile/\r\nautoreconf -vif\r\n./configure --enable-werror \r\nmake\r\nmake install\r\n```\r\nfor this, some building libraries should be installed, for Debian/Ubuntu it's like:\r\n```bash\r\napt install autoconf autogen automake build-essential libasound2-dev \\\r\n libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n libmpg123-dev pkg-config python\r\n```\r\nbut for other Linux distributions it might be different.\r\n\r\nWhen the binary is compiled, it should be put into location where `soundfile` would search for it (the directory is named `_soundfile_data`), it depends on where`libsdfile` (from the previous step) and `soundfile` were installed, might be something like this:\r\n\r\n```bash\r\ncp /usr/local/lib/libsndfile.so /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\ncp /usr/local/lib/libsndfile.la /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\n```\r\n\r\nAnother solution is to not use `soundfile` and apply custom processing function with `torchaudio` while setting `decode=False` in `Audio` feature and passing custom function to `.map`. ",
"Not sure if it may help, but you could also try updating `pip` before installing soundfile"
] | 2023-03-22T10:07:33 | 2023-03-22T13:52:11 | null | CONTRIBUTOR | null | ### Describe the bug
I'm encountering several issues trying to load mp3 audio files using `datasets` on a TPU v4.
The PR https://github.com/huggingface/datasets/pull/5573 updated the audio loading logic to rely solely on the `soundfile`/`libsndfile` libraries for loading audio samples, regardless of their file type.
The installation guide suggests that `libsndfile` is bundled in when `soundfile` is pip installed:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/docs/source/installation.md?plain=1#L70-L71
However, just pip installing `soundfile==0.12.1` throws an error that `libsndfile` is missing:
```
pip install soundfile==0.12.1
```
Then:
```python
>>> soundfile
>>> soundfile.__libsndfile_version__
```
<details>
<summary> Traceback (most recent call last): </summary>
```
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 161, in <module>
import _soundfile_data # ImportError if this doesn't exist
ModuleNotFoundError: No module named '_soundfile_data'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 170, in <module>
raise OSError('sndfile library not found using ctypes.util.find_library')
OSError: sndfile library not found using ctypes.util.find_library
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 192, in <module>
_snd = _ffi.dlopen(_explicit_libname)
OSError: cannot load library 'libsndfile.so': libsndfile.so: cannot open shared object file: No such file or directory
```
</details>
Thus, I've followed the official instructions for installing the `soundfile` package from https://github.com/bastibe/python-soundfile#installation, which states that `libsndfile` needs to be installed separately as:
```
pip install --upgrade soundfile
sudo apt install libsndfile1
```
We can now import `soundfile`:
```python
>>> import soundfile
>>> soundfile.__version__
'0.12.1'
>>> soundfile.__libsndfile_version__
'1.0.28'
```
We see that we have `soundfile==0.12.1`, which matches the `datasets[audio]` package constraints:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/setup.py#L144-L147
But we have `libsndfile==1.0.28`, which is too low for decoding mp3 files:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/src/datasets/config.py#L136-L138
Updating/upgrading the `libsndfile` doesn't change this:
```
sudo apt-get update
sudo apt-get upgrade
```
Is there any other suggestion for how to get a compatible `libsndfile` version? Currently, the version bundled with Ubuntu `apt-get` is too low for decoding mp3 files.
Maybe we could add this under `setup.py` such that we install the correct `libsndfile` version when we do `pip install datasets[audio]`? IMO this would help circumvent such version issues.
### Steps to reproduce the bug
Environment described above. Loading mp3 files:
```python
from datasets import load_dataset
common_voice_es = load_dataset("common_voice", "es", split="validation", streaming=True)
print(next(iter(common_voice_es)))
```
```python
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[4], line 2
1 common_voice_es = load_dataset("common_voice", "es", split="validation", streaming=True)
----> 2 print(next(iter(common_voice_es)))
File ~/datasets/src/datasets/iterable_dataset.py:941, in IterableDataset.__iter__(self)
937 for key, example in ex_iterable:
938 if self.features:
939 # `IterableDataset` automatically fills missing columns with None.
940 # This is done with `_apply_feature_types_on_example`.
--> 941 yield _apply_feature_types_on_example(
942 example, self.features, token_per_repo_id=self._token_per_repo_id
943 )
944 else:
945 yield example
File ~/datasets/src/datasets/iterable_dataset.py:700, in _apply_feature_types_on_example(example, features, token_per_repo_id)
698 encoded_example = features.encode_example(example)
699 # Decode example for Audio feature, e.g.
--> 700 decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
701 return decoded_example
File ~/datasets/src/datasets/features/features.py:1864, in Features.decode_example(self, example, token_per_repo_id)
1850 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1851 """Decode example with custom feature decoding.
1852
1853 Args:
(...)
1861 `dict[str, Any]`
1862 """
-> 1864 return {
1865 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1866 if self._column_requires_decoding[column_name]
1867 else value
1868 for column_name, (feature, value) in zip_dict(
1869 {key: value for key, value in self.items() if key in example}, example
1870 )
1871 }
File ~/datasets/src/datasets/features/features.py:1865, in <dictcomp>(.0)
1850 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1851 """Decode example with custom feature decoding.
1852
1853 Args:
(...)
1861 `dict[str, Any]`
1862 """
1864 return {
-> 1865 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1866 if self._column_requires_decoding[column_name]
1867 else value
1868 for column_name, (feature, value) in zip_dict(
1869 {key: value for key, value in self.items() if key in example}, example
1870 )
1871 }
File ~/datasets/src/datasets/features/features.py:1308, in decode_nested_example(schema, obj, token_per_repo_id)
1305 elif isinstance(schema, (Audio, Image)):
1306 # we pass the token to read and decode files from private repositories in streaming mode
1307 if obj is not None and schema.decode:
-> 1308 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
1309 return obj
File ~/datasets/src/datasets/features/audio.py:167, in Audio.decode_example(self, value, token_per_repo_id)
162 raise RuntimeError(
163 "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
164 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '
165 )
166 elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
--> 167 raise RuntimeError(
168 "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
169 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '
170 )
172 if file is None:
173 token_per_repo_id = token_per_repo_id or {}
RuntimeError: Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`.
```
### Expected behavior
Load mp3 files!
### Environment info
- `datasets` version: 2.10.2.dev0
- Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.13.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- Soundfile version: 0.12.1
- Libsndfile version: 1.0.28 | {
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@mariosasko | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5656). All of your documentation changes will be reflected on that endpoint."
] | 2023-03-21T15:23:29 | 2023-03-22T13:55:30 | null | CONTRIBUTOR | null | Most HTTP(S) downloads from this library support proxy automatically by reading the `HTTP_PROXY` environment variable (et al.) because `requests` is widely used. However, in some parts of the code, `fsspec` is used, which in turn uses `aiohttp` for HTTP(S) requests (as opposed to `requests`), which in turn doesn't support reading proxy env variables by default. This PR enables reading them automatically.
Read [aiohttp docs on using proxies](https://docs.aiohttp.org/en/stable/client_advanced.html?highlight=trust_env#proxy-support).
For context, [the Python library requests](https://requests.readthedocs.io/en/latest/user/advanced/?highlight=http_proxy#proxies) and [the official Python library via `urllib.urlopen` support this automatically by default](https://docs.python.org/3/library/urllib.request.html#urllib.request.urlopen). Many (most common ones?) programs also do the same, including cURL, APT, Wget, and many others. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5655). All of your documentation changes will be reflected on that endpoint.",
"<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) | 0.009691 / 0.011353 (-0.001662) | 0.006160 / 0.011008 (-0.004848) | 0.127528 / 0.038508 (0.089020) | 0.034445 / 0.023109 (0.011335) | 0.391483 / 0.275898 (0.115585) | 0.425922 / 0.323480 (0.102442) | 0.006621 / 0.007986 (-0.001365) | 0.004550 / 0.004328 (0.000221) | 0.099134 / 0.004250 (0.094884) | 0.051089 / 0.037052 (0.014037) | 0.398675 / 0.258489 (0.140186) | 0.456740 / 0.293841 (0.162899) | 0.052279 / 0.128546 (-0.076267) | 0.020878 / 0.075646 (-0.054768) | 0.414954 / 0.419271 (-0.004317) | 0.061903 / 0.043533 (0.018370) | 0.393088 / 0.255139 (0.137949) | 0.410289 / 0.283200 (0.127089) | 0.101684 / 0.141683 (-0.039998) | 1.747102 / 1.452155 (0.294947) | 1.896976 / 1.492716 (0.404260) |\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) | 0.203193 / 0.018006 (0.185187) | 0.495011 / 0.000490 (0.494521) | 0.006290 / 0.000200 (0.006090) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034840 / 0.037411 (-0.002571) | 0.122529 / 0.014526 (0.108003) | 0.133870 / 0.176557 (-0.042686) | 0.207771 / 0.737135 (-0.529364) | 0.141441 / 0.296338 (-0.154897) |\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) | 0.604190 / 0.215209 (0.388981) | 6.040295 / 2.077655 (3.962641) | 2.405703 / 1.504120 (0.901583) | 2.062767 / 1.541195 (0.521572) | 2.079313 / 1.468490 (0.610823) | 1.240107 / 4.584777 (-3.344670) | 5.316583 / 3.745712 (1.570871) | 3.104758 / 5.269862 (-2.165103) | 2.056489 / 4.565676 (-2.509187) | 0.149060 / 0.424275 (-0.275215) | 0.014467 / 0.007607 (0.006860) | 0.736882 / 0.226044 (0.510838) | 7.324142 / 2.268929 (5.055213) | 3.048752 / 55.444624 (-52.395872) | 2.385013 / 6.876477 (-4.491463) | 2.457478 / 2.142072 (0.315405) | 1.459276 / 4.805227 (-3.345951) | 0.253882 / 6.500664 (-6.246782) | 0.076756 / 0.075469 (0.001287) |\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) | 1.499166 / 1.841788 (-0.342622) | 17.294165 / 8.074308 (9.219857) | 20.385668 / 10.191392 (10.194276) | 0.254633 / 0.680424 (-0.425791) | 0.026253 / 0.534201 (-0.507948) | 0.532928 / 0.579283 (-0.046355) | 0.606095 / 0.434364 (0.171731) | 0.615025 / 0.540337 (0.074687) | 0.728651 / 1.386936 (-0.658285) |\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) | 0.009376 / 0.011353 (-0.001977) | 0.005981 / 0.011008 (-0.005027) | 0.109898 / 0.038508 (0.071390) | 0.033746 / 0.023109 (0.010637) | 0.410226 / 0.275898 (0.134328) | 0.470606 / 0.323480 (0.147126) | 0.006706 / 0.007986 (-0.001279) | 0.004482 / 0.004328 (0.000153) | 0.092280 / 0.004250 (0.088030) | 0.047988 / 0.037052 (0.010935) | 0.430628 / 0.258489 (0.172139) | 0.480668 / 0.293841 (0.186827) | 0.052099 / 0.128546 (-0.076447) | 0.018743 / 0.075646 (-0.056903) | 0.112204 / 0.419271 (-0.307068) | 0.059838 / 0.043533 (0.016305) | 0.418230 / 0.255139 (0.163091) | 0.451568 / 0.283200 (0.168368) | 0.107026 / 0.141683 (-0.034657) | 1.708111 / 1.452155 (0.255956) | 1.839268 / 1.492716 (0.346552) |\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) | 0.229558 / 0.018006 (0.211552) | 0.488099 / 0.000490 (0.487609) | 0.004643 / 0.000200 (0.004443) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030461 / 0.037411 (-0.006951) | 0.120993 / 0.014526 (0.106467) | 0.130874 / 0.176557 (-0.045682) | 0.193550 / 0.737135 (-0.543585) | 0.138164 / 0.296338 (-0.158174) |\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) | 0.635709 / 0.215209 (0.420500) | 6.225112 / 2.077655 (4.147457) | 2.639584 / 1.504120 (1.135465) | 2.254487 / 1.541195 (0.713293) | 2.280478 / 1.468490 (0.811988) | 1.205712 / 4.584777 (-3.379065) | 5.367845 / 3.745712 (1.622133) | 3.020207 / 5.269862 (-2.249655) | 2.001897 / 4.565676 (-2.563779) | 0.149582 / 0.424275 (-0.274693) | 0.014867 / 0.007607 (0.007260) | 0.759050 / 0.226044 (0.533006) | 7.692969 / 2.268929 (5.424041) | 3.274009 / 55.444624 (-52.170615) | 2.635529 / 6.876477 (-4.240948) | 2.672960 / 2.142072 (0.530888) | 1.426487 / 4.805227 (-3.378740) | 0.253368 / 6.500664 (-6.247296) | 0.078650 / 0.075469 (0.003181) |\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) | 1.620265 / 1.841788 (-0.221523) | 17.674168 / 8.074308 (9.599860) | 21.120528 / 10.191392 (10.929136) | 0.244205 / 0.680424 (-0.436218) | 0.029646 / 0.534201 (-0.504555) | 0.510948 / 0.579283 (-0.068335) | 0.586255 / 0.434364 (0.151891) | 0.589286 / 0.540337 (0.048949) | 0.736561 / 1.386936 (-0.650375) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de5fe9ae5df84c489e08dcbdc3d2d20272b312c3 \"CML watermark\")\n",
"<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) | 0.007778 / 0.011353 (-0.003575) | 0.005432 / 0.011008 (-0.005577) | 0.098776 / 0.038508 (0.060268) | 0.035196 / 0.023109 (0.012087) | 0.305646 / 0.275898 (0.029748) | 0.342661 / 0.323480 (0.019181) | 0.006513 / 0.007986 (-0.001472) | 0.005897 / 0.004328 (0.001568) | 0.075797 / 0.004250 (0.071547) | 0.056060 / 0.037052 (0.019007) | 0.306645 / 0.258489 (0.048156) | 0.352447 / 0.293841 (0.058606) | 0.037304 / 0.128546 (-0.091242) | 0.012514 / 0.075646 (-0.063132) | 0.334949 / 0.419271 (-0.084323) | 0.051600 / 0.043533 (0.008067) | 0.302302 / 0.255139 (0.047163) | 0.322238 / 0.283200 (0.039038) | 0.106896 / 0.141683 (-0.034787) | 1.483163 / 1.452155 (0.031008) | 1.587483 / 1.492716 (0.094767) |\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) | 0.292318 / 0.018006 (0.274312) | 0.541541 / 0.000490 (0.541051) | 0.008342 / 0.000200 (0.008142) | 0.000339 / 0.000054 (0.000285) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028287 / 0.037411 (-0.009124) | 0.107775 / 0.014526 (0.093250) | 0.119112 / 0.176557 (-0.057445) | 0.174002 / 0.737135 (-0.563134) | 0.126531 / 0.296338 (-0.169808) |\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) | 0.401684 / 0.215209 (0.186475) | 4.024708 / 2.077655 (1.947053) | 1.812763 / 1.504120 (0.308643) | 1.629540 / 1.541195 (0.088345) | 1.731733 / 1.468490 (0.263243) | 0.711066 / 4.584777 (-3.873711) | 3.867499 / 3.745712 (0.121786) | 3.615968 / 5.269862 (-1.653893) | 1.876077 / 4.565676 (-2.689600) | 0.087003 / 0.424275 (-0.337272) | 0.012445 / 0.007607 (0.004838) | 0.499106 / 0.226044 (0.273061) | 4.975920 / 2.268929 (2.706992) | 2.279074 / 55.444624 (-53.165550) | 1.952311 / 6.876477 (-4.924166) | 2.167480 / 2.142072 (0.025408) | 0.855882 / 4.805227 (-3.949346) | 0.171378 / 6.500664 (-6.329287) | 0.066731 / 0.075469 (-0.008738) |\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) | 1.184226 / 1.841788 (-0.657561) | 15.383396 / 8.074308 (7.309088) | 15.069783 / 10.191392 (4.878391) | 0.161489 / 0.680424 (-0.518935) | 0.017763 / 0.534201 (-0.516438) | 0.427103 / 0.579283 (-0.152180) | 0.434295 / 0.434364 (-0.000069) | 0.496848 / 0.540337 (-0.043489) | 0.592572 / 1.386936 (-0.794364) |\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) | 0.008014 / 0.011353 (-0.003339) | 0.005607 / 0.011008 (-0.005401) | 0.076826 / 0.038508 (0.038318) | 0.035283 / 0.023109 (0.012174) | 0.347809 / 0.275898 (0.071911) | 0.382482 / 0.323480 (0.059003) | 0.006276 / 0.007986 (-0.001709) | 0.005978 / 0.004328 (0.001650) | 0.074938 / 0.004250 (0.070687) | 0.054323 / 0.037052 (0.017271) | 0.344027 / 0.258489 (0.085538) | 0.397623 / 0.293841 (0.103783) | 0.037851 / 0.128546 (-0.090695) | 0.012649 / 0.075646 (-0.062997) | 0.086169 / 0.419271 (-0.333103) | 0.051510 / 0.043533 (0.007977) | 0.341112 / 0.255139 (0.085973) | 0.357957 / 0.283200 (0.074757) | 0.110949 / 0.141683 (-0.030734) | 1.479573 / 1.452155 (0.027419) | 1.578572 / 1.492716 (0.085855) |\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) | 0.310678 / 0.018006 (0.292672) | 0.525504 / 0.000490 (0.525015) | 0.000447 / 0.000200 (0.000247) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031262 / 0.037411 (-0.006149) | 0.113801 / 0.014526 (0.099275) | 0.124967 / 0.176557 (-0.051590) | 0.175226 / 0.737135 (-0.561909) | 0.129377 / 0.296338 (-0.166962) |\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) | 0.420672 / 0.215209 (0.205463) | 4.181337 / 2.077655 (2.103682) | 1.985524 / 1.504120 (0.481404) | 1.803468 / 1.541195 (0.262273) | 1.952915 / 1.468490 (0.484425) | 0.710928 / 4.584777 (-3.873849) | 3.886245 / 3.745712 (0.140533) | 3.737837 / 5.269862 (-1.532024) | 1.806859 / 4.565676 (-2.758818) | 0.088461 / 0.424275 (-0.335814) | 0.013125 / 0.007607 (0.005518) | 0.522410 / 0.226044 (0.296365) | 5.232591 / 2.268929 (2.963663) | 2.451188 / 55.444624 (-52.993437) | 2.127725 / 6.876477 (-4.748751) | 2.232859 / 2.142072 (0.090786) | 0.854257 / 4.805227 (-3.950970) | 0.171004 / 6.500664 (-6.329661) | 0.066724 / 0.075469 (-0.008746) |\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) | 1.257700 / 1.841788 (-0.584088) | 15.738605 / 8.074308 (7.664297) | 15.021698 / 10.191392 (4.830306) | 0.147422 / 0.680424 (-0.533002) | 0.017928 / 0.534201 (-0.516273) | 0.428121 / 0.579283 (-0.151162) | 0.432056 / 0.434364 (-0.002308) | 0.498318 / 0.540337 (-0.042020) | 0.591040 / 1.386936 (-0.795896) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ac74267032ef3608779a8c8c4361b95a83ecbcb \"CML watermark\")\n"
] | 2023-03-21T14:18:09 | 2023-03-22T16:31:24 | null | MEMBER | null | Following discussion at https://github.com/huggingface/datasets/pull/5589
Right now `to_iterable_dataset` on images/audio hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images/audios unnecessarily).
I fixed it by providing a generator that yields undecoded examples | {
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https://api.github.com/repos/huggingface/datasets/issues/5654 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5654/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5654/comments | https://api.github.com/repos/huggingface/datasets/issues/5654/events | https://github.com/huggingface/datasets/issues/5654 | 1,633,523,705 | I_kwDODunzps5hXZf5 | 5,654 | Offset overflow when executing Dataset.map | {
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"Upd. the above code works if we replace `25` with `1`, but the result value at key \"hr\" is not a tensor but a list of lists of lists of uint8.\r\n\r\nAdding `train_data.set_format(\"torch\")` after map fixes this, but the original issue remains\r\n\r\n",
"As a workaround, one can replace\r\n`return {\"hr\": torch.stack([crop_transf(tensor) for _ in range(25)])}`\r\nwith\r\n`return {f\"hr_crop_{i}\": crop_transf(tensor) for i in range(25)}`\r\nand then choose appropriate crop randomly in further processing, but I still don't understand why the original approach doesn't work(\r\n"
] | 2023-03-21T09:33:27 | 2023-03-21T10:32:07 | null | NONE | null | ### Describe the bug
Hi, I'm trying to use `.map` method to cache multiple random crops from the image to speed up data processing during training, as the image size is too big.
The map function executes all iterations, and then returns the following error:
```bash
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3353, in _map_single
writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_writer.py", line 582, in finalize
self.write_examples_on_file()
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_writer.py", line 446, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_writer.py", line 555, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/home/ubuntu/miniconda3/envs/enhancement/lib/python3.8/site-packages/datasets/arrow_writer.py", line 567, in write_table
pa_table = pa_table.combine_chunks()
File "pyarrow/table.pxi", line 3315, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
```
Here is the minimal code (`/home/datasets/DIV2K_train_HR` is just a folder of images that can be replaced by any appropriate):
### Steps to reproduce the bug
```python
from glob import glob
import torch
from datasets import Dataset, Image
from torchvision.transforms import PILToTensor, RandomCrop
file_paths = glob("/home/datasets/DIV2K_train_HR/*")
to_tensor = PILToTensor()
crop_transf = RandomCrop(size=256)
def prepare_data(example):
tensor = to_tensor(example["image"].convert("RGB"))
return {"hr": torch.stack([crop_transf(tensor) for _ in range(25)])}
train_data = Dataset.from_dict({"image": file_paths}).cast_column("image", Image())
train_data = train_data.map(
prepare_data,
cache_file_name="/home/datasets/DIV2K_train_HR_crops.tmp",
desc="Caching multiple random crops of image",
remove_columns="image",
)
print(train_data[0].keys(), train_data[0]["hr"].shape)
```
### Expected behavior
Cached file is stored at `"/home/datasets/DIV2K_train_HR_crops.tmp"`, output is `dict_keys(['hr']) torch.Size([25, 3, 256, 256])`
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.10
- Python version: 3.8.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- Pytorch version: 2.0.0+cu117
- torchvision version: 0.15.1+cu117 | {
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"I agree this should be documented"
] | 2023-03-21T05:25:35 | 2023-03-21T13:19:57 | null | NONE | null | ### Describe the bug
[`num_proc`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.DatasetDict.save_to_disk.num_proc) will affect `num_shards`, but it's not documented
### Steps to reproduce the bug
Nothing to reproduce
### Expected behavior
[document of `num_shards`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.DatasetDict.save_to_disk.num_shards) explicitly says that it depends on `max_shard_size`, it should also mention `num_proc`.
### Environment info
datasets main document | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5652). All of your documentation changes will be reflected on that endpoint.",
"<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) | 0.007455 / 0.011353 (-0.003898) | 0.005278 / 0.011008 (-0.005731) | 0.098981 / 0.038508 (0.060473) | 0.029208 / 0.023109 (0.006099) | 0.304132 / 0.275898 (0.028234) | 0.340010 / 0.323480 (0.016530) | 0.005514 / 0.007986 (-0.002472) | 0.003636 / 0.004328 (-0.000692) | 0.076737 / 0.004250 (0.072486) | 0.041985 / 0.037052 (0.004933) | 0.314941 / 0.258489 (0.056452) | 0.346686 / 0.293841 (0.052845) | 0.032528 / 0.128546 (-0.096018) | 0.011795 / 0.075646 (-0.063851) | 0.322122 / 0.419271 (-0.097150) | 0.051548 / 0.043533 (0.008015) | 0.310561 / 0.255139 (0.055422) | 0.329443 / 0.283200 (0.046243) | 0.092820 / 0.141683 (-0.048863) | 1.495764 / 1.452155 (0.043609) | 1.586734 / 1.492716 (0.094018) |\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) | 0.195830 / 0.018006 (0.177824) | 0.422075 / 0.000490 (0.421586) | 0.005483 / 0.000200 (0.005283) | 0.000133 / 0.000054 (0.000078) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023468 / 0.037411 (-0.013943) | 0.097713 / 0.014526 (0.083187) | 0.105331 / 0.176557 (-0.071225) | 0.166237 / 0.737135 (-0.570898) | 0.108924 / 0.296338 (-0.187415) |\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) | 0.671901 / 0.215209 (0.456692) | 6.745691 / 2.077655 (4.668036) | 2.132508 / 1.504120 (0.628388) | 1.693808 / 1.541195 (0.152614) | 1.715282 / 1.468490 (0.246792) | 0.955354 / 4.584777 (-3.629422) | 3.810296 / 3.745712 (0.064584) | 2.214891 / 5.269862 (-3.054970) | 1.461513 / 4.565676 (-3.104164) | 0.109846 / 0.424275 (-0.314430) | 0.013546 / 0.007607 (0.005939) | 0.780046 / 0.226044 (0.554001) | 7.789020 / 2.268929 (5.520091) | 2.602411 / 55.444624 (-52.842213) | 1.995096 / 6.876477 (-4.881380) | 2.009022 / 2.142072 (-0.133051) | 1.069215 / 4.805227 (-3.736012) | 0.179812 / 6.500664 (-6.320852) | 0.068125 / 0.075469 (-0.007344) |\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) | 1.201866 / 1.841788 (-0.639921) | 13.878814 / 8.074308 (5.804506) | 14.179264 / 10.191392 (3.987872) | 0.128908 / 0.680424 (-0.551515) | 0.017257 / 0.534201 (-0.516944) | 0.379500 / 0.579283 (-0.199783) | 0.393308 / 0.434364 (-0.041056) | 0.444700 / 0.540337 (-0.095638) | 0.531043 / 1.386936 (-0.855893) |\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) | 0.007413 / 0.011353 (-0.003940) | 0.005431 / 0.011008 (-0.005577) | 0.078158 / 0.038508 (0.039650) | 0.028837 / 0.023109 (0.005728) | 0.343635 / 0.275898 (0.067737) | 0.383041 / 0.323480 (0.059561) | 0.005283 / 0.007986 (-0.002703) | 0.003673 / 0.004328 (-0.000655) | 0.076461 / 0.004250 (0.072211) | 0.038625 / 0.037052 (0.001573) | 0.341109 / 0.258489 (0.082620) | 0.387027 / 0.293841 (0.093186) | 0.032512 / 0.128546 (-0.096034) | 0.011903 / 0.075646 (-0.063744) | 0.086340 / 0.419271 (-0.332931) | 0.043211 / 0.043533 (-0.000321) | 0.339994 / 0.255139 (0.084855) | 0.370868 / 0.283200 (0.087668) | 0.091679 / 0.141683 (-0.050004) | 1.547188 / 1.452155 (0.095033) | 1.578545 / 1.492716 (0.085829) |\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) | 0.216981 / 0.018006 (0.198975) | 0.412206 / 0.000490 (0.411716) | 0.004243 / 0.000200 (0.004043) | 0.000130 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025392 / 0.037411 (-0.012020) | 0.102577 / 0.014526 (0.088051) | 0.107672 / 0.176557 (-0.068884) | 0.160657 / 0.737135 (-0.576478) | 0.111646 / 0.296338 (-0.184692) |\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) | 0.698815 / 0.215209 (0.483606) | 6.958931 / 2.077655 (4.881276) | 2.344216 / 1.504120 (0.840096) | 1.907752 / 1.541195 (0.366557) | 1.964251 / 1.468490 (0.495761) | 0.950754 / 4.584777 (-3.634023) | 3.829700 / 3.745712 (0.083988) | 3.055565 / 5.269862 (-2.214297) | 1.575851 / 4.565676 (-2.989825) | 0.109227 / 0.424275 (-0.315048) | 0.013163 / 0.007607 (0.005556) | 0.804613 / 0.226044 (0.578569) | 8.015035 / 2.268929 (5.746107) | 2.796358 / 55.444624 (-52.648266) | 2.212561 / 6.876477 (-4.663916) | 2.229918 / 2.142072 (0.087845) | 1.062041 / 4.805227 (-3.743186) | 0.181384 / 6.500664 (-6.319280) | 0.068564 / 0.075469 (-0.006905) |\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) | 1.287904 / 1.841788 (-0.553884) | 14.539222 / 8.074308 (6.464914) | 14.232097 / 10.191392 (4.040705) | 0.130870 / 0.680424 (-0.549554) | 0.016710 / 0.534201 (-0.517491) | 0.384454 / 0.579283 (-0.194829) | 0.391750 / 0.434364 (-0.042614) | 0.443995 / 0.540337 (-0.096343) | 0.526255 / 1.386936 (-0.860681) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bd46874a580b888bdc82b53daace79884f04bc62 \"CML watermark\")\n",
"Arf I need to fix some tests first - sorry",
"<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) | 0.008393 / 0.011353 (-0.002959) | 0.005635 / 0.011008 (-0.005373) | 0.114840 / 0.038508 (0.076332) | 0.039272 / 0.023109 (0.016163) | 0.352116 / 0.275898 (0.076218) | 0.386614 / 0.323480 (0.063134) | 0.006348 / 0.007986 (-0.001638) | 0.005872 / 0.004328 (0.001544) | 0.086437 / 0.004250 (0.082187) | 0.054003 / 0.037052 (0.016951) | 0.350302 / 0.258489 (0.091813) | 0.400148 / 0.293841 (0.106308) | 0.042436 / 0.128546 (-0.086111) | 0.013987 / 0.075646 (-0.061660) | 0.399434 / 0.419271 (-0.019837) | 0.059223 / 0.043533 (0.015690) | 0.354511 / 0.255139 (0.099372) | 0.377764 / 0.283200 (0.094564) | 0.112297 / 0.141683 (-0.029386) | 1.677483 / 1.452155 (0.225328) | 1.784942 / 1.492716 (0.292226) |\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) | 0.233334 / 0.018006 (0.215328) | 0.450575 / 0.000490 (0.450085) | 0.000376 / 0.000200 (0.000176) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031995 / 0.037411 (-0.005416) | 0.126798 / 0.014526 (0.112272) | 0.138453 / 0.176557 (-0.038104) | 0.207360 / 0.737135 (-0.529775) | 0.147744 / 0.296338 (-0.148594) |\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) | 0.481496 / 0.215209 (0.266287) | 4.810495 / 2.077655 (2.732840) | 2.457917 / 1.504120 (0.953797) | 2.300073 / 1.541195 (0.758879) | 2.065595 / 1.468490 (0.597105) | 0.814589 / 4.584777 (-3.770188) | 4.566496 / 3.745712 (0.820784) | 2.386947 / 5.269862 (-2.882914) | 1.531639 / 4.565676 (-3.034037) | 0.099569 / 0.424275 (-0.324706) | 0.014971 / 0.007607 (0.007364) | 0.590359 / 0.226044 (0.364314) | 5.885250 / 2.268929 (3.616322) | 2.706799 / 55.444624 (-52.737826) | 2.324485 / 6.876477 (-4.551992) | 2.452751 / 2.142072 (0.310678) | 0.966955 / 4.805227 (-3.838272) | 0.198165 / 6.500664 (-6.302499) | 0.076877 / 0.075469 (0.001408) |\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) | 1.499085 / 1.841788 (-0.342702) | 17.705516 / 8.074308 (9.631208) | 16.481174 / 10.191392 (6.289782) | 0.191832 / 0.680424 (-0.488592) | 0.021417 / 0.534201 (-0.512784) | 0.519647 / 0.579283 (-0.059636) | 0.498432 / 0.434364 (0.064068) | 0.598206 / 0.540337 (0.057868) | 0.700990 / 1.386936 (-0.685946) |\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) | 0.008746 / 0.011353 (-0.002607) | 0.006052 / 0.011008 (-0.004956) | 0.092938 / 0.038508 (0.054430) | 0.038932 / 0.023109 (0.015823) | 0.406919 / 0.275898 (0.131021) | 0.444325 / 0.323480 (0.120845) | 0.006735 / 0.007986 (-0.001251) | 0.005972 / 0.004328 (0.001643) | 0.088152 / 0.004250 (0.083902) | 0.051009 / 0.037052 (0.013957) | 0.407415 / 0.258489 (0.148926) | 0.481048 / 0.293841 (0.187207) | 0.043268 / 0.128546 (-0.085278) | 0.014574 / 0.075646 (-0.061072) | 0.103555 / 0.419271 (-0.315716) | 0.058251 / 0.043533 (0.014719) | 0.406294 / 0.255139 (0.151155) | 0.429229 / 0.283200 (0.146029) | 0.116977 / 0.141683 (-0.024705) | 1.765885 / 1.452155 (0.313730) | 1.885557 / 1.492716 (0.392841) |\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) | 0.284014 / 0.018006 (0.266008) | 0.458066 / 0.000490 (0.457576) | 0.022286 / 0.000200 (0.022086) | 0.000158 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033971 / 0.037411 (-0.003440) | 0.132030 / 0.014526 (0.117504) | 0.141725 / 0.176557 (-0.034831) | 0.199818 / 0.737135 (-0.537318) | 0.149176 / 0.296338 (-0.147162) |\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) | 0.511463 / 0.215209 (0.296254) | 4.917921 / 2.077655 (2.840267) | 2.382377 / 1.504120 (0.878257) | 2.154599 / 1.541195 (0.613404) | 2.247858 / 1.468490 (0.779368) | 0.834524 / 4.584777 (-3.750253) | 4.560010 / 3.745712 (0.814297) | 2.403055 / 5.269862 (-2.866806) | 1.780784 / 4.565676 (-2.784893) | 0.101409 / 0.424275 (-0.322866) | 0.014657 / 0.007607 (0.007050) | 0.610137 / 0.226044 (0.384093) | 6.051011 / 2.268929 (3.782083) | 2.887357 / 55.444624 (-52.557267) | 2.518225 / 6.876477 (-4.358252) | 2.559654 / 2.142072 (0.417582) | 0.981226 / 4.805227 (-3.824001) | 0.197323 / 6.500664 (-6.303341) | 0.076851 / 0.075469 (0.001382) |\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) | 1.554662 / 1.841788 (-0.287126) | 18.038993 / 8.074308 (9.964685) | 16.719948 / 10.191392 (6.528556) | 0.195641 / 0.680424 (-0.484783) | 0.020699 / 0.534201 (-0.513502) | 0.498949 / 0.579283 (-0.080334) | 0.487775 / 0.434364 (0.053411) | 0.591413 / 0.540337 (0.051075) | 0.708520 / 1.386936 (-0.678416) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#39de0d78224c070be33d0820ec9203018fb721d1 \"CML watermark\")\n",
"Ready for review @mariosasko :)",
"Yea it does behave as expected, but modifying a dataset's features dict is not recommended and can lead to unpredictable behaviors. By copying the features, we make sure users don't modify the dataset's features dict.\r\n\r\nSince the attribute is public, users expect to be able to do whatever they want with it, without checking if they have to copy it or not"
] | 2023-03-20T17:17:23 | 2023-03-22T15:00:00 | null | MEMBER | null | Some users (even internally at HF) are doing
```python
dset_features = dset.features
dset_features.pop(col_to_remove)
dset = dset.map(..., features=dset_features)
```
Right now this causes issues because it modifies the features dict in place before the map.
In this PR I modified `dset.features` to return a copy of the features, so that users can modify it if they want. | {
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} | [] | open | false | null | [] | null | [] | 2023-03-20T12:02:18 | 2023-03-20T12:03:59 | null | NONE | null | ### Describe the bug
save_to_disk() does not expand `~`
1. `dataset = load_datasets("any dataset")`
2. `dataset.save_to_disk("~/data")`
3. a folder named "~" created in current folder
4. FileNotFoundError is raised, because the expanded path does not exist (`/home/<user>/data`)
related issue https://github.com/huggingface/transformers/issues/10628
### Steps to reproduce the bug
As described above.
### Expected behavior
expanduser correctly
### Environment info
- datasets 2.10.1
- python 3.10 | {
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"Can you post a reproducible code snippet of what you tried to do?\r\n\r\n",
"> Can you post a reproducible code snippet of what you tried to do?\n> \n> \n\n```python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"my_folder_name\", split=\"train\")\n```",
"hi @WiNE-iNEFF ! can you please also tell a bit more about how your data is structured (directory structure and filenames patterns)?",
"> hi @WiNE-iNEFF ! can you please also tell a bit more about how your data is structured (directory structure and filenames patterns)?\n\nAll file have format .png converted in RGBA. \nIn main folder \"MyData\" contain 4 folder with images. In function load_dataset i use folder \"MyData\"",
"@WiNE-iNEFF I'm sorry there is still not enough information to answer your question :( For now I can only assume that your [filenames contain split names](https://huggingface.co/docs/datasets/repository_structure#splits-and-file-names) which are somehow incorrectly parsed. \r\nWhat would be the output if you omit `split` while loading? Like just\r\n```python\r\nds = load_dataset(\"MyData\")\r\nprint(ds)\r\n```\r\n\r\n",
"> @WiNE-iNEFF I'm sorry there is still not enough information to answer your question :( For now I can only assume that your [filenames contain split names](https://huggingface.co/docs/datasets/repository_structure#splits-and-file-names) which are somehow incorrectly parsed. \n> What would be the output if you omit `split` while loading? Like just\n> ```python\n> ds = load_dataset(\"MyData\")\n> print(ds)\n> ```\n> \n> \n\n```python\nDataset({\n features: ['image', 'label'],\n num_rows: 4\n})\n```",
"@WiNE-iNEFF My only guess is that 4 images in your data have `\"train\"` string in their names (something like `\"train_image_0.png\"`) and others do not and the loader ignores all the files that do not contain split name in filename. If it's true, please try to remove \"train\" from filenames. Or maybe they are inside a directory named \"train\", then the directory should be renamed (unless you want to put only these 4 specific images to the train but apparently you do not).\r\n\r\nIf there is a bug I cannot investigate it unfortunately because I cannot reproduce your case without some data samples. ",
"> @WiNE-iNEFF My only guess is that 4 images in your data have `\"train\"` string in their names (something like `\"train_image_0.png\"`) and others do not and the loader ignores all the files that do not contain split name in filename. If it's true, please try to remove \"train\" from filenames. Or maybe they are inside a directory named \"train\", then the directory should be renamed (unless you want to put only these 4 specific images to the train but apparently you do not).\n> \n> If there is a bug I cannot investigate it unfortunately because I cannot reproduce your case without some data samples. \n\nI checked my files and some of them do have the words train, valid and test in their names, but the number of such images is more than 500, not 4.",
"@WiNE-iNEFF Probably they are named inconsistently so that the correct pattern for which files should correspond to which split cannot be inferred. You can make it clearer to the loader by removing split names from filenames and putting files in separate folder for each split (you can take a look at the [documentation for imagefolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder)):\r\n```\r\n Fuaimeanna2/\r\n├─ test\r\n│ ├─ label_0\r\n│ │ ├── filename_0.jpg\r\n│ │ └── filename_1.jpg\r\n│ │ └── ...\r\n│ ├─ label_1\r\n│ │ └── ...\r\n│ ├─ label_2\r\n│ │ └── ...\r\n│ └─ label_3\r\n│ └── ...\r\n├─ train\r\n│ ├─ label_0\r\n│ │ └── ...\r\n│ ├─ label_1\r\n│ │ └── ...\r\n│ ├─ label_2\r\n│ │ └── ...\r\n│ └─ label_3\r\n│ └── ...\r\n└── validation\r\n ├─ label_0\r\n │ └── ...\r\n ├─ label_1\r\n │ └── ...\r\n ├─ label_2\r\n │ └── ...\r\n └─ label_3\r\n └── ...\r\n```",
"> @WiNE-iNEFF Probably they are named inconsistently so that the correct pattern for which files should correspond to which split cannot be inferred. You can make it clearer to the loader by removing split names from filenames and putting files in separate folder for each split (you can take a look at the [documentation for imagefolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder)):\n> ```\n> Fuaimeanna2/\n> ├─ test\n> │ ├─ label_0\n> │ │ ├── filename_0.jpg\n> │ │ └── filename_1.jpg\n> │ │ └── ...\n> │ ├─ label_1\n> │ │ └── ...\n> │ ├─ label_2\n> │ │ └── ...\n> │ └─ label_3\n> │ └── ...\n> ├─ train\n> │ ├─ label_0\n> │ │ └── ...\n> │ ├─ label_1\n> │ │ └── ...\n> │ ├─ label_2\n> │ │ └── ...\n> │ └─ label_3\n> │ └── ...\n> └── validation\n> ├─ label_0\n> │ └── ...\n> ├─ label_1\n> │ └── ...\n> ├─ label_2\n> │ └── ...\n> └─ label_3\n> └── ...\n> ```\n\nI have read this documentation more than once. It just wasn't a problem before.",
"Hi,\r\n\r\nYou need to use:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"imagefolder\", split=\"train\", data_dir=\"path_to_your_folder\")\r\n```\r\ninstead of \r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"my_folder_name\", split=\"train\")\r\n```\r\nTo create an image dataset from your local folders."
] | 2023-03-18T13:59:13 | 2023-03-22T12:41:48 | null | NONE | null | I have about 20000 images in my folder which divided into 4 folders with class names.
When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked | {
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https://api.github.com/repos/huggingface/datasets/issues/5649 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5649/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5649/comments | https://api.github.com/repos/huggingface/datasets/issues/5649/events | https://github.com/huggingface/datasets/issues/5649 | 1,630,173,460 | I_kwDODunzps5hKnkU | 5,649 | The index column created with .to_sql() is dependent on the batch_size when writing | {
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"Thanks for reporting, @lsb. \r\n\r\nWe are investigating it.\r\n\r\nOn the other hand, please note that in the next `datasets` release, the index will not be created by default (see #5583). If you would like to have it, you will need to explicitly pass `index=True`. "
] | 2023-03-18T05:25:17 | 2023-03-20T13:16:12 | null | NONE | null | ### Describe the bug
It seems like the "index" column is designed to be unique? The values are only unique per batch. The SQL index is not a unique index.
This can be a problem, for instance, when building a faiss index on a dataset and then trying to match up ids with a sql export.
### Steps to reproduce the bug
```
from datasets import Dataset
import sqlite3
db = sqlite3.connect(":memory:")
nice_numbers = Dataset.from_dict({"nice_number": range(101,106)})
nice_numbers.to_sql("nice1", db, batch_size=1)
nice_numbers.to_sql("nice2", db, batch_size=2)
print(db.execute("select * from nice1").fetchall()) # [(0, 101), (0, 102), (0, 103), (0, 104), (0, 105)]
print(db.execute("select * from nice2").fetchall()) # [(0, 101), (1, 102), (0, 103), (1, 104), (0, 105)]
```
### Expected behavior
I expected the "index" column to be unique
### Environment info
```
% datasets-cli env
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.10.1
- Platform: macOS-13.2.1-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.2
zsh: segmentation fault datasets-cli env
``` | {
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"Thanks for reporting! This can be fixed by setting the format to `arrow` in `flatten_indices` and restoring the original format after the flattening. I'm working on a PR that reduces the number of the `flatten_indices` calls in our codebase and makes `flatten_indices` a no-op when a dataset does not have an indices mapping, so I'll incorporate the fix in that PR."
] | 2023-03-17T12:44:25 | 2023-03-21T13:12:03 | null | NONE | null | ### Describe the bug
Hi,
I noticed that `flatten_indices` throws an error when the batch format is `pandas`. This is probably due to the fact that flatten_indices uses map internally which doesn't accept dataframes as the transformation function output
### Steps to reproduce the bug
tabular_data = pd.DataFrame(np.random.randn(10,10))
tabular_data = datasets.arrow_dataset.Dataset.from_pandas(tabular_data)
tabular_data.with_format("pandas").select([0,1,2,3]).flatten_indices()
### Expected behavior
No error thrown
### Environment info
- `datasets` version: 2.10.1
- Python version: 3.9.5
- PyArrow version: 11.0.0
- Pandas version: 1.4.1 | {
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Make all print statements optional to speed up the development
### Motivation
Im loading multiple tiny datasets and all the print statements make the loading slower
### Your contribution
I can help contribute | {
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"_The documentation is not available anymore as the PR was closed or 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) | 0.009980 / 0.011353 (-0.001373) | 0.006643 / 0.011008 (-0.004366) | 0.140722 / 0.038508 (0.102214) | 0.036693 / 0.023109 (0.013584) | 0.430019 / 0.275898 (0.154121) | 0.463218 / 0.323480 (0.139738) | 0.006977 / 0.007986 (-0.001008) | 0.006488 / 0.004328 (0.002160) | 0.099385 / 0.004250 (0.095134) | 0.047160 / 0.037052 (0.010108) | 0.431440 / 0.258489 (0.172951) | 0.500232 / 0.293841 (0.206391) | 0.057968 / 0.128546 (-0.070578) | 0.020197 / 0.075646 (-0.055449) | 0.438269 / 0.419271 (0.018998) | 0.071149 / 0.043533 (0.027617) | 0.428502 / 0.255139 (0.173363) | 0.486861 / 0.283200 (0.203661) | 0.119855 / 0.141683 (-0.021828) | 1.875372 / 1.452155 (0.423218) | 1.955055 / 1.492716 (0.462339) |\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) | 0.243468 / 0.018006 (0.225462) | 0.547842 / 0.000490 (0.547352) | 0.004885 / 0.000200 (0.004685) | 0.000144 / 0.000054 (0.000089) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031555 / 0.037411 (-0.005856) | 0.125869 / 0.014526 (0.111343) | 0.137816 / 0.176557 (-0.038741) | 0.206581 / 0.737135 (-0.530555) | 0.142976 / 0.296338 (-0.153362) |\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) | 0.624773 / 0.215209 (0.409564) | 6.154861 / 2.077655 (4.077206) | 2.504586 / 1.504120 (1.000466) | 1.989118 / 1.541195 (0.447923) | 2.092280 / 1.468490 (0.623790) | 1.240108 / 4.584777 (-3.344669) | 5.584893 / 3.745712 (1.839181) | 3.075369 / 5.269862 (-2.194492) | 2.174285 / 4.565676 (-2.391391) | 0.141555 / 0.424275 (-0.282720) | 0.016099 / 0.007607 (0.008492) | 0.720543 / 0.226044 (0.494498) | 7.489000 / 2.268929 (5.220071) | 3.239189 / 55.444624 (-52.205435) | 2.525772 / 6.876477 (-4.350704) | 2.773514 / 2.142072 (0.631441) | 1.410084 / 4.805227 (-3.395143) | 0.259252 / 6.500664 (-6.241412) | 0.082573 / 0.075469 (0.007104) |\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) | 1.458186 / 1.841788 (-0.383602) | 17.503738 / 8.074308 (9.429430) | 20.817682 / 10.191392 (10.626290) | 0.231221 / 0.680424 (-0.449203) | 0.032550 / 0.534201 (-0.501651) | 0.559020 / 0.579283 (-0.020263) | 0.592987 / 0.434364 (0.158623) | 0.602661 / 0.540337 (0.062324) | 0.731912 / 1.386936 (-0.655024) |\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) | 0.009543 / 0.011353 (-0.001810) | 0.006953 / 0.011008 (-0.004055) | 0.087651 / 0.038508 (0.049143) | 0.031717 / 0.023109 (0.008608) | 0.437813 / 0.275898 (0.161915) | 0.468448 / 0.323480 (0.144968) | 0.007378 / 0.007986 (-0.000607) | 0.005170 / 0.004328 (0.000842) | 0.102286 / 0.004250 (0.098035) | 0.043643 / 0.037052 (0.006591) | 0.458788 / 0.258489 (0.200299) | 0.519891 / 0.293841 (0.226050) | 0.052875 / 0.128546 (-0.075671) | 0.020518 / 0.075646 (-0.055128) | 0.112675 / 0.419271 (-0.306597) | 0.066390 / 0.043533 (0.022858) | 0.423037 / 0.255139 (0.167898) | 0.420345 / 0.283200 (0.137146) | 0.119221 / 0.141683 (-0.022462) | 1.632244 / 1.452155 (0.180090) | 1.829585 / 1.492716 (0.336869) |\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) | 0.242312 / 0.018006 (0.224305) | 0.547592 / 0.000490 (0.547102) | 0.006520 / 0.000200 (0.006320) | 0.000185 / 0.000054 (0.000131) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032204 / 0.037411 (-0.005207) | 0.113320 / 0.014526 (0.098794) | 0.135667 / 0.176557 (-0.040889) | 0.194360 / 0.737135 (-0.542775) | 0.127934 / 0.296338 (-0.168404) |\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) | 0.648134 / 0.215209 (0.432925) | 6.470574 / 2.077655 (4.392920) | 2.799121 / 1.504120 (1.295001) | 2.160450 / 1.541195 (0.619255) | 2.261648 / 1.468490 (0.793158) | 1.244660 / 4.584777 (-3.340117) | 5.694636 / 3.745712 (1.948923) | 5.316191 / 5.269862 (0.046329) | 2.764551 / 4.565676 (-1.801126) | 0.152225 / 0.424275 (-0.272051) | 0.015959 / 0.007607 (0.008351) | 0.833606 / 0.226044 (0.607562) | 8.099765 / 2.268929 (5.830836) | 3.523005 / 55.444624 (-51.921620) | 2.855126 / 6.876477 (-4.021351) | 2.730849 / 2.142072 (0.588776) | 1.434351 / 4.805227 (-3.370876) | 0.251963 / 6.500664 (-6.248701) | 0.085718 / 0.075469 (0.010249) |\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) | 1.722466 / 1.841788 (-0.119322) | 17.846981 / 8.074308 (9.772673) | 21.578684 / 10.191392 (11.387292) | 0.239987 / 0.680424 (-0.440437) | 0.029189 / 0.534201 (-0.505012) | 0.543181 / 0.579283 (-0.036102) | 0.626527 / 0.434364 (0.192163) | 0.614334 / 0.540337 (0.073997) | 0.745934 / 1.386936 (-0.641002) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c506ad7cd22668f37ec51ff01b7c7f7235b9212 \"CML watermark\")\n",
"<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) | 0.007395 / 0.011353 (-0.003958) | 0.004965 / 0.011008 (-0.006043) | 0.096376 / 0.038508 (0.057868) | 0.033243 / 0.023109 (0.010134) | 0.299990 / 0.275898 (0.024092) | 0.336287 / 0.323480 (0.012807) | 0.005528 / 0.007986 (-0.002458) | 0.004003 / 0.004328 (-0.000326) | 0.072820 / 0.004250 (0.068569) | 0.042867 / 0.037052 (0.005815) | 0.296719 / 0.258489 (0.038230) | 0.337313 / 0.293841 (0.043472) | 0.036809 / 0.128546 (-0.091738) | 0.012239 / 0.075646 (-0.063407) | 0.332351 / 0.419271 (-0.086921) | 0.050449 / 0.043533 (0.006916) | 0.301483 / 0.255139 (0.046344) | 0.316673 / 0.283200 (0.033474) | 0.102526 / 0.141683 (-0.039157) | 1.415429 / 1.452155 (-0.036726) | 1.544381 / 1.492716 (0.051665) |\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) | 0.211158 / 0.018006 (0.193152) | 0.434718 / 0.000490 (0.434228) | 0.003386 / 0.000200 (0.003186) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027945 / 0.037411 (-0.009466) | 0.108743 / 0.014526 (0.094217) | 0.119771 / 0.176557 (-0.056785) | 0.178667 / 0.737135 (-0.558468) | 0.123718 / 0.296338 (-0.172620) |\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) | 0.413908 / 0.215209 (0.198699) | 4.136828 / 2.077655 (2.059174) | 1.932547 / 1.504120 (0.428427) | 1.715389 / 1.541195 (0.174194) | 1.791679 / 1.468490 (0.323189) | 0.692715 / 4.584777 (-3.892062) | 3.741807 / 3.745712 (-0.003905) | 2.066274 / 5.269862 (-3.203587) | 1.314106 / 4.565676 (-3.251570) | 0.087191 / 0.424275 (-0.337084) | 0.012866 / 0.007607 (0.005259) | 0.510012 / 0.226044 (0.283968) | 5.116419 / 2.268929 (2.847490) | 2.408562 / 55.444624 (-53.036063) | 2.002044 / 6.876477 (-4.874433) | 2.121868 / 2.142072 (-0.020204) | 0.837141 / 4.805227 (-3.968086) | 0.166596 / 6.500664 (-6.334068) | 0.063190 / 0.075469 (-0.012279) |\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) | 1.204152 / 1.841788 (-0.637636) | 14.739793 / 8.074308 (6.665485) | 14.403469 / 10.191392 (4.212077) | 0.165781 / 0.680424 (-0.514642) | 0.017826 / 0.534201 (-0.516375) | 0.423527 / 0.579283 (-0.155756) | 0.431410 / 0.434364 (-0.002954) | 0.499422 / 0.540337 (-0.040915) | 0.596116 / 1.386936 (-0.790820) |\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) | 0.007365 / 0.011353 (-0.003988) | 0.005165 / 0.011008 (-0.005844) | 0.073403 / 0.038508 (0.034895) | 0.032542 / 0.023109 (0.009433) | 0.339304 / 0.275898 (0.063406) | 0.371892 / 0.323480 (0.048412) | 0.005544 / 0.007986 (-0.002442) | 0.004108 / 0.004328 (-0.000221) | 0.073750 / 0.004250 (0.069500) | 0.045613 / 0.037052 (0.008561) | 0.366159 / 0.258489 (0.107670) | 0.389864 / 0.293841 (0.096023) | 0.036006 / 0.128546 (-0.092540) | 0.012402 / 0.075646 (-0.063244) | 0.085137 / 0.419271 (-0.334135) | 0.048485 / 0.043533 (0.004952) | 0.334172 / 0.255139 (0.079033) | 0.353168 / 0.283200 (0.069969) | 0.099393 / 0.141683 (-0.042290) | 1.460584 / 1.452155 (0.008429) | 1.518601 / 1.492716 (0.025885) |\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) | 0.227352 / 0.018006 (0.209346) | 0.444211 / 0.000490 (0.443721) | 0.000410 / 0.000200 (0.000210) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029517 / 0.037411 (-0.007894) | 0.115557 / 0.014526 (0.101031) | 0.125855 / 0.176557 (-0.050701) | 0.175214 / 0.737135 (-0.561922) | 0.129324 / 0.296338 (-0.167014) |\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) | 0.429783 / 0.215209 (0.214574) | 4.301159 / 2.077655 (2.223504) | 2.084939 / 1.504120 (0.580819) | 1.887781 / 1.541195 (0.346586) | 2.045712 / 1.468490 (0.577222) | 0.693319 / 4.584777 (-3.891458) | 3.788595 / 3.745712 (0.042883) | 2.087080 / 5.269862 (-3.182781) | 1.325247 / 4.565676 (-3.240429) | 0.085919 / 0.424275 (-0.338356) | 0.012710 / 0.007607 (0.005103) | 0.533432 / 0.226044 (0.307387) | 5.339468 / 2.268929 (3.070540) | 2.578351 / 55.444624 (-52.866273) | 2.224905 / 6.876477 (-4.651572) | 2.301064 / 2.142072 (0.158992) | 0.839622 / 4.805227 (-3.965605) | 0.166523 / 6.500664 (-6.334141) | 0.065254 / 0.075469 (-0.010215) |\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) | 1.262223 / 1.841788 (-0.579565) | 15.042523 / 8.074308 (6.968215) | 14.542719 / 10.191392 (4.351327) | 0.142230 / 0.680424 (-0.538194) | 0.017610 / 0.534201 (-0.516591) | 0.422357 / 0.579283 (-0.156926) | 0.417785 / 0.434364 (-0.016579) | 0.491990 / 0.540337 (-0.048348) | 0.585835 / 1.386936 (-0.801101) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2fcedd2a561fe6f5b6972ad18bfef722e1d2c77 \"CML watermark\")\n"
] | 2023-03-16T16:17:03 | 2023-03-16T17:21:58 | 2023-03-16T17:14:50 | CONTRIBUTOR | null | Fix #5641 | {
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"It looks like an error that we observed once in https://github.com/huggingface/datasets/pull/5166\r\n\r\nCan you try to update `datasets` ?\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\nif it doesn't work, can you make sure you don't have packages installed that may modify `dill`'s behavior, such as `apache-beam` ?",
"@lhoestq That fixed the problem, Thanks :)"
] | 2023-03-16T10:01:28 | 2023-03-17T04:24:51 | 2023-03-17T04:24:51 | NONE | null | ### Describe the bug
I'm using Huggingface Datasets library to load the dataset in google colab
When I do,
> data = train_dataset.select(range(10))
or
> train_datasets = train_dataset.map(
> process_data_to_model_inputs,
> batched=True,
> batch_size=batch_size,
> remove_columns=["article", "abstract"],
> )
I get following error: `module 'dill._dill' has no attribute 'log'`
I've tried downgrading the dill version from latest to 0.2.8, but no luck.
Stack trace:
> ---------------------------------------------------------------------------
> ModuleNotFoundError Traceback (most recent call last)
> /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in _no_cache_fields(obj)
> 367 try:
> --> 368 import transformers as tr
> 369
>
> ModuleNotFoundError: No module named 'transformers'
>
> During handling of the above exception, another exception occurred:
>
> AttributeError Traceback (most recent call last)
> 17 frames
> <ipython-input-13-dd14813880a6> in <module>
> ----> 1 test = train_dataset.select(range(10))
>
> /usr/local/lib/python3.9/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
> 155 }
> 156 # apply actual function
> --> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
> 158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
> 159 # re-apply format to the output
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
> 155 if kwargs.get(fingerprint_name) is None:
> 156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name
> --> 157 kwargs[fingerprint_name] = update_fingerprint(
> 158 self._fingerprint, transform, kwargs_for_fingerprint
> 159 )
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)
> 103 for key in sorted(transform_args):
> 104 hasher.update(key)
> --> 105 hasher.update(transform_args[key])
> 106 return hasher.hexdigest()
> 107
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in update(self, value)
> 55 def update(self, value):
> 56 self.m.update(f"=={type(value)}==".encode("utf8"))
> ---> 57 self.m.update(self.hash(value).encode("utf-8"))
> 58
> 59 def hexdigest(self):
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in hash(cls, value)
> 51 return cls.dispatch[type(value)](cls, value)
> 52 else:
> ---> 53 return cls.hash_default(value)
> 54
> 55 def update(self, value):
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in hash_default(cls, value)
> 44 @classmethod
> 45 def hash_default(cls, value):
> ---> 46 return cls.hash_bytes(dumps(value))
> 47
> 48 @classmethod
>
> /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in dumps(obj)
> 387 file = StringIO()
> 388 with _no_cache_fields(obj):
> --> 389 dump(obj, file)
> 390 return file.getvalue()
> 391
>
> /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in dump(obj, file)
> 359 def dump(obj, file):
> 360 """pickle an object to a file"""
> --> 361 Pickler(file, recurse=True).dump(obj)
> 362 return
> 363
>
> /usr/local/lib/python3.9/dist-packages/dill/_dill.py in dump(self, obj)
> 392 return
> 393
> --> 394 def load_session(filename='/tmp/session.pkl', main=None):
> 395 """update the __main__ module with the state from the session file"""
> 396 if main is None: main = _main_module
>
> /usr/lib/python3.9/pickle.py in dump(self, obj)
> 485 if self.proto >= 4:
> 486 self.framer.start_framing()
> --> 487 self.save(obj)
> 488 self.write(STOP)
> 489 self.framer.end_framing()
>
> /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save(self, obj, save_persistent_id)
> 386 pickler._byref = False # disable pickling by name reference
> 387 pickler._recurse = False # disable pickling recursion for globals
> --> 388 pickler._session = True # is best indicator of when pickling a session
> 389 pickler.dump(main)
> 390 finally:
>
> /usr/lib/python3.9/pickle.py in save(self, obj, save_persistent_id)
> 558 f = self.dispatch.get(t)
> 559 if f is not None:
> --> 560 f(self, obj) # Call unbound method with explicit self
> 561 return
> 562
>
> /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save_singleton(pickler, obj)
>
> /usr/lib/python3.9/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
> 689 write(NEWOBJ)
> 690 else:
> --> 691 save(func)
> 692 save(args)
> 693 write(REDUCE)
>
> /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save(self, obj, save_persistent_id)
> 386 pickler._byref = False # disable pickling by name reference
> 387 pickler._recurse = False # disable pickling recursion for globals
> --> 388 pickler._session = True # is best indicator of when pickling a session
> 389 pickler.dump(main)
> 390 finally:
>
> /usr/lib/python3.9/pickle.py in save(self, obj, save_persistent_id)
> 558 f = self.dispatch.get(t)
> 559 if f is not None:
> --> 560 f(self, obj) # Call unbound method with explicit self
> 561 return
> 562
>
> /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in save_function(pickler, obj)
> 583 dill._dill.log.info("# F1")
> 584 else:
> --> 585 dill._dill.log.info("F2: %s" % obj)
> 586 name = getattr(obj, "__qualname__", getattr(obj, "__name__", None))
> 587 dill._dill.StockPickler.save_global(pickler, obj, name=name)
>
> AttributeError: module 'dill._dill' has no attribute 'log'
### Steps to reproduce the bug
After loading the dataset(eg: https://huggingface.co/datasets/scientific_papers) in google colab
do either
> data = train_dataset.select(range(10))
or
> train_datasets = train_dataset.map(
> process_data_to_model_inputs,
> batched=True,
> batch_size=batch_size,
> remove_columns=["article", "abstract"],
> )
### Expected behavior
The map and select function should work
### Environment info
dataset: https://huggingface.co/datasets/scientific_papers
dill = 0.3.6
python= 3.9.16
transformer = 4.2.0 | {
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"_The documentation is not available anymore as the PR was closed or 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) | 0.008337 / 0.011353 (-0.003016) | 0.005588 / 0.011008 (-0.005421) | 0.110259 / 0.038508 (0.071751) | 0.038928 / 0.023109 (0.015819) | 0.350441 / 0.275898 (0.074543) | 0.378473 / 0.323480 (0.054993) | 0.006369 / 0.007986 (-0.001616) | 0.005730 / 0.004328 (0.001401) | 0.083042 / 0.004250 (0.078792) | 0.048686 / 0.037052 (0.011634) | 0.367561 / 0.258489 (0.109072) | 0.398073 / 0.293841 (0.104232) | 0.043247 / 0.128546 (-0.085299) | 0.013862 / 0.075646 (-0.061785) | 0.386745 / 0.419271 (-0.032527) | 0.060107 / 0.043533 (0.016574) | 0.345450 / 0.255139 (0.090311) | 0.371269 / 0.283200 (0.088069) | 0.117508 / 0.141683 (-0.024175) | 1.689345 / 1.452155 (0.237191) | 1.777119 / 1.492716 (0.284402) |\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) | 0.248248 / 0.018006 (0.230242) | 0.505200 / 0.000490 (0.504710) | 0.015354 / 0.000200 (0.015155) | 0.000794 / 0.000054 (0.000740) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030179 / 0.037411 (-0.007232) | 0.118583 / 0.014526 (0.104057) | 0.131546 / 0.176557 (-0.045010) | 0.196173 / 0.737135 (-0.540962) | 0.140532 / 0.296338 (-0.155807) |\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) | 0.470733 / 0.215209 (0.255524) | 4.758868 / 2.077655 (2.681213) | 2.246731 / 1.504120 (0.742611) | 1.995232 / 1.541195 (0.454037) | 2.057596 / 1.468490 (0.589106) | 0.819227 / 4.584777 (-3.765550) | 4.472093 / 3.745712 (0.726381) | 2.428154 / 5.269862 (-2.841708) | 1.748023 / 4.565676 (-2.817654) | 0.101965 / 0.424275 (-0.322310) | 0.014706 / 0.007607 (0.007098) | 0.600593 / 0.226044 (0.374548) | 5.869565 / 2.268929 (3.600637) | 2.764890 / 55.444624 (-52.679735) | 2.332112 / 6.876477 (-4.544364) | 2.486190 / 2.142072 (0.344118) | 0.979123 / 4.805227 (-3.826104) | 0.199543 / 6.500664 (-6.301121) | 0.075906 / 0.075469 (0.000436) |\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) | 1.397694 / 1.841788 (-0.444094) | 16.910500 / 8.074308 (8.836192) | 16.174131 / 10.191392 (5.982739) | 0.173975 / 0.680424 (-0.506449) | 0.021403 / 0.534201 (-0.512798) | 0.496187 / 0.579283 (-0.083096) | 0.487369 / 0.434364 (0.053005) | 0.565924 / 0.540337 (0.025587) | 0.684965 / 1.386936 (-0.701971) |\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) | 0.008253 / 0.011353 (-0.003100) | 0.005745 / 0.011008 (-0.005263) | 0.085848 / 0.038508 (0.047340) | 0.038753 / 0.023109 (0.015644) | 0.401278 / 0.275898 (0.125379) | 0.433132 / 0.323480 (0.109652) | 0.006112 / 0.007986 (-0.001874) | 0.005973 / 0.004328 (0.001644) | 0.085339 / 0.004250 (0.081088) | 0.053297 / 0.037052 (0.016244) | 0.400265 / 0.258489 (0.141776) | 0.455155 / 0.293841 (0.161314) | 0.043116 / 0.128546 (-0.085430) | 0.013957 / 0.075646 (-0.061689) | 0.099507 / 0.419271 (-0.319764) | 0.058858 / 0.043533 (0.015325) | 0.398030 / 0.255139 (0.142891) | 0.418171 / 0.283200 (0.134971) | 0.114392 / 0.141683 (-0.027291) | 1.683102 / 1.452155 (0.230947) | 1.801427 / 1.492716 (0.308711) |\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) | 0.242271 / 0.018006 (0.224265) | 0.494920 / 0.000490 (0.494430) | 0.007328 / 0.000200 (0.007128) | 0.000144 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034061 / 0.037411 (-0.003351) | 0.146417 / 0.014526 (0.131891) | 0.161079 / 0.176557 (-0.015477) | 0.213999 / 0.737135 (-0.523137) | 0.166704 / 0.296338 (-0.129634) |\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) | 0.491214 / 0.215209 (0.276005) | 4.846946 / 2.077655 (2.769291) | 2.352595 / 1.504120 (0.848475) | 2.114055 / 1.541195 (0.572860) | 2.213537 / 1.468490 (0.745047) | 0.799625 / 4.584777 (-3.785152) | 4.440519 / 3.745712 (0.694807) | 4.476103 / 5.269862 (-0.793758) | 2.249384 / 4.565676 (-2.316292) | 0.098807 / 0.424275 (-0.325468) | 0.014463 / 0.007607 (0.006856) | 0.611793 / 0.226044 (0.385748) | 6.045710 / 2.268929 (3.776782) | 2.865957 / 55.444624 (-52.578667) | 2.454052 / 6.876477 (-4.422425) | 2.606153 / 2.142072 (0.464080) | 0.969057 / 4.805227 (-3.836170) | 0.198499 / 6.500664 (-6.302166) | 0.077012 / 0.075469 (0.001543) |\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) | 1.497020 / 1.841788 (-0.344767) | 17.834277 / 8.074308 (9.759969) | 16.413792 / 10.191392 (6.222400) | 0.201979 / 0.680424 (-0.478445) | 0.020627 / 0.534201 (-0.513574) | 0.499767 / 0.579283 (-0.079516) | 0.496982 / 0.434364 (0.062618) | 0.579554 / 0.540337 (0.039216) | 0.693287 / 1.386936 (-0.693649) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a3fee942ae159ff6cfe6a23b343605e7e12f55 \"CML watermark\")\n",
"<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) | 0.007461 / 0.011353 (-0.003892) | 0.005341 / 0.011008 (-0.005668) | 0.099252 / 0.038508 (0.060744) | 0.034723 / 0.023109 (0.011614) | 0.300980 / 0.275898 (0.025082) | 0.353860 / 0.323480 (0.030380) | 0.006100 / 0.007986 (-0.001885) | 0.004149 / 0.004328 (-0.000180) | 0.074765 / 0.004250 (0.070514) | 0.052226 / 0.037052 (0.015174) | 0.305098 / 0.258489 (0.046609) | 0.357445 / 0.293841 (0.063604) | 0.036129 / 0.128546 (-0.092417) | 0.012482 / 0.075646 (-0.063165) | 0.333321 / 0.419271 (-0.085951) | 0.050489 / 0.043533 (0.006956) | 0.294728 / 0.255139 (0.039589) | 0.322722 / 0.283200 (0.039523) | 0.101226 / 0.141683 (-0.040456) | 1.436787 / 1.452155 (-0.015367) | 1.515784 / 1.492716 (0.023068) |\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) | 0.291836 / 0.018006 (0.273830) | 0.550735 / 0.000490 (0.550245) | 0.003828 / 0.000200 (0.003628) | 0.000113 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028490 / 0.037411 (-0.008922) | 0.109543 / 0.014526 (0.095017) | 0.119451 / 0.176557 (-0.057105) | 0.176721 / 0.737135 (-0.560415) | 0.126711 / 0.296338 (-0.169628) |\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) | 0.418863 / 0.215209 (0.203654) | 4.179167 / 2.077655 (2.101512) | 1.965126 / 1.504120 (0.461006) | 1.775544 / 1.541195 (0.234349) | 1.882667 / 1.468490 (0.414177) | 0.709201 / 4.584777 (-3.875576) | 3.754780 / 3.745712 (0.009068) | 2.175324 / 5.269862 (-3.094538) | 1.477454 / 4.565676 (-3.088223) | 0.085527 / 0.424275 (-0.338748) | 0.012685 / 0.007607 (0.005078) | 0.514276 / 0.226044 (0.288231) | 5.140518 / 2.268929 (2.871589) | 2.436011 / 55.444624 (-53.008614) | 2.114355 / 6.876477 (-4.762122) | 2.278893 / 2.142072 (0.136821) | 0.847825 / 4.805227 (-3.957402) | 0.169579 / 6.500664 (-6.331086) | 0.065306 / 0.075469 (-0.010163) |\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) | 1.190376 / 1.841788 (-0.651411) | 14.756581 / 8.074308 (6.682272) | 14.622610 / 10.191392 (4.431218) | 0.168186 / 0.680424 (-0.512238) | 0.017527 / 0.534201 (-0.516674) | 0.427808 / 0.579283 (-0.151475) | 0.437278 / 0.434364 (0.002914) | 0.509242 / 0.540337 (-0.031095) | 0.602500 / 1.386936 (-0.784436) |\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) | 0.007331 / 0.011353 (-0.004022) | 0.005703 / 0.011008 (-0.005305) | 0.074992 / 0.038508 (0.036484) | 0.034069 / 0.023109 (0.010960) | 0.343513 / 0.275898 (0.067615) | 0.369061 / 0.323480 (0.045582) | 0.006034 / 0.007986 (-0.001951) | 0.004344 / 0.004328 (0.000016) | 0.074678 / 0.004250 (0.070428) | 0.052262 / 0.037052 (0.015210) | 0.364758 / 0.258489 (0.106269) | 0.401130 / 0.293841 (0.107289) | 0.037635 / 0.128546 (-0.090912) | 0.012599 / 0.075646 (-0.063047) | 0.086935 / 0.419271 (-0.332337) | 0.058161 / 0.043533 (0.014628) | 0.338727 / 0.255139 (0.083589) | 0.355957 / 0.283200 (0.072757) | 0.111607 / 0.141683 (-0.030076) | 1.454357 / 1.452155 (0.002202) | 1.591529 / 1.492716 (0.098813) |\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) | 0.284379 / 0.018006 (0.266373) | 0.550720 / 0.000490 (0.550230) | 0.002868 / 0.000200 (0.002668) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028876 / 0.037411 (-0.008535) | 0.110892 / 0.014526 (0.096366) | 0.122519 / 0.176557 (-0.054038) | 0.169774 / 0.737135 (-0.567361) | 0.129381 / 0.296338 (-0.166957) |\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) | 0.429181 / 0.215209 (0.213972) | 4.251016 / 2.077655 (2.173361) | 2.056778 / 1.504120 (0.552658) | 1.860458 / 1.541195 (0.319264) | 1.958923 / 1.468490 (0.490432) | 0.712667 / 4.584777 (-3.872110) | 3.856910 / 3.745712 (0.111198) | 3.374535 / 5.269862 (-1.895327) | 1.846744 / 4.565676 (-2.718932) | 0.087238 / 0.424275 (-0.337037) | 0.012718 / 0.007607 (0.005111) | 0.524654 / 0.226044 (0.298609) | 5.209756 / 2.268929 (2.940827) | 2.494882 / 55.444624 (-52.949743) | 2.201150 / 6.876477 (-4.675327) | 2.274189 / 2.142072 (0.132117) | 0.844728 / 4.805227 (-3.960499) | 0.167467 / 6.500664 (-6.333197) | 0.064018 / 0.075469 (-0.011451) |\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) | 1.273284 / 1.841788 (-0.568503) | 15.104413 / 8.074308 (7.030105) | 15.134025 / 10.191392 (4.942633) | 0.147568 / 0.680424 (-0.532856) | 0.017429 / 0.534201 (-0.516772) | 0.422052 / 0.579283 (-0.157231) | 0.425786 / 0.434364 (-0.008578) | 0.491753 / 0.540337 (-0.048584) | 0.585091 / 1.386936 (-0.801845) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f3d26e74898e0a9dc0d78490104e2e173269ef5b \"CML watermark\")\n"
] | 2023-03-15T20:02:54 | 2023-03-16T14:20:44 | 2023-03-16T14:12:55 | CONTRIBUTOR | null | To address https://github.com/huggingface/datasets/discussions/5593.
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"_The documentation is not available anymore as the PR was closed or 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) | 0.006665 / 0.011353 (-0.004688) | 0.004842 / 0.011008 (-0.006166) | 0.097802 / 0.038508 (0.059294) | 0.032292 / 0.023109 (0.009182) | 0.327522 / 0.275898 (0.051624) | 0.351851 / 0.323480 (0.028371) | 0.005197 / 0.007986 (-0.002789) | 0.003781 / 0.004328 (-0.000547) | 0.073213 / 0.004250 (0.068963) | 0.045819 / 0.037052 (0.008767) | 0.331323 / 0.258489 (0.072834) | 0.376978 / 0.293841 (0.083137) | 0.035014 / 0.128546 (-0.093532) | 0.011853 / 0.075646 (-0.063793) | 0.344031 / 0.419271 (-0.075240) | 0.049094 / 0.043533 (0.005561) | 0.327054 / 0.255139 (0.071915) | 0.349053 / 0.283200 (0.065853) | 0.095413 / 0.141683 (-0.046269) | 1.451593 / 1.452155 (-0.000562) | 1.505568 / 1.492716 (0.012851) |\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) | 0.211624 / 0.018006 (0.193618) | 0.437569 / 0.000490 (0.437079) | 0.003775 / 0.000200 (0.003575) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025915 / 0.037411 (-0.011496) | 0.104085 / 0.014526 (0.089559) | 0.111064 / 0.176557 (-0.065493) | 0.167316 / 0.737135 (-0.569819) | 0.117255 / 0.296338 (-0.179084) |\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) | 0.424241 / 0.215209 (0.209032) | 4.251365 / 2.077655 (2.173710) | 2.074036 / 1.504120 (0.569916) | 1.858022 / 1.541195 (0.316828) | 1.819929 / 1.468490 (0.351439) | 0.704153 / 4.584777 (-3.880624) | 3.750506 / 3.745712 (0.004794) | 3.149836 / 5.269862 (-2.120026) | 1.729540 / 4.565676 (-2.836137) | 0.087287 / 0.424275 (-0.336988) | 0.012304 / 0.007607 (0.004697) | 0.513811 / 0.226044 (0.287767) | 5.129427 / 2.268929 (2.860498) | 2.489253 / 55.444624 (-52.955371) | 2.122746 / 6.876477 (-4.753730) | 2.208528 / 2.142072 (0.066456) | 0.843386 / 4.805227 (-3.961841) | 0.169320 / 6.500664 (-6.331344) | 0.064085 / 0.075469 (-0.011384) |\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) | 1.184361 / 1.841788 (-0.657427) | 14.013478 / 8.074308 (5.939170) | 13.936774 / 10.191392 (3.745382) | 0.138009 / 0.680424 (-0.542415) | 0.017192 / 0.534201 (-0.517009) | 0.420938 / 0.579283 (-0.158345) | 0.413390 / 0.434364 (-0.020974) | 0.500244 / 0.540337 (-0.040094) | 0.582499 / 1.386936 (-0.804437) |\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) | 0.006709 / 0.011353 (-0.004643) | 0.004847 / 0.011008 (-0.006161) | 0.074740 / 0.038508 (0.036232) | 0.032126 / 0.023109 (0.009017) | 0.343248 / 0.275898 (0.067350) | 0.376822 / 0.323480 (0.053342) | 0.005547 / 0.007986 (-0.002439) | 0.005080 / 0.004328 (0.000752) | 0.074634 / 0.004250 (0.070384) | 0.044735 / 0.037052 (0.007682) | 0.357895 / 0.258489 (0.099406) | 0.401150 / 0.293841 (0.107310) | 0.035485 / 0.128546 (-0.093061) | 0.011978 / 0.075646 (-0.063668) | 0.087567 / 0.419271 (-0.331704) | 0.050233 / 0.043533 (0.006701) | 0.337476 / 0.255139 (0.082337) | 0.385064 / 0.283200 (0.101865) | 0.102733 / 0.141683 (-0.038950) | 1.456238 / 1.452155 (0.004083) | 1.539468 / 1.492716 (0.046752) |\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) | 0.203156 / 0.018006 (0.185149) | 0.448898 / 0.000490 (0.448408) | 0.002843 / 0.000200 (0.002644) | 0.000222 / 0.000054 (0.000168) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027836 / 0.037411 (-0.009576) | 0.109889 / 0.014526 (0.095364) | 0.119378 / 0.176557 (-0.057179) | 0.171208 / 0.737135 (-0.565927) | 0.124240 / 0.296338 (-0.172098) |\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) | 0.425374 / 0.215209 (0.210165) | 4.252994 / 2.077655 (2.175339) | 2.006410 / 1.504120 (0.502290) | 1.812821 / 1.541195 (0.271626) | 1.857618 / 1.468490 (0.389128) | 0.714564 / 4.584777 (-3.870213) | 3.803040 / 3.745712 (0.057328) | 2.075452 / 5.269862 (-3.194410) | 1.344868 / 4.565676 (-3.220809) | 0.088705 / 0.424275 (-0.335570) | 0.012481 / 0.007607 (0.004874) | 0.528022 / 0.226044 (0.301977) | 5.268878 / 2.268929 (2.999949) | 2.467858 / 55.444624 (-52.976767) | 2.138681 / 6.876477 (-4.737796) | 2.134928 / 2.142072 (-0.007145) | 0.851518 / 4.805227 (-3.953709) | 0.175085 / 6.500664 (-6.325579) | 0.063555 / 0.075469 (-0.011914) |\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) | 1.265788 / 1.841788 (-0.576000) | 14.683444 / 8.074308 (6.609136) | 14.055848 / 10.191392 (3.864456) | 0.145260 / 0.680424 (-0.535164) | 0.017064 / 0.534201 (-0.517137) | 0.424836 / 0.579283 (-0.154447) | 0.418345 / 0.434364 (-0.016019) | 0.491408 / 0.540337 (-0.048930) | 0.594387 / 1.386936 (-0.792549) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10c3f32c228cc7011ce456498942e6a2a5dc3086 \"CML watermark\")\n",
"<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) | 0.006870 / 0.011353 (-0.004483) | 0.004602 / 0.011008 (-0.006406) | 0.100075 / 0.038508 (0.061567) | 0.028720 / 0.023109 (0.005611) | 0.304212 / 0.275898 (0.028314) | 0.348423 / 0.323480 (0.024943) | 0.005266 / 0.007986 (-0.002720) | 0.003473 / 0.004328 (-0.000855) | 0.077563 / 0.004250 (0.073313) | 0.040066 / 0.037052 (0.003013) | 0.304039 / 0.258489 (0.045550) | 0.348721 / 0.293841 (0.054881) | 0.032127 / 0.128546 (-0.096419) | 0.011583 / 0.075646 (-0.064063) | 0.326853 / 0.419271 (-0.092418) | 0.043158 / 0.043533 (-0.000375) | 0.310111 / 0.255139 (0.054973) | 0.332869 / 0.283200 (0.049670) | 0.088384 / 0.141683 (-0.053299) | 1.509245 / 1.452155 (0.057091) | 1.575393 / 1.492716 (0.082677) |\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) | 0.212839 / 0.018006 (0.194833) | 0.431407 / 0.000490 (0.430918) | 0.002639 / 0.000200 (0.002439) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024945 / 0.037411 (-0.012466) | 0.101312 / 0.014526 (0.086787) | 0.107873 / 0.176557 (-0.068683) | 0.169579 / 0.737135 (-0.567556) | 0.109922 / 0.296338 (-0.186417) |\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) | 0.422091 / 0.215209 (0.206882) | 4.227174 / 2.077655 (2.149519) | 1.957964 / 1.504120 (0.453844) | 1.812076 / 1.541195 (0.270882) | 1.966666 / 1.468490 (0.498176) | 0.698710 / 4.584777 (-3.886067) | 3.431824 / 3.745712 (-0.313888) | 1.898646 / 5.269862 (-3.371215) | 1.172096 / 4.565676 (-3.393581) | 0.083383 / 0.424275 (-0.340892) | 0.012793 / 0.007607 (0.005186) | 0.522501 / 0.226044 (0.296457) | 5.240049 / 2.268929 (2.971121) | 2.349286 / 55.444624 (-53.095338) | 2.051117 / 6.876477 (-4.825360) | 2.255652 / 2.142072 (0.113580) | 0.813668 / 4.805227 (-3.991560) | 0.153770 / 6.500664 (-6.346894) | 0.068323 / 0.075469 (-0.007146) |\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) | 1.197204 / 1.841788 (-0.644584) | 14.146212 / 8.074308 (6.071904) | 14.469765 / 10.191392 (4.278373) | 0.130024 / 0.680424 (-0.550400) | 0.016858 / 0.534201 (-0.517343) | 0.382949 / 0.579283 (-0.196334) | 0.393414 / 0.434364 (-0.040950) | 0.447910 / 0.540337 (-0.092427) | 0.529842 / 1.386936 (-0.857094) |\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) | 0.006903 / 0.011353 (-0.004450) | 0.004695 / 0.011008 (-0.006313) | 0.077457 / 0.038508 (0.038949) | 0.028624 / 0.023109 (0.005514) | 0.340767 / 0.275898 (0.064869) | 0.378811 / 0.323480 (0.055331) | 0.005996 / 0.007986 (-0.001990) | 0.003481 / 0.004328 (-0.000848) | 0.076284 / 0.004250 (0.072034) | 0.042564 / 0.037052 (0.005511) | 0.340908 / 0.258489 (0.082419) | 0.384952 / 0.293841 (0.091111) | 0.032057 / 0.128546 (-0.096489) | 0.011697 / 0.075646 (-0.063949) | 0.085941 / 0.419271 (-0.333331) | 0.042464 / 0.043533 (-0.001069) | 0.339309 / 0.255139 (0.084170) | 0.368105 / 0.283200 (0.084905) | 0.093382 / 0.141683 (-0.048301) | 1.467220 / 1.452155 (0.015065) | 1.563105 / 1.492716 (0.070389) |\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) | 0.260631 / 0.018006 (0.242625) | 0.418155 / 0.000490 (0.417665) | 0.009539 / 0.000200 (0.009339) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025494 / 0.037411 (-0.011917) | 0.106034 / 0.014526 (0.091508) | 0.109878 / 0.176557 (-0.066678) | 0.160754 / 0.737135 (-0.576382) | 0.113226 / 0.296338 (-0.183112) |\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) | 0.442989 / 0.215209 (0.227780) | 4.447040 / 2.077655 (2.369385) | 2.082529 / 1.504120 (0.578409) | 1.876952 / 1.541195 (0.335757) | 1.968341 / 1.468490 (0.499851) | 0.704317 / 4.584777 (-3.880460) | 3.466190 / 3.745712 (-0.279523) | 1.924954 / 5.269862 (-3.344908) | 1.199763 / 4.565676 (-3.365913) | 0.084320 / 0.424275 (-0.339955) | 0.012956 / 0.007607 (0.005349) | 0.538905 / 0.226044 (0.312861) | 5.426593 / 2.268929 (3.157665) | 2.509287 / 55.444624 (-52.935338) | 2.174829 / 6.876477 (-4.701648) | 2.239214 / 2.142072 (0.097141) | 0.810031 / 4.805227 (-3.995196) | 0.153534 / 6.500664 (-6.347130) | 0.069578 / 0.075469 (-0.005891) |\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) | 1.294068 / 1.841788 (-0.547720) | 14.601899 / 8.074308 (6.527591) | 14.469282 / 10.191392 (4.277890) | 0.130024 / 0.680424 (-0.550400) | 0.016895 / 0.534201 (-0.517306) | 0.382583 / 0.579283 (-0.196700) | 0.388938 / 0.434364 (-0.045426) | 0.448416 / 0.540337 (-0.091922) | 0.533261 / 1.386936 (-0.853675) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7b2af47647152d39a3acade256da898cb396e4d9 \"CML watermark\")\n"
] | 2023-03-15T19:32:40 | 2023-03-16T17:23:06 | 2023-03-16T17:15:40 | CONTRIBUTOR | null | For consistency with `pa.Table.from_pydict`, which supports both Python lists and PyArrow arrays as column values.
"Fixes" https://discuss.huggingface.co/t/pyarrow-lib-floatarray-did-not-recognize-python-value-type-when-inferring-an-arrow-data-type/33417 | {
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