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Support for legal NLP datasets (EURLEX, ECtHR cases and EU-REG-IR)
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Add support for two legal NLP datasets:
- EURLEX (https://www.aclweb.org/anthology/P19-1636/)
- ECtHR cases (https://arxiv.org/abs/2103.13084)
- EU-REG-IR (https://arxiv.org/abs/2101.10726)
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Implement Dataset as context manager
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When used as context manager, it would be safely deleted if some exception is raised.
This will avoid
> During handling of the above exception, another exception occurred:
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Support for legal NLP datasets (EURLEX and ECtHR cases)
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Add support for two legal NLP datasets:
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- ECtHR cases (https://arxiv.org/abs/2103.13084)
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Compute WER metric iteratively
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Compute WER metric iteratively to avoid MemoryError.
Fix #2078.
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Fix incorrect assertion in builder.py
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Fix incorrect num_examples comparison assertion in builder.py
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Add more issue templates and customize issue template chooser
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When opening an issue, it is not evident for the users how to choose a blank issue template. There is a link at the bottom of all the other issue templates (`Don’t see your issue here? Open a blank issue.`), but this is not very visible for users. This is the reason why many users finally chose the `add-dataset` template instead (this is more visible) for issues that indeed are not requesting the addition of a new dataset.
~~With this PR, the default blank issue template would be as visible as the other templates (as the `add-dataset` template), thus making easier for the users to choose it.~~
With this PR:
- more issue templates, besides `add-dataset`, are added: `bug-report` and `feature-request`
- the issue template chooser is customized, so that it now includes a link to `Discussions` for questions
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Is there a way to use a GPU only when training an Index in the process of add_faisis_index?
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2021-03-24T21:32:16Z
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2021-03-25T06:31:43Z
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NONE
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Motivation - Some FAISS indexes like IVF consist of the training step that clusters the dataset into a given number of indexes. It would be nice if we can use a GPU to do the training step and covert the index back to CPU as mention in [this faiss example](https://gist.github.com/mdouze/46d6bbbaabca0b9778fca37ed2bcccf6).
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Metadata validation
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2021-03-24T08:52:41Z
|
2021-04-26T08:27:14Z
|
2021-04-26T08:27:13Z
|
CONTRIBUTOR
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- `pydantic` metadata schema with dedicated validators against our taxonomy
- ci script to validate new changes against this schema and start a vertuous loop
- soft validation on tasks ids since we expect the taxonomy to undergo some changes in the near future
for reference with the current validation we have ~365~ 378 datasets with invalid metadata! full error report [_here_.](https://gist.github.com/theo-m/61b3c0c47fc6121d08d3174bd4c2a26b)
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WMT19 Dataset for Kazakh-English is not formatted correctly
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2021-03-23T20:14:47Z
|
2021-03-25T21:36:20Z
| null |
NONE
| null | null | null |
In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error.
The News Commentary v14 parallel data set for kk-en from http://www.statmt.org/wmt19/translation-task.html has a bug here:
> Line 94. The Swiss National Bank, for its part, has been battling with the deflationary effects of the franc’s dramatic appreciation over the past few years. Швейцарияның Ұлттық банкі өз тарапынан, соңғы бірнеше жыл ішінде франк құнының қатты өсуінің дефляциялық әсерімен күресіп келеді.
>
> Line 95. Дефляциялық күштер 2008 жылы терең және ұзаққа созылған жаһандық дағдарысқа байланысты орын алған ірі экономикалық және қаржылық орын алмасулардың арқасында босатылды. Жеке қарыз қаражаты үлесінің қысқаруы орталық банктің рефляцияға жұмсалған күш-жігеріне тұрақты соққан қарсы желдей болды.
>
> Line 96. The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды.
As you can see, line 95 has only the Kazakh translation which should be part of line 96. This causes all of the following English-Kazakh translation pairs to be one off rendering ALL of those translations incorrect. This issue was not fixed when the dataset was imported to Huggingface. By running this code
```
import datasets
from datasets import load_dataset
dataset = load_dataset('wmt19', 'kk-en')
for key in dataset['train']['translation']:
if 'The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008.' in key['kk']:
print(key['en'])
print(key['kk'])
break
```
we get:
> 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды.
> The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate.
which shows that the issue still persists in the Huggingface dataset. The Kazakh sentence matches up to the next English sentence in the dataset instead of the current one.
Please let me know if there's you have any ideas to fix this one-off error from the dataset or if this can be fixed by Huggingface.
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Request to remove S2ORC dataset
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2021-03-23T19:43:06Z
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2021-08-04T19:18:02Z
| null |
NONE
| null | null | null |
Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks!
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Trouble loading wiki_movies
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2021-03-23T18:59:54Z
|
2022-03-30T08:22:58Z
|
2022-03-30T08:22:58Z
|
NONE
| null | null | null |
Hello,
I am trying to load_dataset("wiki_movies") and it gives me this error -
`FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py`
Trying to do `python run_mlm.py \
--model_name_or_path roberta-base \
--dataset_name wiki_movies \` also gives the same error.
Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago.
Thank you!
|
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| 2,103 |
citation, homepage, and license fields of `dataset_info.json` are duplicated many times
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2021-03-23T17:18:09Z
|
2021-04-06T14:39:59Z
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2021-04-06T14:39:59Z
|
NONE
| null | null | null |
This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation.
Example result:
```
"citation": "@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n
```
@lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times.
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Move Dataset.to_csv to csv module
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Move the implementation of `Dataset.to_csv` to module `datasets.io.csv`.
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MIAM dataset - new citation details
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2021-03-23T18:08:10Z
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Hi @lhoestq, I have updated the citations to reference an OpenReview preprint.
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Fix deprecated warning message and docstring
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Fix deprecated warnings:
- Use deprecated Sphinx directive in docstring
- Fix format of deprecated message
- Raise FutureWarning
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load_from_disk takes a long time to load local dataset
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2021-03-23T17:12:16Z
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2021-03-23T17:12:16Z
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NONE
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I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).
Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?
Tagging @lhoestq since you seem to be working on these issues and PRs :)
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SQuAD version
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2021-03-26T09:48:54Z
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2021-03-26T09:48:54Z
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NONE
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Hi~
I want train on squad dataset. What's the version of the squad? Is it 1.1 or 1.0? I'm new in QA, I don't find some descriptions about it.
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fixes issue #1110 by descending further if `obj["_type"]` is a dict
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Check metrics
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CoNLL 2003 dataset not including German
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2021-03-22T19:23:56Z
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2022-09-07T23:34:30Z
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NONE
| null | null | null |
Hello, thanks for all the work on developing and maintaining this amazing platform, which I am enjoying working with!
I was wondering if there is a reason why the German CoNLL 2003 dataset is not included in the [repository](https://github.com/huggingface/datasets/tree/master/datasets/conll2003), since a copy of it could be found in some places on the internet such as GitHub? I could help adding the German data to the hub, unless there are some copyright issues that I am unaware of...
This is considering that many work use the union of CoNLL 2002 and 2003 datasets for comparing cross-lingual NER transfer performance in `en`, `de`, `es`, and `nl`. E.g., [XLM-R](https://www.aclweb.org/anthology/2020.acl-main.747.pdf).
## Adding a Dataset
- **Name:** CoNLL 2003 German
- **Paper:** https://www.aclweb.org/anthology/W03-0419/
- **Data:** https://github.com/huggingface/datasets/tree/master/datasets/conll2003
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Fix: Allows a feature to be named "_type"
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This PR tries to fix issue #1110. Sorry for taking so long to come back to this.
It's a simple fix, but i am not sure if it works for all possible types of `obj`. Let me know what you think @lhoestq
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How to disable making arrow tables in load_dataset ?
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2021-03-21T04:50:07Z
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2022-06-01T16:49:52Z
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2022-06-01T16:49:52Z
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NONE
| null | null | null |
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
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| 2,091 |
Fix copy snippet in docs
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2021-03-20T15:08:22Z
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2021-03-24T08:20:50Z
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2021-03-23T17:18:31Z
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With this change the lines starting with `...` in the code blocks can be properly copied to clipboard.
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| 2,090 |
Add machine translated multilingual STS benchmark dataset
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2021-03-29T13:24:42Z
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2021-03-29T13:00:15Z
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also see here https://github.com/PhilipMay/stsb-multi-mt
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| 2,089 |
Add documentaton for dataset README.md files
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2021-03-20T11:44:38Z
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2021-07-12T17:41:40Z
| null |
CONTRIBUTOR
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Hi,
the dataset README files have special headers.
Somehow a documenation of the allowed values and tags is missing.
Could you add that?
Just to give some concrete questions that should be answered imo:
- which values can be passted to multilinguality?
- what should be passed to language_creators?
- which values should licenses have? What do I say when it is a custom license? Should I add a link?
- how should I choose size_categories ? What are valid ranges?
- what are valid task_categories?
Thanks
Philip
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| 2,088 |
change bibtex template to author instead of authors
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2021-03-20T09:23:44Z
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2021-03-23T15:40:12Z
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Hi,
IMO when using BibTex Author should be used instead of Authors.
See here: http://www.bibtex.org/Using/de/
Thanks
Philip
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Update metadata if dataset features are modified
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2021-03-20T02:05:23Z
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2021-04-09T09:25:33Z
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2021-04-09T09:25:33Z
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This PR adds a decorator that updates the dataset metadata if a previously executed transform modifies its features.
Fixes #2083
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change user permissions to -rw-r--r--
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Fix for #2065
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Fix max_wait_time in requests
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2021-03-19T11:22:26Z
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2021-03-23T15:36:38Z
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it was handled as a min time, not max cc @SBrandeis
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CUAD - Contract Understanding Atticus Dataset
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2021-03-19T09:27:43Z
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2021-04-16T08:50:44Z
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2021-04-16T08:50:44Z
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CONTRIBUTOR
| null | null | null |
## Adding a Dataset
- **Name:** CUAD - Contract Understanding Atticus Dataset
- **Description:** As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community.
- **Paper:** https://arxiv.org/abs/2103.06268
- **Data:** https://github.com/TheAtticusProject/cuad/
- **Motivation:** good domain specific datasets are valuable
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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| 2,083 |
`concatenate_datasets` throws error when changing the order of datasets to concatenate
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2021-03-19T08:29:48Z
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2021-04-09T09:25:33Z
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2021-04-09T09:25:33Z
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MEMBER
| null | null | null |
Hey,
I played around with the `concatenate_datasets(...)` function: https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets
and noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO.
Here is a google colab to reproduce the error: https://colab.research.google.com/drive/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing
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Updated card using information from data statement and datasheet
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2021-03-19T14:29:09Z
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I updated and clarified the REFreSD [data card](https://github.com/mcmillanmajora/datasets/blob/refresd_card/datasets/refresd/README.md) with information from the Eleftheria's [website](https://elbria.github.io/post/refresd/). I added brief descriptions where the initial card referred to the paper, and I also recreated some of the tables in the paper to show relevant dataset statistics.
I'll email Eleftheria to see if she has any comments on the card.
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| 2,081 |
Fix docstrings issues
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Fix docstring issues.
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| 2,080 |
Multidimensional arrays in a Dataset
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2021-03-25T12:46:53Z
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2021-03-25T12:46:53Z
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NONE
| null | null | null |
Hi,
I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row.
The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`)
```
from datasets import Dataset
import pandas as pd
import numpy as np
dataset = pd.DataFrame({
'bbox': [
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])
],
'input_ids': [1, 2, 3, 4]
})
dataset = Dataset.from_pandas(dataset)
```
Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists.
```
import torch
from datasets import Dataset
import pandas as pd
dataset = pd.DataFrame({
'bbox': [
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]]
],
'input_ids': [1, 2, 3, 4]
})
dataset = Dataset.from_pandas(dataset)
def test(examples):
return {'bbbox': torch.Tensor(examples['bbox'])}
dataset = dataset.map(test)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
def test2(examples):
return {'bbbox': torch.stack(examples['bbox'])}
dataset = dataset.map(test2)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
```
Is is possible to support n-D arrays/tensors in datasets?
It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263).
|
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Refactorize Metric.compute signature to force keyword arguments only
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Minor refactoring of Metric.compute signature to force the use of keyword arguments, by using the single star syntax.
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MemoryError when computing WER metric
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[] |
2021-03-18T11:30:05Z
|
2021-05-01T08:31:49Z
|
2021-04-06T07:20:43Z
|
NONE
| null | null | null |
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:
```
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
However, I receive the following exception:
`Traceback (most recent call last):
File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module>
print(wer.compute(predictions=result["predicted"], references=result["target"]))
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute
return wer(references, predictions)
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer
truth, hypothesis, truth_transform, hypothesis_transform, **kwargs
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures
H, S, D, I = _get_operation_counts(truth, hypothesis)
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts
editops = Levenshtein.editops(source_string, destination_string)
MemoryError`
My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
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| 834,649,536 |
MDExOlB1bGxSZXF1ZXN0NTk1NDI0MTYw
| 2,077 |
Bump huggingface_hub version
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2021-03-18T10:54:34Z
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2021-03-18T11:33:26Z
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2021-03-18T11:33:26Z
|
CONTRIBUTOR
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`0.0.2 => 0.0.6`
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| 834,445,296 |
MDU6SXNzdWU4MzQ0NDUyOTY=
| 2,076 |
Issue: Dataset download error
|
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2021-03-18T06:36:06Z
|
2021-03-22T11:52:31Z
| null |
NONE
| null | null | null |
The download link in `iwslt2017.py` file does not seem to work anymore.
For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz`
Would be nice if we could modify it script and use the new downloadable link?
|
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| 834,301,246 |
MDU6SXNzdWU4MzQzMDEyNDY=
| 2,075 |
ConnectionError: Couldn't reach common_voice.py
|
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2021-03-18T01:19:06Z
|
2021-03-20T10:29:41Z
|
2021-03-20T10:29:41Z
|
NONE
| null | null | null |
When I run:
from datasets import load_dataset, load_metric
common_voice_train = load_dataset("common_voice", "zh-CN", split="train+validation")
common_voice_test = load_dataset("common_voice", "zh-CN", split="test")
Got:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/master/datasets/common_voice/common_voice.py
Version:
1.4.1
Thanks! @lhoestq @LysandreJik @thomwolf
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| 2,074 |
Fix size categories in YAML Tags
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2021-03-18T00:02:36Z
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2021-03-23T17:11:10Z
|
2021-03-23T17:11:10Z
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CONTRIBUTOR
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This PR fixes several `size_categories` in YAML tags and makes them consistent. Additionally, I have added a few more categories after `1M`, up to `1T`. I would like to add that to the streamlit app also.
This PR also adds a couple of infos that I found missing.
The code for generating this:
```python
for dataset in sorted(os.listdir('./datasets/')):
if '.' not in dataset and dataset not in ['c4', 'csv', 'downloads', 'cc100', 'ccaligned_multilingual', 'celeb_a', 'chr_en', 'emea', 'glue']:
infos = {}
stats = {}
st = ''
with open(f'datasets/{dataset}/README.md') as f:
d = f.read()
start_dash = d.find('---') + 3
end_dash = d[start_dash:].find('---') + 3
rest_text = d[end_dash + 3:]
try:
full_yaml = OmegaConf.create(d[start_dash:end_dash])
readme = OmegaConf.to_container(full_yaml['size_categories'], resolve=True)
except Exception as e:
print(e)
continue
try:
with open(f'datasets/{dataset}/dataset_infos.json') as f:
data = json.load(f)
except Exception as e:
print(e)
continue # Skip those without infos.
done_set = set([])
num_keys = len(data.keys())
for keys in data:
# dataset = load_dataset('opus100', f'{dirs}')
total = 0
for split in data[keys]['splits']:
total = total + data[keys]['splits'][split]['num_examples']
if total < 1000:
st += "- n<1K" + '\n'
infos[keys] = ["n<1K"]
elif total >= 1000 and total < 10000:
infos[keys] = ["1K<n<10K"]
elif total >= 10000 and total < 100000:
infos[keys] = ["10K<n<100K"]
elif total >= 100000 and total < 1000000:
infos[keys] = ["100K<n<1M"]
elif total >= 1000000 and total < 10000000:
infos[keys] = ["1M<n<10M"]
elif total >= 10000000 and total < 100000000:
infos[keys] = ["10M<n<100M"]
elif total >= 100000000 and total < 1000000000:
infos[keys] = ["100M<n<1B"]
elif total >= 1000000000 and total < 10000000000:
infos[keys] = ["1B<n<10B"]
elif total >= 10000000000 and total < 100000000000:
infos[keys] = ["10B<n<100B"]
elif total >= 100000000000 and total < 1000000000000:
infos[keys] = ["100B<n<1T"]
else:
infos[keys] = ["n>1T"]
done_set = done_set.union(infos[keys])
if (isinstance(readme, list) and list(infos.values())[0] != readme) or (isinstance(readme, dict) and readme != infos):
print('-' * 30)
print(done_set)
print(f"Changing Full YAML for {dataset}")
print(OmegaConf.to_yaml(full_yaml))
if len(done_set) == 1:
full_yaml['size_categories'] = list(done_set)
else:
full_yaml['size_categories'] = dict([(k, v) for k, v in sorted(infos.items(), key=lambda x: x[0])])
full_yaml_string = OmegaConf.to_yaml(full_yaml)
print('-' * 30)
print(full_yaml_string)
inp = input('Do you wish to continue?(Y/N)')
if inp == 'Y':
with open(f'./datasets/{dataset}/README.md', 'w') as f:
f.write('---\n')
f.write(full_yaml_string)
f.write('---')
f.write(rest_text)
else:
break
```
Note that the lower-bound is inclusive. I'm unsure if this is how it is done in the tagging app.
EDIT:
It would be great if there was a way to make the task categories consistent too. For this, the streamlit app can look into all the datasets and check for existing categories and show them in the list. This may add some consistency.
EDIT:
I understand this will not work for cases where only the infos for some of the configs are present, for example: `ccaligned_multingual` has only 5 out of several configs present, and infos has only information about them. Hence, I have skipped a few datasets in the code, if there are more such datasets, then I'll ignore them too.
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| 834,192,501 |
MDExOlB1bGxSZXF1ZXN0NTk1MDYyMzQ2
| 2,073 |
Fixes check of TF_AVAILABLE and TORCH_AVAILABLE
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2021-03-17T21:28:53Z
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2021-03-18T09:09:25Z
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2021-03-18T09:09:24Z
|
MEMBER
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# What is this PR doing
This PR implements the checks if `Tensorflow` and `Pytorch` are available the same way as `transformers` does it. I added the additional checks for the different `Tensorflow` and `torch` versions. #2068
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| 2,072 |
Fix docstring issues
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Fix docstring issues.
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Multiprocessing is slower than single process
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2021-03-18T09:10:23Z
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2021-03-18T09:10:23Z
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CONTRIBUTOR
| null | null | null |
```python
# benchmark_filter.py
import logging
import sys
import time
from datasets import load_dataset, set_caching_enabled
if __name__ == "__main__":
set_caching_enabled(False)
logging.basicConfig(level=logging.DEBUG)
bc = load_dataset("bookcorpus")
now = time.time()
try:
bc["train"].filter(lambda x: len(x["text"]) < 64, num_proc=int(sys.argv[1]))
except Exception as e:
print(f"cancelled: {e}")
elapsed = time.time() - now
print(elapsed)
```
Running `python benchmark_filter.py 1` (20min+) is faster than `python benchmark_filter.py 2` (2hrs+)
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ArrowInvalid issue for squad v2 dataset
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2021-08-04T17:57:16Z
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NONE
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Hello, I am using the huggingface official question answering example notebook (https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb).
In the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error:
`ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178`
My code is as follows:
```
def generate_candidate_questions(examples):
val_questions = examples["question"]
candididate_questions = random.sample(datasets["train"]["question"], len(val_questions))
candididate_questions = [x[:max_length] for x in candididate_questions]
return candididate_questions
def prepare_validation_features(examples, use_mixing=False):
pad_on_right = tokenizer.padding_side == "right"
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
if use_mixing:
candidate_questions = generate_candidate_questions(examples)
tokenized_candidates = tokenizer(
candidate_questions if pad_on_right else examples["context"],
examples["context"] if pad_on_right else candidate_questions,
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
tokenized_examples["example_id"] = []
if use_mixing:
tokenized_examples["candidate_input_ids"] = tokenized_candidates["input_ids"]
tokenized_examples["candidate_attention_mask"] = tokenized_candidates["attention_mask"]
tokenized_examples["candidate_token_type_ids"] = tokenized_candidates["token_type_ids"]
for i in range(len(tokenized_examples["input_ids"])):
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
tokenized_examples["offset_mapping"][i] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
validation_features = datasets["validation"].map(
lambda xs: prepare_validation_features(xs, True),
batched=True,
remove_columns=datasets["validation"].column_names
)
```
I guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help!
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Add and fix docstring for NamedSplit
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Add and fix docstring for `NamedSplit`, which was missing.
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2021-06-14T04:47:30Z
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|
NONE
| null | null | null |
I get en error when running data loading using SageMaker SDK
```
File "main.py", line 34, in <module>
run_training()
File "main.py", line 25, in run_training
dm.setup('fit')
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn
return fn(*args, **kwargs)
File "/opt/ml/code/data_module.py", line 103, in setup
self.dataset[split].set_format(type="torch", columns=self.columns)
File "/opt/conda/lib/python3.6/site-packages/datasets/fingerprint.py", line 337, in wrapper
out = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 995, in set_format
_ = get_formatter(type, **format_kwargs)
File "/opt/conda/lib/python3.6/site-packages/datasets/formatting/__init__.py", line 114, in get_formatter
raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
ValueError: PyTorch needs to be installed to be able to return PyTorch tensors.
```
when trying to execute dataset loading using this notebook https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb, specifically lines
```
self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]
self.dataset[split].set_format(type="torch", columns=self.columns)
```
The SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.4.0-gpu-py3 .
By running container interactively I have checked that torch loading completes successfully by executing `https://github.com/huggingface/datasets/blob/master/src/datasets/config.py#L39`.
Also as a first line in the data loading module I have
```
import os
os.environ["USE_TF"] = "0"
os.environ["USE_TORCH"] = "1"
````
But unfortunately the error stills persists. Any suggestions would be appreciated as I am stack.
Many Thanks!
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CONTRIBUTOR
| null | null | null |
As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2
When using the num_proc argument on windows the whole Python environment crashes and hanging in loop.
For example at the map_to_array part.
An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop
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Fix the docstring rendering of Dataset/DatasetDict.from_csv args.
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| 833,291,432 |
MDU6SXNzdWU4MzMyOTE0MzI=
| 2,065 |
Only user permission of saved cache files, not group
|
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2021-03-17T00:20:22Z
|
2022-06-28T08:10:10Z
|
2021-05-10T06:45:29Z
|
NONE
| null | null | null |
Hello,
It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
|
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| 833,002,360 |
MDExOlB1bGxSZXF1ZXN0NTk0MDczOTQ1
| 2,064 |
Fix ted_talks_iwslt version error
|
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2021-03-16T16:43:45Z
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2021-03-16T18:00:08Z
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2021-03-16T18:00:08Z
|
CONTRIBUTOR
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This PR fixes the bug where the version argument would be passed twice if the dataset configuration was created on the fly.
Fixes #2059
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| 832,993,705 |
MDExOlB1bGxSZXF1ZXN0NTk0MDY2NzI5
| 2,063 |
[Common Voice] Adapt dataset script so that no manual data download is actually needed
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2021-03-16T16:33:44Z
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2021-03-17T09:42:52Z
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2021-03-17T09:42:37Z
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MEMBER
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This PR changes the dataset script so that no manual data dir is needed anymore.
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MDExOlB1bGxSZXF1ZXN0NTkzNzUyNTMz
| 2,062 |
docs: fix missing quotation
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2021-03-17T09:21:57Z
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2021-03-17T09:21:57Z
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CONTRIBUTOR
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The json code misses a quote
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| 832,596,228 |
MDU6SXNzdWU4MzI1OTYyMjg=
| 2,061 |
Cannot load udpos subsets from xtreme dataset using load_dataset()
|
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2021-03-16T09:32:13Z
|
2021-06-18T11:54:11Z
|
2021-06-18T11:54:10Z
|
NONE
| null | null | null |
Hello,
I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error.
Reprex is:
`from datasets import load_dataset `
`dataset = load_dataset('xtreme', 'udpos.English')`
The error is:
`KeyError: '_'`
The full traceback is:
KeyError Traceback (most recent call last)
<ipython-input-5-7181359ea09d> in <module>
1 from datasets import load_dataset
----> 2 dataset = load_dataset('xtreme', 'udpos.English')
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)
738
739 # Download and prepare data
--> 740 builder_instance.download_and_prepare(
741 download_config=download_config,
742 download_mode=download_mode,
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
576 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
577 if not downloaded_from_gcs:
--> 578 self._download_and_prepare(
579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
580 )
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
654 try:
655 # Prepare split will record examples associated to the split
--> 656 self._prepare_split(split_generator, **prepare_split_kwargs)
657 except OSError as e:
658 raise OSError(
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator)
977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose
978 ):
--> 979 example = self.info.features.encode_example(record)
980 writer.write(example)
981 finally:
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example)
946 def encode_example(self, example):
947 example = cast_to_python_objects(example)
--> 948 return encode_nested_example(self, example)
949
950 def encode_batch(self, batch):
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj)
840 # Nested structures: we allow dict, list/tuples, sequences
841 if isinstance(schema, dict):
--> 842 return {
843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
844 }
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0)
841 if isinstance(schema, dict):
842 return {
--> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
844 }
845 elif isinstance(schema, (list, tuple)):
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj)
868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks
869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):
--> 870 return schema.encode_example(obj)
871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation)
872 return obj
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data)
647 # If a string is given, convert to associated integer
648 if isinstance(example_data, str):
--> 649 example_data = self.str2int(example_data)
650
651 # Allowing -1 to mean no label.
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values)
605 if value not in self._str2int:
606 value = value.strip()
--> 607 output.append(self._str2int[str(value)])
608 else:
609 # No names provided, try to integerize
KeyError: '_'
|
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MDExOlB1bGxSZXF1ZXN0NTkzNzIxNzcx
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Filtering refactor
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2021-03-16T09:23:30Z
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2021-10-13T09:09:04Z
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2021-10-13T09:09:03Z
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fix https://github.com/huggingface/datasets/issues/2032
benchmarking is somewhat inconclusive, currently running on `book_corpus` with:
```python
bc = load_dataset("bookcorpus")
now = time.time()
bc.filter(lambda x: len(x["text"]) < 64)
elapsed = time.time() - now
print(elapsed)
```
this branch does it in 233 seconds, master in 1409 seconds.
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| 832,579,156 |
MDU6SXNzdWU4MzI1NzkxNTY=
| 2,059 |
Error while following docs to load the `ted_talks_iwslt` dataset
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2021-03-16T09:12:19Z
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2021-03-16T18:00:31Z
|
2021-03-16T18:00:07Z
|
NONE
| null | null | null |
I am currently trying to load the `ted_talks_iwslt` dataset into google colab.
The [docs](https://huggingface.co/datasets/ted_talks_iwslt) mention the following way of doing so.
```python
dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014")
```
Executing it results in the error attached below.
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-7dcc67154ef9> in <module>()
----> 1 dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014")
4 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)
730 hash=hash,
731 features=features,
--> 732 **config_kwargs,
733 )
734
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, writer_batch_size, *args, **kwargs)
927
928 def __init__(self, *args, writer_batch_size=None, **kwargs):
--> 929 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
930 # Batch size used by the ArrowWriter
931 # It defines the number of samples that are kept in memory before writing them
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)
241 name,
242 custom_features=features,
--> 243 **config_kwargs,
244 )
245
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
337 if "version" not in config_kwargs and hasattr(self, "VERSION") and self.VERSION:
338 config_kwargs["version"] = self.VERSION
--> 339 builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs)
340
341 # otherwise use the config_kwargs to overwrite the attributes
/root/.cache/huggingface/modules/datasets_modules/datasets/ted_talks_iwslt/024d06b1376b361e59245c5878ab8acf9a7576d765f2d0077f61751158e60914/ted_talks_iwslt.py in __init__(self, language_pair, year, **kwargs)
219 description=description,
220 version=datasets.Version("1.1.0", ""),
--> 221 **kwargs,
222 )
223
TypeError: __init__() got multiple values for keyword argument 'version'
```
How to resolve this?
PS: Thanks a lot @huggingface team for creating this great library!
|
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| 2,058 |
Is it possible to convert a `tfds` to HuggingFace `dataset`?
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2021-03-15T20:18:47Z
|
2022-10-05T16:55:18Z
| null |
CONTRIBUTOR
| null | null | null |
I was having some weird bugs with `C4`dataset version of HuggingFace, so I decided to try to download `C4`from `tfds`. I would like to know if it is possible to convert a tfds dataset to HuggingFace dataset format :)
I can also open a new issue reporting the bug I'm receiving with `datasets.load_dataset('c4','en')` in the future if you think that it would be useful.
Thanks!
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| 2,057 |
update link to ZEST dataset
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2021-03-15T19:22:57Z
|
2021-03-16T17:06:28Z
|
2021-03-16T17:06:28Z
|
CONTRIBUTOR
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Updating the link as the original one is no longer working.
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| 831,718,397 |
MDU6SXNzdWU4MzE3MTgzOTc=
| 2,056 |
issue with opus100/en-fr dataset
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2021-03-15T11:32:42Z
|
2021-03-16T15:49:00Z
|
2021-03-16T15:48:59Z
|
NONE
| null | null | null |
Hi
I am running run_mlm.py code of huggingface repo with opus100/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this?
Thanks a lot @lhoestq for your help in advance.
`
thread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/normalizer.rs:382:21
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
63%|██████████████████████████████████████████████████████████▊ | 626/1000 [00:27<00:16, 22.69ba/s]
Traceback (most recent call last):
File "run_mlm.py", line 550, in <module>
main()
File "run_mlm.py", line 412, in main
in zip(data_args.dataset_name, data_args.dataset_config_name)]
File "run_mlm.py", line 411, in <listcomp>
logger) for dataset_name, dataset_config_name\
File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 96, in get_tokenized_dataset
load_from_cache_file=not data_args.overwrite_cache,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in map
for k, dataset in self.items()
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in <dictcomp>
for k, dataset in self.items()
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1309, in map
update_data=update_data,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 204, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/fingerprint.py", line 337, in wrapper
out = func(self, *args, **kwargs)
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1574, in _map_single
batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1490, in apply_function_on_filtered_inputs
function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 89, in tokenize_function
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2347, in __call__
**kwargs,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2532, in batch_encode_plus
**kwargs,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 384, in _batch_encode_plus
is_pretokenized=is_split_into_words,
pyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617
`
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is there a way to override a dataset object saved with save_to_disk?
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2021-03-15T10:50:53Z
|
2021-03-22T04:06:17Z
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2021-03-22T04:06:17Z
|
NONE
| null | null | null |
At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file. Is there a way to override such an object?
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| 2,054 |
Could not find file for ZEST dataset
|
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2021-03-15T09:11:58Z
|
2021-05-03T09:30:24Z
|
2021-05-03T09:30:24Z
|
CONTRIBUTOR
| null | null | null |
I am trying to use zest dataset from Allen AI using below code in colab,
```
!pip install -q datasets
from datasets import load_dataset
dataset = load_dataset("zest")
```
I am getting the following error,
```
Using custom data configuration default
Downloading and preparing dataset zest/default (download: 5.53 MiB, generated: 19.96 MiB, post-processed: Unknown size, total: 25.48 MiB) to /root/.cache/huggingface/datasets/zest/default/0.0.0/1f7a230fbfc964d979bbca0f0130fbab3259fce547ee758ad8aa4f9c9bec6cca...
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-6-18dbbc1a4b8a> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("zest")
9 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
612 )
613 elif response is not None and response.status_code == 404:
--> 614 raise FileNotFoundError("Couldn't find file at {}".format(url))
615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
616 raise ConnectionError("Couldn't reach {}".format(url))
FileNotFoundError: Couldn't find file at https://ai2-datasets.s3-us-west-2.amazonaws.com/zest/zest.zip
```
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| 2,053 |
Add bAbI QA tasks
|
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2021-03-14T13:04:39Z
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2021-03-29T12:41:48Z
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2021-03-29T12:41:48Z
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- **Name:** *The (20) QA bAbI tasks*
- **Description:** *The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. The aim is to classify these tasks into skill sets,so that researchers can identify (and then rectify) the failings of their systems.*
- **Paper:** [arXiv](https://arxiv.org/pdf/1502.05698.pdf)
- **Data:** [Facebook Research Page](https://research.fb.com/downloads/babi/)
- **Motivation:** This is a unique dataset with story-based Question Answering. It is a part of the `bAbI` project by Facebook Research.
**Note**: I have currently added all the 160 configs. If this seems impractical, I can keep only a few. While each `dummy_data.zip` weighs a few KBs, overall it is around 1.3MB for all configurations. This is problematic. Let me know what is to be done.
Thanks :)
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
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MDU6SXNzdWU4MzExMzU3MDQ=
| 2,052 |
Timit_asr dataset repeats examples
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2021-03-14T11:43:43Z
|
2021-03-15T10:37:16Z
|
2021-03-15T10:37:16Z
|
NONE
| null | null | null |
Summary
When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same
Steps to reproduce
As an example, on this code there is the text from the training part:
Code snippet:
```
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
timit['train']['text']
#['Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
```
The same behavior happens for other columns
Expected behavior:
Different info on the actual timit_asr dataset
Actual behavior:
When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same. I've checked datasets 1.3 and the rows are different
Debug info
Streamlit version: (get it with $ streamlit version)
Python version: Python 3.6.12
Using Conda? PipEnv? PyEnv? Pex? Using pip
OS version: Centos-release-7-9.2009.1.el7.centos.x86_64
Additional information
You can check the same behavior on https://huggingface.co/datasets/viewer/?dataset=timit_asr
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MDExOlB1bGxSZXF1ZXN0NTkyNDQ2MDU1
| 2,051 |
Add MDD Dataset
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2021-03-14T00:01:05Z
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2021-03-19T11:15:44Z
|
2021-03-19T10:31:59Z
|
CONTRIBUTOR
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- **Name:** *MDD Dataset*
- **Description:** The Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion), from various movie reviews sources such as MovieLens and OMDb.
- **Paper:** [arXiv](https://arxiv.org/pdf/1511.06931.pdf)
- **Data:** https://research.fb.com/downloads/babi/
- **Motivation:** This is one of the popular dialog datasets, a part of Facebook Research's "bAbI project".
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
**Note**: I haven't included the following from the data files: `entities` (the file containing list of all entities in the first three subtasks), `dictionary`(the dictionary of words they use in their models), `movie_kb`(contains the knowledge base of information about the movies, actors and other entities that are mentioned in the dialogs). Please let me know if those are needed, and if yes, should I make separate configurations for them?
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| 831,006,551 |
MDU6SXNzdWU4MzEwMDY1NTE=
| 2,050 |
Build custom dataset to fine-tune Wav2Vec2
|
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2021-03-13T22:01:10Z
|
2021-03-15T09:27:28Z
|
2021-03-15T09:27:28Z
|
NONE
| null | null | null |
Thank you for your recent tutorial on how to finetune Wav2Vec2 on a custom dataset. The example you gave here (https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) was on the CommonVoice dataset. However, what if I want to load my own dataset? I have a manifest (transcript and their audio files) in a JSON file.
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MDExOlB1bGxSZXF1ZXN0NTkyNDE2MzQ0
| 2,049 |
Fix text-classification tags
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2021-03-13T19:51:42Z
|
2021-03-16T15:47:46Z
|
2021-03-16T15:47:46Z
|
CONTRIBUTOR
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There are different tags for text classification right now: `text-classification` and `text_classification`:
.
This PR fixes it.
|
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MDU6SXNzdWU4MzA5NTM0MzE=
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github is not always available - probably need a back up
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2021-03-13T18:03:32Z
|
2022-04-01T15:27:10Z
|
2022-04-01T15:27:10Z
|
MEMBER
| null | null | null |
Yesterday morning github wasn't working:
```
:/tmp$ wget https://raw.githubusercontent.com/huggingface/datasets/1.4.1/metrics/sacrebleu/sacrebleu.py--2021-03-12 18:35:59-- https://raw.githubusercontent.com/huggingface/datasets/1.4.1/metrics/sacrebleu/sacrebleu.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 500 Internal Server Error
2021-03-12 18:36:11 ERROR 500: Internal Server Error.
```
Suggestion: have a failover system and replicate the data on another system and reach there if gh isn't reachable? perhaps gh can be a master and the replicate a slave - so there is only one true source.
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MDExOlB1bGxSZXF1ZXN0NTkyMTI2NzQ3
| 2,047 |
Multilingual dIalogAct benchMark (miam)
|
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2021-03-12T23:02:55Z
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2021-03-23T10:36:34Z
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2021-03-19T10:47:13Z
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My collaborators (@EmileChapuis, @PierreColombo) and I within the Affective Computing team at Telecom Paris would like to anonymously publish the miam dataset. It is assocated with a publication currently under review. We will update the dataset with full citations once the review period is over.
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add_faisis_index gets very slow when doing it interatively
|
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2021-03-24T22:29:11Z
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2021-03-24T22:29:11Z
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NONE
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As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster?
@lhoestq
```
def training_step(self, batch, batch_idx) -> Dict:
if (not batch_idx==0) and (batch_idx%5==0):
print("******************************************************")
ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder
model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU
model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff
list_of_gpus = ['cuda:2','cuda:3']
c_dir='/custom/cache/dir'
kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir)
print(kb_dataset)
n=len(list_of_gpus) #nunber of dedicated GPUs
kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]
#kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir')
print(self.trainer.global_rank)
dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])
output = [None for _ in list_of_gpus]
#self.trainer.accelerator_connector.accelerator.barrier("embedding_process")
dist.all_gather_object(output, dataset_shards)
#This creation and re-initlaization of the new index
if (self.trainer.global_rank==0): #saving will be done in the main process
combined_dataset = concatenate_datasets(output)
passages_path =self.config.passages_path
logger.info("saving the dataset with ")
#combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage')
combined_dataset.save_to_disk(passages_path)
logger.info("Add faiss index to the dataset that consist of embeddings")
embedding_dataset=combined_dataset
index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)
embedding_dataset.add_faiss_index("embeddings", custom_index=index)
embedding_dataset.get_index("embeddings").save(self.config.index_path)
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| 2,045 |
Preserve column ordering in Dataset.rename_column
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Currently `Dataset.rename_column` doesn't necessarily preserve the order of the columns:
```python
>>> from datasets import Dataset
>>> d = Dataset.from_dict({'sentences': ["s1", "s2"], 'label': [0, 1]})
>>> d
Dataset({
features: ['sentences', 'label'],
num_rows: 2
})
>>> d.rename_column('sentences', 'text')
Dataset({
features: ['label', 'text'],
num_rows: 2
})
```
This PR fixes this.
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| 830,339,905 |
MDExOlB1bGxSZXF1ZXN0NTkxODY2NzM1
| 2,044 |
Add CBT dataset
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2021-03-19T11:10:13Z
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This PR adds the [CBT Dataset](https://arxiv.org/abs/1511.02301).
Note that I have also added the `raw` dataset as a separate configuration. I couldn't find a suitable "task" for it in YAML tags.
The dummy files have one example each, as the examples are slightly big. For `raw` dataset, I just used top few lines, because they are entire books and would take up a lot of space.
Let me know in case of any issues.
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Support pickle protocol for dataset splits defined as ReadInstruction
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Fixes #2022 (+ some style fixes)
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Fix arrow memory checks issue in tests
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The tests currently fail on `master` because the arrow memory verification doesn't return the expected memory evolution when loading an arrow table in memory.
From my experiments, the tests fail only when the full test suite is ran.
This made me think that maybe some arrow objects from other tests were not freeing their memory until they do and cause the memory verifications to fail in other tests.
Collecting the garbage collector before checking the arrow memory usage seems to fix this issue.
I added a context manager `assert_arrow_memory_increases` that we can use in tests and that deals with the gc.
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Doc2dial update data_infos and data_loaders
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MDU6SXNzdWU4MzAxNjkzODc=
| 2,040 |
ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk
|
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2021-03-12T14:27:00Z
|
2021-08-04T18:00:43Z
|
2021-08-04T18:00:43Z
|
NONE
| null | null | null |
Hi there,
I am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):
```python
concatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])
```
Yielding the following error:
```python
ValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.
However datasets' indices [1] come from memory and datasets' indices [0] come from disk.
```
Been trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...
`load_from_disk(PATH_DATA_CLS_A)['train']` yields:
```python
Dataset({
features: ['labels', 'text'],
num_rows: 785
})
```
`load_from_disk(PATH_DATA_CLS_B)['train']` yields:
```python
Dataset({
features: ['labels', 'text'],
num_rows: 3341
})
```
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| 830,047,652 |
MDExOlB1bGxSZXF1ZXN0NTkxNjE3ODY3
| 2,039 |
Doc2dial rc
|
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2021-03-12T11:56:28Z
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2021-03-12T15:32:36Z
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2021-03-12T15:32:36Z
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CONTRIBUTOR
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Added fix to handle the last turn that is a user turn.
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| 830,036,875 |
MDU6SXNzdWU4MzAwMzY4NzU=
| 2,038 |
outdated dataset_infos.json might fail verifications
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2021-03-12T11:41:54Z
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2021-03-16T16:27:40Z
|
2021-03-16T16:27:40Z
|
CONTRIBUTOR
| null | null | null |
The [doc2dial/dataset_infos.json](https://github.com/huggingface/datasets/blob/master/datasets/doc2dial/dataset_infos.json) is outdated. It would fail data_loader when verifying download checksum etc..
Could you please update this file or point me how to update this file?
Thank you.
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| 829,919,685 |
MDExOlB1bGxSZXF1ZXN0NTkxNTA4MTQz
| 2,037 |
Fix: Wikipedia - save memory by replacing root.clear with elem.clear
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2021-03-12T09:22:00Z
|
2021-03-23T06:08:16Z
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2021-03-16T11:01:22Z
|
CONTRIBUTOR
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see: https://github.com/huggingface/datasets/issues/2031
What I did:
- replace root.clear with elem.clear
- remove lines to get root element
- $ make style
- $ make test
- some tests required some pip packages, I installed them.
test results on origin/master and my branch are same. I think it's not related on my modification, isn't it?
```
==================================================================================== short test summary info ====================================================================================
FAILED tests/test_arrow_writer.py::TypedSequenceTest::test_catch_overflow - AssertionError: OverflowError not raised
============================================================= 1 failed, 2332 passed, 5138 skipped, 70 warnings in 91.75s (0:01:31) ==============================================================
make: *** [Makefile:19: test] Error 1
```
Is there anything else I should do?
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| 829,909,258 |
MDU6SXNzdWU4Mjk5MDkyNTg=
| 2,036 |
Cannot load wikitext
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2021-03-12T09:09:39Z
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2021-03-15T08:45:02Z
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2021-03-15T08:44:44Z
|
NONE
| null | null | null |
when I execute these codes
```
>>> from datasets import load_dataset
>>> test_dataset = load_dataset("wikitext")
```
I got an error,any help?
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/wikitext/wikitext.py
```
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| 2,035 |
wiki40b/wikipedia for almost all languages cannot be downloaded
|
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2021-03-11T19:54:54Z
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2021-03-16T14:53:37Z
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NONE
| null | null | null |
Hi
I am trying to download the data as below:
```
from datasets import load_dataset
dataset = load_dataset("wiki40b", "cs")
print(dataset)
```
I am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.
I really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources.
thank you very much.
```
(fast) dara@vgne046:/user/dara/dev/codes/seq2seq$ python test_data.py
Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp/dara/cache_home_2/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...
Traceback (most recent call last):
File "test_data.py", line 3, in <module>
dataset = load_dataset("wiki40b", "cs")
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/load.py", line 746, in load_dataset
use_auth_token=use_auth_token,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/builder.py", line 579, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/builder.py", line 1105, in _download_and_prepare
import apache_beam as beam
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/__init__.py", line 96, in <module>
from apache_beam import io
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/io/__init__.py", line 23, in <module>
from apache_beam.io.avroio import *
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/io/avroio.py", line 55, in <module>
import avro
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 668, in _load_unlocked
File "<frozen importlib._bootstrap>", line 638, in _load_backward_compatible
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/__init__.py", line 34, in <module>
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/__init__.py", line 30, in LoadResource
NotADirectoryError: [Errno 20] Not a directory: '/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/VERSION.txt'
```
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Fix typo
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2021-03-11T17:46:13Z
|
2021-03-11T18:06:25Z
|
2021-03-11T18:06:25Z
|
CONTRIBUTOR
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Change `ENV_XDG_CACHE_HOME ` to `XDG_CACHE_HOME `
|
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| 829,295,339 |
MDExOlB1bGxSZXF1ZXN0NTkwOTgzMDAy
| 2,033 |
Raise an error for outdated sacrebleu versions
|
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2021-03-11T16:08:00Z
|
2021-03-11T17:58:12Z
|
2021-03-11T17:58:12Z
|
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The `sacrebleu` metric seem to only work for sacrecleu>=1.4.12
For example using sacrebleu==1.2.10, an error is raised (from metric/sacrebleu/sacrebleu.py):
```python
def _compute(
self,
predictions,
references,
smooth_method="exp",
smooth_value=None,
force=False,
lowercase=False,
tokenize=scb.DEFAULT_TOKENIZER,
use_effective_order=False,
):
references_per_prediction = len(references[0])
if any(len(refs) != references_per_prediction for refs in references):
raise ValueError("Sacrebleu requires the same number of references for each prediction")
transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
> output = scb.corpus_bleu(
sys_stream=predictions,
ref_streams=transformed_references,
smooth_method=smooth_method,
smooth_value=smooth_value,
force=force,
lowercase=lowercase,
tokenize=tokenize,
use_effective_order=use_effective_order,
)
E TypeError: corpus_bleu() got an unexpected keyword argument 'smooth_method'
/mnt/cache/modules/datasets_modules/metrics/sacrebleu/b390045b3d1dd4abf6a95c4a2a11ee3bcc2b7620b076204d0ddc353fa649fd86/sacrebleu.py:114: TypeError
```
I improved the error message when users have an outdated version of sacrebleu.
The new error message tells the user to update sacrebleu.
cc @LysandreJik
|
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MDU6SXNzdWU4MjkyNTA5MTI=
| 2,032 |
Use Arrow filtering instead of writing a new arrow file for Dataset.filter
|
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2021-03-11T15:18:50Z
|
2021-03-11T17:20:57Z
| null |
MEMBER
| null | null | null |
Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.
Using a mask directly on the arrow table doesn't do any read or write operation therefore it's significantly quicker.
I think there are two cases:
- if the dataset doesn't have an indices mapping, then one can simply use the arrow filtering on the main arrow table `dataset._data.filter(...)`
- if the dataset an indices mapping, then the mask should be applied on the indices mapping table `dataset._indices.filter(...)`
The indices mapping is used to map between the idx at `dataset[idx]` in `__getitem__` and the idx in the actual arrow table.
The new filter method should therefore be faster, and allow users to pass either a filtering function (that returns a boolean given an example), or directly a mask.
Feel free to discuss this idea in this thread :)
One additional note: the refactor at #2025 would make all the pickle-related stuff work directly with the arrow filtering, so that we only need to change the Dataset.filter method without having to deal with pickle.
cc @theo-m @gchhablani
related issues: #1796 #1949
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MDU6SXNzdWU4MjkxMjI3Nzg=
| 2,031 |
wikipedia.py generator that extracts XML doesn't release memory
|
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2021-03-11T12:51:24Z
|
2021-03-22T08:33:52Z
|
2021-03-22T08:33:52Z
|
CONTRIBUTOR
| null | null | null |
I tried downloading Japanese wikipedia, but it always failed because of out of memory maybe.
I found that the generator function that extracts XML data in wikipedia.py doesn't release memory in the loop.
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L464-L502
`root.clear()` intend to clear memory, but it doesn't.
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L490
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L494
I replaced them with `elem.clear()`, then it seems to work correctly.
here is the notebook to reproduce it.
https://gist.github.com/miyamonz/dc06117302b6e85fa51cbf46dde6bb51#file-xtract_content-ipynb
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| 2,030 |
Implement Dataset from text
|
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2021-03-18T13:29:29Z
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Implement `Dataset.from_text`.
Analogue to #1943, #1946.
|
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MDU6SXNzdWU4MjkwOTcyOTA=
| 2,029 |
Loading a faiss index KeyError
|
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NONE
| null | null | null |
I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.
The basic steps are:
1. Create a dataset (dataset1)
2. Create an embeddings column using DPR
3. Add a faiss index to the dataset
4. Save faiss index to a file
5. Create a new dataset (dataset2) with the same text and label information as dataset1
6. Try to load the faiss index from file to dataset2
7. Get `KeyError: "Column embeddings not in the dataset"`
I've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.
https://colab.research.google.com/drive/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing
Ubuntu Version
VERSION="18.04.5 LTS (Bionic Beaver)"
datasets==1.4.1
faiss==1.5.3
faiss-gpu==1.7.0
torch==1.8.0+cu101
transformers==4.3.3
NVIDIA-SMI 460.56
Driver Version: 460.32.03
CUDA Version: 11.2
Tesla K80
I was basically following the steps here: https://huggingface.co/docs/datasets/faiss_and_ea.html#adding-a-faiss-index
I included the exact code from the documentation at the end of the notebook to show that they don't work either.
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Adding PersiNLU reading-comprehension
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Update format columns in Dataset.rename_columns
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Fixes #2026
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MDU6SXNzdWU4MjgxOTQ0Njc=
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KeyError on using map after renaming a column
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2021-03-11T14:38:40Z
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CONTRIBUTOR
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Hi,
I'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function.
Here is what I try:
```python
transform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])])
def prepare_features(examples):
images = []
labels = []
print(examples)
for example_idx, example in enumerate(examples["image"]):
if transform is not None:
images.append(transform(examples["image"][example_idx].permute(2,0,1)))
else:
images.append(examples["image"][example_idx].permute(2,0,1))
labels.append(examples["label"][example_idx])
output = {"label":labels, "image":images}
return output
raw_dataset = load_dataset('cifar10')
raw_dataset.set_format('torch',columns=['img','label'])
raw_dataset = raw_dataset.rename_column('img','image')
features = datasets.Features({
"image": datasets.Array3D(shape=(3,32,32),dtype="float32"),
"label": datasets.features.ClassLabel(names=[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
]),
})
train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)
```
The error:
```python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-54-bf29672c53ee> in <module>()
14 ]),
15 })
---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)
2 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1287 test_inputs = self[:2] if batched else self[0]
1288 test_indices = [0, 1] if batched else 0
-> 1289 update_data = does_function_return_dict(test_inputs, test_indices)
1290 logger.info("Testing finished, running the mapping function on the dataset")
1291
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in does_function_return_dict(inputs, indices)
1258 fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]
1259 processed_inputs = (
-> 1260 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1261 )
1262 does_return_dict = isinstance(processed_inputs, Mapping)
<ipython-input-52-b4dccbafb70d> in prepare_features(examples)
3 labels = []
4 print(examples)
----> 5 for example_idx, example in enumerate(examples["image"]):
6 if transform is not None:
7 images.append(transform(examples["image"][example_idx].permute(2,0,1)))
KeyError: 'image'
```
The print statement inside returns this:
```python
{'label': tensor([6, 9])}
```
Apparently, both `img` and `image` do not exist after renaming.
Note that this code works fine with `img` everywhere.
Notebook: https://colab.research.google.com/drive/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing
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MDExOlB1bGxSZXF1ZXN0NTg5ODk2NjMz
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[Refactor] Use in-memory/memory-mapped/concatenation tables in Dataset
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## Intro
Currently there is one assumption that we need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk with memory mapping (using the dataset._data_files).
This assumption is used for pickling for example:
- in-memory dataset can just be pickled/unpickled in-memory
- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling
## Issues
Because of this assumption, we can't easily implement methods like `Dataset.add_item` to append more rows to a dataset, or `dataset.add_column` to add a column, since we can't mix data from memory and data from the disk.
Moreover, `concatenate_datasets` doesn't work if the datasets to concatenate are not all from memory, or all form the disk.
## Solution provided in this PR
I changed this by allowing several types of Table to be used in the Dataset object.
More specifically I added three pyarrow Table wrappers: InMemoryTable, MemoryMappedTable and ConcatenationTable.
The in-memory and memory-mapped tables implement the pickling behavior described above.
The ConcatenationTable can be made from several tables (either in-memory or memory mapped) called "blocks". Pickling a ConcatenationTable simply pickles the underlying blocks.
## Implementation details
The three tables classes mentioned above all inherit from a `Table` class defined in `table.py`, which is a wrapper of a pyarrow table. The `Table` wrapper implements all the attributes and methods of the underlying pyarrow table.
Regarding the MemoryMappedTable:
Reloading a pyarrow table from the disk makes you lose all the changes you may have applied (slice, rename_columns, drop, cast etc.). Therefore the MemoryMappedTable implements a "replay" mechanism to re-apply the changes when reloading the pyarrow table from the disk.
## Checklist
- [x] add InMemoryTable
- [x] add MemoryMappedTable
- [x] add ConcatenationTable
- [x] Update the ArrowReader to use these new tables depending on the `in_memory` parameter
- [x] Update Dataset.from_xxx methods
- [x] Update load_from_disk and save_to_disk
- [x] Backward compatibility of load_from_disk
- [x] Add tests for the new tables
- [x] Update current tests
- [ ] Documentation
----------
I would be happy to discuss the design of this PR :)
Close #1877
|
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On Jan 18, XQuAD was updated with a new Romanian validation file ([xquad commit link](https://github.com/deepmind/xquad/commit/60cac411649156efb6aab9dd4c9cde787a2c0345))
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NONE
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Hi there,
I am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so:
```python
split = {
'train': ReadInstruction('train', to=90, unit='%'),
'test': ReadInstruction('train', from_=-10, unit='%')
}
dataset = load_dataset(
path='csv', # use 'text' loading script to load from local txt-files
delimiter='\t', # xxx
data_files=text_files, # list of paths to local text files
split=split, # xxx
)
dataset
```
Part of output:
```python
DatasetDict({
train: Dataset({
features: ['sentence', 'sentiment'],
num_rows: 900
})
test: Dataset({
features: ['sentence', 'sentiment'],
num_rows: 100
})
})
```
Afterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however:
```python
dataset['train'].rename_column('sentence', 'text')
```
```python
/usr/local/lib/python3.7/dist-packages/datasets/splits.py in __init__(self, name)
353 for split_name in split_names_from_instruction:
354 if not re.match(_split_re, split_name):
--> 355 raise ValueError(f"Split name should match '{_split_re}'' but got '{split_name}'.")
356
357 def __str__(self):
ValueError: Split name should match '^\w+(\.\w+)*$'' but got 'ReadInstruction('.
```
In particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split.
Thanks in advance! :)
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NONE
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dataset_info.json file saved after using save_to_disk gets corrupted as follows.
Is there a way to disable the cache that will save to /tmp/huggiface/datastes ?
I have a feeling there is a serious issue with cashing.
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Related to this PR: #1998
Additionally, this PR adds the docstart note to the conll2002 dataset card ([link](https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/ned.train) to the raw data with `DOCSTART` lines).
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Replaces `print(...)` in the dataset scripts with the library logger.
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I updated the descriptions of the datasets as they appear in the HF repo and the descriptions of the source datasets according to what I could find from the paper and the references. I'm still a little unclear about some of the fields of the different configs, and there was little info on the word list and name list. I'll contact the authors to see if they have any additional information or suggested changes.
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Hi,
I am creating this draft PR to work on add features similar to [TF datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/core/features). I'll be starting with `Tensor` and `FeatureConnector` classes, and build upon them to add other features as well. This is a work in progress.
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The test at `tests/test_caching.py::RecurseDumpTest::test_dump_ipython_function` was failing in python 3.8 because the ipython function was not properly created.
Fix #2010
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closed
| false | null |
[] | null |
[] |
2021-03-09T13:18:29Z
|
2021-03-10T10:08:37Z
|
2021-03-10T10:08:36Z
|
CONTRIBUTOR
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re: #2009
not super convinced this is better, and while I usually fight against kwargs here it seems to me that it better conveys the relationship to the `_split_generator` method.
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| 825,694,305 |
MDExOlB1bGxSZXF1ZXN0NTg3NzYzMTgx
| 2,013 |
Add Cryptonite dataset
|
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2021-03-09T10:32:11Z
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2021-03-09T19:27:07Z
|
2021-03-09T19:27:06Z
|
CONTRIBUTOR
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cc @aviaefrat who's the original author of the dataset & paper, see https://github.com/aviaefrat/cryptonite
|
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| null | null | true |
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