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NONE
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completed
| null | null |
[
"Thanks for reporting. You're right, workers were under-provisioned due to a manual error, and the job queue was full. It's fixed now."
] |
Dataset Viewer issue for deepklarity/huggingface-spaces-dataset
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2022-07-22T12:14:18Z
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https://api.github.com/repos/huggingface/datasets/issues/4736/comments
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### Link
https://huggingface.co/datasets/deepklarity/huggingface-spaces-dataset/viewer/deepklarity--huggingface-spaces-dataset/train
### Description
Hi Team,
I'm getting the following error on a uploaded dataset. I'm getting the same status for a couple of hours now. The dataset size is `<1MB` and the format is csv, so I'm not sure if it's supposed to take this much time or not.
```
Status code: 400
Exception: Status400Error
Message: The split is being processed. Retry later.
```
Is there any explicit step to be taken to get the viewer to work?
### Owner
Yes
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MEMBER
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] |
Pin rouge_score test dependency
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2022-07-22T07:18:21Z
|
https://api.github.com/repos/huggingface/datasets/issues/4735/comments
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Temporarily pin `rouge_score` (to avoid latest version 0.7.0) until the issue is fixed.
Fix #4734
|
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MEMBER
|
completed
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"We have added a comment on an existing issue opened in their repo: https://github.com/google-research/google-research/issues/1212#issuecomment-1192267130\r\n- https://github.com/google-research/google-research/issues/1212"
] |
Package rouge-score cannot be imported
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2022-07-22T07:15:05Z
|
https://api.github.com/repos/huggingface/datasets/issues/4734/comments
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## Describe the bug
After the today release of `rouge_score-0.0.7` it seems no longer importable. Our CI fails: https://github.com/huggingface/datasets/runs/7463218591?check_suite_focus=true
```
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_configs_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_bigbench
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_rouge
```
with errors:
```
> from rouge_score import rouge_scorer
E ModuleNotFoundError: No module named 'rouge_score'
```
```
E ImportError: To be able to use rouge, you need to install the following dependency: rouge_score.
E Please install it using 'pip install rouge_score' for instance'
```
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NONE
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[
"Fixed by:\r\n- #4735"
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rouge metric
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2022-07-22T07:06:51Z
|
https://api.github.com/repos/huggingface/datasets/issues/4733/comments
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## Describe the bug
A clear and concise description of what the bug is.
Loading Rouge metric gives error after latest rouge-score==0.0.7 release.
Downgrading rougemetric==0.0.4 works fine.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
A clear and concise description of the expected results.
from rouge_score import rouge_scorer, scoring
should run
## Actual results
Specify the actual results or traceback.
File "/root/.cache/huggingface/modules/datasets_modules/metrics/rouge/0ffdb60f436bdb8884d5e4d608d53dbe108e82dac4f494a66f80ef3f647c104f/rouge.py", line 21, in <module>
from rouge_score import rouge_scorer, scoring
ImportError: cannot import name 'rouge_scorer' from 'rouge_score' (unknown location)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version:3.9
- PyArrow version:
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MEMBER
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[
"Thanks for the feedback!\r\n\r\nI think since this issue is closely related to loading, I can add a clearer explanation under [Load > local loading script](https://huggingface.co/docs/datasets/main/en/loading#local-loading-script).",
"That makes sense but I think having a line about it under https://huggingface.co/docs/datasets/installation#source the \"source\" header here would be useful. My mental model of `pip install -e .` does not include the fact that the source files aren't actually being used. ",
"Thanks for sharing your perspective. I think the `load_dataset` function is the only one that pulls from GitHub, and since this use-case is very specific, I don't think we need to include such a broad clarification in the Installation section.\r\n\r\nFeel free to check out the linked PR and let me know if it needs any additional explanation 😊"
] |
Document better that loading a dataset passing its name does not use the local script
|
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As reported by @TrentBrick here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191858596, it could be more clear that loading a dataset by passing its name does not use the (modified) local script of it.
What he did:
- he installed `datasets` from source
- he modified locally `datasets/the_pile/the_pile.py` loading script
- he tried to load it but using `load_dataset("the_pile")` instead of `load_dataset("datasets/the_pile")`
- as explained here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191040245:
- the former does not use the local script, but instead it downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.
He suggests adding a more clear explanation about this. He suggests adding it maybe in [Installation > source](https://huggingface.co/docs/datasets/installation))
CC: @stevhliu
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docs: ✏️ fix TranslationVariableLanguages example
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CONTRIBUTOR
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[
"My bad, `482 * 418 * 50000 * 3 / 1000000 = 30221 MB` ( https://stackoverflow.com/a/42979315 ).\r\n\r\nMeanwhile `256 * 256 * 50000 * 3 / 1000000 = 9830 MB`. We are loading the non-cropped images and that is why we take so much RAM."
] |
Loading imagenet-1k validation split takes much more RAM than expected
|
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## Describe the bug
Loading into memory the validation split of imagenet-1k takes much more RAM than expected. Assuming ImageNet-1k is 150 GB, split is 50000 validation images and 1,281,167 train images, I would expect only about 6 GB loaded in RAM.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imagenet-1k", split="validation")
print(dataset)
"""prints
Dataset({
features: ['image', 'label'],
num_rows: 50000
})
"""
pipe_inputs = dataset["image"]
# and wait :-)
```
## Expected results
Use only < 10 GB RAM when loading the images.
## Actual results
```
Using custom data configuration default
Reusing dataset imagenet-1k (/home/fxmarty/.cache/huggingface/datasets/imagenet-1k/default/1.0.0/a1e9bfc56c3a7350165007d1176b15e9128fcaf9ab972147840529aed3ae52bc)
Killed
```
## Environment info
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- datasets commit: 4e4222f1b6362c2788aec0dd2cd8cede6dd17b80
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Refactor Hub tests
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2022-07-21T14:43:13Z
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https://api.github.com/repos/huggingface/datasets/issues/4729/comments
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This PR refactors `test_upstream_hub` by removing unittests and using the following pytest Hub fixtures:
- `ci_hub_config`
- `set_ci_hub_access_token`: to replace setUp/tearDown
- `temporary_repo` context manager: to replace `try... finally`
- `cleanup_repo`: to delete repo accidentally created if one of the tests fails
This is a preliminary work done to manage unit/integration tests separately.
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[
"Hi @rohitvincent, thanks for reporting.\r\n\r\nUnfortunately I'm not able to reproduce your issue:\r\n```python\r\nIn [2]: from datasets import load_dataset, DownloadMode\r\n ...: load_dataset(path='financial_phrasebank',name='sentences_allagree', download_mode=\"force_redownload\")\r\nDownloading builder script: 6.04kB [00:00, 2.87MB/s] \r\nDownloading metadata: 13.7kB [00:00, 7.24MB/s] \r\nDownloading and preparing dataset financial_phrasebank/sentences_allagree (download: 665.91 KiB, generated: 296.26 KiB, post-processed: Unknown size, total: 962.17 KiB) to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141...\r\nDownloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 682k/682k [00:00<00:00, 7.66MB/s]\r\nDataset financial_phrasebank downloaded and prepared to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 918.80it/s]\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label'],\r\n num_rows: 2264\r\n })\r\n})\r\n```\r\n\r\nAre you able to access the link? https://www.researchgate.net/profile/Pekka-Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip",
"Yes was able to download from the link manually. But still, get the same error when I use load_dataset.",
"Fixed once data files are hosted on the Hub:\r\n- #4598"
] |
load_dataset gives "403" error when using Financial Phrasebank
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2022-07-21T08:43:32Z
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I tried both codes below to download the financial phrasebank dataset (https://huggingface.co/datasets/financial_phrasebank) with the sentences_allagree subset. However, the code gives a 403 error when executed from multiple machines locally or on the cloud.
```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree',download_mode=DownloadMode.FORCE_REDOWNLOAD)
```
```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree')
```
**Error**
ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)
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Dataset Viewer issue for TheNoob3131/mosquito-data
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### Link
https://huggingface.co/datasets/TheNoob3131/mosquito-data/viewer/TheNoob3131--mosquito-data/test
### Description
Dataset preview not showing with large files. Says 'split cache is empty' even though there are train and test splits.
### Owner
_No response_
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Fix broken link to the Hub
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https://api.github.com/repos/huggingface/datasets/issues/4726/comments
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The Markdown link fails to render if it is in the same line as the `<span>`. This PR implements @mishig25's fix by using `<a href=" ">` instead.
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"Thanks for reporting, @TrentBrick. We are addressing the change with their data host server.\r\n\r\nOn the meantime, if you would like to work with your fixed local copy of the_pile script, you should use:\r\n```python\r\nload_dataset(\"path/to/your/local/the_pile/the_pile.py\",...\r\n```\r\ninstead of just `load_dataset(\"the_pile\",...`.\r\n\r\nThe latter downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.",
"@TrentBrick, I have checked the URLs and both hosts work, the original (https://the-eye.eu/) and the mirror (https://mystic.the-eye.eu/). See e.g.:\r\n- https://mystic.the-eye.eu/public/AI/pile/\r\n- https://mystic.the-eye.eu/public/AI/pile_preliminary_components/\r\n\r\nPlease, let me know if you still find any issue loading this dataset by using current server URLs.",
"Great this is working now. Re the download from GitHub... I'm sure thought went into doing this but could it be made more clear maybe here? https://huggingface.co/docs/datasets/installation for example under installing from source? I spent over an hour questioning my sanity as I kept trying to edit this file, uninstall and reinstall the repo, git reset to previous versions of the file etc.",
"Thanks for the quick reply and help too\r\n",
"Thanks @TrentBrick for the suggestion about improving our docs: we should definitely do this if you find they are not clear enough.\r\n\r\nCurrently, our docs explain how to load a dataset from a local loading script here: [Load > Local loading script](https://huggingface.co/docs/datasets/loading#local-loading-script)\r\n\r\nI've opened an issue here:\r\n- #4732\r\n\r\nFeel free to comment on it any additional explanation/suggestion/requirement related to this problem."
] |
the_pile datasets URL broken.
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2022-07-20T20:57:30Z
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https://github.com/huggingface/datasets/pull/3627 changed the Eleuther AI Pile dataset URL from https://the-eye.eu/ to https://mystic.the-eye.eu/ but the latter is now broken and the former works again.
Note that when I git clone the repo and use `pip install -e .` and then edit the URL back the codebase doesn't seem to use this edit so the mystic URL is also cached somewhere else that I can't find?
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"_The documentation is not available anymore as the PR was closed or merged._",
"Added some docs for dask and took your comments into account\r\n\r\ncc @philschmid if you also want to take a look :)",
"Just noticed that it would be more convenient to pass the output dir to download_and_prepare directly, to bypass the caching logic which prepares the dataset at `<cache_dir>/<name>/<version>/<hash>/`. And this way the cache is only used for the downloaded files. What do you think ?\r\n\r\n```python \r\n\r\nbuilder = load_datadet_builder(\"squad\")\r\n# or with a custom cache\r\nbuilder = load_datadet_builder(\"squad\", cache_dir=\"path/to/local/cache/for/downloaded/files\")\r\n\r\n# download and prepare to s3\r\nbuilder.download_and_prepare(\"s3://my_bucket/squad\")\r\n```",
"Might be of interest: \r\nPyTorch and AWS introduced better support for S3 streaming in `torchtext`. \r\n\r\n",
"Having thought about it a bit more, I also agree with @philschmid in that it's important to follow the existing APIs (pandas/dask), which means we should support the following at some point:\r\n\r\n* remote data files resolution for the packaged modules to support `load_dataset(\"<format>\", data_files=\"<fs_url>\")`\r\n* `to_<format>(\"<fs_url>\")`\r\n* `load_from_disk` and `save_to_disk` already expose the `fs` param, but it would be cool to support specifying `fsspec` URLs directly as the source/destination path (perhaps we can then deprecate `fs` to be fully aligned with pandas/dask)\r\n\r\nIMO these are the two main issues with the current approach:\r\n* relying on the builder API to generate the formatted files results in a non-friendly format due to how our caching works (a lot of nested subdirectories)\r\n* this approach still downloads the files needed to generate a dataset locally. Considering one of our goals is to align the streaming API with the non-streaming one, this could be avoided by running `to_<format>` on streamed/iterable datasets",
"Alright I did the last change I wanted to do, here is the final API:\r\n\r\n```python\r\nbuilder = load_dataset_builder(...)\r\nbuilder.download_and_prepare(\"s3://...\", storage_options={\"token\": ...})\r\n```\r\n\r\nand it creates the arrow files directly in the specified directory, not in a nested subdirectory structure as we do in the cache !\r\n\r\n> this approach still downloads the files needed to generate a dataset locally. Considering one of our goals is to align the streaming API with the non-streaming one, this could be avoided by running to_<format> on streamed/iterable datasets\r\n\r\nYup this can be explored in some future work I think. Though to keep things simple and clear I would keep the streaming behaviors only when you load a dataset in streaming mode, and not include it in `download_and_prepare` (because it wouldn't be aligned with the name of the function, which imply to 1. download and 2. prepare ^^). Maybe an API like that can make sense for those who need full streaming\r\n\r\n```python\r\nds = load_dataset(..., streaming=True)\r\nds.to_parquet(\"s3://...\")\r\n```",
"totally agree with your comment on the meaning of \"loading\", I'll update the docs",
"I took your comments into account and reverted all the changes related to `cache_dir` to keep the support for remote `cache_dir` for beam datasets. I also updated the wording in the docs to not use \"load\" when it's not appropriate :)"
] |
Download and prepare as Parquet for cloud storage
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2022-07-20T13:39:02Z
|
https://api.github.com/repos/huggingface/datasets/issues/4724/comments
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Download a dataset as Parquet in a cloud storage can be useful for streaming mode and to use with spark/dask/ray.
This PR adds support for `fsspec` URIs like `s3://...`, `gcs://...` etc. and ads the `file_format` to save as parquet instead of arrow:
```python
from datasets import *
cache_dir = "s3://..."
builder = load_dataset_builder("crime_and_punish", cache_dir=cache_dir)
builder.download_and_prepare(file_format="parquet")
```
EDIT: actually changed the API to
```python
from datasets import *
builder = load_dataset_builder("crime_and_punish")
builder.download_and_prepare("s3://...", file_format="parquet")
```
credentials to cloud storage can be passed using the `storage_options` argument in
For consistency with the BeamBasedBuilder, I name the parquet files `{builder.name}-{split}-xxxxx-of-xxxxx.parquet`. I think this is fine since we'll need to implement parquet sharding after this PR, so that a dataset can be used efficiently with dask for example.
Note that images/audio files are not embedded yet in the parquet files, this will added in a subsequent PR
TODO:
- [x] docs
- [x] tests
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Refactor conftest fixtures
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2022-07-20T12:15:22Z
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https://api.github.com/repos/huggingface/datasets/issues/4723/comments
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Previously, fixture modules `hub_fixtures` and `s3_fixtures`:
- were both at the root test directory
- were imported using `import *`
- as a side effect, the modules `os` and `pytest` were imported from `s3_fixtures` into `conftest`
This PR:
- puts both fixture modules in a dedicated directory `fixtures`
- renames both to: `fixtures.hub` and `fixtures.s3`
- imports them into `conftest` as plugins, using the `pytest_plugins`: this avoids the `import *`
- additionally creates a new fixture module `fixtures.files` with all file-related fixtures
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Docs: Fix same-page haslinks
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2022-07-20T10:04:37Z
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`href="/docs/datasets/quickstart#audio"` implicitly goes to `href="/docs/datasets/{$LATEST_STABLE_VERSION}/quickstart#audio"`. Therefore, https://huggingface.co/docs/datasets/quickstart#audio #audio hashlink does not work since the new docs were not added to v2.3.2 (LATEST_STABLE_VERSION)
to preserve the version, it should be just `href="#audio"`, which will implicilty go to curren_page + #audio element
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[
"Hi ! It looks like a bug in `pyarrow`. If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nTo achieve that you can try to lower the value of `max_shard_size` and also don't use `map` before `push_to_hub`.\r\n\r\nDo you have a minimum reproducible example that we can share with the Arrow team for further debugging ?",
"> If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nYup, I did not encounter this bug when I was testing my script with a slice of <1000 samples for my dataset.\r\n\r\n> Do you have a minimum reproducible example...\r\n\r\nNot sure if I can get more minimal than the script I shared above. Are you asking for a sample json file?\r\nJust generate a random manifest list, I can add that to the above script if that's what you mean?\r\n",
"Actually this is probably linked to this open issue: https://issues.apache.org/jira/browse/ARROW-5030.\r\n\r\nsetting `max_shard_size=\"2GB\"` should do the job (or `max_shard_size=\"1GB\"` if you want to be on the safe side, especially given that there can be some variance in the shard sizes if the dataset is not evenly distributed)"
] |
PyArrow Dataset error when calling `load_dataset`
|
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I_kwDODunzps5OGOHw
| null |
2022-07-20T01:16:03Z
|
https://api.github.com/repos/huggingface/datasets/issues/4721/comments
|
## Describe the bug
I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error:
```sh
File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main
raw_datasets = load_dataset(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split
for key, table in logging.tqdm(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1309, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```
## Steps to reproduce the bug
I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`.
When creating the dataset, I do something like this:
```python
import json
from datasets import Dataset, Audio
# manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text
for line in manifest_lines:
line = line.strip()
if line:
line_dict = json.loads(line)
manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}")
manifest_dict["duration"].append(line_dict["duration"])
manifest_dict["transcription"].append(line_dict["text"])
# Create a HF dataset
dataset = Dataset.from_dict(manifest_dict).cast_column(
"audio", Audio(sampling_rate=16_000),
)
# From the docs for saving to disk
# https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk
def read_audio_file(example):
with open(example["audio"]["path"], "rb") as f:
return {"audio": {"bytes": f.read()}}
dataset = dataset.map(read_audio_file, num_proc=70)
dataset.save_to_disk(f"/audio-data/hf/{artifact_name}")
dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True)
```
Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace.
I am able to load the dataset fine if I use `load_from_disk()`.
## Expected results
`load_dataset()` should behave just like `load_from_disk()` and not cause any errors.
## Actual results
See above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I am using the `huggingface/transformers-pytorch-gpu:latest` image
- `datasets` version: 2.3.0
- Platform: Docker/Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 8.0.0
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open
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https://api.github.com/repos/huggingface/datasets/issues/4720
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https://api.github.com/repos/huggingface/datasets
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2022-09-08T16:47:21Z
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https://github.com/huggingface/datasets/issues/4720
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NONE
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[
"It seems like the list of splits could not be obtained:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"shamikbose89/lancaster_newsbooks\", \"default\")\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/shamikbose89--lancaster_newsbooks/2d1c63d269bf7b9342accce0a95960b1710ab4bc774248878bd80eb96c1afaf7/lancaster_newsbooks.py\", line 73, in _split_generators\r\n data_dir = dl_manager.download_and_extract(_URL)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 916, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 879, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 348, in map_nested\r\n return function(data_struct)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 884, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 388, in _get_extraction_protocol\r\n return _get_extraction_protocol_with_magic_number(f)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 354, in _get_extraction_protocol_with_magic_number\r\n f.seek(0)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 684, in seek\r\n raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nping @huggingface/datasets ",
"Oh, I removed the 'split' key from `kwargs`. I put it back in, but there's still the same error",
"It looks like the data host doesn't support http range requests, which is necessary to glob inside a ZIP archive in streaming mode. Can you try hosting the dataset elsewhere ? Or download each file separately from https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2531 ?",
"@lhoestq Thanks! That seems to have solved it. I can get the splits with the `get_dataset_split_names()` function. The dataset viewer is still not loading properly, though. The new error is\r\n```\r\nStatus code: 400\r\nException: BadZipFile\r\nMessage: File is not a zip file\r\n```\r\n\r\nPS. The dataset loads properly and can be accessed"
] |
Dataset Viewer issue for shamikbose89/lancaster_newsbooks
|
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I_kwDODunzps5OFLYj
| null |
2022-07-19T20:00:07Z
|
https://api.github.com/repos/huggingface/datasets/issues/4720/comments
|
### Link
https://huggingface.co/datasets/shamikbose89/lancaster_newsbooks
### Description
Status code: 400
Exception: ValueError
Message: Cannot seek streaming HTTP file
I am able to use the dataset loading script locally and it also runs when I'm using the one from the hub, but the viewer still doesn't load
### Owner
Yes
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[
"I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated.",
"Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Datasets library, but it has to do with the specific implementation of your dataset on the Hub.\r\n\r\nTherefore, I'm transferring this discussion to your own dataset Community tab: https://huggingface.co/datasets/TheNoob3131/mosquito-data/discussions/1"
] |
Issue loading TheNoob3131/mosquito-data dataset
|
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2022-07-19T17:47:37Z
|
https://api.github.com/repos/huggingface/datasets/issues/4719/comments
|
So my dataset is public in the Huggingface Hub, but when I try to load it using the load_dataset command, it shows that it is downloading the files, but throws a ValueError. When I went to my directory to see if the files were downloaded, the folder was blank.
Here is the error below:
ValueError Traceback (most recent call last)
Input In [8], in <cell line: 3>()
1 from datasets import load_dataset
----> 3 dataset = load_dataset("TheNoob3131/mosquito-data", split="train")
File ~\Anaconda3\lib\site-packages\datasets\load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
Is the dataset in the wrong format or is there some security permission that I should enable?
|
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2022-07-22T13:42:27Z
|
[] |
https://github.com/huggingface/datasets/pull/4718
|
MEMBER
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
Make Extractor accept Path as input
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2022-07-19T13:25:06Z
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https://api.github.com/repos/huggingface/datasets/issues/4718/comments
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This PR:
- Makes `Extractor` accept instance of `Path` as input
- Removes unnecessary castings of `Path` to `str`
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[
"It's currently working, as far as I understand\r\n\r\nhttps://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train\r\n\r\n<img width=\"1556\" alt=\"Capture d’écran 2022-07-19 à 09 24 01\" src=\"https://user-images.githubusercontent.com/1676121/179761130-2d7980b9-c0f6-4093-8b1d-f0a3872fef3f.png\">\r\n\r\n---\r\n\r\nWhat was your issue?"
] |
Dataset Viewer issue for LawalAfeez/englishreview-ds-mini
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2022-07-19T13:19:39Z
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https://api.github.com/repos/huggingface/datasets/issues/4717/comments
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### Link
_No response_
### Description
Unable to view the split data
### Owner
_No response_
|
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Added the "tags" YAML tag, so that users can specify data domain/topics keywords for dataset search
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We're now using `part-of-speech` and not `part-of-speech-tagging`, see discussion here: https://github.com/huggingface/datasets/commit/114c09aff2fa1519597b46fbcd5a8e0c0d3ae020#r78794777
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Fix named split sorting and remove unnecessary casting
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This PR:
- makes `NamedSplit` sortable: so that `sorted()` can be called on them
- removes unnecessary `sorted()` on `dict.keys()`: `dict_keys` view is already like a `set`
- removes unnecessary casting of `NamedSplit` to `str`
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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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Highlight non-commercial license in amazon_reviews_multi dataset card
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Highlight that the licence granted by Amazon only covers non-commercial research use.
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Document how to create a dataset loading script for audio/vision
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Currently, in our docs for Audio/Vision/Text, we explain how to:
- Load data
- Process data
However we only explain how to *Create a dataset loading script* for text data.
I think it would be useful that we add the same for Audio/Vision as these have some specificities different from Text.
See, for example:
- #4697
- and comment there: https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492
CC: @stevhliu
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Add object detection processing tutorial
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The following adds a quick guide on how to process object detection datasets with `albumentations`.
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https://github.com/huggingface/datasets/issues/4709
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CONTRIBUTOR
| null | null | null |
[
"Hi ! That would be awesome to have them indeed, thanks for opening this issue\r\n\r\nI just added you to the WMT org on the HF Hub if you're interested in adding those datasets.\r\n\r\nFeel free to create a dataset repository for each dataset and upload the data files there :) preferably in ZIP archives instead of TAR archives (the current WMT scripts don't support streaming TAR archives, so it would break the dataset preview). We've also had issues with the `statmt.org` host (data unavailable, slow download speed), that's why I think it's better if we re-host the files on the Hub.\r\n\r\n`wmt21` (and wmt22) can be added <s>in this GitHub repository I think</s> on the HF Hub under the `WMT` org (we'll move the previous ones to this org soon as well).\r\nTo add it, you can copy paste the code of the previous one (e.g. wmt19), and add the new data:\r\n- in wmt_utils.py, add the new data subsets. You need to provide the download URLs, as well as the target and source languages\r\n- in wmt21.py (renamed from wmt19.py), you can specify the subsets that WMT21 uses (i.e. the one you just added)\r\n- in wmt_utils.py, define the python function that must be used to parse the subsets you added. To do so, you must go in `_generate_examples` and chose the proper `sub_generator` based on the subset name. For example, the `paracrawl_v3` subset uses the `_parse_tmx` function:\r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/datasets/wmt19/wmt_utils.py#L834-L835\r\n\r\nHopefully the data is in a format that is already supported and there's no need to write a new `_parse_*` function for the new subsets. Let me know if you have questions or if I can help :)",
"@Muennighoff , @lhoestq let me know if you want me to look into this. Happy to help bring WMT21 & WMT22 datasets into 🤗 ! ",
"Hi @srhrshr :) Sure, feel free to create a dataset repository on the Hub and start from the implementation of WMT19 if you want. Then we can move the dataset under the WMT org (we'll move the other ones there as well).\r\n\r\nLet me know if you have questions or if I can help",
"#self-assign",
"Hello @lhoestq ,\r\n\r\nWould it be possible for me to be granted in the WMT organization (on hf ofc) in order to facilitate dataset uploads? I've already initiated the joining process at this link: https://huggingface.co/wmt\r\n\r\nI appreciate your help with this. Thank you!",
"Hi ! Cool I just added you"
] |
WMT21 & WMT22
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2022-07-18T21:05:33Z
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https://api.github.com/repos/huggingface/datasets/issues/4709/comments
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## Adding a Dataset
- **Name:** WMT21 & WMT22
- **Description:** We are going to have three tracks: two small tasks and a large task.
The small tracks evaluate translation between fairly related languages and English (all pairs). The large track uses 101 languages.
- **Paper:** /
- **Data:** https://statmt.org/wmt21/large-scale-multilingual-translation-task.html https://statmt.org/wmt22/large-scale-multilingual-translation-task.html
- **Motivation:** Many more languages than previous WMT versions - Could be very high impact
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
I could also tackle this. I saw the existing logic for WMT models is a bit complex (datasets are stored on the wmt account & retrieved in separate wmt datasets afaict). How long do you think it would take me? @lhoestq
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] |
Fix require torchaudio and refactor test requirements
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2022-07-18T17:24:28Z
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https://api.github.com/repos/huggingface/datasets/issues/4708/comments
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Currently there is a bug in `require_torchaudio` (indeed it is requiring `sox` instead):
```python
def require_torchaudio(test_case):
if find_spec("sox") is None:
...
```
The bug was introduced by:
- #3685
- Commit: https://github.com/huggingface/datasets/pull/3685/commits/b5a3e7122d49c4dcc9333b1d8d18a833fc04b940
which moved
```python
require_sndfile = pytest.mark.skipif(
# In Windows and OS X, soundfile installs sndfile
(sys.platform != "linux" and find_spec("soundfile") is None)
# In Linux, soundfile throws RuntimeError if sndfile not installed with distribution package manager
or (sys.platform == "linux" and find_library("sndfile") is None),
reason="Test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)
require_sox = pytest.mark.skipif(
find_library("sox") is None,
reason="Test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`",
)
require_torchaudio = pytest.mark.skipif(find_spec("torchaudio") is None, reason="Test requires 'torchaudio'")
```
to
```python
def require_sndfile(test_case):
"""
Decorator marking a test that requires soundfile.
These tests are skipped when soundfile isn't installed.
"""
if (sys.platform != "linux" and find_spec("soundfile") is None) or (
sys.platform == "linux" and find_library("sndfile") is None
):
test_case = unittest.skip(
"test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)(test_case)
return test_case
def require_sox(test_case):
"""
Decorator marking a test that requires sox.
These tests are skipped when sox isn't installed.
"""
if find_library("sox") is None:
return unittest.skip("test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`")(
test_case
)
return test_case
def require_torchaudio(test_case):
"""
Decorator marking a test that requires torchaudio.
These tests are skipped when torchaudio isn't installed.
"""
if find_spec("sox") is None:
return unittest.skip("test requires 'torchaudio'")(test_case)
return test_case
```
This PR;
- fixes the bug in `require_torchaudio`
- refactors the test requirements back to using `pytest` instead of `unittest`
- the text in `pytest.skipif` `reason` can be used if needed in a test body: `require_torchaudio.kwargs["reason"]`
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https://github.com/huggingface/datasets/issues/4707
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NONE
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completed
| null | null |
[
"Thanks for reporting. I refreshed the dataset viewer and it now works as expected.\r\n\r\nhttps://huggingface.co/datasets/TheNoob3131/mosquito-data\r\n\r\n<img width=\"1135\" alt=\"Capture d’écran 2022-07-18 à 13 15 22\" src=\"https://user-images.githubusercontent.com/1676121/179566497-e47f1a27-fd84-4a8d-9d7f-2e0f2da803df.png\">\r\n\r\nWe will investigate why it occurred in the first place\r\n",
"By chance, could you provide some details about the operations done on the dataset: was it private? gated?",
"Yes, it was a private dataset, and when I made it public, the Dataset Preview did not work. \r\n\r\nHowever, now when I make the dataset private, it says that the Dataset Preview has been disabled. Why is this?",
"Thanks for the details. For now, the dataset viewer is always disabled on private datasets (see https://huggingface.co/docs/hub/datasets-viewer for more details)",
"Hi, it was working fine for a few hours, but then I can't see the dataset viewer again (public dataset). Why is this still happening?\r\nIt's the same error too:\r\n\r\n",
"OK? This is a bug, thanks for help spotting and reproducing it (it occurs when a dataset is switched to private, then to public). We will be working on it, meanwhile, I've restored the dataset viewer manually again."
] |
Dataset Viewer issue for TheNoob3131/mosquito-data
|
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### Link
_No response_
### Description
Getting this error when trying to view dataset preview:
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv')
### Owner
_No response_
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[] |
https://github.com/huggingface/datasets/pull/4706
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MEMBER
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"I guess the report link is this instead: https://huggingface.co/datasets/xtreme/discussions/1"
] |
Fix empty examples in xtreme dataset for bucc18 config
|
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As reported in https://huggingface.co/muibk, there are empty examples in xtreme/bucc18.de
I applied your fix @mustaszewski
I also used a dict to make the dataset generation much faster
|
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Fix crd3
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As reported in https://huggingface.co/datasets/crd3/discussions/1#62cc377073b2512b81662794, each split of the dataset was containing the same data. This issues comes from a bug in the dataset script
I fixed it and also uploaded the data to hf.co to make the dataset work in streaming mode
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Skip tests only for lz4/zstd params if not installed
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Currently, if `zstandard` or `lz4` are not installed, `test_compression_filesystems` and `test_streaming_dl_manager_extract_all_supported_single_file_compression_types` are skipped for all compression format parameters.
This PR fixes these tests, so that if `zstandard` or `lz4` are not installed, the tests are skipped only for the corresponding compression parameters (`zstd` or `lz4`), whereas the tests are not skipped for all the other compression parameters (`gzip`, `xz` and `bz2`).
Related to:
- #4688
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Make cast in `from_pandas` more robust
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Make the cast in `from_pandas` more robust (as it was done for the packaged modules in https://github.com/huggingface/datasets/pull/4364)
This should be useful in situations like [this one](https://discuss.huggingface.co/t/loading-custom-audio-dataset-and-fine-tuning-model/8836/4).
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"Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a good idea to make our current taxonomy more complex.",
"> Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a good idea to make our current taxonomy more complex.\r\n\r\nThanks, for letting me know. Will you allow the topic tags to be user-generated or only chosen from a list?",
"Thanks for opening this issue @davanstrien.\r\n\r\nAs we discussed last week, the tag approach would be in principle the simpler to be implemented, either the domain tag (with closed vocabulary: more reliable but also more rigid), or the topic tag (with open vocabulary: more flexible for user needs)",
"Hi @davanstrien If i remember correctly this was also discussed inside a hf.co Discussion, would you be able to link it here too?\r\n\r\n(where i suggested using `tags: - foo - bar` IIRC.\r\n\r\nThanks a ton!",
"> Hi @davanstrien If i remember correctly this was also discussed inside a hf.co Discussion, would you be able to link it here too?\r\n> \r\n> (where i suggested using `tags: - foo - bar` IIRC.\r\n> \r\n> Thanks a ton!\r\n\r\nThis doesn't ring a bell - I did a quick search of https://discuss.huggingface.co but didn't find anything. \r\n\r\nThe `tags: ` approach sounds like a good option for this. It would be especially nice if these could suggest existing tags, but this probably won't be easily possible through the current interface. \r\n",
"I opened a PR to add \"tags\" to the YAML validator:\r\nhttps://github.com/huggingface/datasets/pull/4716\r\n\r\nI also added \"tags\" to the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), with suggestions like \"bio\" or \"newspapers\"",
"Thanks @lhoestq for the initiative.\r\n \r\nJust one question: are \"tags\" already supported on the Hub? \r\n\r\nI think they aren't. Thus, the Hub should support them so that they are properly displayed.",
"I think they're not displayed, but at least it should enable users to filter by tag in using `huggingface_hub` or using the appropriate query params on the website (not sure if it's possible yet though)",
"> I think they're not displayed, but at least it should enable users to filter by tag in using `huggingface_hub` or using the appropriate query params on the website (not sure if it's possible yet though)\r\n\r\nI think this would already be a helpful start. I'm happy to try this out with the datasets added to https://huggingface.co/organizations/biglam and use the `huggingface_hub` to filter those datasets using the tags. ",
"Is this abandoned? \r\nI'm looking for a transport logistics dataset; how can I find one?",
"@younes-io Full text search is probably your best bet: https://huggingface.co/search/full-text?type=dataset"
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Domain specific dataset discovery on the Hugging Face hub
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**Is your feature request related to a problem? Please describe.**
## The problem
The datasets hub currently has `8,239` datasets. These datasets span a wide range of different modalities and tasks (currently with a bias towards textual data).
There are various ways of identifying datasets that may be relevant for a particular use case:
- searching
- various filters
Currently, however, there isn't an easy way to identify datasets belonging to a specific domain. For example, I want to browse machine learning datasets related to 'social science' or 'climate change research'.
The ability to identify datasets relating to a specific domain has come up in discussions around the [BigLA](https://github.com/bigscience-workshop/lam/) datasets hackathon https://github.com/bigscience-workshop/lam/discussions/31#discussioncomment-3123610. As part of the hackathon, we're currently collecting datasets related to Libraries, Archives and Museums and making them available via the hub. We currently do this under a Hugging Face organization (https://huggingface.co/biglam). However, going forward, I can see some of these datasets being migrated to sit under an organization that is the custodian of the dataset (for example, a national library the data was originally from). At this point, it becomes more difficult to quickly identify datasets from this domain without relying on search.
This is also related to some existing issues on Github related to metadata on the hub:
- https://github.com/huggingface/datasets/issues/3625
- https://github.com/huggingface/datasets/issues/3877
**Describe the solution you'd like**
### Some possible solutions that may help with this:
#### Enable domain tags (from a controlled vocabulary)
- This would add metadata field to the YAML for the domain a dataset relates to
- Advantages:
- the list is controlled, allowing it to be more easily integrated into the datasets tag app (https://huggingface.co/space/huggingface/datasets-tagging)
- the controlled vocabulary could align with an existing controlled vocabulary
- this additional metadata can be used to perform filtering by domain
- disadvantages
- choosing the best controlled vocab may be difficult
- there are many datasets that are likely to fit into the 'machine learning' domain (i.e. there is a long tail of datasets that aren't in more 'generic' machine learning domain
#### Enable topic tags (user-generated)
Enable 'free form' topic tags for datasets and models. This would be closer to GitHub's repository topics which can be chosen from a controlled list (https://github.com/topics/) but can also be more user/org specific. This could potentially be useful for organizations to also manage their own models and datasets as the number they hold in their org grows. For example, they may create 'topic tags' for a specific project, so it's clearer which datasets /models are related to that project.
#### Collections
This solution would likely be the biggest shift and may require significant changes in the hub fronted. Collections could work in several different ways but would include:
Users can curate particular datasets, models, spaces, etc., into a collection. For example, they may create a collection of 'historic newspapers suitable for training language models'. These collections would not be mutually exclusive, i.e. a dataset can belong to zero, one or many collections. Collections can also potentially be nested under other collections.
This is fairly common on other data reposotiores for example the following collections:
<img width="293" alt="Screenshot 2022-07-18 at 11 50 44" src="https://user-images.githubusercontent.com/8995957/179496445-963ed122-5e26-4574-96e8-41081bce3e2b.png">
all belong under a higher level collection (https://bl.iro.bl.uk/collections/353c908d-b495-4413-b047-87236d2573e3?locale=en).
There are different models one could use for how these collections could be created:
- only within an org
- for any dataset/model
- the owner or a dataset/model has to agree to be added to a collection
- a collection owner can have people suggest additions to their collection
- other models....
These collections could be thematic, related to particular training approaches, curate models with particular inference properties etc. Whilst some of these features may duplicate current/or future tag filters on the hub, they offer the advantage of being flexible and not having to predict what users will want to do upfront.
There is also potential for automating the creation of these collections based on existing metadata. For example, one could collect models trained on a collection of datasets so for example, if we had a collection of 'historic newspapers suitable for training language models' that contained 30 datasets, we could create another collection 'historic newspaper language models' that takes any model on the hub whose metadata says it used one or more of those 30 datasets.
There is also the option of exploring ML approaches to suggest models/datasets may be relevant to a particular collection.
This approach is likely to be quite difficult to implement well and would require significant thought. There is also likely to be a benefit in doing quite a bit of upfront work in curating useful collections to demonstrate the benefits of collections.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
It is possible to collate this information externally, i.e. one could link back to the relevant models/datasets from an external platform.
**Additional context**
Add any other context about the feature request here.
I'm cc'ing others involved in the BigLAM hackathon who may also have thoughts @cakiki @clancyoftheoverflow @albertvillanova
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Added more information in the README about contributors of the Arabic Speech Corpus
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Added more information in the README about contributors and encouraged reading the thesis for more infos
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Support extract lz4 compressed data files
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"Hi, thanks for working on this, but the fix for this has already been merged in https://github.com/huggingface/datasets/pull/4608."
] |
Fix Authentification Error while streaming
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2022-07-18T08:03:41Z
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https://api.github.com/repos/huggingface/datasets/issues/4699/comments
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I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub.
```
from datasets import load_dataset
dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True)
for d in dataset['train']:
print(d)
break # this is for checking
```
This code is an example for streaming private datasets.
when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this,
```
/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)
1007 status=self.status,
1008 message=self.reason,
→ 1009 headers=self.headers,
1010 )
1011
ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet')
```
(this is an example on the dataset has `parquet` extenstion)
It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~".
so I add three lines.
With this change, there is no error anymore(but this code is ad-hoc).
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4698). All of your documentation changes will be reflected on that endpoint.",
"@albertvillanova \r\nAdding the validation split causes these two `assert_called_once` assertions to fail with `AssertionError: Expected 'ArrowWriter' to have been called once. Called 2 times`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/main/tests/test_builder.py#L548-L562\r\n\r\nIt might be better to create a new dummy generator for the streaming tests, WDYT? Alternatively we could test for `self.call_count` equalling 2.",
"@cakiki have you read my comment in the issue page?\r\nhttps://github.com/huggingface/datasets/issues/4637#issuecomment-1175984812",
"Streaming with `split=all` seems to be working, will fix the failing test next",
"Not sure if marking the PR as \"ready for review\" actually notified you, so tagging @albertvillanova just in case :smiley_cat: ",
"cc @lhoestq ",
"Hi @cakiki, still interested in working on this? :) ",
"@albertvillanova So sorry; I have no idea how this slipped through the cracks. Yes, I'd still like to work on this. Is it okay if I DM you on slack?",
"Sure!! And nevermind!"
] |
Enable streaming dataset to use the "all" split
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Fixes #4637
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"Hi @frgfm, thanks for reporting.\r\n\r\nAs the error message says, streaming mode is not supported out of the box when the dataset contains TAR archive files.\r\n\r\nTo make the dataset streamable, you have to use `dl_manager.iter_archive`.\r\n\r\nThere are several examples in other datasets, e.g. food101: https://huggingface.co/datasets/food101/blob/main/food101.py\r\n\r\nAnd yes, as the link you pointed out, for the streaming to be possible, the metadata file must be loaded before all of the images:\r\n- either this is the case when iterating the archive (and you get the metadata file before the images)\r\n- or you have to extract the metadata file by hand and upload it separately to the Hub",
"Hi @albertvillanova :wave:\r\n\r\nThanks! Yeah I saw that but since I didn't have any metadata, I wasn't sure whether I should create them myself.\r\n\r\nSo one last question:\r\nWhat is the metadata supposed to be for archives? The relative path of all files in it?\r\n_(Sorry I'm a bit confused since it's quite hard to debug using the single error message from the data preview :sweat_smile: )_",
"Hi @frgfm, streaming a dataset that contains a TAR file requires some tweaks because (contrary to ZIP files), tha TAR archive does not allow random access to any of the contained member files. Instead they have to be accessed sequentially (in the order in which they were put into the TAR file when created) and yielded.\r\n\r\nSo when iterating over the TAR file content, when an image file is found, we need to yield it (and not keeping it in memory, which will require huge RAM memory for large datasets). But when yielding an image file, we also need to yield with it what we call \"metadata\": the class label, and other textual information (for example, for audio files, sometimes we also add info such as the speaker ID, their sex, their age,...).\r\n\r\nAll this information usually is stored in what we call the metadata file: either a JSON or a CSV/TSV file.\r\n\r\nBut if this is also inside the TAR archive, we need to find this file in the first place when iterating the TAR archive, so that we already have this information when we find an image file and we can yield the image file and its metadata info.\r\n\r\nTherefore:\r\n- either the TAR archive contains the metadata file as the first member when iterating it (something we cannot change as it is done at the creation of the TAR file)\r\n- or if not, then we need to have the metadata file elsewhere\r\n - in these cases, what we do (if the dataset license allows it) is:\r\n - we download the TAR file locally, we extract the metadata file and we host the metadata on the Hub\r\n - we modify the dataset loading script so that it first downloads the metadata file (and reads it) and only then starts iterating the content of the TAR archive file\r\n\r\nSee an example of this process we recently did for \"google/fleurs\" (their metadata files for \"train\" were at the end of the TAR archives, after all audio files): https://huggingface.co/datasets/google/fleurs/discussions/4\r\n- we uploaded the metadata file to the Hub\r\n- we adapted the loading script to use it",
"Hi @albertvillanova :wave: \r\n\r\nThanks, since my last message, I went through the repo of https://huggingface.co/datasets/food101/blob/main/food101.py and managed to get it to work in the end :pray: \r\n\r\nHere it is: https://huggingface.co/datasets/frgfm/imagenette\r\n\r\nI appreciate you opening an issue to document the process, it might help a few!",
"Great to see that you manage to make your dataset streamable. :rocket: \r\n\r\nI'm closing this issue, as for the docs update there is another issue opened:\r\n- #4711"
] |
Trouble with streaming frgfm/imagenette vision dataset with TAR archive
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### Link
https://huggingface.co/datasets/frgfm/imagenette
### Description
Hello there :wave:
Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile:
Basically, I get a:
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far!
Could you point me in the right direction please? :pray:
### Owner
Yes
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"Hi @finiteautomata, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"lince\", \"ner_spaeng\")\r\nDownloading builder script: 20.8kB [00:00, 9.09MB/s] \r\nDownloading metadata: 31.2kB [00:00, 13.5MB/s] \r\nDownloading and preparing dataset lince/ner_spaeng (download: 2.93 MiB, generated: 18.45 MiB, post-processed: Unknown size, total: 21.38 MiB) to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.08M/3.08M [00:01<00:00, 2.73MB/s]\r\nDataset lince downloaded and prepared to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 630.66it/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 33611\r\n })\r\n validation: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 10085\r\n })\r\n test: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 23527\r\n })\r\n})\r\n``` \r\n\r\nPlease note that for this dataset, the original data files are not hosted on the Hugging Face Hub, but on https://ritual.uh.edu\r\nAnd sometimes, the server might be temporarily unavailable, as your error message said (trying to connect to the server timed out):\r\n```\r\nConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError(\"HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))\")))\r\n```\r\nIn these cases you could:\r\n- either contact the owners of the data server where the data is hosted to inform them about the issue in their server\r\n- or re-try after waiting some time: usually these issues are just temporary",
"Great, thanks for checking out!"
] |
Cannot load LinCE dataset
|
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I_kwDODunzps5N6gf7
| null |
2022-07-17T19:01:54Z
|
https://api.github.com/repos/huggingface/datasets/issues/4696/comments
|
## Describe the bug
Cannot load LinCE dataset due to a connection error
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("lince", "ner_spaeng")
```
A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2
## Expected results
It should load the dataset
## Actual results
```python
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-2-fc551ddcebef> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("lince", "ner_spaeng")
10 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
-> 1684 use_auth_token=use_auth_token,
1685 )
1686
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
703 if not downloaded_from_gcs:
704 self._download_and_prepare(
--> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1219
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1222
1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
772
773 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager)
481 def _split_generators(self, dl_manager):
482 """Returns SplitGenerators."""
--> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip")
484 data_dir = os.path.join(lince_dir, self.config.data_dir)
485 return [
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
432
433 def get_recorded_sizes_checksums(self):
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls)
313 num_proc=download_config.num_proc,
314 disable_tqdm=not is_progress_bar_enabled(),
--> 315 desc="Downloading data files",
316 )
317 duration = datetime.now() - start_time
/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
346 # Singleton
347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 348 return function(data_struct)
349
350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled()
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config)
333 # append the relative path to the base_path
334 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 335 return cached_path(url_or_filename, download_config=download_config)
336
337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
195 use_auth_token=download_config.use_auth_token,
196 ignore_url_params=download_config.ignore_url_params,
--> 197 download_desc=download_config.download_desc,
198 )
199 elif os.path.exists(url_or_filename):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
532 if head_error is not None:
--> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
534 elif response is not None:
535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
|
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2022-09-30T14:39:30Z
|
[] |
https://github.com/huggingface/datasets/pull/4695
|
CONTRIBUTOR
| null | true | null |
[
"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for your contribution, @bhavitvyamalik. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest that you create this dataset there. Please, feel free to tell us if you need some help."
] |
Add MANtIS dataset
|
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2022-07-17T15:53:05Z
|
https://api.github.com/repos/huggingface/datasets/issues/4695/comments
|
This PR adds MANtIS dataset.
Arxiv: [https://arxiv.org/abs/1912.04639](https://arxiv.org/abs/1912.04639)
Github: [https://github.com/Guzpenha/MANtIS](https://github.com/Guzpenha/MANtIS)
README and dataset tags are WIP.
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2023-04-26T18:21:09Z
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[] |
https://github.com/huggingface/datasets/issues/4694
|
NONE
| null | null | null |
[
"Hi ! According to https://huggingface.co/docs/datasets/use_with_pytorch#stream-data you can use the pytorch DataLoader with `num_workers>0` to distribute the shards across your workers (it uses `torch.utils.data.get_worker_info()` to get the worker ID and select the right subsets of shards to use)\r\n\r\n<s> EDIT: here is a code example </s>\r\n```python\r\n# ds = ds.with_format(\"torch\")\r\n# dataloader = DataLoader(ds, num_workers=num_workers)\r\n```\r\n\r\nEDIT: `with_format(\"torch\")` is not required, now you can just do\r\n```python\r\ndataloader = DataLoader(ds, num_workers=num_workers)\r\n```",
"@cyk1337 does streaming datasets with multi-gpu works for you? I am testing on one node with multiple gpus, but this is freezing, https://github.com/huggingface/datasets/issues/5123 \r\nIn case you could make this work, could you share with me your data-loading codes?\r\nthank you",
"+1",
"This has been implemented in `datasets` 2.8:\r\n```python\r\nfrom datasets.distributed import split_dataset_by_node\r\n\r\nds = split_dataset_by_node(ds, rank=rank, world_size=world_size)\r\n```\r\n\r\ndocs: https://huggingface.co/docs/datasets/use_with_pytorch#distributed",
"i'm having hanging issues with this when using DDP and allocating the datasets with `split_dataset_by_node` 🤔\r\n\r\n--- \r\n### edit\r\nI don't want to pollute this thread, but for the sake of following up, I observed hanging close to the final iteration of the dataloader. I think this was happening on the final shard. First, I removed the final shard and things worked. Then (including all shards), I reordered the list of shards: `load_dataset('json', data_files=reordered, streaming=True)` and no hang. \r\n\r\nI won't open an issue yet bc I am not quite sure about this observation.",
"@wconnell would you mind opening a different bug issue and giving more details?\r\nhttps://github.com/huggingface/datasets/issues/new?assignees=&labels=&template=bug-report.yml\r\n\r\nThanks."
] |
Distributed data parallel training for streaming datasets
|
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2022-07-17T01:29:43Z
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https://api.github.com/repos/huggingface/datasets/issues/4694/comments
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### Feature request
Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training?
### Motivation
Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation?
### Your contribution
Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do?
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2022-09-23T11:40:11Z
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[] |
https://github.com/huggingface/datasets/pull/4693
|
CONTRIBUTOR
| null | false | null |
[
"_The documentation is not available anymore as the PR was closed or merged._",
"We are closing PRs to dataset scripts because we are moving them to the Hub.\r\n\r\nThanks anyway.\r\n\r\n"
] |
update `samsum` script
|
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2022-07-16T11:53:05Z
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https://api.github.com/repos/huggingface/datasets/issues/4693/comments
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update `samsum` script after #4672 was merged (citation is also updated)
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Unable to cast a column with `Image()` by using the `cast_column()` feature
|
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https://api.github.com/repos/huggingface/datasets/issues/4692/comments
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## Describe the bug
A clear and concise description of what the bug is.
When I create a dataset, then add a column to the created dataset through the `dataset.add_column` feature and then try to cast a column of the dataset (this column contains image paths) with `Image()` by using the `cast_column()` feature, I get the following error - ``` TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ```
When I try and cast the same column, but without doing the `add_column` in the previous step, it works as expected.
## Steps to reproduce the bug
```python
from datasets import Dataset, Image
data_dict = {
"img_path": ["https://picsum.photos/200/300"]
}
dataset = Dataset.from_dict(data_dict)
#NOTE Comment out this line and use cast_column and it works properly
dataset = dataset.add_column("yeet", [1])
#NOTE This line fails to execute properly if `add_column` is called before
dataset = dataset.cast_column("img_path", Image())
# #NOTE This is my current workaround. This seems to work fine with/without `add_column`. While
# # running this, make sure to comment out the `cast_column` line
# new_features = dataset.features.copy()
# new_features["img_path"] = Image()
# dataset = dataset.cast(new_features)
print(dataset)
print(dataset.features)
print(dataset[0])
```
## Expected results
A clear and concise description of the expected results.
Able to successfully use `cast_column` to cast a column containing img_paths to now be Image() features after modifying the dataset using `add_column` in a previous step
## Actual results
Specify the actual results or traceback.
```
Traceback (most recent call last):
File "/home/surya/Desktop/hf_bug_test.py", line 14, in <module>
dataset = dataset.cast_column("img_path", Image())
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1580, in cast_column
dataset._data = dataset._data.cast(dataset.features.arrow_schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1487, in cast
new_tables.append(subtable.cast(subschema, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 834, in cast
return InMemoryTable(table_cast(self.table, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1897, in table_cast
return cast_table_to_schema(table, schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1846, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.9.7
- PyArrow version: 7.0.0
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Dataset Viewer issue for rajistics/indian_food_images
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### Link
https://huggingface.co/datasets/rajistics/indian_food_images/viewer/rajistics--indian_food_images/train
### Description
I have a train/test split in my dataset
<img width="410" alt="Screen Shot 2022-07-15 at 11 44 42 AM" src="https://user-images.githubusercontent.com/6808012/179293215-7b419ec3-3527-46f2-8dad-adbc5568cfa0.png">
t
The dataset viewer works for the test split (images of indian food), but does not show my train split. My guess is maybe there is some corrupt image file that is guessing this. But I have no idea.
The original dataset was pulled from here: https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification?resource=download-directory
### Owner
Yes
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Refactor base extractors
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2022-07-15T17:47:48Z
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https://api.github.com/repos/huggingface/datasets/issues/4690/comments
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This PR:
- Refactors base extractors as subclasses of `BaseExtractor`:
- this is an abstract class defining the interface with:
- `is_extractable`: abstract class method
- `extract`: abstract static method
- Implements abstract `MagicNumberBaseExtractor` (as subclass of `BaseExtractor`):
- this has a default implementation of `is_extractable`
- this improves performance (reducing the number of file reads) by allowing passing already read `magic_number`
- Refactors `Extractor`:
- reads magic number from file only once
This PR deprecates:
```python
is_extractable, extractor = self.extractor.is_extractable(input_path, return_extractor=True)
self.extractor.extract(input_path, output_path, extractor=extractor)
```
and uses more Pythonic instead:
```python
extractor_format = self.extractor.infer_extractor_format(input_path)
self.extractor.extract(input_path, output_path, extractor_format)
```
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Test extractors for all compression formats
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https://api.github.com/repos/huggingface/datasets/issues/4689/comments
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This PR:
- Adds all compression formats to `test_extractor`
- Tests each base extractor for all compression formats
Note that all compression formats are tested except "rar".
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Skip test_extractor only for zstd param if zstandard not installed
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https://api.github.com/repos/huggingface/datasets/issues/4688/comments
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Currently, if `zstandard` is not installed, `test_extractor` is skipped for all compression format parameters.
This PR fixes `test_extractor` so that if `zstandard` is not installed, `test_extractor` is skipped only for the `zstd` compression parameter, that is, it is not skipped for all the other compression parameters (`gzip`, `xz`,...).
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Trigger CI also on push to main
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Currently, new CI (on GitHub Actions) is only triggered on pull requests branches when the base branch is main.
This PR also triggers the CI when a PR is merged to main branch.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4686). All of your documentation changes will be reflected on that endpoint.",
"I wasn't aware of https://github.com/huggingface/datasets/pull/1845 before opening this PR. This issue seems much more complex now ..."
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Align logging with Transformers (again)
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Fix #2832
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Fix mock fsspec
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This PR:
- Removes an unused method from `DummyTestFS`
- Refactors `mock_fsspec` to make it simpler
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[
"Hi! One option is use `map` with a function that overwrites the labels (`dset = dset.map(lamba _: {\"label\": 0}, features=dset.features`)). Or you can use the `remove_column` + `add_column` combination (`dset = dset.remove_columns(\"label\").add_column(\"label\", [0]*len(data)).cast(dset.features)`, but note that this approach creates an in-memory table for the added column instead of writing to disk, which could be problematic for large datasets.",
"Hi! I tried your proposed solution, but it does not solve my problem unfortunately. I am working with a set of protein sequences that have been tokenized with ESM, but some sequences are longer than `max_length`, they have been truncated in the tokenization. So now I want to truncate my labels as well, but that does not work with a mapping (e.g. `dset.map` as you suggested). Specifically, what I did was the following:\r\n\r\n```\r\ndef postprocess_tokenize(tokenized_data):\r\n \"\"\"\r\n adjust label lengths if they dont match.\r\n \"\"\"\r\n if len(tokenized_data['input_ids']) < len(tokenized_data['labels']):\r\n new_labels = tokenized_data['labels'][:len(tokenized_data['input_ids'])]\r\n tokenized_data[\"labels\"] = new_labels\r\n return tokenized_data\r\n\r\ntokenized_data = tokenized_data.map(postprocess_tokenize, batched=True) # this does not adjust the labels...\r\n```\r\n\r\nAny tips on how to do this properly?\r\n\r\nMore generally, I am wondering why the DataCollator supports padding but does not support truncation? Seems odd to me.\r\n\r\nThanks in advance!"
] |
How to assign new values to Dataset?
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Hi, if I want to change some values of the dataset, or add new columns to it, how can I do it?
For example, I want to change all the labels of the SST2 dataset to `0`:
```python
from datasets import load_dataset
data = load_dataset('glue','sst2')
data['train']['label'] = [0]*len(data)
```
I will get the error:
```
TypeError: 'Dataset' object does not support item assignment
```
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Update create dataset card docs
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2022-07-15T00:41:29Z
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https://api.github.com/repos/huggingface/datasets/issues/4683/comments
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This PR proposes removing the [online dataset card creator](https://huggingface.co/datasets/card-creator/) in favor of simply copy/pasting a template and using the [Datasets Tagger app](https://huggingface.co/spaces/huggingface/datasets-tagging) to generate the tags. The Tagger app provides more guidance by showing all possible values a user can select in the dropdown menus, whereas the online dataset card creator doesn't, which can make it difficult to know what tag values to input.
Let me know what you think! :)
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weird issue/bug with columns (dataset iterable/stream mode)
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I have a dataset online (CloverSearch/cc-news-mutlilingual) that has a bunch of columns, two of which are "score_title_maintext" and "score_title_description". the original files are jsonl formatted. I was trying to iterate through via streaming mode and grab all "score_title_description" values, but I kept getting key not found after a certain point of iteration. I found that some json objects in the file don't have "score_title_description". And in SOME cases, this returns a NONE and in others it just gets a key error. Why is there an inconsistency here and how can I fix it?
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completed
| null | null |
[
"Hi, thanks for reporting! If there are no examples in ImageFolder, the `label` column is of type `ClassLabel(names=[])`, which leads to an error in [this line](https://github.com/huggingface/datasets/blob/c15b391942764152f6060b59921b09cacc5f22a6/src/datasets/arrow_writer.py#L387) as `asdict(info)` calls `Features({..., \"label\": {'num_classes': 0, 'names': [], 'id': None, '_type': 'ClassLabel'}})`, which then calls `require_decoding` [here](https://github.com/huggingface/datasets/blob/c15b391942764152f6060b59921b09cacc5f22a6/src/datasets/features/features.py#L1516) on the dict value it does not expect.\r\n\r\nI see two ways to fix this:\r\n* custom `asdict` where `dict_factory` is also applied on the `dict` object itself besides dataclasses (the built-in implementation calls `type(dict_obj)` - this means we also need to fix `Features.to_dict` btw) \r\n* implement `DatasetInfo.to_dict` (though adding `to_dict` to a data class is a bit weird IMO)\r\n\r\n@lhoestq Which one of these approaches do you like more?\r\n",
"Small pref for the first option, it feels weird to know that `Features()` can be called with a dictionary of types defined as dictionaries instead of type instances."
] |
IndexError when loading ImageFolder
|
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2022-07-14T10:57:55Z
|
https://api.github.com/repos/huggingface/datasets/issues/4681/comments
|
## Describe the bug
Loading an image dataset with `imagefolder` throws `IndexError: list index out of range` when the given folder contains a non-image file (like a csv).
## Steps to reproduce the bug
Put a csv file in a folder with images and load it:
```python
import datasets
datasets.load_dataset("imagefolder", data_dir=path/to/folder)
```
## Expected results
I would expect a better error message, like `Unsupported file` or even the dataset loader just ignoring every file that is not an image in that case.
## Actual results
Here is the whole traceback:
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.11.0-051100-generic-x86_64-with-glibc2.27
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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https://github.com/huggingface/datasets/issues/4680
|
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[
"There seems to be an issue with the `C++-snippet-level` config:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"codeparrot/xlcost-text-to-code\", \"C++-snippet-level\")\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 352, in get_dataset_config_info\r\n info.splits = {\r\nTypeError: 'NoneType' object is not iterable\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nI remove the dataset-viewer tag since it's not directly related.\r\n\r\nPinging @huggingface/datasets ",
"Thanks I found that this subset wasn't properly defined the the config, I fixed it. Now I can see the subsets but I get this error for the viewer\r\n````\r\nStatus code: 400\r\nException: Status400Error\r\nMessage: The split cache is empty.\r\n```",
"Yes, the cache is being refreshed, hopefully, it will work in some minutes for all the splits. Some are already here:\r\n\r\nhttps://huggingface.co/datasets/codeparrot/xlcost-text-to-code/viewer/Python-snippet-level/train\r\n\r\n<img width=\"1533\" alt=\"Capture d’écran 2022-07-18 à 12 04 06\" src=\"https://user-images.githubusercontent.com/1676121/179553933-64d874fa-ada9-4b82-900e-082619523c20.png\">\r\n",
"I think all the splits are working as expected now",
"Perfect, thank you!"
] |
Dataset Viewer issue for codeparrot/xlcost-text-to-code
|
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I_kwDODunzps5NwZ7y
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2022-07-14T09:45:50Z
|
https://api.github.com/repos/huggingface/datasets/issues/4680/comments
|
### Link
https://huggingface.co/datasets/codeparrot/xlcost-text-to-code
### Description
Error
```
Server Error
Status code: 400
Exception: TypeError
Message: 'NoneType' object is not iterable
```
Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully.
Thanks!
### Owner
Yes
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https://github.com/huggingface/datasets/pull/4679
|
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[
"Hi ! I think the issue you linked is closed and suggests to use `remove_columns`.\r\n\r\nMoreover if you end up with a dataset with an unnecessarily nested data, please modify your processing functions to not output nested data, or use `map(..., batched=True)` if you function take batches as input",
"Hi @lhoestq , you are right about the issues this pull has steered beyond that issue. I created this [colab notebook](https://colab.research.google.com/drive/16aLu6QrDSV_aUYRdpufl5E4iS08qkUGj?usp=sharing) to present the error. I tried using batch and that won't resolve it either. I'm looking into that error right now.",
"I think you just need to pass one example at a time to your tokenizer, this way you don't end up with nested data:\r\n```python\r\n\r\ndef preprocessFunction(row):\r\n collatedContext = tokenizer.eos_token.join([row[\"context\"+str(i+1)] for i in range(int(AMT_OF_CONTEXT))])\r\n response = row[\"response\"]\r\n tokenizedContext = tokenizer(\r\n collatedContext, max_length=max_context_length, truncation=True # don't pass as a list here\r\n )\r\n with tokenizer.as_target_tokenizer():\r\n tokenized_response = tokenizer(\r\n response, max_length=max_response_length, truncation=True # don't pass a a list here\r\n )\r\n tokenizedContext[\"labels\"] = tokenized_response[\"input_ids\"]\r\n return tokenizedContext\r\n```",
"Yes that is correct, the purpose of this pull is to advise of a more general solution like with `def remove_excess_nesting(self)` or maybe automate the solution (stas00 advised not to automate it as it could \"not be backwards compatible\").",
"I'm not sure I understand how having `remove_excess_nesting` would make more sense than just fixing the preprocessFunction to simply not return nested samples, can you elaborate ?",
"Figuring out the issue can be a bit difficult to figure out. Only until I added batch does it make a little more sense with the error\r\n\r\n> sequence item 0: expected str instance, list found\r\n\r\nbut batch was never intended.\r\n\r\nWhen you run the colab you will notice that only until collating do you learn there is this error. So i figured it would be better to address it during at the `DatasetDict` level.\r\nI think it would be ideal if the user could be notified at the preprocess function.",
"I'm not arguing that `remove_excess_nesting` is the right solution but what I aim to address is dealing with unnecessary nesting as early as possible.",
"> When you run the colab you will notice that only until collating do you learn there is this error.\r\n\r\nI think users can just check the `dataset.features` and they would notice that the data are nested\r\n```python\r\n{\r\n 'input_ids': Sequence(Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), length=-1, id=None)\r\n ...\r\n}\r\n```\r\n\r\nSometime nested data are intentional, so you can't know in advance if it's a user's mistake or something planned.",
"Yes, I understand, it could be intentional and only the collator has problems with it. So, it is not worth handling it any differently in any other non-erroneous data. \r\n\r\nThat being said do you think there is any use for the `remove_excess_nesting` method? Or maybe it should be applied in a different way? If not feel free to close this PR. ",
"I think users can write it and use `map` themselves if needed, it is pretty straightforward to implement.\r\n\r\nI'm closing this PR if you don't mind, and thank you for the discussion :)",
"No problem @lhoestq , thanks for walking me through it."
] |
Added method to remove excess nesting in a DatasetDict
|
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2022-07-13T21:49:37Z
|
https://api.github.com/repos/huggingface/datasets/issues/4679/comments
|
Added the ability for a DatasetDict to remove additional nested layers within its features to avoid conflicts when collating. It is meant to accompany [this PR](https://github.com/huggingface/transformers/pull/18119) to resolve the same issue [#15505](https://github.com/huggingface/transformers/issues/15505).
@stas00 @lhoestq
|
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2022-07-14T13:07:21Z
|
[] |
https://github.com/huggingface/datasets/issues/4678
|
NONE
| null | null | null |
[
"Hi! Calling `take` on an iterable/streamable dataset makes it not possible to shard the dataset, which in turn disables multi-process loading (attempts to split the workload over the shards), so to go past this limitation, you can either use single-process loading in `DataLoader` (`num_workers=None`) or fetch the first `50_000/batch_size` batches in the loop."
] |
Cant pass streaming dataset to dataloader after take()
|
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I_kwDODunzps5NtYP4
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2022-07-13T17:34:18Z
|
https://api.github.com/repos/huggingface/datasets/issues/4678/comments
|
## Describe the bug
I am trying to pass a streaming version of c4 to a dataloader, but it can't be passed after I call `dataset.take(n)`. Some functions such as `shuffle()` can be applied without breaking the dataloader but not take.
## Steps to reproduce the bug
```python
import datasets
import torch
dset = datasets.load_dataset(path='c4', name='en', split="train", streaming=True)
dset = dset.take(50_000)
dset = dset.with_format("torch")
num_workers = 8
batch_size = 512
loader = torch.utils.data.DataLoader(dataset=dset,
batch_size=batch_size,
num_workers=num_workers)
for batch in loader:
...
```
## Expected results
No error thrown when iterating over the dataloader
## Actual results
Original Traceback (most recent call last):
File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/root/.local/lib/python3.9/site-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py", line 48, in __iter__
for key, example in self._iter_shard(shard_idx):
File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 586, in _iter_shard
yield from ex_iterable.shard_data_sources(shard_idx)
File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 60, in shard_data_sources
raise NotImplementedError(f"{type(self)} doesn't implement shard_data_sources yet")
NotImplementedError: <class 'datasets.iterable_dataset.TakeExamplesIterable'> doesn't implement shard_data_sources yet
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.31
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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not_planned
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[
"did you ever fix this? I'm experiencing the same",
"I am having the same issue. Even the simple example from the documentation gives me the 400 Error\r\n\r\n\r\n> from datasets import load_dataset\r\n> \r\n> dataset = load_dataset(\"stevhliu/demo\")\r\n> dataset.push_to_hub(\"processed_demo\")\r\n\r\n\r\n`requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/REDACTED/commit/main (Request ID: e-tPnYTiCdB5KPmSL86dQ)`\r\n\r\nI \"fixed\" it by initializing a new virtual environment with only datasets==2.5.2 installed.\r\n\r\nThe workaround consists of saving to disk then loading from disk and pushing to hub but from the new clean virtual environment."
] |
Random 400 Client Error when pushing dataset
|
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2022-07-12T15:56:44Z
|
https://api.github.com/repos/huggingface/datasets/issues/4677/comments
|
## Describe the bug
When pushing a dataset, the client errors randomly with `Bad Request for url:...`.
At the next call, a new parquet file is created for each shard.
The client may fail at any random shard.
## Steps to reproduce the bug
```python
dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
```
## Expected results
Push all the dataset to the Hub with no duplicates.
If it fails, it should retry or fail, but continue from the last failed shard.
## Actual results
```
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
testing.ipynb Cell 29 in <cell line: 1>()
----> [1](testing.ipynb?line=0) dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4297, in Dataset.push_to_hub(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files)
4291 warnings.warn(
4292 "'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0.",
4293 FutureWarning,
4294 )
4295 max_shard_size = shard_size
-> 4297 repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub(
4298 repo_id=repo_id,
4299 split=split,
4300 private=private,
4301 token=token,
4302 branch=branch,
4303 max_shard_size=max_shard_size,
4304 embed_external_files=embed_external_files,
4305 )
4306 organization, dataset_name = repo_id.split("/")
4307 info_to_dump = self.info.copy()
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4195, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files)
4193 shard.to_parquet(buffer)
4194 uploaded_size += buffer.tell()
-> 4195 _retry(
4196 api.upload_file,
4197 func_kwargs=dict(
4198 path_or_fileobj=buffer.getvalue(),
4199 path_in_repo=shard_path_in_repo,
4200 repo_id=repo_id,
4201 token=token,
4202 repo_type="dataset",
4203 revision=branch,
4204 identical_ok=False,
4205 ),
4206 exceptions=HTTPError,
4207 status_codes=[504],
4208 base_wait_time=2.0,
4209 max_retries=5,
4210 max_wait_time=20.0,
4211 )
4212 shards_path_in_repo.append(shard_path_in_repo)
4214 # Cleanup to remove unused files
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:284, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
--> 284 raise err
285 else:
286 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:281, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
279 while True:
280 try:
--> 281 return func(*func_args, **func_kwargs)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1967, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, identical_ok, commit_message, commit_description, create_pr)
1957 commit_message = (
1958 commit_message
1959 if commit_message is not None
1960 else f"Upload {path_in_repo} with huggingface_hub"
1961 )
1962 operation = CommitOperationAdd(
1963 path_or_fileobj=path_or_fileobj,
1964 path_in_repo=path_in_repo,
1965 )
-> 1967 pr_url = self.create_commit(
1968 repo_id=repo_id,
1969 repo_type=repo_type,
1970 operations=[operation],
1971 commit_message=commit_message,
1972 commit_description=commit_description,
1973 token=token,
1974 revision=revision,
1975 create_pr=create_pr,
1976 )
1977 if pr_url is not None:
1978 re_match = re.match(REGEX_DISCUSSION_URL, pr_url)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1844, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads)
1836 commit_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/commit/{revision}"
1838 commit_resp = requests.post(
1839 url=commit_url,
1840 headers={"Authorization": f"Bearer {token}"},
1841 json=commit_payload,
1842 params={"create_pr": 1} if create_pr else None,
1843 )
-> 1844 _raise_for_status(commit_resp)
1845 return commit_resp.json().get("pullRequestUrl", None)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:84, in _raise_for_status(request)
76 if request.status_code == 401:
77 # The repo was not found and the user is not Authenticated
78 raise RepositoryNotFoundError(
79 f"401 Client Error: Repository Not Found for url: {request.url}. If the"
80 " repo is private, make sure you are authenticated. (Request ID:"
81 f" {request_id})"
82 )
---> 84 _raise_with_request_id(request)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:95, in _raise_with_request_id(request)
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
93 e.args = (e.args[0] + f" (Request ID: {request_id})",) + e.args[1:]
---> 95 raise e
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:90, in _raise_with_request_id(request)
88 request_id = request.headers.get("X-Request-Id")
89 try:
---> 90 request.raise_for_status()
91 except Exception as e:
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
File ~/.local/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1016 http_error_msg = (
1017 f"{self.status_code} Server Error: {reason} for url: {self.url}"
1018 )
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/ORG/DATASET/commit/main (Request ID: a_F0IQAHJdxGKVRYyu1cF)
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.31
- Python version: 3.9.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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|
NONE
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| null | null |
[
"Are you able to reproduce this? My example is small enough that it should be easy to try.",
"Hi! Thanks for reporting and providing a reproducible example. Indeed, by default, `datasets` performs an expensive cast on the values returned by `map` to convert them to one of the types supported by PyArrow (the underlying storage format used by `datasets`). This cast is not needed on NumPy arrays as PyArrow supports them natively, so one way to make this transform faster is to add `return_tensors=\"np\"` to the tokenizer call. \r\n\r\nI think we should mention this in the docs (cc @stevhliu)",
"I tested this tokenize function and indeed noticed a casting. However it seems to only concerns the `offset_mapping` field, which contains a list of tuples, that is converted to a list of lists. Since `pyarrow` also supports tuples, we actually don't need to convert the tuples to lists. \r\n\r\nI think this can be changed here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/src/datasets/features/features.py#L382-L383\r\n\r\n```diff\r\n- if isinstance(obj, list): \r\n+ if isinstance(obj, (list, tuple)): \r\n```\r\n\r\nand here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/src/datasets/features/features.py#L386-L387\r\n\r\n```diff\r\n- return obj if isinstance(obj, list) else [], isinstance(obj, tuple)\r\n+ return obj, False\r\n```\r\n\r\n@srobertjames can you try applying these changes and let us know if it helps ? If so, feel free to open a Pull Request to contribute this improvement if you want :)",
"Wow, adding `return_tensors=\"np\"` sped up my example by a **factor 17x** of and completely eliminated the casting! I'd recommend not only to document it, but to make that the default.\r\n\r\nThe code at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb does not specify `return_tensors=\"np\"` but yet avoids the casting penalty. How does it do that? (The ntbk seems to do `return_overflowing_tokens=True, return_offsets_mapping=True,`).\r\n\r\nAlso, surprisingly enough, using `return_tensors=\"pt\"` (which is my eventual application) yields this error:\r\n```\r\nTypeError: Provided `function` which is applied to all elements of table returns a `dict` of types \r\n[<class 'torch.Tensor'>, <class 'torch.Tensor'>, <class 'torch.Tensor'>, <class 'torch.Tensor'>]. \r\nWhen using `batched=True`, make sure provided `function` returns a `dict` of types like \r\n`(<class 'list'>, <class 'numpy.ndarray'>)`.\r\n```",
"Setting the output to `\"np\"` makes the whole pipeline fast because it moves the data buffers from rust to python to arrow using zero-copy, and also because it does eliminate the casting completely ;)\r\n\r\nHave you had a chance to try eliminating the tuple casting using the trick above ?",
"@lhoestq I just benchmarked the two edits to `features.py` above, and they appear to solve the problem, bringing my original example to within 20% the speed of the output `\"np\"` example. Nice!\r\n\r\nFor a pull request, do you suggest simply following https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md ?",
"Cool ! Sure feel free to follow these instructions to open a PR :) thanks !",
"#take",
"Resolved via https://github.com/huggingface/datasets/pull/4993."
] |
Dataset.map gets stuck on _cast_to_python_objects
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2022-07-12T15:09:58Z
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https://api.github.com/repos/huggingface/datasets/issues/4676/comments
|
## Describe the bug
`Dataset.map`, when fed a Huggingface Tokenizer as its map func, can sometimes spend huge amounts of time doing casts. A minimal example follows.
Not all usages suffer from this. For example, I profiled the preprocessor at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb , and it did _not_ have this problem. However, I'm at a loss to figure out how it avoids it, as the example below is simple and minimal and still has this problem.
This casting, where it occurs, causes the `Dataset.map` to run approximately 7x slower than it runs for code which does not cause this casting.
This may be related to https://github.com/huggingface/datasets/issues/1046 . However, the tokenizer is _not_ set to return Tensors.
## Steps to reproduce the bug
A minimal, self-contained example to reproduce is below:
```python
import transformers
from transformers import AutoTokenizer
from datasets import load_dataset
import torch
import cProfile
pretrained = 'distilbert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(pretrained)
squad = load_dataset('squad')
squad_train = squad['train']
squad_tiny = squad_train.select(range(5000))
assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast)
def tokenize(ds):
tokens = tokenizer(text=ds['question'],
text_pair=ds['context'],
add_special_tokens=True,
padding='max_length',
truncation='only_second',
max_length=160,
stride=32,
return_overflowing_tokens=True,
return_offsets_mapping=True,
)
return tokens
cmd = 'squad_tiny.map(tokenize, batched=True, remove_columns=squad_tiny.column_names)'
cProfile.run(cmd, sort='tottime')
```
## Actual results
The code works, but takes 10-25 sec per batch (about 7x slower than non-casting code), with the following profile. Note that `_cast_to_python_objects` is the culprit.
```
63524075 function calls (58206482 primitive calls) in 121.836 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
5274034/40 68.751 0.000 111.060 2.776 features.py:262(_cast_to_python_objects)
42223832 24.077 0.000 33.310 0.000 {built-in method builtins.isinstance}
16338/20 5.121 0.000 111.053 5.553 features.py:361(<listcomp>)
5274135 4.747 0.000 4.749 0.000 {built-in method _abc._abc_instancecheck}
80/40 4.731 0.059 116.292 2.907 {pyarrow.lib.array}
5274135 4.485 0.000 9.234 0.000 abc.py:96(__instancecheck__)
2661564/2645196 2.959 0.000 4.298 0.000 features.py:1081(_check_non_null_non_empty_recursive)
5 2.786 0.557 2.786 0.557 {method 'encode_batch' of 'tokenizers.Tokenizer' objects}
2668052 0.930 0.000 0.930 0.000 {built-in method builtins.len}
5000 0.930 0.000 0.938 0.000 tokenization_utils_fast.py:187(_convert_encoding)
5 0.750 0.150 0.808 0.162 {method 'to_pydict' of 'pyarrow.lib.Table' objects}
1 0.444 0.444 121.749 121.749 arrow_dataset.py:2501(_map_single)
40 0.375 0.009 116.291 2.907 arrow_writer.py:151(__arrow_array__)
10 0.066 0.007 0.066 0.007 {method 'write_batch' of 'pyarrow.lib._CRecordBatchWriter' objects}
1 0.060 0.060 121.835 121.835 fingerprint.py:409(wrapper)
11387/5715 0.049 0.000 0.175 0.000 {built-in method builtins.getattr}
36 0.049 0.001 0.049 0.001 {pyarrow._compute.call_function}
15000 0.040 0.000 0.040 0.000 _collections_abc.py:719(__iter__)
3 0.023 0.008 0.023 0.008 {built-in method _imp.create_dynamic}
77 0.020 0.000 0.020 0.000 {built-in method builtins.dir}
37 0.019 0.001 0.019 0.001 socket.py:543(send)
15 0.017 0.001 0.017 0.001 tokenization_utils_fast.py:460(<listcomp>)
432/421 0.015 0.000 0.024 0.000 traitlets.py:1388(_notify_observers)
5000 0.015 0.000 0.018 0.000 _collections_abc.py:672(keys)
51 0.014 0.000 0.042 0.001 traitlets.py:276(getmembers)
5 0.014 0.003 3.775 0.755 tokenization_utils_fast.py:392(_batch_encode_plus)
3/1 0.014 0.005 0.035 0.035 {built-in method _imp.exec_dynamic}
5 0.012 0.002 0.950 0.190 tokenization_utils_fast.py:438(<listcomp>)
31626 0.012 0.000 0.012 0.000 {method 'append' of 'list' objects}
1532/1001 0.011 0.000 0.189 0.000 traitlets.py:643(get)
5 0.009 0.002 3.796 0.759 arrow_dataset.py:2631(apply_function_on_filtered_inputs)
51 0.009 0.000 0.062 0.001 traitlets.py:1766(traits)
5 0.008 0.002 3.784 0.757 tokenization_utils_base.py:2632(batch_encode_plus)
368 0.007 0.000 0.044 0.000 traitlets.py:1715(_get_trait_default_generator)
26 0.007 0.000 0.022 0.001 traitlets.py:1186(setup_instance)
51 0.006 0.000 0.010 0.000 traitlets.py:1781(<listcomp>)
80/32 0.006 0.000 0.052 0.002 table.py:1758(cast_array_to_feature)
684 0.006 0.000 0.007 0.000 {method 'items' of 'dict' objects}
4344/1794 0.006 0.000 0.192 0.000 traitlets.py:675(__get__)
...
```
## Environment info
I observed this on both Google colab and my local workstation:
### Google colab
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
### Local
- `datasets` version: 2.3.2
- Platform: Windows-7-6.1.7601-SP1
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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2022-07-14T14:17:46Z
|
[] |
https://github.com/huggingface/datasets/issues/4675
|
NONE
| null | null | null |
[
"Hi! `para_crawl` has a single column of type `Translation`, which stores translation dictionaries. These dictionaries can be stored in a NumPy array but not in a PyTorch tensor since PyTorch only supports numeric types. In `datasets`, the conversion to `torch` works as follows: \r\n1. convert PyArrow table to NumPy arrays \r\n2. convert NumPy arrays to Torch tensors. \r\n\r\nThe 2nd step is problematic for your case as `datasets` attempts to convert the array of dictionaries to a PyTorch tensor. One way to fix this is to use the [preprocessing logic](https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/examples/pytorch/translation/run_translation.py#L440-L458) from the Transformers translation script. And on our side, I think we can replace a NumPy array of dicts with a dict of NumPy array if the feature type is `Translation`/`TranslationVariableLanguages` (one array for each language) to get the official PyTorch error message for strings in such case."
] |
Unable to use dataset with PyTorch dataloader
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I_kwDODunzps5NneXx
| null |
2022-07-12T15:04:04Z
|
https://api.github.com/repos/huggingface/datasets/issues/4675/comments
|
## Describe the bug
When using `.with_format("torch")`, an arrow table is returned and I am unable to use it by passing it to a PyTorch DataLoader: please see the code below.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
ds = load_dataset(
"para_crawl",
name="enfr",
cache_dir="/tmp/test/",
split="train",
keep_in_memory=True,
)
dataloader = DataLoader(ds.with_format("torch"), num_workers=32)
print(next(iter(dataloader)))
```
Is there something I am doing wrong? The documentation does not say much about the behavior of `.with_format()` so I feel like I am a bit stuck here :-/
Thanks in advance for your help!
## Expected results
The code should run with no error
## Actual results
```
AttributeError: 'str' object has no attribute 'dtype'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.18.0-348.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
|
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[] |
https://github.com/huggingface/datasets/issues/4674
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NONE
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completed
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[
"Hi @margotwagner, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your bug: in an environment with datasets-2.3.2 and pyarrow-8.0.0, I can load the datasets without any problem:\r\n```python\r\n>>> ds = load_dataset(\"glue\", \"cola\")\r\n>>> ds\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 8551\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1043\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1063\r\n })\r\n})\r\n\r\n>>> import pyarrow\r\n>>> pyarrow.__version__\r\n8.0.0\r\n>>> from pyarrow.lib import IpcReadOptions\r\n>>> IpcReadOptions\r\npyarrow.lib.IpcReadOptions\r\n```\r\n\r\nI think you may have a problem in your Python environment: maybe you have also an old version of pyarrow that has precedence when importing it.\r\n\r\nCould you please check this (just after you tried to load the dataset and got the error)?\r\n```python\r\n>>> import pyarrow\r\n>>> pyarrow.__version__\r\n``` "
] |
Issue loading datasets -- pyarrow.lib has no attribute
|
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I_kwDODunzps5NkC78
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2022-07-11T22:10:44Z
|
https://api.github.com/repos/huggingface/datasets/issues/4674/comments
|
## Describe the bug
I am trying to load sentiment analysis datasets from huggingface, but any dataset I try to use via load_dataset, I get the same error:
`AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'`
## Steps to reproduce the bug
```python
dataset = load_dataset("glue", "cola")
```
## Expected results
Download datasets without issue.
## Actual results
`AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 8.0.0
- Pandas version: 1.1.0
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[] |
https://github.com/huggingface/datasets/issues/4673
|
NONE
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completed
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[
"Hi @courtneysprouse, thanks for reporting.\r\n\r\nYes, you are right: by default the \"csv\" loader loads all columns as strings. \r\n\r\nYou could tweak this behavior by passing the `feature` argument to `load_dataset`, but it is also true that currently it is not possible to perform some kind of casts, due to lacking of implementation in PyArrow. For example:\r\n```python\r\nimport datasets\r\n\r\nfeatures = datasets.Features(\r\n {\r\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n \"ner_tags\": datasets.Sequence(datasets.Value(\"int32\")),\r\n }\r\n)\r\n\r\nnew_conll = datasets.load_dataset(\"csv\", data_files=\"ner_conll.csv\", features=features)\r\n```\r\ngives `ArrowNotImplementedError` error:\r\n```\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: Unsupported cast from string to list using function cast_list\r\n```\r\n\r\nOn the other hand, if you just would like to save and afterwards load your dataset, you could use `save_to_disk` and `load_from_disk` instead. These functions preserve all data types.\r\n```python\r\n>>> orig_conll.save_to_disk(\"ner_conll\")\r\n\r\n>>> from datasets import load_from_disk\r\n\r\n>>> new_conll = load_from_disk(\"ner_conll\")\r\n>>> new_conll\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 14042\r\n })\r\n validation: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3251\r\n })\r\n test: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3454\r\n })\r\n})\r\n>>> new_conll[\"train\"][0]\r\n{'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\r\n 'id': '0',\r\n 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0],\r\n 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\r\n 'tokens': ['EU',\r\n 'rejects',\r\n 'German',\r\n 'call',\r\n 'to',\r\n 'boycott',\r\n 'British',\r\n 'lamb',\r\n '.']}\r\n>>> new_conll[\"train\"].features\r\n{'chunk_tags': Sequence(feature=ClassLabel(num_classes=23, names=['O', 'B-ADJP', 'I-ADJP', 'B-ADVP', 'I-ADVP', 'B-CONJP', 'I-CONJP', 'B-INTJ', 'I-INTJ', 'B-LST', 'I-LST', 'B-NP', 'I-NP', 'B-PP', 'I-PP', 'B-PRT', 'I-PRT', 'B-SBAR', 'I-SBAR', 'B-UCP', 'I-UCP', 'B-VP', 'I-VP'], id=None), length=-1, id=None),\r\n 'id': Value(dtype='string', id=None),\r\n 'ner_tags': Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None),\r\n 'pos_tags': Sequence(feature=ClassLabel(num_classes=47, names=['\"', \"''\", '#', '$', '(', ')', ',', '.', ':', '``', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB'], id=None), length=-1, id=None),\r\n 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}\r\n```",
"Hi @albertvillanova!\r\n\r\nThanks so much for your suggestions! That worked! "
] |
load_datasets on csv returns everything as a string
|
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I_kwDODunzps5Ni9eb
| null |
2022-07-11T17:30:24Z
|
https://api.github.com/repos/huggingface/datasets/issues/4673/comments
|
## Describe the bug
If you use:
`conll_dataset.to_csv("ner_conll.csv")`
It will create a csv file with all of your data as expected, however when you load it with:
`conll_dataset = load_dataset("csv", data_files="ner_conll.csv")`
everything is read in as a string. For example if I look at everything in 'ner_tags' I get back `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']` instead of what I originally saved which was `[[3, 0, 7, 0, 0, 0, 7, 0, 0], [1, 2], [5, 0]]`
I think maybe there is something funky going on with the csv delimiter
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
#load original conll dataset
orig_conll = load_dataset("conll2003")
#save original conll as a csv
orig_conll.to_csv("ner_conll.csv")
#reload conll data as a csv
new_conll = load_dataset("csv", data_files="ner_conll.csv")`
```
## Expected results
A clear and concise description of the expected results.
I would expect the data be returned as the data type I saved it as. I.e. if I save a list of ints
[[3, 0, 7, 0, 0, 0, 7, 0, 0]], I shouldnt get back a string ['[3 0 7 0 0 0 7 0 0]']
I also get back a string when I pass a list of strings ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']
## Actual results
A list of strings `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']`
A string "['EU' 'rejects' 'German' 'call' 'to' 'boycott' 'British' 'lamb' '.']"
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
|
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2022-07-15T13:14:27Z
|
[] |
https://github.com/huggingface/datasets/pull/4672
|
MEMBER
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Cool! Can you please remove `Fix #3541` from the description as this PR doesn't add support for streaming/`iter_archive`, so it only partially addresses the issue?\r\n\r\nSide note:\r\nI think we can use `libarchive` (`libarchive-c` is a Python package with the bindings) for streaming 7z archives. The only issue with this lib is that it's tricky to install on Windows/Mac."
] |
Support extract 7-zip compressed data files
|
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2022-07-11T15:56:51Z
|
https://api.github.com/repos/huggingface/datasets/issues/4672/comments
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Fix partially #3541, fix #4670.
|
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MEMBER
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[
"Thanks for reporting, @lewtun.\r\n\r\n~We can't load the dataset locally, so I think this is an issue with the loading script (not the viewer).~\r\n\r\n We are investigating...",
"Recently, there was a merged PR related to this dataset:\r\n- #4554\r\n\r\nWe are looking at this...",
"Indeed, the above mentioned PR fixed the loading script (it was not working before).\r\n\r\nI'm forcing the refresh of the Viewer.",
"Please note that the above mentioned PR also made an enhancement in the `datasets` library, required by this loading script. This enhancement will only be available to the Viewer once we make our next release.",
"OK, it's working now.\r\n\r\nhttps://huggingface.co/datasets/wmt16/viewer/ro-en/test\r\n\r\n<img width=\"1434\" alt=\"Capture d’écran 2022-09-08 à 10 15 55\" src=\"https://user-images.githubusercontent.com/1676121/189071665-17d2d149-9b22-42bf-93ac-1a966c3f637a.png\">\r\n",
"Thank you @severo !!"
] |
Dataset Viewer issue for wmt16
|
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I_kwDODunzps5NglB1
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2022-07-11T08:34:11Z
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https://api.github.com/repos/huggingface/datasets/issues/4671/comments
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### Link
https://huggingface.co/datasets/wmt16
### Description
[Reported](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/12#62cb83f14c7f35284e796f9c) by a user of AutoTrain Evaluate. AFAIK this dataset was working 1-2 weeks ago, and I'm not sure how to interpret this error.
```
Status code: 400
Exception: NotImplementedError
Message: This is a abstract method
```
Thanks!
### Owner
No
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https://github.com/huggingface/datasets/issues/4670
|
CONTRIBUTOR
|
completed
| null | null |
[
"Hi @bhavitvyamalik, thanks for reporting.\r\n\r\nYes, currently we do not support 7zip archive compression: I think we should.\r\n\r\nAs a workaround, you could uncompress it explicitly, like done in e.g. `samsum` dataset: \r\n\r\nhttps://github.com/huggingface/datasets/blob/fedf891a08bfc77041d575fad6c26091bc0fce52/datasets/samsum/samsum.py#L106-L110\r\n",
"Related to this issue: https://github.com/huggingface/datasets/issues/3541",
"Sure, let me look into and check what can be done. Will keep you guys updated here!",
"Initially, I thought of solving this without any external dependency. Almost everywhere I saw `lzma` can be used for this but there is a caveat that lzma doesn’t work with 7z archives but only single files. In my case the 7z archive has multiple files so it didn't work. Is it fine to use external library here?",
"Hi @bhavitvyamalik, thanks for your investigation.\r\n\r\nOn Monday, I started a PR that will eventually close this issue as well: I'm linking it to this.\r\n- #4672\r\n\r\nLet me know what you think. "
] |
Can't extract files from `.7z` zipfile using `download_and_extract`
|
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2022-07-10T18:16:49Z
|
https://api.github.com/repos/huggingface/datasets/issues/4670/comments
|
## Describe the bug
I'm adding a new dataset which is a `.7z` zip file in Google drive and contains 3 json files inside. I'm able to download the data files using `download_and_extract` but after downloading it throws this error:
```
>>> dataset = load_dataset("./datasets/mantis/")
Using custom data configuration default
Downloading and preparing dataset mantis/default to /Users/bhavitvyamalik/.cache/huggingface/datasets/mantis/default/1.1.0/611affa804ec53e2055a335cc1b8b213bb5a0b5142d919967729d5ee23c6bab4...
Downloading data: 100%|█████████████████████████████████████████████████████████| 77.2M/77.2M [00:23<00:00, 3.28MB/s]
/Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/load.py", line 1745, in load_dataset
use_auth_token=use_auth_token,
File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
[Errno 20] Not a directory: '/Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6/merged_train.json'
```
just before generating the splits. I checked `fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6` file and it's `7z` zip file (similar to downloaded Google drive file) which means it didn't get unzip. Do I need to unzip it separately and then pass the paths for train,dev,test files in `SplitGenerator`?
## Environment info
- `datasets` version: 1.18.4.dev0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.8
- PyArrow version: 5.0.0
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https://github.com/huggingface/datasets/issues/4669
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NONE
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completed
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[
"I had to use the appropriate token for use_auth_token. Thank you."
] |
loading oscar-corpus/OSCAR-2201 raises an error
|
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2022-07-10T07:09:30Z
|
https://api.github.com/repos/huggingface/datasets/issues/4669/comments
|
## Describe the bug
load_dataset('oscar-2201', 'af')
raises an error:
Traceback (most recent call last):
File "/usr/lib/python3.8/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset
builder_instance = load_dataset_builder(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder
dataset_module = dataset_module_factory(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py
I've tried other permutations such as :
oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-2201', 'af')
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201')
with the same unfortunate result.
## Steps to reproduce the bug
oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-2201', 'af')
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201')
# Sample code to reproduce the bug
```
## Expected results
loaded data
## Actual results
Traceback (most recent call last):
File "/usr/lib/python3.8/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset
builder_instance = load_dataset_builder(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder
dataset_module = dataset_module_factory(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.13.0-37-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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[
"It seems like a private dataset. The viewer is currently not supported on the private datasets."
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Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed
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2022-07-09T18:04:13Z
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https://api.github.com/repos/huggingface/datasets/issues/4668/comments
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### Link
https://huggingface.co/hungnm/multilingual-amazon-review-sentiment
### Description
_No response_
### Owner
Yes
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Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed
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NONE
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completed
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[
"Hi! I agree we should improve the features equality checks to account for this particular case. However, your code fails due to `answer_start` having the dtype `int64` instead of `int32` after loading from JSON (it's not possible to embed type precision info into a JSON file; `save_to_disk` does that for arrow files), which would lead to the concatenation error as PyArrow does not support this sort of type promotion. This can be fixed as follows:\r\n```python\r\ntemp = load_dataset(\"json\", data_files={\"train\": \"output.jsonl\"}, features=squad[\"train\"].features)\r\n``` ",
"That makes sense. I totally missed the `int64` and `int32` part. Thanks for pointing it out! Will close this issue for now."
] |
Issues with concatenating datasets
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2022-07-09T17:45:14Z
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https://api.github.com/repos/huggingface/datasets/issues/4666/comments
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## Describe the bug
It is impossible to concatenate datasets if a feature is sequence of dict in one dataset and a dict of sequence in another. But based on the document, it should be automatically converted.
> A [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence) with a internal dictionary feature will be automatically converted into a dictionary of lists. This behavior is implemented to have a compatilbity layer with the TensorFlow Datasets library but may be un-wanted in some cases. If you don’t want this behavior, you can use a python list instead of the [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence).
## Steps to reproduce the bug
```python
from datasets import concatenate_datasets, load_dataset
squad = load_dataset("squad_v2")
squad["train"].to_json("output.jsonl", lines=True)
temp = load_dataset("json", data_files={"train": "output.jsonl"})
concatenate_datasets([temp["train"], squad["train"]])
```
## Expected results
No error executing that code
## Actual results
```
ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)} or Value("null").
```
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.4-arm64-arm-64bit
- Python version: 3.8.11
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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"Hi @aleSuglia, thanks for reporting.\r\n\r\nWe are having a look at it. \r\n\r\nWe transfer this issue to the Community tab of the corresponding Hub dataset: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/discussions"
] |
Unable to create dataset having Python dataset script only
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2022-07-09T11:45:46Z
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https://api.github.com/repos/huggingface/datasets/issues/4665/comments
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## Describe the bug
Hi there,
I'm trying to add the following dataset to Huggingface datasets: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/blob/
I'm trying to do so using the CLI commands but seems that this command generates the wrong `dataset_info.json` file (you can find it in the repo already):
```
datasets-cli test Heriot-WattUniversity/dialog-babi/dialog_babi.py --save_infos --all-configs
```
while it errors when I remove the python script:
```
datasets-cli test Heriot-WattUniversity/dialog-babi/ --save_infos --all-configs
```
The error message is the following:
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /Users/as2180/workspace/Heriot-WattUniversity/dialog-babi with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.4-arm64-arm-64bit
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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| null | false | null |
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi @khushmeeet, thanks for your contribution.\r\n\r\nBut wouldn't it be better to add this dataset to the Hub? \r\n- https://huggingface.co/docs/datasets/share\r\n- https://huggingface.co/docs/datasets/dataset_script",
"Hi @albertvillanova \r\n\r\nDataset is added to Hub - https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset",
"Great, so I guess we can close this issue, as the dataset is already available on the Hub.",
"OK I read the discussion on:\r\n- #4504\r\n\r\nCurrently, priority is adding datasets to the Hub, not here on GitHub.\r\n\r\nIf you would like to contribute the loading script and all the metadata you generated (README + JSON files), you could:\r\n- Either make a PR to the existing dataset on the Hub\r\n- Create a new dataset on the Hub:\r\n - Either under your personal namespace\r\n - or even more professionally, under the namespace `stanfordSVL` (Stanford Vision and Learning Lab: https://svl.stanford.edu/)\r\n\r\nYou can use the Community tab to ping us if you need help or have any questions."
] |
Add stanford dog dataset
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2022-07-09T04:46:07Z
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https://api.github.com/repos/huggingface/datasets/issues/4664/comments
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This PR is for adding dataset, related to issue #4504.
We are adding Stanford dog breed dataset. It is a multi class image classification dataset.
Details can be found here - http://vision.stanford.edu/aditya86/ImageNetDogs/
Tests on dummy data is failing currently, which I am looking into.
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Add text decorators
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This PR adds some decoration to text about different modalities to make it more obvious separate guides exist for audio, vision, and text. The goal is to make it easier for users to discover these guides!
TODO:
- [x] Open PR to support new Tailwind classes
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Fix: conll2003 - fix empty example
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As reported in https://huggingface.co/datasets/conll2003/discussions/2#62c45a14f93fc97e8260532f, there was an extra empty example at the end of the dataset
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https://github.com/huggingface/datasets/issues/4661
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NONE
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[
"I can confirm that if I run one job first that processes the dataset, then I can run any jobs in parallel with no problem (no write-concurrency anymore...). ",
"Hi! That's weird. It seems like the error points to the `mkstemp` function, but the official docs state the following:\r\n```\r\nThere are no race conditions in the file’s creation, assuming that the platform properly implements the [os.O_EXCL](https://docs.python.org/3/library/os.html#os.O_EXCL) flag for [os.open()](https://docs.python.org/3/library/os.html#os.open)\r\n```\r\nSo this could mean your platform doesn't support that flag.\r\n\r\n~~Can you please check if wrapping the temp file creation (the line `tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)` in `_map_single`) with the `multiprocess.Lock` fixes the issue?~~\r\nPerhaps wrapping the temp file creation in `_map_single` with `filelock` could work:\r\n```python\r\nwith FileLock(lock_path):\r\n tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)\r\n```\r\nCan you please check if that helps?"
] |
Concurrency bug when using same cache among several jobs
|
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I_kwDODunzps5NY6Eg
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2022-07-08T01:58:11Z
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https://api.github.com/repos/huggingface/datasets/issues/4661/comments
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## Describe the bug
I used to see this bug with an older version of the datasets. It seems to persist.
This is my concrete scenario: I launch several evaluation jobs on a cluster in which I share the file system and I share the cache directory used by huggingface libraries. The evaluation jobs read the same *.csv files. If my jobs get all scheduled pretty much at the same time, there are all kinds of weird concurrency errors. Sometime it crashes silently. This time I got lucky that it crashed with a stack trace that I can share and maybe you get to the bottom of this. If you don't have a similar setup available, it may be hard to reproduce as you really need two jobs accessing the same file at the same time to see this type of bug.
## Steps to reproduce the bug
I'm running a modified version of `run_glue.py` script adapted to my use case. I've seen the same problem when running some glue datasets as well (so it's not specific to loading the datasets from csv files).
## Expected results
No crash, concurrent access to the (intermediate) files just fine.
## Actual results
Crashes due to races/concurrency bugs.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.18.0-348.23.1.el8_5.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 8.0.0
- Pandas version: 1.1.0
Stack trace that I just got with the crash (I've obfuscated some names, it should still be quite informative):
```
Running tokenizer on dataset: 0%| | 0/3 [00:00<?, ?ba/s]
Traceback (most recent call last):
File "../../src/models//run_*******.py", line 600, in <module>
main()
File "../../src/models//run_*******.py", line 444, in main
raw_datasets = raw_datasets.map(
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 770, in map
{
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 771, in <dictcomp>
k: dataset.map(
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2376, in map
return self._map_single(
File "/*******/envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 551, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 518, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/*******/envs/tr-crt/lib/python3.8/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2776, in _map_single
buf_writer, writer, tmp_file = init_buffer_and_writer()
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2696, in init_buffer_and_writer
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False)
File "/*******//envs/tr-crt/lib/python3.8/tempfile.py", line 541, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/*******//envs/tr-crt/lib/python3.8/tempfile.py", line 250, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '/*******/cache-transformers//transformers/csv/default-ef9cd184210742a7/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58/tmps8l6j5yc'
```
As I ran 100s of experiments last year for an empirical paper, I ran into this type of bugs several times. I found several bandaid/work-arounds, e.g., run one job first that caches the dataset => eliminate concurrency; OR use unique caches => eliminate concurrency (but increase storage space), etc. and it all works fine.
I'd like to help you fixing this bug as it's really annoying to always apply the work arounds. Let me know what other info from my side could help you figure out the issue.
Thanks for your help!
|
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"_The documentation is not available anymore as the PR was closed or merged._",
"Good catch ! Sorry I forgot (again) about windows paths when writing this x)"
] |
Fix _resolve_single_pattern_locally on Windows with multiple drives
|
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2022-07-07T09:57:30Z
|
https://api.github.com/repos/huggingface/datasets/issues/4660/comments
|
Currently, when `_resolve_single_pattern_locally` is called from a different drive than the one in `pattern`, it raises an exception:
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\io\parquet.py:35: in __init__
**kwargs,
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\builder.py:287: in __init__
sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:761: in from_local_or_remote
if not isinstance(patterns_for_key, DataFilesList)
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:723: in from_local_or_remote
data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:321: in resolve_patterns_locally_or_by_urls
for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:239: in _resolve_single_pattern_locally
for filepath in glob_iter
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:242: in <listcomp>
os.path.relpath(filepath, base_path), os.path.relpath(pattern, base_path)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
path = 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pytest-of-runneradmin\\pytest-0\\popen-gw0\\data6\\dataset.parquet'
start = '/'
...
E ValueError: path is on mount 'C:', start on mount 'D:'
```
This PR makes sure that `base_path` is in the same drive as `pattern`.
|
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2022-07-12T11:30:20Z
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks a lot @albertvillanova ! I hope we're finally done with flakiness on windows ^^\r\n\r\nAlso thanks for paying extra attention to billing and avoiding running unnecessary jobs. Though for certain aspects (see my comments), I think it's worth having the extra jobs to make our life easier",
"~@lhoestq I think you forgot to add your comments?~\r\n\r\nI had missed it among all the other comments...",
"@lhoestq, I'm specially enthusiastic with the fail-fast policy: it was in my TODO list for a long time. I really think it will have a positive impact (I would love to know the spent time saving it will enable, besides the carbon footprint reduction). :wink: \r\n\r\nSo yes, as you said above, let's give it a try at least. If we encounter any inconvenience, we can easily disable it.\r\n\r\nQuestion: I guess I have to disable CircleCI CI before merging this PR?\r\n\r\n"
] |
Transfer CI to GitHub Actions
|
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2022-07-07T09:29:47Z
|
https://api.github.com/repos/huggingface/datasets/issues/4659/comments
|
This PR transfers CI from CircleCI to GitHub Actions. The implementation in GitHub Actions tries to be as faithful as possible to the implementation in CircleCI and get the same output results (exceptions below).
**IMPORTANT NOTE**: The fast-fail policy (described below) is not finally implemented, so that:
- we can continue merging PRs with CI in red because of some random error returned by the Hub
- it is not annoying for maintainers to have to relaunch failed CI jobs
See comments here: https://github.com/huggingface/datasets/pull/4659#discussion_r918802348
Differences in the implementation in GitHub Actions compared to the CircleCI one:
- This PR introduces some *fail-fast* mechanisms to significantly reduce the total time CI is running, both because of environmental impact and because CI in GitHub Actions billing depends on the minutes per month running time (see [About billing for GitHub Actions](https://docs.github.com/en/billing/managing-billing-for-github-actions/about-billing-for-github-actions)):
- All tests *depend* on `check_code_quality` job: only if `check_code_quality` passes, then the other test jobs are launched
- The tests are implemented with a matrix strategy (cross-product: OS and PyArrow versions) and fail-fast: if any of the 4 processes fails, the others are cancelled
- OS dependencies for Linux (see table below)
| OS dependencies | Passed tests | Skipped tests |
| --- | ---: | ---: |
| libsndfile1-dev | 4786 | 3119 |
| libsndfile1 | 4786 | 3119 |
| libsndfile1, sox | 4788 | 3117 |
- This PR replaces `libsndfile1-dev` with `libsndfile1`: the same number of passing tests but less packages installed
- This PR adds `sox`: required by MP3 tests (2 more tests are passed: 4788 instead of 4786)
- For tests using PyArrow 6, this PR uses 6.0.1 instead of 6.0.0
TO DO:
- [ ] Remove old CircleCI CI: kept for the moment to compare stability and performance
Close #4658.
## Comparison between CircleCI and GitHub Actions
| | | CircleCI | GitHub Actions |
| --- | --- | ---: | ---: |
| Ubuntu, pyarrow-latest ||||
|| Passed tests | 4786 | 4788 |
|| Duration | 11m 0s | 10m 10s |
| Windows, pyarrow-latest ||||
|| Passed tests | 4783 | 4783 |
|| Duration | 29m 59s | 22m 56s |
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Transfer CI tests to GitHub Actions
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2022-07-07T08:10:50Z
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Let's try CI tests using GitHub Actions to see if they are more stable than on CircleCI.
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2022-07-12T16:14:52Z
|
[] |
https://github.com/huggingface/datasets/issues/4657
|
NONE
|
completed
| null | null |
[
"Hey, It's already present [here](https://huggingface.co/datasets/squad_v2) ",
"Hi! This dataset is indeed already available on the Hub. Closing."
] |
Add SQuAD2.0 Dataset
|
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I_kwDODunzps5NSrrd
| null |
2022-07-07T03:19:36Z
|
https://api.github.com/repos/huggingface/datasets/issues/4657/comments
|
## Adding a Dataset
- **Name:** *SQuAD2.0*
- **Description:** *Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.*
- **Paper:** *https://aclanthology.org/P18-2124.pdf*
- **Data:** *https://rajpurkar.github.io/SQuAD-explorer/*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
|
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2022-07-14T02:20:12Z
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https://github.com/huggingface/datasets/issues/4656
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NONE
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[
"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/Amazon-QA)."
] |
Add Amazon-QA Dataset
|
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2022-07-07T03:15:11Z
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https://api.github.com/repos/huggingface/datasets/issues/4656/comments
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## Adding a Dataset
- **Name:** *Amazon-QA*
- **Description:** *The dataset is .jsonl format, where each line in the file is a json string that corresponds to a question, existing answers to the question and the extracted review snippets (relevant to the question).*
- **Paper:** *https://github.com/amazonqa/amazonqa/tree/master/paper*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon-qa.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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2022-07-14T02:16:33Z
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https://github.com/huggingface/datasets/issues/4655
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NONE
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[
"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/simple-wiki)."
] |
Simple Wikipedia
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2022-07-07T02:51:26Z
|
https://api.github.com/repos/huggingface/datasets/issues/4655/comments
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## Adding a Dataset
- **Name:** *Simple Wikipedia*
- **Description:** *Two different versions of the data set now exist. Both were generated by aligning Simple English Wikipedia and English Wikipedia. A complete description of the extraction process can be found in "Simple English Wikipedia: A New Simplification Task", William Coster and David Kauchak (2011).*
- **Paper:** *https://aclanthology.org/P11-2117/*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/SimpleWiki.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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2022-07-14T02:13:50Z
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https://github.com/huggingface/datasets/issues/4654
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NONE
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[
"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/QQP_triplets)."
] |
Add Quora Question Triplets Dataset
|
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2022-07-07T02:43:42Z
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https://api.github.com/repos/huggingface/datasets/issues/4654/comments
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## Adding a Dataset
- **Name:** *Quora Question Triplets*
- **Description:** *This dataset consists of over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair.*
- **Paper:**
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates_triplets.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
|
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2022-07-14T02:12:39Z
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[
"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/altlex)."
] |
Add Altlex dataset
|
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2022-07-07T02:23:02Z
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https://api.github.com/repos/huggingface/datasets/issues/4653/comments
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## Adding a Dataset
- **Name:** *Altlex*
- **Description:** *Git repository for software associated with the 2016 ACL paper "Identifying Causal Relations Using Parallel Wikipedia Articles.”*
- **Paper:** *https://aclanthology.org/P16-1135.pdf*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/altlex.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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2022-07-14T02:11:48Z
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https://github.com/huggingface/datasets/issues/4652
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NONE
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completed
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[
"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/sentence-compression)."
] |
Add Sentence Compression Dataset
|
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2022-07-07T02:13:46Z
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https://api.github.com/repos/huggingface/datasets/issues/4652/comments
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## Adding a Dataset
- **Name:** *Sentence Compression*
- **Description:** *Large corpus of uncompressed and compressed sentences from news articles.*
- **Paper:** *https://www.aclweb.org/anthology/D13-1155/*
- **Data:** *https://github.com/google-research-datasets/sentence-compression/tree/master/data*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Add Flickr 30k Dataset
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2022-07-07T01:59:08Z
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https://api.github.com/repos/huggingface/datasets/issues/4651/comments
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## Adding a Dataset
- **Name:** *Flickr 30k*
- **Description:** *To produce the denotation graph, we have created an image caption corpus consisting of 158,915 crowd-sourced captions describing 31,783 images. This is an extension of our previous Flickr 8k Dataset. The new images and captions focus on people involved in everyday activities and events.*
- **Paper:** *https://transacl.org/ojs/index.php/tacl/article/view/229/33*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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2022-07-14T02:07:49Z
|
[] |
https://github.com/huggingface/datasets/issues/4650
|
NONE
| null | null | null |
[
"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/SPECTER)"
] |
Add SPECTER dataset
|
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2022-07-07T01:41:32Z
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https://api.github.com/repos/huggingface/datasets/issues/4650/comments
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## Adding a Dataset
- **Name:** *SPECTER*
- **Description:** *SPECTER: Document-level Representation Learning using Citation-informed Transformers*
- **Paper:** *https://doi.org/10.18653/v1/2020.acl-main.207*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/specter_train_triples.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Add PAQ dataset
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2022-07-07T01:29:42Z
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https://api.github.com/repos/huggingface/datasets/issues/4649/comments
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## Adding a Dataset
- **Name:** *PAQ*
- **Description:** *This repository contains code and models to support the research paper PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them*
- **Paper:** *https://arxiv.org/abs/2102.07033*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/PAQ_pairs.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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2022-07-14T02:03:40Z
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Add WikiAnswers dataset
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2022-07-07T01:06:37Z
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https://api.github.com/repos/huggingface/datasets/issues/4648/comments
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## Adding a Dataset
- **Name:** *WikiAnswers*
- **Description:** *The WikiAnswers corpus contains clusters of questions tagged by WikiAnswers users as paraphrases. Each cluster optionally contains an answer provided by WikiAnswers users.*
- **Paper:** *https://dl.acm.org/doi/10.1145/2623330.2623677*
- **Data:** *https://github.com/afader/oqa#wikianswers-corpus*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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2022-07-06T19:49:18Z
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https://github.com/huggingface/datasets/issues/4647
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NONE
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Add Reddit dataset
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2022-07-06T19:49:18Z
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https://api.github.com/repos/huggingface/datasets/issues/4647/comments
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## Adding a Dataset
- **Name:** *Reddit comments (2015-2018)*
- **Description:** *Reddit is an American social news aggregation website, where users can post links, and take part in discussions on these posts. These threaded discussions provide a large corpus, which is converted into a conversational dataset using the tools in this directory.*
- **Paper:** *https://arxiv.org/abs/1904.06472*
- **Data:** *https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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2022-07-06T15:56:21Z
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] |
Set HF_SCRIPTS_VERSION to main
|
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2022-07-06T15:43:21Z
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https://api.github.com/repos/huggingface/datasets/issues/4645/comments
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After renaming "master" to "main", the CI fails with
```
AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/main/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at /home/circleci/datasets/_dummy/_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py"
```
This is because in the CI we were still using `HF_SCRIPTS_VERSION=master`. I changed it to "main"
|
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[Minor fix] Typo correction
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2022-07-06T15:37:02Z
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recieve -> receive
|
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"_The documentation is not available anymore as the PR was closed or merged._",
"All the mentions I found on google were simple URLs that will be redirected, so it's fine. I also checked the spaces and we should be good:\r\n- dalle-mini used to install the master branch but [it's no longer the case](https://huggingface.co/spaces/flax-community/dalle-mini/commit/b78c972afd5c2d2bed087be6479fe5c9c6cfa741)\r\n- same for [logo generator](https://huggingface.co/spaces/tom-doerr/logo_generator/commit/a9ea330e518870d0ca8f65abb56f71d86750d8e4)\r\n- I opened a PR to fix [vision-datasets-viewer](https://huggingface.co/spaces/nateraw/vision-datasets-viewer/discussions/1)\r\n",
"Ok let's rename the branch, and then we can merge this PR"
] |
Rename master to main
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2022-07-06T13:34:30Z
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https://api.github.com/repos/huggingface/datasets/issues/4643/comments
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This PR renames mentions of "master" by "main" in the code base for several cases:
- set the default dataset script version to "main" if the local installation of `datasets` is a dev installation
- update URLs to this github repository to use "main"
- update the DVC benchmark
- update the github workflows
- update docstrings
- update tests to compare the changes in dataset cards against "main"
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MEMBER
|
completed
| null | null |
[
"Thanks for reporting @lewtun.\r\n\r\nI confirm there is an issue with streaming: it does not stream locally. ",
"Oh, after investigation, the source of the issue is in the Hub dataset loading script.\r\n\r\nI'm opening a PR on the Hub dataset.",
"I've opened a PR on their Hub dataset to support streaming: https://huggingface.co/datasets/ccdv/pubmed-summarization/discussions/2"
] |
Streaming issue for ccdv/pubmed-summarization
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I_kwDODunzps5NO4vz
| null |
2022-07-06T12:13:07Z
|
https://api.github.com/repos/huggingface/datasets/issues/4642/comments
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### Link
https://huggingface.co/datasets/ccdv/pubmed-summarization
### Description
This was reported by a [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/7). It seems like streaming doesn't work due to the way the dataset loading script is defined?
```
Status code: 400
Exception: FileNotFoundError
Message: https://huggingface.co/datasets/ccdv/pubmed-summarization/resolve/main/train.zip/train.txt
```
### Owner
No
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|
MEMBER
|
completed
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[
"Thanks for reporting, @lewtun.\r\n\r\nIt works locally in streaming mode:\r\n```\r\n{'bid': 27681,\r\n 'is_aggregate': True,\r\n 'source': 'cliffnotes',\r\n 'chapter_path': 'all_chapterized_books/27681-chapters/chapters_1_to_2.txt',\r\n 'summary_path': 'finished_summaries/cliffnotes/The Last of the Mohicans/section_1_part_0.txt',\r\n 'book_id': 'The Last of the Mohicans.chapters 1-2',\r\n 'summary_id': 'chapters 1-2',\r\n 'content': None,\r\n 'summary': '{\"name\": \"Chapters 1-2\", \"url\": \"https://web.archive.org/web/20201101053205/https://www.cliffsnotes.com/literature/l/the-last-of-the-mohicans/summary-and-analysis/chapters-12\", \"summary\": \"Before any characters appear, the time and geography are made clear. Though it is the last war that England and France waged for a country that neither would retain, the wilderness between the forces still has to be...\r\n```\r\n\r\nI'm forcing the refresh of the preview. ",
"The preview appears as expected once the refresh forced.",
"Thank you @albertvillanova 🤗 !"
] |
Dataset Viewer issue for kmfoda/booksum
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I_kwDODunzps5NOcti
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2022-07-06T10:38:16Z
|
https://api.github.com/repos/huggingface/datasets/issues/4641/comments
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### Link
https://huggingface.co/datasets/kmfoda/booksum
### Description
A [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/9) discovered this dataset cannot be streamed due to:
```
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/kmfoda/booksum/resolve/47953f583d6967f086cb16a2f4d2346e9834024d/test.csv')
```
I'm not sure why it says "Unauthorized" since it's just a bunch of CSV files in a repo
### Owner
No
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2022-07-06T15:19:55Z
|
[] |
https://github.com/huggingface/datasets/pull/4640
|
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4640). All of your documentation changes will be reflected on that endpoint."
] |
Support all split in streaming mode
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2022-07-06T08:56:38Z
|
https://api.github.com/repos/huggingface/datasets/issues/4640/comments
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Fix #4637.
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2022-07-06T07:41:32Z
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https://github.com/huggingface/datasets/issues/4639
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MEMBER
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[] |
Add HaGRID -- HAnd Gesture Recognition Image Dataset
|
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2022-07-06T07:41:32Z
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https://api.github.com/repos/huggingface/datasets/issues/4639/comments
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## Adding a Dataset
- **Name:** HaGRID -- HAnd Gesture Recognition Image Dataset
- **Description:** We introduce a large image dataset HaGRID (HAnd Gesture Recognition Image Dataset) for hand gesture recognition (HGR) systems. You can use it for image classification or image detection tasks. Proposed dataset allows to build HGR systems, which can be used in video conferencing services (Zoom, Skype, Discord, Jazz etc.), home automation systems, the automotive sector, etc.
- **Paper:** https://arxiv.org/abs/2206.08219
- **Data:** https://github.com/hukenovs/hagrid
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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2022-10-03T09:34:36Z
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https://github.com/huggingface/datasets/pull/4638
|
NONE
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[
"CircleCL reported two errors, but I didn't find the reason. The error message:\r\n```\r\n_________________ ERROR collecting tests/test_dataset_cards.py _________________\r\ntests/test_dataset_cards.py:53: in <module>\r\n @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\ntests/test_dataset_cards.py:35: in get_changed_datasets\r\n diff_output = check_output([\"git\", \"diff\", \"--name-only\", \"origin/master...HEAD\"], cwd=repo_path)\r\n../.pyenv/versions/3.6.15/lib/python3.6/subprocess.py:356: in check_output\r\n **kwargs).stdout\r\n../.pyenv/versions/3.6.15/lib/python3.6/subprocess.py:438: in run\r\n output=stdout, stderr=stderr)\r\nE subprocess.CalledProcessError: Command '['git', 'diff', '--name-only', 'origin/master...HEAD']' returned non-zero exit status 128.\r\n\r\n=========================== short test summary info ============================\r\nERROR tests/test_dataset_cards.py - subprocess.CalledProcessError: Command '[...\r\nERROR tests/test_dataset_cards.py - subprocess.CalledProcessError: Command '[...\r\n= 4011 passed, 2357 skipped, 2 xfailed, 1 xpassed, 116 warnings, 2 errors in 284.32s (0:04:44) =\r\n\r\nExited with code exit status 1\r\n```\r\nI'm not sure if it was caused by this PR ...\r\n\r\nI ran `tests/test_dataset_cards.py` in my local environment, and it passed:\r\n```\r\n(venv)$ pytest tests/test_dataset_cards.py\r\n============================== test session starts ==============================\r\nplatform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0\r\nrootdir: /home/zhangjunbo/src/datasets\r\nplugins: forked-1.4.0, datadir-1.3.1, xdist-2.5.0\r\ncollected 1531 items\r\n\r\ntests/test_dataset_cards.py ..... [100%]\r\n======================= 766 passed, 765 skipped in 2.55s ========================\r\n```\r\n",
"@sanchit-gandhi could you also maybe take a quick look? :-)",
"Thanks for your contribution, @jimbozhang. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help.",
"> Thanks for your contribution, @jimbozhang. Are you still interested in adding this dataset?\r\n> \r\n> We are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n> \r\n> We would suggest you create this dataset there. Please, feel free to tell us if you need some help.\r\n\r\nYes, I just planned to finish this dataset these days, and this suggestion is just in time! Thanks a lot!\r\nI will create this dataset to Hugging Face Hub soon, maybe this week."
] |
The speechocean762 dataset
|
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2022-07-06T06:17:30Z
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https://api.github.com/repos/huggingface/datasets/issues/4638/comments
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[speechocean762](https://www.openslr.org/101/) is a non-native English corpus for pronunciation scoring tasks. It is free for both commercial and non-commercial use.
I believe it will be easier to use if it could be available on Hugging Face.
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CONTRIBUTOR
| null | null | null |
[
"Thanks for reporting @cakiki.\r\n\r\nYes, this is a bug. We are investigating it.",
"@albertvillanova Nice! Let me know if it's something I can fix my self; would love to contribtue!",
"@cakiki I was working on this but if you would like to contribute, go ahead. I will close my PR. ;)\r\n\r\nFor the moment I just pushed the test (to see if it impacts other tests).",
"It impacted the test `test_generator_based_download_and_prepare` and I have fixed this.\r\n\r\nSo that you can copy the test I implemented in my PR and then implement a fix for this issue that passes the test `tests/test_builder.py::test_builder_as_streaming_dataset`.",
"Hi @cakiki are you still interested in working on this? Are you planning to open a PR?",
"Hi @albertvillanova ! Sorry it took so long; I wanted to spend this weekend working on it."
] |
The "all" split breaks streaming
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2022-07-05T21:56:49Z
|
https://api.github.com/repos/huggingface/datasets/issues/4637/comments
|
## Describe the bug
Not sure if this is a bug or just the way streaming works, but setting `streaming=True` did not work when setting `split="all"`
## Steps to reproduce the bug
The following works:
```python
ds = load_dataset('super_glue', 'wsc.fixed', split='all')
```
The following throws `ValueError: Bad split: all. Available splits: ['train', 'validation', 'test']`:
```python
ds = load_dataset('super_glue', 'wsc.fixed', split='all', streaming=True)
```
## Expected results
An iterator over all splits.
## Actual results
I had to do the following to achieve the desired result:
```python
from itertools import chain
ds = load_dataset('super_glue', 'wsc.fixed', streaming=True)
it = chain.from_iterable(ds.values())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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CONTRIBUTOR
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completed
| null | null |
[] |
Add info in docs about behavior of download_config.num_proc
|
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2022-07-05T17:01:00Z
|
https://api.github.com/repos/huggingface/datasets/issues/4636/comments
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**Is your feature request related to a problem? Please describe.**
I went to override `download_config.num_proc` and was confused about what was happening under the hood. It would be nice to have the behavior documented a bit better so folks know what's happening when they use it.
**Describe the solution you'd like**
- Add note about how the default number of workers is 16. Related code:
https://github.com/huggingface/datasets/blob/7bcac0a6a0fc367cc068f184fa132b8de8dfa11d/src/datasets/download/download_manager.py#L299-L302
- Add note that if the number of workers is higher than the number of files to download, it won't use multiprocessing.
**Describe alternatives you've considered**
maybe it would also be nice to set `num_proc` = `num_files` when `num_proc` > `num_files`.
**Additional context**
...
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