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https://api.github.com/repos/huggingface/datasets/issues/5487 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5487/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5487/comments | https://api.github.com/repos/huggingface/datasets/issues/5487/events | https://github.com/huggingface/datasets/issues/5487 | 1,564,480,121 | I_kwDODunzps5dQBJ5 | 5,487 | Incorrect filepath for dill module | {'login': 'avivbrokman', 'id': 35349273, 'node_id': 'MDQ6VXNlcjM1MzQ5Mjcz', 'avatar_url': 'https://avatars.githubusercontent.com/u/35349273?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/avivbrokman', 'html_url': 'https://github.com/avivbrokman', 'followers_url': 'https://api.github.com/users/avivbrokman/followers', 'following_url': 'https://api.github.com/users/avivbrokman/following{/other_user}', 'gists_url': 'https://api.github.com/users/avivbrokman/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/avivbrokman/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/avivbrokman/subscriptions', 'organizations_url': 'https://api.github.com/users/avivbrokman/orgs', 'repos_url': 'https://api.github.com/users/avivbrokman/repos', 'events_url': 'https://api.github.com/users/avivbrokman/events{/privacy}', 'received_events_url': 'https://api.github.com/users/avivbrokman/received_events', 'type': 'User', 'site_admin': False} | [] | open | false | null | [] | null | [
"Hi! The correct path is still `dill._dill.XXXX` in the latest release. What do you get when you run `python -c \"import dill; print(dill.__version__)\"` in your environment?",
"`0.3.6` I feel like that's bad news, because it's probably not the issue.\r\n\r\nMy mistake, about the wrong path guess. I think I didn't notice that the first `dill` in the path isn't supposed to be included in the path specification in python.\r\n<img width=\"146\" alt=\"Screen Shot 2023-01-31 at 12 58 32 PM\" src=\"https://user-images.githubusercontent.com/35349273/215844209-74af6a8f-9bff-4c75-9495-44c658c8e9f7.png\">\r\n",
"Hi, @avivbrokman, this issue you report appeared only with old versions of dill. See:\r\n- #288\r\n\r\nAre you sure you are in the right Python environment?\r\n- Please note that Jupyter (where I guess you get the error) may have multiple execution backends (IPython kernels) that might be different from the Python environment your are using to get the dill version\r\n - Have you run `import dill; print(dill.__version__)` in the same Jupyter/IPython that you were using when you got the error while executing `import datasets`?",
"I'm using spyder, and I am still getting `0.3.6` for `dill`, so unfortunately #288 isn't applicable, I think. However, I found something odd that I believe is a clue: \r\n\r\n```\r\nimport inspect\r\nimport dill\r\n\r\ninspect.getfile(dill)\r\n>>> '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill/__init__.py'\r\n```\r\n\r\nI checked out the directory, and there is no `dill` subdirectory within '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill`, as there should be. Rather, `_dill.py` is in '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill` itself. \r\n\r\n If I run `pip install dill` or `pip install --upgrade dill`, I get the message `Requirement already satisfied: dill in ./opt/anaconda3/lib/python3.9/site-packages (0.3.6)`. If I run `conda upgrade dill`, I get the message `Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.` a couple of times, followed by\r\n\r\n```\r\nSolving environment: failed\r\nSolving environment: / \r\nFound conflicts! Looking for incompatible packages.\r\n```\r\n\r\nAnd then terminal proceeds to list conflicts between different packages I have.\r\n\r\nThis is all very strange to me because I recently uninstalled and reinstalled `anaconda`.\r\n",
"As I said above, I guess this is not a problem with `datasets`. I think you have different Python environments: one with the new dill version (the one you get while using pip) and other with the old dill version (the one where you get the AttributeError).\r\n\r\nYou should update `dill` in the Python environment you are using within spyder.\r\n\r\nPlease note that the `_dill` module is present in the `dill` package since their 2.8.0 version."
] | 2023-01-31T15:01:08Z | 2023-01-31T15:01:08Z | null | NONE | null | ### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import datasets
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/__init__.py", line 43, in <module>
from .arrow_dataset import Dataset
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 66, in <module>
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 27, in <module>
from .features import Features, Image, Value
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/__init__.py", line 17, in <module>
from .audio import Audio
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/audio.py", line 12, in <module>
from ..download.streaming_download_manager import xopen
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/__init__.py", line 9, in <module>
from .download_manager import DownloadManager, DownloadMode
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/download_manager.py", line 36, in <module>
from ..utils.py_utils import NestedDataStructure, map_nested, size_str
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 602, in <module>
class Pickler(dill.Pickler):
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 605, in Pickler
dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())
AttributeError: module 'dill' has no attribute '_dill'
```
Looking at the github source code for dill, it appears that `datasets` has a bug or is not compatible with the latest `dill`. Specifically, rather than `dill._dill.XXXX` it should be `dill.dill._dill.XXXX`. But given the popularity of `datasets` I feel confused about me being the first person to have this issue, so it makes me wonder if I'm misdiagnosing the issue.
### Steps to reproduce the bug
Install `dill` and `datasets` packages and then `import datasets`
### Expected behavior
I expect `datasets` to import.
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.13
- PyArrow version: 11.0.0
- Pandas version: 1.4.4 | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5487/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5487/timeline | null | null | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/5486 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5486/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5486/comments | https://api.github.com/repos/huggingface/datasets/issues/5486/events | https://github.com/huggingface/datasets/issues/5486 | 1,564,059,749 | I_kwDODunzps5dOahl | 5,486 | Adding `sep` to TextConfig | {'login': 'omar-araboghli', 'id': 29576434, 'node_id': 'MDQ6VXNlcjI5NTc2NDM0', 'avatar_url': 'https://avatars.githubusercontent.com/u/29576434?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/omar-araboghli', 'html_url': 'https://github.com/omar-araboghli', 'followers_url': 'https://api.github.com/users/omar-araboghli/followers', 'following_url': 'https://api.github.com/users/omar-araboghli/following{/other_user}', 'gists_url': 'https://api.github.com/users/omar-araboghli/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/omar-araboghli/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/omar-araboghli/subscriptions', 'organizations_url': 'https://api.github.com/users/omar-araboghli/orgs', 'repos_url': 'https://api.github.com/users/omar-araboghli/repos', 'events_url': 'https://api.github.com/users/omar-araboghli/events{/privacy}', 'received_events_url': 'https://api.github.com/users/omar-araboghli/received_events', 'type': 'User', 'site_admin': False} | [] | open | false | null | [] | null | [
"Hi @omar-araboghli, thanks for your proposal.\r\n\r\nHave you tried to use \"csv\" loader instead of \"text\"? That already has a `sep` argument.",
"Hi @albertvillanova, thanks for the quick response!\r\n\r\nIndeed, I have been trying to use `csv` instead of `text`. However I am still not able to define range of rows as one sequence, that is achievable with passing `sample_by='paragraph'` to the `TextConfig`\r\n\r\nFor instance, the below code\r\n\r\n```python\r\nimport datasets\r\n\r\ndataset = datasets.load_dataset(\r\n path='csv',\r\n data_files={'train': TRAINING_SET_PATH},\r\n sep='\\t',\r\n header=None,\r\n column_names=['tokens', 'pos_tags', 'chunk_tags', 'ner_tags']\r\n)\r\n```\r\n\r\nleads to \r\n\r\n```python\r\ndataset\r\n>>> DatasetDict({\r\n train: Dataset({\r\n features: ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 62543\r\n })\r\n})\r\n\r\ndataset['train'][0]\r\n>>> {'tokens': 'Distribution',\r\n 'pos_tags': 'NN',\r\n 'chunk_tags': 'O',\r\n 'ner_tags': 'O'\r\n}\r\n```\r\nIs there a way to deal with multiple csv rows as one dataset instance, where each column is a sequence of those rows ?"
] | 2023-01-31T10:39:53Z | 2023-01-31T14:50:18Z | null | NONE | null | I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination with `sample_by='paragraph'` to parse a paragraph into an array for each column ? If so, I am happy to contribute!
## Environment
* `python 3.8.10`
* `datasets 2.9.0`
## Snippet of `train.txt`
```txt
Distribution NN O O
and NN O O
dynamics NN O O
of NN O O
electron NN O B-RP
complexes NN O I-RP
in NN O O
cyanobacterial NN O B-R
membranes NN O I-R
The NN O O
occurrence NN O O
of NN O O
prostaglandin NN O B-R
F2α NN O I-R
in NN O O
Pharbitis NN O B-R
seedlings NN O I-R
grown NN O O
under NN O O
short NN O B-P
days NN O I-P
or NN O I-P
days NN O I-P
```
## Current Behaviour
```python
# defining 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] here would fail with `ValueError: Length of names (4) does not match length of arrays (1)`
dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='line')
dataset['train']['tokens'][0]
>>> 'Distribution\tNN\tO\tO'
```
## Expected Behaviour / Suggestion
```python
# suppose we defined 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags']
dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='paragraph', sep='\t')
dataset['train']['tokens'][0]
>>> ['Distribution', 'and', 'dynamics', ... ]
dataset['train']['ner_tags'][0]
>>> ['O', 'O', 'O', ... ]
``` | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5486/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5486/timeline | null | null | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/5485 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5485/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5485/comments | https://api.github.com/repos/huggingface/datasets/issues/5485/events | https://github.com/huggingface/datasets/pull/5485 | 1,563,002,829 | PR_kwDODunzps5I2ER2 | 5,485 | Add section in tutorial for IterableDataset | {'login': 'stevhliu', 'id': 59462357, 'node_id': 'MDQ6VXNlcjU5NDYyMzU3', 'avatar_url': 'https://avatars.githubusercontent.com/u/59462357?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/stevhliu', 'html_url': 'https://github.com/stevhliu', 'followers_url': 'https://api.github.com/users/stevhliu/followers', 'following_url': 'https://api.github.com/users/stevhliu/following{/other_user}', 'gists_url': 'https://api.github.com/users/stevhliu/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/stevhliu/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/stevhliu/subscriptions', 'organizations_url': 'https://api.github.com/users/stevhliu/orgs', 'repos_url': 'https://api.github.com/users/stevhliu/repos', 'events_url': 'https://api.github.com/users/stevhliu/events{/privacy}', 'received_events_url': 'https://api.github.com/users/stevhliu/received_events', 'type': 'User', 'site_admin': False} | [] | open | false | null | [] | null | [
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008492 / 0.011353 (-0.002861) | 0.004717 / 0.011008 (-0.006292) | 0.101111 / 0.038508 (0.062602) | 0.029129 / 0.023109 (0.006019) | 0.307564 / 0.275898 (0.031666) | 0.367038 / 0.323480 (0.043558) | 0.007105 / 0.007986 (-0.000881) | 0.003622 / 0.004328 (-0.000706) | 0.078370 / 0.004250 (0.074120) | 0.036960 / 0.037052 (-0.000093) | 0.315612 / 0.258489 (0.057123) | 0.353601 / 0.293841 (0.059760) | 0.032900 / 0.128546 (-0.095647) | 0.011405 / 0.075646 (-0.064241) | 0.322331 / 0.419271 (-0.096940) | 0.040823 / 0.043533 (-0.002710) | 0.306734 / 0.255139 (0.051595) | 0.328155 / 0.283200 (0.044955) | 0.087169 / 0.141683 (-0.054514) | 1.460543 / 1.452155 (0.008389) | 1.498094 / 1.492716 (0.005378) |\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) | 0.011863 / 0.018006 (-0.006143) | 0.416315 / 0.000490 (0.415826) | 0.003463 / 0.000200 (0.003263) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023219 / 0.037411 (-0.014192) | 0.096469 / 0.014526 (0.081943) | 0.105960 / 0.176557 (-0.070596) | 0.148993 / 0.737135 (-0.588142) | 0.108112 / 0.296338 (-0.188226) |\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) | 0.415662 / 0.215209 (0.200453) | 4.155111 / 2.077655 (2.077456) | 1.834943 / 1.504120 (0.330823) | 1.622752 / 1.541195 (0.081557) | 1.701630 / 1.468490 (0.233140) | 0.690596 / 4.584777 (-3.894181) | 3.399385 / 3.745712 (-0.346327) | 3.140521 / 5.269862 (-2.129341) | 1.609152 / 4.565676 (-2.956524) | 0.082132 / 0.424275 (-0.342143) | 0.012343 / 0.007607 (0.004735) | 0.532715 / 0.226044 (0.306670) | 5.323032 / 2.268929 (3.054104) | 2.326625 / 55.444624 (-53.118000) | 1.944263 / 6.876477 (-4.932213) | 1.994015 / 2.142072 (-0.148058) | 0.813805 / 4.805227 (-3.991422) | 0.149233 / 6.500664 (-6.351431) | 0.065318 / 0.075469 (-0.010151) |\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) | 1.212441 / 1.841788 (-0.629347) | 13.979069 / 8.074308 (5.904761) | 14.003998 / 10.191392 (3.812606) | 0.146956 / 0.680424 (-0.533468) | 0.028564 / 0.534201 (-0.505637) | 0.392370 / 0.579283 (-0.186913) | 0.399695 / 0.434364 (-0.034669) | 0.473481 / 0.540337 (-0.066856) | 0.562625 / 1.386936 (-0.824311) |\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) | 0.006821 / 0.011353 (-0.004532) | 0.004570 / 0.011008 (-0.006438) | 0.076217 / 0.038508 (0.037709) | 0.028888 / 0.023109 (0.005779) | 0.345431 / 0.275898 (0.069533) | 0.389246 / 0.323480 (0.065766) | 0.005939 / 0.007986 (-0.002046) | 0.003356 / 0.004328 (-0.000973) | 0.075880 / 0.004250 (0.071629) | 0.041427 / 0.037052 (0.004374) | 0.344481 / 0.258489 (0.085992) | 0.398508 / 0.293841 (0.104667) | 0.031801 / 0.128546 (-0.096745) | 0.011763 / 0.075646 (-0.063884) | 0.085600 / 0.419271 (-0.333672) | 0.042656 / 0.043533 (-0.000876) | 0.345893 / 0.255139 (0.090754) | 0.376910 / 0.283200 (0.093711) | 0.092451 / 0.141683 (-0.049232) | 1.461222 / 1.452155 (0.009068) | 1.555822 / 1.492716 (0.063106) |\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) | 0.235781 / 0.018006 (0.217774) | 0.418485 / 0.000490 (0.417995) | 0.005560 / 0.000200 (0.005360) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025410 / 0.037411 (-0.012001) | 0.103780 / 0.014526 (0.089254) | 0.110183 / 0.176557 (-0.066374) | 0.151097 / 0.737135 (-0.586039) | 0.112539 / 0.296338 (-0.183799) |\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) | 0.436686 / 0.215209 (0.221477) | 4.341594 / 2.077655 (2.263940) | 2.062309 / 1.504120 (0.558190) | 1.857461 / 1.541195 (0.316267) | 1.947204 / 1.468490 (0.478713) | 0.699641 / 4.584777 (-3.885136) | 3.406983 / 3.745712 (-0.338729) | 3.294705 / 5.269862 (-1.975157) | 1.360582 / 4.565676 (-3.205095) | 0.083025 / 0.424275 (-0.341250) | 0.012461 / 0.007607 (0.004854) | 0.537767 / 0.226044 (0.311722) | 5.393316 / 2.268929 (3.124387) | 2.516692 / 55.444624 (-52.927932) | 2.163987 / 6.876477 (-4.712490) | 2.220480 / 2.142072 (0.078408) | 0.810648 / 4.805227 (-3.994579) | 0.151820 / 6.500664 (-6.348844) | 0.068080 / 0.075469 (-0.007389) |\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) | 1.279382 / 1.841788 (-0.562405) | 13.989947 / 8.074308 (5.915638) | 14.039229 / 10.191392 (3.847836) | 0.141071 / 0.680424 (-0.539352) | 0.017118 / 0.534201 (-0.517083) | 0.381558 / 0.579283 (-0.197725) | 0.390407 / 0.434364 (-0.043957) | 0.440920 / 0.540337 (-0.099418) | 0.525478 / 1.386936 (-0.861458) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eeedb5167d150888a640cd70ca63d6d72bbe1043 \"CML watermark\")\n"
] | 2023-01-30T18:43:04Z | 2023-01-31T10:42:55Z | null | MEMBER | null | Introduces an `IterableDataset` and how to access it in the tutorial section. It also adds a brief next step section at the end to provide a path for users who want more explanation and a path for users who want something more practical and learn how to preprocess these dataset types. It'll complement the awesome new doc introduced in:
- #5410 | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5485/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5485/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5485', 'html_url': 'https://github.com/huggingface/datasets/pull/5485', 'diff_url': 'https://github.com/huggingface/datasets/pull/5485.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5485.patch', 'merged_at': None} | true |
https://api.github.com/repos/huggingface/datasets/issues/5484 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5484/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5484/comments | https://api.github.com/repos/huggingface/datasets/issues/5484/events | https://github.com/huggingface/datasets/pull/5484 | 1,562,877,070 | PR_kwDODunzps5I1oaq | 5,484 | Update docs for `nyu_depth_v2` dataset | {'login': 'awsaf49', 'id': 36858976, 'node_id': 'MDQ6VXNlcjM2ODU4OTc2', 'avatar_url': 'https://avatars.githubusercontent.com/u/36858976?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/awsaf49', 'html_url': 'https://github.com/awsaf49', 'followers_url': 'https://api.github.com/users/awsaf49/followers', 'following_url': 'https://api.github.com/users/awsaf49/following{/other_user}', 'gists_url': 'https://api.github.com/users/awsaf49/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/awsaf49/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/awsaf49/subscriptions', 'organizations_url': 'https://api.github.com/users/awsaf49/orgs', 'repos_url': 'https://api.github.com/users/awsaf49/repos', 'events_url': 'https://api.github.com/users/awsaf49/events{/privacy}', 'received_events_url': 'https://api.github.com/users/awsaf49/received_events', 'type': 'User', 'site_admin': False} | [] | open | false | null | [] | null | [
"I think I need to create another PR on https://huggingface.co/datasets/huggingface/documentation-images/tree/main/datasets for hosting the images there?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5484). All of your documentation changes will be reflected on that endpoint.",
"Thanks for the update @awsaf49 !",
"> Thanks a lot for the updates!\r\n> \r\n> Just some minor things remain and the we should be good to ship this 🚀\r\n\r\n@sayakpaul I have updated the minor things. Please approve the workflows"
] | 2023-01-30T17:37:08Z | 2023-01-31T14:40:24Z | null | NONE | null | This PR will fix the issue mentioned in #5461.
cc: @sayakpaul @lhoestq
| {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5484/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5484/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5484', 'html_url': 'https://github.com/huggingface/datasets/pull/5484', 'diff_url': 'https://github.com/huggingface/datasets/pull/5484.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5484.patch', 'merged_at': None} | true |
https://api.github.com/repos/huggingface/datasets/issues/5483 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5483/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5483/comments | https://api.github.com/repos/huggingface/datasets/issues/5483/events | https://github.com/huggingface/datasets/issues/5483 | 1,560,894,690 | I_kwDODunzps5dCVzi | 5,483 | Unable to upload dataset | {'login': 'yuvalkirstain', 'id': 57996478, 'node_id': 'MDQ6VXNlcjU3OTk2NDc4', 'avatar_url': 'https://avatars.githubusercontent.com/u/57996478?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/yuvalkirstain', 'html_url': 'https://github.com/yuvalkirstain', 'followers_url': 'https://api.github.com/users/yuvalkirstain/followers', 'following_url': 'https://api.github.com/users/yuvalkirstain/following{/other_user}', 'gists_url': 'https://api.github.com/users/yuvalkirstain/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/yuvalkirstain/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/yuvalkirstain/subscriptions', 'organizations_url': 'https://api.github.com/users/yuvalkirstain/orgs', 'repos_url': 'https://api.github.com/users/yuvalkirstain/repos', 'events_url': 'https://api.github.com/users/yuvalkirstain/events{/privacy}', 'received_events_url': 'https://api.github.com/users/yuvalkirstain/received_events', 'type': 'User', 'site_admin': False} | [] | closed | false | null | [] | null | [
"Seems to work now, perhaps it was something internal with our university's network."
] | 2023-01-28T15:18:26Z | 2023-01-29T08:09:49Z | 2023-01-29T08:09:49Z | NONE | null | ### Describe the bug
Uploading a simple dataset ends with an exception
### Steps to reproduce the bug
I created a new conda env with python 3.10, pip installed datasets and:
```python
>>> from datasets import load_dataset, load_from_disk, Dataset
>>> d = Dataset.from_dict({"text": ["hello"] * 2})
>>> d.push_to_hub("ttt111")
/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_hf_folder.py:92: UserWarning: A token has been found in `/a/home/cc/students/cs/kirstain/.huggingface/token`. This is the old path where tokens were stored. The new location is `/home/olab/kirstain/.cache/huggingface/token` which is configurable using `HF_HOME` environment variable. Your token has been copied to this new location. You can now safely delete the old token file manually or use `huggingface-cli logout`.
warnings.warn(
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 279.94ba/s]
Upload 1 LFS files: 0%| | 0/1 [00:02<?, ?it/s]
Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:04<?, ?it/s]
Traceback (most recent call last):
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 264, in hf_raise_for_status
response.raise_for_status()
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://s3.us-east-1.amazonaws.com/lfs.huggingface.co/repos/cf/0c/cf0c5ab8a3f729e5f57a8b79a36ecea64a31126f13218591c27ed9a1c7bd9b41/ece885a4bb6bbc8c1bb51b45542b805283d74590f72cd4c45d3ba76628570386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230128T151640Z&X-Amz-Expires=900&X-Amz-Signature=89e78e9a9d70add7ed93d453334f4f93c6f29d889d46750a1f2da04af73978db&X-Amz-SignedHeaders=host&x-amz-storage-class=INTELLIGENT_TIERING&x-id=PutObject
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 334, in _inner_upload_lfs_object
return _upload_lfs_object(
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 391, in _upload_lfs_object
lfs_upload(
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/lfs.py", line 273, in lfs_upload
_upload_single_part(
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/lfs.py", line 305, in _upload_single_part
hf_raise_for_status(upload_res)
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 318, in hf_raise_for_status
raise HfHubHTTPError(str(e), response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError: 403 Client Error: Forbidden for url: https://s3.us-east-1.amazonaws.com/lfs.huggingface.co/repos/cf/0c/cf0c5ab8a3f729e5f57a8b79a36ecea64a31126f13218591c27ed9a1c7bd9b41/ece885a4bb6bbc8c1bb51b45542b805283d74590f72cd4c45d3ba76628570386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230128T151640Z&X-Amz-Expires=900&X-Amz-Signature=89e78e9a9d70add7ed93d453334f4f93c6f29d889d46750a1f2da04af73978db&X-Amz-SignedHeaders=host&x-amz-storage-class=INTELLIGENT_TIERING&x-id=PutObject
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 4909, in push_to_hub
repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub(
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 4804, in _push_parquet_shards_to_hub
_retry(
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 281, in _retry
return func(*func_args, **func_kwargs)
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 124, in _inner_fn
return fn(*args, **kwargs)
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2537, in upload_file
commit_info = self.create_commit(
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 124, in _inner_fn
return fn(*args, **kwargs)
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2346, in create_commit
upload_lfs_files(
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 124, in _inner_fn
return fn(*args, **kwargs)
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 346, in upload_lfs_files
thread_map(
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/tqdm/contrib/concurrent.py", line 94, in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/tqdm/contrib/concurrent.py", line 76, in _executor_map
return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 621, in result_iterator
yield _result_or_cancel(fs.pop())
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 319, in _result_or_cancel
return fut.result(timeout)
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 458, in result
return self.__get_result()
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 338, in _inner_upload_lfs_object
raise RuntimeError(
RuntimeError: Error while uploading 'data/train-00000-of-00001-6df93048e66df326.parquet' to the Hub.
```
### Expected behavior
The dataset should be uploaded without any exceptions
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-4.15.0-65-generic-x86_64-with-glibc2.27
- Python version: 3.10.9
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/5482 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5482/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5482/comments | https://api.github.com/repos/huggingface/datasets/issues/5482/events | https://github.com/huggingface/datasets/issues/5482 | 1,560,853,137 | I_kwDODunzps5dCLqR | 5,482 | Reload features from Parquet metadata | {'login': 'lhoestq', 'id': 42851186, 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/lhoestq', 'html_url': 'https://github.com/lhoestq', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'type': 'User', 'site_admin': False} | [{'id': 1935892871, 'node_id': 'MDU6TGFiZWwxOTM1ODkyODcx', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/enhancement', 'name': 'enhancement', 'color': 'a2eeef', 'default': True, 'description': 'New feature or request'}] | open | false | null | [] | null | [] | 2023-01-28T13:12:31Z | 2023-01-28T13:13:44Z | null | MEMBER | null | The idea would be to allow this :
```python
ds.to_parquet("my_dataset/ds.parquet")
reloaded = load_dataset("my_dataset")
assert ds.features == reloaded.features
```
And it should also work with Image and Audio types (right now they're reloaded as a dict type)
This can be implemented by storing and reading the feature types in the parquet metadata, as we do for arrow files. | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5482/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5482/timeline | null | null | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/5481 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5481/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5481/comments | https://api.github.com/repos/huggingface/datasets/issues/5481/events | https://github.com/huggingface/datasets/issues/5481 | 1,560,468,195 | I_kwDODunzps5dAtrj | 5,481 | Load a cached dataset as iterable | {'login': 'lhoestq', 'id': 42851186, 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/lhoestq', 'html_url': 'https://github.com/lhoestq', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'type': 'User', 'site_admin': False} | [{'id': 1935892871, 'node_id': 'MDU6TGFiZWwxOTM1ODkyODcx', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/enhancement', 'name': 'enhancement', 'color': 'a2eeef', 'default': True, 'description': 'New feature or request'}] | open | false | null | [] | null | [
"Can I work on this issue? I am pretty new to this.",
"Hi ! Sure :) you can comment `#self-assign` to assign yourself to this issue.\r\n\r\nI can give you some pointers to get started:\r\n\r\n`load_dataset` works roughly this way:\r\n1. it instantiate a dataset builder using `load_dataset_builder()`\r\n2. the builder download and prepare the dataset as Arrow files in the cache using `download_and_prepare()`\r\n3. the builder returns a Dataset object with `as_dataset()`\r\n\r\nOne way to approach this would be to implement `as_iterable_dataset()` in `builder.py`.\r\n\r\nAnd similarly to `as_dataset()`, you can use the `ArrowReader`. It has a `get_file_instructions()` method that can be helpful. It gives you the files to read as list of dictionaries with those keys: `filename`, `skip` and `take`.\r\n\r\nThe `skip` and `take` arguments are used in case the user wants to load a subset of the dataset, e.g.\r\n```python\r\nload_dataset(..., split=\"train[:10]\")\r\n```\r\n\r\nLet me know if you have questions or if I can help :)",
"This use-case is a bit specific, and `load_dataset` already has enough parameters (plus, `streaming=True` also returns an iterable dataset, so we would have to explain the difference), so I think it would be better to add `IterableDataset.from_file` to the API (more flexible and aligned with the goal from https://github.com/huggingface/datasets/issues/3444) instead.",
"> This use-case is a bit specific\r\n\r\nThis allows to use `datasets` for large scale training where map-style datasets are too slow and use too much memory in PyTorch. So I would still consider adding it.\r\n\r\nAlternatively we could add this feature one level bellow:\r\n```python\r\nbuilder = load_dataset_builder(...)\r\nbuilder.download_and_prepare()\r\nids = builder.as_iterable_dataset()\r\n```",
"Yes, I see how this can be useful. Still, I think `Dataset.to_iterable` + `IterableDataset.from_file` would be much cleaner in terms of the API design (and more flexible since `load_dataset` can only access the \"initial\" (unprocessed) version of a dataset).\r\n\r\nAnd since it can be tricky to manually find the \"initial\" version of a dataset in the cache, maybe `load_dataset` could return an iterable dataset streamed from the cache if `streaming=True` and the cache is up-to-date. ",
"> This allows to use datasets for large scale training where map-style datasets are too slow and use too much memory in PyTorch.\r\n\r\nI second that. e.g. In my last experiment Oscar-en uses 16GB RSS RAM per process and when using multiple processes the host quickly runs out cpu memory. ",
">And since it can be tricky to manually find the \"initial\" version of a dataset in the cache, maybe load_dataset could return an iterable dataset streamed from the cache if streaming=True and the cache is up-to-date.\r\n\r\nThis is exactly the need on JeanZay (HPC) - I have the dataset cache ready, but the compute node is offline, so making streaming work off a local cache would address that need.\r\n\r\nIf you will have a working POC I can be the tester. ",
"> Yes, I see how this can be useful. Still, I think Dataset.to_iterable + IterableDataset.from_file would be much cleaner in terms of the API design (and more flexible since load_dataset can only access the \"initial\" (unprocessed) version of a dataset).\r\n\r\nI like `IterableDataset.from_file` as well. On the other hand `Dataset.to_iterable` first requires to load a Dataset object, which can take time depending on your hardware and your dataset size (sometimes 1h+).\r\n\r\n> And since it can be tricky to manually find the \"initial\" version of a dataset in the cache, maybe load_dataset could return an iterable dataset streamed from the cache if streaming=True and the cache is up-to-date.\r\n\r\nThat would definitely do the job. I was suggesting a different parameter just to make explicit the difference between\r\n- streaming from the raw data\r\n- streaming from the local cache\r\n\r\nBut I'd be fine with streaming from cache is the cache is up-to-date since it's always faster. We could log a message as usual to make it explicit that the cache is used",
"> I was suggesting a different parameter just to make explicit the difference between\r\n\r\nMosaicML's `streaming` library does the same (tries to stream from the local cache if possible), so logging a message should be explicit enough :).",
"Ok ! Sounds good then :)"
] | 2023-01-27T21:43:51Z | 2023-01-31T10:53:47Z | null | MEMBER | null | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00 | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5481/reactions', 'total_count': 3, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 3, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5481/timeline | null | null | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/5480 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5480/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5480/comments | https://api.github.com/repos/huggingface/datasets/issues/5480/events | https://github.com/huggingface/datasets/pull/5480 | 1,560,364,866 | PR_kwDODunzps5ItY2y | 5,480 | Select columns of Dataset or DatasetDict | {'login': 'daskol', 'id': 9336514, 'node_id': 'MDQ6VXNlcjkzMzY1MTQ=', 'avatar_url': 'https://avatars.githubusercontent.com/u/9336514?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/daskol', 'html_url': 'https://github.com/daskol', 'followers_url': 'https://api.github.com/users/daskol/followers', 'following_url': 'https://api.github.com/users/daskol/following{/other_user}', 'gists_url': 'https://api.github.com/users/daskol/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/daskol/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/daskol/subscriptions', 'organizations_url': 'https://api.github.com/users/daskol/orgs', 'repos_url': 'https://api.github.com/users/daskol/repos', 'events_url': 'https://api.github.com/users/daskol/events{/privacy}', 'received_events_url': 'https://api.github.com/users/daskol/received_events', 'type': 'User', 'site_admin': False} | [] | open | false | null | [] | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5480). All of your documentation changes will be reflected on that endpoint."
] | 2023-01-27T20:06:16Z | 2023-01-31T15:49:14Z | null | NONE | null | Close #5474 and #5468. | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5480/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5480/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5480', 'html_url': 'https://github.com/huggingface/datasets/pull/5480', 'diff_url': 'https://github.com/huggingface/datasets/pull/5480.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5480.patch', 'merged_at': None} | true |
https://api.github.com/repos/huggingface/datasets/issues/5479 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5479/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5479/comments | https://api.github.com/repos/huggingface/datasets/issues/5479/events | https://github.com/huggingface/datasets/issues/5479 | 1,560,357,590 | I_kwDODunzps5dASrW | 5,479 | audiofolder works on local env, but creates empty dataset in a remote one, what dependencies could I be missing/outdated | {'login': 'jcho19', 'id': 107211437, 'node_id': 'U_kgDOBmPqrQ', 'avatar_url': 'https://avatars.githubusercontent.com/u/107211437?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/jcho19', 'html_url': 'https://github.com/jcho19', 'followers_url': 'https://api.github.com/users/jcho19/followers', 'following_url': 'https://api.github.com/users/jcho19/following{/other_user}', 'gists_url': 'https://api.github.com/users/jcho19/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/jcho19/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jcho19/subscriptions', 'organizations_url': 'https://api.github.com/users/jcho19/orgs', 'repos_url': 'https://api.github.com/users/jcho19/repos', 'events_url': 'https://api.github.com/users/jcho19/events{/privacy}', 'received_events_url': 'https://api.github.com/users/jcho19/received_events', 'type': 'User', 'site_admin': False} | [] | closed | false | null | [] | null | [] | 2023-01-27T20:01:22Z | 2023-01-29T05:23:14Z | 2023-01-29T05:23:14Z | NONE | null | ### Describe the bug
I'm using a custom audio dataset (400+ audio files) in the correct format for audiofolder. Although loading the dataset with audiofolder works in one local setup, it doesn't in a remote one (it just creates an empty dataset). I have both ffmpeg and libndfile installed on both computers, what could be missing/need to be updated in the one that doesn't work? On the remote env, libsndfile is 1.0.28 and ffmpeg is 4.2.1.
from datasets import load_dataset
ds = load_dataset("audiofolder", data_dir="...")
Here is the output (should be generating 400+ rows):
Downloading and preparing dataset audiofolder/default to ...
Downloading data files: 0%| | 0/2 [00:00<?, ?it/s]
Downloading data files: 0it [00:00, ?it/s]
Extracting data files: 0it [00:00, ?it/s]
Generating train split: 0 examples [00:00, ? examples/s]
Dataset audiofolder downloaded and prepared to ... Subsequent calls will reuse this data.
0%| | 0/1 [00:00<?, ?it/s]
DatasetDict({
train: Dataset({
features: ['audio', 'transcription'],
num_rows: 1
})
})
Here is my pip environment in the one that doesn't work (uses torch 1.11.a0 from shared env):
Package Version
------------------- -------------------
aiofiles 22.1.0
aiohttp 3.8.3
aiosignal 1.3.1
altair 4.2.1
anyio 3.6.2
appdirs 1.4.4
argcomplete 2.0.0
argon2-cffi 20.1.0
astunparse 1.6.3
async-timeout 4.0.2
attrs 21.2.0
audioread 3.0.0
backcall 0.2.0
bleach 4.0.0
certifi 2021.10.8
cffi 1.14.6
charset-normalizer 2.0.12
click 8.1.3
contourpy 1.0.7
cycler 0.11.0
datasets 2.9.0
debugpy 1.4.1
decorator 5.0.9
defusedxml 0.7.1
dill 0.3.6
distlib 0.3.4
entrypoints 0.3
evaluate 0.4.0
expecttest 0.1.3
fastapi 0.89.1
ffmpy 0.3.0
filelock 3.6.0
fonttools 4.38.0
frozenlist 1.3.3
fsspec 2023.1.0
future 0.18.2
gradio 3.16.2
h11 0.14.0
httpcore 0.16.3
httpx 0.23.3
huggingface-hub 0.12.0
idna 3.3
ipykernel 6.2.0
ipython 7.26.0
ipython-genutils 0.2.0
ipywidgets 7.6.3
jedi 0.18.0
Jinja2 3.0.1
jiwer 2.5.1
joblib 1.2.0
jsonschema 3.2.0
jupyter 1.0.0
jupyter-client 6.1.12
jupyter-console 6.4.0
jupyter-core 4.7.1
jupyterlab-pygments 0.1.2
jupyterlab-widgets 1.0.0
kiwisolver 1.4.4
Levenshtein 0.20.2
librosa 0.9.2
linkify-it-py 1.0.3
llvmlite 0.39.1
markdown-it-py 2.1.0
MarkupSafe 2.0.1
matplotlib 3.6.3
matplotlib-inline 0.1.2
mdit-py-plugins 0.3.3
mdurl 0.1.2
mistune 0.8.4
multidict 6.0.4
multiprocess 0.70.14
nbclient 0.5.4
nbconvert 6.1.0
nbformat 5.1.3
nest-asyncio 1.5.1
notebook 6.4.3
numba 0.56.4
numpy 1.20.3
orjson 3.8.5
packaging 21.0
pandas 1.5.3
pandocfilters 1.4.3
parso 0.8.2
pexpect 4.8.0
pickleshare 0.7.5
Pillow 9.4.0
pip 22.3.1
pipx 1.1.0
platformdirs 2.5.2
pooch 1.6.0
prometheus-client 0.11.0
prompt-toolkit 3.0.19
psutil 5.9.0
ptyprocess 0.7.0
pyarrow 10.0.1
pycparser 2.20
pycryptodome 3.16.0
pydantic 1.10.4
pydub 0.25.1
Pygments 2.10.0
pyparsing 2.4.7
pyrsistent 0.18.0
python-dateutil 2.8.2
python-multipart 0.0.5
pytz 2022.7.1
PyYAML 6.0
pyzmq 22.2.1
qtconsole 5.1.1
QtPy 1.10.0
rapidfuzz 2.13.7
regex 2022.10.31
requests 2.27.1
resampy 0.4.2
responses 0.18.0
rfc3986 1.5.0
scikit-learn 1.2.1
scipy 1.6.3
Send2Trash 1.8.0
setuptools 65.5.1
shiboken6 6.3.1
shiboken6-generator 6.3.1
six 1.16.0
sniffio 1.3.0
soundfile 0.11.0
starlette 0.22.0
terminado 0.11.0
testpath 0.5.0
threadpoolctl 3.1.0
tokenizers 0.13.2
toolz 0.12.0
torch 1.11.0a0+gitunknown
tornado 6.1
tqdm 4.64.1
traitlets 5.0.5
transformers 4.27.0.dev0
types-dataclasses 0.6.4
typing_extensions 4.1.1
uc-micro-py 1.0.1
urllib3 1.26.9
userpath 1.8.0
uvicorn 0.20.0
virtualenv 20.14.1
wcwidth 0.2.5
webencodings 0.5.1
websockets 10.4
wheel 0.37.1
widgetsnbextension 3.5.1
xxhash 3.2.0
yarl 1.8.2
### Steps to reproduce the bug
Create a pip environment with the packages listed above (make sure ffmpeg and libsndfile is installed with same versions listed above).
Create a custom audio dataset and load it in with load_dataset("audiofolder", ...)
### Expected behavior
load_dataset should create a dataset with 400+ rows.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.0
- PyArrow version: 10.0.1
- Pandas version: 1.5.3 | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5479/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5479/timeline | null | completed | null | null | false |
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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==6.0.0\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) | 0.008167 / 0.011353 (-0.003186) | 0.004404 / 0.011008 (-0.006605) | 0.100462 / 0.038508 (0.061954) | 0.028835 / 0.023109 (0.005726) | 0.326759 / 0.275898 (0.050861) | 0.355150 / 0.323480 (0.031670) | 0.007200 / 0.007986 (-0.000786) | 0.003293 / 0.004328 (-0.001035) | 0.078006 / 0.004250 (0.073756) | 0.033298 / 0.037052 (-0.003754) | 0.307119 / 0.258489 (0.048630) | 0.337689 / 0.293841 (0.043848) | 0.033016 / 0.128546 (-0.095530) | 0.011383 / 0.075646 (-0.064263) | 0.321989 / 0.419271 (-0.097283) | 0.039793 / 0.043533 (-0.003740) | 0.295388 / 0.255139 (0.040249) | 0.322694 / 0.283200 (0.039494) | 0.082989 / 0.141683 (-0.058694) | 1.496701 / 1.452155 (0.044546) | 1.548861 / 1.492716 (0.056145) |\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) | 0.176587 / 0.018006 (0.158580) | 0.397660 / 0.000490 (0.397170) | 0.001063 / 0.000200 (0.000863) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022386 / 0.037411 (-0.015025) | 0.096380 / 0.014526 (0.081854) | 0.103032 / 0.176557 (-0.073525) | 0.135050 / 0.737135 (-0.602086) | 0.105941 / 0.296338 (-0.190397) |\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) | 0.430989 / 0.215209 (0.215780) | 4.310309 / 2.077655 (2.232654) | 2.142596 / 1.504120 (0.638477) | 1.952043 / 1.541195 (0.410848) | 1.817803 / 1.468490 (0.349312) | 0.690026 / 4.584777 (-3.894751) | 3.315413 / 3.745712 (-0.430299) | 3.370336 / 5.269862 (-1.899525) | 1.668707 / 4.565676 (-2.896970) | 0.081860 / 0.424275 (-0.342415) | 0.012493 / 0.007607 (0.004886) | 0.527779 / 0.226044 (0.301735) | 5.318732 / 2.268929 (3.049804) | 2.467029 / 55.444624 (-52.977596) | 2.247171 / 6.876477 (-4.629306) | 2.270825 / 2.142072 (0.128752) | 0.802288 / 4.805227 (-4.002939) | 0.148895 / 6.500664 (-6.351770) | 0.064967 / 0.075469 (-0.010503) |\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) | 1.259304 / 1.841788 (-0.582484) | 13.662441 / 8.074308 (5.588133) | 14.074662 / 10.191392 (3.883270) | 0.152907 / 0.680424 (-0.527516) | 0.028340 / 0.534201 (-0.505861) | 0.397356 / 0.579283 (-0.181927) | 0.392600 / 0.434364 (-0.041764) | 0.467935 / 0.540337 (-0.072402) | 0.539890 / 1.386936 (-0.847046) |\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) | 0.006156 / 0.011353 (-0.005197) | 0.004371 / 0.011008 (-0.006637) | 0.076391 / 0.038508 (0.037883) | 0.026455 / 0.023109 (0.003346) | 0.339816 / 0.275898 (0.063917) | 0.370032 / 0.323480 (0.046552) | 0.004614 / 0.007986 (-0.003372) | 0.003200 / 0.004328 (-0.001129) | 0.075408 / 0.004250 (0.071157) | 0.034100 / 0.037052 (-0.002953) | 0.341232 / 0.258489 (0.082743) | 0.380290 / 0.293841 (0.086449) | 0.031021 / 0.128546 (-0.097525) | 0.011562 / 0.075646 (-0.064084) | 0.085564 / 0.419271 (-0.333708) | 0.041431 / 0.043533 (-0.002102) | 0.359570 / 0.255139 (0.104431) | 0.366919 / 0.283200 (0.083719) | 0.088242 / 0.141683 (-0.053441) | 1.460703 / 1.452155 (0.008548) | 1.534351 / 1.492716 (0.041635) |\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) | 0.225703 / 0.018006 (0.207697) | 0.395014 / 0.000490 (0.394524) | 0.000385 / 0.000200 (0.000185) | 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) | 0.023975 / 0.037411 (-0.013436) | 0.098658 / 0.014526 (0.084132) | 0.105043 / 0.176557 (-0.071513) | 0.139988 / 0.737135 (-0.597148) | 0.106854 / 0.296338 (-0.189484) |\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) | 0.442454 / 0.215209 (0.227245) | 4.430860 / 2.077655 (2.353205) | 2.084823 / 1.504120 (0.580704) | 1.870421 / 1.541195 (0.329226) | 1.901618 / 1.468490 (0.433128) | 0.699214 / 4.584777 (-3.885563) | 3.336911 / 3.745712 (-0.408801) | 1.856479 / 5.269862 (-3.413383) | 1.166496 / 4.565676 (-3.399180) | 0.083189 / 0.424275 (-0.341086) | 0.012293 / 0.007607 (0.004686) | 0.543147 / 0.226044 (0.317102) | 5.452030 / 2.268929 (3.183101) | 2.506689 / 55.444624 (-52.937936) | 2.168186 / 6.876477 (-4.708291) | 2.172277 / 2.142072 (0.030205) | 0.813554 / 4.805227 (-3.991673) | 0.152074 / 6.500664 (-6.348590) | 0.066891 / 0.075469 (-0.008579) |\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) | 1.278635 / 1.841788 (-0.563153) | 13.690232 / 8.074308 (5.615924) | 13.403201 / 10.191392 (3.211809) | 0.128171 / 0.680424 (-0.552253) | 0.016687 / 0.534201 (-0.517514) | 0.378645 / 0.579283 (-0.200638) | 0.382922 / 0.434364 (-0.051442) | 0.467483 / 0.540337 (-0.072854) | 0.559026 / 1.386936 (-0.827910) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b262d411ec0e252615a140c4e3e60e7dbd38eef1 \"CML watermark\")\n"
] | 2023-01-27T20:01:22Z | 2023-01-30T19:22:21Z | 2023-01-30T19:15:26Z | MEMBER | null | From this [feedback](https://discuss.huggingface.co/t/nonmatchingsplitssizeserror/30033) on the forum, thought I'd include a tip for recomputing the metadata numbers if it is your own dataset. | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5478/reactions', 'total_count': 1, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 1, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5478/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5478', 'html_url': 'https://github.com/huggingface/datasets/pull/5478', 'diff_url': 'https://github.com/huggingface/datasets/pull/5478.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5478.patch', 'merged_at': '2023-01-30T19:15:26Z'} | true |
https://api.github.com/repos/huggingface/datasets/issues/5477 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5477/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5477/comments | https://api.github.com/repos/huggingface/datasets/issues/5477/events | https://github.com/huggingface/datasets/issues/5477 | 1,559,909,892 | I_kwDODunzps5c-lYE | 5,477 | Unpin sqlalchemy once issue is fixed | {'login': 'albertvillanova', 'id': 8515462, 'node_id': 'MDQ6VXNlcjg1MTU0NjI=', 'avatar_url': 'https://avatars.githubusercontent.com/u/8515462?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/albertvillanova', 'html_url': 'https://github.com/albertvillanova', 'followers_url': 'https://api.github.com/users/albertvillanova/followers', 'following_url': 'https://api.github.com/users/albertvillanova/following{/other_user}', 'gists_url': 'https://api.github.com/users/albertvillanova/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/albertvillanova/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/albertvillanova/subscriptions', 'organizations_url': 'https://api.github.com/users/albertvillanova/orgs', 'repos_url': 'https://api.github.com/users/albertvillanova/repos', 'events_url': 'https://api.github.com/users/albertvillanova/events{/privacy}', 'received_events_url': 'https://api.github.com/users/albertvillanova/received_events', 'type': 'User', 'site_admin': False} | [] | open | false | null | [] | null | [] | 2023-01-27T15:01:55Z | 2023-01-27T15:01:55Z | null | MEMBER | null | Once the source issue is fixed:
- pandas-dev/pandas#51015
we should revert the pin introduced in:
- #5476 | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5477/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5477/timeline | null | null | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/5476 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5476/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5476/comments | https://api.github.com/repos/huggingface/datasets/issues/5476/events | https://github.com/huggingface/datasets/pull/5476 | 1,559,594,684 | PR_kwDODunzps5IqwC_ | 5,476 | Pin sqlalchemy | {'login': 'lhoestq', 'id': 42851186, 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/lhoestq', 'html_url': 'https://github.com/lhoestq', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'type': 'User', 'site_admin': False} | [] | closed | false | null | [] | null | [
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012442 / 0.011353 (0.001089) | 0.006274 / 0.011008 (-0.004734) | 0.128249 / 0.038508 (0.089741) | 0.040117 / 0.023109 (0.017008) | 0.383725 / 0.275898 (0.107827) | 0.510494 / 0.323480 (0.187014) | 0.009037 / 0.007986 (0.001051) | 0.008256 / 0.004328 (0.003927) | 0.105329 / 0.004250 (0.101079) | 0.046909 / 0.037052 (0.009857) | 0.401980 / 0.258489 (0.143491) | 0.461332 / 0.293841 (0.167491) | 0.065629 / 0.128546 (-0.062917) | 0.020043 / 0.075646 (-0.055604) | 0.453773 / 0.419271 (0.034501) | 0.063456 / 0.043533 (0.019923) | 0.384458 / 0.255139 (0.129319) | 0.449699 / 0.283200 (0.166499) | 0.118197 / 0.141683 (-0.023486) | 1.915080 / 1.452155 (0.462925) | 1.957132 / 1.492716 (0.464416) |\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) | 0.209657 / 0.018006 (0.191651) | 0.592478 / 0.000490 (0.591988) | 0.004137 / 0.000200 (0.003937) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029607 / 0.037411 (-0.007804) | 0.129559 / 0.014526 (0.115033) | 0.148326 / 0.176557 (-0.028231) | 0.190506 / 0.737135 (-0.546629) | 0.143177 / 0.296338 (-0.153162) |\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) | 0.626166 / 0.215209 (0.410957) | 6.612680 / 2.077655 (4.535026) | 2.432354 / 1.504120 (0.928234) | 2.051482 / 1.541195 (0.510287) | 2.055822 / 1.468490 (0.587332) | 1.210099 / 4.584777 (-3.374678) | 5.498117 / 3.745712 (1.752405) | 3.054838 / 5.269862 (-2.215024) | 2.182875 / 4.565676 (-2.382802) | 0.144518 / 0.424275 (-0.279757) | 0.014132 / 0.007607 (0.006525) | 0.801805 / 0.226044 (0.575761) | 7.911235 / 2.268929 (5.642307) | 3.372762 / 55.444624 (-52.071862) | 2.517266 / 6.876477 (-4.359210) | 2.515329 / 2.142072 (0.373256) | 1.501731 / 4.805227 (-3.303497) | 0.252569 / 6.500664 (-6.248096) | 0.080987 / 0.075469 (0.005518) |\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) | 1.709880 / 1.841788 (-0.131907) | 18.640340 / 8.074308 (10.566032) | 23.560908 / 10.191392 (13.369516) | 0.265680 / 0.680424 (-0.414744) | 0.046438 / 0.534201 (-0.487763) | 0.571973 / 0.579283 (-0.007310) | 0.642425 / 0.434364 (0.208061) | 0.698167 / 0.540337 (0.157830) | 0.842132 / 1.386936 (-0.544804) |\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) | 0.009268 / 0.011353 (-0.002085) | 0.006052 / 0.011008 (-0.004956) | 0.133448 / 0.038508 (0.094939) | 0.034417 / 0.023109 (0.011308) | 0.435573 / 0.275898 (0.159675) | 0.479642 / 0.323480 (0.156162) | 0.008016 / 0.007986 (0.000030) | 0.006616 / 0.004328 (0.002288) | 0.106256 / 0.004250 (0.102005) | 0.048995 / 0.037052 (0.011942) | 0.450056 / 0.258489 (0.191567) | 0.511027 / 0.293841 (0.217187) | 0.052928 / 0.128546 (-0.075618) | 0.020824 / 0.075646 (-0.054822) | 0.450105 / 0.419271 (0.030834) | 0.062729 / 0.043533 (0.019196) | 0.438887 / 0.255139 (0.183748) | 0.468732 / 0.283200 (0.185532) | 0.116101 / 0.141683 (-0.025582) | 1.909689 / 1.452155 (0.457534) | 2.042007 / 1.492716 (0.549291) |\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) | 0.198265 / 0.018006 (0.180259) | 0.541799 / 0.000490 (0.541309) | 0.003938 / 0.000200 (0.003738) | 0.000116 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035933 / 0.037411 (-0.001478) | 0.130754 / 0.014526 (0.116229) | 0.146143 / 0.176557 (-0.030414) | 0.202042 / 0.737135 (-0.535094) | 0.155648 / 0.296338 (-0.140691) |\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) | 0.691123 / 0.215209 (0.475914) | 6.708370 / 2.077655 (4.630715) | 2.957120 / 1.504120 (1.453000) | 2.558350 / 1.541195 (1.017155) | 2.611271 / 1.468490 (1.142781) | 1.327355 / 4.584777 (-3.257422) | 5.755975 / 3.745712 (2.010263) | 3.295556 / 5.269862 (-1.974305) | 2.159831 / 4.565676 (-2.405845) | 0.161409 / 0.424275 (-0.262866) | 0.015470 / 0.007607 (0.007863) | 0.840611 / 0.226044 (0.614567) | 8.550064 / 2.268929 (6.281136) | 3.832013 / 55.444624 (-51.612612) | 3.032909 / 6.876477 (-3.843568) | 3.155651 / 2.142072 (1.013578) | 1.612486 / 4.805227 (-3.192741) | 0.273789 / 6.500664 (-6.226875) | 0.085618 / 0.075469 (0.010149) |\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) | 1.808376 / 1.841788 (-0.033412) | 18.267614 / 8.074308 (10.193306) | 21.047679 / 10.191392 (10.856286) | 0.259089 / 0.680424 (-0.421335) | 0.029211 / 0.534201 (-0.504990) | 0.556303 / 0.579283 (-0.022980) | 0.625264 / 0.434364 (0.190900) | 0.680814 / 0.540337 (0.140476) | 0.810146 / 1.386936 (-0.576790) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#20ea76c80e07acad78cf67198a4046a982feda21 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008779 / 0.011353 (-0.002574) | 0.004644 / 0.011008 (-0.006364) | 0.099814 / 0.038508 (0.061306) | 0.029830 / 0.023109 (0.006721) | 0.299159 / 0.275898 (0.023261) | 0.354815 / 0.323480 (0.031335) | 0.006968 / 0.007986 (-0.001018) | 0.003521 / 0.004328 (-0.000808) | 0.077687 / 0.004250 (0.073437) | 0.035019 / 0.037052 (-0.002034) | 0.309548 / 0.258489 (0.051059) | 0.345228 / 0.293841 (0.051387) | 0.033644 / 0.128546 (-0.094902) | 0.011564 / 0.075646 (-0.064083) | 0.321835 / 0.419271 (-0.097437) | 0.041798 / 0.043533 (-0.001735) | 0.298190 / 0.255139 (0.043051) | 0.328874 / 0.283200 (0.045674) | 0.088175 / 0.141683 (-0.053508) | 1.481755 / 1.452155 (0.029600) | 1.503085 / 1.492716 (0.010369) |\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) | 0.170930 / 0.018006 (0.152924) | 0.422155 / 0.000490 (0.421666) | 0.001708 / 0.000200 (0.001509) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022588 / 0.037411 (-0.014824) | 0.095775 / 0.014526 (0.081249) | 0.103939 / 0.176557 (-0.072618) | 0.138441 / 0.737135 (-0.598694) | 0.107896 / 0.296338 (-0.188442) |\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) | 0.418243 / 0.215209 (0.203034) | 4.171432 / 2.077655 (2.093777) | 1.906029 / 1.504120 (0.401909) | 1.698174 / 1.541195 (0.156979) | 1.748339 / 1.468490 (0.279849) | 0.691026 / 4.584777 (-3.893751) | 3.393354 / 3.745712 (-0.352358) | 2.722412 / 5.269862 (-2.547450) | 1.462439 / 4.565676 (-3.103238) | 0.084713 / 0.424275 (-0.339562) | 0.012131 / 0.007607 (0.004524) | 0.522153 / 0.226044 (0.296109) | 5.197916 / 2.268929 (2.928988) | 2.314270 / 55.444624 (-53.130354) | 1.986599 / 6.876477 (-4.889878) | 2.012757 / 2.142072 (-0.129315) | 0.802540 / 4.805227 (-4.002687) | 0.148673 / 6.500664 (-6.351991) | 0.065924 / 0.075469 (-0.009545) |\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) | 1.263790 / 1.841788 (-0.577998) | 13.874784 / 8.074308 (5.800476) | 13.842276 / 10.191392 (3.650884) | 0.149002 / 0.680424 (-0.531422) | 0.028550 / 0.534201 (-0.505651) | 0.396913 / 0.579283 (-0.182370) | 0.401543 / 0.434364 (-0.032821) | 0.473754 / 0.540337 (-0.066583) | 0.560455 / 1.386936 (-0.826481) |\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) | 0.006724 / 0.011353 (-0.004629) | 0.004507 / 0.011008 (-0.006502) | 0.098447 / 0.038508 (0.059939) | 0.027888 / 0.023109 (0.004779) | 0.428956 / 0.275898 (0.153058) | 0.451557 / 0.323480 (0.128077) | 0.005056 / 0.007986 (-0.002929) | 0.003363 / 0.004328 (-0.000965) | 0.075990 / 0.004250 (0.071740) | 0.038688 / 0.037052 (0.001635) | 0.421550 / 0.258489 (0.163061) | 0.459480 / 0.293841 (0.165639) | 0.031408 / 0.128546 (-0.097138) | 0.011559 / 0.075646 (-0.064088) | 0.320054 / 0.419271 (-0.099217) | 0.041917 / 0.043533 (-0.001616) | 0.420878 / 0.255139 (0.165739) | 0.444813 / 0.283200 (0.161613) | 0.090409 / 0.141683 (-0.051274) | 1.490058 / 1.452155 (0.037904) | 1.645206 / 1.492716 (0.152489) |\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) | 0.221105 / 0.018006 (0.203099) | 0.407537 / 0.000490 (0.407047) | 0.000410 / 0.000200 (0.000210) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024658 / 0.037411 (-0.012754) | 0.099230 / 0.014526 (0.084705) | 0.107788 / 0.176557 (-0.068769) | 0.143040 / 0.737135 (-0.594096) | 0.109440 / 0.296338 (-0.186899) |\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) | 0.453303 / 0.215209 (0.238094) | 4.520376 / 2.077655 (2.442722) | 2.133909 / 1.504120 (0.629789) | 1.926996 / 1.541195 (0.385801) | 2.019870 / 1.468490 (0.551380) | 0.707423 / 4.584777 (-3.877354) | 3.391903 / 3.745712 (-0.353809) | 1.860661 / 5.269862 (-3.409201) | 1.159940 / 4.565676 (-3.405736) | 0.083773 / 0.424275 (-0.340502) | 0.012228 / 0.007607 (0.004621) | 0.554666 / 0.226044 (0.328622) | 5.567564 / 2.268929 (3.298636) | 2.636718 / 55.444624 (-52.807907) | 2.240215 / 6.876477 (-4.636262) | 2.218951 / 2.142072 (0.076879) | 0.817167 / 4.805227 (-3.988060) | 0.151633 / 6.500664 (-6.349032) | 0.066515 / 0.075469 (-0.008954) |\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) | 1.296665 / 1.841788 (-0.545123) | 13.997898 / 8.074308 (5.923590) | 13.286607 / 10.191392 (3.095215) | 0.148906 / 0.680424 (-0.531518) | 0.016600 / 0.534201 (-0.517601) | 0.377459 / 0.579283 (-0.201824) | 0.379938 / 0.434364 (-0.054426) | 0.461628 / 0.540337 (-0.078709) | 0.550592 / 1.386936 (-0.836344) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#053f51a3e2adb762236eb29dd02791307f45f02f \"CML watermark\")\n"
] | 2023-01-27T11:26:38Z | 2023-01-27T12:06:51Z | 2023-01-27T11:57:48Z | MEMBER | null | since sqlalchemy update to 2.0.0 the CI started to fail: https://github.com/huggingface/datasets/actions/runs/4023742457/jobs/6914976514
the error comes from pandas: https://github.com/pandas-dev/pandas/issues/51015 | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5476/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5476/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5476', 'html_url': 'https://github.com/huggingface/datasets/pull/5476', 'diff_url': 'https://github.com/huggingface/datasets/pull/5476.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5476.patch', 'merged_at': '2023-01-27T11:57:48Z'} | true |
https://api.github.com/repos/huggingface/datasets/issues/5475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5475/comments | https://api.github.com/repos/huggingface/datasets/issues/5475/events | https://github.com/huggingface/datasets/issues/5475 | 1,559,030,149 | I_kwDODunzps5c7OmF | 5,475 | Dataset scan time is much slower than using native arrow | {'login': 'jonny-cyberhaven', 'id': 121845112, 'node_id': 'U_kgDOB0M1eA', 'avatar_url': 'https://avatars.githubusercontent.com/u/121845112?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/jonny-cyberhaven', 'html_url': 'https://github.com/jonny-cyberhaven', 'followers_url': 'https://api.github.com/users/jonny-cyberhaven/followers', 'following_url': 'https://api.github.com/users/jonny-cyberhaven/following{/other_user}', 'gists_url': 'https://api.github.com/users/jonny-cyberhaven/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/jonny-cyberhaven/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jonny-cyberhaven/subscriptions', 'organizations_url': 'https://api.github.com/users/jonny-cyberhaven/orgs', 'repos_url': 'https://api.github.com/users/jonny-cyberhaven/repos', 'events_url': 'https://api.github.com/users/jonny-cyberhaven/events{/privacy}', 'received_events_url': 'https://api.github.com/users/jonny-cyberhaven/received_events', 'type': 'User', 'site_admin': False} | [] | closed | false | null | [] | null | [
"Hi ! In your code you only iterate on the Arrow buffers - you don't actually load the data as python objects. For a fair comparison, you can modify your code using:\r\n```diff\r\n- for _ in range(0, len(table), bsz):\r\n- _ = {k:table[k][_ : _ + bsz] for k in cols}\r\n+ for _ in range(0, len(table), bsz):\r\n+ _ = {k:table[k][_ : _ + bsz].to_pylist() for k in cols}\r\n```\r\n\r\nI re-ran your code and got a speed ratio of 1.00x and 1.02x",
"Ah I see, datasets is implicitly making this conversion. Thanks for pointing that out!\r\n\r\nIf it's not too much, I would also suggest updating some of your docs with the same `.to_pylist()` conversion in the code snippet that follows [here](https://huggingface.co/course/chapter5/4?fw=pt#:~:text=let%E2%80%99s%20run%20a%20little%20speed%20test%20by%20iterating%20over%20all%20the%20elements%20in%20the%20PubMed%20Abstracts%20dataset%3A).",
"This code snippet shows `datasets` code that reads the Arrow data as python objects already, there is no need to add to_pylist. Or were you thinking about something else ?"
] | 2023-01-27T01:32:25Z | 2023-01-30T16:17:11Z | 2023-01-30T16:17:11Z | CONTRIBUTOR | null | ### Describe the bug
I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.
I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that explains this phenomenon?
### Steps to reproduce the bug
https://colab.research.google.com/drive/11EtHDaGAf1DKCpvYnAPJUW-LFfAcDzHY?usp=sharing
### Expected behavior
I expect scan times to be on par with using pyarrow directly.
### Environment info
standard colab environment | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5475/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5475/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/5474 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5474/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5474/comments | https://api.github.com/repos/huggingface/datasets/issues/5474/events | https://github.com/huggingface/datasets/issues/5474 | 1,558,827,155 | I_kwDODunzps5c6dCT | 5,474 | Column project operation on `datasets.Dataset` | {'login': 'daskol', 'id': 9336514, 'node_id': 'MDQ6VXNlcjkzMzY1MTQ=', 'avatar_url': 'https://avatars.githubusercontent.com/u/9336514?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/daskol', 'html_url': 'https://github.com/daskol', 'followers_url': 'https://api.github.com/users/daskol/followers', 'following_url': 'https://api.github.com/users/daskol/following{/other_user}', 'gists_url': 'https://api.github.com/users/daskol/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/daskol/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/daskol/subscriptions', 'organizations_url': 'https://api.github.com/users/daskol/orgs', 'repos_url': 'https://api.github.com/users/daskol/repos', 'events_url': 'https://api.github.com/users/daskol/events{/privacy}', 'received_events_url': 'https://api.github.com/users/daskol/received_events', 'type': 'User', 'site_admin': False} | [{'id': 1935892871, 'node_id': 'MDU6TGFiZWwxOTM1ODkyODcx', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/enhancement', 'name': 'enhancement', 'color': 'a2eeef', 'default': True, 'description': 'New feature or request'}] | open | false | null | [] | null | [
"Hi ! This would be a nice addition indeed :) This sounds like a duplicate of https://github.com/huggingface/datasets/issues/5468\r\n\r\n> Not sure. Some of my PRs are still open and some do not have any discussions.\r\n\r\nSorry to hear that, feel free to ping me on those PRs"
] | 2023-01-26T21:47:53Z | 2023-01-27T12:23:29Z | null | NONE | null | ### Feature request
There is no operation to select a subset of columns of original dataset. Expected API follows.
```python
a = Dataset.from_dict({
'int': [0, 1, 2]
'char': ['a', 'b', 'c'],
'none': [None] * 3,
})
b = a.project('int', 'char') # usually, .select()
print(a.column_names) # stdout: ['int', 'char', 'none']
print(b.column_names) # stdout: ['int', 'char']
```
Method project can easily accept not only column names (as a `str)` but univariant function applied to corresponding column as an example. Or keyword arguments can be used in order to rename columns in advance (see `pandas`, `pyspark`, `pyarrow`, and SQL)..
### Motivation
Projection is a typical operation in every data processing library. And it is a basic block of a well-known data manipulation language like SQL. Without this operation `datasets.Dataset` interface is not complete.
### Your contribution
Not sure. Some of my PRs are still open and some do not have any discussions. | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5474/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5474/timeline | null | null | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/5473 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5473/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5473/comments | https://api.github.com/repos/huggingface/datasets/issues/5473/events | https://github.com/huggingface/datasets/pull/5473 | 1,558,668,197 | PR_kwDODunzps5Inm9h | 5,473 | Set dev version | {'login': 'lhoestq', 'id': 42851186, 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/lhoestq', 'html_url': 'https://github.com/lhoestq', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'type': 'User', 'site_admin': False} | [] | closed | false | null | [] | null | [
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008959 / 0.011353 (-0.002394) | 0.004549 / 0.011008 (-0.006460) | 0.102012 / 0.038508 (0.063504) | 0.030122 / 0.023109 (0.007013) | 0.303731 / 0.275898 (0.027833) | 0.344418 / 0.323480 (0.020938) | 0.007199 / 0.007986 (-0.000787) | 0.003415 / 0.004328 (-0.000913) | 0.079784 / 0.004250 (0.075534) | 0.034894 / 0.037052 (-0.002158) | 0.304739 / 0.258489 (0.046250) | 0.359457 / 0.293841 (0.065616) | 0.034194 / 0.128546 (-0.094352) | 0.011348 / 0.075646 (-0.064298) | 0.324340 / 0.419271 (-0.094931) | 0.041071 / 0.043533 (-0.002461) | 0.304437 / 0.255139 (0.049298) | 0.335517 / 0.283200 (0.052317) | 0.087787 / 0.141683 (-0.053895) | 1.467293 / 1.452155 (0.015138) | 1.543529 / 1.492716 (0.050813) |\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) | 0.187654 / 0.018006 (0.169648) | 0.426558 / 0.000490 (0.426068) | 0.003585 / 0.000200 (0.003385) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023410 / 0.037411 (-0.014001) | 0.097065 / 0.014526 (0.082539) | 0.105358 / 0.176557 (-0.071198) | 0.140941 / 0.737135 (-0.596195) | 0.109484 / 0.296338 (-0.186855) |\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) | 0.420334 / 0.215209 (0.205125) | 4.223235 / 2.077655 (2.145581) | 1.866213 / 1.504120 (0.362093) | 1.673829 / 1.541195 (0.132634) | 1.757828 / 1.468490 (0.289337) | 0.702203 / 4.584777 (-3.882574) | 3.426192 / 3.745712 (-0.319521) | 1.950392 / 5.269862 (-3.319470) | 1.286139 / 4.565676 (-3.279538) | 0.082858 / 0.424275 (-0.341417) | 0.012587 / 0.007607 (0.004980) | 0.531920 / 0.226044 (0.305876) | 5.344425 / 2.268929 (3.075497) | 2.337875 / 55.444624 (-53.106749) | 1.967713 / 6.876477 (-4.908764) | 2.022075 / 2.142072 (-0.119997) | 0.829267 / 4.805227 (-3.975961) | 0.151712 / 6.500664 (-6.348952) | 0.066617 / 0.075469 (-0.008852) |\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) | 1.251867 / 1.841788 (-0.589921) | 13.861756 / 8.074308 (5.787448) | 14.236309 / 10.191392 (4.044917) | 0.138215 / 0.680424 (-0.542209) | 0.028600 / 0.534201 (-0.505601) | 0.395890 / 0.579283 (-0.183393) | 0.403971 / 0.434364 (-0.030393) | 0.479033 / 0.540337 (-0.061305) | 0.564019 / 1.386936 (-0.822917) |\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) | 0.006845 / 0.011353 (-0.004508) | 0.004544 / 0.011008 (-0.006464) | 0.098719 / 0.038508 (0.060211) | 0.029082 / 0.023109 (0.005973) | 0.426011 / 0.275898 (0.150113) | 0.447185 / 0.323480 (0.123705) | 0.005203 / 0.007986 (-0.002783) | 0.004790 / 0.004328 (0.000462) | 0.076446 / 0.004250 (0.072196) | 0.040649 / 0.037052 (0.003596) | 0.414810 / 0.258489 (0.156321) | 0.452082 / 0.293841 (0.158241) | 0.031842 / 0.128546 (-0.096704) | 0.011575 / 0.075646 (-0.064071) | 0.320710 / 0.419271 (-0.098561) | 0.044994 / 0.043533 (0.001461) | 0.415645 / 0.255139 (0.160506) | 0.435235 / 0.283200 (0.152035) | 0.091756 / 0.141683 (-0.049927) | 1.493900 / 1.452155 (0.041746) | 1.592353 / 1.492716 (0.099637) |\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) | 0.264710 / 0.018006 (0.246703) | 0.410553 / 0.000490 (0.410064) | 0.024497 / 0.000200 (0.024297) | 0.000232 / 0.000054 (0.000178) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024452 / 0.037411 (-0.012959) | 0.102673 / 0.014526 (0.088147) | 0.107787 / 0.176557 (-0.068770) | 0.147368 / 0.737135 (-0.589767) | 0.112127 / 0.296338 (-0.184211) |\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) | 0.471294 / 0.215209 (0.256085) | 4.711638 / 2.077655 (2.633983) | 2.436819 / 1.504120 (0.932699) | 2.238540 / 1.541195 (0.697345) | 2.334134 / 1.468490 (0.865644) | 0.697668 / 4.584777 (-3.887108) | 3.414332 / 3.745712 (-0.331380) | 2.783248 / 5.269862 (-2.486614) | 1.529599 / 4.565676 (-3.036078) | 0.082626 / 0.424275 (-0.341649) | 0.012385 / 0.007607 (0.004778) | 0.580486 / 0.226044 (0.354441) | 5.837914 / 2.268929 (3.568986) | 2.915129 / 55.444624 (-52.529495) | 2.606254 / 6.876477 (-4.270223) | 2.659031 / 2.142072 (0.516958) | 0.810431 / 4.805227 (-3.994796) | 0.151666 / 6.500664 (-6.348998) | 0.066873 / 0.075469 (-0.008596) |\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) | 1.259933 / 1.841788 (-0.581855) | 14.052388 / 8.074308 (5.978080) | 13.356141 / 10.191392 (3.164749) | 0.138416 / 0.680424 (-0.542008) | 0.016582 / 0.534201 (-0.517619) | 0.378110 / 0.579283 (-0.201173) | 0.385089 / 0.434364 (-0.049275) | 0.465299 / 0.540337 (-0.075038) | 0.559780 / 1.386936 (-0.827156) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d2859fd4d4beca33f21539a6e1df9a7f012cbd10 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.011945 / 0.011353 (0.000592) | 0.006128 / 0.011008 (-0.004880) | 0.128926 / 0.038508 (0.090418) | 0.037708 / 0.023109 (0.014599) | 0.373449 / 0.275898 (0.097551) | 0.423567 / 0.323480 (0.100088) | 0.009848 / 0.007986 (0.001863) | 0.006097 / 0.004328 (0.001769) | 0.098275 / 0.004250 (0.094024) | 0.043199 / 0.037052 (0.006147) | 0.376848 / 0.258489 (0.118359) | 0.441819 / 0.293841 (0.147978) | 0.055094 / 0.128546 (-0.073453) | 0.019704 / 0.075646 (-0.055942) | 0.422746 / 0.419271 (0.003474) | 0.061764 / 0.043533 (0.018231) | 0.381056 / 0.255139 (0.125917) | 0.419343 / 0.283200 (0.136144) | 0.116720 / 0.141683 (-0.024963) | 1.763913 / 1.452155 (0.311759) | 1.872306 / 1.492716 (0.379589) |\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) | 0.198651 / 0.018006 (0.180645) | 0.560565 / 0.000490 (0.560075) | 0.004269 / 0.000200 (0.004069) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027307 / 0.037411 (-0.010104) | 0.128276 / 0.014526 (0.113750) | 0.129015 / 0.176557 (-0.047542) | 0.167269 / 0.737135 (-0.569866) | 0.143955 / 0.296338 (-0.152384) |\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) | 0.564954 / 0.215209 (0.349745) | 5.810570 / 2.077655 (3.732916) | 2.456382 / 1.504120 (0.952262) | 2.115809 / 1.541195 (0.574614) | 2.097363 / 1.468490 (0.628873) | 1.189712 / 4.584777 (-3.395065) | 5.318287 / 3.745712 (1.572575) | 2.965763 / 5.269862 (-2.304099) | 2.177958 / 4.565676 (-2.387719) | 0.144135 / 0.424275 (-0.280140) | 0.014348 / 0.007607 (0.006741) | 0.781715 / 0.226044 (0.555670) | 7.688349 / 2.268929 (5.419421) | 3.189260 / 55.444624 (-52.255365) | 2.552340 / 6.876477 (-4.324137) | 2.559312 / 2.142072 (0.417240) | 1.490755 / 4.805227 (-3.314473) | 0.257908 / 6.500664 (-6.242756) | 0.082016 / 0.075469 (0.006547) |\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) | 1.565735 / 1.841788 (-0.276053) | 17.660338 / 8.074308 (9.586030) | 19.493573 / 10.191392 (9.302181) | 0.241310 / 0.680424 (-0.439114) | 0.043485 / 0.534201 (-0.490716) | 0.557397 / 0.579283 (-0.021886) | 0.624385 / 0.434364 (0.190021) | 0.634601 / 0.540337 (0.094264) | 0.743140 / 1.386936 (-0.643796) |\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) | 0.010134 / 0.011353 (-0.001219) | 0.005858 / 0.011008 (-0.005150) | 0.128741 / 0.038508 (0.090232) | 0.036769 / 0.023109 (0.013660) | 0.470894 / 0.275898 (0.194996) | 0.524302 / 0.323480 (0.200822) | 0.006830 / 0.007986 (-0.001156) | 0.006166 / 0.004328 (0.001838) | 0.094875 / 0.004250 (0.090625) | 0.051201 / 0.037052 (0.014148) | 0.493992 / 0.258489 (0.235503) | 0.510540 / 0.293841 (0.216699) | 0.056354 / 0.128546 (-0.072192) | 0.020512 / 0.075646 (-0.055134) | 0.417809 / 0.419271 (-0.001463) | 0.061941 / 0.043533 (0.018408) | 0.498883 / 0.255139 (0.243744) | 0.480762 / 0.283200 (0.197563) | 0.110753 / 0.141683 (-0.030930) | 1.914096 / 1.452155 (0.461941) | 1.941338 / 1.492716 (0.448622) |\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) | 0.237955 / 0.018006 (0.219949) | 0.518136 / 0.000490 (0.517647) | 0.000475 / 0.000200 (0.000275) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032947 / 0.037411 (-0.004465) | 0.127857 / 0.014526 (0.113331) | 0.133911 / 0.176557 (-0.042646) | 0.188406 / 0.737135 (-0.548729) | 0.143939 / 0.296338 (-0.152400) |\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) | 0.787553 / 0.215209 (0.572344) | 6.976572 / 2.077655 (4.898918) | 2.897964 / 1.504120 (1.393844) | 2.545906 / 1.541195 (1.004711) | 2.622111 / 1.468490 (1.153620) | 1.278283 / 4.584777 (-3.306494) | 5.650447 / 3.745712 (1.904734) | 4.955835 / 5.269862 (-0.314027) | 2.767946 / 4.565676 (-1.797731) | 0.149385 / 0.424275 (-0.274890) | 0.014340 / 0.007607 (0.006733) | 0.861774 / 0.226044 (0.635730) | 8.660985 / 2.268929 (6.392057) | 3.685611 / 55.444624 (-51.759014) | 2.963087 / 6.876477 (-3.913390) | 3.020746 / 2.142072 (0.878673) | 1.538908 / 4.805227 (-3.266319) | 0.285875 / 6.500664 (-6.214789) | 0.080337 / 0.075469 (0.004867) |\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) | 1.575155 / 1.841788 (-0.266633) | 17.548946 / 8.074308 (9.474638) | 19.954104 / 10.191392 (9.762712) | 0.242025 / 0.680424 (-0.438398) | 0.025586 / 0.534201 (-0.508615) | 0.515676 / 0.579283 (-0.063607) | 0.607035 / 0.434364 (0.172671) | 0.633597 / 0.540337 (0.093259) | 0.744577 / 1.386936 (-0.642359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6529cada7879496bf18dd686e4d281de81d6203c \"CML watermark\")\n"
] | 2023-01-26T19:34:44Z | 2023-01-26T19:47:34Z | 2023-01-26T19:38:30Z | MEMBER | null | null | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5473/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5473/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5473', 'html_url': 'https://github.com/huggingface/datasets/pull/5473', 'diff_url': 'https://github.com/huggingface/datasets/pull/5473.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5473.patch', 'merged_at': '2023-01-26T19:38:30Z'} | true |
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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==6.0.0\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) | 0.008578 / 0.011353 (-0.002775) | 0.004535 / 0.011008 (-0.006473) | 0.100694 / 0.038508 (0.062186) | 0.029570 / 0.023109 (0.006460) | 0.296384 / 0.275898 (0.020486) | 0.354405 / 0.323480 (0.030925) | 0.006962 / 0.007986 (-0.001024) | 0.003405 / 0.004328 (-0.000924) | 0.077275 / 0.004250 (0.073025) | 0.036623 / 0.037052 (-0.000429) | 0.309844 / 0.258489 (0.051355) | 0.340343 / 0.293841 (0.046502) | 0.033626 / 0.128546 (-0.094920) | 0.011433 / 0.075646 (-0.064214) | 0.322659 / 0.419271 (-0.096612) | 0.040509 / 0.043533 (-0.003024) | 0.294002 / 0.255139 (0.038863) | 0.323259 / 0.283200 (0.040059) | 0.088023 / 0.141683 (-0.053660) | 1.462039 / 1.452155 (0.009885) | 1.495401 / 1.492716 (0.002684) |\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) | 0.218614 / 0.018006 (0.200608) | 0.482359 / 0.000490 (0.481869) | 0.001216 / 0.000200 (0.001016) | 0.000081 / 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) | 0.023167 / 0.037411 (-0.014245) | 0.098468 / 0.014526 (0.083942) | 0.108273 / 0.176557 (-0.068284) | 0.139991 / 0.737135 (-0.597144) | 0.109032 / 0.296338 (-0.187307) |\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) | 0.421526 / 0.215209 (0.206317) | 4.216808 / 2.077655 (2.139153) | 1.860550 / 1.504120 (0.356431) | 1.654518 / 1.541195 (0.113323) | 1.699064 / 1.468490 (0.230574) | 0.691489 / 4.584777 (-3.893287) | 3.401885 / 3.745712 (-0.343827) | 2.792860 / 5.269862 (-2.477001) | 1.516269 / 4.565676 (-3.049408) | 0.081627 / 0.424275 (-0.342648) | 0.012556 / 0.007607 (0.004949) | 0.531535 / 0.226044 (0.305491) | 5.320752 / 2.268929 (3.051823) | 2.314502 / 55.444624 (-53.130123) | 1.967118 / 6.876477 (-4.909359) | 2.008252 / 2.142072 (-0.133821) | 0.809730 / 4.805227 (-3.995497) | 0.148112 / 6.500664 (-6.352552) | 0.064821 / 0.075469 (-0.010648) |\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) | 1.269754 / 1.841788 (-0.572033) | 13.884200 / 8.074308 (5.809892) | 13.914390 / 10.191392 (3.722998) | 0.150176 / 0.680424 (-0.530248) | 0.028463 / 0.534201 (-0.505738) | 0.398723 / 0.579283 (-0.180561) | 0.400433 / 0.434364 (-0.033931) | 0.485169 / 0.540337 (-0.055169) | 0.565995 / 1.386936 (-0.820941) |\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) | 0.006479 / 0.011353 (-0.004874) | 0.004504 / 0.011008 (-0.006504) | 0.097905 / 0.038508 (0.059397) | 0.027140 / 0.023109 (0.004031) | 0.408742 / 0.275898 (0.132844) | 0.448707 / 0.323480 (0.125228) | 0.004819 / 0.007986 (-0.003166) | 0.004761 / 0.004328 (0.000433) | 0.075456 / 0.004250 (0.071205) | 0.036282 / 0.037052 (-0.000771) | 0.405961 / 0.258489 (0.147472) | 0.449411 / 0.293841 (0.155570) | 0.031159 / 0.128546 (-0.097387) | 0.011693 / 0.075646 (-0.063954) | 0.321124 / 0.419271 (-0.098147) | 0.041369 / 0.043533 (-0.002164) | 0.408070 / 0.255139 (0.152931) | 0.428704 / 0.283200 (0.145504) | 0.086839 / 0.141683 (-0.054844) | 1.477772 / 1.452155 (0.025617) | 1.555913 / 1.492716 (0.063197) |\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) | 0.239494 / 0.018006 (0.221488) | 0.410785 / 0.000490 (0.410295) | 0.000989 / 0.000200 (0.000789) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023805 / 0.037411 (-0.013607) | 0.097904 / 0.014526 (0.083378) | 0.106437 / 0.176557 (-0.070120) | 0.140555 / 0.737135 (-0.596580) | 0.107169 / 0.296338 (-0.189170) |\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) | 0.470233 / 0.215209 (0.255024) | 4.700451 / 2.077655 (2.622797) | 2.391712 / 1.504120 (0.887592) | 2.191125 / 1.541195 (0.649930) | 2.268924 / 1.468490 (0.800434) | 0.692421 / 4.584777 (-3.892356) | 3.387117 / 3.745712 (-0.358595) | 1.881731 / 5.269862 (-3.388130) | 1.155759 / 4.565676 (-3.409917) | 0.082040 / 0.424275 (-0.342236) | 0.012687 / 0.007607 (0.005080) | 0.567556 / 0.226044 (0.341511) | 5.701408 / 2.268929 (3.432480) | 2.864368 / 55.444624 (-52.580256) | 2.512073 / 6.876477 (-4.364404) | 2.546078 / 2.142072 (0.404005) | 0.795939 / 4.805227 (-4.009288) | 0.150078 / 6.500664 (-6.350586) | 0.067644 / 0.075469 (-0.007825) |\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) | 1.281681 / 1.841788 (-0.560107) | 13.967107 / 8.074308 (5.892799) | 13.293648 / 10.191392 (3.102256) | 0.128027 / 0.680424 (-0.552397) | 0.016791 / 0.534201 (-0.517410) | 0.379400 / 0.579283 (-0.199884) | 0.386847 / 0.434364 (-0.047517) | 0.469859 / 0.540337 (-0.070478) | 0.564203 / 1.386936 (-0.822733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#90832b5e33774ea8ec35ccb92ac14649a345bdbe \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008701 / 0.011353 (-0.002652) | 0.004564 / 0.011008 (-0.006444) | 0.100578 / 0.038508 (0.062070) | 0.029209 / 0.023109 (0.006100) | 0.315308 / 0.275898 (0.039410) | 0.381022 / 0.323480 (0.057542) | 0.007152 / 0.007986 (-0.000834) | 0.003511 / 0.004328 (-0.000817) | 0.078361 / 0.004250 (0.074110) | 0.035394 / 0.037052 (-0.001658) | 0.331076 / 0.258489 (0.072586) | 0.366613 / 0.293841 (0.072772) | 0.033466 / 0.128546 (-0.095080) | 0.011521 / 0.075646 (-0.064126) | 0.322178 / 0.419271 (-0.097093) | 0.040891 / 0.043533 (-0.002641) | 0.320418 / 0.255139 (0.065279) | 0.345199 / 0.283200 (0.062000) | 0.087906 / 0.141683 (-0.053777) | 1.476801 / 1.452155 (0.024646) | 1.497738 / 1.492716 (0.005022) |\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) | 0.178094 / 0.018006 (0.160087) | 0.408317 / 0.000490 (0.407827) | 0.001825 / 0.000200 (0.001625) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022402 / 0.037411 (-0.015010) | 0.097104 / 0.014526 (0.082578) | 0.105361 / 0.176557 (-0.071196) | 0.139728 / 0.737135 (-0.597407) | 0.109613 / 0.296338 (-0.186725) |\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) | 0.418245 / 0.215209 (0.203036) | 4.155655 / 2.077655 (2.078000) | 1.865892 / 1.504120 (0.361772) | 1.659003 / 1.541195 (0.117809) | 1.725649 / 1.468490 (0.257159) | 0.688733 / 4.584777 (-3.896044) | 3.323529 / 3.745712 (-0.422184) | 1.867807 / 5.269862 (-3.402054) | 1.157740 / 4.565676 (-3.407936) | 0.081947 / 0.424275 (-0.342329) | 0.012471 / 0.007607 (0.004864) | 0.529333 / 0.226044 (0.303288) | 5.284898 / 2.268929 (3.015970) | 2.321741 / 55.444624 (-53.122883) | 1.975683 / 6.876477 (-4.900794) | 2.029691 / 2.142072 (-0.112381) | 0.810212 / 4.805227 (-3.995015) | 0.148185 / 6.500664 (-6.352479) | 0.064594 / 0.075469 (-0.010875) |\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) | 1.183391 / 1.841788 (-0.658396) | 13.574760 / 8.074308 (5.500452) | 14.215015 / 10.191392 (4.023623) | 0.150776 / 0.680424 (-0.529648) | 0.029058 / 0.534201 (-0.505143) | 0.404071 / 0.579283 (-0.175212) | 0.401289 / 0.434364 (-0.033075) | 0.490946 / 0.540337 (-0.049392) | 0.582292 / 1.386936 (-0.804644) |\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) | 0.006695 / 0.011353 (-0.004658) | 0.004499 / 0.011008 (-0.006510) | 0.097633 / 0.038508 (0.059125) | 0.027606 / 0.023109 (0.004496) | 0.413191 / 0.275898 (0.137293) | 0.441896 / 0.323480 (0.118416) | 0.005703 / 0.007986 (-0.002283) | 0.004608 / 0.004328 (0.000280) | 0.074392 / 0.004250 (0.070141) | 0.037966 / 0.037052 (0.000913) | 0.410736 / 0.258489 (0.152247) | 0.448581 / 0.293841 (0.154740) | 0.031594 / 0.128546 (-0.096952) | 0.011597 / 0.075646 (-0.064049) | 0.319632 / 0.419271 (-0.099639) | 0.041189 / 0.043533 (-0.002343) | 0.407120 / 0.255139 (0.151981) | 0.433416 / 0.283200 (0.150216) | 0.089932 / 0.141683 (-0.051751) | 1.453919 / 1.452155 (0.001764) | 1.545892 / 1.492716 (0.053176) |\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) | 0.224302 / 0.018006 (0.206296) | 0.415519 / 0.000490 (0.415029) | 0.000407 / 0.000200 (0.000207) | 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) | 0.024104 / 0.037411 (-0.013307) | 0.098202 / 0.014526 (0.083676) | 0.106416 / 0.176557 (-0.070140) | 0.141090 / 0.737135 (-0.596045) | 0.110188 / 0.296338 (-0.186150) |\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) | 0.478252 / 0.215209 (0.263043) | 4.739684 / 2.077655 (2.662029) | 2.419040 / 1.504120 (0.914920) | 2.217705 / 1.541195 (0.676510) | 2.303288 / 1.468490 (0.834798) | 0.696682 / 4.584777 (-3.888095) | 3.401962 / 3.745712 (-0.343750) | 1.886015 / 5.269862 (-3.383846) | 1.175084 / 4.565676 (-3.390592) | 0.083064 / 0.424275 (-0.341211) | 0.012613 / 0.007607 (0.005006) | 0.579105 / 0.226044 (0.353060) | 5.792119 / 2.268929 (3.523191) | 2.889778 / 55.444624 (-52.554846) | 2.537438 / 6.876477 (-4.339039) | 2.574814 / 2.142072 (0.432741) | 0.803438 / 4.805227 (-4.001789) | 0.151912 / 6.500664 (-6.348752) | 0.068291 / 0.075469 (-0.007178) |\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) | 1.286002 / 1.841788 (-0.555786) | 14.179443 / 8.074308 (6.105135) | 13.443939 / 10.191392 (3.252547) | 0.152427 / 0.680424 (-0.527996) | 0.017248 / 0.534201 (-0.516953) | 0.378734 / 0.579283 (-0.200549) | 0.382276 / 0.434364 (-0.052087) | 0.465323 / 0.540337 (-0.075014) | 0.556454 / 1.386936 (-0.830482) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b5672a956d5de864e6f5550e493527d962d6ae55 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008675 / 0.011353 (-0.002678) | 0.004537 / 0.011008 (-0.006471) | 0.100179 / 0.038508 (0.061671) | 0.029307 / 0.023109 (0.006198) | 0.294687 / 0.275898 (0.018789) | 0.356868 / 0.323480 (0.033388) | 0.006992 / 0.007986 (-0.000994) | 0.003380 / 0.004328 (-0.000949) | 0.076961 / 0.004250 (0.072710) | 0.036047 / 0.037052 (-0.001005) | 0.308037 / 0.258489 (0.049548) | 0.341089 / 0.293841 (0.047248) | 0.033416 / 0.128546 (-0.095131) | 0.011534 / 0.075646 (-0.064112) | 0.322976 / 0.419271 (-0.096296) | 0.040894 / 0.043533 (-0.002639) | 0.296501 / 0.255139 (0.041362) | 0.324605 / 0.283200 (0.041405) | 0.086713 / 0.141683 (-0.054970) | 1.502784 / 1.452155 (0.050630) | 1.535013 / 1.492716 (0.042297) |\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) | 0.186647 / 0.018006 (0.168641) | 0.411003 / 0.000490 (0.410514) | 0.003594 / 0.000200 (0.003394) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023704 / 0.037411 (-0.013707) | 0.096154 / 0.014526 (0.081629) | 0.103671 / 0.176557 (-0.072885) | 0.138878 / 0.737135 (-0.598258) | 0.106947 / 0.296338 (-0.189391) |\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) | 0.417180 / 0.215209 (0.201970) | 4.149579 / 2.077655 (2.071925) | 1.865763 / 1.504120 (0.361643) | 1.669722 / 1.541195 (0.128527) | 1.722345 / 1.468490 (0.253855) | 0.695910 / 4.584777 (-3.888867) | 3.342266 / 3.745712 (-0.403446) | 1.884568 / 5.269862 (-3.385294) | 1.265013 / 4.565676 (-3.300664) | 0.081836 / 0.424275 (-0.342439) | 0.012371 / 0.007607 (0.004764) | 0.522997 / 0.226044 (0.296953) | 5.225434 / 2.268929 (2.956506) | 2.304701 / 55.444624 (-53.139924) | 1.949067 / 6.876477 (-4.927410) | 2.016347 / 2.142072 (-0.125725) | 0.809850 / 4.805227 (-3.995377) | 0.148396 / 6.500664 (-6.352268) | 0.063340 / 0.075469 (-0.012129) |\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) | 1.224621 / 1.841788 (-0.617167) | 13.814223 / 8.074308 (5.739915) | 13.879728 / 10.191392 (3.688336) | 0.149530 / 0.680424 (-0.530894) | 0.028439 / 0.534201 (-0.505762) | 0.392726 / 0.579283 (-0.186557) | 0.396894 / 0.434364 (-0.037469) | 0.474395 / 0.540337 (-0.065943) | 0.569090 / 1.386936 (-0.817847) |\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) | 0.006483 / 0.011353 (-0.004870) | 0.004527 / 0.011008 (-0.006481) | 0.098038 / 0.038508 (0.059530) | 0.027239 / 0.023109 (0.004130) | 0.441773 / 0.275898 (0.165875) | 0.471448 / 0.323480 (0.147968) | 0.005034 / 0.007986 (-0.002951) | 0.004732 / 0.004328 (0.000403) | 0.075036 / 0.004250 (0.070785) | 0.036711 / 0.037052 (-0.000341) | 0.442634 / 0.258489 (0.184145) | 0.476479 / 0.293841 (0.182638) | 0.031303 / 0.128546 (-0.097243) | 0.011642 / 0.075646 (-0.064005) | 0.320750 / 0.419271 (-0.098521) | 0.048698 / 0.043533 (0.005165) | 0.441205 / 0.255139 (0.186066) | 0.464845 / 0.283200 (0.181645) | 0.092716 / 0.141683 (-0.048967) | 1.510028 / 1.452155 (0.057874) | 1.574065 / 1.492716 (0.081349) |\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) | 0.220756 / 0.018006 (0.202750) | 0.393971 / 0.000490 (0.393482) | 0.002506 / 0.000200 (0.002306) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024455 / 0.037411 (-0.012956) | 0.100164 / 0.014526 (0.085638) | 0.108053 / 0.176557 (-0.068504) | 0.142973 / 0.737135 (-0.594163) | 0.110108 / 0.296338 (-0.186231) |\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) | 0.473639 / 0.215209 (0.258430) | 4.737521 / 2.077655 (2.659866) | 2.466208 / 1.504120 (0.962088) | 2.272608 / 1.541195 (0.731413) | 2.349255 / 1.468490 (0.880764) | 0.699928 / 4.584777 (-3.884849) | 3.348443 / 3.745712 (-0.397269) | 2.604611 / 5.269862 (-2.665250) | 1.543080 / 4.565676 (-3.022597) | 0.082627 / 0.424275 (-0.341648) | 0.012251 / 0.007607 (0.004644) | 0.569949 / 0.226044 (0.343905) | 5.732316 / 2.268929 (3.463388) | 2.913541 / 55.444624 (-52.531084) | 2.560584 / 6.876477 (-4.315892) | 2.615192 / 2.142072 (0.473120) | 0.803822 / 4.805227 (-4.001406) | 0.150821 / 6.500664 (-6.349843) | 0.067128 / 0.075469 (-0.008341) |\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) | 1.272278 / 1.841788 (-0.569510) | 13.783339 / 8.074308 (5.709030) | 13.243601 / 10.191392 (3.052209) | 0.136421 / 0.680424 (-0.544003) | 0.016565 / 0.534201 (-0.517636) | 0.381102 / 0.579283 (-0.198181) | 0.386166 / 0.434364 (-0.048197) | 0.474249 / 0.540337 (-0.066089) | 0.566826 / 1.386936 (-0.820110) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b5672a956d5de864e6f5550e493527d962d6ae55 \"CML watermark\")\n"
] | 2023-01-26T19:29:42Z | 2023-01-26T19:40:44Z | 2023-01-26T19:33:00Z | MEMBER | null | null | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5472/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5472/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5472', 'html_url': 'https://github.com/huggingface/datasets/pull/5472', 'diff_url': 'https://github.com/huggingface/datasets/pull/5472.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5472.patch', 'merged_at': '2023-01-26T19:33:00Z'} | true |
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"_The documentation is not available anymore as the PR was closed or merged._",
"I thought this issue was resolved in my parallel `to_tf_dataset` PR! I changed the default `num_test_batches` in `_get_output_signature` to 20 and used a test batch size of 1 to maximize variance to detect shorter samples. I think it's still okay to have this PR, though - but I'd use the new value of 20 as the default!",
"@Rocketknight1 You're right - I didn't have the most recent changes to the default values. Updated now to 20! I still think it would be good to have it configurable from the `to_tf_dataset` call so the user has the option to either make it more robust if many samples are needed, or faster if only one is needed. That, and I selfishly want it for faster tests. ",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.010441 / 0.011353 (-0.000912) | 0.005605 / 0.011008 (-0.005404) | 0.115712 / 0.038508 (0.077204) | 0.040907 / 0.023109 (0.017797) | 0.357673 / 0.275898 (0.081775) | 0.415427 / 0.323480 (0.091947) | 0.008827 / 0.007986 (0.000842) | 0.006069 / 0.004328 (0.001740) | 0.088985 / 0.004250 (0.084735) | 0.048461 / 0.037052 (0.011409) | 0.362065 / 0.258489 (0.103576) | 0.393643 / 0.293841 (0.099802) | 0.043844 / 0.128546 (-0.084703) | 0.013757 / 0.075646 (-0.061889) | 0.390993 / 0.419271 (-0.028278) | 0.053612 / 0.043533 (0.010079) | 0.348688 / 0.255139 (0.093549) | 0.377818 / 0.283200 (0.094619) | 0.115762 / 0.141683 (-0.025920) | 1.751826 / 1.452155 (0.299672) | 1.773326 / 1.492716 (0.280609) |\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) | 0.220668 / 0.018006 (0.202662) | 0.536830 / 0.000490 (0.536340) | 0.000467 / 0.000200 (0.000267) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031500 / 0.037411 (-0.005911) | 0.125796 / 0.014526 (0.111270) | 0.137539 / 0.176557 (-0.039017) | 0.184651 / 0.737135 (-0.552484) | 0.145707 / 0.296338 (-0.150632) |\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) | 0.465876 / 0.215209 (0.250667) | 4.637711 / 2.077655 (2.560056) | 2.132335 / 1.504120 (0.628215) | 1.862593 / 1.541195 (0.321398) | 1.961701 / 1.468490 (0.493211) | 0.800551 / 4.584777 (-3.784226) | 4.453321 / 3.745712 (0.707608) | 4.291030 / 5.269862 (-0.978832) | 2.256685 / 4.565676 (-2.308991) | 0.097787 / 0.424275 (-0.326488) | 0.014116 / 0.007607 (0.006509) | 0.593395 / 0.226044 (0.367351) | 5.885774 / 2.268929 (3.616845) | 2.666224 / 55.444624 (-52.778400) | 2.276673 / 6.876477 (-4.599803) | 2.358190 / 2.142072 (0.216117) | 0.981398 / 4.805227 (-3.823829) | 0.196997 / 6.500664 (-6.303668) | 0.077020 / 0.075469 (0.001550) |\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) | 1.365646 / 1.841788 (-0.476142) | 17.418157 / 8.074308 (9.343849) | 15.838749 / 10.191392 (5.647357) | 0.172749 / 0.680424 (-0.507675) | 0.033711 / 0.534201 (-0.500490) | 0.513306 / 0.579283 (-0.065978) | 0.503201 / 0.434364 (0.068837) | 0.608954 / 0.540337 (0.068616) | 0.734697 / 1.386936 (-0.652239) |\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) | 0.008749 / 0.011353 (-0.002604) | 0.005738 / 0.011008 (-0.005270) | 0.084946 / 0.038508 (0.046438) | 0.040386 / 0.023109 (0.017277) | 0.398698 / 0.275898 (0.122800) | 0.435843 / 0.323480 (0.112363) | 0.006812 / 0.007986 (-0.001174) | 0.004567 / 0.004328 (0.000239) | 0.085857 / 0.004250 (0.081607) | 0.054791 / 0.037052 (0.017738) | 0.400381 / 0.258489 (0.141892) | 0.460313 / 0.293841 (0.166472) | 0.042299 / 0.128546 (-0.086247) | 0.014128 / 0.075646 (-0.061519) | 0.100497 / 0.419271 (-0.318775) | 0.058356 / 0.043533 (0.014823) | 0.399774 / 0.255139 (0.144635) | 0.428210 / 0.283200 (0.145011) | 0.122084 / 0.141683 (-0.019598) | 1.683519 / 1.452155 (0.231365) | 1.798024 / 1.492716 (0.305307) |\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) | 0.255058 / 0.018006 (0.237051) | 0.488831 / 0.000490 (0.488342) | 0.008349 / 0.000200 (0.008149) | 0.000183 / 0.000054 (0.000129) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034870 / 0.037411 (-0.002541) | 0.131818 / 0.014526 (0.117292) | 0.143607 / 0.176557 (-0.032949) | 0.197413 / 0.737135 (-0.539722) | 0.148970 / 0.296338 (-0.147368) |\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) | 0.492831 / 0.215209 (0.277622) | 4.963085 / 2.077655 (2.885430) | 2.367803 / 1.504120 (0.863683) | 2.145535 / 1.541195 (0.604340) | 2.289452 / 1.468490 (0.820962) | 0.812691 / 4.584777 (-3.772086) | 4.554068 / 3.745712 (0.808356) | 2.377126 / 5.269862 (-2.892735) | 1.537243 / 4.565676 (-3.028433) | 0.099742 / 0.424275 (-0.324534) | 0.014757 / 0.007607 (0.007149) | 0.628714 / 0.226044 (0.402670) | 6.240197 / 2.268929 (3.971268) | 2.961929 / 55.444624 (-52.482696) | 2.533436 / 6.876477 (-4.343040) | 2.642619 / 2.142072 (0.500547) | 0.976002 / 4.805227 (-3.829225) | 0.197912 / 6.500664 (-6.302752) | 0.078767 / 0.075469 (0.003297) |\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) | 1.522863 / 1.841788 (-0.318925) | 18.210504 / 8.074308 (10.136196) | 15.664172 / 10.191392 (5.472780) | 0.178510 / 0.680424 (-0.501914) | 0.020852 / 0.534201 (-0.513349) | 0.501757 / 0.579283 (-0.077526) | 0.496542 / 0.434364 (0.062178) | 0.624958 / 0.540337 (0.084620) | 0.746960 / 1.386936 (-0.639976) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da7f09ed65411c5941de45c372a8aa8d5e55b431 \"CML watermark\")\n"
] | 2023-01-26T18:09:40Z | 2023-01-27T18:16:45Z | 2023-01-27T18:08:36Z | CONTRIBUTOR | null | `to_tf_dataset` calls can be very costly because of the number of test batches drawn during `_get_output_signature`. The test batches are draw in order to estimate the shapes when creating the tensorflow dataset. This is necessary when the shapes can be irregular, but not in cases when the tensor shapes are the same across all samples. This PR adds an option to change the number of batches drawn, so the user can speed this conversion up.
Running the following, and modifying `num_test_batches`
```
import time
from datasets import load_dataset
from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()
dataset = load_dataset("beans")
dataset = dataset["train"].with_format("np")
start = time.time()
dataset = dataset.to_tf_dataset(
columns=["image"],
label_cols=["label"],
batch_size=8,
collate_fn=data_collator,
num_test_batches=NUM_TEST_BATCHES,
)
end = time.time()
print(end - start)
```
NUM_TEST_BATCHES=200: 0.8197s
NUM_TEST_BATCHES=50: 0.3070s
NUM_TEST_BATCHES=2: 0.1417s
NUM_TEST_BATCHES=1: 0.1352s | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5471/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5471/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5471', 'html_url': 'https://github.com/huggingface/datasets/pull/5471', 'diff_url': 'https://github.com/huggingface/datasets/pull/5471.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5471.patch', 'merged_at': '2023-01-27T18:08:36Z'} | true |
https://api.github.com/repos/huggingface/datasets/issues/5470 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5470/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5470/comments | https://api.github.com/repos/huggingface/datasets/issues/5470/events | https://github.com/huggingface/datasets/pull/5470 | 1,558,542,611 | PR_kwDODunzps5InLw9 | 5,470 | Update dataset card creation | {'login': 'stevhliu', 'id': 59462357, 'node_id': 'MDQ6VXNlcjU5NDYyMzU3', 'avatar_url': 'https://avatars.githubusercontent.com/u/59462357?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/stevhliu', 'html_url': 'https://github.com/stevhliu', 'followers_url': 'https://api.github.com/users/stevhliu/followers', 'following_url': 'https://api.github.com/users/stevhliu/following{/other_user}', 'gists_url': 'https://api.github.com/users/stevhliu/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/stevhliu/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/stevhliu/subscriptions', 'organizations_url': 'https://api.github.com/users/stevhliu/orgs', 'repos_url': 'https://api.github.com/users/stevhliu/repos', 'events_url': 'https://api.github.com/users/stevhliu/events{/privacy}', 'received_events_url': 'https://api.github.com/users/stevhliu/received_events', 'type': 'User', 'site_admin': False} | [] | closed | false | null | [] | null | [
"_The documentation is not available anymore as the PR was closed or merged._",
"The CI failure is unrelated to your PR - feel free to merge :)",
"Haha thanks, you read my mind :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008332 / 0.011353 (-0.003021) | 0.004556 / 0.011008 (-0.006452) | 0.102239 / 0.038508 (0.063731) | 0.029332 / 0.023109 (0.006222) | 0.296189 / 0.275898 (0.020291) | 0.355746 / 0.323480 (0.032266) | 0.007705 / 0.007986 (-0.000281) | 0.003488 / 0.004328 (-0.000840) | 0.079142 / 0.004250 (0.074891) | 0.034980 / 0.037052 (-0.002073) | 0.307460 / 0.258489 (0.048971) | 0.345944 / 0.293841 (0.052103) | 0.033815 / 0.128546 (-0.094731) | 0.011603 / 0.075646 (-0.064044) | 0.322097 / 0.419271 (-0.097175) | 0.043753 / 0.043533 (0.000220) | 0.296706 / 0.255139 (0.041567) | 0.323195 / 0.283200 (0.039996) | 0.092295 / 0.141683 (-0.049388) | 1.542556 / 1.452155 (0.090401) | 1.571896 / 1.492716 (0.079180) |\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) | 0.191075 / 0.018006 (0.173069) | 0.407394 / 0.000490 (0.406905) | 0.002033 / 0.000200 (0.001833) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023175 / 0.037411 (-0.014236) | 0.094774 / 0.014526 (0.080248) | 0.105782 / 0.176557 (-0.070775) | 0.146608 / 0.737135 (-0.590528) | 0.107519 / 0.296338 (-0.188819) |\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) | 0.421516 / 0.215209 (0.206306) | 4.201091 / 2.077655 (2.123436) | 1.880285 / 1.504120 (0.376165) | 1.676333 / 1.541195 (0.135139) | 1.734301 / 1.468490 (0.265811) | 0.688504 / 4.584777 (-3.896273) | 3.370289 / 3.745712 (-0.375423) | 3.127661 / 5.269862 (-2.142201) | 1.562570 / 4.565676 (-3.003106) | 0.081687 / 0.424275 (-0.342588) | 0.012334 / 0.007607 (0.004727) | 0.524125 / 0.226044 (0.298080) | 5.245595 / 2.268929 (2.976667) | 2.332622 / 55.444624 (-53.112002) | 1.973212 / 6.876477 (-4.903265) | 2.006507 / 2.142072 (-0.135565) | 0.807126 / 4.805227 (-3.998101) | 0.148254 / 6.500664 (-6.352411) | 0.064240 / 0.075469 (-0.011229) |\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) | 1.206880 / 1.841788 (-0.634907) | 13.854877 / 8.074308 (5.780569) | 13.806772 / 10.191392 (3.615380) | 0.144380 / 0.680424 (-0.536044) | 0.028492 / 0.534201 (-0.505709) | 0.393854 / 0.579283 (-0.185429) | 0.402210 / 0.434364 (-0.032154) | 0.462138 / 0.540337 (-0.078199) | 0.537480 / 1.386936 (-0.849456) |\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) | 0.006692 / 0.011353 (-0.004661) | 0.004529 / 0.011008 (-0.006479) | 0.077925 / 0.038508 (0.039417) | 0.027824 / 0.023109 (0.004715) | 0.342288 / 0.275898 (0.066390) | 0.375071 / 0.323480 (0.051591) | 0.004889 / 0.007986 (-0.003097) | 0.003353 / 0.004328 (-0.000975) | 0.076198 / 0.004250 (0.071947) | 0.037797 / 0.037052 (0.000744) | 0.347834 / 0.258489 (0.089345) | 0.384200 / 0.293841 (0.090359) | 0.032184 / 0.128546 (-0.096362) | 0.011674 / 0.075646 (-0.063972) | 0.086242 / 0.419271 (-0.333029) | 0.044465 / 0.043533 (0.000932) | 0.341712 / 0.255139 (0.086573) | 0.366908 / 0.283200 (0.083709) | 0.091526 / 0.141683 (-0.050156) | 1.495798 / 1.452155 (0.043643) | 1.571700 / 1.492716 (0.078984) |\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) | 0.221962 / 0.018006 (0.203955) | 0.393095 / 0.000490 (0.392605) | 0.000385 / 0.000200 (0.000185) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024365 / 0.037411 (-0.013046) | 0.099278 / 0.014526 (0.084753) | 0.105940 / 0.176557 (-0.070617) | 0.141334 / 0.737135 (-0.595802) | 0.110898 / 0.296338 (-0.185440) |\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) | 0.446150 / 0.215209 (0.230941) | 4.471441 / 2.077655 (2.393786) | 2.124864 / 1.504120 (0.620744) | 1.909950 / 1.541195 (0.368755) | 1.970085 / 1.468490 (0.501595) | 0.706711 / 4.584777 (-3.878066) | 3.380336 / 3.745712 (-0.365376) | 1.866106 / 5.269862 (-3.403756) | 1.160657 / 4.565676 (-3.405019) | 0.082786 / 0.424275 (-0.341489) | 0.012470 / 0.007607 (0.004862) | 0.537620 / 0.226044 (0.311575) | 5.390588 / 2.268929 (3.121659) | 2.539137 / 55.444624 (-52.905488) | 2.191867 / 6.876477 (-4.684610) | 2.236212 / 2.142072 (0.094139) | 0.810756 / 4.805227 (-3.994471) | 0.150933 / 6.500664 (-6.349731) | 0.066141 / 0.075469 (-0.009328) |\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) | 1.271595 / 1.841788 (-0.570193) | 13.840013 / 8.074308 (5.765705) | 13.334443 / 10.191392 (3.143051) | 0.150096 / 0.680424 (-0.530328) | 0.016919 / 0.534201 (-0.517282) | 0.375534 / 0.579283 (-0.203749) | 0.387203 / 0.434364 (-0.047161) | 0.463500 / 0.540337 (-0.076838) | 0.553496 / 1.386936 (-0.833440) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f2e47230c13f977bcebdc4380623f59da67a75f \"CML watermark\")\n"
] | 2023-01-26T17:57:51Z | 2023-01-27T16:27:00Z | 2023-01-27T16:20:10Z | MEMBER | null | Encourages users to create a dataset card on the Hub directly with the new metadata ui + import dataset card template instead of telling users to manually create and upload one. | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5470/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5470/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5470', 'html_url': 'https://github.com/huggingface/datasets/pull/5470', 'diff_url': 'https://github.com/huggingface/datasets/pull/5470.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5470.patch', 'merged_at': '2023-01-27T16:20:10Z'} | true |
https://api.github.com/repos/huggingface/datasets/issues/5469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5469/comments | https://api.github.com/repos/huggingface/datasets/issues/5469/events | https://github.com/huggingface/datasets/pull/5469 | 1,558,346,906 | PR_kwDODunzps5Imhk2 | 5,469 | Remove deprecated `shard_size` arg from `.push_to_hub()` | {'login': 'polinaeterna', 'id': 16348744, 'node_id': 'MDQ6VXNlcjE2MzQ4NzQ0', 'avatar_url': 'https://avatars.githubusercontent.com/u/16348744?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/polinaeterna', 'html_url': 'https://github.com/polinaeterna', 'followers_url': 'https://api.github.com/users/polinaeterna/followers', 'following_url': 'https://api.github.com/users/polinaeterna/following{/other_user}', 'gists_url': 'https://api.github.com/users/polinaeterna/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/polinaeterna/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/polinaeterna/subscriptions', 'organizations_url': 'https://api.github.com/users/polinaeterna/orgs', 'repos_url': 'https://api.github.com/users/polinaeterna/repos', 'events_url': 'https://api.github.com/users/polinaeterna/events{/privacy}', 'received_events_url': 'https://api.github.com/users/polinaeterna/received_events', 'type': 'User', 'site_admin': False} | [] | closed | false | null | [] | null | [
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008272 / 0.011353 (-0.003081) | 0.004494 / 0.011008 (-0.006515) | 0.100764 / 0.038508 (0.062256) | 0.028741 / 0.023109 (0.005632) | 0.309020 / 0.275898 (0.033122) | 0.354184 / 0.323480 (0.030704) | 0.007455 / 0.007986 (-0.000531) | 0.003377 / 0.004328 (-0.000951) | 0.078472 / 0.004250 (0.074222) | 0.034719 / 0.037052 (-0.002333) | 0.312787 / 0.258489 (0.054298) | 0.342878 / 0.293841 (0.049037) | 0.033326 / 0.128546 (-0.095221) | 0.011519 / 0.075646 (-0.064127) | 0.323556 / 0.419271 (-0.095716) | 0.039929 / 0.043533 (-0.003604) | 0.304627 / 0.255139 (0.049488) | 0.322876 / 0.283200 (0.039677) | 0.086410 / 0.141683 (-0.055273) | 1.502607 / 1.452155 (0.050453) | 1.577953 / 1.492716 (0.085237) |\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) | 0.192861 / 0.018006 (0.174855) | 0.406008 / 0.000490 (0.405519) | 0.001075 / 0.000200 (0.000875) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023351 / 0.037411 (-0.014060) | 0.096086 / 0.014526 (0.081561) | 0.104641 / 0.176557 (-0.071915) | 0.141940 / 0.737135 (-0.595195) | 0.109266 / 0.296338 (-0.187073) |\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) | 0.416496 / 0.215209 (0.201287) | 4.161581 / 2.077655 (2.083926) | 1.815357 / 1.504120 (0.311238) | 1.609536 / 1.541195 (0.068341) | 1.654105 / 1.468490 (0.185615) | 0.693947 / 4.584777 (-3.890830) | 3.349029 / 3.745712 (-0.396683) | 1.883968 / 5.269862 (-3.385893) | 1.287988 / 4.565676 (-3.277688) | 0.081765 / 0.424275 (-0.342511) | 0.012373 / 0.007607 (0.004766) | 0.517186 / 0.226044 (0.291142) | 5.200892 / 2.268929 (2.931964) | 2.247414 / 55.444624 (-53.197211) | 1.910601 / 6.876477 (-4.965876) | 1.965407 / 2.142072 (-0.176666) | 0.814386 / 4.805227 (-3.990841) | 0.149295 / 6.500664 (-6.351369) | 0.064667 / 0.075469 (-0.010802) |\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) | 1.247258 / 1.841788 (-0.594530) | 13.837355 / 8.074308 (5.763047) | 13.850454 / 10.191392 (3.659062) | 0.136078 / 0.680424 (-0.544346) | 0.028322 / 0.534201 (-0.505878) | 0.391394 / 0.579283 (-0.187889) | 0.407494 / 0.434364 (-0.026870) | 0.473784 / 0.540337 (-0.066554) | 0.562953 / 1.386936 (-0.823983) |\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) | 0.006559 / 0.011353 (-0.004794) | 0.004546 / 0.011008 (-0.006462) | 0.099527 / 0.038508 (0.061019) | 0.027428 / 0.023109 (0.004319) | 0.344276 / 0.275898 (0.068377) | 0.377897 / 0.323480 (0.054417) | 0.004913 / 0.007986 (-0.003072) | 0.003338 / 0.004328 (-0.000990) | 0.077589 / 0.004250 (0.073339) | 0.038819 / 0.037052 (0.001766) | 0.343165 / 0.258489 (0.084676) | 0.386228 / 0.293841 (0.092387) | 0.031753 / 0.128546 (-0.096794) | 0.011756 / 0.075646 (-0.063890) | 0.322537 / 0.419271 (-0.096735) | 0.049865 / 0.043533 (0.006332) | 0.340493 / 0.255139 (0.085354) | 0.372179 / 0.283200 (0.088980) | 0.099669 / 0.141683 (-0.042013) | 1.487841 / 1.452155 (0.035686) | 1.527400 / 1.492716 (0.034683) |\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) | 0.180782 / 0.018006 (0.162776) | 0.393494 / 0.000490 (0.393004) | 0.003004 / 0.000200 (0.002804) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024997 / 0.037411 (-0.012415) | 0.098232 / 0.014526 (0.083707) | 0.107869 / 0.176557 (-0.068688) | 0.141042 / 0.737135 (-0.596093) | 0.109551 / 0.296338 (-0.186787) |\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) | 0.477115 / 0.215209 (0.261906) | 4.783928 / 2.077655 (2.706273) | 2.435725 / 1.504120 (0.931605) | 2.233111 / 1.541195 (0.691916) | 2.341097 / 1.468490 (0.872607) | 0.694304 / 4.584777 (-3.890473) | 3.345687 / 3.745712 (-0.400025) | 1.886932 / 5.269862 (-3.382929) | 1.155585 / 4.565676 (-3.410092) | 0.082867 / 0.424275 (-0.341408) | 0.012420 / 0.007607 (0.004813) | 0.576575 / 0.226044 (0.350530) | 5.777691 / 2.268929 (3.508762) | 2.882219 / 55.444624 (-52.562405) | 2.543613 / 6.876477 (-4.332864) | 2.578939 / 2.142072 (0.436866) | 0.803143 / 4.805227 (-4.002084) | 0.151929 / 6.500664 (-6.348735) | 0.067777 / 0.075469 (-0.007693) |\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) | 1.282711 / 1.841788 (-0.559077) | 13.942771 / 8.074308 (5.868463) | 13.376206 / 10.191392 (3.184814) | 0.152916 / 0.680424 (-0.527508) | 0.016619 / 0.534201 (-0.517582) | 0.375141 / 0.579283 (-0.204142) | 0.381660 / 0.434364 (-0.052704) | 0.465090 / 0.540337 (-0.075247) | 0.555068 / 1.386936 (-0.831868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10a6a638e0feb955f7b607b4433ee715c30acccf \"CML watermark\")\n"
] | 2023-01-26T15:40:56Z | 2023-01-26T17:37:51Z | 2023-01-26T17:30:59Z | CONTRIBUTOR | null | The docstrings say that it was supposed to be deprecated since version 2.4.0, can we remove it? | {'url': 'https://api.github.com/repos/huggingface/datasets/issues/5469/reactions', 'total_count': 0, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 0, 'eyes': 0} | https://api.github.com/repos/huggingface/datasets/issues/5469/timeline | null | null | false | {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/5469', 'html_url': 'https://github.com/huggingface/datasets/pull/5469', 'diff_url': 'https://github.com/huggingface/datasets/pull/5469.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/5469.patch', 'merged_at': '2023-01-26T17:30:59Z'} | true |
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