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https://api.github.com/repos/huggingface/datasets/issues/2486 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2486/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2486/comments | https://api.github.com/repos/huggingface/datasets/issues/2486/events | https://github.com/huggingface/datasets/pull/2486 | 919,174,898 | MDExOlB1bGxSZXF1ZXN0NjY4NTI2Njg3 | 2,486 | Add Rico Dataset | [
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] | closed | false | null | 2 | 2021-06-11T20:17:41Z | 2022-10-03T09:38:18Z | 2022-10-03T09:38:18Z | null | Hi there!
I'm wanting to add the Rico datasets for software engineering type data to y'alls awesome library. However, as I have started coding, I've ran into a few hiccups so I thought it best to open the PR early to get a bit of discussion on how the Rico datasets should be added to the `datasets` lib.
1) There are 7 different datasets under Rico and so I was wondering, should I make a folder for each or should I put them as different configurations of a single dataset?
You can see the datasets available for Rico here: http://interactionmining.org/rico
2) As of right now, I have a semi working version of the first dataset which has pairs of screenshots and hierarchies from android applications. However, these screenshots are very large (1440, 2560, 3) and there are 66,000 of them so I am not able to perform the processing that the `datasets` lib does after downloading and extracting the dataset since I run out of memory very fast. Is there a way to have `datasets` lib not put everything into memory while it is processing the dataset?
2.1) If there is not a way, would it be better to just return the path to the screenshots instead of the actual image?
3) The hierarchies are JSON objects and looking through the documentation of `datasets`, I didn't see any feature that I could use for this type of data. So, for now I just have it being read in as a string, is this okay or should I be doing it differently?
4) One of the Rico datasets is a bunch of animations (GIFs), is there a `datasets` feature that I can put this type of data into or should I just return the path as a string?
I appreciate any and all help I can get for this PR, I think the Rico datasets will be an awesome addition to the library :nerd_face: ! | {
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"Hi ! Thanks for adding this dataset :)\r\n\r\nRegarding your questions:\r\n1. We can have them as different configuration of the `rico` dataset\r\n2. Yes please use the path to the image and not open the image directly, so that we can let users open the image one at at time during training if they want to for exam... |
https://api.github.com/repos/huggingface/datasets/issues/717 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/717/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/717/comments | https://api.github.com/repos/huggingface/datasets/issues/717/events | https://github.com/huggingface/datasets/pull/717 | 714,959,268 | MDExOlB1bGxSZXF1ZXN0NDk3OTUwOTA2 | 717 | Fixes #712 Error in the Overview.ipynb notebook | [] | closed | false | null | 0 | 2020-10-05T15:50:41Z | 2020-10-06T06:31:43Z | 2020-10-05T16:25:41Z | null | Fixes #712 Error in the Overview.ipynb notebook by adding `with_details=True` parameter to `list_datasets` function in Cell 3 of **overview** notebook | {
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https://api.github.com/repos/huggingface/datasets/issues/1890 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1890/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1890/comments | https://api.github.com/repos/huggingface/datasets/issues/1890/events | https://github.com/huggingface/datasets/pull/1890 | 809,395,586 | MDExOlB1bGxSZXF1ZXN0NTc0MjY0OTMx | 1,890 | Reformat dataset cards section titles | [] | closed | false | null | 0 | 2021-02-16T15:11:47Z | 2021-02-16T15:12:34Z | 2021-02-16T15:12:33Z | null | Titles are formatted like [Foo](#foo) instead of just Foo | {
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https://api.github.com/repos/huggingface/datasets/issues/4672 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4672/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4672/comments | https://api.github.com/repos/huggingface/datasets/issues/4672/events | https://github.com/huggingface/datasets/pull/4672 | 1,300,911,467 | PR_kwDODunzps47NEfV | 4,672 | Support extract 7-zip compressed data files | [] | closed | false | null | 2 | 2022-07-11T15:56:51Z | 2022-07-15T13:14:27Z | 2022-07-15T13:02:07Z | null | Fix partially #3541, fix #4670. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Cool! Can you please remove `Fix #3541` from the description as this PR doesn't add support for streaming/`iter_archive`, so it only partially addresses the issue?\r\n\r\nSide note:\r\nI think we can use `libarchive` (`libarchive-c` ... |
https://api.github.com/repos/huggingface/datasets/issues/3257 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3257/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3257/comments | https://api.github.com/repos/huggingface/datasets/issues/3257/events | https://github.com/huggingface/datasets/issues/3257 | 1,052,118,365 | I_kwDODunzps4-tg1d | 3,257 | Use f-strings for string formatting | [
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] | closed | false | null | 5 | 2021-11-12T16:02:15Z | 2021-11-17T16:18:38Z | 2021-11-17T16:18:38Z | null | f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.
> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`. | {
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"Hi, I would be glad to help with this. Is there anyone else working on it?",
"Hi, I would be glad to work on this too.",
"#self-assign",
"Hi @Carlosbogo,\r\n\r\nwould you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has ope... |
https://api.github.com/repos/huggingface/datasets/issues/469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/469/comments | https://api.github.com/repos/huggingface/datasets/issues/469/events | https://github.com/huggingface/datasets/issues/469 | 671,876,963 | MDU6SXNzdWU2NzE4NzY5NjM= | 469 | invalid data type 'str' at _convert_outputs in arrow_dataset.py | [] | closed | false | null | 9 | 2020-08-03T07:48:29Z | 2023-07-20T15:54:17Z | 2023-07-20T15:54:17Z | null | I trying to build multi label text classifier model using Transformers lib.
I'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error
File "C:\***\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
I'm using pyarrow 1.0.0. And I have simple custom data set with Text and Integer Label.
Ex: Data
Text , Label #Column Header
I'm facing an Network issue, 1
I forgot my password, 2
Error StackTrace:
File "C:\**\transformers\trainer.py", line 492, in train
for step, inputs in enumerate(epoch_iterator):
File "C:\**\tqdm\std.py", line 1104, in __iter__
for obj in iterable:
File "C:\**\torch\utils\data\dataloader.py", line 345, in __next__
data = self._next_data()
File "C:\**\torch\utils\data\dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\nlp\arrow_dataset.py", line 414, in __getitem__
output_all_columns=self._output_all_columns,
File "C:\**\nlp\arrow_dataset.py", line 403, in _getitem
outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns
File "C:\**\nlp\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
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"Hi ! Did you try to set the output format to pytorch ? (or tensorflow if you're using tensorflow)\r\nIt can be done with `dataset.set_format(\"torch\", columns=columns)` (or \"tensorflow\").\r\n\r\nNote that for pytorch, string columns can't be converted to `torch.Tensor`, so you have to specify in `columns=` the... |
https://api.github.com/repos/huggingface/datasets/issues/3656 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3656/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3656/comments | https://api.github.com/repos/huggingface/datasets/issues/3656/events | https://github.com/huggingface/datasets/issues/3656 | 1,120,510,823 | I_kwDODunzps5CyaNn | 3,656 | checksum error subjqa dataset | [
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] | closed | false | null | 2 | 2022-02-01T10:53:33Z | 2022-02-10T10:56:59Z | 2022-02-10T10:56:38Z | null | ## Describe the bug
I get a checksum error when loading the `subjqa` dataset (used in the transformers book).
## Steps to reproduce the bug
```python
from datasets import load_dataset
subjqa = load_dataset("subjqa","electronics")
```
## Expected results
Loading the dataset
## Actual results
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-2-d2857d460155> in <module>()
2 from datasets import load_dataset
3
----> 4 subjqa = load_dataset("subjqa","electronics")
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/lewtun/SubjQA/archive/refs/heads/master.zip']
```
## Environment info
Google colab
- `datasets` version: 1.18.2
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0 | {
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"Hi @RensDimmendaal, \r\n\r\nI'm sorry but I can't reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: ds = load_dataset(\"subjqa\", \"electronics\")\r\nDownloading builder script: 9.15kB [00:00, 4.10MB/s] ... |
https://api.github.com/repos/huggingface/datasets/issues/5618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5618/comments | https://api.github.com/repos/huggingface/datasets/issues/5618/events | https://github.com/huggingface/datasets/issues/5618 | 1,612,977,934 | I_kwDODunzps5gJBcO | 5,618 | Unpin fsspec < 2023.3.0 once issue fixed | [] | closed | false | null | 0 | 2023-03-07T08:41:51Z | 2023-03-07T13:39:03Z | 2023-03-07T13:39:03Z | null | Unpin `fsspec` upper version once root cause of our CI break is fixed.
See:
- #5614 | {
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https://api.github.com/repos/huggingface/datasets/issues/2472 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2472/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2472/comments | https://api.github.com/repos/huggingface/datasets/issues/2472/events | https://github.com/huggingface/datasets/issues/2472 | 917,463,821 | MDU6SXNzdWU5MTc0NjM4MjE= | 2,472 | Fix automatic generation of Zenodo DOI | [
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} | 4 | 2021-06-10T15:15:46Z | 2021-06-14T16:49:42Z | 2021-06-14T16:49:42Z | null | After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as "Received", instead of in green as "Published".
I have contacted Zenodo support to fix this issue.
TODO:
- [x] Check with Zenodo to fix the issue
- [x] Check BibTeX entry is right | {
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"I have received a reply from Zenodo support:\r\n> We are currently investigating and fixing this issue related to GitHub releases. As soon as we have solved it we will reach back to you.",
"Other repo maintainers had the same problem with Zenodo. \r\n\r\nThere is an open issue on their GitHub repo: zenodo/zenodo... |
https://api.github.com/repos/huggingface/datasets/issues/6086 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6086/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6086/comments | https://api.github.com/repos/huggingface/datasets/issues/6086/events | https://github.com/huggingface/datasets/issues/6086 | 1,825,009,268 | I_kwDODunzps5sx250 | 6,086 | Support `fsspec` in `Dataset.to_<format>` methods | [
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] | open | false | null | 0 | 2023-07-27T19:08:37Z | 2023-07-27T19:08:37Z | null | null | Supporting this should be fairly easy.
Requested on the forum [here](https://discuss.huggingface.co/t/how-can-i-convert-a-loaded-dataset-in-to-a-parquet-file-and-save-it-to-the-s3/48353). | {
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"Hi @mariosasko unless someone's already working on it, I guess I can tackle it!",
"Hi! Sure, feel free to tackle this.",
"#self-assign",
"I'm assuming this should just cover `to_csv`, `to_parquet`, and `to_json`, right? As `to_list` and `to_dict` just return Python objects, `to_pandas` returns a `pandas.Data... |
https://api.github.com/repos/huggingface/datasets/issues/5478 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5478/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5478/comments | https://api.github.com/repos/huggingface/datasets/issues/5478/events | https://github.com/huggingface/datasets/pull/5478 | 1,560,357,583 | PR_kwDODunzps5ItXQG | 5,478 | Tip for recomputing metadata | [] | closed | false | null | 2 | 2023-01-27T20:01:22Z | 2023-01-30T19:22:21Z | 2023-01-30T19:15:26Z | null | From this [feedback](https://discuss.huggingface.co/t/nonmatchingsplitssizeserror/30033) on the forum, thought I'd include a tip for recomputing the metadata numbers if it is your own dataset. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/4144 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4144/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4144/comments | https://api.github.com/repos/huggingface/datasets/issues/4144/events | https://github.com/huggingface/datasets/pull/4144 | 1,200,016,983 | PR_kwDODunzps42ARmu | 4,144 | Fix splits in local packaged modules, local datasets without script and hub datasets without script | [] | closed | false | null | 7 | 2022-04-11T13:57:33Z | 2022-04-29T09:12:14Z | 2022-04-28T21:02:45Z | null | fixes #4150
I suggest to infer splits structure from files when `data_dir` is passed with `get_patterns_locally`, analogous to what's done in `LocalDatasetModuleFactoryWithoutScript` with `self.path`, instead of generating files with `data_dir/**` patterns and putting them all into a single default (train) split.
I would also suggest to align `HubDatasetModuleFactoryWithoutScript` and `LocalDatasetModuleFactoryWithoutScript` with this logic (remove `data_files = os.path.join(data_dir, "**")`). It's not reflected in the current code now as I'd like to discuss it cause I might be unaware of some use cases. @lhoestq @mariosasko @albertvillanova WDYT? | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks !\r\nI'm in favor of this change, even though it's a breaking change:\r\n\r\nif you had a dataset\r\n```\r\ndata/\r\n train.csv\r\n test.csv\r\n```\r\n\r\nthen running this code would now return both train and test splits:\r... |
https://api.github.com/repos/huggingface/datasets/issues/1168 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1168/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1168/comments | https://api.github.com/repos/huggingface/datasets/issues/1168/events | https://github.com/huggingface/datasets/pull/1168 | 757,740,780 | MDExOlB1bGxSZXF1ZXN0NTMzMDYzNjgy | 1,168 | Add Naver sentiment movie corpus | [] | closed | false | null | 1 | 2020-12-05T17:25:23Z | 2020-12-07T13:34:09Z | 2020-12-07T13:34:09Z | null | This PR adds the [Naver sentiment movie corpus](https://github.com/e9t/nsmc), a dataset containing Korean movie reviews from Naver, the most commonly used search engine in Korea. This dataset is often used to benchmark models on Korean NLP tasks, as seen in [this paper](https://www.aclweb.org/anthology/2020.lrec-1.199.pdf). | {
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"Closed via #1252 "
] |
https://api.github.com/repos/huggingface/datasets/issues/1646 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1646/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1646/comments | https://api.github.com/repos/huggingface/datasets/issues/1646/events | https://github.com/huggingface/datasets/pull/1646 | 775,499,344 | MDExOlB1bGxSZXF1ZXN0NTQ2MTY4MTk3 | 1,646 | Add missing homepage in some dataset cards | [] | closed | false | null | 0 | 2020-12-28T17:09:48Z | 2021-01-04T14:08:57Z | 2021-01-04T14:08:56Z | null | In some dataset cards the homepage field in the `Dataset Description` section was missing/empty | {
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https://api.github.com/repos/huggingface/datasets/issues/1360 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1360/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1360/comments | https://api.github.com/repos/huggingface/datasets/issues/1360/events | https://github.com/huggingface/datasets/pull/1360 | 760,088,419 | MDExOlB1bGxSZXF1ZXN0NTM0OTc4NzM0 | 1,360 | add wisesight1000 | [] | closed | false | null | 0 | 2020-12-09T07:41:30Z | 2020-12-10T14:28:41Z | 2020-12-10T14:28:41Z | null | `wisesight1000` contains Thai social media texts randomly drawn from the full `wisesight-sentiment`, tokenized by human annotators. Out of the labels `neg` (negative), `neu` (neutral), `pos` (positive), `q` (question), 250 samples each. Some texts are removed because they look like spam.Because these samples are representative of real world content, we believe having these annotaed samples will allow the community to robustly evaluate tokenization algorithms. | {
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https://api.github.com/repos/huggingface/datasets/issues/4608 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4608/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4608/comments | https://api.github.com/repos/huggingface/datasets/issues/4608/events | https://github.com/huggingface/datasets/pull/4608 | 1,290,298,002 | PR_kwDODunzps46pm9A | 4,608 | Fix xisfile, xgetsize, xisdir, xlistdir in private repo | [] | closed | false | null | 2 | 2022-06-30T15:23:21Z | 2022-07-06T12:45:59Z | 2022-07-06T12:34:19Z | null | `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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"_The documentation is not available anymore as the PR was closed or merged._",
"Added tests for xisfile, xgetsize, xlistdir and xglob for private repos, and also tests for xwalk that was untested"
] |
https://api.github.com/repos/huggingface/datasets/issues/4761 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4761/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4761/comments | https://api.github.com/repos/huggingface/datasets/issues/4761/events | https://github.com/huggingface/datasets/issues/4761 | 1,321,068,411 | I_kwDODunzps5Oved7 | 4,761 | parallel searching in multi-gpu setting using faiss | [] | open | false | null | 26 | 2022-07-28T14:57:03Z | 2023-07-21T02:07:10Z | null | null | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | {
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} | https://api.github.com/repos/huggingface/datasets/issues/4761/timeline | null | null | null | null | false | [
"And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`.",
"Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs\r\n\r\n> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.\r\n\r... |
https://api.github.com/repos/huggingface/datasets/issues/5853 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5853/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5853/comments | https://api.github.com/repos/huggingface/datasets/issues/5853/events | https://github.com/huggingface/datasets/pull/5853 | 1,708,092,786 | PR_kwDODunzps5QaZLP | 5,853 | [docs] Redirects, migrated from nginx | [] | closed | false | null | 3 | 2023-05-12T19:19:27Z | 2023-05-15T10:37:19Z | 2023-05-15T10:30:14Z | null | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@mishig25 note that it's not exactly the same behavior as in nginx as here it interacts a bit with the `version` and the `language`\r\n\r\nShould be close enough, though.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow=... |
https://api.github.com/repos/huggingface/datasets/issues/5259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5259/comments | https://api.github.com/repos/huggingface/datasets/issues/5259/events | https://github.com/huggingface/datasets/issues/5259 | 1,453,555,923 | I_kwDODunzps5Wo4DT | 5,259 | datasets 2.7 introduces sharding error | [] | closed | false | null | 3 | 2022-11-17T15:36:52Z | 2022-12-24T01:44:02Z | 2022-11-18T12:52:05Z | null | ### Describe the bug
dataset fails to load with runtime error
`RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.`
### Steps to reproduce the bug
With datasets[audio] 2.7 loaded, and logged into hugging face,
`data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)`
creates the error.
Full stack trace:
```---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-7-8cb9ca0f79f0>](https://localhost:8080/#) in <module>
----> 1 data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)
5 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) 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, num_proc, **config_kwargs)
1745 try_from_hf_gcs=try_from_hf_gcs,
1746 use_auth_token=use_auth_token,
-> 1747 num_proc=num_proc,
1748 )
1749
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
824 verify_infos=verify_infos,
825 **prepare_split_kwargs,
--> 826 **download_and_prepare_kwargs,
827 )
828 # Sync info
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1554 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
1555 super()._download_and_prepare(
-> 1556 dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs
1557 )
1558
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
911 try:
912 # Prepare split will record examples associated to the split
--> 913 self._prepare_split(split_generator, **prepare_split_kwargs)
914 except OSError as e:
915 raise OSError(
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1362 fpath = path_join(self._output_dir, fname)
1363
-> 1364 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1365 if num_input_shards <= 1 and num_proc is not None:
1366 logger.warning(
[/usr/local/lib/python3.7/dist-packages/datasets/utils/sharding.py](https://localhost:8080/#) in _number_of_shards_in_gen_kwargs(gen_kwargs)
16 + "\n".join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items())
17 + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
---> 18 + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
19 )
20 )
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.```
### Expected behavior
the dataset loads in datasets version 2.6.1 and should load with datasets 2.7
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | {
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"I notice a comment in the code says:\r\n`Having lists of different sizes makes sharding ambigious, raise an error in this case until we decide how to define sharding without ambiguity for users` \r\n \r\n ... which suggests this update was pushed knowing that it might break some things. But, it didn't seem to h... |
https://api.github.com/repos/huggingface/datasets/issues/1409 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1409/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1409/comments | https://api.github.com/repos/huggingface/datasets/issues/1409/events | https://github.com/huggingface/datasets/pull/1409 | 760,593,932 | MDExOlB1bGxSZXF1ZXN0NTM1Mzk5OTI1 | 1,409 | Adding the ASSIN dataset | [] | closed | false | null | 1 | 2020-12-09T19:07:00Z | 2020-12-09T19:18:12Z | 2020-12-09T19:15:52Z | null | Adding the ASSIN dataset, a Portuguese language dataset for Natural Language Inference and Semantic Similarity Scoring | {
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"I wrongly commited data from another branch in this PR, I'll close this a reopen another PR with the fixed branch"
] |
https://api.github.com/repos/huggingface/datasets/issues/645 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/645/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/645/comments | https://api.github.com/repos/huggingface/datasets/issues/645/events | https://github.com/huggingface/datasets/pull/645 | 704,542,234 | MDExOlB1bGxSZXF1ZXN0NDg5NDQ5MjAx | 645 | Don't use take on dataset table in pyarrow 1.0.x | [] | closed | false | null | 3 | 2020-09-18T17:31:34Z | 2020-09-19T16:46:32Z | 2020-09-19T16:46:31Z | null | Fix #615 | {
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"I tried lower batch sizes and it didn't accelerate filter (quite the opposite actually).\r\nThe slow-down also appears for pyarrow 0.17.1 for some reason, not sure it comes from these changes",
"I just checked the benchmarks of other PRs and some of them had 300s (!!) for filter. This needs some investigation.."... |
https://api.github.com/repos/huggingface/datasets/issues/2129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2129/comments | https://api.github.com/repos/huggingface/datasets/issues/2129/events | https://github.com/huggingface/datasets/issues/2129 | 843,033,656 | MDU6SXNzdWU4NDMwMzM2NTY= | 2,129 | How to train BERT model with next sentence prediction? | [] | closed | false | null | 4 | 2021-03-29T06:48:03Z | 2021-04-01T04:58:40Z | 2021-04-01T04:58:40Z | null | Hello.
I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction
like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ?
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"Hi !\r\nWe're not using `TextDatasetForNextSentencePrediction` in `datasets`.\r\nAlthough you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.",
"Thanks.\r\n\r\nDo you mean that `TextDatasetForNextSentencePrediction... |
https://api.github.com/repos/huggingface/datasets/issues/689 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/689/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/689/comments | https://api.github.com/repos/huggingface/datasets/issues/689/events | https://github.com/huggingface/datasets/pull/689 | 712,095,262 | MDExOlB1bGxSZXF1ZXN0NDk1NjMzNjMy | 689 | Switch to pandas reader for text dataset | [] | closed | false | null | 1 | 2020-09-30T16:28:12Z | 2020-09-30T16:45:32Z | 2020-09-30T16:45:31Z | null | Following the discussion in #622 , it appears that there's no appropriate ways to use the payrrow csv reader to read text files because of the separator.
In this PR I switched to pandas to read the file.
Moreover pandas allows to read the file by chunk, which means that you can build the arrow dataset from a text file that is bigger than RAM (we used to have to shard text files an mentioned in https://github.com/huggingface/datasets/issues/610#issuecomment-691672919)
From a test that I did locally on a 1GB text file, the pyarrow reader used to run in 150ms while the new one takes 650ms (multithreading off for pyarrow). This is probably due to chunking since I am having the same speed difference by calling `read()` and calling `read(chunksize)` + `readline()` to read the text file. | {
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"If the windows tests in the CI pass, today will be a happy day"
] |
https://api.github.com/repos/huggingface/datasets/issues/1054 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1054/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1054/comments | https://api.github.com/repos/huggingface/datasets/issues/1054/events | https://github.com/huggingface/datasets/pull/1054 | 756,265,688 | MDExOlB1bGxSZXF1ZXN0NTMxODM3NzQ0 | 1,054 | Add dataset - SemEval 2014 - Task 1 | [] | closed | false | null | 1 | 2020-12-03T14:52:59Z | 2020-12-04T00:52:44Z | 2020-12-04T00:52:44Z | null | Adding the dataset of SemEval 2014 Task 1
Found the dataset under the shared Google Sheet > Recurring Task Datasets
Task Homepage - https://alt.qcri.org/semeval2014/task1
Thank you! | {
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"Added the dataset card.\r\nRequesting another review."
] |
https://api.github.com/repos/huggingface/datasets/issues/2334 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2334/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2334/comments | https://api.github.com/repos/huggingface/datasets/issues/2334/events | https://github.com/huggingface/datasets/pull/2334 | 879,810,107 | MDExOlB1bGxSZXF1ZXN0NjMzNTAzNTEw | 2,334 | Updating the DART file checksums in GEM | [] | closed | false | null | 1 | 2021-05-07T21:53:44Z | 2021-05-07T22:18:10Z | 2021-05-07T22:18:10Z | null | The DART files were just updated on the source GitHub
https://github.com/Yale-LILY/dart/commit/34b3c872da4811523e334f1631e54ca8105dffab | {
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"@sebastianGehrmann "
] |
https://api.github.com/repos/huggingface/datasets/issues/2332 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2332/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2332/comments | https://api.github.com/repos/huggingface/datasets/issues/2332/events | https://github.com/huggingface/datasets/pull/2332 | 879,041,608 | MDExOlB1bGxSZXF1ZXN0NjMyNzk1NDE4 | 2,332 | Add note about indices mapping in save_to_disk docstring | [] | closed | false | null | 0 | 2021-05-07T13:49:42Z | 2021-05-07T17:20:48Z | 2021-05-07T17:20:48Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4994 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4994/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4994/comments | https://api.github.com/repos/huggingface/datasets/issues/4994/events | https://github.com/huggingface/datasets/issues/4994 | 1,379,084,015 | I_kwDODunzps5SMybv | 4,994 | delete the hardcoded license list in `datasets` | [] | closed | false | null | 0 | 2022-09-20T09:14:41Z | 2022-09-22T11:45:47Z | 2022-09-22T11:45:47Z | null | > Feel free to delete the license list in `datasets` [...]
>
> Also FYI in #4926 I also removed all the validation steps anyway (language, license, types etc.)
_Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238401662_
> [...], in my opinion we can just delete this file from `datasets`, the validation is happening hub-side anyways now?
_Originally posted by @julien-c in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238390659_ | {
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https://api.github.com/repos/huggingface/datasets/issues/396 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/396/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/396/comments | https://api.github.com/repos/huggingface/datasets/issues/396/events | https://github.com/huggingface/datasets/pull/396 | 657,477,952 | MDExOlB1bGxSZXF1ZXN0NDQ5NTg3MDQ4 | 396 | Fix memory issue when doing select | [] | closed | false | null | 0 | 2020-07-15T16:15:04Z | 2020-07-16T08:07:32Z | 2020-07-16T08:07:31Z | null | We were passing the `nlp.Dataset` object to get the hash for the new dataset's file name.
Fix #395 | {
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https://api.github.com/repos/huggingface/datasets/issues/1037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1037/comments | https://api.github.com/repos/huggingface/datasets/issues/1037/events | https://github.com/huggingface/datasets/pull/1037 | 755,975,586 | MDExOlB1bGxSZXF1ZXN0NTMxNTk2NDkx | 1,037 | Fix docs indentation issues | [] | closed | false | null | 2 | 2020-12-03T08:21:34Z | 2020-12-22T16:01:15Z | 2020-12-22T16:01:15Z | null | Replace tabs with spaces. | {
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"is this an issue ?",
"Yes @lhoestq, look at the docs site. For example, in https://huggingface.co/docs/datasets/add_dataset.html, look at the indentation in the code block under the sentence:\r\n> Here are the features of the SQuAD dataset for instance, which is taken from the squad dataset loading script:"
] |
https://api.github.com/repos/huggingface/datasets/issues/2514 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2514/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2514/comments | https://api.github.com/repos/huggingface/datasets/issues/2514/events | https://github.com/huggingface/datasets/issues/2514 | 924,417,172 | MDU6SXNzdWU5MjQ0MTcxNzI= | 2,514 | Can datasets remove duplicated rows? | [
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] | open | false | null | 7 | 2021-06-17T23:35:38Z | 2022-09-10T14:43:26Z | null | null | **Is your feature request related to a problem? Please describe.**
i find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..
**Describe the solution you'd like**
have a functionality of " remove duplicated rows"
**Describe alternatives you've considered**
convert dataset to pandas, remove duplicate, and convert back...
**Additional context**
no | {
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"Hi ! For now this is probably the best option.\r\nWe might add a feature like this in the feature as well.\r\n\r\nDo you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\nOtherwise we can have do the deduplication in memory like pandas but I feel like this is going to b... |
https://api.github.com/repos/huggingface/datasets/issues/2401 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2401/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2401/comments | https://api.github.com/repos/huggingface/datasets/issues/2401/events | https://github.com/huggingface/datasets/issues/2401 | 899,910,521 | MDU6SXNzdWU4OTk5MTA1MjE= | 2,401 | load_dataset('natural_questions') fails with "ValueError: External features info don't match the dataset" | [
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] | closed | false | null | 4 | 2021-05-24T18:38:53Z | 2021-06-09T09:07:25Z | 2021-06-09T09:07:25Z | null | ## Describe the bug
load_dataset('natural_questions') throws ValueError
## Steps to reproduce the bug
```python
from datasets import load_dataset
datasets = load_dataset('natural_questions', split='validation[:10]')
```
## Expected results
Call to load_dataset returns data.
## Actual results
```
Using custom data configuration default
Reusing dataset natural_questions (/mnt/d/huggingface/datasets/natural_questions/default/0.0.2/19bc04755018a3ad02ee74f7045cde4ba9b4162cb64450a87030ab786b123b76)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-d55ab8a8cc1c> in <module>
----> 1 datasets = load_dataset('natural_questions', split='validation[:10]', cache_dir='/mnt/d/huggingface/datasets')
~/miniconda3/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)
756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
757 )
--> 758 ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
759 if save_infos:
760 builder_instance._save_infos()
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in as_dataset(self, split, run_post_process, ignore_verifications, in_memory)
735
736 # Create a dataset for each of the given splits
--> 737 datasets = utils.map_nested(
738 partial(
739 self._build_single_dataset,
~/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)
193 # Singleton
194 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 195 return function(data_struct)
196
197 disable_tqdm = bool(logger.getEffectiveLevel() > INFO)
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _build_single_dataset(self, split, run_post_process, ignore_verifications, in_memory)
762
763 # Build base dataset
--> 764 ds = self._as_dataset(
765 split=split,
766 in_memory=in_memory,
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _as_dataset(self, split, in_memory)
838 in_memory=in_memory,
839 )
--> 840 return Dataset(**dataset_kwargs)
841
842 def _post_process(self, dataset: Dataset, resources_paths: Dict[str, str]) -> Optional[Dataset]:
~/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)
271 assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object"
272 if self.info.features.type != inferred_features.type:
--> 273 raise ValueError(
274 "External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format(
275 self.info.features, self.info.features.type, inferred_features, inferred_features.type
ValueError: External features info don't match the dataset:
Got
{'id': Value(dtype='string', id=None), 'document': {'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'html': Value(dtype='string', id=None), 'tokens': Sequence(feature={'token': Value(dtype='string', id=None), 'is_html': Value(dtype='bool', id=None)}, length=-1, id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': Sequence(feature={'id': Value(dtype='string', id=None), 'long_answer': {'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None)}, 'short_answers': Sequence(feature={'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}, length=-1, id=None), 'yes_no_answer': ClassLabel(num_classes=2, names=['NO', 'YES'], names_file=None, id=None)}, length=-1, id=None)}
with type
struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<start_token: int64, end_token: int64, start_byte: int64, end_byte: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<title: string, url: string, html: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>>, id: string, question: struct<text: string, tokens: list<item: string>>>
but expected something like
{'id': Value(dtype='string', id=None), 'document': {'html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'tokens': {'is_html': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None), 'token': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'url': Value(dtype='string', id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': {'id': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'long_answer': [{'end_byte': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'start_token': Value(dtype='int64', id=None)}], 'short_answers': [{'end_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'end_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}], 'yes_no_answer': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}}
with type
struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<end_byte: int64, end_token: int64, start_byte: int64, start_token: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<html: string, title: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>, url: string>, id: string, question: struct<text: string, tokens: list<item: string>>>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyTorch version (GPU?): 1.6.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
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"I faced the similar problem. Downgrading datasets to 1.5.0 fixed it.",
"Thanks for reporting, I'm looking into it",
"I just opened #2438 to fix this :)",
"Hi ! This has been fixed in the 1.8.0 release of `datasets`"
] |
https://api.github.com/repos/huggingface/datasets/issues/5128 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5128/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5128/comments | https://api.github.com/repos/huggingface/datasets/issues/5128/events | https://github.com/huggingface/datasets/pull/5128 | 1,412,783,855 | PR_kwDODunzps5A_k9s | 5,128 | Make filename matching more robust | [] | closed | false | null | 3 | 2022-10-18T08:22:48Z | 2022-10-28T13:07:38Z | 2022-10-28T13:05:06Z | null | Fix #5046 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> I think we should also modify one of the metadata files in the `folder_based_builder` tests to make sure \"./\" is ignored now in the `file_name`\r\n\r\n@mariosasko what do you mean here? I'm not sure which metadata file I should m... |
https://api.github.com/repos/huggingface/datasets/issues/6019 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6019/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6019/comments | https://api.github.com/repos/huggingface/datasets/issues/6019/events | https://github.com/huggingface/datasets/pull/6019 | 1,799,532,822 | PR_kwDODunzps5VPAlD | 6,019 | Improve logging | [] | closed | false | null | 13 | 2023-07-11T18:30:23Z | 2023-07-12T19:34:14Z | 2023-07-12T17:19:28Z | null | Adds the StreamHandler (as `hfh` and `transformers` do) to the library's logger to log INFO messages and logs the messages about "loading a cached result" (and some other warnings) as INFO
(Also removes the `leave=False` arg in the progress bars to be consistent with `hfh` and `transformers` - progress bars serve as an indicator that a result is not cached, so it makes more sense not to delete them)
Fix #2832, fix https://github.com/huggingface/datasets/issues/1948, fix https://github.com/huggingface/datasets/issues/5444 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/295 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/295/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/295/comments | https://api.github.com/repos/huggingface/datasets/issues/295/events | https://github.com/huggingface/datasets/issues/295 | 643,245,412 | MDU6SXNzdWU2NDMyNDU0MTI= | 295 | Improve input warning for evaluation metrics | [] | closed | false | null | 0 | 2020-06-22T17:28:57Z | 2020-06-23T14:47:37Z | 2020-06-23T14:47:37Z | null | Hi,
I am the author of `bert_score`. Recently, we received [ an issue ](https://github.com/Tiiiger/bert_score/issues/62) reporting a problem in using `bert_score` from the `nlp` package (also see #238 in this repo). After looking into this, I realized that the problem arises from the format `nlp.Metric` takes input.
Here is a minimal example:
```python
import nlp
scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
scorer.add(lp, lg)
score = scorer.compute(lang="en")
```
The problem in the above code is that `scorer.add()` expects a list of strings as input for the references. As a result, the `scorer` here would take a list of characters in `lg` to be the references. The correct implementation would be calling
```python
scorer.add(lp, [lg])
```
I just want to raise this issue to you to prevent future user errors of a similar kind. I assume some simple type checking can prevent this from happening?
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/2080 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2080/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2080/comments | https://api.github.com/repos/huggingface/datasets/issues/2080/events | https://github.com/huggingface/datasets/issues/2080 | 835,023,000 | MDU6SXNzdWU4MzUwMjMwMDA= | 2,080 | Multidimensional arrays in a Dataset | [] | closed | false | null | 2 | 2021-03-18T16:29:14Z | 2021-03-25T12:46:53Z | 2021-03-25T12:46:53Z | null | Hi,
I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row.
The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`)
```
from datasets import Dataset
import pandas as pd
import numpy as np
dataset = pd.DataFrame({
'bbox': [
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])
],
'input_ids': [1, 2, 3, 4]
})
dataset = Dataset.from_pandas(dataset)
```
Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists.
```
import torch
from datasets import Dataset
import pandas as pd
dataset = pd.DataFrame({
'bbox': [
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]]
],
'input_ids': [1, 2, 3, 4]
})
dataset = Dataset.from_pandas(dataset)
def test(examples):
return {'bbbox': torch.Tensor(examples['bbox'])}
dataset = dataset.map(test)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
def test2(examples):
return {'bbbox': torch.stack(examples['bbox'])}
dataset = dataset.map(test2)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
```
Is is possible to support n-D arrays/tensors in datasets?
It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263). | {
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"Hi !\r\n\r\nThis is actually supported ! but not yet in `from_pandas`.\r\nYou can use `from_dict` for now instead:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n 'bbox': [\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,... |
https://api.github.com/repos/huggingface/datasets/issues/4684 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4684/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4684/comments | https://api.github.com/repos/huggingface/datasets/issues/4684/events | https://github.com/huggingface/datasets/issues/4684 | 1,305,554,654 | I_kwDODunzps5N0S7e | 4,684 | How to assign new values to Dataset? | [
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] | closed | false | null | 2 | 2022-07-15T04:17:57Z | 2023-03-20T15:50:41Z | 2022-10-10T11:53:38Z | null | 
Hi, if I want to change some values of the dataset, or add new columns to it, how can I do it?
For example, I want to change all the labels of the SST2 dataset to `0`:
```python
from datasets import load_dataset
data = load_dataset('glue','sst2')
data['train']['label'] = [0]*len(data)
```
I will get the error:
```
TypeError: 'Dataset' object does not support item assignment
``` | {
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} | https://api.github.com/repos/huggingface/datasets/issues/4684/timeline | null | completed | null | null | false | [
"Hi! One option is use `map` with a function that overwrites the labels (`dset = dset.map(lamba _: {\"label\": 0}, features=dset.features`)). Or you can use the `remove_column` + `add_column` combination (`dset = dset.remove_columns(\"label\").add_column(\"label\", [0]*len(data)).cast(dset.features)`, but note that... |
https://api.github.com/repos/huggingface/datasets/issues/1549 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1549/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1549/comments | https://api.github.com/repos/huggingface/datasets/issues/1549/events | https://github.com/huggingface/datasets/pull/1549 | 765,612,905 | MDExOlB1bGxSZXF1ZXN0NTM5MDA3MTU4 | 1,549 | Generics kb new branch | [] | closed | false | null | 0 | 2020-12-13T19:33:10Z | 2020-12-21T13:55:09Z | 2020-12-21T13:55:09Z | null | Datasets need manual downloads. Have thus created dummy data as well. But pytest on real and dummy data are failing.
I have completed the readme , tags and other required things. I need to create the metadata json once tests get successful.
Opening a PR while working with Yacine Jernite to resolve my pytest issues. | {
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https://api.github.com/repos/huggingface/datasets/issues/4591 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4591/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4591/comments | https://api.github.com/repos/huggingface/datasets/issues/4591/events | https://github.com/huggingface/datasets/issues/4591 | 1,288,021,332 | I_kwDODunzps5MxaVU | 4,591 | Can't push Images to hub with manual Dataset | [
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] | closed | false | null | 1 | 2022-06-29T00:01:23Z | 2022-07-08T12:01:36Z | 2022-07-08T12:01:35Z | null | ## Describe the bug
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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"Hi, thanks for reporting! This issue stems from the changes introduced in https://github.com/huggingface/datasets/pull/4282 (cc @lhoestq), in which list casts are ignored if they don't change the list type (required to preserve `null` values). And `push_to_hub` does a special cast to embed external image files but... |
https://api.github.com/repos/huggingface/datasets/issues/51 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/51/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/51/comments | https://api.github.com/repos/huggingface/datasets/issues/51/events | https://github.com/huggingface/datasets/pull/51 | 613,266,668 | MDExOlB1bGxSZXF1ZXN0NDE0MDUyOTYw | 51 | [Testing] Improved testing structure | [] | closed | false | null | 1 | 2020-05-06T12:03:07Z | 2020-05-07T22:07:19Z | 2020-05-06T13:20:18Z | null | This PR refactors the test design a bit and puts the mock download manager in the `utils` files as it is just a test helper class.
as @mariamabarham pointed out, creating a dummy folder structure can be quite hard to grasp.
This PR tries to change that to some extent.
It follows the following logic for the `dummy` folder structure now:
1.) The data bulider has no config -> the `dummy` folder structure is:
`dummy/<version>/dummy_data.zip`
2) The data builder has >= 1 configs -> the `dummy` folder structure is:
`dummy/<config_name_1>/<version>/dummy_data.zip`
`dummy/<config_name_2>/<version>/dummy_data.zip`
Now, the difficult part is how to create the `dummy_data.zip` file. There are two cases:
A) The `data_urs` parameter inserted into the `download_and_extract` fn is a **string**:
-> the `dummy_data.zip` file zips the folder:
`dummy_data/<relative_path_of_folder_structure_of_url>`
B) The `data_urs` parameter inserted into the `download_and_extract` fn is a **dict**:
-> the `dummy_data.zip` file zips the folder:
`dummy_data/<relative_path_of_folder_structure_of_url_behind _key_1>`
`dummy_data/<relative_path_of_folder_structure_of_url_behind _key_2>`
By relative folder structure I mean `url_path.split('./')[-1]`. As an example the dataset **xquad** by deepmind has the following url path behind the key `de`: `https://github.com/deepmind/xquad/blob/master/xquad.de.json`
-> This means that the relative url path should be `xquad.de.json`.
@mariamabarham B) is a change from how is was before and I think is makes more sense.
While before the `dummy_data.zip` file for xquad with config `de` looked like:
`dummy_data/de` it would now look like `dummy_data/xquad.de.json`. I think this is better and easier to understand.
Therefore there are currently 6 tests that would have to have changed their dummy folder structure, but which can easily be done (30min).
I also added a function: `print_dummy_data_folder_structure` that prints out the expected structures when testing which should be quite helpful. | {
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"Awesome!\r\nLet's have this in the doc at the end :-)"
] |
https://api.github.com/repos/huggingface/datasets/issues/158 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/158/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/158/comments | https://api.github.com/repos/huggingface/datasets/issues/158/events | https://github.com/huggingface/datasets/pull/158 | 620,396,658 | MDExOlB1bGxSZXF1ZXN0NDE5NjUyNTQy | 158 | add Toronto Books Corpus | [] | closed | false | null | 0 | 2020-05-18T17:54:45Z | 2020-06-11T07:49:15Z | 2020-05-19T07:34:56Z | null | This PR adds the Toronto Books Corpus.
.
It on consider TMX and plain text files (Moses) defined in the table **Statistics and TMX/Moses Downloads** [here](http://opus.nlpl.eu/Books.php ) | {
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https://api.github.com/repos/huggingface/datasets/issues/1793 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1793/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1793/comments | https://api.github.com/repos/huggingface/datasets/issues/1793/events | https://github.com/huggingface/datasets/pull/1793 | 796,940,299 | MDExOlB1bGxSZXF1ZXN0NTY0MDMzMjk0 | 1,793 | Minor fix the docstring of load_metric | [] | closed | false | null | 0 | 2021-01-29T14:47:35Z | 2021-01-29T16:53:32Z | 2021-01-29T16:53:32Z | null | Minor fix:
- duplicated attributes
- format fix | {
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https://api.github.com/repos/huggingface/datasets/issues/1936 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1936/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1936/comments | https://api.github.com/repos/huggingface/datasets/issues/1936/events | https://github.com/huggingface/datasets/pull/1936 | 814,726,512 | MDExOlB1bGxSZXF1ZXN0NTc4NjY3NTQ4 | 1,936 | [WIP] Adding Support for Reading Pandas Category | [] | closed | false | null | 6 | 2021-02-23T18:32:54Z | 2022-03-09T18:46:22Z | 2022-03-09T18:46:22Z | null | @lhoestq - continuing our conversation from https://github.com/huggingface/datasets/issues/1906#issuecomment-784247014
The goal of this PR is to support `Dataset.from_pandas(df)` where the dataframe contains a Category.
Just the 4 line change below actually does seem to work:
```
>>> from datasets import Dataset
>>> import pandas as pd
>>> df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
>>> ds = Dataset.from_pandas(df)
>>> ds.to_pandas()
0
0 a
1 b
2 c
3 a
>>> ds.to_pandas().dtypes
0 category
dtype: object
```
save_to_disk, etc. all seem to work as well. The main things that are theoretically "incorrect" if we leave this are:
```
>>> ds.features.type
StructType(struct<0: int64>)
```
there are a decent number of references to this property in the library, but I can't find anything that seems to actually break as a result of this being int64 vs. dictionary? I think the gist of my question is: a) do we *need* to change the dtype of Classlabel and have get_nested_type return a pyarrow.DictionaryType instead of int64? and b) do you *want* it to change? The biggest challenge I see to implementing this correctly is that the data will need to be passed in along with the pyarrow schema when instantiating the Classlabel (I *think* this is unavoidable, since the type itself doesn't contain the actual label values) which could be a fairly intrusive change - e.g. `from_arrow_schema`'s interface would need to change to include optional arrow data? Once we start going down this path of modifying the public interfaces I am admittedly feeling a little bit outside of my comfort zone
Additionally I think `int2str`, `str2int`, and `encode_example` probably won't work - but I can't find any usages of them in the library itself. | {
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"Thanks ! could you maybe add a few tests in test_arrow_dataset.py to make sure from_pandas works as expected with categorical types ?\r\n\r\nIn particular I'm pretty sure that if you now try to `cast` the dataset to the same features at its current features, it will break instead of just being a no-op.\r\nThis is ... |
https://api.github.com/repos/huggingface/datasets/issues/4248 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4248/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4248/comments | https://api.github.com/repos/huggingface/datasets/issues/4248/events | https://github.com/huggingface/datasets/issues/4248 | 1,218,460,444 | I_kwDODunzps5IoDsc | 4,248 | conll2003 dataset loads original data. | [
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] | closed | false | null | 1 | 2022-04-28T09:33:31Z | 2022-07-18T07:15:48Z | 2022-07-18T07:15:48Z | null | ## Describe the bug
I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text.
Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ?
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
dataset = load_dataset("conll2003")
```
## Expected results
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
## Actual results
```python
print(dataset)
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 219554
})
test: Dataset({
features: ['text'],
num_rows: 50350
})
validation: Dataset({
features: ['text'],
num_rows: 55044
})
})
```
```python
for i in range(20):
print(dataset['train'][i])
{'text': '-DOCSTART- -X- -X- O'}
{'text': ''}
{'text': 'EU NNP B-NP B-ORG'}
{'text': 'rejects VBZ B-VP O'}
{'text': 'German JJ B-NP B-MISC'}
{'text': 'call NN I-NP O'}
{'text': 'to TO B-VP O'}
{'text': 'boycott VB I-VP O'}
{'text': 'British JJ B-NP B-MISC'}
{'text': 'lamb NN I-NP O'}
{'text': '. . O O'}
{'text': ''}
{'text': 'Peter NNP B-NP B-PER'}
{'text': 'Blackburn NNP I-NP I-PER'}
{'text': ''}
{'text': 'BRUSSELS NNP B-NP B-LOC'}
{'text': '1996-08-22 CD I-NP O'}
{'text': ''}
{'text': 'The DT B-NP O'}
{'text': 'European NNP I-NP B-ORG'}
```
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"Thanks for reporting @sue99.\r\n\r\nUnfortunately. I'm not able to reproduce your problem:\r\n```python\r\nIn [1]: import datasets\r\n ...: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"conll2003\")\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n fea... |
https://api.github.com/repos/huggingface/datasets/issues/2009 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2009/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2009/comments | https://api.github.com/repos/huggingface/datasets/issues/2009/events | https://github.com/huggingface/datasets/issues/2009 | 825,541,366 | MDU6SXNzdWU4MjU1NDEzNjY= | 2,009 | Ambiguous documentation | [
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] | closed | false | null | 2 | 2021-03-09T08:42:11Z | 2021-03-12T15:01:34Z | 2021-03-12T15:01:34Z | null | https://github.com/huggingface/datasets/blob/2ac9a0d24a091989f869af55f9f6411b37ff5188/templates/new_dataset_script.py#L156-L158
Looking at the template, I find this documentation line to be confusing, the method parameters don't include the `gen_kwargs` so I'm unclear where they're coming from.
Happy to push a PR with a clearer statement when I understand the meaning. | {
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"Hi @theo-m !\r\n\r\nA few lines above this line, you'll find that the `_split_generators` method returns a list of `SplitGenerator`s objects:\r\n\r\n```python\r\ndatasets.SplitGenerator(\r\n name=datasets.Split.VALIDATION,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n ... |
https://api.github.com/repos/huggingface/datasets/issues/4334 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4334/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4334/comments | https://api.github.com/repos/huggingface/datasets/issues/4334/events | https://github.com/huggingface/datasets/pull/4334 | 1,234,103,477 | PR_kwDODunzps43uguB | 4,334 | Adding eval metadata for billsum | [] | closed | false | null | 0 | 2022-05-12T14:49:08Z | 2022-05-12T14:49:24Z | 2022-05-12T14:49:24Z | null | Adding eval metadata for billsum | {
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https://api.github.com/repos/huggingface/datasets/issues/1438 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1438/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1438/comments | https://api.github.com/repos/huggingface/datasets/issues/1438/events | https://github.com/huggingface/datasets/pull/1438 | 760,962,193 | MDExOlB1bGxSZXF1ZXN0NTM1NzAzMTEw | 1,438 | A descriptive name for my changes | [] | closed | false | null | 3 | 2020-12-10T06:47:24Z | 2020-12-15T10:36:27Z | 2020-12-15T10:36:26Z | null | hind encorp resubmited | {
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"I have noticed that the master branch of your fork has diverged from the one of the repo. This is probably what causes the mess in the github diff \"Files changed\".\r\n\r\nI would suggest to re-fork the `datasets` repo and recreate a new branch and a new PR. ",
"You're pretty close to having all things ready to... |
https://api.github.com/repos/huggingface/datasets/issues/3058 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3058/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3058/comments | https://api.github.com/repos/huggingface/datasets/issues/3058/events | https://github.com/huggingface/datasets/issues/3058 | 1,022,612,664 | I_kwDODunzps4889S4 | 3,058 | Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader. | [
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] | closed | false | null | 2 | 2021-10-11T11:54:59Z | 2022-01-19T14:03:49Z | 2022-01-19T14:03:49Z | null | ## Describe the bug
I have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`.
## Steps to reproduce the bug
Run the `run_mlm_no_trainer.py` and the given script on this [link](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3.
## Expected results
The data batchs are fetched from the data loader and train.
## Actual results
The first time to fetch data batch occurs error.
`Traceback (most recent call last):
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors
tensor = as_tensor(value)
ValueError: too many dimensions 'str'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "src/original_run_mlm_no_trainer.py", line 528, in <module>
main()
File "src/original_run_mlm_no_trainer.py", line 488, in main
for step, batch in enumerate(train_dataloader):
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/accelerate/data_loader.py", line 303, in __iter__
for batch in super().__iter__():
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
data = self._next_data()
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 557, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
return self.collate_fn(data)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 41, in __call__
return self.torch_call(features)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 671, in torch_call
batch = self.tokenizer.pad(examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2774, in pad
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 210, in __init__
self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 722, in convert_to_tensors
"Unable to create tensor, you should probably activate truncation and/or padding "
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.
`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.6
- PyArrow version: 5.0.0
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"Hi ! I think this issue is more related to the `transformers` project. Could you open an issue on https://github.com/huggingface/transformers ?\r\n\r\nAnyway I think the issue could be that both wikipedia and bookcorpusopen have an additional \"title\" column, contrary to wikitext which only has a \"text\" column.... |
https://api.github.com/repos/huggingface/datasets/issues/5449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5449/comments | https://api.github.com/repos/huggingface/datasets/issues/5449/events | https://github.com/huggingface/datasets/pull/5449 | 1,550,801,453 | PR_kwDODunzps5INgD9 | 5,449 | Support fsspec 2023.1.0 in CI | [] | closed | false | null | 2 | 2023-01-20T12:53:17Z | 2023-01-20T13:32:50Z | 2023-01-20T13:26:03Z | null | Support fsspec 2023.1.0 in CI.
In the 2023.1.0 fsspec release, they replaced the type of `fsspec.registry`:
- from `ReadOnlyRegistry`, with an attribute called `target`
- to `MappingProxyType`, without that attribute
Consequently, we need to change our `mock_fsspec` fixtures, that were using the `target` attribute.
Fix #5448. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/1483 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1483/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1483/comments | https://api.github.com/repos/huggingface/datasets/issues/1483/events | https://github.com/huggingface/datasets/pull/1483 | 762,712,337 | MDExOlB1bGxSZXF1ZXN0NTM3MjMxMzQ4 | 1,483 | Added Times of India News Headlines Dataset | [] | closed | false | null | 3 | 2020-12-11T18:12:38Z | 2020-12-14T18:08:08Z | 2020-12-14T18:08:08Z | null | Dataset name: Times of India News Headlines
link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DPQMQH | {
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"@lhoestq @abhishekkrthakur what happened here ?\r\n",
"@lhoestq everything alright here ?",
"@tanmoyio please have patience. @lhoestq has to look at 150+ PRs and it may take time. The PR looks good to me but we wait for his confirmation :) 🤗 "
] |
https://api.github.com/repos/huggingface/datasets/issues/5476 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5476/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5476/comments | https://api.github.com/repos/huggingface/datasets/issues/5476/events | https://github.com/huggingface/datasets/pull/5476 | 1,559,594,684 | PR_kwDODunzps5IqwC_ | 5,476 | Pin sqlalchemy | [] | closed | false | null | 3 | 2023-01-27T11:26:38Z | 2023-01-27T12:06:51Z | 2023-01-27T11:57:48Z | null | since sqlalchemy update to 2.0.0 the CI started to fail: https://github.com/huggingface/datasets/actions/runs/4023742457/jobs/6914976514
the error comes from pandas: https://github.com/pandas-dev/pandas/issues/51015 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/2640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2640/comments | https://api.github.com/repos/huggingface/datasets/issues/2640/events | https://github.com/huggingface/datasets/pull/2640 | 943,591,055 | MDExOlB1bGxSZXF1ZXN0Njg5MjAxMDkw | 2,640 | Fix docstrings | [] | closed | false | {
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} | 0 | 2021-07-13T16:09:14Z | 2021-07-15T06:51:01Z | 2021-07-15T06:06:12Z | null | Fix rendering of some docstrings. | {
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https://api.github.com/repos/huggingface/datasets/issues/3706 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3706/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3706/comments | https://api.github.com/repos/huggingface/datasets/issues/3706/events | https://github.com/huggingface/datasets/issues/3706 | 1,132,218,874 | I_kwDODunzps5DfEn6 | 3,706 | Unable to load dataset 'big_patent' | [
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] | closed | false | null | 5 | 2022-02-11T09:48:34Z | 2022-02-14T15:26:03Z | 2022-02-14T15:26:03Z | null | ## Describe the bug
Unable to load the "big_patent" dataset
## Steps to reproduce the bug
```python
load_dataset('big_patent', 'd', 'validation')
```
## Expected results
Download big_patents' validation split from the 'd' subset
## Getting an error saying:
{FileNotFoundError}Local file ..\huggingface\datasets\downloads\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\bigPatentData\train.tar.gz doesn't exist
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:1.18.3
- Platform: Windows
- Python version:3.8
- PyArrow version:7.0.0
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"Hi @ankitk2109,\r\n\r\nHave you tried passing the split name with the keyword `split=`? See e.g. an example in our Quick Start docs: https://huggingface.co/docs/datasets/quickstart.html#load-the-dataset-and-model\r\n```python\r\n ds = load_dataset(\"big_patent\", \"d\", split=\"validation\")",
"Hi @albertvillano... |
https://api.github.com/repos/huggingface/datasets/issues/142 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/142/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/142/comments | https://api.github.com/repos/huggingface/datasets/issues/142/events | https://github.com/huggingface/datasets/pull/142 | 619,450,068 | MDExOlB1bGxSZXF1ZXN0NDE4OTU0OTc1 | 142 | [WMT] Add all wmt | [] | closed | false | null | 0 | 2020-05-16T11:28:46Z | 2020-05-17T12:18:21Z | 2020-05-17T12:18:20Z | null | This PR adds all wmt datasets scripts. At the moment the script is **not** functional for the language pairs "cs-en", "ru-en", "hi-en" because apparently it takes up to a week to get the manual data for these datasets: see http://ufal.mff.cuni.cz/czeng.
The datasets are fully functional though for the "big" language pairs "de-en" and "fr-en".
Overall I think the scripts are very messy and might need a big refactoring at some point.
For now I think there are good to merge (most dataset configs can be used). I will add "cs", "ru" and "hi" when the manual data is available. | {
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https://api.github.com/repos/huggingface/datasets/issues/5877 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5877/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5877/comments | https://api.github.com/repos/huggingface/datasets/issues/5877/events | https://github.com/huggingface/datasets/issues/5877 | 1,717,983,961 | I_kwDODunzps5mZlrZ | 5,877 | Request for text deduplication feature | [
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] | open | false | null | 3 | 2023-05-20T01:56:00Z | 2023-07-26T21:42:14Z | null | null | ### Feature request
It would be great if there would be support for high performance, highly scalable text deduplication algorithms as part of the datasets library.
### Motivation
Motivated by this blog post https://huggingface.co/blog/dedup and this library https://github.com/google-research/deduplicate-text-datasets, but slightly frustrated by how its not very easy to work with these tools I am proposing this feature.
### Your contribution
I would be happy to contribute to the development effort of this feature. would love to collaborate with others in the development effort. | {
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"The \"exact match\" deduplication will be possible when we resolve https://github.com/huggingface/datasets/issues/2514 (first, https://github.com/apache/arrow/issues/30950 needs to be addressed on the Arrow side). In the meantime, you can use Polars or DuckDB (e.g., via [datasets-sql](https://github.com/mariosasko... |
https://api.github.com/repos/huggingface/datasets/issues/3717 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3717/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3717/comments | https://api.github.com/repos/huggingface/datasets/issues/3717/events | https://github.com/huggingface/datasets/issues/3717 | 1,137,183,015 | I_kwDODunzps5DyAkn | 3,717 | wrong condition in `Features ClassLabel encode_example` | [
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] | closed | false | null | 1 | 2022-02-14T11:44:35Z | 2022-02-14T15:09:36Z | 2022-02-14T15:07:43Z | null | ## Describe the bug
The `encode_example` function in *features.py* seems to have a wrong condition.
```python
if not -1 <= example_data < self.num_classes:
raise ValueError(f"Class label {example_data:d} greater than configured num_classes {self.num_classes}")
```
## Expected results
The `not - 1` condition change the result of the condition. For instance, if `example_data` equals 4 and ` self.num_classes` equals 4 too, `example_data < self.num_classes` will give `False` as expected . But if i add the `not - 1` condition, `not -1 <= example_data < self.num_classes` will give `True` and raise an exception.
## Environment info
- `datasets` version: 1.18.3
- Python version: 3.8.10
- PyArrow version: 7.00
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"Hi @Tudyx, \r\n\r\nPlease note that in Python, the boolean NOT operator (`not`) has lower precedence than comparison operators (`<=`, `<`), thus the expression you mention is equivalent to:\r\n```python\r\n not (-1 <= example_data < self.num_classes)\r\n```\r\n\r\nAlso note that as expected, the exception is raise... |
https://api.github.com/repos/huggingface/datasets/issues/4814 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4814/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4814/comments | https://api.github.com/repos/huggingface/datasets/issues/4814/events | https://github.com/huggingface/datasets/issues/4814 | 1,333,356,230 | I_kwDODunzps5PeWbG | 4,814 | Support CSV as metadata file format in AudioFolder/ImageFolder | [
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] | closed | false | null | 0 | 2022-08-09T14:36:49Z | 2022-08-31T11:59:08Z | 2022-08-31T11:59:08Z | null | Requested here: https://discuss.huggingface.co/t/how-to-structure-an-image-dataset-repo-using-the-image-folder-approach/21004. CSV is also used in AutoTrain for specifying metadata in image datasets. | {
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https://api.github.com/repos/huggingface/datasets/issues/4914 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4914/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4914/comments | https://api.github.com/repos/huggingface/datasets/issues/4914/events | https://github.com/huggingface/datasets/pull/4914 | 1,355,482,624 | PR_kwDODunzps4-CFyN | 4,914 | Support streaming swda dataset | [] | closed | false | null | 1 | 2022-08-30T09:46:28Z | 2022-08-30T11:16:33Z | 2022-08-30T11:14:16Z | null | Support streaming swda dataset. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/1434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1434/comments | https://api.github.com/repos/huggingface/datasets/issues/1434/events | https://github.com/huggingface/datasets/pull/1434 | 760,821,474 | MDExOlB1bGxSZXF1ZXN0NTM1NTg3NjEx | 1,434 | add_sofc_materials_articles | [] | closed | false | null | 1 | 2020-12-10T02:15:02Z | 2020-12-17T09:59:54Z | 2020-12-17T09:59:54Z | null | adding [SOFC-Exp Corpus](https://arxiv.org/abs/2006.03039) | {
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"Hey @lhoestq , thanks for the feedback on this! I updated the `_generate_examples` with some comments on the process, and reduced the `dummy_data.zip` down quite a bit as well. \r\n\r\nFor the dummy data, I reduced the text to only three sentences, and aligned the corresponding entity/token/sentence annotations to... |
https://api.github.com/repos/huggingface/datasets/issues/4105 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4105/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4105/comments | https://api.github.com/repos/huggingface/datasets/issues/4105/events | https://github.com/huggingface/datasets/issues/4105 | 1,194,297,119 | I_kwDODunzps5HL4cf | 4,105 | push to hub fails with huggingface-hub 0.5.0 | [
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] | closed | false | null | 5 | 2022-04-06T08:59:57Z | 2022-04-13T14:30:47Z | 2022-04-13T14:30:47Z | null | ## Describe the bug
`ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id"
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("rubrix/news_test")
ds.push_to_hub("<your-user>/news_test", token="<your-token>")
```
## Expected results
The dataset is successfully uploaded
## Actual results
An error validation is raised:
```bash
if repo_id and (name or organization):
> raise ValueError(
"Only pass `repo_id` and leave deprecated `name` and "
"`organization` to be None."
E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.1
- `huggingface-hub`: 0.5
- Platform: macOS
- Python version: 3.8.12
- PyArrow version: 6.0.0
cc @adrinjalali
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"Hi ! Indeed there was a breaking change in `huggingface_hub` 0.5.0 in `HfApi.create_repo`, which is called here in `datasets` by passing the org name in both the `repo_id` and the `organization` arguments:\r\n\r\nhttps://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arr... |
https://api.github.com/repos/huggingface/datasets/issues/3507 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3507/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3507/comments | https://api.github.com/repos/huggingface/datasets/issues/3507/events | https://github.com/huggingface/datasets/issues/3507 | 1,091,214,808 | I_kwDODunzps5BCp3Y | 3,507 | Discuss whether support canonical datasets w/o dataset_infos.json and/or dummy data | [
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"default": fals... | closed | false | null | 17 | 2021-12-30T17:04:25Z | 2022-11-04T15:31:38Z | 2022-11-04T15:31:37Z | null | I open this PR to have a public discussion about this topic and make a decision.
As previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?
On the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:
- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)
- we are migrating canonical datasets to the Hub
Do we really need to continue testing them in out CI?
Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).
Feel free to ping other people for the discussion.
CC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw | {
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"IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work), so that for datasets that have a working dataset preview, we can remove the dummy data IMO. On the other hand, it see... |
https://api.github.com/repos/huggingface/datasets/issues/266 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/266/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/266/comments | https://api.github.com/repos/huggingface/datasets/issues/266/events | https://github.com/huggingface/datasets/pull/266 | 637,156,392 | MDExOlB1bGxSZXF1ZXN0NDMzMTk1NDgw | 266 | Add sort, shuffle, test_train_split and select methods | [] | closed | false | null | 4 | 2020-06-11T16:22:20Z | 2020-06-18T16:23:25Z | 2020-06-18T16:23:24Z | null | Add a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``.
This is the default behavior in the library.
Fix #147 #166 #259 | {
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"Nice !\r\n\r\nAlso it looks like we can have a train_test_split method for free:\r\n```python\r\ntrain_indices, test_indices = train_test_split(range(len(dataset)))\r\ntrain = dataset.sort(indices=train_indices)\r\ntest = dataset.sort(indices=test_indices)\r\n```\r\n\r\nand a shuffling method for free:\r\n```pytho... |
https://api.github.com/repos/huggingface/datasets/issues/3659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3659/comments | https://api.github.com/repos/huggingface/datasets/issues/3659/events | https://github.com/huggingface/datasets/issues/3659 | 1,120,913,672 | I_kwDODunzps5Cz8kI | 3,659 | push_to_hub but preview not working | [
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] | closed | false | null | 1 | 2022-02-01T16:23:57Z | 2022-02-09T08:00:37Z | 2022-02-09T08:00:37Z | null | ## Dataset viewer issue for '*happifyhealth/twitter_pnn*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/happifyhealth/twitter_pnn)*
I used
```
dataset.push_to_hub("happifyhealth/twitter_pnn")
```
but the preview is not working.
Am I the one who added this dataset ? Yes
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} | https://api.github.com/repos/huggingface/datasets/issues/3659/timeline | null | completed | null | null | false | [
"Hi @thomas-happify, please note that the preview may take some time before rendering the data.\r\n\r\nI've seen it is already working.\r\n\r\nI close this issue. Please feel free to reopen it if the problem arises again."
] |
https://api.github.com/repos/huggingface/datasets/issues/2599 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2599/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2599/comments | https://api.github.com/repos/huggingface/datasets/issues/2599/events | https://github.com/huggingface/datasets/pull/2599 | 937,980,229 | MDExOlB1bGxSZXF1ZXN0Njg0NDQ2MTYx | 2,599 | Update processing.rst with other export formats | [] | closed | false | {
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} | 0 | 2021-07-06T14:50:38Z | 2021-07-12T14:10:16Z | 2021-07-07T08:05:48Z | null | Add other supported export formats than CSV in the docs. | {
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https://api.github.com/repos/huggingface/datasets/issues/4235 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4235/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4235/comments | https://api.github.com/repos/huggingface/datasets/issues/4235/events | https://github.com/huggingface/datasets/issues/4235 | 1,216,952,640 | I_kwDODunzps5IiTlA | 4,235 | How to load VERY LARGE dataset? | [
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] | closed | false | null | 1 | 2022-04-27T07:50:13Z | 2023-07-25T15:07:57Z | 2023-07-25T15:07:57Z | null | ### System Info
```shell
I am using transformer trainer while meeting the issue.
The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency.
I wonder if there are any tricks like Sharding in huggingface trainer.
Looking forward to your reply.
```
### Who can help?
Trainer: @sgugger
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
None
### Expected behavior
```shell
I wonder if there are any tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html.
Thanks a lot!
```
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"The `Trainer` support `IterableDataset`, not just datasets."
] |
https://api.github.com/repos/huggingface/datasets/issues/4696 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4696/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4696/comments | https://api.github.com/repos/huggingface/datasets/issues/4696/events | https://github.com/huggingface/datasets/issues/4696 | 1,307,183,099 | I_kwDODunzps5N6gf7 | 4,696 | Cannot load LinCE dataset | [
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] | closed | false | null | 2 | 2022-07-17T19:01:54Z | 2022-07-18T09:20:40Z | 2022-07-18T07:24:22Z | null | ## Describe the bug
Cannot load LinCE dataset due to a connection error
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("lince", "ner_spaeng")
```
A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2
## Expected results
It should load the dataset
## Actual results
```python
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-2-fc551ddcebef> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("lince", "ner_spaeng")
10 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
-> 1684 use_auth_token=use_auth_token,
1685 )
1686
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
703 if not downloaded_from_gcs:
704 self._download_and_prepare(
--> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1219
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1222
1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
772
773 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager)
481 def _split_generators(self, dl_manager):
482 """Returns SplitGenerators."""
--> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip")
484 data_dir = os.path.join(lince_dir, self.config.data_dir)
485 return [
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
432
433 def get_recorded_sizes_checksums(self):
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls)
313 num_proc=download_config.num_proc,
314 disable_tqdm=not is_progress_bar_enabled(),
--> 315 desc="Downloading data files",
316 )
317 duration = datetime.now() - start_time
/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
346 # Singleton
347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 348 return function(data_struct)
349
350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled()
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config)
333 # append the relative path to the base_path
334 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 335 return cached_path(url_or_filename, download_config=download_config)
336
337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
195 use_auth_token=download_config.use_auth_token,
196 ignore_url_params=download_config.ignore_url_params,
--> 197 download_desc=download_config.download_desc,
198 )
199 elif os.path.exists(url_or_filename):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
532 if head_error is not None:
--> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
534 elif response is not None:
535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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} | https://api.github.com/repos/huggingface/datasets/issues/4696/timeline | null | completed | null | null | false | [
"Hi @finiteautomata, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"lince\", \"ner_spaeng\")\r\nDownloading builder script: 20.8kB [00:00, 9.09MB/s] ... |
https://api.github.com/repos/huggingface/datasets/issues/1069 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1069/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1069/comments | https://api.github.com/repos/huggingface/datasets/issues/1069/events | https://github.com/huggingface/datasets/pull/1069 | 756,425,737 | MDExOlB1bGxSZXF1ZXN0NTMxOTcyNjYz | 1,069 | Test | [] | closed | false | null | 0 | 2020-12-03T18:01:45Z | 2020-12-04T04:24:18Z | 2020-12-04T04:24:11Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/3011 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3011/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3011/comments | https://api.github.com/repos/huggingface/datasets/issues/3011/events | https://github.com/huggingface/datasets/issues/3011 | 1,014,935,713 | I_kwDODunzps48frCh | 3,011 | load_dataset_builder should error if "name" does not exist? | [
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"descrip... | open | false | null | 1 | 2021-10-04T09:20:46Z | 2022-09-20T13:05:07Z | null | null | ```
import datasets as ds
builder = ds.load_dataset_builder('sent_comp', name="doesnotexist")
builder.info.config_name
```
returns
```
'doesnotexist'
```
Shouldn't it raise an error instead?
For this dataset, the only valid values for `name` should be: `"default"` or `None` (ie. argument not passed) | {
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"Yes I think it should raise an error. Currently it looks like it instantiates a custom configuration with the name given by the user:\r\nhttps://github.com/huggingface/datasets/blob/ba27ce33bf568374cf23a07669fdd875b5718bc2/src/datasets/builder.py#L391-L397"
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https://api.github.com/repos/huggingface/datasets/issues/2635 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2635/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2635/comments | https://api.github.com/repos/huggingface/datasets/issues/2635/events | https://github.com/huggingface/datasets/pull/2635 | 943,030,999 | MDExOlB1bGxSZXF1ZXN0Njg4Njk5OTM5 | 2,635 | Streaming for the CSV loader | [] | closed | false | null | 0 | 2021-07-13T09:08:58Z | 2021-07-13T15:19:38Z | 2021-07-13T15:19:37Z | null | It was not using `open` in the builder. Therefore `pd.read_csv` was downloading the full file to start yielding rows.
Indeed, when streaming, `open` is extended to support reading from remote file progressively. | {
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https://api.github.com/repos/huggingface/datasets/issues/3517 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3517/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3517/comments | https://api.github.com/repos/huggingface/datasets/issues/3517/events | https://github.com/huggingface/datasets/pull/3517 | 1,092,726,651 | PR_kwDODunzps4wemwU | 3,517 | Add CPPE-5 dataset | [] | closed | false | null | 1 | 2022-01-03T18:31:20Z | 2022-01-19T02:23:37Z | 2022-01-05T18:53:02Z | null | Adds the recently released CPPE-5 dataset. | {
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"Thanks so much, @mariosasko and @lhoestq , much appreciated!"
] |
https://api.github.com/repos/huggingface/datasets/issues/3289 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3289/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3289/comments | https://api.github.com/repos/huggingface/datasets/issues/3289/events | https://github.com/huggingface/datasets/pull/3289 | 1,056,323,715 | PR_kwDODunzps4uqf79 | 3,289 | Unpin markdown for build_docs now that it's fixed | [] | closed | false | null | 0 | 2021-11-17T16:22:53Z | 2021-11-17T16:23:09Z | 2021-11-17T16:23:08Z | null | `markdown`'s bug has been fixed, so this PR reverts #3286 | {
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https://api.github.com/repos/huggingface/datasets/issues/3861 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3861/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3861/comments | https://api.github.com/repos/huggingface/datasets/issues/3861/events | https://github.com/huggingface/datasets/issues/3861 | 1,162,702,044 | I_kwDODunzps5FTWzc | 3,861 | big_patent cased version | [
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] | closed | false | null | 2 | 2022-03-08T14:08:55Z | 2023-04-21T14:32:03Z | 2023-04-21T14:32:03Z | null | Hi! I am interested in working with the big_patent dataset.
In Tensorflow, there are a number of versions of the dataset:
- 1.0.0 : lower cased tokenized words
- 2.0.0 : Update to use cased raw strings
- 2.1.2 (default): Fix update to cased raw strings.
The version in the huggingface `datasets` library is the 1.0.0. I would be very interested in using the 2.1.2 cased version (used more, recently, for example in the Pegasus paper), but it does not seem to be supported (I tried using the `revision` parameter in `load_datasets`). Is there a way to already load it, or would it be possible to add that version? | {
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"To follow up on this: the cased and uncased versions actually contain different content, and the cased one is easier since it contains a Summary of the Invention in the input.\r\n\r\nSee the paper describing the issue here:\r\nhttps://aclanthology.org/2022.gem-1.34/",
"Thanks for proposing the addition of the ca... |
https://api.github.com/repos/huggingface/datasets/issues/3438 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3438/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3438/comments | https://api.github.com/repos/huggingface/datasets/issues/3438/events | https://github.com/huggingface/datasets/pull/3438 | 1,081,302,203 | PR_kwDODunzps4v52Va | 3,438 | Update supported versions of Python in setup.py | [] | closed | false | null | 0 | 2021-12-15T17:30:12Z | 2021-12-20T14:22:13Z | 2021-12-20T14:22:12Z | null | Update the list of supported versions of Python in `setup.py` to keep the PyPI project description updated. | {
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https://api.github.com/repos/huggingface/datasets/issues/596 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/596/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/596/comments | https://api.github.com/repos/huggingface/datasets/issues/596/events | https://github.com/huggingface/datasets/pull/596 | 696,928,139 | MDExOlB1bGxSZXF1ZXN0NDgyOTM5MTgw | 596 | [style/quality] Moving to isort 5.0.0 + style/quality on datasets and metrics | [] | closed | false | null | 1 | 2020-09-09T15:47:21Z | 2020-09-10T10:05:04Z | 2020-09-10T10:05:03Z | null | Move the repo to isort 5.0.0.
Also start testing style/quality on datasets and metrics.
Specific rule: we allow F401 (unused imports) in metrics to be able to add imports to detect early on missing dependencies.
Maybe we could add this in datasets but while cleaning this I've seen many example of really unused imports in dataset so maybe it's better to have it as a line-by-line nova instead of a general rule like in metrics. | {
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"Ready for review @lhoestq, just updated a few 156 files here"
] |
https://api.github.com/repos/huggingface/datasets/issues/442 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/442/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/442/comments | https://api.github.com/repos/huggingface/datasets/issues/442/events | https://github.com/huggingface/datasets/issues/442 | 666,201,810 | MDU6SXNzdWU2NjYyMDE4MTA= | 442 | [Suggestion] Glue Diagnostic Data with Labels | [
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] | open | false | null | 0 | 2020-07-27T10:59:58Z | 2020-08-24T15:13:20Z | null | null | Hello! First of all, thanks for setting up this useful project!
I've just realised you provide the the [Glue Diagnostics Data](https://huggingface.co/nlp/viewer/?dataset=glue&config=ax) without labels, indicating in the `GlueConfig` that you've only a test set.
Yet, the data with labels is available, too (see also [here](https://gluebenchmark.com/diagnostics#introduction)):
https://www.dropbox.com/s/ju7d95ifb072q9f/diagnostic-full.tsv?dl=1
Have you considered incorporating it? | {
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https://api.github.com/repos/huggingface/datasets/issues/6012 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6012/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6012/comments | https://api.github.com/repos/huggingface/datasets/issues/6012/events | https://github.com/huggingface/datasets/issues/6012 | 1,795,575,432 | I_kwDODunzps5rBk6I | 6,012 | [FR] Transform Chaining, Lazy Mapping | [
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] | open | false | null | 6 | 2023-07-09T21:40:21Z | 2023-07-14T13:12:40Z | null | null | ### Feature request
Currently using a `map` call processes and duplicates the whole dataset, which takes both time and disk space.
The solution is to allow lazy mapping, which is essentially a saved chain of transforms that are applied on the fly whenever a slice of the dataset is requested.
The API should look like `map`, as `set_transform` changes the current dataset while `map` returns another dataset.
### Motivation
Lazy processing allows lower disk usage and faster experimentation.
### Your contribution
_ | {
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"You can use `with_transform` to get a new dataset object.\r\n\r\nSupport for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex. ",
"> You can use `with_transform` to get a new datas... |
https://api.github.com/repos/huggingface/datasets/issues/144 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/144/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/144/comments | https://api.github.com/repos/huggingface/datasets/issues/144/events | https://github.com/huggingface/datasets/pull/144 | 619,477,367 | MDExOlB1bGxSZXF1ZXN0NDE4OTY5NjA1 | 144 | [AWS tests] AWS test should not run for canonical datasets | [] | closed | false | null | 0 | 2020-05-16T13:39:30Z | 2020-05-16T13:44:34Z | 2020-05-16T13:44:33Z | null | AWS tests should in general not run for canonical datasets. Only local tests will run in this case. This way a PR is able to pass when adding a new dataset.
This PR changes to logic to the following:
1) All datasets that are present in `nlp/datasets` are tested only locally. This way when one adds a canonical dataset, the PR includes his dataset in the tests.
2) All datasets that are only present on AWS, such as `webis/tl_dr` atm are tested only on AWS.
I think the testing structure might need a bigger refactoring and better documentation very soon.
Merging for now to unblock new PRs @thomwolf @mariamabarham . | {
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https://api.github.com/repos/huggingface/datasets/issues/4898 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4898/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4898/comments | https://api.github.com/repos/huggingface/datasets/issues/4898/events | https://github.com/huggingface/datasets/issues/4898 | 1,351,851,254 | I_kwDODunzps5Qk5z2 | 4,898 | Dataset Viewer issue for timit_asr | [] | closed | false | null | 5 | 2022-08-26T07:12:05Z | 2022-10-03T12:40:28Z | 2022-10-03T12:40:27Z | null | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | {
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} | https://api.github.com/repos/huggingface/datasets/issues/4898/timeline | null | completed | null | null | false | [
"Yes, the dataset viewer is based on `datasets`, and the following does not work:\r\n\r\n```\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names('timit_asr')\r\nDownloading builder script: 7.48kB [00:00, 6.69MB/s]\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/data... |
https://api.github.com/repos/huggingface/datasets/issues/5685 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5685/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5685/comments | https://api.github.com/repos/huggingface/datasets/issues/5685/events | https://github.com/huggingface/datasets/issues/5685 | 1,646,048,667 | I_kwDODunzps5iHLWb | 5,685 | Broken Image render on the hub website | [] | closed | false | null | 3 | 2023-03-29T15:25:30Z | 2023-03-30T07:54:25Z | 2023-03-30T07:54:25Z | null | ### Describe the bug
Hi :wave:
Not sure if this is the right place to ask, but I am trying to load a huge amount of datasets on the hub (:partying_face: ) but I am facing a little issue with the `image` type
See this [dataset](https://huggingface.co/datasets/Francesco/cell-towers), basically for some reason the first image has numerical bytes inside, not sure if that is okay, but the image render feature **doesn't work**
So the dataset is stored in the following way
```python
builder.download_and_prepare(output_dir=str(output_dir))
ds = builder.as_dataset(split="train")
# [NOTE] no idea how to push it from the builder folder
ds.push_to_hub(repo_id=repo_id)
builder.as_dataset(split="validation").push_to_hub(repo_id=repo_id)
ds = builder.as_dataset(split="test")
ds.push_to_hub(repo_id=repo_id)
```
The build is this class
```python
class COCOLikeDatasetBuilder(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"image_id": datasets.Value("int64"),
"image": datasets.Image(),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"objects": datasets.Sequence(
{
"id": datasets.Value("int64"),
"area": datasets.Value("int64"),
"bbox": datasets.Sequence(
datasets.Value("float32"), length=4
),
"category": datasets.ClassLabel(names=categories),
}
),
}
)
return datasets.DatasetInfo(
description=description,
features=features,
homepage=homepage,
license=license,
citation=citation,
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotation_file_path": "train/_annotations.coco.json",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"annotation_file_path": "test/_annotations.coco.json",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotation_file_path": "valid/_annotations.coco.json",
"files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, annotation_file_path, files):
def process_annot(annot, category_id_to_category):
return {
"id": annot["id"],
"area": annot["area"],
"bbox": annot["bbox"],
"category": category_id_to_category[annot["category_id"]],
}
image_id_to_image = {}
idx = 0
# This loop relies on the ordering of the files in the archive:
# Annotation files come first, then the images.
for path, f in files:
file_name = os.path.basename(path)
if annotation_file_path in path:
annotations = json.load(f)
category_id_to_category = {
category["id"]: category["name"]
for category in annotations["categories"]
}
print(category_id_to_category)
image_id_to_annotations = collections.defaultdict(list)
for annot in annotations["annotations"]:
image_id_to_annotations[annot["image_id"]].append(annot)
image_id_to_image = {
annot["file_name"]: annot for annot in annotations["images"]
}
elif file_name in image_id_to_image:
image = image_id_to_image[file_name]
objects = [
process_annot(annot, category_id_to_category)
for annot in image_id_to_annotations[image["id"]]
]
print(file_name)
yield idx, {
"image_id": image["id"],
"image": {"path": path, "bytes": f.read()},
"width": image["width"],
"height": image["height"],
"objects": objects,
}
idx += 1
```
Basically, I want to add to the hub every dataset I come across on coco format
Thanks
Fra
### Steps to reproduce the bug
In this case, you can just navigate on the [dataset](https://huggingface.co/datasets/Francesco/cell-towers)
### Expected behavior
I was expecting the image rendering feature to work
### Environment info
Not a lot to share, I am using `datasets` from a fresh venv | {
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"Hi! \r\n\r\nYou can fix the viewer by adding the `dataset_info` YAML field deleted in https://huggingface.co/datasets/Francesco/cell-towers/commit/b95b59ddd91ebe9c12920f0efe0ed415cd0d4298 back to the metadata section of the card. \r\n\r\nTo avoid this issue in the feature, you can use `huggingface_hub`'s [RepoCard... |
https://api.github.com/repos/huggingface/datasets/issues/1192 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1192/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1192/comments | https://api.github.com/repos/huggingface/datasets/issues/1192/events | https://github.com/huggingface/datasets/pull/1192 | 757,839,671 | MDExOlB1bGxSZXF1ZXN0NTMzMTM0NjI3 | 1,192 | Add NewsPH_NLI dataset | [] | closed | false | null | 0 | 2020-12-06T04:00:31Z | 2020-12-07T15:39:43Z | 2020-12-07T15:39:43Z | null | This PR adds the NewsPH-NLI Dataset, the first benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing.
Link to the paper: https://arxiv.org/pdf/2010.11574.pdf
Link to the dataset/repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks | {
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https://api.github.com/repos/huggingface/datasets/issues/3053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3053/comments | https://api.github.com/repos/huggingface/datasets/issues/3053/events | https://github.com/huggingface/datasets/issues/3053 | 1,022,076,905 | I_kwDODunzps4866fp | 3,053 | load_dataset('the_pile_openwebtext2') produces ArrowInvalid, value too large to fit in C integer type | [
{
"color": "d73a4a",
"default": true,
"description": "Something isn't working",
"id": 1935892857,
"name": "bug",
"node_id": "MDU6TGFiZWwxOTM1ODkyODU3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/bug"
}
] | closed | false | null | 5 | 2021-10-10T19:55:21Z | 2023-02-24T14:02:20Z | 2023-02-24T14:02:20Z | null | ## Describe the bug
When loading `the_pile_openwebtext2`, we get the error `pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type`
## Steps to reproduce the bug
```python
import datasets
ds = datasets.load_dataset('the_pile_openwebtext2')
```
## Expected results
Should download the dataset, convert it to an arrow file, and return a working Dataset object.
## Actual results
The download works, but conversion to the arrow file fails as follows:
```
>>> ds = datasets.load_dataset('the_pile_openwebtext2')
Downloading and preparing dataset openwebtext2/plain_text (download: 27.33 GiB, generated: 63.86 GiB
, post-processed: Unknown size, total: 91.19 GiB) to /home/davidbau/.cache/huggingface/datasets/open
webtext2/plain_text/1.0.0/c48ec73ba3483bac673463f48f67e9a4fd8cb49a9d6ec4fb957f0b424b97cf25...
Traceback (most recent call last):
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/builder.py", line 1133,
in _prepare_split
writer.write(example, key)
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
366, in write
self.write_examples_on_file()
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
311, in write_examples_on_file
pa_array = pa.array(typed_sequence)
File "pyarrow/array.pxi", line 222, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
115, in __arrow_array__
out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)
File "pyarrow/array.pxi", line 305, in pyarrow.lib.array
File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
```
- Platform: Ubuntu 20.04
- Python version: python 3.9
- PyArrow version: 3.0.0
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} | https://api.github.com/repos/huggingface/datasets/issues/3053/timeline | null | completed | null | null | false | [
"I encountered the same bug using different datasets.\r\nany suggestions?",
"+1, can reproduce here!",
"I get the same error\r\nPlatform: Windows 10\r\nPython: python 3.8.8\r\nPyArrow: 5.0",
"I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this i... |
https://api.github.com/repos/huggingface/datasets/issues/4362 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4362/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4362/comments | https://api.github.com/repos/huggingface/datasets/issues/4362/events | https://github.com/huggingface/datasets/pull/4362 | 1,238,680,112 | PR_kwDODunzps439bkf | 4,362 | Update dataset_infos for UDHN/udhr dataset | [] | closed | false | null | 5 | 2022-05-17T13:52:59Z | 2022-06-08T19:20:11Z | 2022-06-08T19:11:21Z | null | Checksum update to `udhr` for issue #4361 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for contributing @leondz.\r\n\r\nThe checksums of the files have changed because more languages have been added:\r\n- the new language codes need to be added to the dataset card (README file)\r\n- I think the dataset version n... |
https://api.github.com/repos/huggingface/datasets/issues/1496 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1496/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1496/comments | https://api.github.com/repos/huggingface/datasets/issues/1496/events | https://github.com/huggingface/datasets/pull/1496 | 763,091,663 | MDExOlB1bGxSZXF1ZXN0NTM3NTc4NzQw | 1,496 | Add Multi-Dimensional Gender Bias classification data | [] | closed | false | null | 0 | 2020-12-12T00:17:37Z | 2020-12-14T21:14:55Z | 2020-12-14T21:14:55Z | null | https://parl.ai/projects/md_gender/
Mostly has the ABOUT dimension since the others are inferred from other datasets in most cases.
I tried to keep the dummy data small but one of the configs has 140 splits ( > 56KB data) | {
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https://api.github.com/repos/huggingface/datasets/issues/2257 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2257/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2257/comments | https://api.github.com/repos/huggingface/datasets/issues/2257/events | https://github.com/huggingface/datasets/pull/2257 | 866,755,203 | MDExOlB1bGxSZXF1ZXN0NjIyNTkwMDQw | 2,257 | added metrics for CUAD | [] | closed | false | null | 3 | 2021-04-24T14:09:54Z | 2021-04-29T09:53:38Z | 2021-04-27T16:16:32Z | null | For now I've added F1, AUPR, Precision at 80% recall, and Precision at 90%. Last 3 metrics were reported in the [paper](https://arxiv.org/pdf/2103.06268.pdf). Please let me know if we require `exact_match` metric too here | {
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} | true | [
"> For now I've added F1, AUPR, Precision at 80% recall, and Precision at 90%. Last 3 metrics were reported in the [paper](https://arxiv.org/pdf/2103.06268.pdf). Please let me know if we require `exact_match` metric too here\r\n\r\n@bhavitvyamalik I guess the mentioned metrics are enough but it would be better if ... |
https://api.github.com/repos/huggingface/datasets/issues/886 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/886/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/886/comments | https://api.github.com/repos/huggingface/datasets/issues/886/events | https://github.com/huggingface/datasets/pull/886 | 750,829,314 | MDExOlB1bGxSZXF1ZXN0NTI3NDU1MDU5 | 886 | Fix wikipedia custom config | [] | closed | false | null | 1 | 2020-11-25T13:44:12Z | 2021-06-25T05:24:16Z | 2020-11-25T15:42:13Z | null | It should be possible to use the wikipedia dataset with any `language` and `date`.
However it was not working as noticed in #784 . Indeed the custom wikipedia configurations were not enabled for some reason.
I fixed that and was able to run
```python
from datasets import load_dataset
load_dataset("./datasets/wikipedia", language="zh", date="20201120", beam_runner='DirectRunner')
```
cc @stvhuang @SamuelCahyawijaya
Fix #784 | {
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"I think this issue is still not resolve yet. Please check my comment in the following issue, thanks.\r\n[#577](https://github.com/huggingface/datasets/issues/577#issuecomment-868122769)"
] |
https://api.github.com/repos/huggingface/datasets/issues/5918 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5918/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5918/comments | https://api.github.com/repos/huggingface/datasets/issues/5918/events | https://github.com/huggingface/datasets/issues/5918 | 1,735,313,549 | I_kwDODunzps5nbsiN | 5,918 | File not found for audio dataset | [] | open | false | null | 1 | 2023-06-01T02:15:29Z | 2023-06-11T06:02:25Z | null | null | ### Describe the bug
After loading an audio dataset, and looking at a sample entry, the `path` element, which is supposed to be the path to the audio file, doesn't actually exist.
### Steps to reproduce the bug
Run bug.py:
```py
import os.path
from datasets import load_dataset
def run() -> None:
cv13 = load_dataset(
"mozilla-foundation/common_voice_13_0",
"hi",
split="train",
)
print(cv13[0])
audio_file = cv13[0]["path"]
if not os.path.exists(audio_file):
raise ValueError(f'File {audio_file} does not exist.')
if __name__ == "__main__":
run()
```
The result (on my machine):
```json
{'client_id': '0f018a99663f33afbb7d38aee281fb1afcfd07f9e7acd00383f604e1e17c38d6ed8adf1bd2ccbf927a52c5adefb8ac4b158ce27a7c2ed9581e71202eb302dfb3', 'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'audio': {'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'array': array([ 6.46234854e-26, -1.35709319e-25, -8.07793567e-26, ...,
1.06425944e-07, 4.46417090e-08, 2.61451660e-09]), 'sampling_rate': 48000}, 'sentence': 'हमने उसका जन्मदिन मनाया।', 'up_votes': 2, 'down_votes': 0, 'age': '', 'gender': '', 'accent': '', 'locale': 'hi', 'segment': '' ', 'variant': ''}
```
```txt
Traceback (most recent call last):
File "F:\eo-reco\bug.py", line 18, in <module>
run()
File "F:\eo-reco\bug.py", line 15, in run
raise ValueError(f'File {audio_file} does not exist.')
ValueError: File C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\common_voice_hi_26008353.mp3 does not exist.
```
### Expected behavior
The `path` element points to the correct file, which happens to be:
```
C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\hi_train_0\common_voice_hi_26008353.mp3
```
That is, there's an extra directory `hi_train_0` that is not in the `path` element.
### Environment info
- `datasets` version: 2.12.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.11.3
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
- | {
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"load_dataset () did not work for loading local files either "
] |
https://api.github.com/repos/huggingface/datasets/issues/2149 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2149/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2149/comments | https://api.github.com/repos/huggingface/datasets/issues/2149/events | https://github.com/huggingface/datasets/issues/2149 | 844,734,076 | MDU6SXNzdWU4NDQ3MzQwNzY= | 2,149 | Telugu subset missing for xtreme tatoeba dataset | [] | closed | false | null | 2 | 2021-03-30T15:26:34Z | 2022-10-05T13:28:30Z | 2022-10-05T13:28:30Z | null | from nlp import load_dataset
train_dataset = load_dataset('xtreme', 'tatoeba.tel')['validation']
ValueError: BuilderConfig tatoeba.tel not found.
but language tel is actually included in xtreme:
https://github.com/google-research/xtreme/blob/master/utils_preprocess.py
def tatoeba_preprocess(args):
lang3_dict = {
'afr':'af', 'ara':'ar', 'bul':'bg', 'ben':'bn',
'deu':'de', 'ell':'el', 'spa':'es', 'est':'et',
'eus':'eu', 'pes':'fa', 'fin':'fi', 'fra':'fr',
'heb':'he', 'hin':'hi', 'hun':'hu', 'ind':'id',
'ita':'it', 'jpn':'ja', 'jav':'jv', 'kat':'ka',
'kaz':'kk', 'kor':'ko', 'mal':'ml', 'mar':'mr',
'nld':'nl', 'por':'pt', 'rus':'ru', 'swh':'sw',
'tam':'ta', **_'tel':'te'_**, 'tha':'th', 'tgl':'tl', <----here
'tur':'tr', 'urd':'ur', 'vie':'vi', 'cmn':'zh',
'eng':'en',
} | {
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} | https://api.github.com/repos/huggingface/datasets/issues/2149/timeline | null | completed | null | null | false | [
"Good catch ! Thanks for reporting\r\n\r\nI just opened #2180 to fix this",
"Fixed in #2180"
] |
https://api.github.com/repos/huggingface/datasets/issues/5295 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5295/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5295/comments | https://api.github.com/repos/huggingface/datasets/issues/5295/events | https://github.com/huggingface/datasets/issues/5295 | 1,464,006,743 | I_kwDODunzps5XQvhX | 5,295 | Extractions failed when .zip file located on read-only path (e.g., SageMaker FastFile mode) | [] | closed | false | null | 2 | 2022-11-25T03:59:43Z | 2023-07-21T14:39:09Z | 2023-07-21T14:39:09Z | null | ### Describe the bug
Hi,
`load_dataset()` does not work .zip files located on a read-only directory. Looks like it's because Dataset creates a lock file in the [same directory](https://github.com/huggingface/datasets/blob/df4bdd365f2abb695f113cbf8856a925bc70901b/src/datasets/utils/extract.py) as the .zip file.
Encountered this when attempting `load_dataset()` on a datadir with SageMaker FastFile mode.
### Steps to reproduce the bug
```python
# Showing relevant lines only.
hyperparameters = {
"dataset_name": "ydshieh/coco_dataset_script",
"dataset_config_name": 2017,
"data_dir": "/opt/ml/input/data/coco",
"cache_dir": "/tmp/huggingface-cache", # Fix dataset complains out-of-space.
...
}
estimator = PyTorch(
base_job_name="clip",
source_dir="../src/sm-entrypoint",
entry_point="run_clip.py", # Transformers/src/examples/pytorch/contrastive-image-text/run_clip.py
framework_version="1.12",
py_version="py38",
hyperparameters=hyperparameters,
instance_count=1,
instance_type="ml.p3.16xlarge",
volume_size=100,
distribution={"smdistributed": {"dataparallel": {"enabled": True}}},
)
fast_file = lambda x: TrainingInput(x, input_mode='FastFile')
estimator.fit(
{
"pre-trained": fast_file("s3://vm-sagemakerr-us-east-1/clip/pre-trained-checkpoint/"),
"coco": fast_file("s3://vm-sagemakerr-us-east-1/clip/coco-zip-files/"),
}
)
```
Error message:
```text
ErrorMessage "OSError: [Errno 30] Read-only file system: '/opt/ml/input/data/coco/image_info_test2017.zip.lock'
"""
The above exception was the direct cause of the following exception
Traceback (most recent call last)
File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/opt/conda/lib/python3.8/site-packages/mpi4py/__main__.py", line 7, in <module>
main()
File "/opt/conda/lib/python3.8/site-packages/mpi4py/run.py", line 198, in main
run_command_line(args)
File "/opt/conda/lib/python3.8/site-packages/mpi4py/run.py", line 47, in run_command_line
run_path(sys.argv[0], run_name='__main__')
File "/opt/conda/lib/python3.8/runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "/opt/conda/lib/python3.8/runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "run_clip_smddp.py", line 594, in <module>
File "run_clip_smddp.py", line 327, in main
dataset = load_dataset(
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare
super()._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 891, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/ydshieh--coco_dataset_script/e033205c0266a54c10be132f9264f2a39dcf893e798f6756d224b1ff5078998f/coco_dataset_script.py", line 123, in _split_generators
archive_path = dl_manager.download_and_extract(_DL_URLS)
File "/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py", line 447, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py", line 419, in extract
extracted_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 472, in map_nested
mapped = pool.map(_single_map_nested, split_kwds)
File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 364, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 771, in get
raise self._value
OSError: [Errno 30] Read-only file system: '/opt/ml/input/data/coco/image_info_test2017.zip.lock'"
```
### Expected behavior
`load_dataset()` to succeed, just like when .zip file is passed in SageMaker File mode.
### Environment info
* datasets-2.7.1
* transformers-4.24.0
* python-3.8
* torch-1.12
* SageMaker PyTorch DLC | {
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"Hi ! Thanks for reporting. Indeed the lock file should be placed in a directory with write permission (e.g. in the directory where the archive is extracted).",
"I opened https://github.com/huggingface/datasets/pull/5320 to fix this - it places the lock file in the cache directory instead of trying to put in next... |
https://api.github.com/repos/huggingface/datasets/issues/285 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/285/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/285/comments | https://api.github.com/repos/huggingface/datasets/issues/285/events | https://github.com/huggingface/datasets/pull/285 | 641,360,702 | MDExOlB1bGxSZXF1ZXN0NDM2NjAyMjk4 | 285 | Consistent formatting of citations | [] | closed | false | null | 1 | 2020-06-18T16:25:23Z | 2020-06-22T08:09:25Z | 2020-06-22T08:09:24Z | null | #283 | {
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"Circle CI shuold be green :-) "
] |
https://api.github.com/repos/huggingface/datasets/issues/5952 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5952/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5952/comments | https://api.github.com/repos/huggingface/datasets/issues/5952/events | https://github.com/huggingface/datasets/pull/5952 | 1,756,481,591 | PR_kwDODunzps5S-OIh | 5,952 | Add Arrow builder docs | [] | closed | false | null | 3 | 2023-06-14T09:42:46Z | 2023-06-14T14:42:31Z | 2023-06-14T14:34:39Z | null | following https://github.com/huggingface/datasets/pull/5944 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/4148 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4148/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4148/comments | https://api.github.com/repos/huggingface/datasets/issues/4148/events | https://github.com/huggingface/datasets/issues/4148 | 1,201,169,242 | I_kwDODunzps5HmGNa | 4,148 | fix confusing bleu metric example | [
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] | closed | false | null | 0 | 2022-04-12T06:18:26Z | 2022-04-13T14:16:34Z | 2022-04-13T14:16:34Z | null | **Is your feature request related to a problem? Please describe.**
I would like to see the example in "Metric Card for BLEU" changed.
The 0th element in the predictions list is not closed in square brackets, and the 1st list is missing a comma.
The BLEU score are calculated correctly, but it is difficult to understand, so it would be helpful if you could correct this.
```
>> predictions = [
... ["hello", "there", "general", "kenobi", # <- no closing square bracket.
... ["foo", "bar" "foobar"] # <- no comma between "bar" and "foobar"
... ]
>>> references = [
... [["hello", "there", "general", "kenobi"]],
... [["foo", "bar", "foobar"]]
... ]
>>> bleu = datasets.load_metric("bleu")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results)
{'bleu': 0.6370964381207871, ...
```
**Describe the solution you'd like**
```
>> predictions = [
... ["hello", "there", "general", "kenobi", # <- no closing square bracket.
... ["foo", "bar" "foobar"] # <- no comma between "bar" and "foobar"
... ]
# and
>>> print(results)
{'bleu':1.0, ...
```
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https://api.github.com/repos/huggingface/datasets/issues/4356 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4356/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4356/comments | https://api.github.com/repos/huggingface/datasets/issues/4356/events | https://github.com/huggingface/datasets/pull/4356 | 1,236,846,308 | PR_kwDODunzps433OsB | 4,356 | Fix dataset builder default version | [] | closed | false | null | 2 | 2022-05-16T09:05:10Z | 2022-05-30T13:56:58Z | 2022-05-30T13:47:54Z | null | Currently, when using a custom config (subclass of `BuilderConfig`), default version set at the builder level is ignored: we must set default version in the custom config class.
However, when loading a dataset with `config_kwargs` (for a configuration not present in `BUILDER_CONFIGS`), the default version set in the custom config is ignored and "0.0.0" is used instead:
```python
ds = load_dataset("wikipedia", language="co", date="20220501", beam_runner="DirectRunner")
```
generates the following config:
```python
WikipediaConfig(name='20220501.co', version=0.0.0, data_dir=None, data_files=None, description='Wikipedia dataset for co, parsed from 20220501 dump.')
```
with version "0.0.0" instead of "2.0.0".
See as a counter-example, when the config is present in `BUILDER_CONFIGS`:
```python
ds = load_dataset("wikipedia", "20220301.fr", beam_runner="DirectRunner")
```
generates the following config:
```python
WikipediaConfig(name='20220301.fr', version=2.0.0, data_dir=None, data_files=None, description='Wikipedia dataset for fr, parsed from 20220301 dump.')
```
with correct version "2.0.0", as set in the custom config class.
The reason for this is that `DatasetBuilder` has a default VERSION ("0.0.0") that overwrites the default version set at the custom config class.
This PR:
- Removes the default VERSION at `DatasetBuilder` (set to None, so that the class attribute exists but it does not override the custom config default version).
- Note that the `BuilderConfig` class already sets a default version = "0.0.0"; no need to pass this from the builder. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"This PR requires one of these other PRs being merged first:\r\n- #4359 \r\n- huggingface/doc-builder#211"
] |
https://api.github.com/repos/huggingface/datasets/issues/1963 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1963/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1963/comments | https://api.github.com/repos/huggingface/datasets/issues/1963/events | https://github.com/huggingface/datasets/issues/1963 | 818,289,967 | MDU6SXNzdWU4MTgyODk5Njc= | 1,963 | bug in SNLI dataset | [] | closed | false | null | 1 | 2021-02-28T19:36:20Z | 2022-10-05T13:13:46Z | 2022-10-05T13:13:46Z | null | Hi
There is label of -1 in train set of SNLI dataset, please find the code below:
```
import numpy as np
import datasets
data = datasets.load_dataset("snli")["train"]
labels = []
for d in data:
labels.append(d["label"])
print(np.unique(labels))
```
and results:
`[-1 0 1 2]`
version of datasets used:
`datasets 1.2.1 <pip>
`
thanks for your help. @lhoestq | {
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} | https://api.github.com/repos/huggingface/datasets/issues/1963/timeline | null | completed | null | null | false | [
"Hi ! The labels -1 correspond to the examples without gold labels in the original snli dataset.\r\nFeel free to remove these examples if you don't need them by using\r\n```python\r\ndata = data.filter(lambda x: x[\"label\"] != -1)\r\n```"
] |
https://api.github.com/repos/huggingface/datasets/issues/5653 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5653/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5653/comments | https://api.github.com/repos/huggingface/datasets/issues/5653/events | https://github.com/huggingface/datasets/issues/5653 | 1,633,254,159 | I_kwDODunzps5hWXsP | 5,653 | Doc: save_to_disk, `num_proc` will affect `num_shards`, but it's not documented | [
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"color": "7057... | closed | false | null | 1 | 2023-03-21T05:25:35Z | 2023-03-24T16:36:23Z | 2023-03-24T16:36:23Z | null | ### Describe the bug
[`num_proc`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.DatasetDict.save_to_disk.num_proc) will affect `num_shards`, but it's not documented
### Steps to reproduce the bug
Nothing to reproduce
### Expected behavior
[document of `num_shards`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.DatasetDict.save_to_disk.num_shards) explicitly says that it depends on `max_shard_size`, it should also mention `num_proc`.
### Environment info
datasets main document | {
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"I agree this should be documented"
] |
https://api.github.com/repos/huggingface/datasets/issues/493 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/493/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/493/comments | https://api.github.com/repos/huggingface/datasets/issues/493/events | https://github.com/huggingface/datasets/pull/493 | 676,527,351 | MDExOlB1bGxSZXF1ZXN0NDY1ODIxOTA0 | 493 | Fix wmt zh-en url | [] | closed | false | null | 1 | 2020-08-11T02:14:52Z | 2020-08-11T02:22:28Z | 2020-08-11T02:22:12Z | null | I verified that
```
wget https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00
```
runs in 2 minutes. | {
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"this doesn't work. I can decompress the file after download locally."
] |
https://api.github.com/repos/huggingface/datasets/issues/1995 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1995/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1995/comments | https://api.github.com/repos/huggingface/datasets/issues/1995/events | https://github.com/huggingface/datasets/pull/1995 | 822,878,431 | MDExOlB1bGxSZXF1ZXN0NTg1NDI5NTg0 | 1,995 | [Timit_asr] Make sure not only the first sample is used | [] | closed | false | null | 4 | 2021-03-05T08:42:51Z | 2021-06-30T06:25:53Z | 2021-03-05T08:58:59Z | null | When playing around with timit I noticed that only the first sample is used for all indices. I corrected this typo so that the dataset is correctly loaded. | {
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"cc @lhoestq @vrindaprabhu",
"Failing `run (push)` is unrelated -> merging",
"Thanks for fixing this, it was affecting my runs for https://github.com/huggingface/transformers/pull/10581/",
"I am seeing this very late! Sorry for the blunder everyone! :("
] |
https://api.github.com/repos/huggingface/datasets/issues/3469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3469/comments | https://api.github.com/repos/huggingface/datasets/issues/3469/events | https://github.com/huggingface/datasets/pull/3469 | 1,085,882,664 | PR_kwDODunzps4wIrOV | 3,469 | Fix METEOR missing NLTK's omw-1.4 | [] | closed | false | null | 1 | 2021-12-21T14:19:11Z | 2021-12-21T14:52:28Z | 2021-12-21T14:49:28Z | null | NLTK 3.6.6 now requires `omw-1.4` to be downloaded for METEOR to work.
This should fix the CI on master | {
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"I also modified the doctest call to raise the exception that doctest may catch, instead of `doctest.UnexpectedException`.\r\nThis will make debugging easier if it happens again"
] |
https://api.github.com/repos/huggingface/datasets/issues/6037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6037/comments | https://api.github.com/repos/huggingface/datasets/issues/6037/events | https://github.com/huggingface/datasets/issues/6037 | 1,805,887,184 | I_kwDODunzps5ro6bQ | 6,037 | Documentation links to examples are broken | [] | closed | false | null | 2 | 2023-07-15T04:54:50Z | 2023-07-17T22:35:14Z | 2023-07-17T15:10:32Z | null | ### Describe the bug
The links at the bottom of [add_dataset](https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html) to examples of specific datasets are all broken, for example
- text classification: [ag_news](https://github.com/huggingface/datasets/blob/master/datasets/ag_news/ag_news.py) (original data are in csv files)
### Steps to reproduce the bug
Click on links to examples from latest documentation
### Expected behavior
Links should be up to date - it might be more stable to link to https://huggingface.co/datasets/ag_news/blob/main/ag_news.py
### Environment info
dataset v1.2.1 | {
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"These docs are outdated (version 1.2.1 is over two years old). Please refer to [this](https://huggingface.co/docs/datasets/dataset_script) version instead.\r\n\r\nInitially, we hosted datasets in this repo, but now you can find them [on the HF Hub](https://huggingface.co/datasets) (e.g. the [`ag_news`](https://hug... |
https://api.github.com/repos/huggingface/datasets/issues/6001 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6001/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6001/comments | https://api.github.com/repos/huggingface/datasets/issues/6001/events | https://github.com/huggingface/datasets/pull/6001 | 1,782,516,627 | PR_kwDODunzps5UVMMh | 6,001 | Align `column_names` type check with type hint in `sort` | [] | closed | false | null | 3 | 2023-06-30T13:15:50Z | 2023-06-30T14:18:32Z | 2023-06-30T14:11:24Z | null | Fix #5998 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/3120 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3120/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3120/comments | https://api.github.com/repos/huggingface/datasets/issues/3120/events | https://github.com/huggingface/datasets/pull/3120 | 1,031,574,511 | PR_kwDODunzps4tcril | 3,120 | Correctly update metadata to preserve features when concatenating datasets with axis=1 | [] | closed | false | null | 0 | 2021-10-20T15:54:58Z | 2021-10-22T08:28:51Z | 2021-10-21T14:50:21Z | null | This PR correctly updates metadata to preserve higher-level feature types (e.g. `ClassLabel`) in `datasets.concatenate_datasets` when `axis=1`. Previously, we would delete the feature metadata in `datasets.concatenate_datasets` if `axis=1` and restore the feature types from the arrow table schema in `Dataset.__init__`. However, this approach only works for simple feature types (e.g. `Value`).
Fixes #3111 | {
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https://api.github.com/repos/huggingface/datasets/issues/863 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/863/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/863/comments | https://api.github.com/repos/huggingface/datasets/issues/863/events | https://github.com/huggingface/datasets/pull/863 | 744,954,534 | MDExOlB1bGxSZXF1ZXN0NTIyNTk0Mjg1 | 863 | Add clear_cache parameter in the test command | [] | closed | false | null | 0 | 2020-11-17T17:52:29Z | 2020-11-18T14:44:25Z | 2020-11-18T14:44:24Z | null | For certain datasets like OSCAR #348 there are lots of different configurations and each one of them can take a lot of disk space.
I added a `--clear_cache` flag to the `datasets-cli test` command to be able to clear the cache after each configuration test to avoid filling up the disk. It should enable an easier generation for the `dataset_infos.json` file for OSCAR. | {
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