id int64 599M 3.29B | url stringlengths 58 61 | html_url stringlengths 46 51 | number int64 1 7.72k | title stringlengths 1 290 | state stringclasses 2 values | comments int64 0 70 | 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 ⌀ | user_login stringlengths 3 26 | labels listlengths 0 4 | body stringlengths 0 228k ⌀ | is_pull_request bool 2 classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,310,253,552 | https://api.github.com/repos/huggingface/datasets/issues/4721 | https://github.com/huggingface/datasets/issues/4721 | 4,721 | PyArrow Dataset error when calling `load_dataset` | open | 3 | 2022-07-20T01:16:03 | 2022-07-22T14:11:47 | null | piraka9011 | [
"bug"
] | ## Describe the bug
I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error:
```sh
File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main
raw_datasets = load_dataset(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split
for key, table in logging.tqdm(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1309, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```
## Steps to reproduce the bug
I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`.
When creating the dataset, I do something like this:
```python
import json
from datasets import Dataset, Audio
# manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text
for line in manifest_lines:
line = line.strip()
if line:
line_dict = json.loads(line)
manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}")
manifest_dict["duration"].append(line_dict["duration"])
manifest_dict["transcription"].append(line_dict["text"])
# Create a HF dataset
dataset = Dataset.from_dict(manifest_dict).cast_column(
"audio", Audio(sampling_rate=16_000),
)
# From the docs for saving to disk
# https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk
def read_audio_file(example):
with open(example["audio"]["path"], "rb") as f:
return {"audio": {"bytes": f.read()}}
dataset = dataset.map(read_audio_file, num_proc=70)
dataset.save_to_disk(f"/audio-data/hf/{artifact_name}")
dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True)
```
Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace.
I am able to load the dataset fine if I use `load_from_disk()`.
## Expected results
`load_dataset()` should behave just like `load_from_disk()` and not cause any errors.
## Actual results
See above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I am using the `huggingface/transformers-pytorch-gpu:latest` image
- `datasets` version: 2.3.0
- Platform: Docker/Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 8.0.0
| false |
1,309,980,195 | https://api.github.com/repos/huggingface/datasets/issues/4720 | https://github.com/huggingface/datasets/issues/4720 | 4,720 | Dataset Viewer issue for shamikbose89/lancaster_newsbooks | closed | 4 | 2022-07-19T20:00:07 | 2022-09-08T16:47:21 | 2022-09-08T16:47:21 | shamikbose | [] | ### Link
https://huggingface.co/datasets/shamikbose89/lancaster_newsbooks
### Description
Status code: 400
Exception: ValueError
Message: Cannot seek streaming HTTP file
I am able to use the dataset loading script locally and it also runs when I'm using the one from the hub, but the viewer still doesn't load
### Owner
Yes | false |
1,309,854,492 | https://api.github.com/repos/huggingface/datasets/issues/4719 | https://github.com/huggingface/datasets/issues/4719 | 4,719 | Issue loading TheNoob3131/mosquito-data dataset | closed | 2 | 2022-07-19T17:47:37 | 2022-07-20T06:46:57 | 2022-07-20T06:46:02 | thenerd31 | [] | 
So my dataset is public in the Huggingface Hub, but when I try to load it using the load_dataset command, it shows that it is downloading the files, but throws a ValueError. When I went to my directory to see if the files were downloaded, the folder was blank.
Here is the error below:
ValueError Traceback (most recent call last)
Input In [8], in <cell line: 3>()
1 from datasets import load_dataset
----> 3 dataset = load_dataset("TheNoob3131/mosquito-data", split="train")
File ~\Anaconda3\lib\site-packages\datasets\load.py:1679, 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)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
Is the dataset in the wrong format or is there some security permission that I should enable? | false |
1,309,520,453 | https://api.github.com/repos/huggingface/datasets/issues/4718 | https://github.com/huggingface/datasets/pull/4718 | 4,718 | Make Extractor accept Path as input | closed | 1 | 2022-07-19T13:25:06 | 2022-07-22T13:42:27 | 2022-07-22T13:29:43 | albertvillanova | [] | This PR:
- Makes `Extractor` accept instance of `Path` as input
- Removes unnecessary castings of `Path` to `str` | true |
1,309,512,483 | https://api.github.com/repos/huggingface/datasets/issues/4717 | https://github.com/huggingface/datasets/issues/4717 | 4,717 | Dataset Viewer issue for LawalAfeez/englishreview-ds-mini | closed | 1 | 2022-07-19T13:19:39 | 2022-07-20T08:32:57 | 2022-07-20T08:32:57 | lawalAfeez820 | [
"dataset-viewer"
] | ### Link
_No response_
### Description
Unable to view the split data
### Owner
_No response_ | false |
1,309,455,838 | https://api.github.com/repos/huggingface/datasets/issues/4716 | https://github.com/huggingface/datasets/pull/4716 | 4,716 | Support "tags" yaml tag | closed | 3 | 2022-07-19T12:34:31 | 2022-07-20T13:44:50 | 2022-07-20T13:31:56 | lhoestq | [] | Added the "tags" YAML tag, so that users can specify data domain/topics keywords for dataset search | true |
1,309,405,980 | https://api.github.com/repos/huggingface/datasets/issues/4715 | https://github.com/huggingface/datasets/pull/4715 | 4,715 | Fix POS tags | closed | 2 | 2022-07-19T11:52:54 | 2022-07-19T12:54:34 | 2022-07-19T12:41:16 | lhoestq | [] | We're now using `part-of-speech` and not `part-of-speech-tagging`, see discussion here: https://github.com/huggingface/datasets/commit/114c09aff2fa1519597b46fbcd5a8e0c0d3ae020#r78794777 | true |
1,309,265,682 | https://api.github.com/repos/huggingface/datasets/issues/4714 | https://github.com/huggingface/datasets/pull/4714 | 4,714 | Fix named split sorting and remove unnecessary casting | closed | 3 | 2022-07-19T09:48:28 | 2022-07-22T09:39:45 | 2022-07-22T09:10:57 | albertvillanova | [] | This PR:
- makes `NamedSplit` sortable: so that `sorted()` can be called on them
- removes unnecessary `sorted()` on `dict.keys()`: `dict_keys` view is already like a `set`
- removes unnecessary casting of `NamedSplit` to `str` | true |
1,309,184,756 | https://api.github.com/repos/huggingface/datasets/issues/4713 | https://github.com/huggingface/datasets/pull/4713 | 4,713 | Document installation of sox OS dependency for audio | closed | 1 | 2022-07-19T08:42:35 | 2022-07-21T08:16:59 | 2022-07-21T08:04:15 | albertvillanova | [] | The `sox` OS package needs being installed manually using the distribution package manager.
This PR adds this explanation to the docs. | true |
1,309,177,302 | https://api.github.com/repos/huggingface/datasets/issues/4712 | https://github.com/huggingface/datasets/pull/4712 | 4,712 | Highlight non-commercial license in amazon_reviews_multi dataset card | closed | 1 | 2022-07-19T08:36:20 | 2022-07-27T16:09:40 | 2022-07-27T15:57:41 | sbroadhurst-hf | [] | Highlight that the licence granted by Amazon only covers non-commercial research use. | true |
1,309,138,570 | https://api.github.com/repos/huggingface/datasets/issues/4711 | https://github.com/huggingface/datasets/issues/4711 | 4,711 | Document how to create a dataset loading script for audio/vision | closed | 1 | 2022-07-19T08:03:40 | 2023-07-25T16:07:52 | 2023-07-25T16:07:52 | albertvillanova | [
"documentation"
] | Currently, in our docs for Audio/Vision/Text, we explain how to:
- Load data
- Process data
However we only explain how to *Create a dataset loading script* for text data.
I think it would be useful that we add the same for Audio/Vision as these have some specificities different from Text.
See, for example:
- #4697
- and comment there: https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492
CC: @stevhliu
| false |
1,308,958,525 | https://api.github.com/repos/huggingface/datasets/issues/4710 | https://github.com/huggingface/datasets/pull/4710 | 4,710 | Add object detection processing tutorial | closed | 3 | 2022-07-19T04:23:46 | 2022-07-21T20:10:35 | 2022-07-21T19:56:42 | nateraw | [] | The following adds a quick guide on how to process object detection datasets with `albumentations`. | true |
1,308,633,093 | https://api.github.com/repos/huggingface/datasets/issues/4709 | https://github.com/huggingface/datasets/issues/4709 | 4,709 | WMT21 & WMT22 | open | 7 | 2022-07-18T21:05:33 | 2023-06-20T09:02:11 | null | Muennighoff | [
"good first issue",
"dataset request"
] | ## Adding a Dataset
- **Name:** WMT21 & WMT22
- **Description:** We are going to have three tracks: two small tasks and a large task.
The small tracks evaluate translation between fairly related languages and English (all pairs). The large track uses 101 languages.
- **Paper:** /
- **Data:** https://statmt.org/wmt21/large-scale-multilingual-translation-task.html https://statmt.org/wmt22/large-scale-multilingual-translation-task.html
- **Motivation:** Many more languages than previous WMT versions - Could be very high impact
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
I could also tackle this. I saw the existing logic for WMT models is a bit complex (datasets are stored on the wmt account & retrieved in separate wmt datasets afaict). How long do you think it would take me? @lhoestq
| false |
1,308,279,700 | https://api.github.com/repos/huggingface/datasets/issues/4708 | https://github.com/huggingface/datasets/pull/4708 | 4,708 | Fix require torchaudio and refactor test requirements | closed | 1 | 2022-07-18T17:24:28 | 2022-07-22T06:30:56 | 2022-07-22T06:18:11 | albertvillanova | [] | Currently there is a bug in `require_torchaudio` (indeed it is requiring `sox` instead):
```python
def require_torchaudio(test_case):
if find_spec("sox") is None:
...
```
The bug was introduced by:
- #3685
- Commit: https://github.com/huggingface/datasets/pull/3685/commits/b5a3e7122d49c4dcc9333b1d8d18a833fc04b940
which moved
```python
require_sndfile = pytest.mark.skipif(
# In Windows and OS X, soundfile installs sndfile
(sys.platform != "linux" and find_spec("soundfile") is None)
# In Linux, soundfile throws RuntimeError if sndfile not installed with distribution package manager
or (sys.platform == "linux" and find_library("sndfile") is None),
reason="Test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)
require_sox = pytest.mark.skipif(
find_library("sox") is None,
reason="Test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`",
)
require_torchaudio = pytest.mark.skipif(find_spec("torchaudio") is None, reason="Test requires 'torchaudio'")
```
to
```python
def require_sndfile(test_case):
"""
Decorator marking a test that requires soundfile.
These tests are skipped when soundfile isn't installed.
"""
if (sys.platform != "linux" and find_spec("soundfile") is None) or (
sys.platform == "linux" and find_library("sndfile") is None
):
test_case = unittest.skip(
"test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)(test_case)
return test_case
def require_sox(test_case):
"""
Decorator marking a test that requires sox.
These tests are skipped when sox isn't installed.
"""
if find_library("sox") is None:
return unittest.skip("test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`")(
test_case
)
return test_case
def require_torchaudio(test_case):
"""
Decorator marking a test that requires torchaudio.
These tests are skipped when torchaudio isn't installed.
"""
if find_spec("sox") is None:
return unittest.skip("test requires 'torchaudio'")(test_case)
return test_case
```
This PR;
- fixes the bug in `require_torchaudio`
- refactors the test requirements back to using `pytest` instead of `unittest`
- the text in `pytest.skipif` `reason` can be used if needed in a test body: `require_torchaudio.kwargs["reason"]` | true |
1,308,251,405 | https://api.github.com/repos/huggingface/datasets/issues/4707 | https://github.com/huggingface/datasets/issues/4707 | 4,707 | Dataset Viewer issue for TheNoob3131/mosquito-data | closed | 6 | 2022-07-18T17:07:19 | 2022-07-18T19:44:46 | 2022-07-18T17:15:50 | thenerd31 | [
"dataset-viewer"
] | ### Link
_No response_
### Description
Getting this error when trying to view dataset preview:
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv')
### Owner
_No response_ | false |
1,308,198,454 | https://api.github.com/repos/huggingface/datasets/issues/4706 | https://github.com/huggingface/datasets/pull/4706 | 4,706 | Fix empty examples in xtreme dataset for bucc18 config | closed | 2 | 2022-07-18T16:22:46 | 2022-07-19T06:41:14 | 2022-07-19T06:29:17 | lhoestq | [] | As reported in https://huggingface.co/muibk, there are empty examples in xtreme/bucc18.de
I applied your fix @mustaszewski
I also used a dict to make the dataset generation much faster | true |
1,308,161,794 | https://api.github.com/repos/huggingface/datasets/issues/4705 | https://github.com/huggingface/datasets/pull/4705 | 4,705 | Fix crd3 | closed | 1 | 2022-07-18T15:53:44 | 2022-07-21T17:18:44 | 2022-07-21T17:06:30 | lhoestq | [] | As reported in https://huggingface.co/datasets/crd3/discussions/1#62cc377073b2512b81662794, each split of the dataset was containing the same data. This issues comes from a bug in the dataset script
I fixed it and also uploaded the data to hf.co to make the dataset work in streaming mode | true |
1,308,147,876 | https://api.github.com/repos/huggingface/datasets/issues/4704 | https://github.com/huggingface/datasets/pull/4704 | 4,704 | Skip tests only for lz4/zstd params if not installed | closed | 1 | 2022-07-18T15:41:40 | 2022-07-19T13:02:31 | 2022-07-19T12:49:18 | albertvillanova | [] | Currently, if `zstandard` or `lz4` are not installed, `test_compression_filesystems` and `test_streaming_dl_manager_extract_all_supported_single_file_compression_types` are skipped for all compression format parameters.
This PR fixes these tests, so that if `zstandard` or `lz4` are not installed, the tests are skipped only for the corresponding compression parameters (`zstd` or `lz4`), whereas the tests are not skipped for all the other compression parameters (`gzip`, `xz` and `bz2`).
Related to:
- #4688 | true |
1,307,844,097 | https://api.github.com/repos/huggingface/datasets/issues/4703 | https://github.com/huggingface/datasets/pull/4703 | 4,703 | Make cast in `from_pandas` more robust | closed | 1 | 2022-07-18T11:55:49 | 2022-07-22T11:17:42 | 2022-07-22T11:05:24 | mariosasko | [] | Make the cast in `from_pandas` more robust (as it was done for the packaged modules in https://github.com/huggingface/datasets/pull/4364)
This should be useful in situations like [this one](https://discuss.huggingface.co/t/loading-custom-audio-dataset-and-fine-tuning-model/8836/4). | true |
1,307,793,811 | https://api.github.com/repos/huggingface/datasets/issues/4702 | https://github.com/huggingface/datasets/issues/4702 | 4,702 | Domain specific dataset discovery on the Hugging Face hub | open | 11 | 2022-07-18T11:14:03 | 2024-02-12T09:53:43 | null | davanstrien | [
"enhancement"
] | **Is your feature request related to a problem? Please describe.**
## The problem
The datasets hub currently has `8,239` datasets. These datasets span a wide range of different modalities and tasks (currently with a bias towards textual data).
There are various ways of identifying datasets that may be relevant for a particular use case:
- searching
- various filters
Currently, however, there isn't an easy way to identify datasets belonging to a specific domain. For example, I want to browse machine learning datasets related to 'social science' or 'climate change research'.
The ability to identify datasets relating to a specific domain has come up in discussions around the [BigLA](https://github.com/bigscience-workshop/lam/) datasets hackathon https://github.com/bigscience-workshop/lam/discussions/31#discussioncomment-3123610. As part of the hackathon, we're currently collecting datasets related to Libraries, Archives and Museums and making them available via the hub. We currently do this under a Hugging Face organization (https://huggingface.co/biglam). However, going forward, I can see some of these datasets being migrated to sit under an organization that is the custodian of the dataset (for example, a national library the data was originally from). At this point, it becomes more difficult to quickly identify datasets from this domain without relying on search.
This is also related to some existing issues on Github related to metadata on the hub:
- https://github.com/huggingface/datasets/issues/3625
- https://github.com/huggingface/datasets/issues/3877
**Describe the solution you'd like**
### Some possible solutions that may help with this:
#### Enable domain tags (from a controlled vocabulary)
- This would add metadata field to the YAML for the domain a dataset relates to
- Advantages:
- the list is controlled, allowing it to be more easily integrated into the datasets tag app (https://huggingface.co/space/huggingface/datasets-tagging)
- the controlled vocabulary could align with an existing controlled vocabulary
- this additional metadata can be used to perform filtering by domain
- disadvantages
- choosing the best controlled vocab may be difficult
- there are many datasets that are likely to fit into the 'machine learning' domain (i.e. there is a long tail of datasets that aren't in more 'generic' machine learning domain
#### Enable topic tags (user-generated)
Enable 'free form' topic tags for datasets and models. This would be closer to GitHub's repository topics which can be chosen from a controlled list (https://github.com/topics/) but can also be more user/org specific. This could potentially be useful for organizations to also manage their own models and datasets as the number they hold in their org grows. For example, they may create 'topic tags' for a specific project, so it's clearer which datasets /models are related to that project.
#### Collections
This solution would likely be the biggest shift and may require significant changes in the hub fronted. Collections could work in several different ways but would include:
Users can curate particular datasets, models, spaces, etc., into a collection. For example, they may create a collection of 'historic newspapers suitable for training language models'. These collections would not be mutually exclusive, i.e. a dataset can belong to zero, one or many collections. Collections can also potentially be nested under other collections.
This is fairly common on other data reposotiores for example the following collections:
<img width="293" alt="Screenshot 2022-07-18 at 11 50 44" src="https://user-images.githubusercontent.com/8995957/179496445-963ed122-5e26-4574-96e8-41081bce3e2b.png">
all belong under a higher level collection (https://bl.iro.bl.uk/collections/353c908d-b495-4413-b047-87236d2573e3?locale=en).
There are different models one could use for how these collections could be created:
- only within an org
- for any dataset/model
- the owner or a dataset/model has to agree to be added to a collection
- a collection owner can have people suggest additions to their collection
- other models....
These collections could be thematic, related to particular training approaches, curate models with particular inference properties etc. Whilst some of these features may duplicate current/or future tag filters on the hub, they offer the advantage of being flexible and not having to predict what users will want to do upfront.
There is also potential for automating the creation of these collections based on existing metadata. For example, one could collect models trained on a collection of datasets so for example, if we had a collection of 'historic newspapers suitable for training language models' that contained 30 datasets, we could create another collection 'historic newspaper language models' that takes any model on the hub whose metadata says it used one or more of those 30 datasets.
There is also the option of exploring ML approaches to suggest models/datasets may be relevant to a particular collection.
This approach is likely to be quite difficult to implement well and would require significant thought. There is also likely to be a benefit in doing quite a bit of upfront work in curating useful collections to demonstrate the benefits of collections.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
It is possible to collate this information externally, i.e. one could link back to the relevant models/datasets from an external platform.
**Additional context**
Add any other context about the feature request here.
I'm cc'ing others involved in the BigLAM hackathon who may also have thoughts @cakiki @clancyoftheoverflow @albertvillanova | false |
1,307,689,625 | https://api.github.com/repos/huggingface/datasets/issues/4701 | https://github.com/huggingface/datasets/pull/4701 | 4,701 | Added more information in the README about contributors of the Arabic Speech Corpus | closed | 0 | 2022-07-18T09:48:03 | 2022-07-28T10:33:05 | 2022-07-28T10:33:05 | nawarhalabi | [] | Added more information in the README about contributors and encouraged reading the thesis for more infos | true |
1,307,599,161 | https://api.github.com/repos/huggingface/datasets/issues/4700 | https://github.com/huggingface/datasets/pull/4700 | 4,700 | Support extract lz4 compressed data files | closed | 1 | 2022-07-18T08:41:31 | 2022-07-18T14:43:59 | 2022-07-18T14:31:47 | albertvillanova | [] | null | true |
1,307,555,592 | https://api.github.com/repos/huggingface/datasets/issues/4699 | https://github.com/huggingface/datasets/pull/4699 | 4,699 | Fix Authentification Error while streaming | closed | 1 | 2022-07-18T08:03:41 | 2022-07-20T13:10:44 | 2022-07-20T13:10:43 | hkjeon13 | [] | I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub.
```
from datasets import load_dataset
dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True)
for d in dataset['train']:
print(d)
break # this is for checking
```
This code is an example for streaming private datasets.
when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this,
```
/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)
1007 status=self.status,
1008 message=self.reason,
→ 1009 headers=self.headers,
1010 )
1011
ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet')
```
(this is an example on the dataset has `parquet` extenstion)
It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~".
so I add three lines.
With this change, there is no error anymore(but this code is ad-hoc). | true |
1,307,539,585 | https://api.github.com/repos/huggingface/datasets/issues/4698 | https://github.com/huggingface/datasets/pull/4698 | 4,698 | Enable streaming dataset to use the "all" split | closed | 9 | 2022-07-18T07:47:39 | 2025-05-21T13:17:19 | 2025-05-21T13:17:19 | cakiki | [] | Fixes #4637 | true |
1,307,332,253 | https://api.github.com/repos/huggingface/datasets/issues/4697 | https://github.com/huggingface/datasets/issues/4697 | 4,697 | Trouble with streaming frgfm/imagenette vision dataset with TAR archive | closed | 5 | 2022-07-18T02:51:09 | 2022-08-01T15:10:57 | 2022-08-01T15:10:57 | frgfm | [
"streaming"
] | ### Link
https://huggingface.co/datasets/frgfm/imagenette
### Description
Hello there :wave:
Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile:
Basically, I get a:
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far!
Could you point me in the right direction please? :pray:
### Owner
Yes | false |
1,307,183,099 | https://api.github.com/repos/huggingface/datasets/issues/4696 | https://github.com/huggingface/datasets/issues/4696 | 4,696 | Cannot load LinCE dataset | closed | 2 | 2022-07-17T19:01:54 | 2022-07-18T09:20:40 | 2022-07-18T07:24:22 | finiteautomata | [
"bug"
] | ## Describe the bug
Cannot load LinCE dataset due to a connection error
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("lince", "ner_spaeng")
```
A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2
## Expected results
It should load the dataset
## Actual results
```python
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-2-fc551ddcebef> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("lince", "ner_spaeng")
10 frames
/usr/local/lib/python3.7/dist-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)
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
-> 1684 use_auth_token=use_auth_token,
1685 )
1686
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
703 if not downloaded_from_gcs:
704 self._download_and_prepare(
--> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1219
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1222
1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
772
773 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager)
481 def _split_generators(self, dl_manager):
482 """Returns SplitGenerators."""
--> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip")
484 data_dir = os.path.join(lince_dir, self.config.data_dir)
485 return [
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
432
433 def get_recorded_sizes_checksums(self):
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls)
313 num_proc=download_config.num_proc,
314 disable_tqdm=not is_progress_bar_enabled(),
--> 315 desc="Downloading data files",
316 )
317 duration = datetime.now() - start_time
/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
346 # Singleton
347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 348 return function(data_struct)
349
350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled()
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config)
333 # append the relative path to the base_path
334 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 335 return cached_path(url_or_filename, download_config=download_config)
336
337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
195 use_auth_token=download_config.use_auth_token,
196 ignore_url_params=download_config.ignore_url_params,
--> 197 download_desc=download_config.download_desc,
198 )
199 elif os.path.exists(url_or_filename):
/usr/local/lib/python3.7/dist-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)
531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
532 if head_error is not None:
--> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
534 elif response is not None:
535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
| false |
1,307,134,701 | https://api.github.com/repos/huggingface/datasets/issues/4695 | https://github.com/huggingface/datasets/pull/4695 | 4,695 | Add MANtIS dataset | closed | 2 | 2022-07-17T15:53:05 | 2022-09-30T14:39:30 | 2022-09-30T14:37:16 | bhavitvyamalik | [
"dataset contribution"
] | This PR adds MANtIS dataset.
Arxiv: [https://arxiv.org/abs/1912.04639](https://arxiv.org/abs/1912.04639)
Github: [https://github.com/Guzpenha/MANtIS](https://github.com/Guzpenha/MANtIS)
README and dataset tags are WIP. | true |
1,306,958,380 | https://api.github.com/repos/huggingface/datasets/issues/4694 | https://github.com/huggingface/datasets/issues/4694 | 4,694 | Distributed data parallel training for streaming datasets | open | 6 | 2022-07-17T01:29:43 | 2023-04-26T18:21:09 | null | cyk1337 | [
"enhancement"
] | ### Feature request
Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training?
### Motivation
Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation?
### Your contribution
Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do? | false |
1,306,788,322 | https://api.github.com/repos/huggingface/datasets/issues/4693 | https://github.com/huggingface/datasets/pull/4693 | 4,693 | update `samsum` script | closed | 2 | 2022-07-16T11:53:05 | 2022-09-23T11:40:11 | 2022-09-23T11:37:57 | bhavitvyamalik | [
"dataset contribution"
] | update `samsum` script after #4672 was merged (citation is also updated) | true |
1,306,609,680 | https://api.github.com/repos/huggingface/datasets/issues/4692 | https://github.com/huggingface/datasets/issues/4692 | 4,692 | Unable to cast a column with `Image()` by using the `cast_column()` feature | closed | 1 | 2022-07-15T22:56:03 | 2022-07-19T13:36:24 | 2022-07-19T13:36:24 | skrishnan99 | [
"bug"
] | ## Describe the bug
A clear and concise description of what the bug is.
When I create a dataset, then add a column to the created dataset through the `dataset.add_column` feature and then try to cast a column of the dataset (this column contains image paths) with `Image()` by using the `cast_column()` feature, I get the following error - ``` TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ```
When I try and cast the same column, but without doing the `add_column` in the previous step, it works as expected.
## Steps to reproduce the bug
```python
from datasets import Dataset, Image
data_dict = {
"img_path": ["https://picsum.photos/200/300"]
}
dataset = Dataset.from_dict(data_dict)
#NOTE Comment out this line and use cast_column and it works properly
dataset = dataset.add_column("yeet", [1])
#NOTE This line fails to execute properly if `add_column` is called before
dataset = dataset.cast_column("img_path", Image())
# #NOTE This is my current workaround. This seems to work fine with/without `add_column`. While
# # running this, make sure to comment out the `cast_column` line
# new_features = dataset.features.copy()
# new_features["img_path"] = Image()
# dataset = dataset.cast(new_features)
print(dataset)
print(dataset.features)
print(dataset[0])
```
## Expected results
A clear and concise description of the expected results.
Able to successfully use `cast_column` to cast a column containing img_paths to now be Image() features after modifying the dataset using `add_column` in a previous step
## Actual results
Specify the actual results or traceback.
```
Traceback (most recent call last):
File "/home/surya/Desktop/hf_bug_test.py", line 14, in <module>
dataset = dataset.cast_column("img_path", Image())
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1580, in cast_column
dataset._data = dataset._data.cast(dataset.features.arrow_schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1487, in cast
new_tables.append(subtable.cast(subschema, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 834, in cast
return InMemoryTable(table_cast(self.table, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1897, in table_cast
return cast_table_to_schema(table, schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1846, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.9.7
- PyArrow version: 7.0.0
| false |
1,306,389,656 | https://api.github.com/repos/huggingface/datasets/issues/4691 | https://github.com/huggingface/datasets/issues/4691 | 4,691 | Dataset Viewer issue for rajistics/indian_food_images | closed | 1 | 2022-07-15T19:03:15 | 2022-07-18T15:02:03 | 2022-07-18T15:02:03 | rajshah4 | [
"dataset-viewer"
] | ### Link
https://huggingface.co/datasets/rajistics/indian_food_images/viewer/rajistics--indian_food_images/train
### Description
I have a train/test split in my dataset
<img width="410" alt="Screen Shot 2022-07-15 at 11 44 42 AM" src="https://user-images.githubusercontent.com/6808012/179293215-7b419ec3-3527-46f2-8dad-adbc5568cfa0.png">
t
The dataset viewer works for the test split (images of indian food), but does not show my train split. My guess is maybe there is some corrupt image file that is guessing this. But I have no idea.
The original dataset was pulled from here: https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification?resource=download-directory
### Owner
Yes | false |
1,306,321,975 | https://api.github.com/repos/huggingface/datasets/issues/4690 | https://github.com/huggingface/datasets/pull/4690 | 4,690 | Refactor base extractors | closed | 1 | 2022-07-15T17:47:48 | 2022-07-18T08:46:56 | 2022-07-18T08:34:49 | albertvillanova | [] | This PR:
- Refactors base extractors as subclasses of `BaseExtractor`:
- this is an abstract class defining the interface with:
- `is_extractable`: abstract class method
- `extract`: abstract static method
- Implements abstract `MagicNumberBaseExtractor` (as subclass of `BaseExtractor`):
- this has a default implementation of `is_extractable`
- this improves performance (reducing the number of file reads) by allowing passing already read `magic_number`
- Refactors `Extractor`:
- reads magic number from file only once
This PR deprecates:
```python
is_extractable, extractor = self.extractor.is_extractable(input_path, return_extractor=True)
self.extractor.extract(input_path, output_path, extractor=extractor)
```
and uses more Pythonic instead:
```python
extractor_format = self.extractor.infer_extractor_format(input_path)
self.extractor.extract(input_path, output_path, extractor_format)
``` | true |
1,306,230,203 | https://api.github.com/repos/huggingface/datasets/issues/4689 | https://github.com/huggingface/datasets/pull/4689 | 4,689 | Test extractors for all compression formats | closed | 1 | 2022-07-15T16:29:55 | 2022-07-15T17:47:02 | 2022-07-15T17:35:24 | albertvillanova | [] | This PR:
- Adds all compression formats to `test_extractor`
- Tests each base extractor for all compression formats
Note that all compression formats are tested except "rar". | true |
1,306,100,488 | https://api.github.com/repos/huggingface/datasets/issues/4688 | https://github.com/huggingface/datasets/pull/4688 | 4,688 | Skip test_extractor only for zstd param if zstandard not installed | closed | 1 | 2022-07-15T14:23:47 | 2022-07-15T15:27:53 | 2022-07-15T15:15:24 | albertvillanova | [] | Currently, if `zstandard` is not installed, `test_extractor` is skipped for all compression format parameters.
This PR fixes `test_extractor` so that if `zstandard` is not installed, `test_extractor` is skipped only for the `zstd` compression parameter, that is, it is not skipped for all the other compression parameters (`gzip`, `xz`,...). | true |
1,306,021,415 | https://api.github.com/repos/huggingface/datasets/issues/4687 | https://github.com/huggingface/datasets/pull/4687 | 4,687 | Trigger CI also on push to main | closed | 1 | 2022-07-15T13:11:29 | 2022-07-15T13:47:21 | 2022-07-15T13:35:23 | albertvillanova | [] | Currently, new CI (on GitHub Actions) is only triggered on pull requests branches when the base branch is main.
This PR also triggers the CI when a PR is merged to main branch. | true |
1,305,974,924 | https://api.github.com/repos/huggingface/datasets/issues/4686 | https://github.com/huggingface/datasets/pull/4686 | 4,686 | Align logging with Transformers (again) | closed | 2 | 2022-07-15T12:24:29 | 2023-09-24T10:05:34 | 2023-07-11T18:29:27 | mariosasko | [] | Fix #2832 | true |
1,305,861,708 | https://api.github.com/repos/huggingface/datasets/issues/4685 | https://github.com/huggingface/datasets/pull/4685 | 4,685 | Fix mock fsspec | closed | 1 | 2022-07-15T10:23:12 | 2022-07-15T13:05:03 | 2022-07-15T12:52:40 | albertvillanova | [] | This PR:
- Removes an unused method from `DummyTestFS`
- Refactors `mock_fsspec` to make it simpler | true |
1,305,554,654 | https://api.github.com/repos/huggingface/datasets/issues/4684 | https://github.com/huggingface/datasets/issues/4684 | 4,684 | How to assign new values to Dataset? | closed | 2 | 2022-07-15T04:17:57 | 2023-03-20T15:50:41 | 2022-10-10T11:53:38 | beyondguo | [
"enhancement"
] | 
Hi, if I want to change some values of the dataset, or add new columns to it, how can I do it?
For example, I want to change all the labels of the SST2 dataset to `0`:
```python
from datasets import load_dataset
data = load_dataset('glue','sst2')
data['train']['label'] = [0]*len(data)
```
I will get the error:
```
TypeError: 'Dataset' object does not support item assignment
``` | false |
1,305,443,253 | https://api.github.com/repos/huggingface/datasets/issues/4683 | https://github.com/huggingface/datasets/pull/4683 | 4,683 | Update create dataset card docs | closed | 1 | 2022-07-15T00:41:29 | 2022-07-18T17:26:00 | 2022-07-18T13:24:10 | stevhliu | [
"documentation"
] | This PR proposes removing the [online dataset card creator](https://huggingface.co/datasets/card-creator/) in favor of simply copy/pasting a template and using the [Datasets Tagger app](https://huggingface.co/spaces/huggingface/datasets-tagging) to generate the tags. The Tagger app provides more guidance by showing all possible values a user can select in the dropdown menus, whereas the online dataset card creator doesn't, which can make it difficult to know what tag values to input.
Let me know what you think! :) | true |
1,304,788,215 | https://api.github.com/repos/huggingface/datasets/issues/4682 | https://github.com/huggingface/datasets/issues/4682 | 4,682 | weird issue/bug with columns (dataset iterable/stream mode) | open | 0 | 2022-07-14T13:26:47 | 2022-07-14T13:26:47 | null | eunseojo | [] | I have a dataset online (CloverSearch/cc-news-mutlilingual) that has a bunch of columns, two of which are "score_title_maintext" and "score_title_description". the original files are jsonl formatted. I was trying to iterate through via streaming mode and grab all "score_title_description" values, but I kept getting key not found after a certain point of iteration. I found that some json objects in the file don't have "score_title_description". And in SOME cases, this returns a NONE and in others it just gets a key error. Why is there an inconsistency here and how can I fix it? | false |
1,304,617,484 | https://api.github.com/repos/huggingface/datasets/issues/4681 | https://github.com/huggingface/datasets/issues/4681 | 4,681 | IndexError when loading ImageFolder | closed | 2 | 2022-07-14T10:57:55 | 2022-07-25T12:37:54 | 2022-07-25T12:37:54 | johko | [
"bug"
] | ## Describe the bug
Loading an image dataset with `imagefolder` throws `IndexError: list index out of range` when the given folder contains a non-image file (like a csv).
## Steps to reproduce the bug
Put a csv file in a folder with images and load it:
```python
import datasets
datasets.load_dataset("imagefolder", data_dir=path/to/folder)
```
## Expected results
I would expect a better error message, like `Unsupported file` or even the dataset loader just ignoring every file that is not an image in that case.
## Actual results
Here is the whole traceback:
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.11.0-051100-generic-x86_64-with-glibc2.27
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| false |
1,304,534,770 | https://api.github.com/repos/huggingface/datasets/issues/4680 | https://github.com/huggingface/datasets/issues/4680 | 4,680 | Dataset Viewer issue for codeparrot/xlcost-text-to-code | closed | 5 | 2022-07-14T09:45:50 | 2022-07-18T16:37:00 | 2022-07-18T16:04:36 | loubnabnl | [] | ### Link
https://huggingface.co/datasets/codeparrot/xlcost-text-to-code
### Description
Error
```
Server Error
Status code: 400
Exception: TypeError
Message: 'NoneType' object is not iterable
```
Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully.
Thanks!
### Owner
Yes | false |
1,303,980,648 | https://api.github.com/repos/huggingface/datasets/issues/4679 | https://github.com/huggingface/datasets/pull/4679 | 4,679 | Added method to remove excess nesting in a DatasetDict | closed | 11 | 2022-07-13T21:49:37 | 2022-07-21T15:55:26 | 2022-07-21T10:55:02 | CakeCrusher | [] | Added the ability for a DatasetDict to remove additional nested layers within its features to avoid conflicts when collating. It is meant to accompany [this PR](https://github.com/huggingface/transformers/pull/18119) to resolve the same issue [#15505](https://github.com/huggingface/transformers/issues/15505).
@stas00 @lhoestq | true |
1,303,741,432 | https://api.github.com/repos/huggingface/datasets/issues/4678 | https://github.com/huggingface/datasets/issues/4678 | 4,678 | Cant pass streaming dataset to dataloader after take() | open | 1 | 2022-07-13T17:34:18 | 2022-07-14T13:07:21 | null | zankner | [
"bug"
] | ## Describe the bug
I am trying to pass a streaming version of c4 to a dataloader, but it can't be passed after I call `dataset.take(n)`. Some functions such as `shuffle()` can be applied without breaking the dataloader but not take.
## Steps to reproduce the bug
```python
import datasets
import torch
dset = datasets.load_dataset(path='c4', name='en', split="train", streaming=True)
dset = dset.take(50_000)
dset = dset.with_format("torch")
num_workers = 8
batch_size = 512
loader = torch.utils.data.DataLoader(dataset=dset,
batch_size=batch_size,
num_workers=num_workers)
for batch in loader:
...
```
## Expected results
No error thrown when iterating over the dataloader
## Actual results
Original Traceback (most recent call last):
File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/root/.local/lib/python3.9/site-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py", line 48, in __iter__
for key, example in self._iter_shard(shard_idx):
File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 586, in _iter_shard
yield from ex_iterable.shard_data_sources(shard_idx)
File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 60, in shard_data_sources
raise NotImplementedError(f"{type(self)} doesn't implement shard_data_sources yet")
NotImplementedError: <class 'datasets.iterable_dataset.TakeExamplesIterable'> doesn't implement shard_data_sources yet
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.31
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| false |
1,302,258,440 | https://api.github.com/repos/huggingface/datasets/issues/4677 | https://github.com/huggingface/datasets/issues/4677 | 4,677 | Random 400 Client Error when pushing dataset | closed | 2 | 2022-07-12T15:56:44 | 2023-02-07T13:54:10 | 2023-02-07T13:54:10 | msis | [
"bug"
] | ## Describe the bug
When pushing a dataset, the client errors randomly with `Bad Request for url:...`.
At the next call, a new parquet file is created for each shard.
The client may fail at any random shard.
## Steps to reproduce the bug
```python
dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
```
## Expected results
Push all the dataset to the Hub with no duplicates.
If it fails, it should retry or fail, but continue from the last failed shard.
## Actual results
```
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
testing.ipynb Cell 29 in <cell line: 1>()
----> [1](testing.ipynb?line=0) dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4297, in Dataset.push_to_hub(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files)
4291 warnings.warn(
4292 "'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0.",
4293 FutureWarning,
4294 )
4295 max_shard_size = shard_size
-> 4297 repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub(
4298 repo_id=repo_id,
4299 split=split,
4300 private=private,
4301 token=token,
4302 branch=branch,
4303 max_shard_size=max_shard_size,
4304 embed_external_files=embed_external_files,
4305 )
4306 organization, dataset_name = repo_id.split("/")
4307 info_to_dump = self.info.copy()
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4195, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files)
4193 shard.to_parquet(buffer)
4194 uploaded_size += buffer.tell()
-> 4195 _retry(
4196 api.upload_file,
4197 func_kwargs=dict(
4198 path_or_fileobj=buffer.getvalue(),
4199 path_in_repo=shard_path_in_repo,
4200 repo_id=repo_id,
4201 token=token,
4202 repo_type="dataset",
4203 revision=branch,
4204 identical_ok=False,
4205 ),
4206 exceptions=HTTPError,
4207 status_codes=[504],
4208 base_wait_time=2.0,
4209 max_retries=5,
4210 max_wait_time=20.0,
4211 )
4212 shards_path_in_repo.append(shard_path_in_repo)
4214 # Cleanup to remove unused files
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:284, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
--> 284 raise err
285 else:
286 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:281, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
279 while True:
280 try:
--> 281 return func(*func_args, **func_kwargs)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1967, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, identical_ok, commit_message, commit_description, create_pr)
1957 commit_message = (
1958 commit_message
1959 if commit_message is not None
1960 else f"Upload {path_in_repo} with huggingface_hub"
1961 )
1962 operation = CommitOperationAdd(
1963 path_or_fileobj=path_or_fileobj,
1964 path_in_repo=path_in_repo,
1965 )
-> 1967 pr_url = self.create_commit(
1968 repo_id=repo_id,
1969 repo_type=repo_type,
1970 operations=[operation],
1971 commit_message=commit_message,
1972 commit_description=commit_description,
1973 token=token,
1974 revision=revision,
1975 create_pr=create_pr,
1976 )
1977 if pr_url is not None:
1978 re_match = re.match(REGEX_DISCUSSION_URL, pr_url)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1844, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads)
1836 commit_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/commit/{revision}"
1838 commit_resp = requests.post(
1839 url=commit_url,
1840 headers={"Authorization": f"Bearer {token}"},
1841 json=commit_payload,
1842 params={"create_pr": 1} if create_pr else None,
1843 )
-> 1844 _raise_for_status(commit_resp)
1845 return commit_resp.json().get("pullRequestUrl", None)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:84, in _raise_for_status(request)
76 if request.status_code == 401:
77 # The repo was not found and the user is not Authenticated
78 raise RepositoryNotFoundError(
79 f"401 Client Error: Repository Not Found for url: {request.url}. If the"
80 " repo is private, make sure you are authenticated. (Request ID:"
81 f" {request_id})"
82 )
---> 84 _raise_with_request_id(request)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:95, in _raise_with_request_id(request)
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
93 e.args = (e.args[0] + f" (Request ID: {request_id})",) + e.args[1:]
---> 95 raise e
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:90, in _raise_with_request_id(request)
88 request_id = request.headers.get("X-Request-Id")
89 try:
---> 90 request.raise_for_status()
91 except Exception as e:
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
File ~/.local/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1016 http_error_msg = (
1017 f"{self.status_code} Server Error: {reason} for url: {self.url}"
1018 )
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/ORG/DATASET/commit/main (Request ID: a_F0IQAHJdxGKVRYyu1cF)
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.31
- Python version: 3.9.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| false |
1,302,202,028 | https://api.github.com/repos/huggingface/datasets/issues/4676 | https://github.com/huggingface/datasets/issues/4676 | 4,676 | Dataset.map gets stuck on _cast_to_python_objects | closed | 9 | 2022-07-12T15:09:58 | 2022-10-03T13:01:04 | 2022-10-03T13:01:03 | srobertjames | [
"bug",
"good first issue"
] | ## Describe the bug
`Dataset.map`, when fed a Huggingface Tokenizer as its map func, can sometimes spend huge amounts of time doing casts. A minimal example follows.
Not all usages suffer from this. For example, I profiled the preprocessor at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb , and it did _not_ have this problem. However, I'm at a loss to figure out how it avoids it, as the example below is simple and minimal and still has this problem.
This casting, where it occurs, causes the `Dataset.map` to run approximately 7x slower than it runs for code which does not cause this casting.
This may be related to https://github.com/huggingface/datasets/issues/1046 . However, the tokenizer is _not_ set to return Tensors.
## Steps to reproduce the bug
A minimal, self-contained example to reproduce is below:
```python
import transformers
from transformers import AutoTokenizer
from datasets import load_dataset
import torch
import cProfile
pretrained = 'distilbert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(pretrained)
squad = load_dataset('squad')
squad_train = squad['train']
squad_tiny = squad_train.select(range(5000))
assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast)
def tokenize(ds):
tokens = tokenizer(text=ds['question'],
text_pair=ds['context'],
add_special_tokens=True,
padding='max_length',
truncation='only_second',
max_length=160,
stride=32,
return_overflowing_tokens=True,
return_offsets_mapping=True,
)
return tokens
cmd = 'squad_tiny.map(tokenize, batched=True, remove_columns=squad_tiny.column_names)'
cProfile.run(cmd, sort='tottime')
```
## Actual results
The code works, but takes 10-25 sec per batch (about 7x slower than non-casting code), with the following profile. Note that `_cast_to_python_objects` is the culprit.
```
63524075 function calls (58206482 primitive calls) in 121.836 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
5274034/40 68.751 0.000 111.060 2.776 features.py:262(_cast_to_python_objects)
42223832 24.077 0.000 33.310 0.000 {built-in method builtins.isinstance}
16338/20 5.121 0.000 111.053 5.553 features.py:361(<listcomp>)
5274135 4.747 0.000 4.749 0.000 {built-in method _abc._abc_instancecheck}
80/40 4.731 0.059 116.292 2.907 {pyarrow.lib.array}
5274135 4.485 0.000 9.234 0.000 abc.py:96(__instancecheck__)
2661564/2645196 2.959 0.000 4.298 0.000 features.py:1081(_check_non_null_non_empty_recursive)
5 2.786 0.557 2.786 0.557 {method 'encode_batch' of 'tokenizers.Tokenizer' objects}
2668052 0.930 0.000 0.930 0.000 {built-in method builtins.len}
5000 0.930 0.000 0.938 0.000 tokenization_utils_fast.py:187(_convert_encoding)
5 0.750 0.150 0.808 0.162 {method 'to_pydict' of 'pyarrow.lib.Table' objects}
1 0.444 0.444 121.749 121.749 arrow_dataset.py:2501(_map_single)
40 0.375 0.009 116.291 2.907 arrow_writer.py:151(__arrow_array__)
10 0.066 0.007 0.066 0.007 {method 'write_batch' of 'pyarrow.lib._CRecordBatchWriter' objects}
1 0.060 0.060 121.835 121.835 fingerprint.py:409(wrapper)
11387/5715 0.049 0.000 0.175 0.000 {built-in method builtins.getattr}
36 0.049 0.001 0.049 0.001 {pyarrow._compute.call_function}
15000 0.040 0.000 0.040 0.000 _collections_abc.py:719(__iter__)
3 0.023 0.008 0.023 0.008 {built-in method _imp.create_dynamic}
77 0.020 0.000 0.020 0.000 {built-in method builtins.dir}
37 0.019 0.001 0.019 0.001 socket.py:543(send)
15 0.017 0.001 0.017 0.001 tokenization_utils_fast.py:460(<listcomp>)
432/421 0.015 0.000 0.024 0.000 traitlets.py:1388(_notify_observers)
5000 0.015 0.000 0.018 0.000 _collections_abc.py:672(keys)
51 0.014 0.000 0.042 0.001 traitlets.py:276(getmembers)
5 0.014 0.003 3.775 0.755 tokenization_utils_fast.py:392(_batch_encode_plus)
3/1 0.014 0.005 0.035 0.035 {built-in method _imp.exec_dynamic}
5 0.012 0.002 0.950 0.190 tokenization_utils_fast.py:438(<listcomp>)
31626 0.012 0.000 0.012 0.000 {method 'append' of 'list' objects}
1532/1001 0.011 0.000 0.189 0.000 traitlets.py:643(get)
5 0.009 0.002 3.796 0.759 arrow_dataset.py:2631(apply_function_on_filtered_inputs)
51 0.009 0.000 0.062 0.001 traitlets.py:1766(traits)
5 0.008 0.002 3.784 0.757 tokenization_utils_base.py:2632(batch_encode_plus)
368 0.007 0.000 0.044 0.000 traitlets.py:1715(_get_trait_default_generator)
26 0.007 0.000 0.022 0.001 traitlets.py:1186(setup_instance)
51 0.006 0.000 0.010 0.000 traitlets.py:1781(<listcomp>)
80/32 0.006 0.000 0.052 0.002 table.py:1758(cast_array_to_feature)
684 0.006 0.000 0.007 0.000 {method 'items' of 'dict' objects}
4344/1794 0.006 0.000 0.192 0.000 traitlets.py:675(__get__)
...
```
## Environment info
I observed this on both Google colab and my local workstation:
### Google colab
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
### Local
- `datasets` version: 2.3.2
- Platform: Windows-7-6.1.7601-SP1
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| false |
1,302,193,649 | https://api.github.com/repos/huggingface/datasets/issues/4675 | https://github.com/huggingface/datasets/issues/4675 | 4,675 | Unable to use dataset with PyTorch dataloader | open | 1 | 2022-07-12T15:04:04 | 2022-07-14T14:17:46 | null | BlueskyFR | [
"bug"
] | ## Describe the bug
When using `.with_format("torch")`, an arrow table is returned and I am unable to use it by passing it to a PyTorch DataLoader: please see the code below.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
ds = load_dataset(
"para_crawl",
name="enfr",
cache_dir="/tmp/test/",
split="train",
keep_in_memory=True,
)
dataloader = DataLoader(ds.with_format("torch"), num_workers=32)
print(next(iter(dataloader)))
```
Is there something I am doing wrong? The documentation does not say much about the behavior of `.with_format()` so I feel like I am a bit stuck here :-/
Thanks in advance for your help!
## Expected results
The code should run with no error
## Actual results
```
AttributeError: 'str' object has no attribute 'dtype'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.18.0-348.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| false |
1,301,294,844 | https://api.github.com/repos/huggingface/datasets/issues/4674 | https://github.com/huggingface/datasets/issues/4674 | 4,674 | Issue loading datasets -- pyarrow.lib has no attribute | closed | 1 | 2022-07-11T22:10:44 | 2023-02-28T18:06:55 | 2023-02-28T18:06:55 | margotwagner | [
"bug"
] | ## Describe the bug
I am trying to load sentiment analysis datasets from huggingface, but any dataset I try to use via load_dataset, I get the same error:
`AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'`
## Steps to reproduce the bug
```python
dataset = load_dataset("glue", "cola")
```
## Expected results
Download datasets without issue.
## Actual results
`AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 8.0.0
- Pandas version: 1.1.0
| false |
1,301,010,331 | https://api.github.com/repos/huggingface/datasets/issues/4673 | https://github.com/huggingface/datasets/issues/4673 | 4,673 | load_datasets on csv returns everything as a string | closed | 3 | 2022-07-11T17:30:24 | 2024-11-05T03:55:10 | 2022-07-12T13:33:08 | courtneysprouse | [
"bug"
] | ## Describe the bug
If you use:
`conll_dataset.to_csv("ner_conll.csv")`
It will create a csv file with all of your data as expected, however when you load it with:
`conll_dataset = load_dataset("csv", data_files="ner_conll.csv")`
everything is read in as a string. For example if I look at everything in 'ner_tags' I get back `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']` instead of what I originally saved which was `[[3, 0, 7, 0, 0, 0, 7, 0, 0], [1, 2], [5, 0]]`
I think maybe there is something funky going on with the csv delimiter
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
#load original conll dataset
orig_conll = load_dataset("conll2003")
#save original conll as a csv
orig_conll.to_csv("ner_conll.csv")
#reload conll data as a csv
new_conll = load_dataset("csv", data_files="ner_conll.csv")`
```
## Expected results
A clear and concise description of the expected results.
I would expect the data be returned as the data type I saved it as. I.e. if I save a list of ints
[[3, 0, 7, 0, 0, 0, 7, 0, 0]], I shouldnt get back a string ['[3 0 7 0 0 0 7 0 0]']
I also get back a string when I pass a list of strings ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']
## Actual results
A list of strings `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']`
A string "['EU' 'rejects' 'German' 'call' 'to' 'boycott' 'British' 'lamb' '.']"
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
| false |
1,300,911,467 | https://api.github.com/repos/huggingface/datasets/issues/4672 | https://github.com/huggingface/datasets/pull/4672 | 4,672 | Support extract 7-zip compressed data files | closed | 2 | 2022-07-11T15:56:51 | 2022-07-15T13:14:27 | 2022-07-15T13:02:07 | albertvillanova | [] | Fix partially #3541, fix #4670. | true |
1,300,385,909 | https://api.github.com/repos/huggingface/datasets/issues/4671 | https://github.com/huggingface/datasets/issues/4671 | 4,671 | Dataset Viewer issue for wmt16 | closed | 6 | 2022-07-11T08:34:11 | 2022-09-13T13:27:02 | 2022-09-08T08:16:06 | lewtun | [
"dataset-viewer"
] | ### Link
https://huggingface.co/datasets/wmt16
### Description
[Reported](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/12#62cb83f14c7f35284e796f9c) by a user of AutoTrain Evaluate. AFAIK this dataset was working 1-2 weeks ago, and I'm not sure how to interpret this error.
```
Status code: 400
Exception: NotImplementedError
Message: This is a abstract method
```
Thanks!
### Owner
No | false |
1,299,984,246 | https://api.github.com/repos/huggingface/datasets/issues/4670 | https://github.com/huggingface/datasets/issues/4670 | 4,670 | Can't extract files from `.7z` zipfile using `download_and_extract` | closed | 5 | 2022-07-10T18:16:49 | 2022-07-15T13:02:07 | 2022-07-15T13:02:07 | bhavitvyamalik | [
"bug"
] | ## Describe the bug
I'm adding a new dataset which is a `.7z` zip file in Google drive and contains 3 json files inside. I'm able to download the data files using `download_and_extract` but after downloading it throws this error:
```
>>> dataset = load_dataset("./datasets/mantis/")
Using custom data configuration default
Downloading and preparing dataset mantis/default to /Users/bhavitvyamalik/.cache/huggingface/datasets/mantis/default/1.1.0/611affa804ec53e2055a335cc1b8b213bb5a0b5142d919967729d5ee23c6bab4...
Downloading data: 100%|█████████████████████████████████████████████████████████| 77.2M/77.2M [00:23<00:00, 3.28MB/s]
/Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/load.py", line 1745, in load_dataset
use_auth_token=use_auth_token,
File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
[Errno 20] Not a directory: '/Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6/merged_train.json'
```
just before generating the splits. I checked `fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6` file and it's `7z` zip file (similar to downloaded Google drive file) which means it didn't get unzip. Do I need to unzip it separately and then pass the paths for train,dev,test files in `SplitGenerator`?
## Environment info
- `datasets` version: 1.18.4.dev0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.8
- PyArrow version: 5.0.0 | false |
1,299,848,003 | https://api.github.com/repos/huggingface/datasets/issues/4669 | https://github.com/huggingface/datasets/issues/4669 | 4,669 | loading oscar-corpus/OSCAR-2201 raises an error | closed | 1 | 2022-07-10T07:09:30 | 2022-07-11T09:27:49 | 2022-07-11T09:27:49 | vitalyshalumov | [
"bug"
] | ## Describe the bug
load_dataset('oscar-2201', 'af')
raises an error:
Traceback (most recent call last):
File "/usr/lib/python3.8/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset
builder_instance = load_dataset_builder(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder
dataset_module = dataset_module_factory(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py
I've tried other permutations such as :
oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-2201', 'af')
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201')
with the same unfortunate result.
## Steps to reproduce the bug
oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-2201', 'af')
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201')
# Sample code to reproduce the bug
```
## Expected results
loaded data
## Actual results
Traceback (most recent call last):
File "/usr/lib/python3.8/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset
builder_instance = load_dataset_builder(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder
dataset_module = dataset_module_factory(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.13.0-37-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| false |
1,299,735,893 | https://api.github.com/repos/huggingface/datasets/issues/4668 | https://github.com/huggingface/datasets/issues/4668 | 4,668 | Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed | closed | 1 | 2022-07-09T18:04:13 | 2022-07-11T07:47:47 | 2022-07-11T07:47:47 | ghost | [
"dataset-viewer"
] | ### Link
https://huggingface.co/hungnm/multilingual-amazon-review-sentiment
### Description
_No response_
### Owner
Yes | false |
1,299,735,703 | https://api.github.com/repos/huggingface/datasets/issues/4667 | https://github.com/huggingface/datasets/issues/4667 | 4,667 | Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed | closed | 0 | 2022-07-09T18:03:15 | 2022-07-11T07:47:15 | 2022-07-11T07:47:15 | ghost | [
"duplicate"
] | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | false |
1,299,732,238 | https://api.github.com/repos/huggingface/datasets/issues/4666 | https://github.com/huggingface/datasets/issues/4666 | 4,666 | Issues with concatenating datasets | closed | 2 | 2022-07-09T17:45:14 | 2022-07-12T17:16:15 | 2022-07-12T17:16:14 | ChenghaoMou | [
"bug"
] | ## Describe the bug
It is impossible to concatenate datasets if a feature is sequence of dict in one dataset and a dict of sequence in another. But based on the document, it should be automatically converted.
> A [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence) with a internal dictionary feature will be automatically converted into a dictionary of lists. This behavior is implemented to have a compatilbity layer with the TensorFlow Datasets library but may be un-wanted in some cases. If you don’t want this behavior, you can use a python list instead of the [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence).
## Steps to reproduce the bug
```python
from datasets import concatenate_datasets, load_dataset
squad = load_dataset("squad_v2")
squad["train"].to_json("output.jsonl", lines=True)
temp = load_dataset("json", data_files={"train": "output.jsonl"})
concatenate_datasets([temp["train"], squad["train"]])
```
## Expected results
No error executing that code
## Actual results
```
ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)} or Value("null").
```
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.4-arm64-arm-64bit
- Python version: 3.8.11
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
| false |
1,299,652,638 | https://api.github.com/repos/huggingface/datasets/issues/4665 | https://github.com/huggingface/datasets/issues/4665 | 4,665 | Unable to create dataset having Python dataset script only | closed | 1 | 2022-07-09T11:45:46 | 2022-07-11T07:10:09 | 2022-07-11T07:10:01 | aleSuglia | [
"bug"
] | ## Describe the bug
Hi there,
I'm trying to add the following dataset to Huggingface datasets: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/blob/
I'm trying to do so using the CLI commands but seems that this command generates the wrong `dataset_info.json` file (you can find it in the repo already):
```
datasets-cli test Heriot-WattUniversity/dialog-babi/dialog_babi.py --save_infos --all-configs
```
while it errors when I remove the python script:
```
datasets-cli test Heriot-WattUniversity/dialog-babi/ --save_infos --all-configs
```
The error message is the following:
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /Users/as2180/workspace/Heriot-WattUniversity/dialog-babi with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.4-arm64-arm-64bit
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | false |
1,299,571,212 | https://api.github.com/repos/huggingface/datasets/issues/4664 | https://github.com/huggingface/datasets/pull/4664 | 4,664 | Add stanford dog dataset | closed | 5 | 2022-07-09T04:46:07 | 2022-07-15T13:30:32 | 2022-07-15T13:15:42 | khushmeeet | [] | This PR is for adding dataset, related to issue #4504.
We are adding Stanford dog breed dataset. It is a multi class image classification dataset.
Details can be found here - http://vision.stanford.edu/aditya86/ImageNetDogs/
Tests on dummy data is failing currently, which I am looking into. | true |
1,299,298,693 | https://api.github.com/repos/huggingface/datasets/issues/4663 | https://github.com/huggingface/datasets/pull/4663 | 4,663 | Add text decorators | closed | 1 | 2022-07-08T17:51:48 | 2022-07-18T18:33:14 | 2022-07-18T18:20:49 | stevhliu | [] | This PR adds some decoration to text about different modalities to make it more obvious separate guides exist for audio, vision, and text. The goal is to make it easier for users to discover these guides!
TODO:
- [x] Open PR to support new Tailwind classes | true |
1,298,845,369 | https://api.github.com/repos/huggingface/datasets/issues/4662 | https://github.com/huggingface/datasets/pull/4662 | 4,662 | Fix: conll2003 - fix empty example | closed | 1 | 2022-07-08T10:49:13 | 2022-07-08T14:14:53 | 2022-07-08T14:02:42 | lhoestq | [] | As reported in https://huggingface.co/datasets/conll2003/discussions/2#62c45a14f93fc97e8260532f, there was an extra empty example at the end of the dataset | true |
1,298,374,944 | https://api.github.com/repos/huggingface/datasets/issues/4661 | https://github.com/huggingface/datasets/issues/4661 | 4,661 | Concurrency bug when using same cache among several jobs | open | 3 | 2022-07-08T01:58:11 | 2025-04-10T13:21:23 | null | ioana-blue | [
"bug"
] | ## Describe the bug
I used to see this bug with an older version of the datasets. It seems to persist.
This is my concrete scenario: I launch several evaluation jobs on a cluster in which I share the file system and I share the cache directory used by huggingface libraries. The evaluation jobs read the same *.csv files. If my jobs get all scheduled pretty much at the same time, there are all kinds of weird concurrency errors. Sometime it crashes silently. This time I got lucky that it crashed with a stack trace that I can share and maybe you get to the bottom of this. If you don't have a similar setup available, it may be hard to reproduce as you really need two jobs accessing the same file at the same time to see this type of bug.
## Steps to reproduce the bug
I'm running a modified version of `run_glue.py` script adapted to my use case. I've seen the same problem when running some glue datasets as well (so it's not specific to loading the datasets from csv files).
## Expected results
No crash, concurrent access to the (intermediate) files just fine.
## Actual results
Crashes due to races/concurrency bugs.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.18.0-348.23.1.el8_5.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 8.0.0
- Pandas version: 1.1.0
Stack trace that I just got with the crash (I've obfuscated some names, it should still be quite informative):
```
Running tokenizer on dataset: 0%| | 0/3 [00:00<?, ?ba/s]
Traceback (most recent call last):
File "../../src/models//run_*******.py", line 600, in <module>
main()
File "../../src/models//run_*******.py", line 444, in main
raw_datasets = raw_datasets.map(
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 770, in map
{
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 771, in <dictcomp>
k: dataset.map(
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2376, in map
return self._map_single(
File "/*******/envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 551, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 518, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/*******/envs/tr-crt/lib/python3.8/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2776, in _map_single
buf_writer, writer, tmp_file = init_buffer_and_writer()
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2696, in init_buffer_and_writer
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False)
File "/*******//envs/tr-crt/lib/python3.8/tempfile.py", line 541, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/*******//envs/tr-crt/lib/python3.8/tempfile.py", line 250, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '/*******/cache-transformers//transformers/csv/default-ef9cd184210742a7/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58/tmps8l6j5yc'
```
As I ran 100s of experiments last year for an empirical paper, I ran into this type of bugs several times. I found several bandaid/work-arounds, e.g., run one job first that caches the dataset => eliminate concurrency; OR use unique caches => eliminate concurrency (but increase storage space), etc. and it all works fine.
I'd like to help you fixing this bug as it's really annoying to always apply the work arounds. Let me know what other info from my side could help you figure out the issue.
Thanks for your help!
| false |
1,297,128,387 | https://api.github.com/repos/huggingface/datasets/issues/4660 | https://github.com/huggingface/datasets/pull/4660 | 4,660 | Fix _resolve_single_pattern_locally on Windows with multiple drives | closed | 2 | 2022-07-07T09:57:30 | 2022-07-07T17:03:36 | 2022-07-07T16:52:07 | albertvillanova | [] | Currently, when `_resolve_single_pattern_locally` is called from a different drive than the one in `pattern`, it raises an exception:
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\io\parquet.py:35: in __init__
**kwargs,
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\builder.py:287: in __init__
sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:761: in from_local_or_remote
if not isinstance(patterns_for_key, DataFilesList)
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:723: in from_local_or_remote
data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:321: in resolve_patterns_locally_or_by_urls
for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:239: in _resolve_single_pattern_locally
for filepath in glob_iter
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:242: in <listcomp>
os.path.relpath(filepath, base_path), os.path.relpath(pattern, base_path)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
path = 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pytest-of-runneradmin\\pytest-0\\popen-gw0\\data6\\dataset.parquet'
start = '/'
...
E ValueError: path is on mount 'C:', start on mount 'D:'
```
This PR makes sure that `base_path` is in the same drive as `pattern`. | true |
1,297,094,140 | https://api.github.com/repos/huggingface/datasets/issues/4659 | https://github.com/huggingface/datasets/pull/4659 | 4,659 | Transfer CI to GitHub Actions | closed | 4 | 2022-07-07T09:29:47 | 2022-07-12T11:30:20 | 2022-07-12T11:18:25 | albertvillanova | [] | This PR transfers CI from CircleCI to GitHub Actions. The implementation in GitHub Actions tries to be as faithful as possible to the implementation in CircleCI and get the same output results (exceptions below).
**IMPORTANT NOTE**: The fast-fail policy (described below) is not finally implemented, so that:
- we can continue merging PRs with CI in red because of some random error returned by the Hub
- it is not annoying for maintainers to have to relaunch failed CI jobs
See comments here: https://github.com/huggingface/datasets/pull/4659#discussion_r918802348
Differences in the implementation in GitHub Actions compared to the CircleCI one:
- This PR introduces some *fail-fast* mechanisms to significantly reduce the total time CI is running, both because of environmental impact and because CI in GitHub Actions billing depends on the minutes per month running time (see [About billing for GitHub Actions](https://docs.github.com/en/billing/managing-billing-for-github-actions/about-billing-for-github-actions)):
- All tests *depend* on `check_code_quality` job: only if `check_code_quality` passes, then the other test jobs are launched
- The tests are implemented with a matrix strategy (cross-product: OS and PyArrow versions) and fail-fast: if any of the 4 processes fails, the others are cancelled
- OS dependencies for Linux (see table below)
| OS dependencies | Passed tests | Skipped tests |
| --- | ---: | ---: |
| libsndfile1-dev | 4786 | 3119 |
| libsndfile1 | 4786 | 3119 |
| libsndfile1, sox | 4788 | 3117 |
- This PR replaces `libsndfile1-dev` with `libsndfile1`: the same number of passing tests but less packages installed
- This PR adds `sox`: required by MP3 tests (2 more tests are passed: 4788 instead of 4786)
- For tests using PyArrow 6, this PR uses 6.0.1 instead of 6.0.0
TO DO:
- [ ] Remove old CircleCI CI: kept for the moment to compare stability and performance
Close #4658.
## Comparison between CircleCI and GitHub Actions
| | | CircleCI | GitHub Actions |
| --- | --- | ---: | ---: |
| Ubuntu, pyarrow-latest ||||
|| Passed tests | 4786 | 4788 |
|| Duration | 11m 0s | 10m 10s |
| Windows, pyarrow-latest ||||
|| Passed tests | 4783 | 4783 |
|| Duration | 29m 59s | 22m 56s | | true |
1,297,001,390 | https://api.github.com/repos/huggingface/datasets/issues/4658 | https://github.com/huggingface/datasets/issues/4658 | 4,658 | Transfer CI tests to GitHub Actions | closed | 0 | 2022-07-07T08:10:50 | 2022-07-12T11:18:25 | 2022-07-12T11:18:25 | albertvillanova | [] | Let's try CI tests using GitHub Actions to see if they are more stable than on CircleCI. | false |
1,296,743,133 | https://api.github.com/repos/huggingface/datasets/issues/4657 | https://github.com/huggingface/datasets/issues/4657 | 4,657 | Add SQuAD2.0 Dataset | closed | 2 | 2022-07-07T03:19:36 | 2022-07-12T16:14:52 | 2022-07-12T16:14:52 | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *SQuAD2.0*
- **Description:** *Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.*
- **Paper:** *https://aclanthology.org/P18-2124.pdf*
- **Data:** *https://rajpurkar.github.io/SQuAD-explorer/*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,740,266 | https://api.github.com/repos/huggingface/datasets/issues/4656 | https://github.com/huggingface/datasets/issues/4656 | 4,656 | Add Amazon-QA Dataset | closed | 1 | 2022-07-07T03:15:11 | 2022-07-14T02:20:12 | 2022-07-14T02:20:12 | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *Amazon-QA*
- **Description:** *The dataset is .jsonl format, where each line in the file is a json string that corresponds to a question, existing answers to the question and the extracted review snippets (relevant to the question).*
- **Paper:** *https://github.com/amazonqa/amazonqa/tree/master/paper*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon-qa.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,720,896 | https://api.github.com/repos/huggingface/datasets/issues/4655 | https://github.com/huggingface/datasets/issues/4655 | 4,655 | Simple Wikipedia | closed | 1 | 2022-07-07T02:51:26 | 2022-07-14T02:16:33 | 2022-07-14T02:16:33 | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *Simple Wikipedia*
- **Description:** *Two different versions of the data set now exist. Both were generated by aligning Simple English Wikipedia and English Wikipedia. A complete description of the extraction process can be found in "Simple English Wikipedia: A New Simplification Task", William Coster and David Kauchak (2011).*
- **Paper:** *https://aclanthology.org/P11-2117/*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/SimpleWiki.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,716,119 | https://api.github.com/repos/huggingface/datasets/issues/4654 | https://github.com/huggingface/datasets/issues/4654 | 4,654 | Add Quora Question Triplets Dataset | closed | 1 | 2022-07-07T02:43:42 | 2022-07-14T02:13:50 | 2022-07-14T02:13:50 | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *Quora Question Triplets*
- **Description:** *This dataset consists of over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair.*
- **Paper:**
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates_triplets.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,702,834 | https://api.github.com/repos/huggingface/datasets/issues/4653 | https://github.com/huggingface/datasets/issues/4653 | 4,653 | Add Altlex dataset | closed | 1 | 2022-07-07T02:23:02 | 2022-07-14T02:12:39 | 2022-07-14T02:12:39 | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *Altlex*
- **Description:** *Git repository for software associated with the 2016 ACL paper "Identifying Causal Relations Using Parallel Wikipedia Articles.”*
- **Paper:** *https://aclanthology.org/P16-1135.pdf*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/altlex.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,697,498 | https://api.github.com/repos/huggingface/datasets/issues/4652 | https://github.com/huggingface/datasets/issues/4652 | 4,652 | Add Sentence Compression Dataset | closed | 1 | 2022-07-07T02:13:46 | 2022-07-14T02:11:48 | 2022-07-14T02:11:48 | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *Sentence Compression*
- **Description:** *Large corpus of uncompressed and compressed sentences from news articles.*
- **Paper:** *https://www.aclweb.org/anthology/D13-1155/*
- **Data:** *https://github.com/google-research-datasets/sentence-compression/tree/master/data*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,689,414 | https://api.github.com/repos/huggingface/datasets/issues/4651 | https://github.com/huggingface/datasets/issues/4651 | 4,651 | Add Flickr 30k Dataset | closed | 1 | 2022-07-07T01:59:08 | 2022-07-14T02:09:45 | 2022-07-14T02:09:45 | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *Flickr 30k*
- **Description:** *To produce the denotation graph, we have created an image caption corpus consisting of 158,915 crowd-sourced captions describing 31,783 images. This is an extension of our previous Flickr 8k Dataset. The new images and captions focus on people involved in everyday activities and events.*
- **Paper:** *https://transacl.org/ojs/index.php/tacl/article/view/229/33*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,680,037 | https://api.github.com/repos/huggingface/datasets/issues/4650 | https://github.com/huggingface/datasets/issues/4650 | 4,650 | Add SPECTER dataset | open | 1 | 2022-07-07T01:41:32 | 2022-07-14T02:07:49 | null | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *SPECTER*
- **Description:** *SPECTER: Document-level Representation Learning using Citation-informed Transformers*
- **Paper:** *https://doi.org/10.18653/v1/2020.acl-main.207*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/specter_train_triples.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,673,712 | https://api.github.com/repos/huggingface/datasets/issues/4649 | https://github.com/huggingface/datasets/issues/4649 | 4,649 | Add PAQ dataset | closed | 1 | 2022-07-07T01:29:42 | 2022-07-14T02:06:27 | 2022-07-14T02:06:27 | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *PAQ*
- **Description:** *This repository contains code and models to support the research paper PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them*
- **Paper:** *https://arxiv.org/abs/2102.07033*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/PAQ_pairs.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,659,335 | https://api.github.com/repos/huggingface/datasets/issues/4648 | https://github.com/huggingface/datasets/issues/4648 | 4,648 | Add WikiAnswers dataset | closed | 1 | 2022-07-07T01:06:37 | 2022-07-14T02:03:40 | 2022-07-14T02:03:40 | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *WikiAnswers*
- **Description:** *The WikiAnswers corpus contains clusters of questions tagged by WikiAnswers users as paraphrases. Each cluster optionally contains an answer provided by WikiAnswers users.*
- **Paper:** *https://dl.acm.org/doi/10.1145/2623330.2623677*
- **Data:** *https://github.com/afader/oqa#wikianswers-corpus*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,311,270 | https://api.github.com/repos/huggingface/datasets/issues/4647 | https://github.com/huggingface/datasets/issues/4647 | 4,647 | Add Reddit dataset | open | 0 | 2022-07-06T19:49:18 | 2022-07-06T19:49:18 | null | omarespejel | [
"dataset request"
] | ## Adding a Dataset
- **Name:** *Reddit comments (2015-2018)*
- **Description:** *Reddit is an American social news aggregation website, where users can post links, and take part in discussions on these posts. These threaded discussions provide a large corpus, which is converted into a conversational dataset using the tools in this directory.*
- **Paper:** *https://arxiv.org/abs/1904.06472*
- **Data:** *https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
| false |
1,296,027,785 | https://api.github.com/repos/huggingface/datasets/issues/4645 | https://github.com/huggingface/datasets/pull/4645 | 4,645 | Set HF_SCRIPTS_VERSION to main | closed | 1 | 2022-07-06T15:43:21 | 2022-07-06T15:56:21 | 2022-07-06T15:45:05 | lhoestq | [] | After renaming "master" to "main", the CI fails with
```
AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/main/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at /home/circleci/datasets/_dummy/_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py"
```
This is because in the CI we were still using `HF_SCRIPTS_VERSION=master`. I changed it to "main" | true |
1,296,018,052 | https://api.github.com/repos/huggingface/datasets/issues/4644 | https://github.com/huggingface/datasets/pull/4644 | 4,644 | [Minor fix] Typo correction | closed | 1 | 2022-07-06T15:37:02 | 2022-07-06T15:56:32 | 2022-07-06T15:45:16 | cakiki | [] | recieve -> receive | true |
1,295,852,650 | https://api.github.com/repos/huggingface/datasets/issues/4643 | https://github.com/huggingface/datasets/pull/4643 | 4,643 | Rename master to main | closed | 3 | 2022-07-06T13:34:30 | 2022-07-06T15:36:46 | 2022-07-06T15:25:08 | lhoestq | [] | This PR renames mentions of "master" by "main" in the code base for several cases:
- set the default dataset script version to "main" if the local installation of `datasets` is a dev installation
- update URLs to this github repository to use "main"
- update the DVC benchmark
- update the github workflows
- update docstrings
- update tests to compare the changes in dataset cards against "main"
| true |
1,295,748,083 | https://api.github.com/repos/huggingface/datasets/issues/4642 | https://github.com/huggingface/datasets/issues/4642 | 4,642 | Streaming issue for ccdv/pubmed-summarization | closed | 3 | 2022-07-06T12:13:07 | 2022-07-06T14:17:34 | 2022-07-06T14:17:34 | lewtun | [] | ### Link
https://huggingface.co/datasets/ccdv/pubmed-summarization
### Description
This was reported by a [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/7). It seems like streaming doesn't work due to the way the dataset loading script is defined?
```
Status code: 400
Exception: FileNotFoundError
Message: https://huggingface.co/datasets/ccdv/pubmed-summarization/resolve/main/train.zip/train.txt
```
### Owner
No | false |
1,295,633,250 | https://api.github.com/repos/huggingface/datasets/issues/4641 | https://github.com/huggingface/datasets/issues/4641 | 4,641 | Dataset Viewer issue for kmfoda/booksum | closed | 3 | 2022-07-06T10:38:16 | 2022-07-06T13:25:28 | 2022-07-06T11:58:06 | lewtun | [
"dataset-viewer"
] | ### Link
https://huggingface.co/datasets/kmfoda/booksum
### Description
A [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/9) discovered this dataset cannot be streamed due to:
```
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/kmfoda/booksum/resolve/47953f583d6967f086cb16a2f4d2346e9834024d/test.csv')
```
I'm not sure why it says "Unauthorized" since it's just a bunch of CSV files in a repo
### Owner
No | false |
1,295,495,699 | https://api.github.com/repos/huggingface/datasets/issues/4640 | https://github.com/huggingface/datasets/pull/4640 | 4,640 | Support all split in streaming mode | open | 1 | 2022-07-06T08:56:38 | 2022-07-06T15:19:55 | null | albertvillanova | [] | Fix #4637. | true |
1,295,367,322 | https://api.github.com/repos/huggingface/datasets/issues/4639 | https://github.com/huggingface/datasets/issues/4639 | 4,639 | Add HaGRID -- HAnd Gesture Recognition Image Dataset | open | 0 | 2022-07-06T07:41:32 | 2022-07-06T07:41:32 | null | osanseviero | [
"dataset request"
] | ## Adding a Dataset
- **Name:** HaGRID -- HAnd Gesture Recognition Image Dataset
- **Description:** We introduce a large image dataset HaGRID (HAnd Gesture Recognition Image Dataset) for hand gesture recognition (HGR) systems. You can use it for image classification or image detection tasks. Proposed dataset allows to build HGR systems, which can be used in video conferencing services (Zoom, Skype, Discord, Jazz etc.), home automation systems, the automotive sector, etc.
- **Paper:** https://arxiv.org/abs/2206.08219
- **Data:** https://github.com/hukenovs/hagrid
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false |
1,295,233,315 | https://api.github.com/repos/huggingface/datasets/issues/4638 | https://github.com/huggingface/datasets/pull/4638 | 4,638 | The speechocean762 dataset | closed | 4 | 2022-07-06T06:17:30 | 2022-10-03T09:34:36 | 2022-10-03T09:34:36 | jimbozhang | [
"dataset contribution"
] | [speechocean762](https://www.openslr.org/101/) is a non-native English corpus for pronunciation scoring tasks. It is free for both commercial and non-commercial use.
I believe it will be easier to use if it could be available on Hugging Face. | true |
1,294,818,236 | https://api.github.com/repos/huggingface/datasets/issues/4637 | https://github.com/huggingface/datasets/issues/4637 | 4,637 | The "all" split breaks streaming | open | 6 | 2022-07-05T21:56:49 | 2022-07-15T13:59:30 | null | cakiki | [
"bug"
] | ## Describe the bug
Not sure if this is a bug or just the way streaming works, but setting `streaming=True` did not work when setting `split="all"`
## Steps to reproduce the bug
The following works:
```python
ds = load_dataset('super_glue', 'wsc.fixed', split='all')
```
The following throws `ValueError: Bad split: all. Available splits: ['train', 'validation', 'test']`:
```python
ds = load_dataset('super_glue', 'wsc.fixed', split='all', streaming=True)
```
## Expected results
An iterator over all splits.
## Actual results
I had to do the following to achieve the desired result:
```python
from itertools import chain
ds = load_dataset('super_glue', 'wsc.fixed', streaming=True)
it = chain.from_iterable(ds.values())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| false |
1,294,547,836 | https://api.github.com/repos/huggingface/datasets/issues/4636 | https://github.com/huggingface/datasets/issues/4636 | 4,636 | Add info in docs about behavior of download_config.num_proc | closed | 0 | 2022-07-05T17:01:00 | 2022-07-28T10:40:32 | 2022-07-28T10:40:32 | nateraw | [
"enhancement"
] | **Is your feature request related to a problem? Please describe.**
I went to override `download_config.num_proc` and was confused about what was happening under the hood. It would be nice to have the behavior documented a bit better so folks know what's happening when they use it.
**Describe the solution you'd like**
- Add note about how the default number of workers is 16. Related code:
https://github.com/huggingface/datasets/blob/7bcac0a6a0fc367cc068f184fa132b8de8dfa11d/src/datasets/download/download_manager.py#L299-L302
- Add note that if the number of workers is higher than the number of files to download, it won't use multiprocessing.
**Describe alternatives you've considered**
maybe it would also be nice to set `num_proc` = `num_files` when `num_proc` > `num_files`.
**Additional context**
...
| false |
1,294,475,931 | https://api.github.com/repos/huggingface/datasets/issues/4635 | https://github.com/huggingface/datasets/issues/4635 | 4,635 | Dataset Viewer issue for vadis/sv-ident | closed | 6 | 2022-07-05T15:48:13 | 2022-07-06T07:13:33 | 2022-07-06T07:12:14 | e-tornike | [
"dataset-viewer"
] | ### Link
https://huggingface.co/datasets/vadis/sv-ident/viewer/default/validation
### Description
Error message when loading validation split in the viewer:
```
Status code: 400
Exception: Status400Error
Message: The split cache is empty.
```
### Owner
_No response_ | false |
1,294,405,251 | https://api.github.com/repos/huggingface/datasets/issues/4634 | https://github.com/huggingface/datasets/issues/4634 | 4,634 | Can't load the Hausa audio dataset | closed | 1 | 2022-07-05T14:47:36 | 2022-09-13T14:07:32 | 2022-09-13T14:07:32 | moro23 | [] | common_voice_train = load_dataset("common_voice", "ha", split="train+validation") | false |
1,294,367,783 | https://api.github.com/repos/huggingface/datasets/issues/4633 | https://github.com/huggingface/datasets/pull/4633 | 4,633 | [data_files] Only match separated split names | closed | 5 | 2022-07-05T14:18:11 | 2022-07-18T13:20:29 | 2022-07-18T13:07:33 | lhoestq | [] | As reported in https://github.com/huggingface/datasets/issues/4477, the current pattern matching to infer which file goes into which split is too permissive. For example a file "contest.py" would be considered part of a test split (it contains "test") and "seqeval.py" as well (it contains "eval").
In this PR I made the pattern matching more robust by only matching split names **between separators**. The supported separators are dots, dashes, spaces and underscores.
I updated the docs accordingly.
One detail about the tests: I had to update one test because it was using `PurePath.match` as a reference for globbing, but it doesn't support the `[..]` glob pattern. Therefore I added a `mock_fs` context manager that can be used to easily define a dummy filesystem with certain files in it and run pattern matching tests. Its code comes mostly from test_streaming_download_manager.py
Close https://github.com/huggingface/datasets/issues/4477 | true |
1,294,166,880 | https://api.github.com/repos/huggingface/datasets/issues/4632 | https://github.com/huggingface/datasets/issues/4632 | 4,632 | 'sort' method sorts one column only | closed | 3 | 2022-07-05T11:25:26 | 2023-07-25T15:04:27 | 2023-07-25T15:04:27 | shachardon | [] | The 'sort' method changes the order of one column only (the one defined by the argument 'column'), thus creating a mismatch between a sample fields. I would expect it to change the order of the samples as a whole, based on the 'column' order. | false |
1,293,545,900 | https://api.github.com/repos/huggingface/datasets/issues/4631 | https://github.com/huggingface/datasets/pull/4631 | 4,631 | Update WinoBias README | closed | 1 | 2022-07-04T20:24:40 | 2022-07-07T13:23:32 | 2022-07-07T13:11:47 | sashavor | [] | I'm adding some information about Winobias that I got from the paper :smile:
I think this makes it a bit clearer! | true |
1,293,470,728 | https://api.github.com/repos/huggingface/datasets/issues/4630 | https://github.com/huggingface/datasets/pull/4630 | 4,630 | fix(dataset_wrappers): Fixes access to fsspec.asyn in torch_iterable_dataset.py. | closed | 1 | 2022-07-04T18:26:55 | 2022-07-05T15:19:52 | 2022-07-05T15:08:21 | gugarosa | [] | Fix #4612.
Apparently, newest `fsspec` versions do not allow access to attribute-based modules if they are not imported, such as `fsspec.async`.
Thus, @mariosasko suggested to add the missing part to the module import to allow for its access. | true |
1,293,418,800 | https://api.github.com/repos/huggingface/datasets/issues/4629 | https://github.com/huggingface/datasets/issues/4629 | 4,629 | Rename repo default branch to main | closed | 0 | 2022-07-04T17:16:10 | 2022-07-06T15:49:57 | 2022-07-06T15:49:57 | albertvillanova | [
"maintenance"
] | Rename repository default branch to `main` (instead of current `master`).
Once renamed, users will have to manually update their local repos:
- [ ] Upstream:
```
git branch -m master main
git fetch upstream main
git branch -u upstream/main main
git remote set-head upstream -a
```
- [ ] Origin:
Rename fork default branch as well at: https://github.com/USERNAME/lam/settings/branches
Then:
```
git fetch origin main
git remote set-head origin -a
```
CC: @sgugger | false |
1,293,361,308 | https://api.github.com/repos/huggingface/datasets/issues/4628 | https://github.com/huggingface/datasets/pull/4628 | 4,628 | Fix time type `_arrow_to_datasets_dtype` conversion | closed | 1 | 2022-07-04T16:20:15 | 2022-07-07T14:08:38 | 2022-07-07T13:57:12 | mariosasko | [] | Fix #4620
The issue stems from the fact that `pa.array([time_data]).type` returns `DataType(time64[unit])`, which doesn't expose the `unit` attribute, instead of `Time64Type(time64[unit])`. I believe this is a bug in PyArrow. Luckily, the both types have the same `str()`, so in this PR I call `pa.type_for_alias(str(type))` to convert them both to the `Time64Type(time64[unit])` format.
cc @severo | true |
1,293,287,798 | https://api.github.com/repos/huggingface/datasets/issues/4627 | https://github.com/huggingface/datasets/pull/4627 | 4,627 | fixed duplicate calculation of spearmanr function in metrics wrapper. | closed | 3 | 2022-07-04T15:02:01 | 2022-07-07T12:41:09 | 2022-07-07T12:41:09 | benlipkin | [] | During _compute, the scipy.stats spearmanr function was called twice, redundantly, once for calculating the score and once for calculating the p-value, under the conditional branch where return_pvalue=True. I adjusted the _compute function to execute the spearmanr function once, store the results tuple in a temporary variable, and then pass the indexed contents to the expected keys of the returned dictionary. | true |
1,293,256,269 | https://api.github.com/repos/huggingface/datasets/issues/4626 | https://github.com/huggingface/datasets/issues/4626 | 4,626 | Add non-commercial licensing info for datasets for which we removed tags | open | 1 | 2022-07-04T14:32:43 | 2022-07-08T14:27:29 | null | lhoestq | [] | We removed several YAML tags saying that certain datasets can't be used for commercial purposes: https://github.com/huggingface/datasets/pull/4613#discussion_r911919753
Reason for this is that we only allow tags that are part of our [supported list of licenses](https://github.com/huggingface/datasets/blob/84fc3ad73c85de4eda5d152dfede7671491449cb/src/datasets/utils/resources/standard_licenses.tsv)
We should update the Licensing Information section of the concerned dataset cards, now that the non-commercial tag doesn't exist anymore for certain datasets | false |
1,293,163,744 | https://api.github.com/repos/huggingface/datasets/issues/4625 | https://github.com/huggingface/datasets/pull/4625 | 4,625 | Unpack `dl_manager.iter_files` to allow parallization | closed | 2 | 2022-07-04T13:16:58 | 2022-07-05T11:11:54 | 2022-07-05T11:00:48 | mariosasko | [] | Iterate over data files outside `dl_manager.iter_files` to allow parallelization in streaming mode.
(The issue reported [here](https://discuss.huggingface.co/t/dataset-only-have-n-shard-1-when-has-multiple-shards-in-repo/19887))
PS: Another option would be to override `FilesIterable.__getitem__` to make it indexable and check for that type in `_shard_kwargs` and `n_shards,` but IMO this solution adds too much unnecessary complexity. | true |
1,293,085,058 | https://api.github.com/repos/huggingface/datasets/issues/4624 | https://github.com/huggingface/datasets/pull/4624 | 4,624 | Remove all paperswithcode_id: null | closed | 3 | 2022-07-04T12:11:32 | 2023-09-24T10:05:19 | 2022-07-04T13:10:38 | lhoestq | [] | On the Hub there is a validation error on the `paperswithcode_id` tag when the value is `null`:
<img width="686" alt="image" src="https://user-images.githubusercontent.com/42851186/177151825-93d341c5-25bd-41ab-96c2-c0b516d51c68.png">
We've been using `null` to specify that we checked on pwc but the dataset doesn't exist there.
To have the validation working again we can simply remove all the `paperswithcode_id: null`.
cc @julien-c | true |
1,293,042,894 | https://api.github.com/repos/huggingface/datasets/issues/4623 | https://github.com/huggingface/datasets/issues/4623 | 4,623 | Loading MNIST as Pytorch Dataset | open | 4 | 2022-07-04T11:33:10 | 2022-07-04T14:40:50 | null | jameschapman19 | [
"bug"
] | ## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
| false |
1,293,031,939 | https://api.github.com/repos/huggingface/datasets/issues/4622 | https://github.com/huggingface/datasets/pull/4622 | 4,622 | Fix ImageFolder with parameters drop_metadata=True and drop_labels=False (when metadata.jsonl is present) | closed | 5 | 2022-07-04T11:23:20 | 2022-07-15T14:37:23 | 2022-07-15T14:24:24 | polinaeterna | [] | Will fix #4621
ImageFolder raises `KeyError: 'label'` with params `drop_metadata=True` and `drop_labels=False` (if there is at least one metadata.jsonl file a data directory). This happens because metadata files are collected inside `analyze()` function regardless of `drop_metadata` value. And then the following condition doesn't pass: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/imagefolder/imagefolder.py#L167
So I suggest to double check it inside `analyze()` not to collect metadata files if they are not needed. (and labels too, to be consistent)
---
Also, I added a test to check if labels are inferred correctly from directories names in general (because we didn't have it) :) | true |
1,293,030,128 | https://api.github.com/repos/huggingface/datasets/issues/4621 | https://github.com/huggingface/datasets/issues/4621 | 4,621 | ImageFolder raises an error with parameters drop_metadata=True and drop_labels=False when metadata.jsonl is present | closed | 0 | 2022-07-04T11:21:44 | 2022-07-15T14:24:24 | 2022-07-15T14:24:24 | polinaeterna | [
"bug"
] | ## Describe the bug
If you pass `drop_metadata=True` and `drop_labels=False` when a `data_dir` contains at least one `matadata.jsonl` file, you will get a KeyError. This is probably not a very useful case but we shouldn't get an error anyway. Asking users to move metadata files manually outside `data_dir` or pass features manually (when there is a tool that can infer them automatically) don't look like a good idea to me either.
## Steps to reproduce the bug
### Clone an example dataset from the Hub
```bash
git clone https://huggingface.co/datasets/nateraw/test-imagefolder-metadata
```
### Try to load it
```python
from datasets import load_dataset
ds = load_dataset("test-imagefolder-metadata", drop_metadata=True, drop_labels=False)
```
or even just
```python
ds = load_dataset("test-imagefolder-metadata", drop_metadata=True)
```
as `drop_labels=False` is a default value.
## Expected results
A DatasetDict object with two features: `"image"` and `"label"`.
## Actual results
```
Traceback (most recent call last):
File "/home/polina/workspace/datasets/debug.py", line 18, in <module>
ds = load_dataset(
File "/home/polina/workspace/datasets/src/datasets/load.py", line 1732, in load_dataset
builder_instance.download_and_prepare(
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1218, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1596, in encode_example
return encode_nested_example(self, example)
File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in encode_nested_example
{
File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in <dictcomp>
{
File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in zip_dict
yield key, tuple(d[key] for d in dicts)
File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in <genexpr>
yield key, tuple(d[key] for d in dicts)
KeyError: 'label'
```
## Environment info
`datasets` master branch
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.14.0-1042-oem-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1
- Pandas version: 1.4.1
| false |
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