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3.29B
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7.72k
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values | comments
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| created_at
timestamp[s]date 2020-04-14 10:18:02
2025-08-05 09:28:51
| updated_at
timestamp[s]date 2020-04-27 16:04:17
2025-08-05 11:39:56
| closed_at
timestamp[s]date 2020-04-14 12:01:40
2025-08-01 05:15:45
β | user_login
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listlengths 0
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stringlengths 0
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β | is_pull_request
bool 2
classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,179,381,021
|
https://api.github.com/repos/huggingface/datasets/issues/4007
|
https://github.com/huggingface/datasets/issues/4007
| 4,007
|
set_format does not work with multi dimension tensor
|
closed
| 4
| 2022-03-24T11:27:43
| 2022-03-30T07:28:57
| 2022-03-24T14:39:29
|
phihung
|
[
"bug"
] |
## Describe the bug
set_format only transforms the last dimension of a multi-dimension list to tensor
## Steps to reproduce the bug
```python
import torch
from datasets import Dataset
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]})
# ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result
ds = ds.with_format("torch")
print(ds[0])
```
## Expected results
```
{'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]}
```
## Actual results
```
{'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- datasets version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0
| false
|
1,179,367,195
|
https://api.github.com/repos/huggingface/datasets/issues/4006
|
https://github.com/huggingface/datasets/pull/4006
| 4,006
|
Use audio feature in ASR task template
|
closed
| 1
| 2022-03-24T11:15:22
| 2022-03-24T17:19:29
| 2022-03-24T16:48:02
|
lhoestq
|
[] |
The AutomaticSpeechRecognition task template is outdated: it still uses the file path column as input instead of the audio column.
I changed that and updated all the datasets as well as the tests.
The only community dataset that will need to be updated is `facebook/multilingual_librispeech`. It has almost zero usage unfortunately (probably because users load the duplicate `multilingual_librispeech` directly instead), but it means we can update it.
(this makes me think that we should deprecate `multilingual_librispeech` it and redirect users to `facebook/multilingual_librispeech`).
This PR is also useful for the AudioFolder in https://github.com/huggingface/datasets/pull/3963
| true
|
1,179,365,663
|
https://api.github.com/repos/huggingface/datasets/issues/4005
|
https://github.com/huggingface/datasets/issues/4005
| 4,005
|
Yelp not working
|
closed
| 6
| 2022-03-24T11:14:00
| 2022-03-25T14:59:57
| 2022-03-25T14:56:10
|
patrickvonplaten
|
[] |
## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
| false
|
1,179,320,795
|
https://api.github.com/repos/huggingface/datasets/issues/4004
|
https://github.com/huggingface/datasets/pull/4004
| 4,004
|
ASSIN 2 dataset: replace broken Google Drive _URLS by links on github
|
closed
| 1
| 2022-03-24T10:37:39
| 2022-03-28T14:01:46
| 2022-03-28T13:56:39
|
ruanchaves
|
[] |
Closes #4003 .
Fixes checksum error. Replaces Google Drive urls by the files hosted here: [Multilingual Transformer Ensembles for Portuguese Natural Language Tasks](https://github.com/ruanchaves/assin)
| true
|
1,179,286,877
|
https://api.github.com/repos/huggingface/datasets/issues/4003
|
https://github.com/huggingface/datasets/issues/4003
| 4,003
|
ASSIN2 dataset checksum bug
|
closed
| 6
| 2022-03-24T10:08:50
| 2022-04-27T14:14:45
| 2022-03-28T13:56:39
|
ruanchaves
|
[
"bug"
] |
## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
| false
|
1,179,263,787
|
https://api.github.com/repos/huggingface/datasets/issues/4002
|
https://github.com/huggingface/datasets/pull/4002
| 4,002
|
Support streaming conll2012_ontonotesv5 dataset
|
closed
| 1
| 2022-03-24T09:49:56
| 2022-03-24T10:53:41
| 2022-03-24T10:48:47
|
albertvillanova
|
[] |
Use another URL whit a single ZIP file (instead of previous one with a ZIP file inside another ZIP file).
| true
|
1,179,231,418
|
https://api.github.com/repos/huggingface/datasets/issues/4001
|
https://github.com/huggingface/datasets/issues/4001
| 4,001
|
How to use generate this multitask dataset for SQUAD? I am getting a value error.
|
closed
| 4
| 2022-03-24T09:21:51
| 2022-03-26T09:48:21
| 2022-03-26T03:35:43
|
gsk1692
|
[] |
## Dataset viewer issue for 'squad_multitask*'
**Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask
*short description of the issue*
I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine.
I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format')
Error:
Status code: 400
Exception: TypeError
Message: argument of type 'Value' is not iterable
Kindly advice.
| false
|
1,178,844,616
|
https://api.github.com/repos/huggingface/datasets/issues/4000
|
https://github.com/huggingface/datasets/issues/4000
| 4,000
|
load_dataset error: sndfile library not found
|
closed
| 4
| 2022-03-24T01:52:32
| 2022-03-25T17:53:33
| 2022-03-25T17:53:33
|
i-am-neo
|
[
"bug"
] |
## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 136/136 [00:00<00:00, 36004.88it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 136/136 [00:01<00:00, 79.10it/s]
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 18/18 [00:00<00:00, 25343.23it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 18/18 [00:00<00:00, 2874.78it/s]
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 16/16 [00:00<00:00, 27950.38it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
| false
|
1,178,685,280
|
https://api.github.com/repos/huggingface/datasets/issues/3999
|
https://github.com/huggingface/datasets/pull/3999
| 3,999
|
Docs maintenance
|
closed
| 1
| 2022-03-23T21:27:33
| 2022-03-30T17:01:45
| 2022-03-30T16:56:38
|
stevhliu
|
[
"documentation"
] |
This PR links some functions to the API reference. These functions previously only showed up in code format because the path to the actual API was incorrect.
| true
|
1,178,631,986
|
https://api.github.com/repos/huggingface/datasets/issues/3998
|
https://github.com/huggingface/datasets/pull/3998
| 3,998
|
Fix Audio.encode_example() when writing an array
|
closed
| 2
| 2022-03-23T20:32:13
| 2022-03-29T14:21:44
| 2022-03-29T14:16:13
|
polinaeterna
|
[] |
Closes #3996
| true
|
1,178,566,568
|
https://api.github.com/repos/huggingface/datasets/issues/3997
|
https://github.com/huggingface/datasets/pull/3997
| 3,997
|
Sync Features dictionaries
|
closed
| 1
| 2022-03-23T19:23:51
| 2022-04-13T15:52:27
| 2022-04-13T15:46:19
|
mariosasko
|
[] |
This PR adds a wrapper to the `Features` class to keep the secondary dict, `_column_requires_decoding`, aligned with the main dict (as discussed in https://github.com/huggingface/datasets/pull/3723#discussion_r806912731).
A more elegant approach would be to subclass `UserDict` and override `__setitem__` and `__delitem__`, but this PR doesn't implement it for the following reasons:
* it requires replacing all occurrences of `isinstance(obj, dict)` with `isinstance(obj, Mapping)`, which is five times slower than `isinstance(obj, dict)` on my machine, in `features.py`
* is a breaking change, i.e., `isinstance(Features(...), dict)` would return `False` after it
* IMO, it makes sense to be consistent in the user-facing API and subclass either `dict` or `UserDict`. The problem with the latter is that it can't be used for `DatasetDict` because `DatasetDict` exposes the `data` property, which is also used by `UserDict`, so this would result in a collision.
| true
|
1,178,415,905
|
https://api.github.com/repos/huggingface/datasets/issues/3996
|
https://github.com/huggingface/datasets/issues/3996
| 3,996
|
Audio.encode_example() throws an error when writing example from array
|
closed
| 3
| 2022-03-23T17:11:47
| 2022-03-29T14:16:13
| 2022-03-29T14:16:13
|
polinaeterna
|
[
"bug"
] |
## Describe the bug
When trying to do `Audio().encode_example()` with preexisting array (see [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L73)), `sf.write()` throws you an error:
`TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7f4218c0db30>`
## Steps to reproduce the bug
### Sample code to reproduce the bug
```python
# download sample file
!wget https://huggingface.co/datasets/polinaeterna/test_encode_example/resolve/main/common_voice_vi_21824030.mp3
arr, sr = librosa.load("common_voice_vi_21824030.mp3")
Audio().encode_example({
"path": "common_voice_vi_21824030.mp3",
"array": arr,
"sampling_rate":sr
})
```
## Expected results
An encoded example (`{"bytes": b'....', "path": 'path'}`)
## Actual results
```python
TypeError Traceback (most recent call last)
Input In [3], in <module>
1 arr, sr = librosa.load("common_voice_vi_21824030.mp3")
----> 3 Audio().encode_example({
4 "path": "common_voice_vi_21824030.mp3",
5 "array": arr,
6 "sampling_rate":sr
7 })
File ~/workspace/datasets/src/datasets/features/audio.py:75, in Audio.encode_example(self, value)
73 elif isinstance(value, dict) and "array" in value:
74 buffer = BytesIO()
---> 75 sf.write(buffer, value["array"], value["sampling_rate"])
76 return {"bytes": buffer.getvalue(), "path": value.get("path")}
77 elif value.get("bytes") is not None or value.get("path") is not None:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:314, in write(file, data, samplerate, subtype, endian, format, closefd)
312 else:
313 channels = data.shape[1]
--> 314 with SoundFile(file, 'w', samplerate, channels,
315 subtype, endian, format, closefd) as f:
316 f.write(data)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:627, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
625 mode_int = _check_mode(mode)
626 self._mode = mode
--> 627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1416, in _create_info_struct(file, mode, samplerate, channels, format, subtype, endian)
1414 original_format = format
1415 if format is None:
-> 1416 format = _get_format_from_filename(file, mode)
1417 assert isinstance(format, (_unicode, str))
1418 else:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1457, in _get_format_from_filename(file, mode)
1455 pass
1456 if format.upper() not in _formats and 'r' not in mode:
-> 1457 raise TypeError("No format specified and unable to get format from "
1458 "file extension: {0!r}".format(file))
1459 return format
TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7fd8daf88180>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets master
- Platform: Ubuntu 20.04
- Python version: python 3.8.12
- PyArrow version: 6.0.1
## Solution
I guess we just need to add `format` arg in [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L75) like this:
```python
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
```
BTW discovered this when trying to decode audio in mp3 format without torchaudio (would be useful for TensorFlow users), like this:
```python
from datasets import load_dataset, Features, Audio
ds = load_dataset("common_voice", "vi", split="test")
ds = ds.remove_columns("audio")
ds.select(range(3)) # 3 samples just for testing
def load_mp3_with_librosa(example):
arr, sr = librosa.load(example["path"])
example["audio"] = {
"path": example["path"],
"array": arr,
"sampling_rate": sr
}
return example
updated_dataset = ds.map(lambda example: load_mp3_with_librosa(example),
features=Features(
{"audio": Audio(decode=False)}
))
```
@lhoestq @mariosasko @albertvillanova am I right in my logic? do we agree that we can set wav as the format? π€
| false
|
1,178,232,623
|
https://api.github.com/repos/huggingface/datasets/issues/3995
|
https://github.com/huggingface/datasets/pull/3995
| 3,995
|
Close `PIL.Image` file handler in `Image.decode_example`
|
closed
| 1
| 2022-03-23T14:51:48
| 2022-03-23T18:24:52
| 2022-03-23T18:19:27
|
mariosasko
|
[] |
Closes the file handler of the PIL image object in `Image.decode_example` to avoid the `Too many open files` error.
To pass [the image equality checks](https://app.circleci.com/pipelines/github/huggingface/datasets/10774/workflows/d56670e6-16bb-4c64-b601-a152c5acf5ed/jobs/65825) in CI, `Image.decode_example` calls `image.load()` regardless of how the image object is created (not only for the `PIL.Image.open(local_path)` case). This is needed because `load()` sets the `readonly` attribute of a `PIL.Image` object to 0 (it's 1 after `PIL.Image.open(file_like)`), and in the older PIL versions (only fixed on main), that attribute is considered in `PIL.Image.__eq__`. More info can be found here: https://github.com/python-pillow/Pillow/issues/5926.
Fix #3985
| true
|
1,178,211,138
|
https://api.github.com/repos/huggingface/datasets/issues/3994
|
https://github.com/huggingface/datasets/pull/3994
| 3,994
|
Change audio column from string path to Audio feature in ASR task
|
closed
| 0
| 2022-03-23T14:34:52
| 2022-03-23T15:43:43
| 2022-03-23T15:43:43
|
polinaeterna
|
[] |
Will fix #3990
| true
|
1,178,201,495
|
https://api.github.com/repos/huggingface/datasets/issues/3993
|
https://github.com/huggingface/datasets/issues/3993
| 3,993
|
Streaming dataset + interleave + DataLoader hangs with multiple workers
|
open
| 5
| 2022-03-23T14:27:29
| 2023-02-28T14:14:24
| null |
jpilaul
|
[
"bug"
] |
## Describe the bug
Interleaving multiple iterable datasets that use `load_dataset` on streaming mode hangs when passed to `torch.utils.data.DataLoader` with multiple workers.
## Steps to reproduce the bug
```python
from datasets import interleave_datasets, load_dataset
from torch.utils.data import DataLoader
en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
fr_dataset = load_dataset('oscar', "unshuffled_deduplicated_fr", split='train', streaming=True)
it_dataset = load_dataset('oscar', "unshuffled_deduplicated_it", split='train', streaming=True)
de_dataset = load_dataset('oscar', "unshuffled_deduplicated_de", split='train', streaming=True)
multilingual_dataset = interleave_datasets([en_dataset, fr_dataset, de_dataset, it_dataset])
multilingual_dataset = multilingual_dataset.with_format('torch')
next(iter(multilingual_dataset)) # works fairly fast
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=4)
for batch in dataloader:
print(len(batch)) # prints nothing after 30 min of waiting
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=0)
for batch in dataloader:
print(len(batch)) # prints right away
```
## Expected results
It should be able to iterate the dataset with multiple workers.
## Actual results
Prints with results with `next(iter(multilingual_dataset)) ` and `num_workers=0` but it prints nothing with `num_workers=4` or any number above 0.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.1.dev0
- `pytorch` version: 1.10.0+cu113
- Python version: 3.7
- PyArrow version: 6.0.1
| false
|
1,177,946,153
|
https://api.github.com/repos/huggingface/datasets/issues/3992
|
https://github.com/huggingface/datasets/issues/3992
| 3,992
|
Image column is not decoded in map when using with with_transform
|
closed
| 1
| 2022-03-23T10:51:13
| 2022-12-13T16:59:06
| 2022-12-13T16:59:06
|
phihung
|
[
"bug"
] |
## Describe the bug
Image column is not _decoded_ in **map** when using with `with_transform`
## Steps to reproduce the bug
```python
from datasets import Image, Dataset
def add_C(batch):
batch["C"] = batch["A"]
return batch
ds = Dataset.from_dict({"A": ["image.png"]}).cast_column("A", Image())
ds = ds.with_transform(lambda x: x) # <= This line causes the problem
ds = ds.map(add_C, batched=True)
print(ds[0])
```
## Expected results
```
{'C': <PIL.PngImagePlugin.PngImageFile>, ...}
```
## Actual results
```
{'C': {'bytes': None, 'path': 'image.png'}, ...}
```
If we remove the `with_transform` line, we get the expected result.
## Environment info
- `datasets` version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0
| false
|
1,177,362,901
|
https://api.github.com/repos/huggingface/datasets/issues/3991
|
https://github.com/huggingface/datasets/issues/3991
| 3,991
|
Add Lung Image Database Consortium image collection (LIDC-IDRI) dataset
|
open
| 0
| 2022-03-22T22:16:05
| 2022-03-23T12:57:16
| null |
omarespejel
|
[
"dataset request",
"vision"
] |
## Adding a Dataset
- **Name:** *Lung Image Database Consortium image collection (LIDC-IDRI)*
- **Description:** *Consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.*
- **Data:** *[link to the Github repository or current dataset location](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)*
- **Motivation:** *Key dataset in the healthcare community*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
FYI @osanseviero @abidlabs
| false
|
1,176,976,247
|
https://api.github.com/repos/huggingface/datasets/issues/3990
|
https://github.com/huggingface/datasets/issues/3990
| 3,990
|
Improve AutomaticSpeechRecognition task template
|
closed
| 2
| 2022-03-22T15:41:08
| 2022-03-23T17:12:40
| 2022-03-23T17:12:40
|
polinaeterna
|
[
"enhancement"
] |
**Is your feature request related to a problem? Please describe.**
[AutomaticSpeechRecognition task template](https://github.com/huggingface/datasets/blob/master/src/datasets/tasks/automatic_speech_recognition.py) is outdated as it uses path to audiofile as an audio column instead of a Audio feature itself (I guess it's because Audio feature didn't exist at the time this template was created).
**Describe the solution you'd like**
Change audio columns from string path to Audio feature.
| false
|
1,176,955,078
|
https://api.github.com/repos/huggingface/datasets/issues/3989
|
https://github.com/huggingface/datasets/pull/3989
| 3,989
|
Remove old wikipedia leftovers
|
closed
| 3
| 2022-03-22T15:25:46
| 2022-03-31T15:35:26
| 2022-03-31T15:30:16
|
albertvillanova
|
[] |
After updating Wikipedia dataset, remove old wikipedia leftovers from doc.
| true
|
1,176,858,540
|
https://api.github.com/repos/huggingface/datasets/issues/3988
|
https://github.com/huggingface/datasets/pull/3988
| 3,988
|
More consistent references in docs
|
closed
| 2
| 2022-03-22T14:18:41
| 2022-03-22T17:06:32
| 2022-03-22T16:50:44
|
mariosasko
|
[] |
Aligns the internal references with style discussed in https://github.com/huggingface/datasets/pull/3980.
cc @stevhliu
| true
|
1,176,481,659
|
https://api.github.com/repos/huggingface/datasets/issues/3987
|
https://github.com/huggingface/datasets/pull/3987
| 3,987
|
Fix Faiss custom_index device
|
closed
| 1
| 2022-03-22T09:11:24
| 2022-03-24T12:18:59
| 2022-03-24T12:14:12
|
albertvillanova
|
[] |
Currently, if both `custom_index` and `device` are passed to `FaissIndex`, `device` is silently ignored.
This PR fixes this by raising a ValueError if both arguments are passed.
Alternatively, the `custom_index` could be transferred to the target `device`.
| true
|
1,176,429,565
|
https://api.github.com/repos/huggingface/datasets/issues/3986
|
https://github.com/huggingface/datasets/issues/3986
| 3,986
|
Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface)
|
open
| 5
| 2022-03-22T08:23:21
| 2023-03-06T16:55:04
| null |
kelvinAI
|
[
"bug"
] |
## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
| false
|
1,175,982,937
|
https://api.github.com/repos/huggingface/datasets/issues/3985
|
https://github.com/huggingface/datasets/issues/3985
| 3,985
|
[image feature] Too many files open error when image feature is returned as a path
|
closed
| 0
| 2022-03-21T21:54:05
| 2022-03-23T18:19:27
| 2022-03-23T18:19:27
|
apsdehal
|
[
"bug"
] |
## Describe the bug
PR in context: #3967. If I load the dataset in this PR (TextVQA), and do a simple list comprehension on the dataset, I get `Too many open files error`. This is happening due to the way we are loading the image feature when a str path is returned from the `_generate_examples`. Specifically at https://github.com/huggingface/datasets/blob/508eb4ab5d52f590baa677b4f64b1cc069139f7b/src/datasets/features/image.py#L110, we are open the file handle to the image but never closing it. This in my understanding is causing the issue.
## Steps to reproduce the bug
Pull the PR locally and run the following code
```python
from datasets import load_dataset
dataset = load_dataset("./datasets/textvqa")["train"]
data = [item for item in dataset]
# Error happens
```
## Expected results
List comprehension should work smoothly
## Actual results
`Too many open files error`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.1.dev0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.10.0
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| false
|
1,175,822,117
|
https://api.github.com/repos/huggingface/datasets/issues/3984
|
https://github.com/huggingface/datasets/issues/3984
| 3,984
|
Local and automatic tests fail
|
closed
| 1
| 2022-03-21T19:07:37
| 2023-07-25T15:18:40
| 2023-07-25T15:18:40
|
MarkusSagen
|
[
"bug"
] |
## Describe the bug
Running the tests from CircleCI on a PR or locally fails, even with no changes. Tests seem to fail on `test_metric_common.py`
## Steps to reproduce the bug
```shell
git clone https://huggingface/datasets.git
cd datasets
```
```python
python -m pip install -e .
pytest
```
## Expected results
All tests passing
## Actual results
```
tests/test_metric_common.py:91:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../.pyenv/versions/3.8.5/lib/python3.8/doctest.py:1336: in __run
exec(compile(example.source, filename, "single",
<doctest datasets_modules.metrics.ter.c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155.ter.Ter[3]>:1: in <module>
???
../datasets/src/datasets/metric.py:430: in compute
output = self._compute(**inputs, **compute_kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = Metric(name: "ter", features: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Val...ences=references)
>>> print(results)
{'score': 0.0, 'num_edits': 0, 'ref_length': 6.5}
""", stored examples: 0)
predictions = ['hello there general kenobi', 'foo bar foobar']
references = [['hello there general kenobi', 'hello there !'], ['foo bar foobar', 'foo bar foobar']]
normalized = False, no_punct = False, asian_support = False, case_sensitive = False
def _compute(
self,
predictions,
references,
normalized: bool = False,
no_punct: bool = False,
asian_support: bool = False,
case_sensitive: bool = False,
):
references_per_prediction = len(references[0])
if any(len(refs) != references_per_prediction for refs in references):
raise ValueError("Sacrebleu requires the same number of references for each prediction")
transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
> sb_ter = TER(normalized, no_punct, asian_support, case_sensitive)
E TypeError: __init__() takes 2 positional arguments but 5 were given
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/metrics/ter/c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155/ter.py:130: TypeError
------------------------------ Captured stdout call -------------------------------
Trying:
predictions = ["hello there general kenobi", "foo bar foobar"]
Expecting nothing
ok
Trying:
references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
Expecting nothing
ok
Trying:
ter = datasets.load_metric("ter")
Expecting nothing
ok
Trying:
results = ter.compute(predictions=predictions, references=references)
Expecting nothing
================================ warnings summary =================================
../.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
from imp import load_source
../datasets/src/datasets/commands/test.py:35
/home/markussagen/datasets/src/datasets/commands/test.py:35: PytestCollectionWarning: cannot collect test class 'TestCommand' because it has a __init__ constructor (from: tests/commands/test_test.py)
class TestCommand(BaseDatasetsCLICommand):
tests/commands/test_test.py:33
/home/markussagen/mydataset/tests/commands/test_test.py:33: PytestCollectionWarning: cannot collect test class 'TestCommandArgs' because it has a __new__ constructor (from: tests/commands/test_test.py)
class TestCommandArgs:
tests/test_arrow_dataset.py: 760 warnings
tests/test_formatting.py: 60 warnings
tests/test_search.py: 31 warnings
tests/features/test_array_xd.py: 117 warnings
/home/markussagen/datasets/src/datasets/formatting/formatting.py:197: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
(isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
tests/test_arrow_dataset.py: 154 warnings
tests/features/test_array_xd.py: 1 warning
/home/markussagen/datasets/src/datasets/formatting/formatting.py:201: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
tests/test_arrow_dataset.py: 60 warnings
/home/markussagen/datasets/src/datasets/arrow_dataset.py:3105: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
elif np.issubdtype(values.dtype, np.str):
tests/test_arrow_dataset.py: 138 warnings
tests/test_formatting.py: 21 warnings
/home/markussagen/datasets/src/datasets/formatting/tf_formatter.py:69: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
data_struct.dtype == np.object
tests/test_arrow_dataset.py: 240 warnings
tests/test_formatting.py: 20 warnings
/home/markussagen/datasets/src/datasets/formatting/torch_formatter.py:49: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects
tests/test_arrow_dataset.py: 12 warnings
tests/test_search.py: 2 warnings
tests/features/test_array_xd.py: 6 warnings
tests/features/test_image.py: 4 warnings
/home/markussagen/datasets/src/datasets/features/features.py:1129: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
[0] + [len(arr) for arr in l_arr], dtype=np.object
tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_banking77
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/banking77/aec0289529599d4572d76ab00c8944cb84f88410ad0c9e7da26189d31f62a55b/banking77.py:24: DeprecationWarning: invalid escape sequence \~
_CITATION = """\
tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_universal_dependencies
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/universal_dependencies/065e728dfe9a8371434a6e87132c2386a6eacab1a076d3a12aa417b994e6ef7d/universal_dependencies.py:6: DeprecationWarning: invalid escape sequence \=
_CITATION = """\
tests/test_filesystem.py: 105 warnings
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/responses/__init__.py:398: DeprecationWarning: stream argument is deprecated. Use stream parameter in request directly
warn(
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
/home/markussagen/datasets/src/datasets/formatting/jax_formatter.py:57: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data_struct.dtype == np.object: # jax arrays cannot be instantied from an array of objects
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py:3567: UserWarning: Explicitly requested dtype <class 'jax._src.numpy.lax_numpy.int64'> requested in array is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.
lax._check_user_dtype_supported(dtype, "array")
tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/apscheduler/util.py:95: PytzUsageWarning: The zone attribute is specific to pytz's interface; please migrate to a new time zone provider. For more details on how to do so, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html
if obj.zone == 'local':
tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features
_audio
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/librosa/core/constantq.py:1059: DeprecationWarning: `np.complex` is a deprecated alias for the builtin `complex`. To silence this warning, use `complex` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.complex128` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
dtype=np.complex,
tests/features/test_array_xd.py::test_array_xd_with_none
/home/markussagen/mydataset/tests/features/test_array_xd.py:338: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
assert isinstance(arr, np.ndarray) and arr.dtype == np.object and arr.shape == (3,)
-- Docs: https://docs.pytest.org/en/stable/warnings.html
============================= short test summary info =============================
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_bleurt - I...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_chrf - Att...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_code_eval
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_comet - Im...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_competition_math
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_coval - Im...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_perplexity
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_ter - Type...
```
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-5.16.11-76051611-generic-x86_64-with-glibc2.33
- Python version: 3.8.5
- PyArrow version: 5.0.0
| false
|
1,175,759,412
|
https://api.github.com/repos/huggingface/datasets/issues/3983
|
https://github.com/huggingface/datasets/issues/3983
| 3,983
|
Infinitely attempting lock
|
closed
| 4
| 2022-03-21T18:11:57
| 2024-05-09T08:24:34
| 2022-05-06T16:12:18
|
jyrr
|
[] |
I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this?
| false
|
1,175,478,099
|
https://api.github.com/repos/huggingface/datasets/issues/3982
|
https://github.com/huggingface/datasets/pull/3982
| 3,982
|
Exclude Google Drive tests of the CI
|
closed
| 2
| 2022-03-21T14:34:16
| 2022-03-31T16:38:02
| 2022-03-21T14:51:35
|
lhoestq
|
[] |
These tests make the CI spam the Google Drive API, the CI now gets banned by Google Drive very often.
I think we can just skip these tests from the CI for now.
In the future we could have a CI job that runs only once a day or once a week for such cases
cc @albertvillanova @mariosasko @severo
Close #3415
| true
|
1,175,423,517
|
https://api.github.com/repos/huggingface/datasets/issues/3981
|
https://github.com/huggingface/datasets/pull/3981
| 3,981
|
Add TER metric card
|
closed
| 1
| 2022-03-21T13:54:36
| 2022-03-29T13:57:11
| 2022-03-29T13:51:40
|
emibaylor
|
[] |
Add TER metric card
This card is still missing content for the following sections:
- **Limitations & Biases**
- **Values from Papers**
If anyone has any ideas for either of the above, feel free to either add them or point me to them and I'll add them!
| true
|
1,175,412,905
|
https://api.github.com/repos/huggingface/datasets/issues/3980
|
https://github.com/huggingface/datasets/pull/3980
| 3,980
|
Add tip on how to speed up loading with ImageFolder
|
closed
| 5
| 2022-03-21T13:45:58
| 2022-03-22T13:39:45
| 2022-03-22T13:34:56
|
mariosasko
|
[] |
This PR does two things:
* adds a tip on how to speed up loading of a large number of files with ImageFolder (motivated by [this issue](https://github.com/huggingface/datasets/issues/3960))
* replaces the current references to the `Dataset` methods in the Image Processing doc with their fully qualified counterparts (to align it with the Audio Processing doc)
cc @stevhliu
| true
|
1,175,258,969
|
https://api.github.com/repos/huggingface/datasets/issues/3979
|
https://github.com/huggingface/datasets/pull/3979
| 3,979
|
Fix google drive streaming for small files
|
closed
| 4
| 2022-03-21T11:38:46
| 2023-09-24T09:55:19
| 2022-03-21T14:25:58
|
lhoestq
|
[] |
Google drive did another change recently, following #3787 #3843 .
In particular Google Drive now returns 403 for GET requests with `confirm=t` when a files doesn't have a virus warning message. I fixed this by passing `confirm=t` if and only if when there is one (i.e. when status code is 200 for HEAD)
| true
|
1,175,226,456
|
https://api.github.com/repos/huggingface/datasets/issues/3978
|
https://github.com/huggingface/datasets/issues/3978
| 3,978
|
I can't view HFcallback dataset for ASR Space
|
open
| 4
| 2022-03-21T11:07:49
| 2023-09-25T12:19:53
| null |
kingabzpro
|
[] |
## Dataset viewer issue for '*Urdu-ASR-flags*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)*
*I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.*
Am I the one who added this dataset ? Yes
| false
|
1,175,049,927
|
https://api.github.com/repos/huggingface/datasets/issues/3977
|
https://github.com/huggingface/datasets/issues/3977
| 3,977
|
Adapt `docs/README.md` for datasets
|
closed
| 1
| 2022-03-21T08:26:49
| 2023-02-27T10:32:37
| 2023-02-27T10:32:37
|
qqaatw
|
[
"documentation"
] |
## Describe the bug
Currently `docs/README.md` is a direct copy from `transformers`, we should probably adapt this file for `datasets`.
| false
|
1,175,043,780
|
https://api.github.com/repos/huggingface/datasets/issues/3976
|
https://github.com/huggingface/datasets/pull/3976
| 3,976
|
Fix main classes reference in docs
|
closed
| 3
| 2022-03-21T08:19:46
| 2022-04-12T14:19:39
| 2022-04-12T14:19:38
|
qqaatw
|
[] |
Currently the section index (on the page's right side) of the [main classes reference](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes) incorrectly displays `Tensor returned:`, this PR fixes this issue by wrapping code examples in this page with markdown code block.
There are other examples in datasets library having this issue.
| true
|
1,174,678,942
|
https://api.github.com/repos/huggingface/datasets/issues/3975
|
https://github.com/huggingface/datasets/pull/3975
| 3,975
|
Update many missing tags to dataset README's
|
closed
| 0
| 2022-03-20T20:42:27
| 2022-03-21T18:39:52
| 2022-03-21T18:39:52
|
MarkusSagen
|
[] |
I've started to go through the datasets available and noticed that there are 127 datasets that does not have all the tags so I started filling them in; starting with some of the most common and QA datasets
Not 100% certain that the task_id is correct for SuperGLUE
If anyone is browsing the issues and would like to help make Hugging face datasets even more feature complete and awesome, feel free to use this tool I wrote to find the missing tags in the [datacards](https://github.com/Hugging-Face-Supporter/datacards)
| true
|
1,174,485,044
|
https://api.github.com/repos/huggingface/datasets/issues/3974
|
https://github.com/huggingface/datasets/pull/3974
| 3,974
|
Add XFUN dataset
|
closed
| 8
| 2022-03-20T09:24:54
| 2022-10-03T09:38:16
| 2022-10-03T09:36:22
|
qqaatw
|
[
"dataset contribution"
] |
This PR adds XFUN dataset.
Home page and repository: https://github.com/doc-analysis/XFUND
Source code: https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/data/datasets/xfun.py
| true
|
1,174,455,431
|
https://api.github.com/repos/huggingface/datasets/issues/3973
|
https://github.com/huggingface/datasets/issues/3973
| 3,973
|
ConnectionError and SSLError
|
closed
| 6
| 2022-03-20T06:45:37
| 2022-03-30T08:13:32
| 2022-03-30T08:13:32
|
yanyu2015
|
[
"bug"
] |
code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
| false
|
1,174,402,033
|
https://api.github.com/repos/huggingface/datasets/issues/3972
|
https://github.com/huggingface/datasets/pull/3972
| 3,972
|
Adding Roman Urdu Hate Speech dataset
|
closed
| 3
| 2022-03-20T00:19:26
| 2022-03-25T15:56:19
| 2022-03-25T15:51:20
|
bp-high
|
[] |
This Pull request will add the Roman Urdu Hate speech Dataset.
| true
|
1,174,329,442
|
https://api.github.com/repos/huggingface/datasets/issues/3971
|
https://github.com/huggingface/datasets/pull/3971
| 3,971
|
Applied index-filters on scores in search.py.
|
closed
| 1
| 2022-03-19T18:43:42
| 2022-04-12T14:48:23
| 2022-04-12T14:41:58
|
vishalsrao
|
[] |
Updated search.py to resolve the issue mentioned in https://github.com/huggingface/datasets/issues/3961.
Applied index-filters on scores in get_nearest_examples and get_nearest_examples_batch methods of search.py.
| true
|
1,174,327,367
|
https://api.github.com/repos/huggingface/datasets/issues/3970
|
https://github.com/huggingface/datasets/pull/3970
| 3,970
|
Apply index-filters on scores in get_nearest_examples and get_nearestβ¦
|
closed
| 0
| 2022-03-19T18:32:31
| 2022-03-19T18:38:12
| 2022-03-19T18:38:12
|
vishalsrao
|
[] |
Updated search.py to resolve the issue mentioned in https://github.com/huggingface/datasets/issues/3961.
Applied index-filters on scores in get_nearest_examples and get_nearest_examples_batch methods of search.py.
| true
|
1,174,273,824
|
https://api.github.com/repos/huggingface/datasets/issues/3969
|
https://github.com/huggingface/datasets/issues/3969
| 3,969
|
Cannot preview cnn_dailymail dataset
|
closed
| 10
| 2022-03-19T14:08:57
| 2022-04-20T15:52:49
| 2022-04-20T15:52:49
|
hasan-besh
|
[] |
## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| false
|
1,174,193,962
|
https://api.github.com/repos/huggingface/datasets/issues/3968
|
https://github.com/huggingface/datasets/issues/3968
| 3,968
|
Cannot preview 'indonesian-nlp/eli5_id' dataset
|
closed
| 5
| 2022-03-19T06:54:09
| 2022-03-24T16:34:24
| 2022-03-24T16:34:24
|
cahya-wirawan
|
[
"dataset-viewer"
] |
## Dataset viewer issue for '*indonesian-nlp/eli5_id*'
**Link:** https://huggingface.co/datasets/indonesian-nlp/eli5_id
I can not see the dataset preview.
```
Server Error
Status code: 400
Exception: Status400Error
Message: Not found. Maybe the cache is missing, or maybe the dataset does not exist.
```
Am I the one who added this dataset ? Yes
| false
|
1,174,107,128
|
https://api.github.com/repos/huggingface/datasets/issues/3967
|
https://github.com/huggingface/datasets/pull/3967
| 3,967
|
[feat] Add TextVQA dataset
|
closed
| 3
| 2022-03-18T23:29:39
| 2022-05-05T06:51:31
| 2022-05-05T06:44:29
|
apsdehal
|
[] |
This would be the first classification-based vision-and-language dataset in the datasets library.
Currently, the dataset downloads everything you need beforehand. See the [paper](https://arxiv.org/abs/1904.08920) for more details.
Test Plan:
- Ran the full and the dummy data test locally
| true
|
1,173,883,084
|
https://api.github.com/repos/huggingface/datasets/issues/3966
|
https://github.com/huggingface/datasets/pull/3966
| 3,966
|
Create metric card for BERTScore
|
closed
| 1
| 2022-03-18T18:21:56
| 2022-03-22T13:35:28
| 2022-03-22T13:30:56
|
sashavor
|
[] |
Proposing a metric card for BERTScore
| true
|
1,173,708,739
|
https://api.github.com/repos/huggingface/datasets/issues/3965
|
https://github.com/huggingface/datasets/issues/3965
| 3,965
|
TypeError: Couldn't cast array of type for JSONLines dataset
|
closed
| 1
| 2022-03-18T15:17:53
| 2022-05-06T16:13:51
| 2022-05-06T16:13:51
|
lewtun
|
[
"bug"
] |
## Describe the bug
One of the [course participants](https://discuss.huggingface.co/t/chapter-5-questions/11744/20?u=lewtun) is having trouble loading a JSONLines dataset that's composed of the GitHub issues from `spacy` (see stack trace below).
This reminds me a bit of #2799 where one can load the dataset in `pandas` but not in `datasets` and perhaps increasing the `block_size` is needed again.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns 'https://huggingface.co/datasets/Evan/spaCy-github-issues/resolve/main/spacy-issues.jsonl'
data_files = hf_hub_url(repo_id="Evan/spaCy-github-issues", filename="spacy-issues.jsonl", repo_type="dataset")
# throws TypeError: Couldn't cast array of type
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas - note this take a while as the file is >2GB
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to pandas.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset
builder_instance.download_and_prepare(
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/builder.py", line 683, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/builder.py", line 1136, in _prepare_split
writer.write_table(table)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_writer.py", line 511, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1121, in table_cast
return cast_table_to_features(table, Features.from_arrow_schema(schema))
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1102, in cast_table_to_features
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1102, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 944, in wrapper
return func(array, *args, **kwargs)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 918, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 918, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1086, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 944, in wrapper
return func(array, *args, **kwargs)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 920, in wrapper
return func(array, *args, **kwargs)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1019, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<url: string, html_url: string, labels_url: string, id: int64, node_id: string, number: int64, title: string, description: string, creator: struct<login: string, id: int64, node_id: string, avatar_url: string, gravatar_id: string, url: string, html_url: string, followers_url: string, following_url: string, gists_url: string, starred_url: string, subscriptions_url: string, organizations_url: string, repos_url: string, events_url: string, received_events_url: string, type: string, site_admin: bool>, open_issues: int64, closed_issues: int64, state: string, created_at: timestamp[s], updated_at: timestamp[s], due_on: null, closed_at: timestamp[s]>
to
null
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.7
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| false
|
1,173,564,993
|
https://api.github.com/repos/huggingface/datasets/issues/3964
|
https://github.com/huggingface/datasets/issues/3964
| 3,964
|
Add default Audio Loader
|
closed
| 0
| 2022-03-18T12:58:55
| 2022-08-22T14:20:46
| 2022-08-22T14:20:46
|
polinaeterna
|
[
"enhancement"
] |
**Is your feature request related to a problem? Please describe.**
Writing a custom loading dataset script might be a bit challenging for users.
**Describe the solution you'd like**
Add default Audio loader (analogous to ImageFolder) for small datasets with standard directory structure.
**Describe alternatives you've considered**
Create a custom loading script? that's what users doing now.
| false
|
1,173,492,562
|
https://api.github.com/repos/huggingface/datasets/issues/3963
|
https://github.com/huggingface/datasets/pull/3963
| 3,963
|
Add Audio Folder
|
closed
| 14
| 2022-03-18T11:40:09
| 2022-06-15T16:33:19
| 2022-06-15T16:33:19
|
polinaeterna
|
[] |
Would resolve #3964
AudioFolder loads a .txt file with transcriptions and creates a dataset with all audiofiles in provided directory that has a transcription (independently of the directory structure) as a single split (train).
Can be loaded via:
```python
# for local dirs
dataset = load_dataset("audiofolder", data_dir="/path/to/folder", transcripts_filename="transcripts.txt")
```
```python
# for local and remote zip archives
dataset = load_dataset("audiofolder", data_files="path/to/archive/archive.zip", transcripts_filename="transcripts.txt")
```
default transcriptions filename is `transcripts.txt`. it should have the following structure:
```
audio_id_1 transcription text 1
audio_id_1 transcription text 1
```
separator is `\t`!
---
sorry for first old commits from other branch, don't know how that happened...
| true
|
1,173,482,291
|
https://api.github.com/repos/huggingface/datasets/issues/3962
|
https://github.com/huggingface/datasets/pull/3962
| 3,962
|
Fix flatten of Sequence feature type
|
closed
| 1
| 2022-03-18T11:27:42
| 2022-03-21T14:40:47
| 2022-03-21T14:36:12
|
lhoestq
|
[] |
The `Sequence` features type is not correctly flattened if it contains a dictionary. This PR fixes this, and I added a test case for this.
Close https://github.com/huggingface/datasets/issues/3795
| true
|
1,173,223,086
|
https://api.github.com/repos/huggingface/datasets/issues/3961
|
https://github.com/huggingface/datasets/issues/3961
| 3,961
|
Scores from Index at extra positions are not filtered out
|
closed
| 2
| 2022-03-18T06:13:23
| 2022-04-12T14:41:58
| 2022-04-12T14:41:58
|
vishalsrao
|
[
"bug"
] |
If a FAISS index has fewer records than the requested number of top results (k), then it returns -1 in indices for the additional positions. The get_nearest_examples method only filters out the extra results from the dataset samples. It would be better to filter out extra scores too.
Reference: https://github.com/huggingface/datasets/blob/2.0.0/src/datasets/search.py#L693
| false
|
1,173,148,884
|
https://api.github.com/repos/huggingface/datasets/issues/3960
|
https://github.com/huggingface/datasets/issues/3960
| 3,960
|
Load local dataset error
|
open
| 13
| 2022-03-18T03:32:49
| 2023-08-02T17:12:20
| null |
TXacs
|
[
"bug",
"dataset bug"
] |
When i used the datasets==1.11.0οΌ it's all right. Util update the latest version, it get the error like this:
```
>>> from datasets import load_dataset
>>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train'], 'validation': ['/ssd/datasets/imagenet/pytorch/val']}
>>> ds = load_dataset('nateraw/image-folder', data_files=data_files, cache_dir='./', task='image-classification')
[] https://huggingface.co/datasets/nateraw/image-folder/resolve/main/ /dat/txacs/git/txacs/examples/image-classification/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/load.py", line 1671, in load_dataset
**config_kwargs,
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/load.py", line 1521, in load_dataset_builder
**config_kwargs,
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/builder.py", line 1031, in __init__
super().__init__(*args, **kwargs)
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/builder.py", line 255, in __init__
sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 584, in from_local_or_remote
if not isinstance(patterns_for_key, DataFilesList)
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 546, in from_local_or_remote
data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 196, in resolve_patterns_locally_or_by_urls
for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 146, in _resolve_single_pattern_locally
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find '/ssd/datasets/imagenet/pytorch/train' at /dat/txacs/git/txacs/examples/image-classification/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main
```
I need some help to solve the problem, thanks!
| false
|
1,172,872,695
|
https://api.github.com/repos/huggingface/datasets/issues/3959
|
https://github.com/huggingface/datasets/issues/3959
| 3,959
|
Medium-sized dataset conversion from pandas causes a crash
|
closed
| 3
| 2022-03-17T20:20:35
| 2022-12-12T17:14:06
| 2022-04-20T12:35:37
|
Antymon
|
[
"bug"
] |
Hi, I am suffering from the following issue:
## Describe the bug
Conversion to arrow dataset from pandas dataframe of a certain size deterministically causes the following crash:
```
File "/home/datasets_crash.py", line 7, in <module>
arrow=datasets.Dataset.from_pandas(d)
File "/home/.conda/envs/tools/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 783, in from_pandas
table = InMemoryTable.from_pandas(
File "/home/.conda/envs/tools/lib/python3.9/site-packages/datasets/table.py", line 379, in from_pandas
return cls(pa.Table.from_pandas(*args, **kwargs))
File "pyarrow/table.pxi", line 1487, in pyarrow.lib.Table.from_pandas
File "pyarrow/table.pxi", line 1532, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 1181, in pyarrow.lib.Table.validate
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array invalid: Invalid: Struct child array #1 has length smaller than expected for struct array (1192457 < 1192458)
```
## Steps to reproduce the bug
I have a dataset made from replicated single example mocking a dict representation of a publication.
I copy over this example 140k times and create a pandas frame.
I use 'Dataset.from_pandas' and boom
```python
# Sample code to reproduce the bug
import copy
import datasets
import pandas
# serialized dict is quite long to be realistic representation of a publication content
paper_as_dict=eval("{'article_id': '2020-11-05T14:25:05.321Z02bc3286-91b7-486a-9c74-4f457fbc586a', 'sections': [{'section_id': 'body.0', 'paragraphs': [{'sentences': ['11010111001000000011010011110011101110111011000100001010011100101001111010110111101011101111101010101110001111011110111010111', '1101100110110010010101010100110011000111001100100000011100010111010000011100001101111000000011010111001111001010101111110011010010111011000110100110010', '101011011000010100000010011001011011000000110011011110000101001110110000010001100110111100011100110101010010110000101', '1101101110101010101000000010101011111001111000101000110001110100111000100000011001110100110000110100111011001010110011101001001110']}]}, {'section_id': 'body.1', 'paragraphs': [{'sentences': ['11111100100100111000101001011110100110011001011011001001100110100111011010000110011000010001010100101110001001101011110111110101111100001001001000011110110010110011100110110111110011100011111000101010111010101011001110000100000001001010010010011101111100011010', '10101000110000110111110011101111000101010010001001010000001111001100000010001000001110111110010011101000000111011', '111010011111101111110011111110110001000111100101001000100110101111110000111000111111110000101001101000110011010111011101001010110110001000100000001110001111100110110001110001001100011010100110100010100111000110110100010010100101011110000110000101010010001110101100000']}, {'sentences': ['111110011110110110001111001101011110010110100011101010110101011001101110110111100000111101010110011110111101001111000101110001001010010101100111111001001000011101000100110000101', '011101101101111101001100101010000010111101100101110100101000001100010100110011010010100001101001110111100011010011011111000111111101110001010111010011010110001000010101100110000100010110101110110011001010011001100111101100001001', '1110001011011010101001100001110001110001000111111111101110100001011101101001110100000110000011010001101010101110101110101101001010100100010000000010110010010010', '11101111000111111100111110010000111101110010010101001111011001111110011000011100110001010010000100101010', '111000110110110010101100010010100001100100110010101000001000011101000100101011011010000011001011011111001101100001110010100001111110111001001010101100100110001011011100000101010010000000001100010000101100110110111101110010100010011101110110111010011011000011001010111011100000000010101001011000100000011010100011101001011001010010011110100100']}, {'sentences': ['001101111100001101001001001110000110010101011101001001111111011000111001111011101011110111000000100001110110101110001010001111110100010', '0000110010110101001100011011000011001101001110001000000110010101000011101011110110000000100111000001010000101011111011110001001100001110101010101110101011111000000011001111011110001010010111010000100100000001111001011100101111010101111001001101100101001101111000111011010110010001010010010111010000001101101111100101000111101011001000101', '00000101100101100111101010000101011100101100001100011001100100001100001010001010010011001001111001000010100010000110100111110000001000101000111100010111110011000100000111100010000100010111100010101', '111100110010100110000010010101010101110011110100000101110000000111010101111001011110010101001110000001001000010110010010011110111110010110100101110011001101110111001111100011100100011110010010100101011111111']}, {'sentences': ['1100001110101111000001011001100110001011100011110110010011001000101000011110010101010011011000111010000101010011010000000111011001000010100101000011111101000000000101111000', '1110101000100110001111000011000101110111001100101010011001100011010011111111111010101011010101010011000101001100100000110010100110110110110001101100', '00010001100100101100100111111110111111101000100110101111101111110101110001010001011100000000000011010101101001111010001110101101110011001011111101110100010000111101', '011100011101011001000110010110100100000010100010010110011000000010101110011111111101010010010001100110101010010001100010110011110001011011101010111111100100110110010111101001100101010111001', '10111000011010101111110110011010101011111001000001010010111111010010111111100100010100110100101101110100110011001000110100000111000100110000001000111010', '0010011111111011100111010001111001011101001010000010110000010111000101001101000011101110100100000000100100010010101010100011100101001000100110110000010111111110000011011101111000111010']}]}, {'section_id': 'body.2.0', 'paragraphs': [{'sentences': ['110010010011001110100100011001111100010011110111101011011011001010010010010011101011', '000110101110011011101011000000100011111000001100011011110101101011000110011010001010001101101100000111100101001011111001001101111', '1000011100100000100100100010010000111011000100110010000011110111100110110001101001010100011111010100101000111', '11110111111000110010000000000100010010110001100010001010000111011000101100011010010101110110011010110101001101110011101011101100000001000100101011010110110100101011101010010101101000011110000010101011001011000001000000001010110000100010000100011110101001111100001000100000111000001010011111111110101010100011011000010000111000110', '1001000111011000111110001111111001100001000000101000111011101101100101010110001101000000001111010111100011111000000100001001110', '100110010111010101111010100000010001110101111001010010001100001110100100100101110011010101001000100101000100100011001110001100111000010010011011000010011010010000110001000000100011110010110110011010001100111010111110011']}, {'sentences': ['10010101011100010111011111001001001010100011001001111101101001000000001111101110000111101011000001001011101110101001100010010001101111001110000100010010001001101111011111110010011011110011', '110001110010110000101111000000110010010010100000010100001111101101000101100000000110000000011111011001111000010110110001011010011011101100100110011000100110101010111010111111000111001111010110010001001110100001011011000110000000111101110000001111011011101110100000100010000110001000000110100000', '101010000000010000110110111000110000100111000001110100101101101010001010010010101010100111010110001001000101011110010011001001001110111001101101100100011110011011110101100010110111001010000001000110100000001010011111111110111010011110001001110100011011000101011000110110011011010110100100011111111011100111110110000110011011110110110011101010101111001101010110101000000001100101111010000101110', '1010100110111111111000110110111110010100000100001110101110111001011000010001110110001111111110000101001001110010001110000111010101111010111111011100100011100111111101101111000010001100101000010001100110110100110111111100100011001011000001111110010100110111000010011110111011001101100000101011111110101000011000010', '00000001110000101001110101110011101001110011000111111101111101111000010011100000101000001011001110', '101000111010010000011010011010011010010010100010110100011100100111011101010100101110100111010001000000', '01101000110001101011001101100010100011011010000000001010101000010101000110100010000000110001110001010010000000101101000011000100000110011101100001010100011111101010010110001101110101010111101100001110000011001101', '0010010111000011110010011110001010100000111100001011010100100010101010010011101101100110001001111001000110000111011110010000110101010110111111010110100000011010001001010001000110001101101000101110001011110000101101110000110010110010111001100010011011100011', '00110111110000000100110111101011000100100110001000001001101011001000010100100001100111100110000110110101111010000010101000000101000011001011101001', '0100100001000111001110110110000001000100111001101101110100100111010111110001110010110111100110011111001001000011101110100101111011000110100000111010011101']}, {'sentences': ['100001001011101111111100110111011110001101111101100001000110110000100101011000000100000', '10101001001111110101001010100110011110101101001']}]}, {'section_id': 'body.2.0.0', 'paragraphs': [{'sentences': ['1110101100001100011000101000010000100010101101010110101011100101110110110111010101001100100000000111011001000100011110101011111010100101001010000010001001101010100011110010101110011001100010000100110011000011101010001000111001000001100', '101000000011001001110101000100101010000111000111100010010001111111100110001100000100011010011010010101101111010101010000110011101001111001111011111001110001010000110101101011101111010000001100', '01100001011110010100000101001101111101010011100010011001011110110010010011100101000', '0011100111000101111000010001111100000111000101110001111010001100001000111010000101100001110101100111111', '00001100000011110001011010010110000000111110110001111000110000011011001110000000100011001010110000010000010001101010101100000010011011000101011111100010010', '1011101011101111000001100100111000011000010010011110011000110111010010111100111101100110011010000110000111000110111110101111000001000010011101111000110000100011110101101101001101000110010000001000010011011010101100', '1000010011100011100000010011011111111110101101111011101010010111000000101011000000110101111000010011', '01100000110011001110101111101101011001011101000010001100101010100011010101010100111011011110100010100111', '011011010100011011110010101000110001111110110']}]}, {'section_id': 'body.2.0.1', 'paragraphs': [{'sentences': ['00111011011101000100100111000001101001011000111100100010101001010011001011000010011111001100000100010001100101110011001000110001101011010111011111011000010011010010111010011111101000110111011100010011100111111110110111011', '011011010101101101010000001011010110011111011110100111010101010110001101000010011111000011100', '110001000110010000000111101110111110101110111000101000010001110101000101001000111000010001011101010000110001010001101001001110111110111010111010011101000101101010000', '001000111110100110000001111100000111001110111001110111001000111010001001100111001101000001001001010111000111011100001111011001111110001011000111110011111101011101000100101001111011100001000110101010101111111110011111111011000101110001000000000100111011111011001100111', '11010101100010010100010010010101001011001011000001100010101111111101001101110011001010010100000111010101', '01110000110011111000110010011010000011100000010010001111100010010100100001011011111110001100', '011101111100011101100111110101111001101010010001001110101100001101000000111000']}]}, {'section_id': 'body.2.0.2', 'paragraphs': [{'sentences': ['0111011000110100110000001011001110111000011110100111011000000001000010001111111001101111011100101110101101000111000101000010000111011010110000011101111110111110100111000111000011', '00100110111000110101100111000110100010011010010101001010011000000101000110100110011010011111000100000011000000010001010000100111101011111111101010001111010000001011100001110100000101001101101010011011101000', '000001110001010010100101010100010101001100011001001101101101110111011111101010010111010110110111011110101100001000011110111011001', '0001110010111110100110110011000001111100100100110101011010010101010100101000010101000100101000011011', '1000010010010101001100101110010111010100000110101110000000111001111111001011111010000011110001011001001001000101', '0001111100111010010100010111010110011011000000001111010010110001000011010001100111101110001110000011010101111100001000011010110100000100100001111011110110000000101000010001111001010010110101110111101101110111000100', '1000101100001000100001101110111110000100000001000010101111010011010010010111011010100011001000100100001010001100110']}]}, {'section_id': 'body.2.0.3', 'paragraphs': [{'sentences': ['1010100111100011110110101011100001011010011010100100010011000110111000001010010110111001001101111000010100100110101001010001010001000110010000001', '100010101010100111000011111101010100101110011000100011100100100111000010000011001010010111011010000101010011011110111001010110', '0110000110110110110011011000011010010000001010011000010001011110110010000100011111010100110111111010010111000101111', '10100100000011100010110110011111011011101101111000001001010100001001011010000011001010101100000', '1011111111100001001100000010000100110010101000010100111111110010110011101110000101101011101', '10001111110000011100100000101100000000010000100000011100110000011110111010011101010111101001111000100000000110000011010010001100110111100001001011101011001111110010100111001001010001010011010010010111001101110101110000101011', '101101111111101101010010000110111110000110000111001001010011111101011001011010101100010100110101101011100111100100110010001011110001110010000011101100100100001001110010000010011111100110101']}]}, {'section_id': 'body.2.1', 'paragraphs': [{'sentences': ['1010010011010011001111111001000110010001101111101011001011011000101001010101010001000110100011110101110001110110111010010010100100111000101100100101111110100000011111001101010111101010100101011011110111111110', '000010101101111100000110010110011001111100001101011101000100010001001001000000101101000001110000011010111100000010010000010101110101100010011000101110110111111001000101000111000110100001001100001010101010100011', '0000000011101110111100100010111100101010110001111101110110010000100100010000101001101111001111001001100110010011010000101001110010000000100101011101001010100100011101101001011000010111110100101010110110011001110000110010010111110110101100001011101001100111010001000010111010001010000100010010011110111100110011100011111101101000011100111110101010100110001100100000100011011010111000111110010110100010111101001001101000001100100010000111110000011101111100111101000000000']}, {'sentences': ['01011000010110011000000101101000110101011010100111011001001001100001101101111101111001101111100101111001101011011001011110110110110100001100111111010100101110111111101000101100101010110011111011100101101010100110111001111100100011001110011101000110100000001100001100110001110101001000011010000110101011010000001111100100000100101110011000001001010011011101100011000001100000011', '1001100000101000000011110100110001100001101001100011010000111111010110101111001000100111000011010100100000110110001', '10010011000110110111010110000010010000000111101000100101100111101101001100111110101001001111100001110011110000010101000001000000010100011011110011000100110101001100110111111001101000011010100110000000011110001000101010101000110010010']}]}, {'section_id': 'body.2.2', 'paragraphs': [{'sentences': ['000011000000010011000001101111000101000111111111111010001011110000011001010111010101010110001111110000010', '10101001101011101010001111011000110100000100011110010001100111111101101100010010111110110101101011000011000001101110010111011111100111110000000101110010111', '100001011110010111010110001101101001100000000001000010110101011001111100101101101111010010111111000000111001111010011111000100010001111011110001010000110010101010111110100101011011100001010101000001011011111111101', '1000110111111011101000110101001111111111000100011001000011010100001010011110001111010011011111000111011100101001011111001000010101110110101000111011111111010010001101001010110111000011110101011000010000110', '1011100000100000010101101111001001100110111000010001011010111111000000001010101001111011101011010101101001111101101100101001011101000011011010001001101100100111101111111100010011010101111011100001100001000100101100100110101000010000011000000011001100000110000001', '0001001101111001111111010000001101010110110110100110110100000100110101101010010101011000010010111011000010111110000001110101110111000010011000100110111001000111011000100101110111111', '0110010010011000011010001111001100101001100001001000010100101100010110000000101010110001001010001100111101010001110010010000111011100101101010111111101001100010001011100110010100110111010101000100001110000101110011111011111000010101010110101100010010111100100010010100111110111100101010100011101001110110010000011110001010101010000100010000100111001111011101', '000001010000010001100000101011000000110101000100010111111100101111111000110111001001110110101111110011100001001000011001010000011011', '0101101001010101001101010100011000111011001000100001110100110011100000001001010110001101010110011100111111100101101111101111011001111111110010111010011011011111011011110000101011010', '11000001110111000001100100001110000111001010000101011011101010111001011100010010010111111111000011111110010111100011100110001001100011111010100111110111001110010', '0100010110100001010101110111100011100100010111111011101001100101111110101011010010101111001000101001111000001110001100011001110010100110101100110100100000001010101101011110011001000101100111001001001110100', '100000100010011111001101010000100110011110001100000010010110110100000111111011010100101111010111001110101000100001111101001110000011010110000010100', '00100110000011100101000110110001000011101000011010101000010001111011100001111111001011100111101000001000000110110001000101111010010010001100111', '0110110100011001110011001111100010101001011111011001011001101101010010101101110101010100001000100100000111101110001001110111000110011101101010100000101', '0011111010010011011101010110100110000011000011100100101011011001110110001110001111000011010111011000110100111111011101110111000010010000011011010011011100000011101100110110100100000010110101110100110101001100111011101001010111011011110100110101110010011011010001010111110011001000010100010101010010110010010110000100110001000011010011000100101011010100100111010']}]}]}")
d=pandas.DataFrame.from_records(copy.deepcopy(paper_as_dict) for _ in range(140_100))
arrow=datasets.Dataset.from_pandas(d)
```
## Expected results
The dataset should be converted without error.
## Actual results
Error `pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array invalid: Invalid: Struct child array #1 has length smaller than expected for struct array (1192457 < 1192458)`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.18.4 pandas==1.3.5
- Platform: macOS 11.6 or CentOS Linux 7 (Core)
- Python version: Python 3.9.7
- PyArrow version: pyarrow==3.0.0
| false
|
1,172,657,981
|
https://api.github.com/repos/huggingface/datasets/issues/3958
|
https://github.com/huggingface/datasets/pull/3958
| 3,958
|
Update Wikipedia metadata
|
closed
| 2
| 2022-03-17T17:50:05
| 2022-03-21T12:26:48
| 2022-03-21T12:26:47
|
albertvillanova
|
[] |
This PR updates:
- dataset card
- metadata JSON
| true
|
1,172,401,455
|
https://api.github.com/repos/huggingface/datasets/issues/3957
|
https://github.com/huggingface/datasets/pull/3957
| 3,957
|
Fix xtreme s metrics
|
closed
| 2
| 2022-03-17T13:39:04
| 2022-03-18T13:46:19
| 2022-03-18T13:42:16
|
patrickvonplaten
|
[] |
We in fact do need BABEL in xtreme-s
| true
|
1,172,272,327
|
https://api.github.com/repos/huggingface/datasets/issues/3956
|
https://github.com/huggingface/datasets/issues/3956
| 3,956
|
TypeError: __init__() missing 1 required positional argument: 'scheme'
|
closed
| 8
| 2022-03-17T11:43:13
| 2023-11-21T04:26:20
| 2022-03-28T08:00:01
|
amirj
|
[
"bug"
] |
## Describe the bug
Based on [this tutorial](https://huggingface.co/docs/datasets/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
squad = load_dataset('squad', split='validation')
squad.add_elasticsearch_index("context", host="localhost", port="9200")
```
## Expected results
[Creating an elastic index based on the provided tutorial](https://huggingface.co/docs/datasets/faiss_es#elasticsearch)
## Actual results
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-8fb51aa33961> in <module>
1 from datasets import load_dataset
2 squad = load_dataset('squad', split='validation')
----> 3 squad.add_elasticsearch_index("context", host="localhost", port="9200")
~/opt/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)
3777 """
3778 with self.formatted_as(type=None, columns=[column]):
-> 3779 super().add_elasticsearch_index(
3780 column=column,
3781 index_name=index_name,
~/opt/anaconda3/lib/python3.8/site-packages/datasets/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)
587 """
588 index_name = index_name if index_name is not None else column
--> 589 es_index = ElasticSearchIndex(
590 host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config
591 )
~/opt/anaconda3/lib/python3.8/site-packages/datasets/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)
123 from elasticsearch import Elasticsearch # noqa: F811
124
--> 125 self.es_client = es_client if es_client is not None else Elasticsearch([{"host": host, "port": str(port)}])
126 self.es_index_name = (
127 es_index_name
~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)
310
311 if _transport is None:
--> 312 node_configs = client_node_configs(
313 hosts,
314 cloud_id=cloud_id,
~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/utils.py in client_node_configs(hosts, cloud_id, **kwargs)
99 else:
100 assert hosts is not None
--> 101 node_configs = hosts_to_node_configs(hosts)
102
103 # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.
~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/utils.py in hosts_to_node_configs(hosts)
142
143 elif isinstance(host, Mapping):
--> 144 node_configs.append(host_mapping_to_node_config(host))
145 else:
146 raise ValueError(
~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/utils.py in host_mapping_to_node_config(host)
209 options["path_prefix"] = options.pop("url_prefix")
210
--> 211 return NodeConfig(**options) # type: ignore
212
213
TypeError: __init__() missing 1 required positional argument: 'scheme'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Mac
- Python version: 3.8.0
- PyArrow version: 7.0.0
- ElaticSearch Info:
{
"name" : "byname",
"cluster_name" : "elasticsearch_brew",
"cluster_uuid" : "9xkjrltiQIG0J95ciWhqRA",
"version" : {
"number" : "7.10.2-SNAPSHOT",
"build_flavor" : "oss",
"build_type" : "tar",
"build_hash" : "unknown",
"build_date" : "2021-01-16T01:41:27.115673Z",
"build_snapshot" : true,
"lucene_version" : "8.7.0",
"minimum_wire_compatibility_version" : "6.8.0",
"minimum_index_compatibility_version" : "6.0.0-beta1"
},
"tagline" : "You Know, for Search"
}
| false
|
1,172,246,647
|
https://api.github.com/repos/huggingface/datasets/issues/3955
|
https://github.com/huggingface/datasets/pull/3955
| 3,955
|
Remove unncessary 'pylint disable' message in ReadMe
|
closed
| 0
| 2022-03-17T11:16:55
| 2022-04-12T14:28:35
| 2022-04-12T14:28:35
|
Datta0
|
[] | null | true
|
1,172,141,664
|
https://api.github.com/repos/huggingface/datasets/issues/3954
|
https://github.com/huggingface/datasets/issues/3954
| 3,954
|
The dataset preview is not available for tdklab/Hebrew_Squad_v1.1 dataset
|
closed
| 6
| 2022-03-17T09:38:11
| 2022-04-20T12:39:07
| 2022-04-20T12:39:07
|
MatanBenChorin
|
[] |
## Dataset viewer issue for 'tdklab/Hebrew_Squad_v1.1'
**Link:** https://huggingface.co/api/datasets/tdklab/Hebrew_Squad_v1.1?full=true
The dataset preview is not available for this dataset.
Am I the one who added this dataset ? Yes
| false
|
1,172,123,736
|
https://api.github.com/repos/huggingface/datasets/issues/3953
|
https://github.com/huggingface/datasets/issues/3953
| 3,953
|
Add ImageNet Sketch
|
closed
| 2
| 2022-03-17T09:20:31
| 2022-05-23T18:05:29
| 2022-05-23T18:05:29
|
NielsRogge
|
[
"dataset request",
"vision"
] |
## Adding a Dataset
- **Name:** ImageNet Sketch
- **Description:** ImageNet-Sketch is a dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale.
- **Paper:** [Learning Robust Global Representations by Penalizing Local Predictive Power](https://arxiv.org/abs/1905.13549)
- **Data:** https://github.com/HaohanWang/ImageNet-Sketch
- **Motivation:** Allows for evaluating the robustness of vision models.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false
|
1,171,895,531
|
https://api.github.com/repos/huggingface/datasets/issues/3952
|
https://github.com/huggingface/datasets/issues/3952
| 3,952
|
Checksum error for glue sst2, stsb, rte etc datasets
|
closed
| 1
| 2022-03-17T03:45:47
| 2022-03-17T07:10:15
| 2022-03-17T07:10:14
|
ravindra-ut
|
[
"bug"
] |
## Describe the bug
Checksum error for glue sst2, stsb, rte etc datasets
## Steps to reproduce the bug
```python
>>> nlp.load_dataset('glue', 'sst2')
Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown sizetotal: 11.90 MiB) to
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 73.0/73.0 [00:00<00:00, 18.2kB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/3.8/lib/python/site-packages/nlp/load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 462, in download_and_prepare
self._download_and_prepare(
File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 521, in _download_and_prepare
verify_checksums(
File "/Library/Python/3.8/lib/python/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']
```
## Expected results
dataset load should succeed without checksum error.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/3.8/lib/python/site-packages/nlp/load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 462, in download_and_prepare
self._download_and_prepare(
File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 521, in _download_and_prepare
verify_checksums(
File "/Library/Python/3.8/lib/python/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']
```
## Environment info
- `datasets` version: '1.18.3'
- Platform: Mac OS
- Python version: Python 3.8.9
- PyArrow version: '7.0.0'
| false
|
1,171,568,814
|
https://api.github.com/repos/huggingface/datasets/issues/3951
|
https://github.com/huggingface/datasets/issues/3951
| 3,951
|
Forked streaming datasets try to `open` data urls rather than use network
|
closed
| 1
| 2022-03-16T21:21:02
| 2022-06-10T20:47:26
| 2022-06-10T20:47:26
|
dlwh
|
[
"bug"
] |
## Describe the bug
Building on #3950, if you bypass the pickling problem you still can't use the dataset. Somehow something gets confused and the forked processes try to `open` urls rather than anything else.
## Steps to reproduce the bug
```python
from multiprocessing import freeze_support
import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets
import torch.utils.data
# work around #3950
class TorchIterableDataset(datasets.IterableDataset, torch.utils.data.IterableDataset):
pass
def _ensure_format(v: datasets.IterableDataset) -> datasets.IterableDataset:
return TorchIterableDataset(v._ex_iterable, v.info, v.split, "torch", v._shuffling)
if __name__ == '__main__':
freeze_support()
ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
ds = _ensure_format(ds)
model = AutoModelForCausalLM.from_pretrained("distilgpt2")
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
```
## Expected results
I'd expect the dataset to load the url correctly and produce examples.
## Actual results
```
warnings.warn(
***** Running training *****
Num examples = 8000
Num Epochs = 9223372036854775807
Instantaneous batch size per device = 8
Total train batch size (w. parallel, distributed & accumulation) = 8
Gradient Accumulation steps = 1
Total optimization steps = 1000
0%| | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last):
File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 22, in <module>
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train
for step, inputs in enumerate(epoch_iterator):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
data = self._next_data()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise
raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
for key, example in self._iter():
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 494, in _iter
yield from ex_iterable
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/Users/dlwh/.cache/huggingface/modules/datasets_modules/datasets/oscar/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2/oscar.py", line 358, in _generate_examples
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/en/en_part_1.txt.gz'
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 27, in poll
pid, sts = os.waitpid(self.pid, flag)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 6932) is killed by signal: Terminated: 15.
0%| | 0/1000 [00:02<?, ?it/s]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| false
|
1,171,560,585
|
https://api.github.com/repos/huggingface/datasets/issues/3950
|
https://github.com/huggingface/datasets/issues/3950
| 3,950
|
Streaming Datasets don't work with Transformers Trainer when dataloader_num_workers>1
|
closed
| 1
| 2022-03-16T21:14:11
| 2022-06-10T20:47:26
| 2022-06-10T20:47:26
|
dlwh
|
[
"bug",
"good first issue"
] |
## Describe the bug
Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.
## Steps to reproduce the bug
```python
import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets
ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("torch")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
```
## Expected results
For this code I'd expect a crash related to not having preprocessed the data, but instead we get a pickling error.
## Actual results
```
0%| | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last):
File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 7, in <module>
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train
for step, inputs in enumerate(epoch_iterator):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__
return self._get_iterator()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 918, in __init__
w.start()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'iterable_dataset.<locals>.TorchIterableDataset'
0%| | 0/1000 [00:00<?, ?it/s]
```
This immediate crash can be fixed by not using a local class to make the `TorchIterableDataset` (Note that you have to do with_format("torch") or you get an exception because the dataset has no len) However, any lambdas etc used as maps will also trigger this crash. A more permanent fix would be to move away from multiprocessing and instead use something like pathos or multiprocessing_on_dill (https://stackoverflow.com/questions/19984152/what-can-multiprocessing-and-dill-do-together)
Note that if you bypass this crash you get another crash. (I'll file a separate bug).
## Environment info
- `datasets` version: 2.0.0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| false
|
1,171,467,981
|
https://api.github.com/repos/huggingface/datasets/issues/3949
|
https://github.com/huggingface/datasets/pull/3949
| 3,949
|
Remove GLEU metric
|
closed
| 1
| 2022-03-16T19:35:31
| 2022-04-12T20:43:26
| 2022-04-12T20:37:09
|
emibaylor
|
[] |
Remove the GLEU metric as it is not actually implemented.
| true
|
1,171,460,560
|
https://api.github.com/repos/huggingface/datasets/issues/3948
|
https://github.com/huggingface/datasets/pull/3948
| 3,948
|
Google BLEU Metric Card
|
closed
| 1
| 2022-03-16T19:27:17
| 2022-03-21T16:04:26
| 2022-03-21T16:04:25
|
emibaylor
|
[] |
Add metric card for Google BLEU (GLEU) metric
One thing I noticed while writing this up is that, while this metric was made specifically to be better than BLEU at the sentence level instead of the corpus level, the current implementation only allows the calculation of the corpus-level statistic. I think changing this would be a good thing to put on the to do list for the future.
| true
|
1,171,452,854
|
https://api.github.com/repos/huggingface/datasets/issues/3947
|
https://github.com/huggingface/datasets/pull/3947
| 3,947
|
BLEU metric card
|
closed
| 2
| 2022-03-16T19:20:07
| 2022-03-29T14:59:50
| 2022-03-29T14:54:14
|
emibaylor
|
[] |
Add BLEU metric card
| true
|
1,171,239,287
|
https://api.github.com/repos/huggingface/datasets/issues/3946
|
https://github.com/huggingface/datasets/pull/3946
| 3,946
|
Add newline to text dataset builder for controlling universal newlines mode
|
closed
| 3
| 2022-03-16T16:11:11
| 2023-09-24T10:10:50
| 2023-09-24T10:10:47
|
albertvillanova
|
[] |
Fix #3804.
| true
|
1,171,222,257
|
https://api.github.com/repos/huggingface/datasets/issues/3945
|
https://github.com/huggingface/datasets/pull/3945
| 3,945
|
Fix comet metric
|
closed
| 4
| 2022-03-16T15:56:47
| 2022-03-22T15:10:12
| 2022-03-22T15:05:30
|
lhoestq
|
[] |
The COMET metric has been broken for a while since big breaking changes happened. We did not catch them in the CI because the slow test mocks the download_model function that was changed.
This PR fixes the metric, updates the download_model mock and updates the doctest.
| true
|
1,171,209,510
|
https://api.github.com/repos/huggingface/datasets/issues/3944
|
https://github.com/huggingface/datasets/pull/3944
| 3,944
|
Create README.md
|
closed
| 1
| 2022-03-16T15:46:26
| 2022-03-17T17:50:54
| 2022-03-17T17:47:05
|
sashavor
|
[] |
Proposing COMET metric card
| true
|
1,171,185,070
|
https://api.github.com/repos/huggingface/datasets/issues/3943
|
https://github.com/huggingface/datasets/pull/3943
| 3,943
|
[Doc] Don't use v for version tags on GitHub
|
closed
| 1
| 2022-03-16T15:28:30
| 2022-03-17T11:46:26
| 2022-03-17T11:46:25
|
sgugger
|
[] |
This removes the `v` automatically used by `doc-builder` for versions.
| true
|
1,171,177,122
|
https://api.github.com/repos/huggingface/datasets/issues/3942
|
https://github.com/huggingface/datasets/issues/3942
| 3,942
|
reddit_tifu dataset: Checksums didn't match for dataset source files
|
closed
| 3
| 2022-03-16T15:23:30
| 2022-03-16T15:57:43
| 2022-03-16T15:39:25
|
XingxingZhang
|
[
"bug",
"duplicate"
] |
## Describe the bug
When loading the reddit_tifu dataset, it throws the exception "Checksums didn't match for dataset source files"
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
print(datasets.__version__)
# load_dataset('billsum')
load_dataset('reddit_tifu', 'short')
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: mac os
- Python version: Python 3.7.6
- PyArrow version: 3.0.0
| false
|
1,171,132,709
|
https://api.github.com/repos/huggingface/datasets/issues/3941
|
https://github.com/huggingface/datasets/issues/3941
| 3,941
|
billsum dataset: Checksums didn't match for dataset source files:
|
closed
| 3
| 2022-03-16T14:52:08
| 2024-03-13T12:11:35
| 2022-03-16T15:46:44
|
XingxingZhang
|
[
"bug"
] |
## Describe the bug
When loading the `billsum` dataset, it throws the exception "Checksums didn't match for dataset source files"
```
File "virtualenv_projects/codex/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx']
```
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
print(datasets.__version__)
load_dataset('billsum')
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: mac os
- Python version: Python 3.7.6
- PyArrow version: 3.0.0
| false
|
1,171,106,853
|
https://api.github.com/repos/huggingface/datasets/issues/3940
|
https://github.com/huggingface/datasets/pull/3940
| 3,940
|
Create CoVAL metric card
|
closed
| 1
| 2022-03-16T14:31:49
| 2022-03-18T17:37:59
| 2022-03-18T17:35:14
|
sashavor
|
[] |
Initial CoVAL metric card
| true
|
1,170,882,331
|
https://api.github.com/repos/huggingface/datasets/issues/3939
|
https://github.com/huggingface/datasets/issues/3939
| 3,939
|
Source links broken
|
closed
| 8
| 2022-03-16T11:17:47
| 2022-03-19T04:41:32
| 2022-03-19T04:41:32
|
qqaatw
|
[
"bug"
] |
## Describe the bug
The source links of v2.0.0 docs are broken:
For exmaple, clicking the source button of this [class](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.ClassLabel) will direct users to `https://github.com/huggingface/datasets/blob/v2.0.0/src/datasets/features/features.py#L747`
here, the `v2.0.0` should be `2.0.0`.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
Redirecting to this link: `https://github.com/huggingface/datasets/blob/2.0.0/src/datasets/features/features.py#L747`
## Actual results
Described above.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
| false
|
1,170,875,417
|
https://api.github.com/repos/huggingface/datasets/issues/3938
|
https://github.com/huggingface/datasets/pull/3938
| 3,938
|
Avoid info log messages from transformers in FrugalScore metric
|
closed
| 1
| 2022-03-16T11:11:29
| 2022-03-17T08:37:25
| 2022-03-17T08:37:24
|
albertvillanova
|
[] |
Fix #3928.
| true
|
1,170,832,006
|
https://api.github.com/repos/huggingface/datasets/issues/3937
|
https://github.com/huggingface/datasets/issues/3937
| 3,937
|
Missing languages in lvwerra/github-code dataset
|
closed
| 5
| 2022-03-16T10:32:03
| 2022-03-22T07:09:23
| 2022-03-21T14:50:47
|
Eytan-S
|
[
"Dataset discussion"
] |
Hi,
I'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!
I've noticed that two languages are missing from the dataset: TypeScript and Scala.
Looks like they're also omitted from the query you used to get the original code.
Are there any plans to add them in the future?
Thanks!
| false
|
1,170,713,473
|
https://api.github.com/repos/huggingface/datasets/issues/3936
|
https://github.com/huggingface/datasets/pull/3936
| 3,936
|
Fix Wikipedia version and re-add tests
|
closed
| 1
| 2022-03-16T08:48:04
| 2022-03-16T17:04:07
| 2022-03-16T17:04:05
|
albertvillanova
|
[] |
To keep backward compatibility when loading using "wikipedia" dataset ID (https://huggingface.co/datasets/wikipedia), we have created the pre-processed data for the same languages we were offering before, but with updated date "20220301":
- de
- en
- fr
- frr
- it
- simple
These pre-processed data can be accessed, e.g.:
```python
ds = load_dataset("wikipedia", "20220301.frr", split="train")
```
The next step will be to offer the pre-processed data for many other languages, but when loading using "wikimedia/wikipedia": https://huggingface.co/datasets/wikimedia/wikipedia
| true
|
1,170,292,492
|
https://api.github.com/repos/huggingface/datasets/issues/3934
|
https://github.com/huggingface/datasets/pull/3934
| 3,934
|
Create MAUVE metric card
|
closed
| 1
| 2022-03-15T21:36:07
| 2022-03-18T17:38:14
| 2022-03-18T17:34:13
|
sashavor
|
[] |
Proposing a MAUVE metric card
| true
|
1,170,253,605
|
https://api.github.com/repos/huggingface/datasets/issues/3933
|
https://github.com/huggingface/datasets/pull/3933
| 3,933
|
Update README.md
|
closed
| 1
| 2022-03-15T20:52:05
| 2022-03-17T17:51:24
| 2022-03-17T17:47:37
|
sashavor
|
[] |
Fixing missing triple quote
| true
|
1,170,221,773
|
https://api.github.com/repos/huggingface/datasets/issues/3932
|
https://github.com/huggingface/datasets/pull/3932
| 3,932
|
Create SARI metric card
|
closed
| 1
| 2022-03-15T20:37:23
| 2022-03-18T17:37:01
| 2022-03-18T17:32:55
|
sashavor
|
[] |
SARI metric card! (do we have an expert in text simplification to validate?.. :sweat_smile: )
| true
|
1,170,097,208
|
https://api.github.com/repos/huggingface/datasets/issues/3931
|
https://github.com/huggingface/datasets/pull/3931
| 3,931
|
Add align_labels_with_mapping docs
|
closed
| 1
| 2022-03-15T19:24:57
| 2022-03-18T16:28:31
| 2022-03-18T16:24:33
|
stevhliu
|
[
"documentation"
] |
This PR documents the `align_labels_with_mapping` function to ensure predicted labels are aligned with the dataset, or to assign a different mapping of labels to ids (requested by @mariosasko π ).
For this specific code sample, the current dataset has a `mixed` label that the original [dataset](https://huggingface.co/datasets/poem_sentiment#data-fields) didn't. Is there a way to remove this label so it is completely aligned with the original dataset mappings? Otherwise, I'll just leave it as it is.
| true
|
1,170,087,793
|
https://api.github.com/repos/huggingface/datasets/issues/3930
|
https://github.com/huggingface/datasets/pull/3930
| 3,930
|
Create README.md
|
closed
| 1
| 2022-03-15T19:16:59
| 2022-04-04T15:23:15
| 2022-04-04T15:17:28
|
sashavor
|
[] |
Creating a README for IndicGLUE
cc @mcmillanmajora for fact checking in terms of languages (also, are there any limitations of the dataset or eval metric that I'm not aware of?)
| true
|
1,170,066,235
|
https://api.github.com/repos/huggingface/datasets/issues/3929
|
https://github.com/huggingface/datasets/issues/3929
| 3,929
|
Load a local dataset twice
|
closed
| 1
| 2022-03-15T18:59:26
| 2022-03-16T09:55:09
| 2022-03-16T09:54:06
|
caush
|
[
"bug"
] |
## Describe the bug
Load a local "dataset" composed of two csv files twice.
## Steps to reproduce the bug
Put the two joined files in a repository named "Data".
Then in python:
import datasets as ds
ds.load_dataset('Data', data_files = {'file1.csv', 'file2.csv'})
## Expected results
Should give something like (because files have only one data row):
Title, clicks
Truc et astuce, 123
Machin, 12
## Actual results
Gives
Title, clicks
Truc et astuce, 123
Machin, 12
Truc et astuce, 123
Machin, 12
## Environment info
[file1.csv](https://github.com/huggingface/datasets/files/8256322/file1.csv)
[file2.csv](https://github.com/huggingface/datasets/files/8256323/file2.csv)
- `datasets` version: 2.0.0
- Platform: Linux-5.4.0-65-generic-x86_64-with-glibc2.10
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| false
|
1,170,017,132
|
https://api.github.com/repos/huggingface/datasets/issues/3928
|
https://github.com/huggingface/datasets/issues/3928
| 3,928
|
Frugal score deprecations
|
closed
| 1
| 2022-03-15T18:10:42
| 2022-03-17T08:37:24
| 2022-03-17T08:37:24
|
ierezell
|
[
"bug"
] |
## Describe the bug
The frugal score returns a really verbose output with warnings that can be easily changed.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets.load import load_metric
frugal = load_metric("frugalscore")
frugal.compute(predictions=["Do you like spinachis"], references=["Do you like spinach"])
```
## Expected results
A clear and concise description of the expected results.
```
{'scores': [0.9946]}
```
## Actual results
Specify the actual results or traceback.
```
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 864.09ba/s]
Using amp half precision backend
The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1. If sentence2, sentence1 are not expected by `BertForSequenceClassification.forward`, you can safely ignore this message.
***** Running Prediction *****
Num examples = 1
Batch size = 64
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 4644.85it/s]
{'scores': [0.9946]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
| false
|
1,170,016,465
|
https://api.github.com/repos/huggingface/datasets/issues/3927
|
https://github.com/huggingface/datasets/pull/3927
| 3,927
|
Update main readme
|
closed
| 2
| 2022-03-15T18:09:59
| 2022-03-29T10:13:47
| 2022-03-29T10:08:20
|
lhoestq
|
[] |
The main readme was still focused on text datasets - I extended it by mentioning that we also support image and audio datasets
| true
|
1,169,945,052
|
https://api.github.com/repos/huggingface/datasets/issues/3926
|
https://github.com/huggingface/datasets/pull/3926
| 3,926
|
Doc maintenance
|
closed
| 1
| 2022-03-15T17:00:46
| 2022-03-15T19:27:15
| 2022-03-15T19:27:12
|
stevhliu
|
[
"documentation"
] |
This PR adds some minor maintenance to the docs. The main fix is properly linking to pages in the callouts because some of the links would just redirect to a non-existent section on the same page.
| true
|
1,169,913,769
|
https://api.github.com/repos/huggingface/datasets/issues/3925
|
https://github.com/huggingface/datasets/pull/3925
| 3,925
|
Fix main_classes docs index
|
closed
| 3
| 2022-03-15T16:33:46
| 2022-03-22T13:49:11
| 2022-03-22T13:44:04
|
lhoestq
|
[] |
Currently the `main_classes` documentation has a wrong index. I believe this comes from issues in the examples of the Translation feature types
| true
|
1,169,805,813
|
https://api.github.com/repos/huggingface/datasets/issues/3924
|
https://github.com/huggingface/datasets/pull/3924
| 3,924
|
Document cases for github datasets
|
closed
| 2
| 2022-03-15T15:10:10
| 2022-04-05T18:33:15
| 2022-03-15T15:41:23
|
lhoestq
|
[] |
In general we recommend adding the new dataset under a username or organization in the Hugging Face Hub at [hf.co/datasets](hf.co/datasets), but users can still add a dataset on github in some cases.
I added a paragraph in the documentation to explain in which cases it can make more sense to open a PR on github:
- when you need the dataset to be reviewed
- when you need long-term maintenance from the HF team
- when thereβs no clear org name / namespace that you can put the dataset under
| true
|
1,169,773,869
|
https://api.github.com/repos/huggingface/datasets/issues/3923
|
https://github.com/huggingface/datasets/pull/3923
| 3,923
|
Add methods to IterableDatasetDict
|
closed
| 5
| 2022-03-15T14:46:03
| 2022-07-06T15:40:20
| 2022-03-15T16:45:06
|
lhoestq
|
[] |
Following the new methods added in #3826 and https://github.com/huggingface/datasets/pull/3862 I added several methods to IterableDatasetDict:
- map
- filter
- shuffle
- with_format
- cast
- cast_column
- remove_columns
- rename_column
- rename_columns
| true
|
1,169,761,293
|
https://api.github.com/repos/huggingface/datasets/issues/3922
|
https://github.com/huggingface/datasets/pull/3922
| 3,922
|
Fix NonMatchingChecksumError in MultiWOZ 2.2 dataset
|
closed
| 2
| 2022-03-15T14:36:28
| 2022-03-15T16:07:04
| 2022-03-15T16:07:03
|
albertvillanova
|
[] |
Fix #2957
| true
|
1,169,749,338
|
https://api.github.com/repos/huggingface/datasets/issues/3921
|
https://github.com/huggingface/datasets/pull/3921
| 3,921
|
Fix NonMatchingChecksumError in CRD3 dataset
|
closed
| 2
| 2022-03-15T14:27:14
| 2022-03-15T15:54:27
| 2022-03-15T15:54:26
|
albertvillanova
|
[] |
Fix #3051
| true
|
1,169,532,807
|
https://api.github.com/repos/huggingface/datasets/issues/3920
|
https://github.com/huggingface/datasets/issues/3920
| 3,920
|
'datasets.features' is not a package
|
closed
| 2
| 2022-03-15T11:14:23
| 2022-03-16T09:17:12
| 2022-03-16T09:17:12
|
Arij-Aladel
|
[] |
@albertvillanova
python 3.9
os: ubuntu 20.04
In conda environment
torch installed by
```/env/bin/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html```
datasets package is installed by
```
/env/bin/pip install datasets==1.8.0
```
During runing the code I have this error
```
[6]<stderr>: File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 875, in find_class
[6]<stderr>: return super().find_class(mod_name, name)
[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package
```
precisely this error appears when
torch.load('data_file.pt')
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 607, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 882, in _load
result = unpickler.load()
File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 875, in find_class
return super().find_class(mod_name, name)
ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package
```
Why I am getting this error?
| false
|
1,169,497,210
|
https://api.github.com/repos/huggingface/datasets/issues/3919
|
https://github.com/huggingface/datasets/issues/3919
| 3,919
|
AttributeError: 'DatasetDict' object has no attribute 'features'
|
closed
| 2
| 2022-03-15T10:46:59
| 2022-03-17T04:16:14
| 2022-03-17T04:16:14
|
jswapnil10
|
[
"bug"
] |
## Describe the bug
Receiving the error when trying to check for Dataset features
## Steps to reproduce the bug
from datasets import Dataset
dataset = Dataset.from_pandas(df[['id', 'words', 'bboxes', 'ner_tags', 'image_path']])
dataset.features
## Expected results
A clear and concise description of the expected results.
## Actual results
Getting the following errror
AttributeError: 'DatasetDict' object has no attribute 'features'
## Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 1.18.4
- Platform: Linux-4.14.252-131.483.amzn1.x86_64-x86_64-with-glibc2.9
- Python version: 3.6.13
- PyArrow version: 6.0.1
| false
|
1,169,366,117
|
https://api.github.com/repos/huggingface/datasets/issues/3918
|
https://github.com/huggingface/datasets/issues/3918
| 3,918
|
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files
|
closed
| 3
| 2022-03-15T08:53:45
| 2022-03-16T15:36:58
| 2022-03-15T14:01:25
|
willowdong
|
[
"bug",
"duplicate"
] |
## Describe the bug
Can't load the dataset
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset('multi_news')
dataset_2=load_dataset("reddit_tifu", "long")
## Actual results
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']
## Environment info
- `datasets` version: 1.18.4
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.0
- PyArrow version: 6.0.1
| false
|
1,168,906,154
|
https://api.github.com/repos/huggingface/datasets/issues/3917
|
https://github.com/huggingface/datasets/pull/3917
| 3,917
|
Create README.md
|
closed
| 1
| 2022-03-14T21:08:10
| 2022-03-17T17:45:39
| 2022-03-17T17:45:39
|
sashavor
|
[] |
This follows the same structure as the GLUE metric card, hope that works for everyone :)
| true
|
1,168,869,191
|
https://api.github.com/repos/huggingface/datasets/issues/3916
|
https://github.com/huggingface/datasets/pull/3916
| 3,916
|
Create README.md for GLUE
|
closed
| 1
| 2022-03-14T20:27:22
| 2022-03-15T17:06:57
| 2022-03-15T17:06:56
|
sashavor
|
[] |
I still have a hesitation regarding the format of inputs -- whether it's a list or a list of lists? -- hopefully @lhoestq will be able to clarify.
Also tagging @yjernite for the Limitations section. Happy to hear your thoughts!
| true
|
1,168,848,101
|
https://api.github.com/repos/huggingface/datasets/issues/3915
|
https://github.com/huggingface/datasets/pull/3915
| 3,915
|
Metric card template
|
closed
| 6
| 2022-03-14T20:07:08
| 2022-05-04T10:44:09
| 2022-05-04T10:37:06
|
emibaylor
|
[] |
Adding a metric card template, based on ideas and edits from @sashavor and I, as well as from comments from @lhoestq and others (thank you!).
All feedback is welcome, but am especially curious about feedback in terms of:
- things that should be included but aren't
- things that are included but should be changed or removed
- the instructions I included, and whether they should be added to, clarified, or deleted altogether
| true
|
1,168,777,880
|
https://api.github.com/repos/huggingface/datasets/issues/3914
|
https://github.com/huggingface/datasets/pull/3914
| 3,914
|
Use templates for doc-builidng jobs
|
closed
| 2
| 2022-03-14T18:53:06
| 2022-03-17T15:02:59
| 2022-03-17T15:02:58
|
sgugger
|
[] |
This PR updates the jobs for all doc-building related things by using the templates introduced on `doc-builder`. By putting those once there, we make sure every repo gets the latest fixes on the doc-building github actions :-)
Note: all libraries must share the same docker image for those doc-building jobs. For now, all the one used (`huggingface/transformers-doc-builder`) contains all extra steps of the datasets install for docbuling (mainly libsndfile) but if in the future some additional steps are necessary on top of `pip install -e .[dev]`, this docker image will need to be updated with the extra deps.
| true
|
1,168,723,950
|
https://api.github.com/repos/huggingface/datasets/issues/3913
|
https://github.com/huggingface/datasets/pull/3913
| 3,913
|
Deterministic split order in DatasetDict.map
|
closed
| 3
| 2022-03-14T17:58:37
| 2023-09-24T09:55:10
| 2022-03-15T10:45:15
|
lhoestq
|
[] |
The order in which the splits are processed by `map` is not deterministic in `DatasetDict.map`. This can cause caching issues when the processing function is stateful and sensible to the order in which examples are processed
Close https://github.com/huggingface/datasets/issues/3847
| true
|
1,168,720,098
|
https://api.github.com/repos/huggingface/datasets/issues/3912
|
https://github.com/huggingface/datasets/pull/3912
| 3,912
|
add draft of registering function for pandas
|
closed
| 3
| 2022-03-14T17:54:29
| 2023-09-24T09:55:01
| 2023-01-24T12:57:10
|
lvwerra
|
[] |
This PR adds a register function for `pandas`. It allows to directly push `DataFrame` objects to the hub and in return loading datasets on the hub from `DataFrame`. The motivation for this integration is to enable the vast number of `pandas` users to be able to easily push `DataFrames` to the hub.
Here is an example:
```python
import pandas as pd
from datasets import register_pandas
register_pandas()
# push to hub
df = pd.DataFrame.from_dict({"test": [1,2,3]})
df.push_to_hub("my_test")
# load from hub
df_retrieved = pd.DataFrame.load_from_hub("lvwerra/my_test")
```
It follows a similar philosophy as the `tqdm` [integration](https://github.com/tqdm/tqdm#pandas-integration). Also see [this issue](https://github.com/pandas-dev/pandas/issues/46000) on the `pandas` repository.
This is just a rough draft of what such integration could look like but I would like appreciate some feedback on this: is this something you would like to add the library and is this the way to go? cc @lhoestq @albertvillanova @julien-c
| true
|
1,168,652,374
|
https://api.github.com/repos/huggingface/datasets/issues/3911
|
https://github.com/huggingface/datasets/pull/3911
| 3,911
|
Create README.md for CER metric
|
closed
| 1
| 2022-03-14T16:54:51
| 2022-03-17T17:49:40
| 2022-03-17T17:45:54
|
sashavor
|
[] |
Initial proposal for a CER metric card
cc @patrickvonplaten - wdyt this time around? :smile:
| true
|
1,168,579,694
|
https://api.github.com/repos/huggingface/datasets/issues/3910
|
https://github.com/huggingface/datasets/pull/3910
| 3,910
|
Fix text loader to split only on universal newlines
|
closed
| 6
| 2022-03-14T15:54:58
| 2022-03-15T16:16:11
| 2022-03-15T16:16:09
|
albertvillanova
|
[] |
Currently, `text` loader breaks on a superset of universal newlines, which also contains Unicode line boundaries. See: https://docs.python.org/3/library/stdtypes.html#str.splitlines
However, the expected behavior is to get the lines splitted only on universal newlines: "\n", "\r\n" and "\r".
See: oscar-corpus/corpus#18
Fix #3729.
| true
|
1,168,578,058
|
https://api.github.com/repos/huggingface/datasets/issues/3909
|
https://github.com/huggingface/datasets/issues/3909
| 3,909
|
Error loading file audio when downloading the Common Voice dataset directly from the Hub
|
closed
| 8
| 2022-03-14T15:53:50
| 2023-03-02T15:31:27
| 2023-03-02T15:31:26
|
aliceinland
|
[
"bug"
] |
## Describe the bug
When loading the Common_Voice dataset, by downloading it directly from the Hugging Face hub, some files can not be opened.
## Steps to reproduce the bug
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "it", split="test")
#test_dataset = load_dataset('csv', data_files = {'test': '/workspace/Dataset/Common_Voice/cv-corpus80/it/test.csv'})
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\β\'\οΏ½]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
```
## Expected results
The common voice dataset downloaded and correctly loaded whit the use of the hugging face datasets library.
## Actual results
The error is:
```python
0ex [00:00, ?ex/s]
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-48-ef87f4129e6e> in <module>
7 return batch
8
----> 9 test_dataset = test_dataset.map(speech_file_to_array_fn)
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
2107
2108 if num_proc is None or num_proc == 1:
-> 2109 return self._map_single(
2110 function=function,
2111 with_indices=with_indices,
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
516 self: "Dataset" = kwargs.pop("self")
517 # apply actual function
--> 518 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
519 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
520 for dataset in datasets:
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
483 }
484 # apply actual function
--> 485 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
486 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
487 # re-apply format to the output
/opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
411 # Call actual function
412
--> 413 out = func(self, *args, **kwargs)
414
415 # Update fingerprint of in-place transforms + update in-place history of transforms
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2465 if not batched:
2466 for i, example in enumerate(pbar):
-> 2467 example = apply_function_on_filtered_inputs(example, i, offset=offset)
2468 if update_data:
2469 if i == 0:
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)
2372 if with_rank:
2373 additional_args += (rank,)
-> 2374 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
2375 if update_data is None:
2376 # Check if the function returns updated examples
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in decorated(item, *args, **kwargs)
2067 )
2068 # Use the LazyDict internally, while mapping the function
-> 2069 result = f(decorated_item, *args, **kwargs)
2070 # Return a standard dict
2071 return result.data if isinstance(result, LazyDict) else result
<ipython-input-48-ef87f4129e6e> in speech_file_to_array_fn(batch)
3 def speech_file_to_array_fn(batch):
4 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
----> 5 speech_array, sampling_rate = torchaudio.load(batch["path"])
6 batch["speech"] = resampler(speech_array).squeeze().numpy()
7 return batch
/opt/conda/lib/python3.8/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)
150 filepath, frame_offset, num_frames, normalize, channels_first, format)
151 filepath = os.fspath(filepath)
--> 152 return torch.ops.torchaudio.sox_io_load_audio_file(
153 filepath, frame_offset, num_frames, normalize, channels_first, format)
154
RuntimeError: Error loading audio file: failed to open file common_voice_it_17415776.mp3 ```
## Environment info
- `datasets` version: 1.18.4
- Platform: Linux-5.4.0-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 7.0.0
| false
|
1,168,576,963
|
https://api.github.com/repos/huggingface/datasets/issues/3908
|
https://github.com/huggingface/datasets/pull/3908
| 3,908
|
Update README.md for SQuAD v2 metric
|
closed
| 1
| 2022-03-14T15:53:10
| 2022-03-15T17:04:11
| 2022-03-15T17:04:11
|
sashavor
|
[] |
Putting "Values from popular papers" as a subsection of "Output values"
| true
|
1,168,575,998
|
https://api.github.com/repos/huggingface/datasets/issues/3907
|
https://github.com/huggingface/datasets/pull/3907
| 3,907
|
Update README.md for SQuAD metric
|
closed
| 1
| 2022-03-14T15:52:31
| 2022-03-15T17:04:20
| 2022-03-15T17:04:19
|
sashavor
|
[] |
Putting "Values from popular papers" as a subsection of "Output values"
| true
|
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