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https://api.github.com/repos/huggingface/datasets/issues/4305 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4305/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4305/comments | https://api.github.com/repos/huggingface/datasets/issues/4305/events | https://github.com/huggingface/datasets/pull/4305 | 1,231,099,934 | PR_kwDODunzps43kt4P | 4,305 | Fixes FrugalScore | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4305). All of your documentation changes will be reflected on that endpoint.",
"> predictions and references are swapped. Basically Frugalscore is commutative, however some tiny differences can occur if we swap the references and the predictions. I decided to swap them just to obtain the exact results as reported in the paper.\r\n\r\nWhat is the order of magnitude of the difference ? Do you know what causes this ?\r\n\r\n> I switched to dynamic padding that was was used in the training, forcing the padding to max_length introduces errors for some reason that I ignore.\r\n\r\nWhat error ?"
] | 1,652,186,646,000 | 1,652,263,399,000 | null | CONTRIBUTOR | null | There are two minor modifications in this PR:
1) `predictions` and `references` are swapped. Basically Frugalscore is commutative, however some tiny differences can occur if we swap the references and the predictions. I decided to swap them just to obtain the exact results as reported in the paper.
2) I switched to dynamic padding that was was used in the training, forcing the padding to `max_length` introduces errors for some reason that I ignore.
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https://api.github.com/repos/huggingface/datasets/issues/4304 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4304/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4304/comments | https://api.github.com/repos/huggingface/datasets/issues/4304/events | https://github.com/huggingface/datasets/issues/4304 | 1,231,047,051 | I_kwDODunzps5JYEmL | 4,304 | Language code search does direct matches | {
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"Thanks for reporting ! I forwarded the issue to the front-end team :)\r\n\r\nWill keep you posted !\r\n\r\nI also changed the tagging app to suggest two letters code for now."
] | 1,652,183,956,000 | 1,652,186,322,000 | null | CONTRIBUTOR | null | ## Describe the bug
Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search.
## Steps to reproduce the bug
1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL))
2. Look for datasets using the full code
3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq))
Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`.
One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :)
## Expected results
Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`).
## Actual results
The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches.
## Environment info
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"The CI failure is unrelated to this PR and fixed on master, merging :)"
] | 1,652,174,498,000 | 1,652,259,015,000 | 1,652,259,014,000 | CONTRIBUTOR | null | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The Hub doesn't allow these characters in the YAML tags, and git push fails if you want to push a dataset card containing these characters.",
"Ok, let me rename the bad config names :) I think I can also keep backward compatibility with a warning",
"Almost done with it btw, will submit a PR that shows all the configuration name changes (from a bit more than 20 datasets)",
"Please, let me know when the renaming of configs is done. If not enough bandwidth, I can take care of it...",
"Will focus on this this afternoon ;)",
"I realized when renaming all the configurations with dots in https://github.com/huggingface/datasets/pull/4365 that it's not ideal for certain cases. For example:\r\n- many configurations have a version like \"1.0.0\" in their names\r\n- to avoid breaking changes we need to replace dots with underscores in the user input and show a warning, which hurts the experience\r\n- our second most downloaded dataset at the moment is affected: `newsgroup`\r\n- if we disallow dots, then we'll never be able to make the [allenai/c4](https://huggingface.co/datasets/allenai/c4) work with its different configurations since they contain dots, and we can't rename them because they are the official download links\r\n\r\nI was thinking of other alternatives:\r\n1. just stop separating tags per config name completely, and have a single flat YAML for all configurations. Dataset search doesn't use this info anyway\r\n2. use another YAML structure to avoid having config names as keys, such as\r\n```yaml\r\nlanguages:\r\n- config: 20220301_en\r\n values:\r\n - en\r\n```\r\n\r\nI'm down for 1, to keep things simple",
"@lhoestq I agree:\r\n- better not changing config names (so that we do not introduce any braking change)\r\n- therefore, we should not use them as keys\r\n\r\nIn relation with the proposed solutions, I have no strong opinion:\r\n- option 1 is simpler and aligns better with current usage on the Hub (configs are ignored)\r\n- however:\r\n - we will lose all the information per config we already have (for those datasets containing config keys; contributors made an effort to put that information per config)\r\n - and this information might be useful on the Hub in the future, in case we would like to enrich the search feature with more granularity; this is only applicable if this feature could eventually make sense\r\n\r\nSo, no strong opinion...",
"Closing in favor of https://github.com/huggingface/datasets/pull/4367"
] | 1,652,161,966,000 | 1,653,040,110,000 | 1,653,039,620,000 | MEMBER | null | Currently, when mirroring datasets on the Hub, the license tags are hacked: removed of characters "." and "$". On the contrary, this hacking is not applied to community datasets on the Hub. This generates multiple variants of the same tag on the Hub.
I guess this hacking is no longer necessary:
- it is not applied to community datasets
- all canonical datasets are validated by maintainers before being merged: CI + maintainers make sure license tags are the right ones
Fix #4298. | {
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https://api.github.com/repos/huggingface/datasets/issues/4301 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4301/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4301/comments | https://api.github.com/repos/huggingface/datasets/issues/4301/events | https://github.com/huggingface/datasets/pull/4301 | 1,230,401,256 | PR_kwDODunzps43idlE | 4,301 | Add ImageNet-Sketch dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I think you can go ahead with uploading the data, and also ping the author in parallel. I think the images may subject to copyright anyway (scrapped from google image) so the dataset author is not allowed to set a license to the data.\r\n\r\nI think it's fine to upload the dataset as soon as we mention explicitly that the images may be subject to copyright."
] | 1,652,139,525,000 | 1,653,329,654,000 | 1,653,329,129,000 | CONTRIBUTOR | null | This PR adds the ImageNet-Sketch dataset and resolves #3953 . | {
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] | 1,652,131,826,000 | 1,653,495,795,000 | 1,653,470,413,000 | MEMBER | null | This PR adds API code examples for loading methods, let me know if I've missed any important parameters we should showcase :)
I was a bit confused about `inspect_dataset` and `inspect_metric`. The `path` parameter says it will accept a dataset identifier from the Hub. But when I try the identifier `rotten_tomatoes`, it gives me:
```py
from datasets import inspect_dataset
inspect_dataset('rotten_tomatoes', local_path='/content/rotten_tomatoes')
FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory.
```
Does the user need to have an existing copy of `rotten_tomatoes.py` on their local drive (in which case, it seems like the same option as the first option in `path`)? | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the reviews @apsdehal and @lhoestq! As suggested by @lhoestq, I'll separate the train/val/test splits, apply the validation split fixes and shuffle the images files to simplify the script and make streaming faster.",
"@apsdehal I dismissed your review as it's no longer relevant after the data files changes suggested by @lhoestq. "
] | 1,652,129,358,000 | 1,653,490,499,000 | 1,653,489,976,000 | CONTRIBUTOR | null | Remove the manual download code from `imagenet-1k` to make it a regular dataset. | {
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"we'll add the same server-side metadata validation system as for hf.co/models soon-ish\r\n\r\n(you can check on hf.co/models that licenses are \"clean\")",
"Fixed by #4367."
] | 1,652,104,292,000 | 1,653,040,310,000 | 1,653,040,310,000 | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata.
**Describe the solution you'd like**
I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) .
**Describe alternatives you've considered**
None
**Additional context**
None
**Priority**
Low
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"@lhoestq @albertvillanova `update-task-list` branch does not exist anymore, should point to `main` now i guess",
"Thanks for reporting, fixing it now",
"It's up again :)"
] | 1,652,103,905,000 | 1,652,107,465,000 | 1,652,107,465,000 | CONTRIBUTOR | null | ## Describe the bug
The neat hf spaces app for generating YAML tags for dataset `README.md`s is down
## Steps to reproduce the bug
1. Visit https://huggingface.co/spaces/huggingface/datasets-tagging
## Expected results
There'll be a HF spaces web app for generating dataset metadata YAML
## Actual results
There's an error message; here's the step where it breaks:
```
Step 18/29 : RUN pip install -r requirements.txt
---> Running in e88bfe7e7e0c
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/huggingface/datasets.git@update-task-list (from -r requirements.txt (line 4))
Cloning https://github.com/huggingface/datasets.git (to revision update-task-list) to /tmp/pip-req-build-bm8t0r0k
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/datasets.git /tmp/pip-req-build-bm8t0r0k
WARNING: Did not find branch or tag 'update-task-list', assuming revision or ref.
Running command git checkout -q update-task-list
error: pathspec 'update-task-list' did not match any file(s) known to git
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
```
## Environment info
- Platform: Linux / Brave
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4296). All of your documentation changes will be reflected on that endpoint."
] | 1,652,095,102,000 | 1,652,096,518,000 | null | MEMBER | null | Fix #3488. | {
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```
TypeError: _download_and_prepare() got an unexpected keyword argument 'save_infos'
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] | 1,651,994,866,000 | 1,655,225,340,000 | 1,655,222,820,000 | CONTRIBUTOR | null | The `load_from_cache` parameter of `map` should be `load_from_cache_file`. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,860,931,000 | 1,653,501,913,000 | 1,653,501,396,000 | MEMBER | null | This PR adds API code examples for the remaining functions in the Main classes. I wasn't too familiar with some of the functions (`decode_batch`, `decode_column`, `decode_example`, etc.) so please feel free to add an example of usage and I can fill in the rest :) | {
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https://api.github.com/repos/huggingface/datasets/issues/4291 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4291/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4291/comments | https://api.github.com/repos/huggingface/datasets/issues/4291/events | https://github.com/huggingface/datasets/issues/4291 | 1,227,777,500 | I_kwDODunzps5JLmXc | 4,291 | Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message | {
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"Hi @leondz, thanks for reporting.\r\n\r\nIndeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions.\r\n\r\nIn particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`.\r\n\r\nLet me know if you need some help: I could push a commit to your repo with the fix.",
"Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)"
] | 1,651,838,607,000 | 1,652,084,758,000 | 1,652,084,758,000 | CONTRIBUTOR | null | ### Link
https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train
### Description
The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss?
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/4290 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4290/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4290/comments | https://api.github.com/repos/huggingface/datasets/issues/4290/events | https://github.com/huggingface/datasets/pull/4290 | 1,227,592,826 | PR_kwDODunzps43Zr08 | 4,290 | Update README.md | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4290). All of your documentation changes will be reflected on that endpoint.",
"@albertvillanova Kindly check :)"
] | 1,651,827,171,000 | 1,653,231,871,000 | null | CONTRIBUTOR | null | Updating readme in medmcqa dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/4288 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4288/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4288/comments | https://api.github.com/repos/huggingface/datasets/issues/4288/events | https://github.com/huggingface/datasets/pull/4288 | 1,226,821,732 | PR_kwDODunzps43XLKi | 4,288 | Add missing `faiss` import to fix https://github.com/huggingface/datasets/issues/4287 | {
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https://api.github.com/repos/huggingface/datasets/issues/4287 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4287/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4287/comments | https://api.github.com/repos/huggingface/datasets/issues/4287/events | https://github.com/huggingface/datasets/issues/4287 | 1,226,806,652 | I_kwDODunzps5JH5V8 | 4,287 | "NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None | {
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"So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above.\r\n\r\nAs it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered.\r\n\r\nSo on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU.\r\n\r\nI'll add the code in a PR linked to this issue in case you want to merge it!",
"Adding here the complete error traceback!\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/alvarobartt/lol.py\", line 12, in <module>\r\n ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3656, in add_faiss_index\r\n super().add_faiss_index(\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 478, in add_faiss_index\r\n faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True)\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 281, in add_vectors\r\n self.faiss_index = self._faiss_index_to_device(index, self.device)\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 327, in _faiss_index_to_device\r\n faiss_res = faiss.StandardGpuResources()\r\nNameError: name 'faiss' is not defined\r\n```",
"Closed as https://github.com/huggingface/datasets/pull/4288 already merged! :hugs:"
] | 1,651,763,385,000 | 1,652,190,799,000 | 1,652,190,799,000 | CONTRIBUTOR | null | ## Describe the bug
When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception.
All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
import torch
torch.set_grad_enabled(False)
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
from datasets import load_dataset
ds = load_dataset('crime_and_punish', split='train[:100]')
ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()})
ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`
```
## Expected results
A new column named `embeddings` in the dataset that we're adding the index to.
## Actual results
An exception is triggered with the following message `NameError: name 'faiss' is not defined`.
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,761,260,000 | 1,652,184,604,000 | 1,652,184,158,000 | CONTRIBUTOR | null | This language is present in [Wikimedia's WIT](https://huggingface.co/datasets/wikimedia/wit_base) dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/4285 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4285/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4285/comments | https://api.github.com/repos/huggingface/datasets/issues/4285/events | https://github.com/huggingface/datasets/pull/4285 | 1,226,374,831 | PR_kwDODunzps43VtEa | 4,285 | Update LexGLUE README.md | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,739,810,000 | 1,651,757,944,000 | 1,651,757,615,000 | CONTRIBUTOR | null | Update the leaderboard based on the latest results presented in the ACL 2022 version of the article. | {
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https://api.github.com/repos/huggingface/datasets/issues/4284 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4284/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4284/comments | https://api.github.com/repos/huggingface/datasets/issues/4284/events | https://github.com/huggingface/datasets/issues/4284 | 1,226,200,727 | I_kwDODunzps5JFlaX | 4,284 | Issues in processing very large datasets | {
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] | open | false | null | [] | null | [
"Hi ! `datasets` doesn't load the dataset in memory. Instead it uses memory mapping to load your dataset from your disk (it is stored as arrow files). Do you know at what point you have RAM issues exactly ?\r\n\r\nHow big are your graph_data_train dictionaries btw ?"
] | 1,651,726,869,000 | 1,652,184,923,000 | null | NONE | null | ## Describe the bug
I'm trying to add a feature called "subgraph" to CNN/DM dataset (modifications on run_summarization.py of Huggingface Transformers script) --- I'm not quite sure if I'm doing it the right way, though--- but the main problem appears when the training starts where the error ` [OSError: [Errno 12] Cannot allocate memory]` appears. I suppose this problem roots in RAM issues and how the dataset is loaded during training, but I have no clue of what I can do to fix it. Observing the dataset's cache directory, I see that it takes ~600GB of memory and that's why I believe special care is needed when loading it into the memory.
Here are my modifications to `run_summarization.py` code.
```
# loading pre-computed dictionary where keys are 'id' of article and values are corresponding subgraph
graph_data_train = get_graph_data('train')
graph_data_validation = get_graph_data('val')
...
...
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function_train,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
```
And here is the modified preprocessed function:
```
def preprocess_function_train(examples):
inputs, targets, sub_graphs, ids = [], [], [], []
for i in range(len(examples[text_column])):
if examples[text_column][i] is not None and examples[summary_column][i] is not None:
# if examples['doc_id'][i] in graph_data.keys():
inputs.append(examples[text_column][i])
targets.append(examples[summary_column][i])
sub_graphs.append(graph_data_train[examples['id'][i]])
ids.append(examples['id'][i])
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True,
sub_graphs=sub_graphs, ids=ids)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == "max_length" and data_args.ignore_pad_token_for_loss:
labels["input_ids"] = [
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
]
model_inputs["labels"] = labels["input_ids"]
return model_inputs
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/4283 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4283/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4283/comments | https://api.github.com/repos/huggingface/datasets/issues/4283/events | https://github.com/huggingface/datasets/pull/4283 | 1,225,686,988 | PR_kwDODunzps43Tnxo | 4,283 | Fix filesystem docstring | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,686,162,000 | 1,651,854,722,000 | 1,651,818,137,000 | MEMBER | null | This PR untangles the `S3FileSystem` docstring so the [parameters](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#parameters) are properly displayed. | {
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https://api.github.com/repos/huggingface/datasets/issues/4282 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4282/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4282/comments | https://api.github.com/repos/huggingface/datasets/issues/4282/events | https://github.com/huggingface/datasets/pull/4282 | 1,225,616,545 | PR_kwDODunzps43TZYL | 4,282 | Don't do unnecessary list type casting to avoid replacing None values by empty lists | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Quick question about the message in the warning. You say \"will be fixed in a future major version\" but don't you mean \"will raise an error in a future major version\"?",
"Right ! Good catch, thanks, I updated the message to say \"will raise an error in a future major version\""
] | 1,651,682,221,000 | 1,651,833,838,000 | 1,651,833,420,000 | MEMBER | null | In certain cases, `None` values are replaced by empty lists when casting feature types.
It happens every time you cast an array of nested lists like [None, [0, 1, 2, 3]] to a different type (to change the integer precision for example). In this case you'd get [[], [0, 1, 2, 3]] for example. This issue comes from PyArrow, see the discussion in https://github.com/huggingface/datasets/issues/3676
This issue also happens when no type casting is needed, because casting is supposed to be a no-op in this case. But as https://github.com/huggingface/datasets/issues/3676 shown, it's not the case and `None` are replaced by empty lists even if we cast to the exact same type.
In this PR I just workaround this bug in the case where no type casting is needed. In particular, I only call `pa.ListArray.from_arrays` only when necessary.
I also added a warning when some `None` are effectively replaced by empty lists. I wanted to raise an error in this case, but maybe we should wait a major update to do so
This PR fixes this particular case, that is occurring in `run_qa.py` in `transformers`:
```python
from datasets import Dataset
ds = Dataset.from_dict({"a": range(4)})
ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"])
print(ds.to_pandas())
# before:
# b
# 0 [None, [0]]
# 1 [[], [0]]
# 2 [[], [0]]
# 3 [[], [0]]
#
# now:
# b
# 0 [None, [0]]
# 1 [None, [0]]
# 2 [None, [0]]
# 3 [None, [0]]
```
cc @sgugger | {
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https://api.github.com/repos/huggingface/datasets/issues/4281 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4281/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4281/comments | https://api.github.com/repos/huggingface/datasets/issues/4281/events | https://github.com/huggingface/datasets/pull/4281 | 1,225,556,939 | PR_kwDODunzps43TNBm | 4,281 | Remove a copy-paste sentence in dataset cards | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The non-passing tests have nothing to do with this PR."
] | 1,651,678,915,000 | 1,651,826,283,000 | 1,651,689,196,000 | MEMBER | null | Remove the following copy-paste sentence from dataset cards:
```
We show detailed information for up to 5 configurations of the dataset.
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4280 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4280/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4280/comments | https://api.github.com/repos/huggingface/datasets/issues/4280/events | https://github.com/huggingface/datasets/pull/4280 | 1,225,446,844 | PR_kwDODunzps43S2xg | 4,280 | Add missing features to commonsense_qa dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@albertvillanova it adds question_concept and id which is great. I suppose we'll talk about staying true to the format on another PR. ",
"Yes, let's merge this PR as it is: it adds missing features.\r\n\r\nA subsequent PR may address the request on changing the dataset feature structure."
] | 1,651,674,266,000 | 1,651,847,037,000 | 1,651,846,606,000 | MEMBER | null | Fix partially #4275. | {
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https://api.github.com/repos/huggingface/datasets/issues/4279 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4279/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4279/comments | https://api.github.com/repos/huggingface/datasets/issues/4279/events | https://github.com/huggingface/datasets/pull/4279 | 1,225,300,273 | PR_kwDODunzps43SXw5 | 4,279 | Update minimal PyArrow version warning | {
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] | 1,651,667,169,000 | 1,651,740,658,000 | 1,651,740,227,000 | CONTRIBUTOR | null | Update the minimal PyArrow version warning (should've been part of #4250). | {
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https://api.github.com/repos/huggingface/datasets/issues/4278 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4278/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4278/comments | https://api.github.com/repos/huggingface/datasets/issues/4278/events | https://github.com/huggingface/datasets/pull/4278 | 1,225,122,123 | PR_kwDODunzps43RyTs | 4,278 | Add missing features to openbookqa dataset for additional config | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Let's merge this PR as it is: it adds missing features.\r\n\r\nA subsequent PR may address the request on changing the data feature structure."
] | 1,651,656,170,000 | 1,651,842,800,000 | 1,651,842,361,000 | MEMBER | null | Fix partially #4276. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hmm, not sure why the Windows tests are failing with:\r\n\r\n```\r\nDid not find path entry C:\\tools\\miniconda3\\bin\r\nC:\\tools\\miniconda3\\envs\\py37\\python.exe: No module named pytest\r\n```\r\n\r\nEdit: running the CI again fixed the problem 🙃 ",
"> One last nit and we can merge then\r\n\r\nThanks, done!"
] | 1,651,648,516,000 | 1,651,851,735,000 | 1,651,851,391,000 | MEMBER | null | This PR extends the `Dataset.align_labels_with_mapping()` method to support alignment of label mappings between datasets and models for token classification (e.g. NER).
Example of usage:
```python
from datasets import load_dataset
ner_ds = load_dataset("conll2003", split="train")
# returns [3, 0, 7, 0, 0, 0, 7, 0, 0]
ner_ds[0]["ner_tags"]
# hypothetical model mapping with O <--> B-LOC
label2id = {
"B-LOC": "0",
"B-MISC": "7",
"B-ORG": "3",
"B-PER": "1",
"I-LOC": "6",
"I-MISC": "8",
"I-ORG": "4",
"I-PER": "2",
"O": "5"
}
ner_aligned_ds = ner_ds.align_labels_with_mapping(label2id, "ner_tags")
# returns [3, 5, 7, 5, 5, 5, 7, 5, 5]
ner_aligned_ds[0]["ner_tags"]
```
Context: we need this in AutoTrain to automatically align datasets / models during evaluation. cc @abhishekkrthakur | {
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"Thanks for reporting, @vblagoje.\r\n\r\nIndeed, I noticed some of these issues while reviewing this PR:\r\n- #4259 \r\n\r\nThis is in my TODO list. ",
"Ok, awesome @albertvillanova How about #4275 ?",
"On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency.\r\n\r\nFor example, other datasets also flatten \"question.stem\" into \"question\":\r\n- ai2_arc:\r\n ```python\r\n question = data[\"question\"][\"stem\"]\r\n choices = data[\"question\"][\"choices\"]\r\n text_choices = [choice[\"text\"] for choice in choices]\r\n label_choices = [choice[\"label\"] for choice in choices]\r\n yield id_, {\r\n \"id\": id_,\r\n \"answerKey\": answerkey,\r\n \"question\": question,\r\n \"choices\": {\"text\": text_choices, \"label\": label_choices},\r\n }\r\n ```\r\n- commonsense_qa:\r\n ```python\r\n question = data[\"question\"]\r\n stem = question[\"stem\"]\r\n yield id_, {\r\n \"answerKey\": answerkey,\r\n \"question\": stem,\r\n \"choices\": {\"label\": labels, \"text\": texts},\r\n }\r\n ```\r\n- cos_e:\r\n ```python\r\n \"question\": cqa[\"question\"][\"stem\"],\r\n ```\r\n- qasc\r\n- quartz\r\n- wiqa\r\n\r\nExceptions:\r\n- exams\r\n\r\nI think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.",
"@albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq ",
"I'm opening a PR that adds the missing fields.\r\n\r\nLet's agree on the feature structure: @lhoestq @mariosasko @polinaeterna ",
"IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).",
"I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new. ",
"I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets.\r\n\r\nThere is always the tension between:\r\n- preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset),\r\n- and on the other hand performing some king of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain \"standard\" structure).\r\n - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model\r\n\r\nRecently, in the BigScience BioMedical hackathon, they adopted a different approach:\r\n- they implement a \"source\" config, respecting the original structure as much as possible\r\n- they implement additional config for each task, with a \"standard\" nested structure per task, which is most useful for users.",
"@albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best. "
] | 1,651,643,512,000 | 1,652,013,223,000 | null | CONTRIBUTOR | null | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanScore'],
- 'clarity': row['clarity'],
- 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2 | {
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"Thanks for reporting, @vblagoje.\r\n\r\nI'm opening a PR to address this. "
] | 1,651,642,739,000 | 1,651,664,478,000 | null | CONTRIBUTOR | null | ## Describe the bug
In short, CommonSenseQA implementation is inconsistent with the original dataset.
More precisely, we need to:
1. Add the dataset matching "id" field. The current dataset, instead, regenerates monotonically increasing id.
2. The [“question”][“stem”] field is flattened into "question". We should match the original dataset and unflatten it
3. Add the missing "question_concept" field in the question tree node
4. Anything else? Go over the data structure of the newly repaired CommonSenseQA and make sure it matches the original
## Expected results
Every data item of the CommonSenseQA should structurally and data-wise match the original CommonSenseQA dataset.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2 | {
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] | 1,651,617,857,000 | 1,651,681,772,000 | 1,651,681,324,000 | MEMBER | null | This PR adds API code examples for `IterableDataset` and `IterableDatasetDicts`. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> This PR fixes #4271.\r\n\r\nThings have not changed when searching \"tqdm\" in the Dataset document. The second result still performs as \"Enable\".",
"Hi @jiangwy99, the fix will appear on the `main` version of the docs:\r\n\r\n\r\n",
"Fixed now, thanks."
] | 1,651,610,877,000 | 1,651,678,947,000 | 1,651,647,516,000 | MEMBER | null | This PR fixes #4271. | {
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"Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)"
] | 1,651,599,896,000 | 1,651,647,515,000 | 1,651,647,515,000 | NONE | null | ## Describe the bug
in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable". | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,591,294,000 | 1,651,826,286,000 | 1,651,594,852,000 | MEMBER | null | CI in PR:
- #4259
was green, but after merging it to master, a code quality error appeared. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,569,847,000 | 1,651,826,289,000 | 1,651,576,579,000 | MEMBER | null | Update metadata of big_patent dataset with:
- license
- point of contact | {
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"It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for [\"word\"](https://en.wiktionary.org/wiki/word),\r\n\r\nPronunciation\r\n([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/\r\n([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/",
"Hi @i-am-neo, thanks for reporting.\r\n\r\nNormally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public.\r\n\r\nAlso note that last commit \"Add metadata\" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well.\r\n\r\n@i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.",
"Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!",
"All datasets are private now. \r\n\r\nRe:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)",
"Thanks a lot, @cakiki.\r\n\r\n@i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets. ",
"Thanks for letting me know, @albertvillanova @cakiki.\r\nAny chance of having a subset alpha version in the meantime? \r\nI only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes.\r\n\r\nWould like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. \r\n\r\n(Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!",
"Hey @i-am-neo,\r\n\r\nCool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)",
"@i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/)\r\nYou're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file",
"thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!",
"thanks @patrickvonplaten. will do - getting my observations together."
] | 1,651,523,665,000 | 1,651,852,410,000 | 1,651,577,028,000 | NONE | null | ## Describe the bug
Error generated when attempting to download dataset
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
ExpectedMoreDownloadedFiles Traceback (most recent call last)
[<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
3 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/4267 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4267/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4267/comments | https://api.github.com/repos/huggingface/datasets/issues/4267/events | https://github.com/huggingface/datasets/pull/4267 | 1,223,214,275 | PR_kwDODunzps43LzOR | 4,267 | Replace data URL in SAMSum dataset within the same repository | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,516,688,000 | 1,651,826,293,000 | 1,651,518,229,000 | MEMBER | null | Replace data URL with one in the same repository. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,510,771,000 | 1,651,740,440,000 | 1,651,740,009,000 | CONTRIBUTOR | null | Adds the HF Speech Bench to Librispeech Dataset Card in place of the Papers With Code Leaderboard. Should improve usage and visibility of this leaderboard! Wondering whether this can also be done for [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) and [8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) through someone with permissions?
cc @patrickvonplaten: more leaderboard promotion! | {
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https://api.github.com/repos/huggingface/datasets/issues/4263 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4263/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4263/comments | https://api.github.com/repos/huggingface/datasets/issues/4263/events | https://github.com/huggingface/datasets/pull/4263 | 1,222,723,083 | PR_kwDODunzps43KLnD | 4,263 | Rename imagenet2012 -> imagenet-1k | {
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"> Later we can add imagenet-21k as a new dataset if we want.\r\n\r\nisn't it what models refer to as `imagenet` already?",
"> isn't it what models refer to as imagenet already?\r\n\r\nI wasn't sure, but it looks like it indeed. Therefore having a dataset `imagenet` for ImageNet 21k makes sense actually.\r\n\r\nEDIT: actually not all `imagenet` tag refer to ImageNet 21k - we will need to correct some of them",
"_The documentation is not available anymore as the PR was closed or merged._",
"should we remove the repo mirror on the hub side or will you do it?"
] | 1,651,487,181,000 | 1,651,513,846,000 | 1,651,509,177,000 | MEMBER | null | On the Hugging Face Hub, users refer to imagenet2012 (from #4178 ) as imagenet-1k in their model tags.
To correctly link models to imagenet, we should rename this dataset `imagenet-1k`.
Later we can add `imagenet-21k` as a new dataset if we want.
Once this one is merged we can delete the `imagenet2012` dataset repository on the Hub.
EDIT: to complete the rationale on why we should name it `imagenet-1k`:
If users specifically added the tag `imagenet-1k` , then it could be for two reasons (not sure which one is predominant), either they
- wanted to make it explicit that it’s not 21k -> the distinction is important for the community
- or they have been following this convention from other models -> the convention implicitly exists already | {
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https://api.github.com/repos/huggingface/datasets/issues/4262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4262/comments | https://api.github.com/repos/huggingface/datasets/issues/4262/events | https://github.com/huggingface/datasets/pull/4262 | 1,222,130,749 | PR_kwDODunzps43IOye | 4,262 | Add YAML tags to Dataset Card rotten tomatoes | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,406,348,000 | 1,651,588,053,000 | 1,651,587,635,000 | CONTRIBUTOR | null | The dataset card for the rotten tomatoes / MR movie review dataset had some missing YAML tags. Hopefully, this also improves the visibility of this dataset now that paperswithcode and huggingface link to eachother. | {
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https://api.github.com/repos/huggingface/datasets/issues/4261 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4261/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4261/comments | https://api.github.com/repos/huggingface/datasets/issues/4261/events | https://github.com/huggingface/datasets/issues/4261 | 1,221,883,779 | I_kwDODunzps5I1HeD | 4,261 | data leakage in `webis/conclugen` dataset | {
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"Hi @xflashxx, thanks for reporting.\r\n\r\nPlease note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis\r\n\r\nWe are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.",
"i'd suggest just pinging the authors here in the issue if possible?",
"Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.",
"Hi @xflashxx and @albertvillanova,\r\n\r\nI have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to \"new\" items. The length of `ids_validation` and `ids_testing` is zero.\r\n\r\nRegarding impact on scores:\r\n1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. \r\n\r\n2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository.\r\n\r\nHope this helps!",
"Thanks @shahbazsyed for your fast fix.\r\n\r\nAs a side note:\r\n- Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de\r\n- Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings."
] | 1,651,340,617,000 | 1,651,557,866,000 | 1,651,557,866,000 | NONE | null | ## Describe the bug
Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results.
Furthermore, all splits contain duplicate samples.
## Steps to reproduce the bug
```python
from datasets import load_dataset
training = load_dataset("webis/conclugen", "base", split="train")
validation = load_dataset("webis/conclugen", "base", split="validation")
testing = load_dataset("webis/conclugen", "base", split="test")
# collect which sample id's are present in the training split
ids_validation = list()
ids_testing = list()
for train_sample in training:
train_argument = train_sample["argument"]
train_conclusion = train_sample["conclusion"]
train_id = train_sample["id"]
# test if current sample is in validation split
if train_argument in validation["argument"]:
for validation_sample in validation:
validation_argument = validation_sample["argument"]
validation_conclusion = validation_sample["conclusion"]
validation_id = validation_sample["id"]
if train_argument == validation_argument and train_conclusion == validation_conclusion:
ids_validation.append(validation_id)
# test if current sample is in test split
if train_argument in testing["argument"]:
for testing_sample in testing:
testing_argument = testing_sample["argument"]
testing_conclusion = testing_sample["conclusion"]
testing_id = testing_sample["id"]
if train_argument == testing_argument and train_conclusion == testing_conclusion:
ids_testing.append(testing_id)
```
## Expected results
Length of both lists `ids_validation` and `ids_testing` should be zero.
## Actual results
Length of `ids_validation` = `2556`
Length of `ids_testing` = `287`
Furthermore, there seems to be duplicate samples in (at least) the *training* split, since:
`print(len(set(ids_validation)))` = `950`
`print(len(set(ids_testing)))` = `101`
All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: macOS-12.3.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0 | {
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"whoops just found https://huggingface.co/datasets/rotten_tomatoes"
] | 1,651,324,773,000 | 1,651,328,185,000 | 1,651,328,185,000 | CONTRIBUTOR | null | Add the MR (Movie Review) dataset. The original dataset contains sentences from Rotten Tomatoes labeled as either "positive" or "negative".
Homepage: [https://www.cs.cornell.edu/people/pabo/movie-review-data/](https://www.cs.cornell.edu/people/pabo/movie-review-data/)
paperswithcode: [https://paperswithcode.com/dataset/mr](https://paperswithcode.com/dataset/mr)
- [ ] I was not able to generate dummy data, the original dataset files have ".pos" and ".neg" as file extensions so the auto-generator does not work. Is it fine like this or should dummy data be added?
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,304,499,000 | 1,651,645,891,000 | 1,651,590,861,000 | CONTRIBUTOR | null | This PR fixes the Bug in the openbookqa dataset as mentioned in this issue #3550.
Fix #3550.
cc. @lhoestq @mariosasko | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Good catch ! Thanks :)\r\n> \r\n> Next time can you describe your fix in the Pull Request description please ?\r\n\r\nThanks. Also whoops, sorry about not being very descriptive. I updated the pull request description, and will keep this in mind for future PRs."
] | 1,651,272,164,000 | 1,651,509,200,000 | 1,651,508,772,000 | MEMBER | null | This pr fixes a couple bugs:
1) the perplexity was calculated with a 0 in the attention mask for the start token, which was causing high perplexity scores that were not correct
2) the documentation was not updated | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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"_The documentation is not available anymore as the PR was closed or merged._",
"CI is failing because some sections are missing in the dataset card, but this is unrelated to this PR - Merging !"
] | 1,651,247,746,000 | 1,651,249,495,000 | 1,651,249,136,000 | MEMBER | null | I hosted the data files in https://huggingface.co/datasets/pubmed_qa. This is allowed because the data is under the MIT license.
cc @stas00 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,220,503,000 | 1,651,826,296,000 | 1,651,249,569,000 | MEMBER | null | This PR replaces data URL in SAMSum dataset:
- original host (arxiv.org) does not allow HTTP Range requests
- we have hosted the data on the Hub (license: CC BY-NC-ND 4.0)
Moreover, it implements support for streaming.
Fix #4146.
Related to: #4236.
CC: @severo | {
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] | 1,651,179,507,000 | 1,651,254,287,000 | 1,651,253,886,000 | CONTRIBUTOR | null | Proposing a metric card for mIoU :rocket:
sorry for spamming you with review requests, @albertvillanova ! :hugs: | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,170,739,000 | 1,651,250,771,000 | 1,651,250,326,000 | CONTRIBUTOR | null | Proposing a metric card for the XTREME-S dataset :hugs: | {
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https://api.github.com/repos/huggingface/datasets/issues/4250 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4250/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4250/comments | https://api.github.com/repos/huggingface/datasets/issues/4250/events | https://github.com/huggingface/datasets/pull/4250 | 1,219,093,830 | PR_kwDODunzps429yjN | 4,250 | Bump PyArrow Version to 6 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Updated meta.yaml as well. Thanks.",
"I'm OK with bumping PyArrow to version 6 to match the version in Colab, but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues.",
"> but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues.\r\n\r\nI agree, not much attention has been payed to extension arrays in the latest developments of Arrow anyway.\r\n\r\nLet's not use them more that what we do right now, and try to remove them at one point"
] | 1,651,169,450,000 | 1,651,657,012,000 | 1,651,656,586,000 | CONTRIBUTOR | null | Fixes #4152
This PR updates the PyArrow version to 6 in setup.py, CI job files .circleci/config.yaml and .github/workflows/benchmarks.yaml files.
This will fix ArrayND error which exists in pyarrow 5. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,141,643,000 | 1,651,826,301,000 | 1,651,162,083,000 | MEMBER | null | Support streaming XGLUE dataset.
Fix #4247.
CC: @severo | {
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"Thanks for reporting @sue99.\r\n\r\nUnfortunately. I'm not able to reproduce your problem:\r\n```python\r\nIn [1]: import datasets\r\n ...: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"conll2003\")\r\n\r\nIn [2]: dataset\r\nOut[2]: \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\r\nIn [3]: dataset[\"train\"][0]\r\nOut[3]: \r\n{'id': '0',\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 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\r\n 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\r\n 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}\r\n```\r\n\r\nJust guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub. "
] | 1,651,138,411,000 | 1,651,163,473,000 | null | NONE | null | ## Describe the bug
I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text.
Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ?
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
dataset = load_dataset("conll2003")
```
## Expected results
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
## Actual results
```python
print(dataset)
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 219554
})
test: Dataset({
features: ['text'],
num_rows: 50350
})
validation: Dataset({
features: ['text'],
num_rows: 55044
})
})
```
```python
for i in range(20):
print(dataset['train'][i])
{'text': '-DOCSTART- -X- -X- O'}
{'text': ''}
{'text': 'EU NNP B-NP B-ORG'}
{'text': 'rejects VBZ B-VP O'}
{'text': 'German JJ B-NP B-MISC'}
{'text': 'call NN I-NP O'}
{'text': 'to TO B-VP O'}
{'text': 'boycott VB I-VP O'}
{'text': 'British JJ B-NP B-MISC'}
{'text': 'lamb NN I-NP O'}
{'text': '. . O O'}
{'text': ''}
{'text': 'Peter NNP B-NP B-PER'}
{'text': 'Blackburn NNP I-NP I-PER'}
{'text': ''}
{'text': 'BRUSSELS NNP B-NP B-LOC'}
{'text': '1996-08-22 CD I-NP O'}
{'text': ''}
{'text': 'The DT B-NP O'}
{'text': 'European NNP I-NP B-ORG'}
```
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"\r\n",
"Thanks for reporting @czq1999.\r\n\r\nNote that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet.\r\n\r\nThat is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming. ",
"Fixed, thanks @albertvillanova !\r\n\r\nhttps://huggingface.co/datasets/xglue\r\n\r\n<img width=\"824\" alt=\"Capture d’écran 2022-04-29 à 10 23 14\" src=\"https://user-images.githubusercontent.com/1676121/165909391-9f98d98a-665a-4e57-822d-8baa2dc9b7c9.png\">\r\n"
] | 1,651,131,050,000 | 1,651,220,608,000 | 1,651,162,083,000 | NONE | null | It seems that something wrong with the data previvew of XGLUE | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,131,015,000 | 1,651,826,308,000 | 1,651,824,847,000 | MEMBER | null | This PR implements support to load a dataset (w/o script) containing TSV files by passing only the dataset name (no need to pass `sep='\t'`):
```python
ds = load_dataset("dataset/name")
```
The refactoring allows for future builder kwargs customizations based on file extension.
Related to #4238. | {
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] | 1,651,099,942,000 | 1,651,256,374,000 | 1,651,255,983,000 | MEMBER | null | This PR adds code examples for `DatasetDict` in the API reference :) | {
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https://api.github.com/repos/huggingface/datasets/issues/4244 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4244/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4244/comments | https://api.github.com/repos/huggingface/datasets/issues/4244/events | https://github.com/huggingface/datasets/pull/4244 | 1,217,732,221 | PR_kwDODunzps425Po6 | 4,244 | task id update | {
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"Reverted the multi-input-text-classification tag from task_categories and added it as task_ids @lhoestq ",
"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,084,094,000 | 1,651,661,033,000 | 1,651,660,597,000 | CONTRIBUTOR | null | changed multi input text classification as task id instead of category | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,080,151,000 | 1,651,081,024,000 | 1,651,080,642,000 | MEMBER | null | We don't need to use extraHeaders anymore for rate limits anymore. Anyway extraHeaders was not working with git LFS because it was passing the wrong auth to S3. | {
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"Hi :)\r\n\r\nI think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with:\r\n\r\n```py\r\npip install -U datasets\r\n```\r\n\r\nThen you can download:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"glue\", \"rte\")\r\n```",
"This appears to work. Thank you!\n\nOn Wed, Apr 27, 2022, 1:18 PM Steven Liu ***@***.***> wrote:\n\n> Hi :)\n>\n> I think your issue may be related to the older nlp library. I was able to\n> download glue with the latest version of datasets. Can you try updating\n> with:\n>\n> pip install -U datasets\n>\n> Then you can download:\n>\n> from datasets import load_datasetds = load_dataset(\"glue\", \"rte\")\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n"
] | 1,651,068,861,000 | 1,651,131,927,000 | 1,651,131,927,000 | NONE | null | ## Describe the bug
I am trying to download the GLUE dataset from the NLP module but get an error (see below).
## Steps to reproduce the bug
```python
import nlp
nlp.__version__ # '0.2.0'
nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
```
## Expected results
I expect the dataset to download without an error.
## Actual results
```
INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports.
INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue
INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py
INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json
INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0)
INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source
INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb
INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805
Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0...
Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s]
INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-7-669a8343dcc1> in <module>
----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
418 verify_infos = not save_infos and not ignore_verifications
419 self._download_and_prepare(
--> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
422 # Sync info
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
458 # Checksums verification
459 if verify_infos:
--> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
461 for split_generator in split_generators:
462 if str(split_generator.split_info.name).lower() == "all":
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)
34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
35 if len(bad_urls) > 0:
---> 36 raise NonMatchingChecksumError(str(bad_urls))
37 logger.info("All the checksums matched successfully.")
38
NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa
- Python version: 3.6.13
- PyArrow version: 6.0.1
- Pandas version: 1.1.5
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"I don't think you need to generate new dummy data, since they're in the same format as the original data.\r\n\r\nThe CI is failing because of this error:\r\n```python\r\n> turn[\"names\"] = turn[\"NAMES\"]\r\nE TypeError: tuple indices must be integers or slices, not str\r\n```\r\n\r\nDo you know what could cause this ? If I understand correctly, `turn` is supposed to be a list of dictionaries right ?",
"> ``` \r\n> \r\n> Do you know what could cause this ? If I understand correctly, turn is supposed to be a list of dictionaries right ?\r\n> ```\r\n\r\nThis is strange. Let me look into this. As per https://github.com/RevanthRameshkumar/CRD3/blob/master/data/aligned%20data/c%3D2/C1E001_2_0.json TURNS is a list of dictionaries. So when we iterate over `row[\"TURNS]\"` each `turn` is essentially a dictionary. Not sure why it's being considered a tuple here."
] | 1,651,062,696,000 | 1,651,236,101,000 | 1,651,236,101,000 | CONTRIBUTOR | null | Modified the `_generate_examples` function to consider all the turns for a chunk id as a single example
Modified the features accordingly
```
"turns": [
{
"names": datasets.features.Sequence(datasets.Value("string")),
"utterances": datasets.features.Sequence(datasets.Value("string")),
"number": datasets.Value("int32"),
}
],
}
```
I wasn't able to run `datasets-cli dummy_data datasets` command. Is there a workaround for this? | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,651,061,750,000 | 1,651,498,137,000 | 1,651,497,723,000 | CONTRIBUTOR | null | The list of use cases did not render on GitHub with the prepended spacing.
Additionally, some typo's we're fixed. | {
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"Hi @loretoparisi, thanks for reporting.\r\n\r\nThere is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode=\"force_redownload\")`.\r\n\r\nPlease, let me know if this fixes your problem.\r\n\r\nI can confirm you that your dataset loads without any problem for me:\r\n```python\r\nIn [2]: ds = load_dataset(\"loretoparisi/tatoeba-sentences\", data_files={\"train\": \"train.csv\", \"test\": \"test.csv\"}, delimiter=\"\\t\", column_names=['label', 'text'])\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['label', 'text'],\r\n num_rows: 8256449\r\n })\r\n test: Dataset({\r\n features: ['label', 'text'],\r\n num_rows: 2061204\r\n })\r\n})\r\n``` ",
"@albertvillanova thank you, it seems it still does not work using:\r\n\r\n```python\r\nsentences = load_dataset(\r\n \"loretoparisi/tatoeba-sentences\",\r\n data_files=data_files,\r\n delimiter='\\t', \r\n column_names=['label', 'text'],\r\n download_mode=\"force_redownload\"\r\n)\r\n```\r\n[This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook!\r\n\r\nThe problem is that the download file's revision for `test.csv` is not correctly parsed\r\n\r\n\r\n\r\nIf you download that file `test.csv` from the repo, the line `\\\\N` is not there anymore (it was there at the first file upload).\r\n\r\nMy impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\\\N`) since are binary files:\r\n\r\n```\r\n!ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519\r\n!ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow\r\n!head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json\r\n```\r\n",
"SOLVED! The problem was the with the file itself, using caching parameter helped indeed.\r\nThanks for helping!"
] | 1,651,056,131,000 | 1,651,076,965,000 | 1,651,076,930,000 | NONE | null | ## Describe the bug
I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error
```
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally:
```python
from datasets import load_dataset_builder
dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences")
print(dataset_builder.cache_dir)
print(dataset_builder.info.features)
print(dataset_builder.info.splits)
```
```
Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd
/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519
None
None
```
and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`.
Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it?
Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last.
Thank you.
## Steps to reproduce the bug
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset(
"loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
)
# You can make this part faster with num_proc=<some int>
sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
sentences = sentences.shuffle()
```
## Expected results
Properly tokenize dataset file `test.csv` without issues.
## Actual results
Specify the actual results or traceback.
```
Downloading data files: 100%
2/2 [00:16<00:00, 7.34s/it]
Downloading data: 100%
391M/391M [00:12<00:00, 36.6MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 40.0MB/s]
Extracting data files: 100%
2/2 [00:00<00:00, 47.66it/s]
Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.
100%
2/2 [00:00<00:00, 25.94it/s]
11%
942339/8256449 [01:55<13:11, 9245.85ex/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>()
12 )
13 # You can make this part faster with num_proc=<some int>
---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
15 sentences = sentences.shuffle()
10 frames
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
- ```
```
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.11.0+cu113 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
- ```
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"Thanks for reporting. I understand it's an error in the dataset script. To reproduce:\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> split_names = ds.get_dataset_split_names(\"mozilla-foundation/common_voice_8_0\", use_auth_token=\"**********\")\r\nDownloading builder script: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10.9k/10.9k [00:00<00:00, 10.9MB/s]\r\nDownloading extra modules: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.98k/2.98k [00:00<00:00, 3.36MB/s]\r\nDownloading extra modules: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 53.1k/53.1k [00:00<00:00, 650kB/s]\r\nNo config specified, defaulting to: common_voice/en\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 280, 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/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py\", line 153, in _split_generators\r\n self._log_download(self.config.name, bundle_version, hf_auth_token)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py\", line 139, in _log_download\r\n email = HfApi().whoami(auth_token)[\"email\"]\r\nKeyError: 'email'\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-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 323, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 285, 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```",
"Thanks for reporting @patrickvonplaten and thanks for the investigation @severo.\r\n\r\nUnfortunately I'm not able to reproduce the error.\r\n\r\nI think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139\r\n```python\r\nfrom huggingface_hub import HfApi, HfFolder\r\n\r\nif isinstance(auth_token, bool):\r\n email = HfApi().whoami(auth_token)\r\nemail = HfApi().whoami(auth_token)[\"email\"]\r\n```\r\n\r\nCould you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?",
"OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email!\r\n\r\n```python\r\n>>> from huggingface_hub import HfApi, HfFolder\r\n>>> auth_token = \"hf_app_******\"\r\n>>> t = HfApi().whoami(auth_token)\r\n>>> t\r\n{'type': 'app', 'name': 'dataset-preview-backend'}\r\n>>> t[\"email\"]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nKeyError: 'email'\r\n```\r\n\r\nNote also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key.\r\n\r\n@SBrandeis @julien-c: do you think the app token should have an email associated, like the users?",
"We can workaround this with\r\n```python\r\nemail = HfApi().whoami(auth_token).get(\"email\", \"system@huggingface.co\")\r\n```\r\nin the common voice scripts",
"Hmmm, does this mean that any person who downloads the common voice dataset will be logged as \"system@huggingface.co\"? If so, it would defeat the purpose of sending the user's email to the commonvoice API, right?",
"I agree with @severo: we cannot set our system email as default, allowing anybody not authenticated to by-pass the Common Voice usage policy.\r\n\r\nAdditionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead.\r\n\r\nCC: @patrickvonplaten @lhoestq @SBrandeis @julien-c ",
"Hmm I don't agree here. \r\n\r\nAnybody can always just bypass the system by setting whatever email. As soon as someone has access to the downloading script it's trivial to tweak the code to not send the \"correct\" email but to just whatever and it would work.\r\n\r\nNote that someone only has visibility on the code after having \"signed\" the access-mechanism so I think we can expect the users to have agreed to not do anything malicious. \r\n\r\nI'm fine with both @lhoestq's solution or we find a way that forces the user to be logged in + being able to load the data for the datasets viewer. Wdyt @lhoestq @severo @albertvillanova ?",
"> Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead.\r\n\r\nYes, I agree we can forget about this @patrickvonplaten. After having had a look at Common Voice website, I've seen they only require sending an email (no auth is inplace on their side, contrary to what I had previously thought). Therefore, currently we impose stronger requirements than them: we require the user having logged in and accepted the access mechanism.\r\n\r\nCurrently the script as it is already requires the user being logged in:\r\n```python\r\nHfApi().whoami(auth_token)\r\n```\r\nthrows an exception if None/invalid auth_token is passed.\r\n\r\nOn the other hand, we should agree on the way to allow the viewer to stream the data.",
"The preview is back now, thanks !"
] | 1,651,053,920,000 | 1,652,185,025,000 | 1,652,185,024,000 | MEMBER | null | https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I first uploaded the data files to the Hub: I think it is a good option because we have git lfs to track versions and changes. Moreover people will be able to make PRs to propose updates on the data files.\r\n- I would have preferred to upload it it to the \"data\" org namespace, but it is already taken (although not used): might be possible to take it?\r\n\r\nAs an alternative (and to be consistent with previous datasets), I also uploaded the data files to our AWS bucket.\r\n\r\nWe should decide which to use (now and for future datasets) and set it here before merging. We should remove the data files for the non-chosen option.\r\n\r\nCC: @lhoestq @mariosasko @polinaeterna ",
"Would it make sense to make the dataset a community one (so, create an organization for it) and store the script and the data in a single repository? Just as it is for most of the datasets. That way we can also access the data using a relative path inside the repo (that's not the point though). The point is that to me it seems a bit more straightforward to store everything in one place. \r\n\r\nI guess the strong argument against this logic is that in this case the canonical version won't work... But maybe there is some redirecting mechanism I don't know about? :)\r\n\r\nAnyway, I'm in favor of hosting data on the Hub instead of AWS :) ",
"I also think storing everything in one place/single repository is the best option.\r\n\r\n@polinaeterna Canonical datasets also support data files (see the [`red_caps` repo](https://huggingface.co/datasets/red_caps/tree/main) for instance) ",
"Thanks @polinaeterna and @mariosasko for your comments.\r\n\r\nYes, definitely it is much better to have everything in the same repo. \r\n\r\nI'm transferring their data files to the Hub under \"big_patent\" and deleting them from the other repo and AWS."
] | 1,651,053,673,000 | 1,654,848,655,000 | 1,651,515,675,000 | MEMBER | null | This PR replaces the Google Drive URL with our Hub one, once the data owners have approved to host their data on the Hub.
Moreover, this PR makes the dataset streamable.
Fix #4217. | {
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"The `Trainer` support `IterableDataset`, not just datasets."
] | 1,651,045,813,000 | 1,651,059,857,000 | null | NONE | null | ### System Info
```shell
I am using transformer trainer while meeting the issue.
The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency.
I wonder if there are any tricks like Sharding in huggingface trainer.
Looking forward to your reply.
```
### Who can help?
Trainer: @sgugger
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
None
### Expected behavior
```shell
I wonder if there are any tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html.
Thanks a lot!
```
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"_The documentation is not available anymore as the PR was closed or merged._",
"Related to: https://github.com/huggingface/autonlp-backend/issues/414 and https://github.com/huggingface/autonlp-backend/issues/424",
"The tests are failing due to the changed metadata:\r\n\r\n```\r\ngot an unexpected keyword argument 'train-eval-index'\r\n```\r\n\r\nI think you can fix this by updating the `DatasetMetadata` class and implementing an appropriate `validate_train_eval_index()` function\r\n\r\n@lhoestq we are working with an arbitrary set of tags for `autoeval config`. See https://github.com/huggingface/autonlp-backend/issues/414\r\nI need to add a validator function though for the tests to pass. Our set is not well-defined as in the rest https://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources. What's a workaround for this?",
"On the question of validating the `train-eval-index` metadata, I think the simplest approach would be to validate that the required fields exist and not worry about their values (which are open-ended).\r\n\r\nFor me, the required fields include:\r\n\r\n* `config`\r\n* `task`\r\n* `task_id`\r\n* `splits` (train / validation / eval)\r\n* `col_mapping`\r\n* `metrics` (checking that each one has `type`, `name`) \r\n\r\nHere I'm using the spec defined in https://github.com/huggingface/autonlp-backend/issues/414 as a guide.\r\n\r\nWDYT @lhoestq ?",
"Makes sense ! Currently the metadata type validator doesn't support subfields - let me open a PR to add it",
"I ended up improving the metadata validation in this PR x)\r\n\r\nIn particular:\r\n- I added support YAML keys with dashes instead of underscores for `train-eval-index`\r\n- I added `train-eval-index` validation with `validate_train_eval_index`. It does nothing fancy, it just checks that it is a list if it exists in the YAML, but feel free to improve it if you want\r\n\r\nLet me know if it sounds good to you ! I think we can improve `validate_train_eval_index` in another PR",
"Come on windows... I didn't do anything advanced...\r\n\r\nAnyway, will try to fix this when I get back home x)",
"> Come on windows... I didn't do anything advanced...\r\n> \r\n> Anyway, will try to fix this when I get back home x)\r\n\r\nHehe, thanks!",
"Thanks, @lhoestq this is great! ",
"Did I just fix it for windows and now it fails on linux ? xD",
"> Did I just fix it for windows and now it fails on linux ? xD\r\n\r\nLooks like the Heisenberg uncertainty principle is at play here - you cannot simultaneously have unit tests passing in both Linux and Windows 😅 ",
"The worst is that the tests pass locally both on my windows and my linux x)",
"Ok fixed it, the issue came from python 3.6 that doesn't return the right `__origin__` for Dict and List types",
"> Alright thanks for adding the first Autoeval config ! :D\r\n\r\nWoohoo! Thank you so much 🤗 ",
"This is cool!"
] | 1,651,037,530,000 | 1,651,843,231,000 | 1,651,774,858,000 | CONTRIBUTOR | null | Added autoeval config to imdb as pilot | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4233). All of your documentation changes will be reflected on that endpoint."
] | 1,651,023,129,000 | 1,651,037,370,000 | 1,651,023,143,000 | CONTRIBUTOR | null | null | {
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"closing in favor of https://github.com/huggingface/datasets/pull/4244"
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https://api.github.com/repos/huggingface/datasets/issues/4231 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4231/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4231/comments | https://api.github.com/repos/huggingface/datasets/issues/4231/events | https://github.com/huggingface/datasets/pull/4231 | 1,216,651,960 | PR_kwDODunzps421pUX | 4,231 | Fix invalid url to CC-Aligned dataset | {
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] | 1,651,021,621,000 | 1,652,720,473,000 | 1,652,719,992,000 | CONTRIBUTOR | null | The CC-Aligned dataset url has changed to https://data.statmt.org/cc-aligned/, the old address http://www.statmt.org/cc-aligned/ is no longer valid | {
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https://api.github.com/repos/huggingface/datasets/issues/4230 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4230/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4230/comments | https://api.github.com/repos/huggingface/datasets/issues/4230/events | https://github.com/huggingface/datasets/issues/4230 | 1,216,643,661 | I_kwDODunzps5IhIJN | 4,230 | Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data? | {
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"Thanks for reporting @beyondguo.\r\n\r\nIndeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip\r\nAnd that URL only contains the English version.",
"The German data requires payment\r\n\r\nThe [original task page](https://www.clips.uantwerpen.be/conll2003/ner/) states \"The German data is a collection of articles from the Frankfurter Rundschau. The named entities have been annotated by people of the University of Antwerp. Only the annotations are available here. In order to build these data sets you need access to the ECI Multilingual Text Corpus. It can be ordered from the Linguistic Data Consortium (2003 non-member price: US$ 35.00).\"\r\n\r\nInflation since 2003 has also affected LDC's prices, and today the dataset [LDC94T5](https://catalog.ldc.upenn.edu/LDC94T5) is available under license for $75 a copy. The [license](https://catalog.ldc.upenn.edu/license/eci-slash-mci-user-agreement.pdf) includes a non-distribution condition, which is probably why the data has not turned up openly.\r\n\r\nThe ACL hold copyright of this data; I'll mail them and anyone I can find at ECI to see if they'll open this up now. After all, it worked with Microsoft 3DMM, why not here too, after 28 years? :)\r\n"
] | 1,651,020,832,000 | 1,652,358,222,000 | null | NONE | null | 
But on huggingface datasets:
Where is the German data? | {
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"thank you @lhoestq!! :hugs: "
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- adds ability to return p-value
- p-value was mentioned in the original docs as a return value, but there was no option to return it. I updated the _compute function slightly to have an option to return the p-value. | {
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https://api.github.com/repos/huggingface/datasets/issues/4223 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4223/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4223/comments | https://api.github.com/repos/huggingface/datasets/issues/4223/events | https://github.com/huggingface/datasets/pull/4223 | 1,216,107,082 | PR_kwDODunzps42z0YV | 4,223 | Add Accuracy Metric Card | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,650,985,846,000 | 1,651,588,065,000 | 1,651,587,647,000 | CONTRIBUTOR | null | - adds accuracy metric card
- updates docstring in accuracy.py
- adds .json file with metric card and docstring information | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Non passing tests are due to other pre-existing errors in dataset cards: not related to this PR."
] | 1,650,983,785,000 | 1,651,826,318,000 | 1,650,991,949,000 | MEMBER | null | I noticed many links were not properly displayed (only text, no link) on the Hub because of wrong syntax, e.g.: https://huggingface.co/datasets/big_patent
This PR fixes all description links in dataset cards. | {
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https://api.github.com/repos/huggingface/datasets/issues/4221 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4221/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4221/comments | https://api.github.com/repos/huggingface/datasets/issues/4221/events | https://github.com/huggingface/datasets/issues/4221 | 1,215,911,182 | I_kwDODunzps5IeVUO | 4,221 | Dictionary Feature | {
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"Hi @jordiae,\r\n\r\nInstead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:\r\n```python\r\n\"list_of_dict_feature\": [\r\n {\r\n \"key1_in_dict\": datasets.Value(\"string\"),\r\n \"key2_in_dict\": datasets.Value(\"int32\"),\r\n ...\r\n }\r\n],\r\n```\r\n\r\nFeel free to re-open this issue if that does not work for your use case.",
"> Hi @jordiae,\r\n> \r\n> Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:\r\n> \r\n> ```python\r\n> \"list_of_dict_feature\": [\r\n> {\r\n> \"key1_in_dict\": datasets.Value(\"string\"),\r\n> \"key2_in_dict\": datasets.Value(\"int32\"),\r\n> ...\r\n> }\r\n> ],\r\n> ```\r\n> \r\n> Feel free to re-open this issue if that does not work for your use case.\r\n\r\nThank you"
] | 1,650,977,418,000 | 1,651,243,939,000 | 1,651,165,498,000 | NONE | null | Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something?
Thank you in advance. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi ! Can you explain why this change is needed ?",
"Facebook recommends installing FAISS using conda (https://github.com/facebookresearch/faiss/blob/main/INSTALL.md). pip does not seem to have the latest version of FAISS. The latest version of faiss is 1.7.2 (https://anaconda.org/conda-forge/faiss), but the latest one available through pip is 1.5.3 (https://pypi.org/project/faiss/). "
] | 1,650,936,043,000 | 1,652,117,374,000 | 1,652,116,929,000 | CONTRIBUTOR | null | null | {
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"Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive.\r\n\r\nSee related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com\r\n\r\nTo quote [@lhoestq](https://github.com/huggingface/datasets/issues/4075#issuecomment-1087362551):\r\n\r\n> PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.\r\n\r\n",
"We should find out if the dataset license allows redistribution and contact the data owners to propose them to host their data on our Hub.",
"The data owners have agreed on hosting their data on the Hub."
] | 1,650,900,705,000 | 1,653,546,583,000 | 1,651,515,675,000 | NONE | null | ## Dataset viewer issue for '*big_patent*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)*
*Unable to view because it says FileNotFound, also cannot download it through the python API*
Am I the one who added this dataset ? No
| {
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https://api.github.com/repos/huggingface/datasets/issues/4216 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4216/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4216/comments | https://api.github.com/repos/huggingface/datasets/issues/4216/events | https://github.com/huggingface/datasets/pull/4216 | 1,214,614,029 | PR_kwDODunzps42u1_w | 4,216 | Avoid recursion error in map if example is returned as dict value | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,650,897,632,000 | 1,651,684,806,000 | 1,651,684,372,000 | CONTRIBUTOR | null | I noticed this bug while answering [this question](https://discuss.huggingface.co/t/correct-way-to-create-a-dataset-from-a-csv-file/15686/11?u=mariosasko).
This code replicates the bug:
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": ex})
```
and this is the fix for it (before this PR):
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": dict(ex)})
```
Internally, this can be fixed by merging two dicts via dict unpacking (instead of `dict.update) `in `Dataset.map`, which avoids creating recursive dictionaries.
P.S. `{**a, **b}` is slightly more performant than `a.update(b)` in my bencmarks. | {
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https://api.github.com/repos/huggingface/datasets/issues/4215 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4215/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4215/comments | https://api.github.com/repos/huggingface/datasets/issues/4215/events | https://github.com/huggingface/datasets/pull/4215 | 1,214,579,162 | PR_kwDODunzps42uuhY | 4,215 | Add `drop_last_batch` to `IterableDataset.map` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,650,896,119,000 | 1,651,593,367,000 | 1,651,592,934,000 | CONTRIBUTOR | null | Addresses this comment: https://github.com/huggingface/datasets/pull/3801#pullrequestreview-901736921 | {
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https://api.github.com/repos/huggingface/datasets/issues/4214 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4214/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4214/comments | https://api.github.com/repos/huggingface/datasets/issues/4214/events | https://github.com/huggingface/datasets/pull/4214 | 1,214,572,430 | PR_kwDODunzps42utC5 | 4,214 | Skip checksum computation in Imagefolder by default | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,650,895,841,000 | 1,651,591,712,000 | 1,651,591,289,000 | CONTRIBUTOR | null | Avoids having to set `ignore_verifications=True` in `load_dataset("imagefolder", ...)` to skip checksum verification and speed up loading.
The user can still pass `DownloadConfig(record_checksums=True)` to not skip this part. | {
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https://api.github.com/repos/huggingface/datasets/issues/4213 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4213/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4213/comments | https://api.github.com/repos/huggingface/datasets/issues/4213/events | https://github.com/huggingface/datasets/pull/4213 | 1,214,510,010 | PR_kwDODunzps42uft_ | 4,213 | ETT time series dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"thank you!\r\n"
] | 1,650,893,178,000 | 1,651,753,161,000 | 1,651,752,635,000 | CONTRIBUTOR | null | Ready for review. | {
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https://api.github.com/repos/huggingface/datasets/issues/4212 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4212/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4212/comments | https://api.github.com/repos/huggingface/datasets/issues/4212/events | https://github.com/huggingface/datasets/pull/4212 | 1,214,498,582 | PR_kwDODunzps42udRf | 4,212 | [Common Voice] Make sure bytes are correctly deleted if `path` exists | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"cool that you noticed that we store unnecessary bytes again :D "
] | 1,650,892,706,000 | 1,651,013,668,000 | 1,651,013,307,000 | MEMBER | null | `path` should be set to local path inside audio feature if exist so that bytes can correctly be deleted. | {
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https://api.github.com/repos/huggingface/datasets/issues/4211 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4211/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4211/comments | https://api.github.com/repos/huggingface/datasets/issues/4211/events | https://github.com/huggingface/datasets/issues/4211 | 1,214,361,837 | I_kwDODunzps5IYbDt | 4,211 | DatasetDict containing Datasets with different features when pushed to hub gets remapped features | {
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"Hi @pietrolesci, thanks for reporting.\r\n\r\nPlease note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different **split**.\r\n\r\nTo handle sub-datasets with different features, we use another approach: use different **configurations** instead of **splits**.\r\n\r\nHowever, for the moment `push_to_hub` does not support specifying different configurations. IMHO, we should implement this.",
"Hi @albertvillanova,\r\n\r\nThanks a lot for your reply! I got it now. The strange thing for me was to have it correctly working (i.e., DatasetDict with different features in some datasets) locally and not on the Hub. It would be great to have configuration supported by `push_to_hub`. Personally, this latter functionality allowed me to iterate rather quickly on dataset curation.\r\n\r\nAgain, thanks for your time @albertvillanova!\r\n\r\nBest,\r\nPietro",
"Hi! Yes, we should override `DatasetDict.__setitem__` and throw an error if features dictionaries are different. `DatasetDict` is a subclass of `dict`, so `DatasetDict.{update/setdefault}` need to be overridden as well. We could avoid this by subclassing `UserDict`, but then we would get the name collision - `DatasetDict.data` vs. `UserDict.data`. This makes me think we should rename the `data` attribute of `DatasetDict`/`Dataset` for easier dict subclassing (would also simplify https://github.com/huggingface/datasets/pull/3997) and to follow good Python practices. Another option is to have a custom `UserDict` class in `py_utils`, but it can be hard to keep this class consistent with the built-in `UserDict`. \r\n\r\n@albertvillanova @lhoestq wdyt?",
"I would keep things simple and keep subclassing dict. Regarding the features check, I guess this can be done only for `push_to_hub` right ? It is the only function right now that requires the underlying datasets to be splits (e.g. train/test) and have the same features.\r\n\r\nNote that later you will be able to push datasets with different features as different dataset **configurations** (similarly to the [GLUE subsets](https://huggingface.co/datasets/glue) for example). We will work on this soon"
] | 1,650,885,774,000 | 1,653,059,730,000 | 1,653,059,730,000 | NONE | null | Hi there,
I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same.
Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli).
In short:
I have 3 feature mapping
```python
Tri_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
}
)
Ent_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]),
}
)
Con_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]),
}
)
```
Then I create different datasets
```python
dataset_splits = {}
for split in df["split"].unique():
print(split)
df_split = df.loc[df["split"] == split].copy()
if split in Tri_dataset:
df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
ds = Dataset.from_pandas(df_split, features=Tri_features)
elif split in Ent_bin_dataset:
df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1})
ds = Dataset.from_pandas(df_split, features=Ent_features)
elif split in Con_bin_dataset:
df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1})
ds = Dataset.from_pandas(df_split, features=Con_features)
else:
print("ERROR:", split)
dataset_splits[split] = ds
datasets = DatasetDict(dataset_splits)
```
I then push to hub
```python
datasets.push_to_hub("pietrolesci/robust_nli", token="<token>")
```
Finally, I load it from the hub
```python
datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli")
```
And I get that
```python
datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features
```
since
```python
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"])
```
gets remapped to
```python
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"])
``` | {
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"Hi! Casting class labels from strings is currently not supported in the CSV loader, but you can get the same result with an additional map as follows:\r\n```python\r\nfrom datasets import load_dataset,Features,Value,ClassLabel\r\nclass_names = [\"cmn\",\"deu\",\"rus\",\"fra\",\"eng\",\"jpn\",\"spa\",\"ita\",\"kor\",\"vie\",\"nld\",\"epo\",\"por\",\"tur\",\"heb\",\"hun\",\"ell\",\"ind\",\"ara\",\"arz\",\"fin\",\"bul\",\"yue\",\"swe\",\"ukr\",\"bel\",\"que\",\"ces\",\"swh\",\"nno\",\"wuu\",\"nob\",\"zsm\",\"est\",\"kat\",\"pol\",\"lat\",\"urd\",\"sqi\",\"isl\",\"fry\",\"afr\",\"ron\",\"fao\",\"san\",\"bre\",\"tat\",\"yid\",\"uig\",\"uzb\",\"srp\",\"qya\",\"dan\",\"pes\",\"slk\",\"eus\",\"cycl\",\"acm\",\"tgl\",\"lvs\",\"kaz\",\"hye\",\"hin\",\"lit\",\"ben\",\"cat\",\"bos\",\"hrv\",\"tha\",\"orv\",\"cha\",\"mon\",\"lzh\",\"scn\",\"gle\",\"mkd\",\"slv\",\"frm\",\"glg\",\"vol\",\"ain\",\"jbo\",\"tok\",\"ina\",\"nds\",\"mal\",\"tlh\",\"roh\",\"ltz\",\"oss\",\"ido\",\"gla\",\"mlt\",\"sco\",\"ast\",\"jav\",\"oci\",\"ile\",\"ota\",\"xal\",\"tel\",\"sjn\",\"nov\",\"khm\",\"tpi\",\"ang\",\"aze\",\"tgk\",\"tuk\",\"chv\",\"hsb\",\"dsb\",\"bod\",\"sme\",\"cym\",\"mri\",\"ksh\",\"kmr\",\"ewe\",\"kab\",\"ber\",\"tpw\",\"udm\",\"lld\",\"pms\",\"lad\",\"grn\",\"mlg\",\"xho\",\"pnb\",\"grc\",\"hat\",\"lao\",\"npi\",\"cor\",\"nah\",\"avk\",\"mar\",\"guj\",\"pan\",\"kir\",\"myv\",\"prg\",\"sux\",\"crs\",\"ckt\",\"bak\",\"zlm\",\"hil\",\"cbk\",\"chr\",\"nav\",\"lkt\",\"enm\",\"arq\",\"lin\",\"abk\",\"pcd\",\"rom\",\"gsw\",\"tam\",\"zul\",\"awa\",\"wln\",\"amh\",\"bar\",\"hbo\",\"mhr\",\"bho\",\"mrj\",\"ckb\",\"osx\",\"pfl\",\"mgm\",\"sna\",\"mah\",\"hau\",\"kan\",\"nog\",\"sin\",\"glv\",\"dng\",\"kal\",\"liv\",\"vro\",\"apc\",\"jdt\",\"fur\",\"che\",\"haw\",\"yor\",\"crh\",\"pdc\",\"ppl\",\"kin\",\"shs\",\"mnw\",\"tet\",\"sah\",\"kum\",\"ngt\",\"nya\",\"pus\",\"hif\",\"mya\",\"moh\",\"wol\",\"tir\",\"ton\",\"lzz\",\"oar\",\"lug\",\"brx\",\"non\",\"mww\",\"hak\",\"nlv\",\"ngu\",\"bua\",\"aym\",\"vec\",\"ibo\",\"tkl\",\"bam\",\"kha\",\"ceb\",\"lou\",\"fuc\",\"smo\",\"gag\",\"lfn\",\"arg\",\"umb\",\"tyv\",\"kjh\",\"oji\",\"cyo\",\"urh\",\"kzj\",\"pam\",\"srd\",\"lmo\",\"swg\",\"mdf\",\"gil\",\"snd\",\"tso\",\"sot\",\"zza\",\"tsn\",\"pau\",\"som\",\"egl\",\"ady\",\"asm\",\"ori\",\"dtp\",\"cho\",\"max\",\"kam\",\"niu\",\"sag\",\"ilo\",\"kaa\",\"fuv\",\"nch\",\"hoc\",\"iba\",\"gbm\",\"sun\",\"war\",\"mvv\",\"pap\",\"ary\",\"kxi\",\"csb\",\"pag\",\"cos\",\"rif\",\"kek\",\"krc\",\"aii\",\"ban\",\"ssw\",\"tvl\",\"mfe\",\"tah\",\"bvy\",\"bcl\",\"hnj\",\"nau\",\"nst\",\"afb\",\"quc\",\"min\",\"tmw\",\"mad\",\"bjn\",\"mai\",\"cjy\",\"got\",\"hsn\",\"gan\",\"tzl\",\"dws\",\"ldn\",\"afh\",\"sgs\",\"krl\",\"vep\",\"rue\",\"tly\",\"mic\",\"ext\",\"izh\",\"sma\",\"jam\",\"cmo\",\"mwl\",\"kpv\",\"koi\",\"bis\",\"ike\",\"run\",\"evn\",\"ryu\",\"mnc\",\"aoz\",\"otk\",\"kas\",\"aln\",\"akl\",\"yua\",\"shy\",\"fkv\",\"gos\",\"fij\",\"thv\",\"zgh\",\"gcf\",\"cay\",\"xmf\",\"tig\",\"div\",\"lij\",\"rap\",\"hrx\",\"cpi\",\"tts\",\"gaa\",\"tmr\",\"iii\",\"ltg\",\"bzt\",\"syc\",\"emx\",\"gom\",\"chg\",\"osp\",\"stq\",\"frr\",\"fro\",\"nys\",\"toi\",\"new\",\"phn\",\"jpa\",\"rel\",\"drt\",\"chn\",\"pli\",\"laa\",\"bal\",\"hdn\",\"hax\",\"mik\",\"ajp\",\"xqa\",\"pal\",\"crk\",\"mni\",\"lut\",\"ayl\",\"ood\",\"sdh\",\"ofs\",\"nus\",\"kiu\",\"diq\",\"qxq\",\"alt\",\"bfz\",\"klj\",\"mus\",\"srn\",\"guc\",\"lim\",\"zea\",\"shi\",\"mnr\",\"bom\",\"sat\",\"szl\"]\r\nfeatures = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})\r\nnum_labels = features['label'].num_classes\r\ndata_files = { \"train\": \"train.csv\", \"test\": \"test.csv\" }\r\nsentences = load_dataset(\r\n \"loretoparisi/tatoeba-sentences\",\r\n data_files=data_files,\r\n delimiter='\\t', \r\n column_names=['label', 'text'],\r\n)\r\n# You can make this part faster with num_proc=<some int>\r\nsentences = sentences.map(lambda ex: features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None, features=features)\r\n```\r\n\r\n@lhoestq IIRC, I suggested adding `cast_to_storage` to `ClassLabel` + `table_cast` to the packaged loaders if the `ClassLabel`/`Image`/`Audio` type is present in `features` to avoid this kind of error, but your concern was speed. IMO shouldn't be a problem if we do `table_cast` only when these features are present.",
"I agree packaged loaders should support `ClassLabel` feature without throwing an error.",
"@albertvillanova @mariosasko thank you, with that change now I get\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n[<ipython-input-9-eeb68eeb9bec>](https://localhost:8080/#) in <module>()\r\n 11 )\r\n 12 # You can make this part faster with num_proc=<some int>\r\n---> 13 sentences = sentences.map(lambda ex: features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None, features=features)\r\n 14 sentences = sentences.shuffle()\r\n\r\n8 frames\r\n[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices)\r\n 2193 if processed_inputs is not None and not isinstance(processed_inputs, (Mapping, pa.Table)):\r\n 2194 raise TypeError(\r\n-> 2195 f\"Provided `function` which is applied to all elements of table returns a variable of type {type(processed_inputs)}. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects.\"\r\n 2196 )\r\n 2197 elif isinstance(indices, list) and isinstance(processed_inputs, Mapping):\r\n\r\nTypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'int'>. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects.\r\n```\r\n\r\nthe error is raised by [this](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L2221)\r\n\r\n```\r\n[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices)\r\n```",
"@mariosasko changed it like\r\n\r\n```python\r\nsentences = sentences.map(lambda ex: {\"label\" : features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None}, features=features)\r\n```\r\n\r\nto avoid the above errorr.",
"Any update on this? Is this correct ?\r\n> @mariosasko changed it like\r\n> \r\n> ```python\r\n> sentences = sentences.map(lambda ex: {\"label\" : features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None}, features=features)\r\n> ```\r\n> \r\n> to avoid the above errorr.\r\n\r\n"
] | 1,650,871,722,000 | 1,653,999,391,000 | 1,653,999,391,000 | NONE | null | ### System Info
```shell
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.10.0+cu111 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
```
### Who can help?
@LysandreJik
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset("loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
features = features
```
ERROR:
```
ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None)
Value(dtype='string', id=None)
Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39
Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...
Downloading data files: 100%
2/2 [00:18<00:00, 8.06s/it]
Downloading data: 100%
391M/391M [00:13<00:00, 35.3MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 36.5MB/s]
Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
15 frames
/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
ValueError: invalid literal for int() with base 10: 'cmn'
```
while loading without `features` it loads without errors
```
sentences = load_dataset("loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text']
)
```
but the `label` col seems to be wrong (without the `ClassLabel` object):
```
sentences['train'].features
{'label': Value(dtype='string', id=None),
'text': Value(dtype='string', id=None)}
```
The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences
Dataset format is:
```
ces Nechci vědět, co je tam uvnitř.
ces Kdo o tom chce slyšet?
deu Tom sagte, er fühle sich nicht wohl.
ber Mel-iyi-d anida-t tura ?
hun Gondom lesz rá rögtön.
ber Mel-iyi-d anida-tt tura ?
deu Ich will dich nicht reden hören.
```
### Expected behavior
```shell
correctly load train and test files.
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4208 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4208/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4208/comments | https://api.github.com/repos/huggingface/datasets/issues/4208/events | https://github.com/huggingface/datasets/pull/4208 | 1,213,716,426 | PR_kwDODunzps42r7bW | 4,208 | Add CMU MoCap Dataset | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4208). All of your documentation changes will be reflected on that endpoint.",
"- Updated the readme.\r\n- Added dummy_data.zip and ran the all the tests.\r\n\r\nThe dataset works for \"asf/amc\" and \"avi\" formats which have a single download link for the complete dataset. But \"c3d\" and \"mpg\" have multiple download links, can we combine and host these links on the Hub since the dataset is free to use ?",
"\"c3d\" and \"mpg\" have multiple download links (part archives) and dl_manager.download_and_extract() extracts the files to multiple paths, is there a way to extract these multiple archives into one folder ? Any other way to go about this ?\r\nCan we combine and host these links on the Hub since the dataset is free to use ?",
"> \"c3d\" and \"mpg\" have multiple download links (part archives) and dl_manager.download_and_extract() extracts the files to multiple paths, is there a way to extract these multiple archives into one folder ? Any other way to go about this ?\r\n\r\nWe store downloaded data under `~/.cache/huggingface/datasets/downloads` (by default), so these downloads are \"hidden\" and won't clutter one's filesystem in an \"obvious way\".",
"> We store downloaded data under ~/.cache/huggingface/datasets/downloads (by default), so these downloads are \"hidden\" and won't clutter one's filesystem in an \"obvious way\".\r\n\r\nYes, the filesystem won't be clustered, but the problem is processing the dataset becomes cumbersome. For eg, for the c3d format has 5 part-downloads, so the folders will be as follows : \r\n```\r\n['~/.cache/huggingface/datasets/downloads/extracted/0e6bf028f490bf18c23ce572d1437c4ef32a74f630e33c26a806250d35cfcdd1', '~/.cache/huggingface/datasets/downloads/extracted/1b44fc5c7a6e031c904545422d449fd964f8ee795b9d1dcb0b6a76d03b50ebe6', '~/.cache/huggingface/datasets/downloads/extracted/137595188e96187c24ce1aa5c78200c7f78816fbd9d6c62354c01b3e6ec550c7', '~/.cache/huggingface/datasets/downloads/extracted/6c0c893e435f36fd79aa0f199f58fe16f01985f039644a7cb094a8c43a15ffd4', '~/.cache/huggingface/datasets/downloads/extracted/45e4703354cbc975e6add66f1b17b716c882b56f44575b033c5926aa5fcfb17f']\r\n```\r\nEach of these folders have a given set of subjects, so we'll be need to write extra code to fetch data from each of these folders, and the mpg format has 12 part-downloads which will lead to 12 folders having certain set of subjects, so it is cumbersome to process them.",
"I have added all the changes that were suggested. We just need to handle the multi-part download for c3d and mpg formats. Easiest way would be to have just one zip for these formats.",
"But we can handle this with a simple mapping that stores the id ranges (for each config), no? And an actual file path is not important during processing.",
"I have added code to handle c3d, mpg formats as well. The data for the mpg format seems incomplete as it contains only 53 rows. I have added a note regarding this in the Data Splits section.",
"The real data test works fine and dummy_data test work fine. There were few missing files which was causing issues, I have fixed it now.\r\n",
"- Reduced the dummy_data size.\r\n- Added sample dataset preprocessing code, it is not complete though.\r\n- Added all changes suggested.\r\n\r\nLet me know if anything else is required. Thank you. :)"
] | 1,650,821,468,000 | 1,654,578,348,000 | null | CONTRIBUTOR | null | Resolves #3457
Dataset Request : Add CMU Graphics Lab Motion Capture dataset [#3457](https://github.com/huggingface/datasets/issues/3457)
This PR adds the CMU MoCap Dataset.
The authors didn't respond even after multiple follow ups, so I ended up crawling the website to get categories, subcategories and description information. Some of the subjects do not have category/subcategory/description as well. I am using a subject to categories, subcategories and description map (metadata file).
Currently the loading of the dataset works for "asf/amc" and "avi" formats since they have a single download link. But "c3d" and "mpg" have multiple download links (part archives) and dl_manager.download_and_extract() extracts the files to multiple paths, is there a way to extract these multiple archives into one folder ? Any other way to go about this ?
Any suggestions/inputs on this would be helpful. Thank you.
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Nerval is a python package that helps evaluate NER models. It creates classification report and confusion matrix at entity level. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,650,639,381,000 | 1,651,591,722,000 | 1,651,591,308,000 | CONTRIBUTOR | null | Minor change to fully align this function with the recent change in Transformers (https://github.com/huggingface/transformers/pull/16891) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"This looks good to me! "
] | 1,650,637,466,000 | 1,651,584,203,000 | 1,651,583,784,000 | CONTRIBUTOR | null | What this PR mainly does:
- add metric card for recall metric
- update docs in recall python file
Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!) | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,650,637,428,000 | 1,651,587,820,000 | 1,651,587,406,000 | CONTRIBUTOR | null | What this PR mainly does:
- add metric card for precision metric
- update docs in precision python file
Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!) | {
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