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https://api.github.com/repos/huggingface/datasets/issues/4662 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4662/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4662/comments | https://api.github.com/repos/huggingface/datasets/issues/4662/events | https://github.com/huggingface/datasets/pull/4662 | 1,298,845,369 | PR_kwDODunzps47GTEc | 4,662 | Fix: conll2003 - fix empty example | {
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} | As reported in https://huggingface.co/datasets/conll2003/discussions/2#62c45a14f93fc97e8260532f, there was an extra empty example at the end of the dataset | {
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] | open | false | null | [] | null | [] | 2022-07-08T01:58:11Z | 2022-07-15T17:11:23Z | null | NONE | null | null | null | ## Describe the bug
I used to see this bug with an older version of the datasets. It seems to persist.
This is my concrete scenario: I launch several evaluation jobs on a cluster in which I share the file system and I share the cache directory used by huggingface libraries. The evaluation jobs read the same *.csv files. If my jobs get all scheduled pretty much at the same time, there are all kinds of weird concurrency errors. Sometime it crashes silently. This time I got lucky that it crashed with a stack trace that I can share and maybe you get to the bottom of this. If you don't have a similar setup available, it may be hard to reproduce as you really need two jobs accessing the same file at the same time to see this type of bug.
## Steps to reproduce the bug
I'm running a modified version of `run_glue.py` script adapted to my use case. I've seen the same problem when running some glue datasets as well (so it's not specific to loading the datasets from csv files).
## Expected results
No crash, concurrent access to the (intermediate) files just fine.
## Actual results
Crashes due to races/concurrency bugs.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.18.0-348.23.1.el8_5.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 8.0.0
- Pandas version: 1.1.0
Stack trace that I just got with the crash (I've obfuscated some names, it should still be quite informative):
```
Running tokenizer on dataset: 0%| | 0/3 [00:00<?, ?ba/s]
Traceback (most recent call last):
File "../../src/models//run_*******.py", line 600, in <module>
main()
File "../../src/models//run_*******.py", line 444, in main
raw_datasets = raw_datasets.map(
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 770, in map
{
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 771, in <dictcomp>
k: dataset.map(
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2376, in map
return self._map_single(
File "/*******/envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 551, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 518, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/*******/envs/tr-crt/lib/python3.8/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2776, in _map_single
buf_writer, writer, tmp_file = init_buffer_and_writer()
File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2696, in init_buffer_and_writer
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False)
File "/*******//envs/tr-crt/lib/python3.8/tempfile.py", line 541, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/*******//envs/tr-crt/lib/python3.8/tempfile.py", line 250, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '/*******/cache-transformers//transformers/csv/default-ef9cd184210742a7/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58/tmps8l6j5yc'
```
As I ran 100s of experiments last year for an empirical paper, I ran into this type of bugs several times. I found several bandaid/work-arounds, e.g., run one job first that caches the dataset => eliminate concurrency; OR use unique caches => eliminate concurrency (but increase storage space), etc. and it all works fine.
I'd like to help you fixing this bug as it's really annoying to always apply the work arounds. Let me know what other info from my side could help you figure out the issue.
Thanks for your help!
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https://api.github.com/repos/huggingface/datasets/issues/4660 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4660/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4660/comments | https://api.github.com/repos/huggingface/datasets/issues/4660/events | https://github.com/huggingface/datasets/pull/4660 | 1,297,128,387 | PR_kwDODunzps47AYDq | 4,660 | Fix _resolve_single_pattern_locally on Windows with multiple drives | {
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/4660"
} | Currently, when `_resolve_single_pattern_locally` is called from a different drive than the one in `pattern`, it raises an exception:
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\io\parquet.py:35: in __init__
**kwargs,
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\builder.py:287: in __init__
sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:761: in from_local_or_remote
if not isinstance(patterns_for_key, DataFilesList)
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:723: in from_local_or_remote
data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:321: in resolve_patterns_locally_or_by_urls
for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:239: in _resolve_single_pattern_locally
for filepath in glob_iter
C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:242: in <listcomp>
os.path.relpath(filepath, base_path), os.path.relpath(pattern, base_path)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
path = 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pytest-of-runneradmin\\pytest-0\\popen-gw0\\data6\\dataset.parquet'
start = '/'
...
E ValueError: path is on mount 'C:', start on mount 'D:'
```
This PR makes sure that `base_path` is in the same drive as `pattern`. | {
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https://api.github.com/repos/huggingface/datasets/issues/4659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4659/comments | https://api.github.com/repos/huggingface/datasets/issues/4659/events | https://github.com/huggingface/datasets/pull/4659 | 1,297,094,140 | PR_kwDODunzps47AQo9 | 4,659 | Transfer CI to GitHub Actions | {
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} | This PR transfers CI from CircleCI to GitHub Actions. The implementation in GitHub Actions tries to be as faithful as possible to the implementation in CircleCI and get the same output results (exceptions below).
**IMPORTANT NOTE**: The fast-fail policy (described below) is not finally implemented, so that:
- we can continue merging PRs with CI in red because of some random error returned by the Hub
- it is not annoying for maintainers to have to relaunch failed CI jobs
See comments here: https://github.com/huggingface/datasets/pull/4659#discussion_r918802348
Differences in the implementation in GitHub Actions compared to the CircleCI one:
- This PR introduces some *fail-fast* mechanisms to significantly reduce the total time CI is running, both because of environmental impact and because CI in GitHub Actions billing depends on the minutes per month running time (see [About billing for GitHub Actions](https://docs.github.com/en/billing/managing-billing-for-github-actions/about-billing-for-github-actions)):
- All tests *depend* on `check_code_quality` job: only if `check_code_quality` passes, then the other test jobs are launched
- The tests are implemented with a matrix strategy (cross-product: OS and PyArrow versions) and fail-fast: if any of the 4 processes fails, the others are cancelled
- OS dependencies for Linux (see table below)
| OS dependencies | Passed tests | Skipped tests |
| --- | ---: | ---: |
| libsndfile1-dev | 4786 | 3119 |
| libsndfile1 | 4786 | 3119 |
| libsndfile1, sox | 4788 | 3117 |
- This PR replaces `libsndfile1-dev` with `libsndfile1`: the same number of passing tests but less packages installed
- This PR adds `sox`: required by MP3 tests (2 more tests are passed: 4788 instead of 4786)
- For tests using PyArrow 6, this PR uses 6.0.1 instead of 6.0.0
TO DO:
- [ ] Remove old CircleCI CI: kept for the moment to compare stability and performance
Close #4658.
## Comparison between CircleCI and GitHub Actions
| | | CircleCI | GitHub Actions |
| --- | --- | ---: | ---: |
| Ubuntu, pyarrow-latest ||||
|| Passed tests | 4786 | 4788 |
|| Duration | 11m 0s | 10m 10s |
| Windows, pyarrow-latest ||||
|| Passed tests | 4783 | 4783 |
|| Duration | 29m 59s | 22m 56s | | {
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] | null | [] | 2022-07-07T08:10:50Z | 2022-07-12T11:18:25Z | 2022-07-12T11:18:25Z | MEMBER | null | null | null | Let's try CI tests using GitHub Actions to see if they are more stable than on CircleCI. | {
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https://api.github.com/repos/huggingface/datasets/issues/4657 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4657/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4657/comments | https://api.github.com/repos/huggingface/datasets/issues/4657/events | https://github.com/huggingface/datasets/issues/4657 | 1,296,743,133 | I_kwDODunzps5NSrrd | 4,657 | Add SQuAD2.0 Dataset | {
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] | closed | false | null | [] | null | [] | 2022-07-07T03:19:36Z | 2022-07-12T16:14:52Z | 2022-07-12T16:14:52Z | NONE | null | null | null | ## Adding a Dataset
- **Name:** *SQuAD2.0*
- **Description:** *Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.*
- **Paper:** *https://aclanthology.org/P18-2124.pdf*
- **Data:** *https://rajpurkar.github.io/SQuAD-explorer/*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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https://api.github.com/repos/huggingface/datasets/issues/4656 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4656/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4656/comments | https://api.github.com/repos/huggingface/datasets/issues/4656/events | https://github.com/huggingface/datasets/issues/4656 | 1,296,740,266 | I_kwDODunzps5NSq-q | 4,656 | Add Amazon-QA Dataset | {
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- **Name:** *Amazon-QA*
- **Description:** *The dataset is .jsonl format, where each line in the file is a json string that corresponds to a question, existing answers to the question and the extracted review snippets (relevant to the question).*
- **Paper:** *https://github.com/amazonqa/amazonqa/tree/master/paper*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon-qa.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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https://api.github.com/repos/huggingface/datasets/issues/4655 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4655/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4655/comments | https://api.github.com/repos/huggingface/datasets/issues/4655/events | https://github.com/huggingface/datasets/issues/4655 | 1,296,720,896 | I_kwDODunzps5NSmQA | 4,655 | Simple Wikipedia | {
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] | closed | false | null | [] | null | [] | 2022-07-07T02:51:26Z | 2022-07-14T02:16:33Z | 2022-07-14T02:16:33Z | NONE | null | null | null | ## Adding a Dataset
- **Name:** *Simple Wikipedia*
- **Description:** *Two different versions of the data set now exist. Both were generated by aligning Simple English Wikipedia and English Wikipedia. A complete description of the extraction process can be found in "Simple English Wikipedia: A New Simplification Task", William Coster and David Kauchak (2011).*
- **Paper:** *https://aclanthology.org/P11-2117/*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/SimpleWiki.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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https://api.github.com/repos/huggingface/datasets/issues/4654 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4654/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4654/comments | https://api.github.com/repos/huggingface/datasets/issues/4654/events | https://github.com/huggingface/datasets/issues/4654 | 1,296,716,119 | I_kwDODunzps5NSlFX | 4,654 | Add Quora Question Triplets Dataset | {
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- **Name:** *Quora Question Triplets*
- **Description:** *This dataset consists of over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair.*
- **Paper:**
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates_triplets.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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https://api.github.com/repos/huggingface/datasets/issues/4653 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4653/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4653/comments | https://api.github.com/repos/huggingface/datasets/issues/4653/events | https://github.com/huggingface/datasets/issues/4653 | 1,296,702,834 | I_kwDODunzps5NSh1y | 4,653 | Add Altlex dataset | {
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- **Name:** *Altlex*
- **Description:** *Git repository for software associated with the 2016 ACL paper "Identifying Causal Relations Using Parallel Wikipedia Articles.”*
- **Paper:** *https://aclanthology.org/P16-1135.pdf*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/altlex.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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https://api.github.com/repos/huggingface/datasets/issues/4652 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4652/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4652/comments | https://api.github.com/repos/huggingface/datasets/issues/4652/events | https://github.com/huggingface/datasets/issues/4652 | 1,296,697,498 | I_kwDODunzps5NSgia | 4,652 | Add Sentence Compression Dataset | {
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- **Name:** *Sentence Compression*
- **Description:** *Large corpus of uncompressed and compressed sentences from news articles.*
- **Paper:** *https://www.aclweb.org/anthology/D13-1155/*
- **Data:** *https://github.com/google-research-datasets/sentence-compression/tree/master/data*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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] | closed | false | null | [] | null | [] | 2022-07-07T01:59:08Z | 2022-07-14T02:09:45Z | 2022-07-14T02:09:45Z | NONE | null | null | null | ## Adding a Dataset
- **Name:** *Flickr 30k*
- **Description:** *To produce the denotation graph, we have created an image caption corpus consisting of 158,915 crowd-sourced captions describing 31,783 images. This is an extension of our previous Flickr 8k Dataset. The new images and captions focus on people involved in everyday activities and events.*
- **Paper:** *https://transacl.org/ojs/index.php/tacl/article/view/229/33*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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] | open | false | null | [] | null | [] | 2022-07-07T01:41:32Z | 2022-07-14T02:07:49Z | null | NONE | null | null | null | ## Adding a Dataset
- **Name:** *SPECTER*
- **Description:** *SPECTER: Document-level Representation Learning using Citation-informed Transformers*
- **Paper:** *https://doi.org/10.18653/v1/2020.acl-main.207*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/specter_train_triples.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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- **Name:** *PAQ*
- **Description:** *This repository contains code and models to support the research paper PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them*
- **Paper:** *https://arxiv.org/abs/2102.07033*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/PAQ_pairs.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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- **Name:** *WikiAnswers*
- **Description:** *The WikiAnswers corpus contains clusters of questions tagged by WikiAnswers users as paraphrases. Each cluster optionally contains an answer provided by WikiAnswers users.*
- **Paper:** *https://dl.acm.org/doi/10.1145/2623330.2623677*
- **Data:** *https://github.com/afader/oqa#wikianswers-corpus*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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] | open | false | null | [] | null | [] | 2022-07-06T19:49:18Z | 2022-07-06T19:49:18Z | null | NONE | null | null | null | ## Adding a Dataset
- **Name:** *Reddit comments (2015-2018)*
- **Description:** *Reddit is an American social news aggregation website, where users can post links, and take part in discussions on these posts. These threaded discussions provide a large corpus, which is converted into a conversational dataset using the tools in this directory.*
- **Paper:** *https://arxiv.org/abs/1904.06472*
- **Data:** *https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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} | After renaming "master" to "main", the CI fails with
```
AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/main/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at /home/circleci/datasets/_dummy/_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py"
```
This is because in the CI we were still using `HF_SCRIPTS_VERSION=master`. I changed it to "main" | {
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} | This PR renames mentions of "master" by "main" in the code base for several cases:
- set the default dataset script version to "main" if the local installation of `datasets` is a dev installation
- update URLs to this github repository to use "main"
- update the DVC benchmark
- update the github workflows
- update docstrings
- update tests to compare the changes in dataset cards against "main"
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https://huggingface.co/datasets/ccdv/pubmed-summarization
### Description
This was reported by a [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/7). It seems like streaming doesn't work due to the way the dataset loading script is defined?
```
Status code: 400
Exception: FileNotFoundError
Message: https://huggingface.co/datasets/ccdv/pubmed-summarization/resolve/main/train.zip/train.txt
```
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https://huggingface.co/datasets/kmfoda/booksum
### Description
A [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/9) discovered this dataset cannot be streamed due to:
```
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/kmfoda/booksum/resolve/47953f583d6967f086cb16a2f4d2346e9834024d/test.csv')
```
I'm not sure why it says "Unauthorized" since it's just a bunch of CSV files in a repo
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- **Name:** HaGRID -- HAnd Gesture Recognition Image Dataset
- **Description:** We introduce a large image dataset HaGRID (HAnd Gesture Recognition Image Dataset) for hand gesture recognition (HGR) systems. You can use it for image classification or image detection tasks. Proposed dataset allows to build HGR systems, which can be used in video conferencing services (Zoom, Skype, Discord, Jazz etc.), home automation systems, the automotive sector, etc.
- **Paper:** https://arxiv.org/abs/2206.08219
- **Data:** https://github.com/hukenovs/hagrid
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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} | [speechocean762](https://www.openslr.org/101/) is a non-native English corpus for pronunciation scoring tasks. It is free for both commercial and non-commercial use.
I believe it will be easier to use if it could be available on Hugging Face. | {
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Not sure if this is a bug or just the way streaming works, but setting `streaming=True` did not work when setting `split="all"`
## Steps to reproduce the bug
The following works:
```python
ds = load_dataset('super_glue', 'wsc.fixed', split='all')
```
The following throws `ValueError: Bad split: all. Available splits: ['train', 'validation', 'test']`:
```python
ds = load_dataset('super_glue', 'wsc.fixed', split='all', streaming=True)
```
## Expected results
An iterator over all splits.
## Actual results
I had to do the following to achieve the desired result:
```python
from itertools import chain
ds = load_dataset('super_glue', 'wsc.fixed', streaming=True)
it = chain.from_iterable(ds.values())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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I went to override `download_config.num_proc` and was confused about what was happening under the hood. It would be nice to have the behavior documented a bit better so folks know what's happening when they use it.
**Describe the solution you'd like**
- Add note about how the default number of workers is 16. Related code:
https://github.com/huggingface/datasets/blob/7bcac0a6a0fc367cc068f184fa132b8de8dfa11d/src/datasets/download/download_manager.py#L299-L302
- Add note that if the number of workers is higher than the number of files to download, it won't use multiprocessing.
**Describe alternatives you've considered**
maybe it would also be nice to set `num_proc` = `num_files` when `num_proc` > `num_files`.
**Additional context**
...
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https://huggingface.co/datasets/vadis/sv-ident/viewer/default/validation
### Description
Error message when loading validation split in the viewer:
```
Status code: 400
Exception: Status400Error
Message: The split cache is empty.
```
### Owner
_No response_ | {
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} | As reported in https://github.com/huggingface/datasets/issues/4477, the current pattern matching to infer which file goes into which split is too permissive. For example a file "contest.py" would be considered part of a test split (it contains "test") and "seqeval.py" as well (it contains "eval").
In this PR I made the pattern matching more robust by only matching split names **between separators**. The supported separators are dots, dashes, spaces and underscores.
I updated the docs accordingly.
One detail about the tests: I had to update one test because it was using `PurePath.match` as a reference for globbing, but it doesn't support the `[..]` glob pattern. Therefore I added a `mock_fs` context manager that can be used to easily define a dummy filesystem with certain files in it and run pattern matching tests. Its code comes mostly from test_streaming_download_manager.py
Close https://github.com/huggingface/datasets/issues/4477 | {
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I think this makes it a bit clearer! | {
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Apparently, newest `fsspec` versions do not allow access to attribute-based modules if they are not imported, such as `fsspec.async`.
Thus, @mariosasko suggested to add the missing part to the module import to allow for its access. | {
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] | null | [] | 2022-07-04T17:16:10Z | 2022-07-06T15:49:57Z | 2022-07-06T15:49:57Z | MEMBER | null | null | null | Rename repository default branch to `main` (instead of current `master`).
Once renamed, users will have to manually update their local repos:
- [ ] Upstream:
```
git branch -m master main
git fetch upstream main
git branch -u upstream/main main
git remote set-head upstream -a
```
- [ ] Origin:
Rename fork default branch as well at: https://github.com/USERNAME/lam/settings/branches
Then:
```
git fetch origin main
git remote set-head origin -a
```
CC: @sgugger | {
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https://api.github.com/repos/huggingface/datasets/issues/4628 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4628/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4628/comments | https://api.github.com/repos/huggingface/datasets/issues/4628/events | https://github.com/huggingface/datasets/pull/4628 | 1,293,361,308 | PR_kwDODunzps46zvFJ | 4,628 | Fix time type `_arrow_to_datasets_dtype` conversion | {
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} | Fix #4620
The issue stems from the fact that `pa.array([time_data]).type` returns `DataType(time64[unit])`, which doesn't expose the `unit` attribute, instead of `Time64Type(time64[unit])`. I believe this is a bug in PyArrow. Luckily, the both types have the same `str()`, so in this PR I call `pa.type_for_alias(str(type))` to convert them both to the `Time64Type(time64[unit])` format.
cc @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/4627 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4627/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4627/comments | https://api.github.com/repos/huggingface/datasets/issues/4627/events | https://github.com/huggingface/datasets/pull/4627 | 1,293,287,798 | PR_kwDODunzps46zfNa | 4,627 | fixed duplicate calculation of spearmanr function in metrics wrapper. | {
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} | During _compute, the scipy.stats spearmanr function was called twice, redundantly, once for calculating the score and once for calculating the p-value, under the conditional branch where return_pvalue=True. I adjusted the _compute function to execute the spearmanr function once, store the results tuple in a temporary variable, and then pass the indexed contents to the expected keys of the returned dictionary. | {
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https://api.github.com/repos/huggingface/datasets/issues/4626 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4626/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4626/comments | https://api.github.com/repos/huggingface/datasets/issues/4626/events | https://github.com/huggingface/datasets/issues/4626 | 1,293,256,269 | I_kwDODunzps5NFYZN | 4,626 | Add non-commercial licensing info for datasets for which we removed tags | {
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} | [] | open | false | null | [] | null | [] | 2022-07-04T14:32:43Z | 2022-07-08T14:27:29Z | null | MEMBER | null | null | null | We removed several YAML tags saying that certain datasets can't be used for commercial purposes: https://github.com/huggingface/datasets/pull/4613#discussion_r911919753
Reason for this is that we only allow tags that are part of our [supported list of licenses](https://github.com/huggingface/datasets/blob/84fc3ad73c85de4eda5d152dfede7671491449cb/src/datasets/utils/resources/standard_licenses.tsv)
We should update the Licensing Information section of the concerned dataset cards, now that the non-commercial tag doesn't exist anymore for certain datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/4625 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4625/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4625/comments | https://api.github.com/repos/huggingface/datasets/issues/4625/events | https://github.com/huggingface/datasets/pull/4625 | 1,293,163,744 | PR_kwDODunzps46zELz | 4,625 | Unpack `dl_manager.iter_files` to allow parallization | {
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} | [] | closed | false | null | [] | null | [] | 2022-07-04T13:16:58Z | 2022-07-05T11:11:54Z | 2022-07-05T11:00:48Z | CONTRIBUTOR | null | false | {
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} | Iterate over data files outside `dl_manager.iter_files` to allow parallelization in streaming mode.
(The issue reported [here](https://discuss.huggingface.co/t/dataset-only-have-n-shard-1-when-has-multiple-shards-in-repo/19887))
PS: Another option would be to override `FilesIterable.__getitem__` to make it indexable and check for that type in `_shard_kwargs` and `n_shards,` but IMO this solution adds too much unnecessary complexity. | {
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https://api.github.com/repos/huggingface/datasets/issues/4624 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4624/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4624/comments | https://api.github.com/repos/huggingface/datasets/issues/4624/events | https://github.com/huggingface/datasets/pull/4624 | 1,293,085,058 | PR_kwDODunzps46yzOK | 4,624 | Remove all paperswithcode_id: null | {
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} | On the Hub there is a validation error on the `paperswithcode_id` tag when the value is `null`:
<img width="686" alt="image" src="https://user-images.githubusercontent.com/42851186/177151825-93d341c5-25bd-41ab-96c2-c0b516d51c68.png">
We've been using `null` to specify that we checked on pwc but the dataset doesn't exist there.
To have the validation working again we can simply remove all the `paperswithcode_id: null`.
cc @julien-c | {
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https://api.github.com/repos/huggingface/datasets/issues/4623 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4623/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4623/comments | https://api.github.com/repos/huggingface/datasets/issues/4623/events | https://github.com/huggingface/datasets/issues/4623 | 1,293,042,894 | I_kwDODunzps5NEkTO | 4,623 | Loading MNIST as Pytorch Dataset | {
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] | open | false | null | [] | null | [] | 2022-07-04T11:33:10Z | 2022-07-04T14:40:50Z | null | NONE | null | null | null | ## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```
## Expected results
Expect to see torch tensors image and label
## Actual results
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
dataset[0]
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
return self._getitem(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
formatted_output = format_table(
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
mapped = [
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
return function(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
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https://api.github.com/repos/huggingface/datasets/issues/4622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4622/comments | https://api.github.com/repos/huggingface/datasets/issues/4622/events | https://github.com/huggingface/datasets/pull/4622 | 1,293,031,939 | PR_kwDODunzps46ynmT | 4,622 | Fix ImageFolder with parameters drop_metadata=True and drop_labels=False (when metadata.jsonl is present) | {
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} | [] | closed | false | null | [] | null | [] | 2022-07-04T11:23:20Z | 2022-07-15T14:37:23Z | 2022-07-15T14:24:24Z | CONTRIBUTOR | null | false | {
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/4622"
} | Will fix #4621
ImageFolder raises `KeyError: 'label'` with params `drop_metadata=True` and `drop_labels=False` (if there is at least one metadata.jsonl file a data directory). This happens because metadata files are collected inside `analyze()` function regardless of `drop_metadata` value. And then the following condition doesn't pass: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/imagefolder/imagefolder.py#L167
So I suggest to double check it inside `analyze()` not to collect metadata files if they are not needed. (and labels too, to be consistent)
---
Also, I added a test to check if labels are inferred correctly from directories names in general (because we didn't have it) :) | {
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https://api.github.com/repos/huggingface/datasets/issues/4621 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4621/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4621/comments | https://api.github.com/repos/huggingface/datasets/issues/4621/events | https://github.com/huggingface/datasets/issues/4621 | 1,293,030,128 | I_kwDODunzps5NEhLw | 4,621 | ImageFolder raises an error with parameters drop_metadata=True and drop_labels=False when metadata.jsonl is present | {
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] | null | [] | 2022-07-04T11:21:44Z | 2022-07-15T14:24:24Z | 2022-07-15T14:24:24Z | CONTRIBUTOR | null | null | null | ## Describe the bug
If you pass `drop_metadata=True` and `drop_labels=False` when a `data_dir` contains at least one `matadata.jsonl` file, you will get a KeyError. This is probably not a very useful case but we shouldn't get an error anyway. Asking users to move metadata files manually outside `data_dir` or pass features manually (when there is a tool that can infer them automatically) don't look like a good idea to me either.
## Steps to reproduce the bug
### Clone an example dataset from the Hub
```bash
git clone https://huggingface.co/datasets/nateraw/test-imagefolder-metadata
```
### Try to load it
```python
from datasets import load_dataset
ds = load_dataset("test-imagefolder-metadata", drop_metadata=True, drop_labels=False)
```
or even just
```python
ds = load_dataset("test-imagefolder-metadata", drop_metadata=True)
```
as `drop_labels=False` is a default value.
## Expected results
A DatasetDict object with two features: `"image"` and `"label"`.
## Actual results
```
Traceback (most recent call last):
File "/home/polina/workspace/datasets/debug.py", line 18, in <module>
ds = load_dataset(
File "/home/polina/workspace/datasets/src/datasets/load.py", line 1732, in load_dataset
builder_instance.download_and_prepare(
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1218, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1596, in encode_example
return encode_nested_example(self, example)
File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in encode_nested_example
{
File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in <dictcomp>
{
File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in zip_dict
yield key, tuple(d[key] for d in dicts)
File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in <genexpr>
yield key, tuple(d[key] for d in dicts)
KeyError: 'label'
```
## Environment info
`datasets` master branch
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.14.0-1042-oem-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1
- Pandas version: 1.4.1
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https://api.github.com/repos/huggingface/datasets/issues/4620 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4620/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4620/comments | https://api.github.com/repos/huggingface/datasets/issues/4620/events | https://github.com/huggingface/datasets/issues/4620 | 1,292,797,878 | I_kwDODunzps5NDoe2 | 4,620 | Data type is not recognized when using datetime.time | {
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] | null | [] | 2022-07-04T08:13:38Z | 2022-07-07T13:57:11Z | 2022-07-07T13:57:11Z | CONTRIBUTOR | null | null | null | ## Describe the bug
Creating a dataset from a pandas dataframe with `datetime.time` format generates an error.
## Steps to reproduce the bug
```python
import pandas as pd
from datetime import time
from datasets import Dataset
df = pd.DataFrame({"feature_name": [time(1, 1, 1)]})
dataset = Dataset.from_pandas(df)
```
## Expected results
The dataset should be created.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 823, in from_pandas
return cls(table, info=info, split=split)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 679, in __init__
inferred_features = Features.from_arrow_schema(arrow_table.schema)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in from_arrow_schema
obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in <dictcomp>
obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1315, in generate_from_arrow_type
return Value(dtype=_arrow_to_datasets_dtype(pa_type))
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 83, in _arrow_to_datasets_dtype
return f"time64[{arrow_type.unit}]"
AttributeError: 'pyarrow.lib.DataType' object has no attribute 'unit'
```
## Environment info
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.13.0-1031-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.4.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/4619 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4619/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4619/comments | https://api.github.com/repos/huggingface/datasets/issues/4619/events | https://github.com/huggingface/datasets/issues/4619 | 1,292,107,275 | I_kwDODunzps5NA_4L | 4,619 | np arrays get turned into native lists | {
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] | open | false | null | [] | null | [] | 2022-07-02T17:54:57Z | 2022-07-03T20:27:07Z | null | NONE | null | null | null | ## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?
## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```
## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.
## Actual results
It's a list.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/4618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4618/comments | https://api.github.com/repos/huggingface/datasets/issues/4618/events | https://github.com/huggingface/datasets/issues/4618 | 1,292,078,225 | I_kwDODunzps5NA4yR | 4,618 | contribute data loading for object detection datasets with yolo data format | {
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] | open | false | null | [] | null | [] | 2022-07-02T15:21:59Z | 2022-07-21T14:10:44Z | null | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
At the moment, HF datasets loads [image classification datasets](https://huggingface.co/docs/datasets/image_process) out-of-the-box. There could be a data loader for loading standard object detection datasets ([original discussion here](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/2))
**Describe the solution you'd like**
I wrote a [custom script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py) to load dataset which has YOLO data format.
**Describe alternatives you've considered**
The script can either be a standalone dataset builder, or a modified version of `ImageFolder`
**Additional context**
I would be happy to contribute to this, but I would do it at a very slow pace (maybe a month or two) as I have my exams approaching 😄
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https://api.github.com/repos/huggingface/datasets/issues/4615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4615/comments | https://api.github.com/repos/huggingface/datasets/issues/4615/events | https://github.com/huggingface/datasets/pull/4615 | 1,291,307,428 | PR_kwDODunzps46tADt | 4,615 | Fix `embed_storage` on features inside lists/sequences | {
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} | [] | closed | false | null | [] | null | [] | 2022-07-01T11:52:08Z | 2022-07-08T12:13:10Z | 2022-07-08T12:01:36Z | CONTRIBUTOR | null | false | {
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} | Add a dedicated function for embed_storage to always preserve the embedded/casted arrays (and to have more control over `embed_storage` in general).
Fix #4591
~~(Waiting for #4608 to be merged to mark this PR as ready for review - required for fixing `xgetsize` in private repos)~~ Done! | {
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https://api.github.com/repos/huggingface/datasets/issues/4614 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4614/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4614/comments | https://api.github.com/repos/huggingface/datasets/issues/4614/events | https://github.com/huggingface/datasets/pull/4614 | 1,291,218,020 | PR_kwDODunzps46ssfw | 4,614 | Ensure ConcatenationTable.cast uses target_schema metadata | {
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} | Currently, `ConcatenationTable.cast` does not use target_schema metadata when casting subtables. This causes an issue when using cast_column and the underlying table is a ConcatenationTable.
Code example of where issue arrises:
```
from datasets import Dataset, Image
column1 = [0, 1]
image_paths = ['/images/image1.jpg', '/images/image2.jpg']
ds = Dataset.from_dict({"column1": column1})
ds = ds.add_column("image", image_paths)
ds.cast_column("image", Image()) # Fails here
```
Output
```
...
TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)}
```
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https://api.github.com/repos/huggingface/datasets/issues/4612 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4612/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4612/comments | https://api.github.com/repos/huggingface/datasets/issues/4612/events | https://github.com/huggingface/datasets/issues/4612 | 1,290,984,660 | I_kwDODunzps5M8tzU | 4,612 | Release 2.3.0 broke custom iterable datasets | {
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] | closed | false | null | [] | null | [] | 2022-07-01T06:46:07Z | 2022-07-05T15:08:21Z | 2022-07-05T15:08:21Z | NONE | null | null | null | ## Describe the bug
Trying to iterate examples from custom iterable dataset fails to bug introduced in `torch_iterable_dataset.py` since the release of 2.3.0.
## Steps to reproduce the bug
```python
next(iter(custom_iterable_dataset))
```
## Expected results
`next(iter(custom_iterable_dataset))` should return examples from the dataset
## Actual results
```
/usr/local/lib/python3.7/dist-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py in _set_fsspec_for_multiprocess()
16 See https://github.com/fsspec/gcsfs/issues/379
17 """
---> 18 fsspec.asyn.iothread[0] = None
19 fsspec.asyn.loop[0] = None
20
AttributeError: module 'fsspec' has no attribute 'asyn'
```
## Environment info
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
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https://api.github.com/repos/huggingface/datasets/issues/4611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4611/comments | https://api.github.com/repos/huggingface/datasets/issues/4611/events | https://github.com/huggingface/datasets/pull/4611 | 1,290,940,874 | PR_kwDODunzps46rxIX | 4,611 | Preserve member order by MockDownloadManager.iter_archive | {
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} | Currently, `MockDownloadManager.iter_archive` yields paths to archive members in an order given by `path.rglob("*")`, which migh not be the same order as in the original archive.
See issue in:
- https://github.com/huggingface/datasets/pull/4579#issuecomment-1172135027
This PR fixes the order of the members yielded by `MockDownloadManager.iter_archive` so that it is the same as in the original archive. | {
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https://api.github.com/repos/huggingface/datasets/issues/4610 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4610/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4610/comments | https://api.github.com/repos/huggingface/datasets/issues/4610/events | https://github.com/huggingface/datasets/issues/4610 | 1,290,603,827 | I_kwDODunzps5M7Q0z | 4,610 | codeparrot/github-code failing to load | {
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] | null | [] | 2022-06-30T20:24:48Z | 2022-07-05T14:24:13Z | 2022-07-05T09:19:56Z | NONE | null | null | null | ## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.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)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/4609 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4609/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4609/comments | https://api.github.com/repos/huggingface/datasets/issues/4609/events | https://github.com/huggingface/datasets/issues/4609 | 1,290,392,083 | I_kwDODunzps5M6dIT | 4,609 | librispeech dataset has to download whole subset when specifing the split to use | {
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] | closed | false | null | [] | null | [] | 2022-06-30T16:38:24Z | 2022-07-12T21:44:32Z | 2022-07-12T21:44:32Z | NONE | null | null | null | ## Describe the bug
librispeech dataset has to download whole subset when specifing the split to use
## Steps to reproduce the bug
see below
# Sample code to reproduce the bug
```
!pip install datasets
from datasets import load_dataset
raw_dataset = load_dataset("librispeech_asr", "clean", split="train.100")
```
## Expected results
The split "train.clean.100" is downloaded.
## Actual results
All four splits in "clean" subset is downloaded.
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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} | [] | closed | false | null | [] | null | [] | 2022-06-30T15:23:21Z | 2022-07-06T12:45:59Z | 2022-07-06T12:34:19Z | MEMBER | null | false | {
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} | `xisfile` is working in a private repository when passing a chained URL to a file inside an archive, e.g. `zip://a.txt::https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`. However it's not working when passing a simple file `https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`.
This is because the authentication headers are not passed correctly in this case.
This is causing dataset streaming to fail in private parquet repositories, as noted in https://github.com/huggingface/datasets/issues/4605
I fixed `xisfile` and the other functions that behave the same way: xgetsize, xisdir and xlistdir
TODO:
- [x] tests
fix https://github.com/huggingface/datasets/issues/4605 | {
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https://api.github.com/repos/huggingface/datasets/issues/4607 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4607/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4607/comments | https://api.github.com/repos/huggingface/datasets/issues/4607/events | https://github.com/huggingface/datasets/pull/4607 | 1,290,171,941 | PR_kwDODunzps46pLnd | 4,607 | Align more metadata with other repo types (models,spaces) | {
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} | see also associated PR on the `datasets-tagging` Space: https://huggingface.co/spaces/huggingface/datasets-tagging/discussions/2 (to merge after this one is merged) | {
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https://api.github.com/repos/huggingface/datasets/issues/4606 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4606/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4606/comments | https://api.github.com/repos/huggingface/datasets/issues/4606/events | https://github.com/huggingface/datasets/issues/4606 | 1,290,083,534 | I_kwDODunzps5M5RzO | 4,606 | evaluation result changes after `datasets` version change | {
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] | open | false | null | [] | null | [] | 2022-06-30T12:43:26Z | 2022-07-04T17:47:32Z | null | NONE | null | null | null | ## Describe the bug
evaluation result changes after `datasets` version change
## Steps to reproduce the bug
1. Train a model on WikiAnn
2. reload the ckpt -> test accuracy becomes same as eval accuracy
3. such behavior is gone after downgrading `datasets`
https://colab.research.google.com/drive/1kYz7-aZRGdayaq-gDTt30tyEgsKlpYOw?usp=sharing
## Expected results
evaluation result shouldn't change before/after `datasets` version changes
## Actual results
evaluation result changes before/after `datasets` version changes
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: colab
- Python version: 3.7.13
- PyArrow version: 6.0.1
Q. How could the evaluation result change before/after `datasets` version changes? | {
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] | null | [] | 2022-06-30T12:23:34Z | 2022-07-06T12:34:19Z | 2022-07-06T12:34:19Z | NONE | null | null | null | ### Link
https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train
### Description
When I try to access this from the website I get this error:
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet')
If I try to load with code I also get the same issue:
```python
dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True)
dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True)
```
### Owner
No | {
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https://api.github.com/repos/huggingface/datasets/issues/4604 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4604/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4604/comments | https://api.github.com/repos/huggingface/datasets/issues/4604/events | https://github.com/huggingface/datasets/pull/4604 | 1,289,963,962 | PR_kwDODunzps46oeju | 4,604 | Update CI Windows orb | {
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} | This PR tries to fix recurrent random CI failures on Windows.
After 2 runs, it seems to have fixed the issue.
Fix #4603. | {
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https://api.github.com/repos/huggingface/datasets/issues/4603 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4603/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4603/comments | https://api.github.com/repos/huggingface/datasets/issues/4603/events | https://github.com/huggingface/datasets/issues/4603 | 1,289,963,331 | I_kwDODunzps5M40dD | 4,603 | CI fails recurrently and randomly on Windows | {
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The windows CI is currently flaky: some dependencies like `aiobotocore`, `multiprocess` and `seqeval` sometimes fail to install.
In particular it seems that building the wheels fail. Here is an example of logs:
```
Building wheel for seqeval (setup.py): started
Running command 'C:\tools\miniconda3\envs\py37\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"'; __file__='"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6'
No parent package detected, impossible to derive `name`
running bdist_wheel
running build
running build_py
package init file 'seqeval\__init__.py' not found (or not a regular file)
package init file 'seqeval\metrics\__init__.py' not found (or not a regular file)
C:\tools\miniconda3\envs\py37\lib\site-packages\setuptools\command\install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
setuptools.SetuptoolsDeprecationWarning,
installing to build\bdist.win-amd64\wheel
running install
running install_lib
warning: install_lib: 'build\lib' does not exist -- no Python modules to install
running install_egg_info
running egg_info
creating UNKNOWN.egg-info
writing UNKNOWN.egg-info\PKG-INFO
writing dependency_links to UNKNOWN.egg-info\dependency_links.txt
writing top-level names to UNKNOWN.egg-info\top_level.txt
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
reading manifest file 'UNKNOWN.egg-info\SOURCES.txt'
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
Copying UNKNOWN.egg-info to build\bdist.win-amd64\wheel\.\UNKNOWN-0.0.0-py3.7.egg-info
running install_scripts
creating build\bdist.win-amd64\wheel\UNKNOWN-0.0.0.dist-info\WHEEL
creating 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6\UNKNOWN-0.0.0-py3-none-any.whl' and adding 'build\bdist.win-amd64\wheel' to it
adding 'UNKNOWN-0.0.0.dist-info/METADATA'
adding 'UNKNOWN-0.0.0.dist-info/WHEEL'
adding 'UNKNOWN-0.0.0.dist-info/top_level.txt'
adding 'UNKNOWN-0.0.0.dist-info/RECORD'
removing build\bdist.win-amd64\wheel
Building wheel for seqeval (setup.py): finished with status 'done'
Created wheel for seqeval: filename=UNKNOWN-0.0.0-py3-none-any.whl size=963 sha256=67eb93a6e1ff4796c5882a13f9fa25bb0d3d103796e2525f9cecf3b2ef26d4b1
Stored in directory: c:\users\circleci\appdata\local\pip\cache\wheels\05\96\ee\7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7
WARNING: Built wheel for seqeval is invalid: Wheel has unexpected file name: expected 'seqeval', got 'UNKNOWN'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4602 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4602/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4602/comments | https://api.github.com/repos/huggingface/datasets/issues/4602/events | https://github.com/huggingface/datasets/pull/4602 | 1,289,950,379 | PR_kwDODunzps46obqi | 4,602 | Upgrade setuptools in windows CI | {
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In particular it seems that building the wheels fail. Here is an example of logs
```
Building wheel for seqeval (setup.py): started
Running command 'C:\tools\miniconda3\envs\py37\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"'; __file__='"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6'
No parent package detected, impossible to derive `name`
running bdist_wheel
running build
running build_py
package init file 'seqeval\__init__.py' not found (or not a regular file)
package init file 'seqeval\metrics\__init__.py' not found (or not a regular file)
C:\tools\miniconda3\envs\py37\lib\site-packages\setuptools\command\install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
setuptools.SetuptoolsDeprecationWarning,
installing to build\bdist.win-amd64\wheel
running install
running install_lib
warning: install_lib: 'build\lib' does not exist -- no Python modules to install
running install_egg_info
running egg_info
creating UNKNOWN.egg-info
writing UNKNOWN.egg-info\PKG-INFO
writing dependency_links to UNKNOWN.egg-info\dependency_links.txt
writing top-level names to UNKNOWN.egg-info\top_level.txt
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
reading manifest file 'UNKNOWN.egg-info\SOURCES.txt'
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
Copying UNKNOWN.egg-info to build\bdist.win-amd64\wheel\.\UNKNOWN-0.0.0-py3.7.egg-info
running install_scripts
creating build\bdist.win-amd64\wheel\UNKNOWN-0.0.0.dist-info\WHEEL
creating 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6\UNKNOWN-0.0.0-py3-none-any.whl' and adding 'build\bdist.win-amd64\wheel' to it
adding 'UNKNOWN-0.0.0.dist-info/METADATA'
adding 'UNKNOWN-0.0.0.dist-info/WHEEL'
adding 'UNKNOWN-0.0.0.dist-info/top_level.txt'
adding 'UNKNOWN-0.0.0.dist-info/RECORD'
removing build\bdist.win-amd64\wheel
Building wheel for seqeval (setup.py): finished with status 'done'
Created wheel for seqeval: filename=UNKNOWN-0.0.0-py3-none-any.whl size=963 sha256=67eb93a6e1ff4796c5882a13f9fa25bb0d3d103796e2525f9cecf3b2ef26d4b1
Stored in directory: c:\users\circleci\appdata\local\pip\cache\wheels\05\96\ee\7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7
WARNING: Built wheel for seqeval is invalid: Wheel has unexpected file name: expected 'seqeval', got 'UNKNOWN'
```
hopefully this fixes the issue
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https://api.github.com/repos/huggingface/datasets/issues/4601 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4601/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4601/comments | https://api.github.com/repos/huggingface/datasets/issues/4601/events | https://github.com/huggingface/datasets/pull/4601 | 1,289,924,715 | PR_kwDODunzps46oWF8 | 4,601 | Upgrade pip in WIN CI | {
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In particular it seems that building the wheels fail. Here is an example of logs
```
Building wheel for seqeval (setup.py): started
Running command 'C:\tools\miniconda3\envs\py37\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"'; __file__='"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6'
No parent package detected, impossible to derive `name`
running bdist_wheel
running build
running build_py
package init file 'seqeval\__init__.py' not found (or not a regular file)
package init file 'seqeval\metrics\__init__.py' not found (or not a regular file)
C:\tools\miniconda3\envs\py37\lib\site-packages\setuptools\command\install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
setuptools.SetuptoolsDeprecationWarning,
installing to build\bdist.win-amd64\wheel
running install
running install_lib
warning: install_lib: 'build\lib' does not exist -- no Python modules to install
running install_egg_info
running egg_info
creating UNKNOWN.egg-info
writing UNKNOWN.egg-info\PKG-INFO
writing dependency_links to UNKNOWN.egg-info\dependency_links.txt
writing top-level names to UNKNOWN.egg-info\top_level.txt
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
reading manifest file 'UNKNOWN.egg-info\SOURCES.txt'
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
Copying UNKNOWN.egg-info to build\bdist.win-amd64\wheel\.\UNKNOWN-0.0.0-py3.7.egg-info
running install_scripts
creating build\bdist.win-amd64\wheel\UNKNOWN-0.0.0.dist-info\WHEEL
creating 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6\UNKNOWN-0.0.0-py3-none-any.whl' and adding 'build\bdist.win-amd64\wheel' to it
adding 'UNKNOWN-0.0.0.dist-info/METADATA'
adding 'UNKNOWN-0.0.0.dist-info/WHEEL'
adding 'UNKNOWN-0.0.0.dist-info/top_level.txt'
adding 'UNKNOWN-0.0.0.dist-info/RECORD'
removing build\bdist.win-amd64\wheel
Building wheel for seqeval (setup.py): finished with status 'done'
Created wheel for seqeval: filename=UNKNOWN-0.0.0-py3-none-any.whl size=963 sha256=67eb93a6e1ff4796c5882a13f9fa25bb0d3d103796e2525f9cecf3b2ef26d4b1
Stored in directory: c:\users\circleci\appdata\local\pip\cache\wheels\05\96\ee\7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7
WARNING: Built wheel for seqeval is invalid: Wheel has unexpected file name: expected 'seqeval', got 'UNKNOWN'
```
I tried to update pip and re-run the CI several times and I couldn't re-experience this issue for now, so I think upgrading pip may solve the issue | {
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https://api.github.com/repos/huggingface/datasets/issues/4600 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4600/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4600/comments | https://api.github.com/repos/huggingface/datasets/issues/4600/events | https://github.com/huggingface/datasets/pull/4600 | 1,289,177,042 | PR_kwDODunzps46l3P1 | 4,600 | Remove multiple config section | {
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} | Hi,
the current implementation of smooth-BLEU contains a bug: it smoothes unigrams as well. Consequently, when both the reference and translation consist of totally different tokens, it anyway returns a non-zero value (please see the attached image).
This however contradicts the source paper suggesting the smooth-BLEU _(Chin-Yew Lin, Franz Josef Och. ORANGE: a method for evaluating automatic evaluation metrics for machine translation. COLING 2004.)_ :
> Add one count to the n-gram hit and total ngram count for n > 1. Therefore, for candidate translations with less than n words, they can still get a positive smoothed BLEU score from shorter n-gram matches; however if nothing matches then they will get zero scores.
This pull request aims at fixing this bug.
I made a pull request in the target repository `tensorflow/nmt`, which implements this script, yet the last commit there is dating 19.02.2019 and I doubt whether this will be fixed promptly. Yet, this bug is critical, for instance for summarization datasets with short summaries (e.g. AESLC), since smoothing needs to be applied there. Therefore, the easiest solution that I found is to fork the repo and download this script directly from the forked fixed repo.
Kind,
Akim Tsvigun
<img width="516" alt="Снимок экрана 2022-06-29 в 17 49 27" src="https://user-images.githubusercontent.com/36672861/176466935-ac579e6d-6a93-4111-ab41-9b33056e7d47.png">
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Fix #4597. | {
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https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train
### Description
As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset:
```
Server error
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No | {
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https://huggingface.co/datasets/universal_dependencies
### Description
invalid json response body at https://datasets-server.huggingface.co/splits?dataset=universal_dependencies reason: Unexpected token I in JSON at position 0
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https://huggingface.co/datasets/cakiki/rosetta-code
### Description
Hello, I just noticed an entry being redacted that shouldn't have been:
`RootMeanSquare@Range[10]` is being displayed as `[email protected][10]`
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} | Issue #4594
Issue: When `datasets.load_from_disk` is wrongly used to load a `DatasetDict`, the error message suggests using `datasets.load_from_disk`, which is the same function that generated the error.
Fix: The appropriate function which should be suggested instead is `datasets.dataset_dict.load_from_disk`.
Changes: Change the suggestion to say "Please use `datasets.dataset_dict.load_from_disk` instead." | {
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https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes | {
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If I create a dataset including an 'Image' feature manually, when pushing to hub decoded images are not pushed,
instead it looks for image where image local path is/used to be.
This doesn't (at least didn't used to) happen with imagefolder. I want to build dataset manually because it is complicated.
This happens even though the dataset is looking like decoded images:
and I use `embed_external_files=True` while `push_to_hub` (same with false)
## Steps to reproduce the bug
```python
from PIL import Image
from datasets import Image as ImageFeature
from datasets import Features,Dataset
#manually create dataset
feats=Features(
{
"images": [ImageFeature()], #same even if explicitly ImageFeature(decode=True)
"input_image": ImageFeature(),
}
)
test_data={"images":[[Image.open("test.jpg"),Image.open("test.jpg"),Image.open("test.jpg")]], "input_image":[Image.open("test.jpg")]}
test_dataset=Dataset.from_dict(test_data,features=feats)
print(test_dataset)
test_dataset.push_to_hub("ceyda/image_test_public",private=False,token="",embed_external_files=True)
# clear cache rm -r ~/.cache/huggingface
# remove "test.jpg" # remove to see that it is looking for image on the local path
test_dataset=load_dataset("ceyda/image_test_public",use_auth_token="")
print(test_dataset)
print(test_dataset['train'][0])
```
## Expected results
should be able to push image bytes if dataset has `Image(decode=True)`
## Actual results
errors because it is trying to decode file from the non existing local path.
```
----> print(test_dataset['train'][0])
File ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py:2154, in Dataset.__getitem__(self, key)
2152 def __getitem__(self, key): # noqa: F811
2153 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2154 return self._getitem(
2155 key,
2156 )
File ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py:2139, in Dataset._getitem(self, key, decoded, **kwargs)
2137 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
2138 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2139 formatted_output = format_table(
2140 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2141 )
2142 return formatted_output
File ~/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
...
-> 3068 fp = builtins.open(filename, "rb")
3069 exclusive_fp = True
3071 try:
FileNotFoundError: [Errno 2] No such file or directory: 'test.jpg'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-1074-azure-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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} | # What does this PR do?
## Summary
*In function `_copy_script_and_other_resources_in_importable_dir`, using string split when generating `meta_path` throws error in the edge case raised in #4540.*
## Additions
-
## Changes
- Changed meta_path to use `os.path.splitext` instead of using `str.split` to generalize code.
## Deletions
-
## Issues Addressed :
Fixes #4540 | {
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- Hosts head_qa data on the Hub instead of Google Drive
- Fixes NonMatchingChecksumError
Fix https://huggingface.co/datasets/head_qa/discussions/1 | {
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However the fingerprint is used to name cache files so we need to make sure it doesn't contain bad characters as mentioned in https://github.com/huggingface/datasets/issues/1718, and that it's not too long | {
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They were on Google Drive.
Fix #4580. | {
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This PR adds binary classification to the task IDs to enable this.
Related AutoTrain issue: https://github.com/huggingface/autonlp-backend/issues/597
cc @abhishekkrthakur @SBrandeis | {
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doing `.add_column("x",x_data)` also removed any `_indexes` on the dataset, decided this shouldn't be the case.
This was because `add_column` was creating a new `Dataset(...)` and wasn't possible to pass indexes on init.
with this PR now can pass 'indexes' on init through `IndexableMixin`
- [x] Added test | {
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https://huggingface.co/datasets/pn_summary/viewer/1.0.0/validation
### Description
Getting an index error on the `validation` and `test` splits:
```
Server error
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
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https://huggingface.co/datasets/multi_news
### Description
Not sure what the index error is referring to here:
```
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
No | {
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However it would be nice to have a no-code way to to this.
For example we could specify different configurations of a dataset (for example, if a dataset contains different languages) with one directory per configuration.
These structures are not supported right now, but would be nice to have:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train.csv
│ └── test.csv
└── fr/
├── train.csv
└── test.csv
```
Or with one directory per split:
```
my_dataset_repository/
├── README.md
├── en/
│ ├── train/
│ │ ├── shard_0.csv
│ │ └── shard_1.csv
│ └── test/
│ ├── shard_0.csv
│ └── shard_1.csv
└── fr/
├── train/
│ ├── shard_0.csv
│ └── shard_1.csv
└── test/
├── shard_0.csv
└── shard_1.csv
```
cc @stevhliu @albertvillanova | {
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Fix #4548
@lhoestq ~~https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 adds support for metadata files placed at the root, and https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 accounts for nested metadata files also, but this results in more complex code. Let me know which one of these two approaches you prefer.~~ Maybe https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 is good enough for now (for the sake of simplicity). https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 breaks the imagefolder tests due to duplicates in the resolved metadata files. One way to fix this would be to resolve the metadata pattern only on parent directories, but this adds even more logic to `_get_data_files_patterns`, so not sure if this is what we should do. | {
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] | null | [] | 2022-06-27T08:35:42Z | 2022-08-23T10:01:02Z | 2022-08-23T10:00:21Z | NONE | null | null | null | It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.dataset, "zh-en")
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<c[hn]: string, en: string, zh: string>
to
struct<en: string, zh: string>
```
So the solution of this problem is to change the original array manually:
```
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if 'zh' not in vo:
tmp['zh'] = vo['c[hn]']
else:
tmp['zh'] = vo['zh']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
```
Therefore, maybe a correct version of original casia2015 file need to be updated | {
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This PR:
- pins `fsspec` min version with fixed BlockSizeError: `fsspec[http]>=2021.11.1`
- https://github.com/fsspec/filesystem_spec/pull/830
- unpins `s3fs==2021.08.1` to align it with `fsspec` requirement: `s3fs>=2021.11.1`
> s3fs 2021.8.1 requires fsspec==2021.08.1
- see discussion: https://github.com/huggingface/datasets/pull/2858/files#r700027326
- updates the following requirements to be compatible with the previous ones and one with each other:
- `aiobotocore==1.4.2` to `aiobotocore>=2.0.1` (required by s3fs>=2021.11.1)
- `boto3==1.17.106` to `boto3>=1.19.8` (to be compatible with aiobotocore>=2.0.1)
- `botocore==1.20.106` to `botocore>=1.22.8` (to be compatible with aiobotocore and boto3)
Fix #4572. | {
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] | null | [] | 2022-06-26T20:24:17Z | 2022-07-21T12:40:01Z | 2022-07-21T12:40:01Z | MEMBER | null | null | null | ### Link
https://huggingface.co/datasets/mlsum/viewer/de/train
### Description
There's seems to be a problem with the download / streaming of this dataset:
```
Server error
Status code: 400
Exception: BadZipFile
Message: File is not a zip file
```
### Owner
No | {
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https://huggingface.co/datasets/gsarti/flores_101
### Description
It seems like streaming isn't supported for this dataset:
```
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://dl.fbaipublicfiles.com/flores101/dataset/flores101_dataset.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
### Owner
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] | closed | false | null | [] | null | [] | 2022-06-26T08:34:05Z | 2022-06-30T11:00:47Z | 2022-06-26T14:36:20Z | CONTRIBUTOR | null | null | null | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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] | null | [] | 2022-06-26T07:32:54Z | 2022-06-27T06:37:48Z | 2022-06-27T06:37:48Z | MEMBER | null | null | null | ### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No | {
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] | closed | false | null | [] | null | [] | 2022-06-25T16:43:56Z | 2022-07-04T14:29:40Z | 2022-07-04T14:29:40Z | CONTRIBUTOR | null | null | null | ### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/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, revision, use_auth_token, task, streaming, **config_kwargs)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
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https://api.github.com/repos/huggingface/datasets/issues/4566 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4566/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4566/comments | https://api.github.com/repos/huggingface/datasets/issues/4566/events | https://github.com/huggingface/datasets/issues/4566 | 1,284,397,594 | I_kwDODunzps5Mjloa | 4,566 | Document link #load_dataset_enhancing_performance points to nowhere | {
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] | open | false | null | [] | null | [] | 2022-06-25T01:18:19Z | 2022-06-29T20:29:54Z | null | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?
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] | closed | false | null | [] | null | [] | 2022-06-24T20:07:54Z | 2022-07-06T19:03:02Z | 2022-07-06T19:03:02Z | NONE | null | null | null | ## Adding a Dataset
- **Name:** UFSC OCPap: Papanicolaou Stained Oral Cytology Dataset (v4)
- **Description:** The UFSC OCPap dataset comprises 9,797 labeled images of 1200x1600 pixels acquired from 5 slides of cancer diagnosed and 3 healthy of oral brush samples, from distinct patients.
- **Paper:** https://dx.doi.org/10.2139/ssrn.4119212
- **Data:** https://data.mendeley.com/datasets/dr7ydy9xbk/1
- **Motivation:** real data of pap stained oral cytology samples
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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Fix #4562. | {
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https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
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