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https://api.github.com/repos/huggingface/datasets/issues/5143 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5143/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5143/comments | https://api.github.com/repos/huggingface/datasets/issues/5143/events | https://github.com/huggingface/datasets/issues/5143 | 1,416,837,186 | I_kwDODunzps5UczhC | 5,143 | DownloadManager Git LFS support | {
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"Hey ! Actually it works, just pass the right URL ;)\r\nThe URL must be the one with “/resolve/”\r\n\r\ne.g. https://huggingface.co/datasets/imagenet-1k/resolve/main/data/test_images.tar.gz\r\n\r\nYou can even pass a relative path to the dl_manager instead, like `dl_manager.download(\"data/test_images.tar.gz\")`",
... | 2022-10-20T15:29:29Z | 2022-10-20T17:17:10Z | 2022-10-20T17:17:10Z | CONTRIBUTOR | null | null | {
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} | ### Feature request
Maybe I'm mistaken but the `DownloadManager` does not support extracting git lfs files out of the box right?
Using `dl_manager.download()` or `dl_manager.download_and_extract()` still returns lfs files afaict.
Is there a good way to write a dataset loading script for a repo with lfs files?
### Motivation
/
### Your contribution
/ | {
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https://api.github.com/repos/huggingface/datasets/issues/4597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4597/comments | https://api.github.com/repos/huggingface/datasets/issues/4597/events | https://github.com/huggingface/datasets/issues/4597 | 1,288,672,007 | I_kwDODunzps5Mz5MH | 4,597 | Streaming issue for financial_phrasebank | {
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"cc @huggingface/datasets: it seems like https://www.researchgate.net/ is flaky for datasets hosting (I put the \"hosted-on-google-drive\" tag since it's the same kind of issue I think)",
"Let's see if their license allows hosting their data on the Hub.",
"License is Creative Commons Attribution-NonCommercial-S... | 2022-06-29T12:45:43Z | 2022-07-01T09:29:36Z | 2022-07-01T09:29:36Z | MEMBER | null | null | {
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} | ### Link
https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train
### Description
As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset:
```
Server error
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No | {
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https://api.github.com/repos/huggingface/datasets/issues/4771 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4771/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4771/comments | https://api.github.com/repos/huggingface/datasets/issues/4771/events | https://github.com/huggingface/datasets/pull/4771 | 1,322,600,725 | PR_kwDODunzps48VjWx | 4,771 | Remove dummy data generation docs | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-29T19:20:46Z | 2022-08-03T00:04:01Z | 2022-08-02T23:50:29Z | MEMBER | null | null | null | This PR removes instructions to generate dummy data since that is no longer necessary for datasets that are uploaded to the Hub instead of our GitHub repo.
Close #4744 | {
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https://api.github.com/repos/huggingface/datasets/issues/5205 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5205/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5205/comments | https://api.github.com/repos/huggingface/datasets/issues/5205/events | https://github.com/huggingface/datasets/pull/5205 | 1,437,221,987 | PR_kwDODunzps5CRO33 | 5,205 | Add missing `DownloadConfig.use_auth_token` value | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-11-05T23:36:36Z | 2022-11-08T08:13:00Z | 2022-11-07T16:20:24Z | MEMBER | null | null | null | This PR solves https://github.com/huggingface/datasets/issues/5204
Now the `token` is propagated so that `DownloadConfig.use_auth_token` value is set before trying to download private files from existing datasets in the Hub. | {
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"It is caused by these code lines: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1688-L1694",
"Also note the information in the docstring: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/datase... | 2024-01-16T11:27:07Z | 2024-02-05T12:29:37Z | 2024-02-05T12:29:37Z | MEMBER | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
config_name="default",
commit_message="Convert dataset to Parquet",
commit_description="Convert dataset to Parquet.",
create_pr=True,
token=token,
)
```
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https://api.github.com/repos/huggingface/datasets/issues/6678 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6678/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6678/comments | https://api.github.com/repos/huggingface/datasets/issues/6678/events | https://github.com/huggingface/datasets/pull/6678 | 2,141,902,154 | PR_kwDODunzps5nQ2ZO | 6,678 | Release: 2.17.1 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6678). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-02-19T09:24:29Z | 2024-02-19T10:03:00Z | 2024-02-19T09:56:52Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7376 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7376/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7376/comments | https://api.github.com/repos/huggingface/datasets/issues/7376/events | https://github.com/huggingface/datasets/pull/7376 | 2,802,621,104 | PR_kwDODunzps6IiO9j | 7,376 | [docs] uv install | {
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} | [] | closed | false | null | [] | null | [] | 2025-01-21T19:15:48Z | 2025-03-14T20:16:35Z | 2025-03-14T20:16:35Z | MEMBER | null | null | null | Proposes adding uv to installation docs (see Slack thread [here](https://huggingface.slack.com/archives/C01N44FJDHT/p1737377177709279) for more context) if you're interested! | {
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https://api.github.com/repos/huggingface/datasets/issues/5302 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5302/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5302/comments | https://api.github.com/repos/huggingface/datasets/issues/5302/events | https://github.com/huggingface/datasets/pull/5302 | 1,464,778,901 | PR_kwDODunzps5DuJJp | 5,302 | Improve `use_auth_token` docstring and deprecate `use_auth_token` in `download_and_prepare` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-11-25T17:09:21Z | 2022-12-09T14:20:15Z | 2022-12-09T14:17:20Z | COLLABORATOR | null | null | null | Clarify in the docstrings what happens when `use_auth_token` is `None` and deprecate the `use_auth_token` param in `download_and_prepare`. | {
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https://api.github.com/repos/huggingface/datasets/issues/5804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5804/comments | https://api.github.com/repos/huggingface/datasets/issues/5804/events | https://github.com/huggingface/datasets/pull/5804 | 1,688,285,666 | PR_kwDODunzps5PX0Dk | 5,804 | Set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5804). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-04-28T10:10:01Z | 2023-04-28T10:18:51Z | 2023-04-28T10:10:29Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6572 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6572/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6572/comments | https://api.github.com/repos/huggingface/datasets/issues/6572/events | https://github.com/huggingface/datasets/pull/6572 | 2,072,384,281 | PR_kwDODunzps5jlCO5 | 6,572 | Adding option for multipart achive download | {
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"On closer examination, this appears to be unnecessary. "
] | 2024-01-09T13:35:44Z | 2024-02-25T08:13:01Z | 2024-02-25T08:13:01Z | NONE | null | null | null | Right now we can only download multiple separate archives or a single file archive, but not multipart archives, such as those produced by `tar --multi-volume`. This PR allows for downloading and extraction of archives split into multiple parts.
With the new `multi_part` field of the `DownloadConfig` set, the downloader will first retrieve all the files and attempt to concatenate them before starting extraction. This will obviously fail if files retrieved are actually multiple separate archives, so the option is set to `False` by default.
Tests and docs incoming. | {
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https://api.github.com/repos/huggingface/datasets/issues/4545 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4545/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4545/comments | https://api.github.com/repos/huggingface/datasets/issues/4545/events | https://github.com/huggingface/datasets/pull/4545 | 1,280,899,028 | PR_kwDODunzps46KV-y | 4,545 | Make DuplicateKeysError more user friendly [For Issue #2556] | {
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"> Nice thanks !\r\n> \r\n> After your changes feel free to mark this PR as \"ready for review\" ;)\r\n\r\nMarking PR ready for review.\r\n\r\n@lhoestq Let me know if there is anything else required or if we are good to go ahead and merge.",
"_The documentation is not available anymore as the PR was closed or mer... | 2022-06-22T21:01:34Z | 2022-06-28T09:37:06Z | 2022-06-28T09:26:04Z | CONTRIBUTOR | null | null | null | # What does this PR do?
## Summary
*DuplicateKeysError error does not provide any information regarding the examples which have the same the key.*
*This information is very helpful for debugging the dataset generator script.*
## Additions
-
## Changes
- Changed `DuplicateKeysError Class` in `src/datasets/keyhash.py` to add current index and duplicate_key_indices to error message.
- Changed `check_duplicate_keys` function in `src/datasets/arrow_writer.py` to find indices of examples with duplicate hash if duplicate keys are found.
## Deletions
-
## To do :
- [x] Find way to find and print path `<Path to Dataset>` in Error message
## Issues Addressed :
Fixes #2556 | {
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https://api.github.com/repos/huggingface/datasets/issues/5719 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5719/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5719/comments | https://api.github.com/repos/huggingface/datasets/issues/5719/events | https://github.com/huggingface/datasets/issues/5719 | 1,659,203,222 | I_kwDODunzps5i5W6W | 5,719 | Array2D feature creates a list of list instead of a numpy array | {
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"Hi! \r\n\r\nYou need to set the format to `np` before indexing the dataset to get NumPy arrays:\r\n```python\r\nfeatures = Features(dict(seq=Array2D((2,2), 'float32'))) \r\nds = Dataset.from_dict(dict(seq=[np.random.rand(2,2)]), features=features)\r\nds.set_format(\"np\")\r\na = ds[0]['seq']\r\n```\r\n\r\n> I th... | 2023-04-07T21:04:08Z | 2023-04-20T15:34:41Z | 2023-04-20T15:34:41Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I'm not sure if this is expected behavior or not. When I create a 2D array using `Array2D`, the data has list type instead of numpy array. I think it should not be the expected behavior especially when I feed a numpy array as input to the data creation function. Why is it converting my array into a list?
Also if I change the first dimension of the `Array2D` shape to None, it's returning array correctly.
### Steps to reproduce the bug
Run this code:
```py
from datasets import Dataset, Features, Array2D
import numpy as np
# you have to change the first dimension of the shape to None to make it return an array
features = Features(dict(seq=Array2D((2,2), 'float32')))
ds = Dataset.from_dict(dict(seq=[np.random.rand(2,2)]), features=features)
a = ds[0]['seq']
print(a)
print(type(a))
```
The following will be printed in stdout:
```
[[0.8127174377441406, 0.3760348856449127], [0.7510159611701965, 0.4322739541530609]]
<class 'list'>
```
### Expected behavior
Each indexed item should be a list or numpy array. Currently, `Array((2,2))` yields a list but `Array((None,2))` yields an array.
### Environment info
- `datasets` version: 2.11.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.13
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 1.4.4
| {
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https://api.github.com/repos/huggingface/datasets/issues/7466 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7466/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7466/comments | https://api.github.com/repos/huggingface/datasets/issues/7466/events | https://github.com/huggingface/datasets/pull/7466 | 2,928,661,327 | PR_kwDODunzps6PHQyp | 7,466 | Fix local pdf loading | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7466). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-03-18T14:09:06Z | 2025-03-18T14:11:52Z | 2025-03-18T14:09:21Z | MEMBER | null | null | null | fir this error when accessing a local pdf
```
File ~/.pyenv/versions/3.12.2/envs/hf-datasets/lib/python3.12/site-packages/pdfminer/psparser.py:220, in PSBaseParser.seek(self, pos)
218 """Seeks the parser to the given position."""
219 log.debug("seek: %r", pos)
--> 220 self.fp.seek(pos)
221 # reset the status for nextline()
222 self.bufpos = pos
ValueError: seek of closed file
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6750 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6750/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6750/comments | https://api.github.com/repos/huggingface/datasets/issues/6750/events | https://github.com/huggingface/datasets/issues/6750 | 2,203,590,658 | I_kwDODunzps6DWCAC | 6,750 | `load_dataset` requires a network connection for local download? | {
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"Are you using `HF_DATASETS_OFFLINE=1` ?",
"> Are you using `HF_DATASETS_OFFLINE=1` ?\r\n\r\nThis doesn't work for me. `datasets=2.18.0`\r\n\r\n`test.py`:\r\n```\r\nimport datasets\r\n\r\ndatasets.utils.logging.set_verbosity_info()\r\n\r\nds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c1... | 2024-03-23T01:06:32Z | 2024-04-15T15:38:52Z | 2024-04-15T15:38:52Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Hi all - I see that in the past a network dependency has been mistakenly introduced into `load_dataset` even for local loads. Is it possible this has happened again?
### Steps to reproduce the bug
```
>>> import datasets
>>> datasets.load_dataset("hh-rlhf")
Repo card metadata block was not found. Setting CardData to empty.
*hangs bc i'm firewalled*
````
stack trace from ctrl-c:
```
^CTraceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/load.py", line 2582, in load_dataset
builder_instance.download_and_prepare(
output_path = get_from_cache( [0/122]
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 532, in get_from_cache
response = http_head(
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 419, in http_head
response = _request_with_retry(
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 304, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/adapters.py", line 487, in send
resp = conn.urlopen(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 363, in connect
self.sock = conn = self._new_conn()
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
```
### Expected behavior
loads the dataset
### Environment info
```
> pip show datasets
Name: datasets
Version: 2.18.0
```
Python 3.10.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5905 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5905/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5905/comments | https://api.github.com/repos/huggingface/datasets/issues/5905/events | https://github.com/huggingface/datasets/issues/5905 | 1,727,541,392 | I_kwDODunzps5m-DCQ | 5,905 | Offer an alternative to Iterable Dataset that allows lazy loading and processing while skipping batches efficiently | {
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"We plan to improve this eventually (see https://github.com/huggingface/datasets/issues/5454 and https://github.com/huggingface/datasets/issues/5380).\r\n\r\n> Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe someth... | 2023-05-26T12:33:02Z | 2023-06-15T13:34:18Z | null | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Feature request
I would like a way to resume training from a checkpoint without waiting for a very long time when using an iterable dataset.
### Motivation
I am training models on the speech-recognition task. I have very large datasets that I can't comfortably store on a disk and also quite computationally intensive audio processing to do. As a result I want to load data from my remote when it is needed and perform all processing on the fly.
I am currently using the iterable dataset feature of _datasets_. It does everything I need with one exception. My issue is that when resuming training at a step n, we have to download all the data and perform the processing of steps < n, just to get the iterable at the right step. In my case it takes almost as long as training for the same steps, which make resuming training from a checkpoint useless in practice.
I understand that the nature of iterators make it probably nearly impossible to quickly resume training.
I thought about a possible solution nonetheless :
I could in fact index my large dataset and make it a mapped dataset. Then I could use set_transform to perform the processing on the fly. Finally, if I'm not mistaken, the _accelerate_ package allows to [skip steps efficiently](https://github.com/huggingface/accelerate/blob/a73898027a211c3f6dc4460351b0ec246aa824aa/src/accelerate/data_loader.py#L827) for a mapped dataset.
Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there.
If not, I could do it using a plain _Pytorch_ dataset. Then I would need to convert it to a _datasets_' dataset to get all the features of _datasets_. Is it something possible ?
### Your contribution
I could provide a PR to allow lazy loading of mapped dataset or the conversion of a mapped _Pytorch_ dataset into a _Datasets_ dataset if you think it is an useful new feature. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7503 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7503/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7503/comments | https://api.github.com/repos/huggingface/datasets/issues/7503/events | https://github.com/huggingface/datasets/issues/7503 | 2,978,512,625 | I_kwDODunzps6xiH7x | 7,503 | Inconsistency between load_dataset and load_from_disk functionality | {
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"Hi ! you can find more info here: https://github.com/huggingface/datasets/issues/5044#issuecomment-1263714347\n\n> What's the recommended approach for this use case? Should I manually process my gsm8k-new dataset to make it compatible with load_dataset? Is there a standard way to convert between these formats?\n\n... | 2025-04-08T03:46:22Z | 2025-04-15T12:39:53Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
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} | ## Issue Description
I've encountered confusion when using `load_dataset` and `load_from_disk` in the datasets library. Specifically, when working offline with the gsm8k dataset, I can load it using a local path:
```python
import datasets
ds = datasets.load_dataset('/root/xxx/datasets/gsm8k', 'main')
```
output:
```text
DatasetDict({
train: Dataset({
features: ['question', 'answer'],
num_rows: 7473
})
test: Dataset({
features: ['question', 'answer'],
num_rows: 1319
})
})
```
This works as expected. However, after processing the dataset (converting answer format from #### to \boxed{})
```python
import datasets
ds = datasets.load_dataset('/root/xxx/datasets/gsm8k', 'main')
ds_train = ds['train']
ds_test = ds['test']
import re
def convert(sample):
solution = sample['answer']
solution = re.sub(r'####\s*(\S+)', r'\\boxed{\1}', solution)
sample = {
'problem': sample['question'],
'solution': solution
}
return sample
ds_train = ds_train.map(convert, remove_columns=['question', 'answer'])
ds_test = ds_test.map(convert,remove_columns=['question', 'answer'])
```
I saved it using save_to_disk:
```python
from datasets.dataset_dict import DatasetDict
data_dict = DatasetDict({
'train': ds_train,
'test': ds_test
})
data_dict.save_to_disk('/root/xxx/datasets/gsm8k-new')
```
But now I can only load it using load_from_disk:
```python
new_ds = load_from_disk('/root/xxx/datasets/gsm8k-new')
```
output:
```text
DatasetDict({
train: Dataset({
features: ['problem', 'solution'],
num_rows: 7473
})
test: Dataset({
features: ['problem', 'solution'],
num_rows: 1319
})
})
```
Attempting to use load_dataset produces unexpected results:
```python
new_ds = load_dataset('/root/xxx/datasets/gsm8k-new')
```
output:
```text
DatasetDict({
train: Dataset({
features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'],
num_rows: 1
})
test: Dataset({
features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'],
num_rows: 1
})
})
```
Questions
1. Why is it designed such that after using `save_to_disk`, the dataset cannot be loaded with `load_dataset`? For small projects with limited code, it might be relatively easy to change all instances of `load_dataset` to `load_from_disk`. However, for complex frameworks like TRL or lighteval, diving into the framework code to change `load_dataset` to `load_from_disk` is extremely tedious and error-prone.
Additionally, `load_from_disk` cannot load datasets directly downloaded from the hub, which means that if you need to modify a dataset, you have to choose between using `load_from_disk` or `load_dataset`. This creates an unnecessary dichotomy in the API and complicates workflow when working with modified datasets.
2. What's the recommended approach for this use case? Should I manually process my gsm8k-new dataset to make it compatible with load_dataset? Is there a standard way to convert between these formats?
thanks~ | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7518 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7518/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7518/comments | https://api.github.com/repos/huggingface/datasets/issues/7518/events | https://github.com/huggingface/datasets/issues/7518 | 2,996,141,825 | I_kwDODunzps6ylX8B | 7,518 | num_proc parallelization works only for first ~10s. | {
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"Hi, can you check if the processes are still alive ? It's a bit weird because `datasets` does check if processes crash and return an error in that case",
"Thank you for reverting quickly. I digged a bit, and realized my disk's IOPS is also limited - which is causing this. will check further and report if it's an... | 2025-04-15T11:44:03Z | 2025-04-15T13:12:13Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
When I try to load an already downloaded dataset with num_proc=64, the speed is very high for the first 10-20 seconds acheiving 30-40K samples / s, and 100% utilization for all cores but it soon drops to <= 1000 with almost 0% utilization for most cores.
### Steps to reproduce the bug
```
// download dataset with cli
!huggingface-cli download --repo-type dataset timm/imagenet-1k-wds --max-workers 32
from datasets import load_dataset
ds = load_dataset("timm/imagenet-1k-wds", num_proc=64)
```
### Expected behavior
100% core utilization throughout.
### Environment info
Azure A100-80GB, 16 cores VM
 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5607 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5607/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5607/comments | https://api.github.com/repos/huggingface/datasets/issues/5607/events | https://github.com/huggingface/datasets/pull/5607 | 1,609,166,035 | PR_kwDODunzps5LQPbG | 5,607 | Fix outdated `verification_mode` values | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-03-03T19:50:29Z | 2023-03-09T17:34:13Z | 2023-03-09T17:27:07Z | CONTRIBUTOR | null | null | null | ~I think it makes sense not to save `dataset_info.json` file to a dataset cache directory when loading dataset with `verification_mode="no_checks"` because otherwise when next time the dataset is loaded **without** `verification_mode="no_checks"`, it will be loaded successfully, despite some values in info might not correspond to the ones in the repo which was the reason for using `verification_mode="no_checks"` first.~
Updated values of `verification_mode` to the current ones in some places ("none" -> "no_checks", "all" -> "all_checks") | {
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https://api.github.com/repos/huggingface/datasets/issues/4560 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4560/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4560/comments | https://api.github.com/repos/huggingface/datasets/issues/4560/events | https://github.com/huggingface/datasets/pull/4560 | 1,283,558,873 | PR_kwDODunzps46TY9n | 4,560 | Add evaluation metadata to imagenet-1k | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"As discussed with @lewtun, we are closing this PR, because it requires first the task names to be aligned between AutoTrain and datasets."
] | 2022-06-24T10:12:41Z | 2023-09-24T09:35:32Z | 2022-09-23T09:37:03Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6247 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6247/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6247/comments | https://api.github.com/repos/huggingface/datasets/issues/6247/events | https://github.com/huggingface/datasets/pull/6247 | 1,901,390,945 | PR_kwDODunzps5amAQ1 | 6,247 | Update create_dataset.mdx | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-09-18T17:06:29Z | 2023-09-19T18:51:49Z | 2023-09-19T18:40:10Z | CONTRIBUTOR | null | null | null | modified , as AudioFolder and ImageFolder not in Dataset Library.
``` from datasets import AudioFolder ``` and ```from datasets import ImageFolder``` to ```from datasets import load_dataset```
```
cannot import name 'AudioFolder' from 'datasets' (/home/eswardivi/miniconda3/envs/Hugformers/lib/python3.10/site-packages/datasets/__init__.py)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4746 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4746/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4746/comments | https://api.github.com/repos/huggingface/datasets/issues/4746/events | https://github.com/huggingface/datasets/issues/4746 | 1,318,486,599 | I_kwDODunzps5OloJH | 4,746 | Dataset Viewer issue for yanekyuk/wikikey | {
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"The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main\r\n\r\nMaybe the viewer can display a better message for empty datasets",
"OK. Closing as it's not an error. We will work on making the error message a lot clearer."
] | 2022-07-26T16:25:16Z | 2022-09-08T08:15:22Z | 2022-09-08T08:15:22Z | NONE | null | null | {
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_No response_
### Description
_No response_
### Owner
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https://api.github.com/repos/huggingface/datasets/issues/6904 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6904/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6904/comments | https://api.github.com/repos/huggingface/datasets/issues/6904/events | https://github.com/huggingface/datasets/pull/6904 | 2,302,912,179 | PR_kwDODunzps5vzRlD | 6,904 | Fix decoding multi part extension | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6904). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"takign the liberty to merge this for the viewer and a new dataset being released",
"<... | 2024-05-17T14:32:57Z | 2024-05-17T14:52:56Z | 2024-05-17T14:46:54Z | MEMBER | null | null | null | e.g. a field named `url.txt` should be a treated as text
I also included a small fix to support .npz correctly | {
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https://api.github.com/repos/huggingface/datasets/issues/5651 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5651/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5651/comments | https://api.github.com/repos/huggingface/datasets/issues/5651/events | https://github.com/huggingface/datasets/issues/5651 | 1,631,967,509 | I_kwDODunzps5hRdkV | 5,651 | expanduser in save_to_disk | {
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"`save_to_disk` should indeed expand `~`. Marking it as a \"good first issue\".",
"#self-assign\r\n\r\nFile path to code: \r\n\r\nhttps://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_dataset.py#L1364\r\n\r\n@RmZeta2718 I created a pull request for this issue. ",
"Hello, \r\nIt says `save_to_di... | 2023-03-20T12:02:18Z | 2023-10-27T14:04:37Z | 2023-10-27T14:04:37Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
save_to_disk() does not expand `~`
1. `dataset = load_datasets("any dataset")`
2. `dataset.save_to_disk("~/data")`
3. a folder named "~" created in current folder
4. FileNotFoundError is raised, because the expanded path does not exist (`/home/<user>/data`)
related issue https://github.com/huggingface/transformers/issues/10628
### Steps to reproduce the bug
As described above.
### Expected behavior
expanduser correctly
### Environment info
- datasets 2.10.1
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"completed": 0,
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} | ### Describe the bug
According the doc here: https://huggingface.co/docs/hub/datasets-file-names-and-splits#custom-split-name
it should infer the split name from the subdir of data or the beg of the name of the files in data.
When doing this manually through web upload it does not work. it uses "train" as a unique split.
example: https://huggingface.co/datasets/eole-nlp/estimator_chatml
### Steps to reproduce the bug
follow the link above
### Expected behavior
there should be two splits "mlqe" and "1720_da"
### Environment info
website | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-19T08:42:35Z | 2022-07-21T08:16:59Z | 2022-07-21T08:04:15Z | MEMBER | null | null | null | The `sox` OS package needs being installed manually using the distribution package manager.
This PR adds this explanation to the docs. | {
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https://api.github.com/repos/huggingface/datasets/issues/6085 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6085/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6085/comments | https://api.github.com/repos/huggingface/datasets/issues/6085/events | https://github.com/huggingface/datasets/pull/6085 | 1,824,985,188 | PR_kwDODunzps5WlAyA | 6,085 | Fix `fsspec` download | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-07-27T18:54:47Z | 2023-07-27T19:06:13Z | null | COLLABORATOR | null | null | null | Testing `ds = load_dataset("audiofolder", data_files="s3://datasets.huggingface.co/SpeechCommands/v0.01/v0.01_test.tar.gz", storage_options={"anon": True})` and trying to fix the issues raised by `fsspec` ...
TODO: fix
```
self.session = aiobotocore.session.AioSession(**self.kwargs)
TypeError: __init__() got an unexpected keyword argument 'hf'
```
by "preparing `storage_options`" for the `fsspec` head/get | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6020 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6020/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6020/comments | https://api.github.com/repos/huggingface/datasets/issues/6020/events | https://github.com/huggingface/datasets/issues/6020 | 1,799,720,536 | I_kwDODunzps5rRY5Y | 6,020 | Inconsistent "The features can't be aligned" error when combining map, multiprocessing, and variable length outputs | {
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"This scenario currently requires explicitly passing the target features (to avoid the error): \r\n```python\r\nimport datasets\r\n\r\n...\r\n\r\nfeatures = dataset.features\r\nfeatures[\"output\"] = = [{\"test\": datasets.Value(\"int64\")}]\r\ntest2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=... | 2023-07-11T20:40:38Z | 2024-10-27T06:30:13Z | null | NONE | null | null | {
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} | ### Describe the bug
I'm using a dataset with map and multiprocessing to run a function that returned a variable length list of outputs. This output list may be empty. Normally this is handled fine, but there is an edge case that crops up when using multiprocessing. In some cases, an empty list result ends up in a dataset shard consisting of a single item. This results in a `The features can't be aligned` error that is difficult to debug because it depends on the number of processes/shards used.
I've reproduced a minimal example below. My current workaround is to fill empty results with a dummy value that I filter after, but this was a weird error that took a while to track down.
### Steps to reproduce the bug
```python
import datasets
dataset = datasets.Dataset.from_list([{'idx':i} for i in range(60)])
def test_func(row, idx):
if idx==58:
return {'output': []}
else:
return {'output' : [{'test':1}, {'test':2}]}
# this works fine
test1 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=4)
# this fails
test2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32)
>ValueError: The features can't be aligned because the key output of features {'idx': Value(dtype='int64', id=None), 'output': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None)} has unexpected type - Sequence(feature=Value(dtype='null', id=None), length=-1, id=None) (expected either [{'test': Value(dtype='int64', id=None)}] or Value("null").
```
The error occurs during the check
```python
_check_if_features_can_be_aligned([dset.features for dset in dsets])
```
When the multiprocessing splitting lines up just right with the empty return value, one of the `dset` in `dsets` will have a single item with an empty list value, causing the error.
### Expected behavior
Expected behavior is the result would be the same regardless of the `num_proc` value used.
### Environment info
Datasets version 2.11.0
Python 3.9.16 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6325 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6325/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6325/comments | https://api.github.com/repos/huggingface/datasets/issues/6325/events | https://github.com/huggingface/datasets/pull/6325 | 1,955,420,178 | PR_kwDODunzps5dcSM3 | 6,325 | Create battery_analysis.py | {
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https://api.github.com/repos/huggingface/datasets/issues/4892 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4892/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4892/comments | https://api.github.com/repos/huggingface/datasets/issues/4892/events | https://github.com/huggingface/datasets/pull/4892 | 1,350,636,499 | PR_kwDODunzps49yCD3 | 4,892 | Add citation to ro_sts and ro_sts_parallel datasets | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4892). All of your documentation changes will be reflected on that endpoint."
] | 2022-08-25T09:51:06Z | 2022-08-25T10:49:56Z | 2022-08-25T10:49:56Z | MEMBER | null | null | null | This PR adds the citation information to `ro_sts_parallel` and `ro_sts_parallel` datasets, once they have replied our request for that information:
- https://github.com/dumitrescustefan/RO-STS/issues/4 | {
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https://api.github.com/repos/huggingface/datasets/issues/5045 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5045/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5045/comments | https://api.github.com/repos/huggingface/datasets/issues/5045/events | https://github.com/huggingface/datasets/issues/5045 | 1,391,287,609 | I_kwDODunzps5S7V05 | 5,045 | Automatically revert to last successful commit to hub when a push_to_hub is interrupted | {
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"Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nMaybe `push_to_hub` be implemented as a single commit @Wauplin ? This way if it fails, the repo is still at the previous (valid) state instead of ending-up in an invalid/incimplete state.",
"> Maybe push_to_hub be imple... | 2022-09-29T18:08:12Z | 2023-10-16T13:30:49Z | 2023-10-16T13:30:49Z | NONE | null | null | {
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} | **Is your feature request related to a problem? Please describe.**
I pushed a modification of a large dataset (remove a column) to the hub. The push was interrupted after some files were committed to the repo. This left the dataset to raise an error on load_dataset() (ValueError couldn’t cast … because column names don’t match). Only by specifying the previous (complete) commit as revision=commit_hash in load_data(), I was able to repair this and after a successful, complete push, the dataset loads without error again.
**Describe the solution you'd like**
Would it make sense to detect an incomplete push_to_hub() and automatically revert to the previous commit/revision?
**Describe alternatives you've considered**
Leave everything as is, the revision parameter in load_dataset() allows to manually fix this problem.
**Additional context**
Provide useful defaults
| {
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https://api.github.com/repos/huggingface/datasets/issues/6988 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6988/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6988/comments | https://api.github.com/repos/huggingface/datasets/issues/6988/events | https://github.com/huggingface/datasets/pull/6988 | 2,364,129,918 | PR_kwDODunzps5zDpXX | 6,988 | [`feat`] Move dataset card creation to method for easier overriding | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6988). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"`Dataset` objects are not made to be subclassed, so I don't think going in that directi... | 2024-06-20T10:47:57Z | 2024-06-21T16:04:58Z | null | MEMBER | null | null | null | Hello!
## Pull Request overview
* Move dataset card creation to method for easier overriding
## Details
It's common for me to fully automatically download, reformat, and upload a dataset (e.g. see https://huggingface.co/datasets?other=sentence-transformers), but one aspect that I cannot easily automate is the dataset card generation. This is because during `push_to_hub`, the dataset card is created in 3 lines of code in a much larger method. To automatically generate a dataset card, I need to either:
1. Subclass `Dataset`/`DatasetDict`, copy the entire `push_to_hub` method to override the ~3 lines used to generate the dataset card. This is not viable as the method is likely to change over time.
2. Use `push_to_hub` normally, then separately download the pushed (but empty) dataset card, update it, and reupload the modified dataset. This works fine, but prevents me from being able to return a `Dataset` to my users which will automatically use a nice dataset card.
So, in this PR I'm proposing to move the dataset generation into another method so that it can be overridden more easily. For example, imagine the following use case:
````python
import json
from typing import Any, Dict, Optional
from datasets import Dataset, load_dataset
from datasets.info import DatasetInfosDict, DatasetInfo
from datasets.utils.metadata import MetadataConfigs
from huggingface_hub import DatasetCardData, DatasetCard
TEMPLATE = r"""---
{dataset_card_data}
---
# Dataset Card for {source_dataset_name} with mined hard negatives
This dataset is a collection of {column_one}-{column_two}-negative triplets from the {source_dataset_name} dataset. See [{source_dataset_name}](https://huggingface.co/datasets/{source_dataset_id}) for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.
## Mining Parameters
The negative samples have been mined using the following parameters:
- `range_min`: {range_min}, i.e. we skip the {range_min} most similar samples
- `range_max`: {range_max}, i.e. we only look at the top {range_max} most similar samples
- `margin`: {margin}, i.e. we require negative similarity + margin < positive similarity, so negative samples can't be more similar than the known true answer
- `sampling_strategy`: {sampling_strategy}, i.e. whether to randomly sample from the candidate negatives or take the "top" negatives
- `num_negatives`: {num_negatives}, i.e. we mine {num_negatives} negatives per question-answer pair
## Dataset Format
- Columns: {column_one}, {column_two}, negative
- Column types: str, str, str
- Example:
```python
{example}
```
"""
class HNMDataset(Dataset):
@classmethod
def from_dict(cls, *args, mining_kwargs: Dict[str, Any], **kwargs) -> "HNMDataset":
dataset = super().from_dict(*args, **kwargs)
dataset.mining_kwargs = mining_kwargs
return dataset
def _create_dataset_card(
self,
dataset_card_data: DatasetCardData,
dataset_card: Optional[DatasetCard],
config_name: str,
info_to_dump: DatasetInfo,
metadata_config_to_dump: MetadataConfigs,
) -> DatasetCard:
if dataset_card:
return dataset_card
DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)
MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)
dataset_card_data.tags = ["sentence-transformers"]
dataset_name = self.mining_kwargs["source_dataset"].info.dataset_name
# Very messy, just as an example:
dataset_id = list(self.mining_kwargs["source_dataset"].info.download_checksums.keys())[0].removeprefix("hf://datasets/").split("@")[0]
content = TEMPLATE.format(**{
"dataset_card_data": str(dataset_card_data),
"source_dataset_name": dataset_name,
"source_dataset_id": dataset_id,
"range_min": self.mining_kwargs["range_min"],
"range_max": self.mining_kwargs["range_max"],
"margin": self.mining_kwargs["margin"],
"sampling_strategy": self.mining_kwargs["sampling_strategy"],
"num_negatives": self.mining_kwargs["num_negatives"],
"column_one": self.column_names[0],
"column_two": self.column_names[1],
"example": json.dumps(self[0], indent=4),
})
return DatasetCard(content)
source_dataset = load_dataset("sentence-transformers/gooaq", split="train[:100]")
dataset = HNMDataset.from_dict({
"query": source_dataset["question"],
"answer": source_dataset["answer"],
# "negative": ... <- In my case, this column would be 'mined' automatically with these parameters
}, mining_kwargs={
"range_min": 10,
"range_max": 20,
"max_score": 0.9,
"margin": 0.1,
"sampling_strategy": "random",
"num_negatives": 3,
"source_dataset": source_dataset,
})
dataset.push_to_hub("tomaarsen/mining_demo", private=True)
````
In this script, I've created a subclass which stores some additional information about how the dataset was generated. It's a bit hacky (e.g. setting a `mining_kwargs` parameter in `from_dict` that wasn't created in `__init__`, but that's just a consequence of how the `from_...` methods don't accept kwargs), but it allows me to create a "hard negatives mining" function that returns a dataset which people can use locally like normal, but if they choose to upload it, then it'll automatically include some information, e.g.: https://huggingface.co/datasets/tomaarsen/mining_demo
This allows others to actually find this dataset (e.g. via the `sentence-transformers` tag) and get an idea of the quality, source, etc. by looking at the model card.
## Note
I'm not fixed on this solution whatsoever: I am also completely fine with other solutions, e.g. a `dataset.set_dataset_card_creator` method that allows me to provide a function without even having to subclass anything. I'm open to all ideas :)
cc @albertvillanova @lhoestq
cc @LysandreJik
- Tom Aarsen | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5236 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5236/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5236/comments | https://api.github.com/repos/huggingface/datasets/issues/5236/events | https://github.com/huggingface/datasets/pull/5236 | 1,448,190,801 | PR_kwDODunzps5C2Hnj | 5,236 | Handle ArrowNotImplementedError caused by try_type being Image or Audio in cast | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Not sure how we can have a test that is relevant for this though - feel free to add one if you have ideas\r\n\r\nYes, this was my reasoning for not adding a test. This change is pretty simple, so I think it's OK not to have a test ... | 2022-11-14T14:38:59Z | 2022-11-14T16:04:29Z | 2022-11-14T16:01:48Z | COLLABORATOR | null | null | null | Handle the `ArrowNotImplementedError` thrown when `try_type` is `Image` or `Audio` and the input array cannot be converted to their storage formats.
Reproducer:
```python
from datasets import Dataset
from PIL import Image
import requests
ds = Dataset.from_dict({"image": [Image.open(requests.get("https://upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png", stream=True).raw)]})
ds.map(lambda x: {"image": True}) # ArrowNotImplementedError
```
PS: This could also be fixed by raising `TypeError` in `{Image, Audio}.cast_storage` for unsupported types instead of passing the array to `array_cast.` | {
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https://api.github.com/repos/huggingface/datasets/issues/5754 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5754/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5754/comments | https://api.github.com/repos/huggingface/datasets/issues/5754/events | https://github.com/huggingface/datasets/pull/5754 | 1,668,755,035 | PR_kwDODunzps5OWozh | 5,754 | Minor tqdm fixes | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-14T18:15:14Z | 2023-04-20T15:27:58Z | 2023-04-20T15:21:00Z | COLLABORATOR | null | null | null | `GeneratorBasedBuilder`'s TQDM bars were not used as context managers. This PR fixes that (missed these bars in https://github.com/huggingface/datasets/pull/5560).
Also, this PR modifies the single-proc `save_to_disk` to fix the issue with the TQDM bar not accumulating the progress in the multi-shard setting (again, this bug was introduced by me in the linked PR 😎) | {
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https://api.github.com/repos/huggingface/datasets/issues/5776 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5776/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5776/comments | https://api.github.com/repos/huggingface/datasets/issues/5776/events | https://github.com/huggingface/datasets/issues/5776 | 1,677,116,100 | I_kwDODunzps5j9sLE | 5,776 | Use Pandas' `read_json` in the JSON builder | {
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} | Instead of PyArrow's `read_json`, we should use `pd.read_json` in the JSON builder for consistency with the CSV and SQL builders (e.g., to address https://github.com/huggingface/datasets/issues/5725).
In Pandas2.0, to get the same performance, we can set the `engine` to "pyarrow". The issue is that Colab still doesn't install Pandas 2.0 by default, so I think it's best to wait for this to be resolved on their side to avoid downgrading decoding performance in scenarios when Pandas 2.0 is not installed. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7372 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7372/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7372/comments | https://api.github.com/repos/huggingface/datasets/issues/7372/events | https://github.com/huggingface/datasets/issues/7372 | 2,791,760,968 | I_kwDODunzps6mZuRI | 7,372 | Inconsistent Behavior Between `load_dataset` and `load_from_disk` When Loading Sharded Datasets | {
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} | [] | open | false | null | [] | null | [] | 2025-01-16T05:47:20Z | 2025-01-16T05:47:20Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Description
I encountered an inconsistency in behavior between `load_dataset` and `load_from_disk` when loading sharded datasets. Here is a minimal example to reproduce the issue:
#### Code 1: Using `load_dataset`
```python
from datasets import Dataset, load_dataset
# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Load the DatasetDict
loaded_datasetdict = load_dataset("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 1350 samples.
- `test` has 150 samples.
#### Code 2: Using `load_from_disk`
```python
from datasets import Dataset, load_from_disk
# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Load the DatasetDict
loaded_datasetdict = load_from_disk("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 450 samples.
- `test` has 50 samples.
### Expected Behavior
I expected both `load_dataset` and `load_from_disk` to load the same dataset, as they are pointing to the same directory. However, the results differ significantly:
- `load_dataset` seems to merge all shards, resulting in a combined dataset.
- `load_from_disk` only loads the last saved dataset, ignoring previous shards.
### Questions
1. Is this behavior intentional? If so, could you clarify the difference between `load_dataset` and `load_from_disk` in the documentation?
2. If this is not intentional, could this be considered a bug?
3. What is the recommended way to handle cases where multiple datasets are saved to the same directory?
Thank you for your time and effort in maintaining this great library! I look forward to your feedback. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7496 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7496/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7496/comments | https://api.github.com/repos/huggingface/datasets/issues/7496/events | https://github.com/huggingface/datasets/issues/7496 | 2,967,345,522 | I_kwDODunzps6w3hly | 7,496 | Json builder: Allow features to override problematic Arrow types | {
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{
"color": "a2eeef",
"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement"
}
] | open | false | null | [] | null | [
"Hi ! It would be cool indeed, currently the JSON data are generally loaded here: \n\nhttps://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/packaged_modules/json/json.py#L137-L140\n\nMaybe we can pass a Arrow `schema` to avoid errors ?"
] | 2025-04-02T19:27:16Z | 2025-04-15T13:06:09Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Feature request
In the JSON builder, use explicitly requested feature types before or while converting to Arrow.
### Motivation
Working with JSON datasets is really hard because of Arrow. At the very least, it seems like it should be possible to work-around these problems by explicitly setting problematic columns's types. But it seems like this is not possible because the features are only used *after* converting to arrow.
Here's a simple example where the Arrow error could potentially be avoided by converting the column to a string: https://colab.research.google.com/drive/16QHRdbUwKSrpwVfGwu8V8AHr8v2dv0dt?usp=sharing
### Your contribution
Maybe with some guidance. I'm not very familiar with arrow or pandas. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6502 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6502/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6502/comments | https://api.github.com/repos/huggingface/datasets/issues/6502/events | https://github.com/huggingface/datasets/pull/6502 | 2,043,771,731 | PR_kwDODunzps5iHPt- | 6,502 | Pickle support for `torch.Generator` objects | {
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} | [] | closed | false | null | [] | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6502). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2023-12-15T13:55:12Z | 2023-12-15T15:04:33Z | 2023-12-15T14:58:22Z | COLLABORATOR | null | null | null | Fix for https://discuss.huggingface.co/t/caching-a-dataset-processed-with-randomness/65616 | {
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https://api.github.com/repos/huggingface/datasets/issues/7486 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7486/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7486/comments | https://api.github.com/repos/huggingface/datasets/issues/7486/events | https://github.com/huggingface/datasets/issues/7486 | 2,954,042,179 | I_kwDODunzps6wExtD | 7,486 | `shared_datadir` fixture is missing | {
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} | [] | closed | false | null | [] | null | [
"OK I was missing the `pytest-datadir` package. Sorry for the noise!"
] | 2025-03-27T18:17:12Z | 2025-03-27T19:49:11Z | 2025-03-27T19:49:10Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Running the tests for the latest release fails due to missing `shared_datadir` fixture.
### Steps to reproduce the bug
Running `pytest` while building a package for Arch Linux leads to these errors:
```
==================================== ERRORS ====================================
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>1] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>2] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>3] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>4] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>5] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>6] _________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
_______________ ERROR at setup of test_dataset_with_pdf_feature ________________
[gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 34
@require_pdfplumber
def test_dataset_with_pdf_feature(shared_datadir):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:34
_________ ERROR at setup of test_pdf_feature_encode_example[<lambda>0] _________
[gw46] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python
file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8
@require_pdfplumber
@pytest.mark.parametrize(
"build_example",
[
lambda pdf_path: pdf_path,
lambda pdf_path: open(pdf_path, "rb").read(),
lambda pdf_path: {"path": pdf_path},
lambda pdf_path: {"path": pdf_path, "bytes": None},
lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()},
lambda pdf_path: {"bytes": open(pdf_path, "rb").read()},
],
)
def test_pdf_feature_encode_example(shared_datadir, build_example):
E fixture 'shared_datadir' not found
> available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file
> use 'pytest --fixtures [testpath]' for help on them.
/build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8
```
### Expected behavior
All fixtures used in tests should be available.
### Environment info
Arch Linux build system, building the [python-datasets](https://gitlab.archlinux.org/archlinux/packaging/packages/python-datasets) package.
There are actually [many deselected tests](https://gitlab.archlinux.org/archlinux/packaging/packages/python-datasets/-/blob/6f97957f0c326cc7b3da6b7f12326305bcaef374/PKGBUILD#L66-148) which were failing on previous releases, but these errors popped up in 3.5.0. | {
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https://api.github.com/repos/huggingface/datasets/issues/5550 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5550/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5550/comments | https://api.github.com/repos/huggingface/datasets/issues/5550/events | https://github.com/huggingface/datasets/pull/5550 | 1,591,409,475 | PR_kwDODunzps5KUl5i | 5,550 | Resolve four broken refs in the docs | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"See the resolved changes [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.Dataset.class_encode_column), [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/pa... | 2023-02-20T08:52:11Z | 2023-02-20T15:16:13Z | 2023-02-20T15:09:13Z | MEMBER | null | null | null | Hello!
## Pull Request overview
* Resolve 4 broken references in the docs
## The problems
Two broken references [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.class_encode_column):

---
One broken reference [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique):

---
One missing reference [here](https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column):

- Tom Aarsen | {
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https://api.github.com/repos/huggingface/datasets/issues/5970 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5970/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5970/comments | https://api.github.com/repos/huggingface/datasets/issues/5970/events | https://github.com/huggingface/datasets/issues/5970 | 1,766,010,356 | I_kwDODunzps5pQy30 | 5,970 | description disappearing from Info when Uploading a Dataset Created with `from_dict` | {
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"Here's a minimal way to reproduce the bug, for the sake of convenience.\r\n````\r\nfrom datasets import Dataset, DatasetInfo, load_dataset\r\n\r\n\r\nepisodes_dict = {\"test\":[1,2,3],\"test2\": [1,2,4]}\r\n\r\nhugging_face_dataset = Dataset.from_dict(\r\n episodes_dict, info=DatasetInfo(description=\"test_str\... | 2023-06-20T19:18:26Z | 2023-06-22T14:23:56Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
When uploading a dataset created locally using `from_dict` with a specified `description` field. It appears before upload, but is missing after upload and re-download.
### Steps to reproduce the bug
I think the most relevant pattern in the code might be the following lines:
```
description_json_str = json.dumps(
{
"dataset_id": dataset.spec.dataset_id,
"env_name": dataset.spec.env_spec.id,
"action_space": serialize_space(dataset.spec.action_space),
"observation_space": serialize_space(dataset.spec.observation_space),
}
)
hugging_face_dataset = Dataset.from_dict(
episodes_dict, info=DatasetInfo(description=description_json_str)
)
```
Which comes from this function https://github.com/balisujohn/minarai/blob/8e023727f0a8488c4451651d9f7a79b981412c40/minari/integrations/hugging_face.py#L39
To replicate,
clone this branch of my Minari fork https://github.com/balisujohn/minarai/tree/dev-huggingface then run
```
python3.8 -m venv env
source env/bin/activate
python3 -m pip install -e .
python3 -m pip install pytest
```
The change the hugging face repo path in the test called `test_hugging_face_push_and_pull_dataset` in `tests/integrations/test_hugging_face.py` to one you have permissions to write to.
Then run:
```
pytest tests/integrations/test_hugging_face.py::test_hugging_face_push_and_pull_dataset
```
### Expected behavior
DATASET INFO BEFORE UPLOADING
DatasetInfo(description='{"dataset_id": "dummy-combo-test-v0", "env_name": "DummyComboEnv-v0", "action_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}]}", "observation_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"component_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [-1.0], \\"high\\": [1.0]}, \\"component_2\\": {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"subcomponent_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, \\"subcomponent_2\\": {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}, {\\"type\\": \\"Discrete\\", \\"dtype\\": \\"int64\\", \\"start\\": 0, \\"n\\": 10}]}}}}}]}]}"}', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None)
...
DATASET INFO AFTER UPLOADING AND DOWNLOADING
DatasetInfo(description='', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits={'train': SplitInfo(name='train', num_bytes=4846, num_examples=60, shard_lengths=None, dataset_name='parquet')}, download_checksums={'https://huggingface.co/datasets/balisujohn/minari_test/resolve/8217b614ff9ba5edc1a30c7df430e92a46f65363/data/train-00000-of-00001-7c5900b93b35745e.parquet': {'num_bytes': 9052, 'checksum': None}}, download_size=9052, post_processing_size=None, dataset_size=4846, size_in_bytes=13898)
...
### Environment info
- `datasets` version: 2.13.0
- Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.2
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https://api.github.com/repos/huggingface/datasets/issues/5525 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5525/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5525/comments | https://api.github.com/repos/huggingface/datasets/issues/5525/events | https://github.com/huggingface/datasets/issues/5525 | 1,580,342,729 | I_kwDODunzps5eMh3J | 5,525 | TypeError: Couldn't cast array of type string to null | {
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"Thanks for reporting, @TJ-Solergibert.\r\n\r\nWe cannot access your Colab notebook: `There was an error loading this notebook. Ensure that the file is accessible and try again.`\r\nCould you please make it publicly accessible?\r\n",
"I swear it's public, I've checked the settings and I've been able to open it in... | 2023-02-10T21:12:36Z | 2023-02-14T17:41:08Z | 2023-02-14T09:35:49Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Processing a dataset I alredy uploaded to the Hub (https://huggingface.co/datasets/tj-solergibert/Europarl-ST) I found that for some splits and some languages (test split, source_lang = "nl") after applying a map function I get the mentioned error.
I alredy tried reseting the shorter strings (reset_cortas function). It only happends with NL, PL, RO and PT. It does not make sense since when processing the other languages I also use the corpus of those that fail and it does not cause any errors.
I suspect that the error may be in this direction:
We use cast_array_to_feature to support casting to custom types like Audio and Image # Also, when trying type "string", we don't want to convert integers or floats to "string". # We only do it if trying_type is False - since this is what the user asks for.
### Steps to reproduce the bug
Here I link a colab notebook to reproduce the error:
https://colab.research.google.com/drive/1JCrS7FlGfu_kFqChMrwKZ_bpabnIMqbP?authuser=1#scrollTo=FBAvlhMxIzpA
### Expected behavior
Data processing does not fail. A correct example can be seen here: https://huggingface.co/datasets/tj-solergibert/Europarl-ST-processed-mt-en
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/7285 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7285/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7285/comments | https://api.github.com/repos/huggingface/datasets/issues/7285/events | https://github.com/huggingface/datasets/pull/7285 | 2,644,488,598 | PR_kwDODunzps6BV3Gu | 7,285 | Release v3.1.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5188 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5188/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5188/comments | https://api.github.com/repos/huggingface/datasets/issues/5188/events | https://github.com/huggingface/datasets/pull/5188 | 1,432,477,139 | PR_kwDODunzps5CBaoQ | 5,188 | add: segmentation guide. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks @osanseviero. Am I good to merge? ",
"I would wait for a second approval just in case :) ",
"Sure :) ",
"Merging since the images have been pushed as LFS files ([PR](https://huggingface.co/datasets/huggingface/documentat... | 2022-11-02T04:34:36Z | 2022-11-04T18:25:57Z | 2022-11-04T18:23:34Z | MEMBER | null | null | null | Closes #5181
I have opened a PR on Hub (https://huggingface.co/datasets/huggingface/documentation-images/discussions/5) to include the images in our central Hub repository. Once the PR is merged I will edit the image links.
I have also prepared a [Colab Notebook](https://colab.research.google.com/drive/1BMDCfOTBnyshoME5RSxn5iQy-TWeFbOA?usp=sharing) in case anyone wants to play.
- [x] Replace the image links | {
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https://api.github.com/repos/huggingface/datasets/issues/5641 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5641/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5641/comments | https://api.github.com/repos/huggingface/datasets/issues/5641/events | https://github.com/huggingface/datasets/issues/5641 | 1,625,942,730 | I_kwDODunzps5g6erK | 5,641 | Features cannot be named "self" | {
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} | ### Describe the bug
Hi,
I noticed that we cannot create a HuggingFace dataset from Pandas DataFrame with a column named `self`.
The error seems to be coming from arguments validation in the `Features.from_dict` function.
### Steps to reproduce the bug
```python
import datasets
dummy_pandas = pd.DataFrame([0,1,2,3], columns = ["self"])
datasets.arrow_dataset.Dataset.from_pandas(dummy_pandas)
```
### Expected behavior
No error thrown
### Environment info
- `datasets` version: 2.8.0
- Python version: 3.9.5
- PyArrow version: 6.0.1
- Pandas version: 1.4.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6010 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6010/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6010/comments | https://api.github.com/repos/huggingface/datasets/issues/6010/events | https://github.com/huggingface/datasets/issues/6010 | 1,793,838,152 | I_kwDODunzps5q68xI | 6,010 | Improve `Dataset`'s string representation | {
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"I want to take a shot at this if possible ",
"Yes, feel free to work on this.\r\n\r\nYou can check the PyArrow Table `__repr__` and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas.",
"@mariosasko are there any other similar issues that I could work on? I see this has been alr... | 2023-07-07T16:38:03Z | 2023-09-01T03:45:07Z | null | COLLABORATOR | null | null | {
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} | Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows.
We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5610 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5610/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5610/comments | https://api.github.com/repos/huggingface/datasets/issues/5610/events | https://github.com/huggingface/datasets/issues/5610 | 1,610,698,006 | I_kwDODunzps5gAU0W | 5,610 | use datasets streaming mode in trainer ddp mode cause memory leak | {
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"Same problem, \r\ntransformers 4.28.1\r\ndatasets 2.12.0\r\n\r\nleak around 100Mb per 10 seconds when use dataloader_num_werker > 0 in training argumennts for transformer train, possile bug in transformers repo, but still not found solution :(\r\n",
"found an article described a problem, may be helpful for someb... | 2023-03-06T05:26:49Z | 2024-03-07T01:11:32Z | null | NONE | null | null | {
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} | ### Describe the bug
use datasets streaming mode in trainer ddp mode cause memory leak
### Steps to reproduce the bug
import os
import time
import datetime
import sys
import numpy as np
import random
import torch
from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler
torch.manual_seed(42)
from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM
from transformers import AdamW, get_linear_schedule_with_warmup
hf_model_path ='./Wenzhong-GPT2-110M'
tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path)
tokenizer.add_special_tokens({'pad_token': '<|pad|>'})
from datasets import load_dataset
gpus=8
max_len = 576
batch_size_node = 17
save_step = 5000
gradient_accumulation = 2
dataloader_num = 4
max_step = 351000*1000//batch_size_node//gradient_accumulation//gpus
#max_step = -1
print("total_step:%d"%(max_step))
import datasets
datasets.version
dataset = load_dataset("text", data_files="./gpt_data_v1/*",split='train',cache_dir='./dataset_cache',streaming=True)
print('load over')
shuffled_dataset = dataset.shuffle(seed=42)
print('shuffle over')
def dataset_tokener(example,max_lenth=max_len):
example['text'] = list(map(lambda x : x.strip()+'<|endoftext|>',example['text'] ))
return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest")
new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"])
print('map over')
configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False)
model = AutoModelForCausalLM.from_pretrained(hf_model_path)
model.resize_token_embeddings(len(tokenizer))
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
from transformers import Trainer,TrainingArguments
import os
print("strat train")
training_args = TrainingArguments(output_dir="./test_trainer",
num_train_epochs=1.0,
report_to="none",
do_train=True,
dataloader_num_workers=dataloader_num,
local_rank=int(os.environ.get('LOCAL_RANK', -1)),
overwrite_output_dir=True,
logging_strategy='steps',
logging_first_step=True,
logging_dir="./logs",
log_on_each_node=False,
per_device_train_batch_size=batch_size_node,
warmup_ratio=0.03,
save_steps=save_step,
save_total_limit=5,
gradient_accumulation_steps=gradient_accumulation,
max_steps=max_step,
disable_tqdm=False,
data_seed=42
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=new_new_dataset,
eval_dataset=None,
tokenizer=tokenizer,
data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False),
#compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
#preprocess_logits_for_metrics=preprocess_logits_for_metrics
#if training_args.do_eval and not is_torch_tpu_available()
#else None,
)
trainer.train(resume_from_checkpoint=True)
### Expected behavior
use the train code uppper
my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb
start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py
here is result:

here is memory usage monitor in 12 hours

every dataloader work allocate over 24gb cpu memory
according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase.
i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak.
### Environment info
pytorch 1.11.0
py 3.8
cuda 11.3
transformers 4.26.1
datasets 2.9.0
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https://api.github.com/repos/huggingface/datasets/issues/5996 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5996/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5996/comments | https://api.github.com/repos/huggingface/datasets/issues/5996/events | https://github.com/huggingface/datasets/pull/5996 | 1,779,294,374 | PR_kwDODunzps5UKP0i | 5,996 | Deprecate `use_auth_token` in favor of `token` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-06-28T16:26:38Z | 2023-07-05T15:22:20Z | 2023-07-03T16:03:33Z | COLLABORATOR | null | null | null | ... to be consistent with `transformers` and `huggingface_hub`. | {
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https://api.github.com/repos/huggingface/datasets/issues/5494 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5494/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5494/comments | https://api.github.com/repos/huggingface/datasets/issues/5494/events | https://github.com/huggingface/datasets/issues/5494 | 1,566,655,348 | I_kwDODunzps5dYUN0 | 5,494 | Update audio installation doc page | {
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"Totally agree, the docs should be in sync with our code.\r\n\r\nIndeed to avoid confusing users, I think we should have updated the docs at the same time as this PR:\r\n- #5167",
"@albertvillanova yeah sure I should have, but I forgot back then, sorry for that 😶",
"No, @polinaeterna, nothing to be sorry about... | 2023-02-01T19:07:50Z | 2023-03-02T16:08:17Z | 2023-03-02T16:08:17Z | CONTRIBUTOR | null | null | {
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} | Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on all linux versions but there is a custom ubuntu repo for it, we have insctructions in the code: https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L327
So we should update the doc page. But first investigate [this issue](5488). | {
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https://api.github.com/repos/huggingface/datasets/issues/4815 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4815/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4815/comments | https://api.github.com/repos/huggingface/datasets/issues/4815/events | https://github.com/huggingface/datasets/issues/4815 | 1,334,078,303 | I_kwDODunzps5PhGtf | 4,815 | Outdated loading script for OPUS ParaCrawl dataset | {
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Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.
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https://api.github.com/repos/huggingface/datasets/issues/4875 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4875/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4875/comments | https://api.github.com/repos/huggingface/datasets/issues/4875/events | https://github.com/huggingface/datasets/issues/4875 | 1,348,095,686 | I_kwDODunzps5QWk7G | 4,875 | `_resolve_features` ignores the token | {
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"Hi ! Your HF_ENDPOINT seems wrong because of the extra \"/\"\r\n```diff\r\n- os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co/\"\r\n+ os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co\"\r\n```\r\n\r\ncan you try again without the extra \"/\" ?",
"Oh, yes, sorry, but it's not the issue.\r... | 2022-08-23T14:57:36Z | 2022-10-17T13:45:47Z | null | COLLABORATOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ## Describe the bug
When calling [`_resolve_features()`](https://github.com/huggingface/datasets/blob/54b532a8a2f5353fdb0207578162153f7b2da2ec/src/datasets/iterable_dataset.py#L1255) on a gated dataset, ie. a dataset which requires a token to be loaded, the token seems to be ignored even if it has been provided to `load_dataset` before.
## Steps to reproduce the bug
```python
import os
os.environ["HF_ENDPOINT"] = "https://hub-ci.huggingface.co/"
hf_token = "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"
from datasets import load_dataset
# public
dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654226756"
config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654226756"
split_name = "train"
iterable_dataset = load_dataset(
dataset_name,
name=config_name,
split=split_name,
streaming=True,
use_auth_token=hf_token,
)
iterable_dataset = iterable_dataset._resolve_features()
print(iterable_dataset.features)
# gated
dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654317644"
config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654317644"
split_name = "train"
iterable_dataset = load_dataset(
dataset_name,
name=config_name,
split=split_name,
streaming=True,
use_auth_token=hf_token,
)
try:
iterable_dataset = iterable_dataset._resolve_features()
except FileNotFoundError as e:
print("FAILS")
```
## Expected results
I expect to have the same result on a public dataset and on a gated (or private) dataset, if the token has been provided.
## Actual results
An exception is thrown on gated datasets.
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.15.0-1017-aws-x86_64-with-glibc2.35
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.4.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6469/comments | https://api.github.com/repos/huggingface/datasets/issues/6469/events | https://github.com/huggingface/datasets/pull/6469 | 2,023,695,839 | PR_kwDODunzps5hC6xf | 6,469 | Don't expand_info in HF glob | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6469). All of your documentation changes will be reflected on that endpoint.",
"Merging this one for now, but lmk if you had other optimizations in mind for the next version of `huggingface_hub`",
"<details>\n<summary>Show be... | 2023-12-04T12:00:37Z | 2023-12-15T13:18:37Z | 2023-12-15T13:12:30Z | MEMBER | null | null | null | Finally fix https://github.com/huggingface/datasets/issues/5537 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-27T14:50:12Z | 2022-07-27T15:03:00Z | 2022-07-27T14:51:06Z | MEMBER | null | null | null | Following https://github.com/huggingface/datasets/pull/4753 there was still a "unknown" langauge tag in `wikipedia` so the job at https://github.com/huggingface/datasets/runs/7542567336?check_suite_focus=true failed for wikipedia | {
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https://api.github.com/repos/huggingface/datasets/issues/4748 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4748/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4748/comments | https://api.github.com/repos/huggingface/datasets/issues/4748/events | https://github.com/huggingface/datasets/pull/4748 | 1,318,874,913 | PR_kwDODunzps48JTEb | 4,748 | Add image classification processing guide | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-27T00:11:11Z | 2022-07-27T17:28:21Z | 2022-07-27T17:16:12Z | MEMBER | null | null | null | This PR follows up on #4710 to separate the object detection and image classification guides. It expands a little more on the original guide to include a more complete example of loading and transforming a whole dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/4937 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4937/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4937/comments | https://api.github.com/repos/huggingface/datasets/issues/4937/events | https://github.com/huggingface/datasets/pull/4937 | 1,363,426,946 | PR_kwDODunzps4-cn6W | 4,937 | Remove deprecated identical_ok | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-06T15:01:24Z | 2022-09-06T22:24:09Z | 2022-09-06T22:21:57Z | MEMBER | null | null | null | `huggingface-hub` says that the `identical_ok` argument of `HfApi.upload_file` is now deprecated, and will be removed soon. It even has no effect at the moment when it's passed:
```python
Args:
...
identical_ok (`bool`, *optional*, defaults to `True`):
Deprecated: will be removed in 0.11.0.
Changing this value has no effect.
...
```
There was only one occurence of `identical_ok=False` but it's maybe not worth adding a check ti verify if the files were the same.
cc @mariosasko | {
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https://api.github.com/repos/huggingface/datasets/issues/7515 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7515/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7515/comments | https://api.github.com/repos/huggingface/datasets/issues/7515/events | https://github.com/huggingface/datasets/issues/7515 | 2,995,082,418 | I_kwDODunzps6yhVSy | 7,515 | `concatenate_datasets` does not preserve Pytorch format for IterableDataset | {
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"Hi ! Oh indeed it would be cool to return the same format in that case. Would you like to submit a PR ? The function that does the concatenation is here:\n\nhttps://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/iterable_dataset.py#L3375-L3380",
"Thank you for the poin... | 2025-04-15T04:36:34Z | 2025-04-16T02:39:16Z | null | NONE | null | null | {
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} | ### Describe the bug
When concatenating datasets with `concatenate_datasets`, I would expect the resulting combined dataset to be in the same format as the inputs (assuming it's consistent). This is indeed the behavior when combining `Dataset`, but not when combining `IterableDataset`. Specifically, when applying `concatenate_datasets` to a list of `IterableDataset` in Pytorch format (i.e. using `.with_format(Pytorch)`), the output `IterableDataset` is not in Pytorch format.
### Steps to reproduce the bug
```
import datasets
ds = datasets.Dataset.from_dict({"a": [1,2,3]})
iterable_ds = ds.to_iterable_dataset()
datasets.concatenate_datasets([ds.with_format("torch")]) # <- this preserves Pytorch format
datasets.concatenate_datasets([iterable_ds.with_format("torch")]) # <- this does NOT preserves Pytorch format
```
### Expected behavior
Pytorch format should be preserved when combining IterableDataset in Pytorch format.
### Environment info
datasets==3.5.0, Python 3.11.11, torch==2.2.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4883/comments | https://api.github.com/repos/huggingface/datasets/issues/4883/events | https://github.com/huggingface/datasets/issues/4883 | 1,349,083,235 | I_kwDODunzps5QaWBj | 4,883 | With dataloader RSS memory consumed by HF datasets monotonically increases | {
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"Are you sure there is a leak? How can I see it? You shared the script but not the output which you believe should indicate a leak.\r\n\r\nI modified your reproduction script to print only once per try as your original was printing too much info and you absolutely must add `gc.collect()` when doing any memory measu... | 2022-08-24T08:42:54Z | 2024-01-23T12:42:40Z | null | CONTRIBUTOR | null | null | {
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} | ## Describe the bug
When the HF datasets is used in conjunction with PyTorch Dataloader, the RSS memory of the process keeps on increasing when it should stay constant.
## Steps to reproduce the bug
Run and observe the output of this snippet which logs RSS memory.
```python
import psutil
import os
from transformers import BertTokenizer
from datasets import load_dataset
from torch.utils.data import DataLoader
BATCH_SIZE = 32
NUM_TRIES = 10
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def transform(x):
x.update(tokenizer(x["text"], return_tensors="pt", max_length=64, padding="max_length", truncation=True))
x.pop("text")
x.pop("label")
return x
dataset = load_dataset("imdb", split="train")
dataset.set_transform(transform)
train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)
mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
count = 0
while count < NUM_TRIES:
for idx, batch in enumerate(train_loader):
mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
print(count, idx, mem_after - mem_before)
count += 1
```
## Expected results
Memory should not increase after initial setup and loading of the dataset
## Actual results
Memory continuously increases as can be seen in the log.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 7.0.0
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/6220 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6220/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6220/comments | https://api.github.com/repos/huggingface/datasets/issues/6220/events | https://github.com/huggingface/datasets/pull/6220 | 1,884,285,980 | PR_kwDODunzps5ZspRb | 6,220 | Set dev version | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-09-06T15:40:33Z | 2023-09-06T15:52:33Z | 2023-09-06T15:41:13Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6491 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6491/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6491/comments | https://api.github.com/repos/huggingface/datasets/issues/6491/events | https://github.com/huggingface/datasets/pull/6491 | 2,037,690,643 | PR_kwDODunzps5hyiTY | 6,491 | Fix metrics dead link | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6491). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2023-12-12T12:51:49Z | 2023-12-21T15:15:08Z | 2023-12-21T15:08:53Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7398 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7398/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7398/comments | https://api.github.com/repos/huggingface/datasets/issues/7398/events | https://github.com/huggingface/datasets/pull/7398 | 2,853,097,869 | PR_kwDODunzps6LNoDk | 7,398 | Release: 3.3.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7398). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-02-14T09:15:03Z | 2025-02-14T09:57:39Z | 2025-02-14T09:57:37Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6269 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6269/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6269/comments | https://api.github.com/repos/huggingface/datasets/issues/6269/events | https://github.com/huggingface/datasets/pull/6269 | 1,919,572,790 | PR_kwDODunzps5bjbDc | 6,269 | Reduce the number of commits in `push_to_hub` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-09-29T16:22:31Z | 2023-10-16T16:03:18Z | 2023-10-16T13:30:46Z | COLLABORATOR | null | null | null | Reduces the number of commits in `push_to_hub` by using the `preupload` API from https://github.com/huggingface/huggingface_hub/pull/1699. Each commit contains a maximum of 50 uploaded files.
A shard's fingerprint no longer needs to be added as a suffix to support resuming an upload, meaning the shards' naming scheme is the same as the initial one.
Also, it adds support for the following params: `create_pr`, `commit_message` and `revision` (`branch` deprecated; unlike the previous implementation, this one creates a branch if the branch does not exist to be consistent with `transformers`).
(Nit) This implementation keeps the markdown section of the generated README.md empty to enable importing the card template (when the card is accessed on the Hub).
Fixes https://github.com/huggingface/datasets/issues/5492, fixes https://github.com/huggingface/datasets/issues/6257, fixes https://github.com/huggingface/datasets/issues/5045, fixes https://github.com/huggingface/datasets/issues/6271
TODO:
- [x] set the minimal version to the next `hfh` release (once it's published) | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-03-22T11:44:11Z | 2024-03-22T14:51:45Z | 2024-03-22T14:45:39Z | MEMBER | null | null | null | Following changes at https://github.com/fsspec/filesystem_spec/pull/1497 for `fsspec>=2024.2.0` | {
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https://api.github.com/repos/huggingface/datasets/issues/7203 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7203/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7203/comments | https://api.github.com/repos/huggingface/datasets/issues/7203/events | https://github.com/huggingface/datasets/pull/7203 | 2,573,154,222 | PR_kwDODunzps598skr | 7,203 | with_format docstring | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7203). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-08T13:05:19Z | 2024-10-08T13:13:12Z | 2024-10-08T13:13:05Z | MEMBER | null | null | null | reported at https://github.com/huggingface/datasets/issues/3444 | {
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https://api.github.com/repos/huggingface/datasets/issues/6929 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6929/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6929/comments | https://api.github.com/repos/huggingface/datasets/issues/6929/events | https://github.com/huggingface/datasets/issues/6929 | 2,322,980,077 | I_kwDODunzps6Kddzt | 6,929 | Avoid downloading the whole dataset when only README.me has been touched on hub. | {
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"you're right, we're tackling this here: https://github.com/huggingface/dataset-viewer/issues/2757",
"@severo : great !"
] | 2024-05-29T10:36:06Z | 2024-05-29T20:51:56Z | null | NONE | null | null | {
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} | ### Feature request
`datasets.load_dataset()` triggers a new download of the **whole dataset** when the README.md file has been touched on huggingface hub, even if data files / parquet files are the exact same.
I think the current behaviour of the load_dataset function is triggered whenever a change of the hash of latest commit on huggingface hub, but is there a clever way to only download again the dataset **if and only if** data is modified ?
### Motivation
The current behaviour is a waste of network bandwidth / disk space / research time.
### Your contribution
I don't have time to submit a PR, but I hope a simple solution will emerge from this issue ! | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5078 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5078/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5078/comments | https://api.github.com/repos/huggingface/datasets/issues/5078/events | https://github.com/huggingface/datasets/pull/5078 | 1,398,335,148 | PR_kwDODunzps5APjkH | 5,078 | Fix header level in Audio docs | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-05T20:22:44Z | 2022-10-06T08:12:23Z | 2022-10-06T08:09:41Z | MEMBER | null | null | null | Fixes header level so `Dataset features` is the doc title instead of `The Audio type`:
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https://api.github.com/repos/huggingface/datasets/issues/5166 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5166/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5166/comments | https://api.github.com/repos/huggingface/datasets/issues/5166/events | https://github.com/huggingface/datasets/pull/5166 | 1,423,629,582 | PR_kwDODunzps5Bj5IQ | 5,166 | Support dill 0.3.6 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I think it hasn't been merged ? https://github.com/uqfoundation/dill/pull/501\r\n\r\nThough I can see that the CI is green because it uses dill 0.3.1.1 - we should probably fix the dill version in both CIs:\r\n- use 0.3.1.1 for the C... | 2022-10-26T08:24:59Z | 2022-10-28T05:41:05Z | 2022-10-28T05:38:14Z | MEMBER | null | null | null | This PR:
- ~~Unpins dill to allow installing dill>=0.3.6~~
- ~~Removes the fix on dill for >=0.3.6 because they implemented a deterministic mode (to be confirmed by @anivegesana)~~
- Pins dill<0.3.7 to allow latest dill 0.3.6
- Implements a fix for dill `save_function` for dill 0.3.6
- Additionally had to implement a fix for dill `save_code` and `_save_regex` for dill 0.3.6
- Fixes the CI so that the latest dill version is tested (besides the minimum 0.3.1.1 required by apache-beam 2.42.0)
Fix #5162. | {
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https://api.github.com/repos/huggingface/datasets/issues/7432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7432/comments | https://api.github.com/repos/huggingface/datasets/issues/7432/events | https://github.com/huggingface/datasets/pull/7432 | 2,887,717,289 | PR_kwDODunzps6M-DI0 | 7,432 | Fix type annotation | {
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"Thanks ! There is https://github.com/huggingface/datasets/pull/7426 already that fixes the issue, I'm closing your PR if you don't mind"
] | 2025-02-28T17:28:20Z | 2025-03-04T15:53:03Z | 2025-03-04T15:53:03Z | CONTRIBUTOR | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7151 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7151/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7151/comments | https://api.github.com/repos/huggingface/datasets/issues/7151/events | https://github.com/huggingface/datasets/pull/7151 | 2,527,577,048 | PR_kwDODunzps57kyY4 | 7,151 | Align filename prefix splitting with WebDataset library | {
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} | [] | closed | false | null | [] | null | [] | 2024-09-16T06:07:39Z | 2024-09-16T15:26:36Z | 2024-09-16T15:26:34Z | MEMBER | null | null | null | Align filename prefix splitting with WebDataset library.
This PR uses the same `base_plus_ext` function as the one used by the `webdataset` library.
Fix #7150.
Related to #7144. | {
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https://api.github.com/repos/huggingface/datasets/issues/4818 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4818/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4818/comments | https://api.github.com/repos/huggingface/datasets/issues/4818/events | https://github.com/huggingface/datasets/pull/4818 | 1,334,941,810 | PR_kwDODunzps48-W7a | 4,818 | Add add cc-by-sa-2.5 license tag | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4818). All of your documentation changes will be reflected on that endpoint.",
"I think we can close this PR because the `standard_licenses.tsv` file was removed from this repo and we no longer perform any dataset card validati... | 2022-08-10T17:18:39Z | 2022-10-04T13:47:24Z | 2022-10-04T13:47:24Z | CONTRIBUTOR | null | null | null | - [ ] add it to moon-landing
- [ ] add it to hub-docs | {
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https://api.github.com/repos/huggingface/datasets/issues/5930 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5930/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5930/comments | https://api.github.com/repos/huggingface/datasets/issues/5930/events | https://github.com/huggingface/datasets/issues/5930 | 1,745,184,395 | I_kwDODunzps5oBWaL | 5,930 | loading private custom dataset script - authentication error | {
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"This issue seems to have been resolved, so I'm closing it."
] | 2023-06-07T06:58:23Z | 2023-06-15T14:49:21Z | 2023-06-15T14:49:20Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Train model with my custom dataset stored in HuggingFace and loaded with the loading script requires authentication but I am not sure how ?
I am logged in in the terminal, in the browser. I receive this error:
/python3.8/site-packages/datasets/utils/file_utils.py", line 566, in get_from_cache
raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels `(ConnectionError('Unauthorized for URL `https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels. Please use the parameter `**`use_auth_token=True`**` after logging in with `**`huggingface-cli login`**`'))
when I added: `use_auth_token=True` and logged in via terminal then I received error:
or the same error in different format:
raise ConnectionError(f"`Couldn't reach {url} (error {response.status_code}`)")
ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels (`error 401`)
### Steps to reproduce the bug
1. cloned transformers library locally:
https://huggingface.co/docs/transformers/v4.15.0/examples :
> git clone https://github.com/huggingface/transformers
> cd transformers
> pip install .
> cd /transformers/examples/pytorch/audio-classification
> pip install -r requirements.txt
2. created **loading script**
> https://huggingface.co/docs/datasets/dataset_script added next to dataset:
3. uploaded **private custom dataset** with loading script to HuggingFace
> https://huggingface.co/docs/datasets/dataset_script
4. added dataset loading script to **local directory** in the above cloned transformers library:
> cd /transformers/examples/pytorch/audio-classification
5. logged in to HuggingFace on local terminal with :
> **huggingface-cli login**
6. run the model with the custom dataset stored on HuggingFace with code: https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/README.md
cd /transformers/examples/pytorch/audio-classification
> python run_audio_classification.py \
> --model_name_or_path facebook/wav2vec2-base \
> --output_dir l/users/flck/outputs/wav2vec2-base-s \
> --overwrite_output_dir \
> --dataset_name s \
> --dataset_config_name s \
> --remove_unused_columns False \
> --do_train \
> --do_eval \
> --fp16 \
> --learning_rate 3e-5 \
> --max_length_seconds 1 \
> --attention_mask False \
> --warmup_ratio 0.1 \
> --num_train_epochs 5 \
> --per_device_train_batch_size 32 \
> --gradient_accumulation_steps 4 \
> --per_device_eval_batch_size 32 \
> --dataloader_num_workers 4 \
> --logging_strategy steps \
> --logging_steps 10 \
> --evaluation_strategy epoch \
> --save_strategy epoch \
> --load_best_model_at_end True \
> --metric_for_best_model accuracy \
> --save_total_limit 3 \
> --seed 0 \
> --push_to_hub \
> **--use_auth_token=True**
### Expected behavior
Be able to train a model the https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/ run_audio_classification.py with private custom dataset stored on HuggingFace.
### Environment info
- datasets version: 2.12.0
- `transformers` version: 4.30.0.dev0
- Platform: Linux-5.4.204-ql-generic-12.0-19-x86_64-with-glibc2.17
- Python version: 3.8.12
- Huggingface_hub version: 0.15.1
- Safetensors version: 0.3.1
- PyTorch version (GPU?): 2.0.1+cu117 (True)
Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] torch==2.0.1
[pip3] torchaudio==2.0.2
[conda] numpy 1.24.3 pypi_0 pypi
[conda] torch 2.0.1 pypi_0 pypi
[conda] torchaudio 2.0.2 pypi_0 pypi
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https://api.github.com/repos/huggingface/datasets/issues/5984 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5984/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5984/comments | https://api.github.com/repos/huggingface/datasets/issues/5984/events | https://github.com/huggingface/datasets/issues/5984 | 1,771,571,458 | I_kwDODunzps5pmAkC | 5,984 | AutoSharding IterableDataset's when num_workers > 1 | {
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{
"color": "a2eeef",
"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement"
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] | open | false | null | [] | null | [
"For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n\r\n@lhoestq @... | 2023-06-23T14:34:20Z | 2024-03-22T15:01:14Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Feature request
Minimal Example
```
import torch
from datasets import IterableDataset
d = IterableDataset.from_file(<file_name>)
dl = torch.utils.data.dataloader.DataLoader(d,num_workers=3)
for sample in dl:
print(sample)
```
Warning:
Too many dataloader workers: 2 (max is dataset.n_shards=1). Stopping 1 dataloader workers.
To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=1. To enable more parallelism, please split the dataset in more files than 1.
Expected Behavior:
Dataset is sharded each cpu uses subset (contiguously - so you can do checkpoint loading/saving)
### Motivation
I have a lot of unused cpu's and would like to be able to shard iterable datasets with pytorch's dataloader when num_workers > 1. This is for a very large single file. I am aware that we can use the `split_dataset_by_node` to ensure that each node (for distributed) gets different shards, but we should extend it so that this also continues for multiple workers.
### Your contribution
If someone points me to what needs to change, I can create a PR. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6916 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6916/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6916/comments | https://api.github.com/repos/huggingface/datasets/issues/6916/events | https://github.com/huggingface/datasets/issues/6916 | 2,311,675,564 | I_kwDODunzps6JyV6s | 6,916 | ```push_to_hub()``` - Prevent Automatic Generation of Splits | {
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"completed": 0,
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} | ### Describe the bug
I currently have a dataset which has not been splited. When pushing the dataset to my hugging face dataset repository, it is split into a testing and training set. How can I prevent the split from happening?
### Steps to reproduce the bug
1. Have a unsplit dataset
```python
Dataset({ features: ['input', 'output', 'Attack', '__index_level_0__'], num_rows: 944685 })
```
2. Push it to huggingface
```python
dataset.push_to_hub(dataset_name)
```
3. On the hugging face dataset repo, the dataset then appears to be splited:

4. Indeed, when loading the dataset from this repo, the dataset is split in two testing and training set.
```python
from datasets import load_dataset, Dataset
dataset = load_dataset("Jetlime/NF-CSE-CIC-IDS2018-v2", streaming=True)
dataset
```
output:
```
IterableDatasetDict({
train: IterableDataset({
features: ['input', 'output', 'Attack', '__index_level_0__'],
n_shards: 2
})
test: IterableDataset({
features: ['input', 'output', 'Attack', '__index_level_0__'],
n_shards: 1
})
```
### Expected behavior
The dataset shall not be splited, as not requested.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.2.0-35-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5262/comments | https://api.github.com/repos/huggingface/datasets/issues/5262/events | https://github.com/huggingface/datasets/issues/5262 | 1,455,171,100 | I_kwDODunzps5WvCYc | 5,262 | AttributeError: 'Value' object has no attribute 'names' | {
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"Hi ! It looks like your \"isDif\" column is a Sequence of Value(\"string\"), not a Sequence of ClassLabel.\r\n\r\nYou can convert your Value(\"string\") feature type to a ClassLabel feature type this way:\r\n```python\r\nfrom datasets import ClassLabel, Sequence\r\n\r\n# provide the label_names yourself\r\nlabel_n... | 2022-11-18T13:58:42Z | 2022-11-22T10:09:24Z | 2022-11-22T10:09:23Z | NONE | null | null | {
"completed": 0,
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} | Hello
I'm trying to build a model for custom token classification
I already followed the token classification course on huggingface
while adapting the code to my work, this message occures :
'Value' object has no attribute 'names'
Here's my code:
`raw_datasets`
generates
DatasetDict({
train: Dataset({
features: ['isDisf', 'pos', 'tokens', 'id'],
num_rows: 14
})
})
`raw_datasets["train"][3]["isDisf"]`
generates
['B_RM', 'I_RM', 'I_RM', 'B_RP', 'I_RP', 'O', 'O']
`dis_feature = raw_datasets["train"].features["isDisf"]
dis_feature`
generates
Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)
and
`label_names = dis_feature.feature.names
label_names`
generates
AttributeError Traceback (most recent call last)
[<ipython-input-28-972fd54a869a>](https://localhost:8080/#) in <module>
----> 1 label_names = dis_feature.feature.names
2 label_names
AttributeError: 'Value' object has
AttributeError: 'Value' object has no attribute 'names'
Thank you for your help | {
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https://api.github.com/repos/huggingface/datasets/issues/5385 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5385/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5385/comments | https://api.github.com/repos/huggingface/datasets/issues/5385/events | https://github.com/huggingface/datasets/issues/5385 | 1,508,535,532 | I_kwDODunzps5Z6mzs | 5,385 | Is `fs=` deprecated in `load_from_disk()` as well? | {
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"Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ",
"> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? I’ll look around for anywh... | 2022-12-22T21:00:45Z | 2023-01-23T10:50:05Z | 2023-01-23T10:50:04Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ### Describe the bug
The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec:
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340
Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ?
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779
### Steps to reproduce the bug
n/a
### Expected behavior
n/a
### Environment info
n/a | {
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https://api.github.com/repos/huggingface/datasets/issues/6879 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6879/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6879/comments | https://api.github.com/repos/huggingface/datasets/issues/6879/events | https://github.com/huggingface/datasets/issues/6879 | 2,282,968,259 | I_kwDODunzps6IE1TD | 6,879 | Batched mapping does not raise an error if values for an existing column are empty | {
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"completed": 0,
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} | ### Describe the bug
Using `Dataset.map(fn, batched=True)` allows resizing the dataset by returning a dict of lists, all of which must be the same size. If they are not the same size, an error like `pyarrow.lib.ArrowInvalid: Column 1 named x expected length 1 but got length 0` is raised.
This is not the case if the function returns an empty list for an existing column in the dataset. In that case, the dataset is silently resized to 0 rows.
### Steps to reproduce the bug
MWE:
```
import datasets
data = datasets.Dataset.from_dict({"test": [1]})
def mapping_fn(examples):
return {"test": [], "y": [1]}
data = data.map(mapping_fn, batched=True)
print(len(data))
```
Note that when returning `"x": []`, the error is raised correctly, also when returning `"test": [1,2]`.
### Expected behavior
Expected an exception: `pyarrow.lib.ArrowInvalid: Column 1 named test expected length 1 but got length 0` or `pyarrow.lib.ArrowInvalid: Column 2 named y expected length 0 but got length 1`.
Any exception would be acceptable.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-5.4.0-153-generic-x86_64-with-glibc2.31
- Python version: 3.11.8
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7010 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7010/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7010/comments | https://api.github.com/repos/huggingface/datasets/issues/7010/events | https://github.com/huggingface/datasets/issues/7010 | 2,379,777,480 | I_kwDODunzps6N2IXI | 7,010 | Re-enable raising error from huggingface-hub FutureWarning in CI | {
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"completed": 0,
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} | Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR:
- #6876
Note that this can only be done once transformers releases the fix:
- https://github.com/huggingface/transformers/pull/31007 | {
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https://api.github.com/repos/huggingface/datasets/issues/6063 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6063/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6063/comments | https://api.github.com/repos/huggingface/datasets/issues/6063/events | https://github.com/huggingface/datasets/pull/6063 | 1,818,679,485 | PR_kwDODunzps5WPtxi | 6,063 | Release: 2.14.0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-07-24T15:41:19Z | 2023-07-24T16:05:16Z | 2023-07-24T15:47:51Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6994 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6994/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6994/comments | https://api.github.com/repos/huggingface/datasets/issues/6994/events | https://github.com/huggingface/datasets/pull/6994 | 2,370,491,689 | PR_kwDODunzps5zYYXr | 6,994 | Fix incorrect rank value in data splitting | {
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"Sure~",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6994). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>... | 2024-06-24T15:07:47Z | 2024-06-26T04:37:35Z | 2024-06-25T16:19:17Z | CONTRIBUTOR | null | null | null | Fix #6990. | {
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https://api.github.com/repos/huggingface/datasets/issues/7502 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7502/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7502/comments | https://api.github.com/repos/huggingface/datasets/issues/7502/events | https://github.com/huggingface/datasets/issues/7502 | 2,977,453,814 | I_kwDODunzps6xeFb2 | 7,502 | `load_dataset` of size 40GB creates a cache of >720GB | {
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"Hi ! Parquet is a compressed format. When you load a dataset, it uncompresses the Parquet data into Arrow data on your disk. That's why you can indeed end up with 720GB of uncompressed data on disk. The uncompression is needed to enable performant dataset objects (especially for random access).\n\nTo save some sto... | 2025-04-07T16:52:34Z | 2025-04-15T15:22:12Z | 2025-04-15T15:22:11Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | Hi there,
I am trying to load a dataset from the Hugging Face Hub and split it into train and validation splits. Somehow, when I try to do it with `load_dataset`, it exhausts my disk quota. So, I tried manually downloading the parquet files from the hub and loading them as follows:
```python
ds = DatasetDict(
{
"train": load_dataset(
"parquet",
data_dir=f"{local_dir}/{tok}",
cache_dir=cache_dir,
num_proc=min(12, os.cpu_count()), # type: ignore
split=ReadInstruction("train", from_=0, to=NUM_TRAIN, unit="abs"), # type: ignore
),
"validation": load_dataset(
"parquet",
data_dir=f"{local_dir}/{tok}",
cache_dir=cache_dir,
num_proc=min(12, os.cpu_count()), # type: ignore
split=ReadInstruction("train", from_=NUM_TRAIN, unit="abs"), # type: ignore
)
}
)
```
which still strangely creates 720GB of cache. In addition, if I remove the raw parquet file folder (`f"{local_dir}/{tok}"` in this example), I am not able to load anything. So, I am left wondering what this cache is doing. Am I missing something? Is there a solution to this problem?
Thanks a lot in advance for your help!
A related issue: https://github.com/huggingface/transformers/issues/10204#issue-809007443.
---
Python: 3.11.11
datasets: 3.5.0
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https://api.github.com/repos/huggingface/datasets/issues/6615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6615/comments | https://api.github.com/repos/huggingface/datasets/issues/6615/events | https://github.com/huggingface/datasets/issues/6615 | 2,098,951,409 | I_kwDODunzps59G3Tx | 6,615 | ... | {
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} | [] | closed | false | null | [] | null | [
"Sorry I posted in the wrong repo, please delete.. thanks!"
] | 2024-01-24T19:37:03Z | 2024-01-24T19:42:30Z | 2024-01-24T19:40:11Z | NONE | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6128 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6128/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6128/comments | https://api.github.com/repos/huggingface/datasets/issues/6128/events | https://github.com/huggingface/datasets/issues/6128 | 1,841,545,493 | I_kwDODunzps5tw8EV | 6,128 | IndexError: Invalid key: 88 is out of bounds for size 0 | {
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"Hi @TomasAndersonFang,\r\n\r\nHave you tried instead to use `torch_compile` in `transformers.TrainingArguments`? https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.torch_compile",
"> \r\n\r\nI tried this and got the following error:\r\n\r\n```\r\nTraceback (mo... | 2023-08-08T15:32:08Z | 2023-12-26T07:51:57Z | 2023-08-11T13:35:09Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib.
### Steps to reproduce the bug
I use the following code to fine-tune Falcon on my private dataset.
```python
import transformers
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
AutoConfig,
DataCollatorForSeq2Seq,
Trainer,
Seq2SeqTrainer,
HfArgumentParser,
Seq2SeqTrainingArguments,
BitsAndBytesConfig,
)
from peft import (
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
prepare_model_for_int8_training,
set_peft_model_state_dict,
)
import torch
import os
import evaluate
import functools
from datasets import load_dataset
import bitsandbytes as bnb
import logging
import json
import copy
from typing import Dict, Optional, Sequence
from dataclasses import dataclass, field
# Lora settings
LORA_R = 8
LORA_ALPHA = 16
LORA_DROPOUT= 0.05
LORA_TARGET_MODULES = ["query_key_value"]
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B")
@dataclass
class DataArguments:
data_path: str = field(default=None, metadata={"help": "Path to the training data."})
train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."})
eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."})
cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."})
num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."})
@dataclass
class TrainingArguments(transformers.TrainingArguments):
# cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
)
is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."})
def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True):
result = tokenizer(
text,
truncation=True,
max_length=max_seq_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != tokenizer.eos_token_id
and len(result["input_ids"]) < max_seq_len
and add_eos_token
):
result["input_ids"].append(tokenizer.eos_token_id)
result["attention_mask"].append(1)
if add_eos_token and len(result["input_ids"]) >= max_seq_len:
result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id
result["attention_mask"][max_seq_len - 1] = 1
result["labels"] = result["input_ids"].copy()
return result
def main():
parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
config = AutoConfig.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
trust_remote_code=True,
)
if training_args.is_lora:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
torch_dtype=torch.float16,
trust_remote_code=True,
load_in_8bit=True,
quantization_config=BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0
),
)
model = prepare_model_for_int8_training(model)
config = LoraConfig(
r=LORA_R,
lora_alpha=LORA_ALPHA,
target_modules=LORA_TARGET_MODULES,
lora_dropout=LORA_DROPOUT,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)
else:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
torch_dtype=torch.float16,
cache_dir=data_args.cache_path,
trust_remote_code=True,
)
model.config.use_cache = False
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
print_trainable_parameters(model)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
model_max_length=training_args.model_max_length,
padding_side="left",
use_fast=True,
trust_remote_code=True,
)
tokenizer.pad_token = tokenizer.eos_token
# Load dataset
def generate_and_tokenize_prompt(sample):
input_text = sample["input"]
target_text = sample["output"] + tokenizer.eos_token
full_text = input_text + target_text
tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512)
tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512)
input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token
tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:]
return tokenized_full_text
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.eval_file is not None:
data_files["eval"] = data_args.eval_file
dataset = load_dataset(data_args.data_path, data_files=data_files)
train_dataset = dataset["train"]
eval_dataset = dataset["eval"]
train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc)
eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc)
data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True)
# Evaluation metrics
def compute_metrics(eval_preds, tokenizer):
metric = evaluate.load('exact_match')
preds, labels = eval_preds
# In case the model returns more than the prediction logits
if isinstance(preds, tuple):
preds = preds[0]
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False)
# Replace -100s in the labels as we can't decode them
labels[labels == -100] = tokenizer.pad_token_id
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False)
# Some simple post-processing
decoded_preds = [pred.strip() for pred in decoded_preds]
decoded_labels = [label.strip() for label in decoded_labels]
result = metric.compute(predictions=decoded_preds, references=decoded_labels)
return {'exact_match': result['exact_match']}
compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer)
model = torch.compile(model)
# Training
trainer = Trainer(
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
args=training_args,
data_collator=data_collator,
compute_metrics=compute_metrics_fn,
)
trainer.train()
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)
tokenizer.save_pretrained(save_directory=training_args.output_dir)
if __name__ == "__main__":
main()
```
When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error:
```
Traceback (most recent call last):
File "falcon_sft.py", line 230, in <module>
main()
File "falcon_sft.py", line 223, in main
trainer.train()
File "python3.10/site-packages/transformers/trainer.py", line 1539, in train
return inner_training_loop(
File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop
for step, inputs in enumerate(epoch_iterator):
File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__
current_batch = next(dataloader_iter)
File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__
batch = self.__getitem__(keys)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__
return self._getitem(key)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table
_check_valid_index_key(key, size)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key
_check_valid_index_key(int(max(key)), size=size)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 88 is out of bounds for size 0
```
So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`?
### Expected behavior
I want to use `torch.compile` in my code.
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.8
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/7484 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7484/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7484/comments | https://api.github.com/repos/huggingface/datasets/issues/7484/events | https://github.com/huggingface/datasets/pull/7484 | 2,953,677,168 | PR_kwDODunzps6Qbevn | 7,484 | release: 3.5.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7484). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-03-27T16:33:27Z | 2025-03-27T16:35:44Z | 2025-03-27T16:34:22Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5009 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5009/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5009/comments | https://api.github.com/repos/huggingface/datasets/issues/5009/events | https://github.com/huggingface/datasets/issues/5009 | 1,381,194,067 | I_kwDODunzps5SU1lT | 5,009 | Error loading StonyBrookNLP/tellmewhy dataset from hub even though local copy loads correctly | {
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"I think this is because some columns are mostly empty lists. In particular the train and validation splits only have empty lists for `val_ann`. Therefore the type inference doesn't know which type is inside (or it would have to scan the other splits first before knowing).\r\n\r\nYou can fix that by specifying the ... | 2022-09-21T16:23:06Z | 2022-09-29T13:07:29Z | 2022-09-29T13:07:29Z | NONE | null | null | {
"completed": 0,
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"total": 0
} | ## Describe the bug
I have added a new dataset with the identifier `StonyBrookNLP/tellmewhy` to the hub. When I load the individual files using my local copy using `dataset = datasets.load_dataset("json", data_files="data/train.jsonl")`, it loads the dataset correctly. However, when I try to load it from the hub, I get an error (pasted below). Additionally, `dataset = datasets.load_dataset("json", data_dir="data/")` throws the same error.
## Steps to reproduce the bug
```python
dataset = datasets.load_dataset('StonyBrookNLP/tellmewhy')
```
## Expected results
Successfully load the `StonyBrookNLP/tellmewhy` dataset.
## Actual results
```
Using custom data configuration StonyBrookNLP--tellmewhy-82712924092694ff
Downloading and preparing dataset json/StonyBrookNLP--tellmewhy to /home/yklal95/.cache/huggingface/datasets/StonyBrookNLP___json/StonyBrookNLP--tellmewhy-82712924092694ff/0.0.0/a3e658c4731e59120d44081ac10bf85dc7e1388126b92338344ce9661907f253...
Downloading data files: 100%|██████████████████████████████| 3/3 [00:00<00:00, 957.46it/s]
Extracting data files: 100%|███████████████████████████████| 3/3 [00:00<00:00, 299.14it/s]
Traceback (most recent call last):
File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 17, in <module>
main(args)
File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 11, in main
dataset = datasets.load_dataset(args.dataset_name)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/load.py", line 1746, in load_dataset
builder_instance.download_and_prepare(
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 1277, in _prepare_split
writer.write_table(table)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/arrow_writer.py", line 524, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 2005, in table_cast
return cast_table_to_schema(table, schema)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1822, in cast_array_to_feature
casted_values = _c(array.values, feature.feature)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper
return func(array, *args, **kwargs)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1853, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper
return func(array, *args, **kwargs)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1761, in array_cast
raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
TypeError: Couldn't cast array of type int64 to null
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-121-generic-x86_64-with-glibc2.27
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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https://api.github.com/repos/huggingface/datasets/issues/6096 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6096/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6096/comments | https://api.github.com/repos/huggingface/datasets/issues/6096/events | https://github.com/huggingface/datasets/pull/6096 | 1,826,731,091 | PR_kwDODunzps5Wq9Hb | 6,096 | Add `fsspec` support for `to_json`, `to_csv`, and `to_parquet` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6096). All of your documentation changes will be reflected on that endpoint.",
"Hi here @lhoestq @mariosasko I just realised this PR is still open, should we close it in case this is something not to include within `datasets`, ... | 2023-07-28T16:36:59Z | 2024-05-28T07:40:30Z | 2024-03-06T11:12:42Z | MEMBER | null | null | null | Hi to whoever is reading this! 🤗 (Most likely @mariosasko)
## What's in this PR?
This PR replaces the `open` from Python with `fsspec.open` and adds the argument `storage_options` for the methods `to_json`, `to_csv`, and `to_parquet`, to allow users to export any 🤗`Dataset` into a file in a file-system as requested at #6086.
## What's missing in this PR?
As per `to_json`, `to_csv`, and `to_parquet` docstrings for the recently included `storage_options` arg, I've scoped it to 2.15.0, so we should check that before merging in case we want to scope that for 2.14.2 instead.
Additionally, should we also add `fsspec` support for the `from_csv`, `from_json`, and `from_parquet` methods? If you want me to do so @mariosasko just let me know and I'll create another PR to support that too!
Fix #6086. | {
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https://api.github.com/repos/huggingface/datasets/issues/4783 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4783/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4783/comments | https://api.github.com/repos/huggingface/datasets/issues/4783/events | https://github.com/huggingface/datasets/pull/4783 | 1,326,375,011 | PR_kwDODunzps48iHey | 4,783 | Docs for creating a loading script for image datasets | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"IMO it would make more sense to add a \"Create image dataset\" page with two main sections - a no-code approach with `imagefolder` + metadata (preferred way), and with a loading script (advanced). It should be clear when to choose wh... | 2022-08-02T20:36:03Z | 2022-09-09T17:08:14Z | 2022-09-07T19:07:34Z | MEMBER | null | null | null | This PR is a first draft of creating a loading script for image datasets. Feel free to let me know if there are any specificities I'm missing for this. 🙂
To do:
- [x] Document how to create different configurations. | {
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https://api.github.com/repos/huggingface/datasets/issues/5894 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5894/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5894/comments | https://api.github.com/repos/huggingface/datasets/issues/5894/events | https://github.com/huggingface/datasets/pull/5894 | 1,724,774,910 | PR_kwDODunzps5RSjot | 5,894 | Force overwrite existing filesystem protocol | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-24T21:41:53Z | 2023-05-25T06:52:08Z | 2023-05-25T06:42:33Z | CONTRIBUTOR | null | null | null | Fix #5876 | {
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https://api.github.com/repos/huggingface/datasets/issues/6381 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6381/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6381/comments | https://api.github.com/repos/huggingface/datasets/issues/6381/events | https://github.com/huggingface/datasets/pull/6381 | 1,975,028,470 | PR_kwDODunzps5eeYty | 6,381 | Add my dataset | {
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"Hi! We do not host datasets in this repo. Instead, you should use `dataset.push_to_hub` to upload the dataset to the HF Hub.",
"@mariosasko could you provide me proper guide to push data on HF hub ",
"You can find this info here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingf... | 2023-11-02T20:59:52Z | 2023-11-08T14:37:46Z | 2023-11-06T15:50:14Z | NONE | null | null | null | ## medical data
**Description:**
This dataset, named "medical data," is a collection of text data from various sources, carefully curated and cleaned for use in natural language processing (NLP) tasks. It consists of a diverse range of text, including articles, books, and online content, covering topics from science to literature.
**Citation:**
If applicable, please include a citation for this dataset to give credit to the original sources or contributors.
**Key Features:**
- Language: The text is primarily in English, but it may include content in other languages as well.
- Use Cases: This dataset is suitable for text classification, language modeling, sentiment analysis, and other NLP tasks.
**Usage:**
To access this dataset, use the `load_your_dataset` function provided in the `your_dataset.py` script within this repository. You can specify the dataset split you need, such as "train," "test," or "validation," to get the data for your specific task.
**Contributors:**
- [Keyur Chaudhari]
**Contact:**
If you have any questions or need assistance regarding this dataset, please feel free to contact [keyurchaudhari536@gmail.com].
Please note that this dataset is shared under a specific license, which can be found in the [LICENSE](link to your dataset's license) file. Make sure to review and adhere to the terms of the license when using this dataset for your projects.
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https://api.github.com/repos/huggingface/datasets/issues/5051 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5051/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5051/comments | https://api.github.com/repos/huggingface/datasets/issues/5051/events | https://github.com/huggingface/datasets/pull/5051 | 1,392,559,503 | PR_kwDODunzps4_8drw | 5,051 | Revert task removal in folder-based builders | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-30T14:50:03Z | 2022-10-03T12:23:35Z | 2022-10-03T12:21:31Z | COLLABORATOR | null | null | null | Reverts the removal of `task_templates` in the folder-based builders. I also added the `AudioClassifaction` task for consistency.
This is needed to fix https://github.com/huggingface/transformers/issues/19177.
I think we should soon deprecate and remove the current task API (and investigate if it's possible to integrate the `train eval index` API), but we need to update the Transformers examples before that so we don't break them.
cc @NielsRogge | {
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https://api.github.com/repos/huggingface/datasets/issues/5056 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5056/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5056/comments | https://api.github.com/repos/huggingface/datasets/issues/5056/events | https://github.com/huggingface/datasets/pull/5056 | 1,394,713,173 | PR_kwDODunzps5ADfxN | 5,056 | Fix broken URL's (GEM) | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5056). All of your documentation changes will be reflected on that endpoint.",
"Thanks, @manandey. We have removed all dataset scripts from this repo. Subsequent PRs should be opened directly on the Hugging Face Hub."
] | 2022-10-03T13:13:22Z | 2022-10-04T13:49:00Z | 2022-10-04T13:48:59Z | CONTRIBUTOR | null | null | null | This PR fixes the broken URL's in GEM. cc. @lhoestq, @albertvillanova | {
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https://api.github.com/repos/huggingface/datasets/issues/5000 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5000/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5000/comments | https://api.github.com/repos/huggingface/datasets/issues/5000/events | https://github.com/huggingface/datasets/issues/5000 | 1,379,709,398 | I_kwDODunzps5SPLHW | 5,000 | Dataset Viewer issue for asapp/slue | {
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"<img width=\"519\" alt=\"Capture d’écran 2022-09-20 à 22 33 47\" src=\"https://user-images.githubusercontent.com/1676121/191358952-1220cb7d-745a-4203-a66b-3c707b25038f.png\">\r\n\r\n```\r\nNot found.\r\n\r\nError code: SplitsResponseNotFound\r\n```\r\n\r\nhttps://datasets-server.huggingface.co/splits?dataset=a... | 2022-09-20T16:45:45Z | 2022-09-27T07:04:03Z | 2022-09-21T07:24:07Z | NONE | null | null | {
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} | ### Link
https://huggingface.co/datasets/asapp/slue/viewer/
### Description
Hi,
I wonder how to get the dataset viewer of our slue dataset to work.
Best,
Felix
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/6619 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6619/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6619/comments | https://api.github.com/repos/huggingface/datasets/issues/6619/events | https://github.com/huggingface/datasets/pull/6619 | 2,102,407,478 | PR_kwDODunzps5lK2VY | 6,619 | Migrate from `setup.cfg` to `pyproject.toml` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6619). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-01-26T15:27:10Z | 2024-01-26T15:53:40Z | 2024-01-26T15:47:32Z | COLLABORATOR | null | null | null | Based on https://github.com/huggingface/huggingface_hub/pull/1971 in `hfh` | {
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https://api.github.com/repos/huggingface/datasets/issues/5883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5883/comments | https://api.github.com/repos/huggingface/datasets/issues/5883/events | https://github.com/huggingface/datasets/pull/5883 | 1,719,527,597 | PR_kwDODunzps5RAkYi | 5,883 | Fix string-encoding, make `batch_size` optional, and minor improvements in `Dataset.to_tf_dataset` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n\r... | 2023-05-22T11:51:07Z | 2023-06-08T11:09:03Z | 2023-06-06T16:49:15Z | MEMBER | null | null | null | ## What's in this PR?
This PR addresses some minor fixes and general improvements in the `to_tf_dataset` method of `datasets.Dataset`, to convert a 🤗HuggingFace Dataset as a TensorFlow Dataset.
The main bug solved in this PR comes with the string-encoding, since for safety purposes the internal conversion of `numpy.arrays` when `dtype` is unicode/string, is to convert it into `numpy.bytes`, more information in the docstring of https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#L210. That's triggered when using `tensorflow.numpy_function` as it's applying another type cast besides the one that `datasets` does, so the casting is applied at least twice per entry/batch. So this means that the definition of the `numpy.unicode_` dtype when the data in the batch is a string, is ignored, and replaced by `numpy.bytes_`.
Besides that, some other minor things have been fixed:
* Made `batch_size` an optional parameter in `to_tf_dataset`
* Map the `tensorflow` output dtypes just once, and not in every `tf.function` call during `map`
* Keep `numpy` formatting in the `datasets.Dataset` if already formatted like it, no need to format it again as `numpy`
* Docstring indentation in `dataset_to_tf` and `multiprocess_dataset_to_tf`
## What's missing in this PR?
I can include some integration tests if needed, to validate that `batch_size` is optional, and that the tensors in the TF-Dataset can be looped over with no issues as before. | {
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https://api.github.com/repos/huggingface/datasets/issues/7515 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7515/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7515/comments | https://api.github.com/repos/huggingface/datasets/issues/7515/events | https://github.com/huggingface/datasets/issues/7515 | 2,995,082,418 | I_kwDODunzps6yhVSy | 7,515 | `concatenate_datasets` does not preserve Pytorch format for IterableDataset | {
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"Hi ! Oh indeed it would be cool to return the same format in that case. Would you like to submit a PR ? The function that does the concatenation is here:\n\nhttps://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/iterable_dataset.py#L3375-L3380",
"Thank you for the poin... | 2025-04-15T04:36:34Z | 2025-04-16T02:39:16Z | null | NONE | null | null | {
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} | ### Describe the bug
When concatenating datasets with `concatenate_datasets`, I would expect the resulting combined dataset to be in the same format as the inputs (assuming it's consistent). This is indeed the behavior when combining `Dataset`, but not when combining `IterableDataset`. Specifically, when applying `concatenate_datasets` to a list of `IterableDataset` in Pytorch format (i.e. using `.with_format(Pytorch)`), the output `IterableDataset` is not in Pytorch format.
### Steps to reproduce the bug
```
import datasets
ds = datasets.Dataset.from_dict({"a": [1,2,3]})
iterable_ds = ds.to_iterable_dataset()
datasets.concatenate_datasets([ds.with_format("torch")]) # <- this preserves Pytorch format
datasets.concatenate_datasets([iterable_ds.with_format("torch")]) # <- this does NOT preserves Pytorch format
```
### Expected behavior
Pytorch format should be preserved when combining IterableDataset in Pytorch format.
### Environment info
datasets==3.5.0, Python 3.11.11, torch==2.2.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5160 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5160/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5160/comments | https://api.github.com/repos/huggingface/datasets/issues/5160/events | https://github.com/huggingface/datasets/issues/5160 | 1,422,193,938 | I_kwDODunzps5UxPUS | 5,160 | Automatically add filename for image/audio folder | {
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"Also cc @anton-l ",
"BTW the exact same holds true for the audio folder",
"I'm fine with adding a new column with the file name personally. Not sure how breaking this is though",
"@patrickvonplaten do you mean just filename or full relative path inside the repo?\r\nI think it shouldn't be breaking, at least ... | 2022-10-25T09:56:49Z | 2022-10-26T16:51:46Z | null | CONTRIBUTOR | null | null | {
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} | ### Feature request
When creating a custom audio of image dataset, it would be great to automatically have access to the filename. It should be both:
a) Automatically displayed in the viewer
b) Automatically added as a column to the dataset when doing `load_dataset`
In `diffusers` our test rely quite heavily on images and audio files now and it's a bit tedious at the moment to download specific images from a datasets repo.
E.g. we have a dataset of images for tests in `diffusers`: https://huggingface.co/datasets/hf-internal-testing/diffusers-images
where it would be extremely nice to have direct access to the filename both visually on the datasets page (@severo ) as well as via the `load_datasets` function. We currently have some akward functionality to download images by path name: https://github.com/huggingface/diffusers/blob/2fb8fafa4b761f6fc144cf75a6f6f0ea6af3a1c1/src/diffusers/utils/testing_utils.py#L131
It would be much nicer to just go over `load_dataset(...)`
### Motivation
Intuitively the filename is something people understand directly. E.g if you upload a folder of images online, it's nice if you recognize the image as well as the filename next to it directly and that you're able to use it right away.
The label on the other hand is less intuitive to understand as you haven't added it yourself.
### Your contribution
Not sure if I have the time to add it myself anytime soon, but it would help us a lot for `diffusers`. | null | {
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} | ### Feature request
Hi,
I like your idea of adapting the datasets library to be usable with JAX. Thank you for that.
However, in your [setup.py](https://github.com/huggingface/datasets/blob/main/setup.py), you enforce old versions of JAX <= 0.3... It is very cumbersome !
What is the rationale for such a limitation ? Can you remove it please ?
Thanks,
### Motivation
This library is unusable with new versions of JAX ?
### Your contribution
Yes. | {
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"Canonical datasets are no longer mentioned in the docs."
] | 2024-06-04T12:09:03Z | 2024-07-01T11:31:25Z | 2024-07-01T11:31:25Z | MEMBER | null | null | {
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"`datasets` doesn't seem to urlencode the directory names here\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/utils/file_utils.py#L109-L111\r\n\r\nfor example we should have\r\n```python\r\nfrom datasets.utils.file_utils import hf_hub_url\r\n\r\nurl = hf_hu... | 2022-10-11T10:05:32Z | 2022-10-13T13:14:20Z | 2022-10-13T13:14:20Z | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
dataset files with `#` symbol their paths aren't read correctly.
## Steps to reproduce the bug
The data in folder `c#`of this [dataset](https://huggingface.co/datasets/loubnabnl/bigcode_csharp) can't be loaded. While the folder `c_sharp` with the same data is loaded properly
```python
ds = load_dataset('loubnabnl/bigcode_csharp', split="train", data_files=["data/c#/*"])
```
```
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/27a3166cff4bb18e11919cafa6f169c0f57483de/data/c#/data_0003.jsonl
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-12.2.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
cc @lhoestq | {
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"Yes your assumption on concatenate/interleave is ok imo.\r\n\r\nIt seems the TypedExamplesIterable can slow down things, it should take formatting into account to not convert numpy arrays to python lists\r\n\r\nright now it's slow (unrelatedly to your PR):\r\n\r\n```python\r\n>>> ds = Dataset.from_dict({\"a\": np.... | 2024-10-09T10:42:05Z | 2024-10-16T11:27:22Z | 2024-10-09T16:04:07Z | CONTRIBUTOR | null | null | null | Fixes example in #7208 - I'm not sure what other checks I should do? @lhoestq
I also haven't thought hard about the concatenate / interleaving example iterables but think this might work assuming that features are either all identical or None? | {
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https://api.github.com/repos/huggingface/datasets/issues/5064 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5064/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5064/comments | https://api.github.com/repos/huggingface/datasets/issues/5064/events | https://github.com/huggingface/datasets/pull/5064 | 1,395,978,143 | PR_kwDODunzps5AHsP0 | 5,064 | Align signature of create/delete_repo with latest hfh | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-04T09:54:53Z | 2022-10-07T17:02:11Z | 2022-10-07T16:59:30Z | MEMBER | null | null | null | This PR aligns the signature of `create_repo`/`delete_repo` with the current one in hfh, by removing deprecated `name` and `organization`, and using `repo_id` instead.
Related to:
- #5063
CC: @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/6273 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6273/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6273/comments | https://api.github.com/repos/huggingface/datasets/issues/6273/events | https://github.com/huggingface/datasets/issues/6273 | 1,920,922,260 | I_kwDODunzps5yfvKU | 6,273 | Broken Link to PubMed Abstracts dataset . | {
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"This has already been reported in the HF Course repo (https://github.com/huggingface/course/issues/623).",
"@lhoestq @albertvillanova @lewtun I don't think we are allowed to host these data files on the Hub (due to DMCA), which means the only option is to use a different dataset in the course (and to re-record t... | 2023-10-01T19:08:48Z | 2024-04-28T02:30:42Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
The link provided for the dataset is broken,
data_files =
[https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst](url)
The
### Steps to reproduce the bug
Steps to reproduce:
1) Head over to [https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt#big-data-datasets-to-the-rescue](url)
2) In the Section "What is the Pile?", you can see a code snippet that contains the broken link.
### Expected behavior
The link should Redirect to the "PubMed Abstracts dataset" as expected .
### Environment info
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https://api.github.com/repos/huggingface/datasets/issues/5437 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5437/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5437/comments | https://api.github.com/repos/huggingface/datasets/issues/5437/events | https://github.com/huggingface/datasets/issues/5437 | 1,536,837,144 | I_kwDODunzps5bmkYY | 5,437 | Can't load png dataset with 4 channel (RGBA) | {
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"Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\r\n\r\n",
"> Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode... | 2023-01-17T18:22:27Z | 2023-01-18T20:20:15Z | 2023-01-18T20:20:15Z | NONE | null | null | {
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} | I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand. | {
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