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https://api.github.com/repos/huggingface/datasets/issues/2771 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2771/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2771/comments | https://api.github.com/repos/huggingface/datasets/issues/2771/events | https://github.com/huggingface/datasets/pull/2771 | 963,257,036 | MDExOlB1bGxSZXF1ZXN0NzA1OTExMDMw | 2,771 | [WIP][Common Voice 7] Add common voice 7.0 | [] | closed | false | null | 2 | 2021-08-07T16:01:10Z | 2021-12-06T23:24:02Z | 2021-12-06T23:24:02Z | null | This PR allows to load the new common voice dataset manually as explained when doing:
```python
from datasets import load_dataset
ds = load_dataset("./datasets/datasets/common_voice_7", "ab")
```
=>
```
Please follow the manual download instructions:
You need to manually the dataset from `https://commonvoice.mozilla.org/en/datasets`.
Make sure you choose the version `Common Voice Corpus 7.0`.
Choose a language of your choice and find the corresponding language-id, *e.g.*, `Abkhaz` with language-id `ab`. The following language-ids are available:
['ab', 'ar', 'as', 'az', 'ba', 'bas', 'be', 'bg', 'br', 'ca', 'cnh', 'cs', 'cv', 'cy', 'de', 'dv', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy-NL', 'ga-IE', 'gl', 'gn', 'ha', 'hi', 'hsb', 'hu', 'hy-AM', 'ia', 'id', 'it', 'ja', 'ka', 'kab', 'kk', 'kmr', 'ky', 'lg', 'lt', 'lv', 'mn', 'mt', 'nl', 'or', 'pa-IN', 'pl', 'pt', 'rm-sursilv', 'rm-vallader', 'ro', 'ru', 'rw', 'sah', 'sk', 'sl', 'sr', 'sv-SE', 'ta', 'th', 'tr', 'tt', 'ug', 'uk', 'ur', 'uz', 'vi', 'vot', 'zh-CN', 'zh-HK', 'zh-TW']
Next, you will have to enter your email address to download the dataset in the `tar.gz` format. Save the file under <path-to-file>.
The file should then be extracted with: ``tar -xvzf <path-to-file>`` which will extract a folder called ``cv-corpus-7.0-2021-07-21``.
The dataset can then be loaded with `datasets.load_dataset("common_voice", <language-id>, data_dir="<path-to-'cv-corpus-7.0-2021-07-21'-folder>", ignore_verifications=True).
```
Having followed those instructions one can then download the data as follows:
```python
from datasets import load_dataset
ds = load_dataset("./datasets/datasets/common_voice_7", "ab", data_dir="./cv-corpus-7.0-2021-07-21/", ignore_verifications=True)
```
## TODO
- [ ] Discuss naming. Is the name ok here "common_voice_7"? The dataset script differs only really in one point from `common_voice.py` in that all the metadata is different (more hours etc...) and that it has to use manual data dir for now
- [ ] Ideally we should get a bundled download link. For `common_voice.py` there is a bundled download link: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/{}.tar.gz` that allows one to directly download the data. However such a link is missing for Common Voice 7. I guess we should try to contact common voice about it and ask whether we could host the data or help otherwise somehow. See: https://github.com/common-voice/common-voice-bundler/issues/15 cc @yjernite
- [ ] I did not compute the dataset.json and it would mean that I'd have to download 76 datasets totalling around 1TB manually before running the checksum command. This just takes too much time. For now the user will have to add a `ignore_verifications=True` to download the data. This step would also be much easier if we could get a bundled link
- [ ] Add dummy data | {
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"Hi ! I think the name `common_voice_7` is fine :)\r\nMoreover if the dataset_infos.json is missing I'm pretty sure you don't need to specify `ignore_verifications=True`",
"Hi, how about to add a new parameter \"version\" in the function load_dataset, something like: \r\n`load_dataset(\"common_voice\", \"lg\", ve... |
https://api.github.com/repos/huggingface/datasets/issues/5603 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5603/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5603/comments | https://api.github.com/repos/huggingface/datasets/issues/5603/events | https://github.com/huggingface/datasets/pull/5603 | 1,607,143,509 | PR_kwDODunzps5LJZzG | 5,603 | Don't compute checksums if not necessary in `datasets-cli test` | [] | closed | false | null | 3 | 2023-03-02T16:42:39Z | 2023-03-03T15:45:32Z | 2023-03-03T15:38:28Z | null | we only need them if there exists a `dataset_infos.json` | {
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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/5111 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5111/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5111/comments | https://api.github.com/repos/huggingface/datasets/issues/5111/events | https://github.com/huggingface/datasets/issues/5111 | 1,408,143,170 | I_kwDODunzps5T7o9C | 5,111 | map and filter not working properly in multiprocessing with the new release 2.6.0 | [
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] | closed | false | null | 14 | 2022-10-13T17:00:55Z | 2022-10-17T08:26:59Z | 2022-10-14T14:59:59Z | null | ## Describe the bug
When mapping is used on a dataset with more than one process, there is a weird behavior when trying to use `filter` , it's like only the samples from one worker are retrieved, one needs to specify the same `num_proc` in filter for it to work properly. This doesn't happen with `datasets` version 2.5.2
In the code below the data is filtered differently when we increase `num_proc` used in `map` although the datsets before and after mapping have identical elements.
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
def preprocess(example):
return example
ds = load_dataset("codeparrot/codeparrot-clean-valid", split="train").select([i for i in range(10)])
ds1 = ds.map(preprocess, num_proc=2)
ds2 = ds.map(preprocess)
# the datasets elements are the same
for i in range(len(ds1)):
assert ds1[i]==ds2[i]
print(f'Target column before filtering {ds1["autogenerated"]}')
print(f'Target column before filtering {ds2["autogenerated"]}')
print(f"datasets version {datasets.__version__}")
ds_filtered_1 = ds1.filter(lambda x: not x["autogenerated"])
ds_filtered_2 = ds2.filter(lambda x: not x["autogenerated"])
# all elements in Target column are false so they should all be kept, but for ds2 only the first 5=num_samples/num_proc are kept
print(ds_filtered_1)
print(ds_filtered_2)
```
```
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 5
})
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 10
})
```
## Expected results
Increasing `num_proc` in mapping shouldn't alter filtering. With the previous version 2.5.2 this doesn't happen
## Actual results
Filtering doesn't work properly when we increase `num_proc` in mapping but not when calling `filter`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.6.0
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | {
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} | https://api.github.com/repos/huggingface/datasets/issues/5111/timeline | null | completed | null | null | false | [
"Same bug exists with `num_proc=1` on colab. `3.7.14 (default, Sep 8 2022, 00:06:44) [GCC 7.5.0]` ",
"Thanks for reporting, @loubnabnl and for the additional information, @PartiallyTyped.\r\n\r\nHowever, I'm not able to reproduce this issue, neither locally nor on Colab:\r\n```\r\nDataset({\r\n features: ['re... |
https://api.github.com/repos/huggingface/datasets/issues/2344 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2344/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2344/comments | https://api.github.com/repos/huggingface/datasets/issues/2344/events | https://github.com/huggingface/datasets/issues/2344 | 885,331,505 | MDU6SXNzdWU4ODUzMzE1MDU= | 2,344 | Is there a way to join multiple datasets in one? | [
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] | open | false | null | 2 | 2021-05-10T23:16:10Z | 2022-10-05T17:27:05Z | null | null | **Is your feature request related to a problem? Please describe.**
I need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2?
**Describe the solution you'd like**
Id like to join them with a merge or join method, just like pandas dataframes.
**Additional context**
If you want to extend an existing dataset with more data, for example for training a language model, you need that functionality. I've not found it in the documentation. | {
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} | https://api.github.com/repos/huggingface/datasets/issues/2344/timeline | null | null | null | null | false | [
"Hi ! We don't have `join`/`merge` on a certain column as in pandas.\r\nMaybe you can just use the [concatenate_datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets) function.\r\n",
"Hi! You can use `datasets_sql` for that now. As o... |
https://api.github.com/repos/huggingface/datasets/issues/3848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3848/comments | https://api.github.com/repos/huggingface/datasets/issues/3848/events | https://github.com/huggingface/datasets/issues/3848 | 1,162,076,902 | I_kwDODunzps5FQ-Lm | 3,848 | NonMatchingChecksumError when checksum is None | [
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] | closed | false | null | 7 | 2022-03-08T00:24:12Z | 2022-03-15T14:37:26Z | 2022-03-15T12:28:23Z | null | I ran into the following error when adding a new dataset:
```bash
expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}}
recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}
verification_name = 'dataset source files'
def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):
if expected_checksums is None:
logger.info("Unable to verify checksums.")
return
if len(set(expected_checksums) - set(recorded_checksums)) > 0:
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
if len(set(recorded_checksums) - set(expected_checksums)) > 0:
raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
for_verification_name = " for " + verification_name if verification_name is not None else ""
if len(bad_urls) > 0:
error_msg = "Checksums didn't match" + for_verification_name + ":\n"
> raise NonMatchingChecksumError(error_msg + str(bad_urls))
E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
E ['https://adversarialglue.github.io/dataset/dev.zip']
src/datasets/utils/info_utils.py:40: NonMatchingChecksumError
```
## Expected results
The dataset downloads correctly, and there is no error.
## Actual results
Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. | {
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"Hi @jxmorris12, thanks for reporting.\r\n\r\nThe objective of `verify_checksums` is to check that both checksums are equal. Therefore if one is None and the other is non-None, they are not equal, and the function accordingly raises a NonMatchingChecksumError. That behavior is expected.\r\n\r\nThe question is: how ... |
https://api.github.com/repos/huggingface/datasets/issues/3716 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3716/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3716/comments | https://api.github.com/repos/huggingface/datasets/issues/3716/events | https://github.com/huggingface/datasets/issues/3716 | 1,136,831,092 | I_kwDODunzps5Dwqp0 | 3,716 | `FaissIndex` to support multiple GPU and `custom_index` | [
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] | closed | false | null | 2 | 2022-02-14T06:21:43Z | 2022-03-07T16:28:56Z | 2022-03-07T16:28:56Z | null | **Is your feature request related to a problem? Please describe.**
Currently, because `device` is of the type `int | None`, to leverage `faiss-gpu`'s multi-gpu support, you need to create a `custom_index`. However, if using a `custom_index` created by e.g. `faiss.index_cpu_to_all_gpus`, then `FaissIndex.save` does not work properly because it checks the device id (which is an int, so no multiple GPUs).
**Describe the solution you'd like**
I would like `FaissIndex` to support multiple GPUs, by passing in a list to `add_faiss_index`.
**Describe alternatives you've considered**
Alternatively, I would like it to at least provide a warning cause it wasn't the behavior that I expected.
**Additional context**
Relavent source code here:
https://github.com/huggingface/datasets/blob/6ed6ac9448311930557810383d2cfd4fe6aae269/src/datasets/search.py#L340-L349
Device management needs changing to support multiple GPUs, probably by `isinstance` calls.
I can provide a PR if you like :)
Thanks for reading!
| {
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"Hi @rentruewang, thansk for reporting and for your PR!!! We should definitely support this. ",
"@albertvillanova Great! :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/5958 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5958/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5958/comments | https://api.github.com/repos/huggingface/datasets/issues/5958/events | https://github.com/huggingface/datasets/pull/5958 | 1,757,265,971 | PR_kwDODunzps5TA3__ | 5,958 | set dev version | [] | closed | false | null | 3 | 2023-06-14T16:26:34Z | 2023-06-14T16:34:55Z | 2023-06-14T16:26:51Z | null | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5958). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... |
https://api.github.com/repos/huggingface/datasets/issues/4845 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4845/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4845/comments | https://api.github.com/repos/huggingface/datasets/issues/4845/events | https://github.com/huggingface/datasets/pull/4845 | 1,337,928,283 | PR_kwDODunzps49IOjf | 4,845 | Mark CI tests as xfail if Hub HTTP error | [] | closed | false | null | 1 | 2022-08-13T10:45:11Z | 2022-08-23T04:57:12Z | 2022-08-23T04:42:26Z | null | In order to make testing more robust (and avoid merges to master with red tests), we could mark tests as xfailed (instead of failed) when the Hub raises some temporary HTTP errors.
This PR:
- marks tests as xfailed only if the Hub raises a 500 error for:
- test_upstream_hub
- makes pytest report the xfailed/xpassed tests.
More tests could also be marked if needed.
Examples of CI failures due to temporary Hub HTTP errors:
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files
- https://github.com/huggingface/datasets/runs/7806855399?check_suite_focus=true
`requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-16603108028233/commit/main (Request ID: aZeAQ5yLktoGHQYBcJ3zo)`
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_no_token
- https://github.com/huggingface/datasets/runs/7840022996?check_suite_focus=true
`requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://s3.us-east-1.amazonaws.com/lfs-staging.huggingface.co/repos/81/e3/81e3b831fa9bf23190ec041f26ef7ff6d6b71c1a937b8ec1ef1f1f05b508c089/caae596caa179cf45e7c9ac0c6d9a9cb0fe2d305291bfbb2d8b648ae26ed38b6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20220815%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220815T144713Z&X-Amz-Expires=900&X-Amz-Signature=5ddddfe8ef2b0601e80ab41c78a4d77d921942b0d8160bcab40ff894095e6823&X-Amz-SignedHeaders=host&x-id=PutObject`
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_private
- https://github.com/huggingface/datasets/runs/7835921082?check_suite_focus=true
`requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hub-ci.huggingface.co/api/repos/create (Request ID: gL_1I7i2dii9leBhlZen-) - Internal Error - We're working hard to fix that as soon as possible!`
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features_image_list
- https://github.com/huggingface/datasets/runs/7835920900?check_suite_focus=true
- This is not 500, but 404:
`requests.exceptions.HTTPError: 404 Client Error: Not Found for url: [https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16605586458339.git/info/lfs/objects](https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16605586458339.git/info/lfs/objects/batch)`
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] |
https://api.github.com/repos/huggingface/datasets/issues/241 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/241/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/241/comments | https://api.github.com/repos/huggingface/datasets/issues/241/events | https://github.com/huggingface/datasets/pull/241 | 631,703,079 | MDExOlB1bGxSZXF1ZXN0NDI4NTQwMDM0 | 241 | Fix empty cache dir | [] | closed | false | null | 2 | 2020-06-05T15:45:22Z | 2020-06-08T08:35:33Z | 2020-06-08T08:35:31Z | null | If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful.
So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future.
This should fix #239
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"Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think",
"> Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think\r\n\r\nNo it shouldn't force to redo... |
https://api.github.com/repos/huggingface/datasets/issues/681 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/681/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/681/comments | https://api.github.com/repos/huggingface/datasets/issues/681/events | https://github.com/huggingface/datasets/pull/681 | 710,075,721 | MDExOlB1bGxSZXF1ZXN0NDkzOTkwMjEz | 681 | Adding missing @property (+2 small flake8 fixes). | [] | closed | false | null | 0 | 2020-09-28T08:53:53Z | 2020-09-28T10:26:13Z | 2020-09-28T10:26:09Z | null | Fixes #678 | {
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https://api.github.com/repos/huggingface/datasets/issues/412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/412/comments | https://api.github.com/repos/huggingface/datasets/issues/412/events | https://github.com/huggingface/datasets/issues/412 | 660,047,139 | MDU6SXNzdWU2NjAwNDcxMzk= | 412 | Unable to load XTREME dataset from disk | [] | closed | false | null | 3 | 2020-07-18T09:55:00Z | 2020-07-21T08:15:44Z | 2020-07-21T08:15:44Z | null | Hi 🤗 team!
## Description of the problem
Following the [docs](https://huggingface.co/nlp/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https://github.com/google-research/xtreme) benchmark.
I have manually downloaded the `AmazonPhotos.zip` file from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset.
As far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path:
```
# path where load_dataset is looking for fr.tar.gz
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/
# path where it actually exists
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/panx_dataset/
```
## Steps to reproduce the problem
1. Manually download the XTREME benchmark from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1)
2. Run the following code snippet
```python
from nlp import load_dataset
# AmazonPhotos.zip is in the root of the folder
dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
```
3. Here is the stack trace
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-4-26786bb5fa93> in <module>
----> 1 dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
430 verify_infos = not save_infos and not ignore_verifications
431 self._download_and_prepare(
--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
433 )
434 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
464 split_dict = SplitDict(dataset_name=self.name)
465 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 466 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
467 # Checksums verification
468 if verify_infos:
/usr/local/lib/python3.6/dist-packages/nlp/datasets/xtreme/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746/xtreme.py in _split_generators(self, dl_manager)
725 panx_dl_dir = dl_manager.extract(panx_path)
726 lang = self.config.name.split(".")[1]
--> 727 lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + ".tar.gz"))
728 return [
729 nlp.SplitGenerator(
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in extract(self, path_or_paths)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)
170 return tuple(mapped)
171 # Singleton
--> 172 return function(data_struct)
173
174
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in <lambda>(path)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
203 elif urlparse(url_or_filename).scheme == "":
204 # File, but it doesn't exist.
--> 205 raise FileNotFoundError("Local file {} doesn't exist".format(url_or_filename))
206 else:
207 # Something unknown
FileNotFoundError: Local file /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/fr.tar.gz doesn't exist
```
## OS and hardware
```
- `nlp` version: 0.3.0
- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.4.0 (True)
- Tensorflow version (GPU?): 2.1.0 (True)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
``` | {
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"Hi @lewtun, you have to provide the full path to the downloaded file for example `/home/lewtum/..`",
"I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !",
"Thanks for the rapid fix @lhoestq!"
] |
https://api.github.com/repos/huggingface/datasets/issues/4528 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4528/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4528/comments | https://api.github.com/repos/huggingface/datasets/issues/4528/events | https://github.com/huggingface/datasets/issues/4528 | 1,276,679,155 | I_kwDODunzps5MGJPz | 4,528 | Memory leak when iterating a Dataset | [
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] | closed | false | null | 5 | 2022-06-20T10:03:14Z | 2022-09-12T08:51:39Z | 2022-09-12T08:51:39Z | null | e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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"Is someone assigned to this issue?",
"The same issue is being debugged here: https://github.com/huggingface/datasets/issues/4883\r\n",
"Here is a modified repro example that makes it easier to see the leak:\r\n\r\n```\r\n$ cat ds2.py\r\nimport gc, sys\r\nimport time\r\nfrom datasets import load_dataset\r\nimpo... |
https://api.github.com/repos/huggingface/datasets/issues/2442 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2442/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2442/comments | https://api.github.com/repos/huggingface/datasets/issues/2442/events | https://github.com/huggingface/datasets/pull/2442 | 909,677,029 | MDExOlB1bGxSZXF1ZXN0NjYwMjE1ODY1 | 2,442 | add english language tags for ~100 datasets | [] | closed | false | null | 1 | 2021-06-02T16:24:56Z | 2021-06-04T09:51:40Z | 2021-06-04T09:51:39Z | null | As discussed on Slack, I have manually checked for ~100 datasets that they have at least one subset in English. This information was missing so adding into the READMEs.
Note that I didn't check all the subsets so it's possible that some of the datasets have subsets in other languages than English... | {
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"Fixing the tags of all the datasets is out of scope for this PR so I'm merging even though the CI fails because of the missing tags"
] |
https://api.github.com/repos/huggingface/datasets/issues/2505 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2505/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2505/comments | https://api.github.com/repos/huggingface/datasets/issues/2505/events | https://github.com/huggingface/datasets/pull/2505 | 921,234,797 | MDExOlB1bGxSZXF1ZXN0NjcwMjY2NjQy | 2,505 | Make numpy arrow extractor faster | [] | closed | false | null | 5 | 2021-06-15T10:11:32Z | 2021-06-28T09:53:39Z | 2021-06-28T09:53:38Z | null | I changed the NumpyArrowExtractor to call directly to_numpy and see if it can lead to speed-ups as discussed in https://github.com/huggingface/datasets/issues/2498
This could make the numpy/torch/tf/jax formatting faster | {
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"Looks like we have a nice speed up in some benchmarks. For example:\r\n- `read_formatted numpy 5000`: 4.584777 sec -> 0.487113 sec\r\n- `read_formatted torch 5000`: 4.565676 sec -> 1.289514 sec",
"Can we convert this draft to PR @lhoestq ?",
"Ready for review ! cc @vblagoje",
"@lhoestq I tried the branch a... |
https://api.github.com/repos/huggingface/datasets/issues/4374 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4374/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4374/comments | https://api.github.com/repos/huggingface/datasets/issues/4374/events | https://github.com/huggingface/datasets/issues/4374 | 1,241,860,535 | I_kwDODunzps5KBUm3 | 4,374 | extremely slow processing when using a custom dataset | [
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"descript... | closed | false | null | 2 | 2022-05-19T14:18:05Z | 2023-07-25T15:07:17Z | 2023-07-25T15:07:16Z | null | ## processing a custom dataset loaded as .txt file is extremely slow, compared to a dataset of similar volume from the hub
I have a large .txt file of 22 GB which i load into HF dataset
`lang_dataset = datasets.load_dataset("text", data_files="hi.txt")`
further i use a pre-processing function to clean the dataset
`lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)`
the following processing takes astronomical time to process, while hoging all the ram.
similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data.
`lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)`
the hours predicted to preprocess are as follows:
huggingface hub dataset: 6.5 hrs
custom loaded dataset: 7000 hrs
note: both the datasets are almost actually same, just provided by different sources with has +/- some samples, only one is hosted on the HF hub and the other is downloaded in a text format.
## Steps to reproduce the bug
```
import datasets
import psutil
import sys
import glob
from fastcore.utils import listify
import re
import gc
def remove_non_indic_sentences(example):
tmp_ls = []
eng_regex = r'[. a-zA-Z0-9ÖÄÅöäå _.,!"\'\/$]*'
for e in listify(example['text']):
matches = re.findall(eng_regex, e)
for match in (str(match).strip() for match in matches if match not in [""," ", " ", ",", " ,", ", ", " , "]):
if len(list(match.split(" "))) > 2:
e = re.sub(match," ",e,count=1)
tmp_ls.append(e)
gc.collect()
example['clean_text'] = tmp_ls
return example
lang_dataset = datasets.load_dataset("text", data_files="hi.txt")
lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)
## same thing work much faster when loading similar dataset from hub
lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", split="train", use_auth_token=True)
lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)
```
## Actual results
similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data.
`lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)
**the hours predicted to preprocess are as follows:**
huggingface hub dataset: 6.5 hrs
custom loaded dataset: 7000 hrs
**i even tried the following:**
- sharding the large 22gb text files into smaller files and loading
- saving the file to disk and then loading
- using lesser num_proc
- using smaller batch size
- processing without batches ie : without `batched=True`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2.dev0
- Platform: Ubuntu 20.04 LTS
- Python version: 3.9.7
- PyArrow version:8.0.0
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"Hi !\r\n\r\nMy guess is that some examples in your dataset are bigger than your RAM, and therefore loading them in RAM to pass them to `remove_non_indic_sentences` takes forever because it might use SWAP memory.\r\n\r\nMaybe several examples in your dataset are grouped together, can you check `len(lang_dataset[\"t... |
https://api.github.com/repos/huggingface/datasets/issues/1280 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1280/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1280/comments | https://api.github.com/repos/huggingface/datasets/issues/1280/events | https://github.com/huggingface/datasets/pull/1280 | 759,151,028 | MDExOlB1bGxSZXF1ZXN0NTM0MTk2MDc0 | 1,280 | disaster response messages dataset | [] | closed | false | null | 2 | 2020-12-08T07:27:16Z | 2020-12-09T16:21:57Z | 2020-12-09T16:21:57Z | null | {
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} | true | [
"I have added the Readme.md as well, the PR is ready for review. \r\n\r\nThank you ",
"Hi @lhoestq I have updated the code and files. Please if you could check once.\r\n\r\nThank you"
] | |
https://api.github.com/repos/huggingface/datasets/issues/1758 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1758/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1758/comments | https://api.github.com/repos/huggingface/datasets/issues/1758/events | https://github.com/huggingface/datasets/issues/1758 | 790,626,116 | MDU6SXNzdWU3OTA2MjYxMTY= | 1,758 | dataset.search() (elastic) cannot reliably retrieve search results | [] | closed | false | null | 2 | 2021-01-21T02:26:37Z | 2021-01-22T00:25:50Z | 2021-01-22T00:25:50Z | null | I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.
I am indexing data that looks like the following from the HF SQuAD 2.0 data set:
```
['57318658e6313a140071d02b',
'56f7165e3d8e2e1400e3733a',
'570e2f6e0b85d914000d7d21',
'5727e58aff5b5019007d97d0',
'5a3b5a503ff257001ab8441f',
'57262fab271a42140099d725']
```
To reproduce the issue, try:
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# build base examples, features set of training data
shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list()
examples = load_dataset("squad_v2").shuffle(seed=1)['train']
features = load_dataset("squad_v2").shuffle(seed=1)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# reorder features by the training process
features = features.select(indices=shuffled_idx)
# get the example ids to match with the "example" data; get unique entries
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions in the examples data set; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# retrieve the example index
example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]
example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]
example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]
len(example_idx_k1) # should be 130319
len(example_idx_k2) # should be 130319
#trial 1 lengths:
# k=1: 130314
# k=3: 130319
# trial 2:
# just run k=3 first: 130310
# try k=1 after k=3: 130319
```
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} | https://api.github.com/repos/huggingface/datasets/issues/1758/timeline | null | completed | null | null | false | [
"Hi !\r\nI tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.\r\nMaybe this is because the index is not updated yet on the ElasticSearch side ?",
"Thanks for the feedback! I added a 30 second \"sleep\" and that seemed to work well!"
] |
https://api.github.com/repos/huggingface/datasets/issues/922 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/922/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/922/comments | https://api.github.com/repos/huggingface/datasets/issues/922/events | https://github.com/huggingface/datasets/pull/922 | 753,559,130 | MDExOlB1bGxSZXF1ZXN0NTI5NjEzOTA4 | 922 | Add XOR QA Dataset | [] | closed | false | null | 4 | 2020-11-30T15:10:54Z | 2020-12-02T03:12:21Z | 2020-12-02T03:12:21Z | null | Added XOR Question Answering Dataset. The link to the dataset can be found [here](https://nlp.cs.washington.edu/xorqa/)
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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"Hi @sumanthd17 \r\n\r\nLooks like a good start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)",
"I followed the instructions mentioned there but my datas... |
https://api.github.com/repos/huggingface/datasets/issues/1252 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1252/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1252/comments | https://api.github.com/repos/huggingface/datasets/issues/1252/events | https://github.com/huggingface/datasets/pull/1252 | 758,511,388 | MDExOlB1bGxSZXF1ZXN0NTMzNjczMDcx | 1,252 | Add Naver sentiment movie corpus | [] | closed | false | null | 0 | 2020-12-07T13:33:45Z | 2020-12-08T14:32:33Z | 2020-12-08T14:21:37Z | null | Supersedes #1168
> 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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https://api.github.com/repos/huggingface/datasets/issues/2560 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2560/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2560/comments | https://api.github.com/repos/huggingface/datasets/issues/2560/events | https://github.com/huggingface/datasets/pull/2560 | 932,143,634 | MDExOlB1bGxSZXF1ZXN0Njc5NTMyODk4 | 2,560 | fix Dataset.map when num_procs > num rows | [] | closed | false | null | 3 | 2021-06-29T02:24:11Z | 2021-06-29T15:00:18Z | 2021-06-29T14:53:31Z | null | closes #2470
## Testing notes
To run updated tests:
```sh
pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s
```
With Python code (to view warning):
```python
from datasets import Dataset
dataset = Dataset.from_dict({"x": ["sample"]})
print(len(dataset))
dataset.map(lambda x: x, num_proc=10)
``` | {
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} | true | [
"Hi ! Thanks for fixing this :)\r\n\r\nLooks like you have tons of changes due to code formatting.\r\nWe're using `black` for this, with a custom line length. To run our code formatting, you just need to run\r\n```\r\nmake style\r\n```\r\n\r\nThen for the windows error in the CI, I'm looking into it. It's probably ... |
https://api.github.com/repos/huggingface/datasets/issues/622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/622/comments | https://api.github.com/repos/huggingface/datasets/issues/622/events | https://github.com/huggingface/datasets/issues/622 | 700,225,826 | MDU6SXNzdWU3MDAyMjU4MjY= | 622 | load_dataset for text files not working | [
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"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library",
"id": 2067388877,
"name": "dataset bug",
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
}
] | closed | false | null | 41 | 2020-09-12T12:49:28Z | 2020-10-28T11:07:31Z | 2020-10-28T11:07:30Z | null | Trying the following snippet, I get different problems on Linux and Windows.
```python
dataset = load_dataset("text", data_files="data.txt")
# or
dataset = load_dataset("text", data_files=["data.txt"])
```
(ps [This example](https://huggingface.co/docs/datasets/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)
The problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.
Linux stack trace:
```
PyTorch version 1.6.0+cu101 available.
Checking /home/bram/.cache/huggingface/datasets/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.json
Using custom data configuration default
Generating dataset text (/home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)
Downloading and preparing dataset text/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...
Dataset not on Hf google storage. Downloading and preparing it from source
Downloading took 0.0 min
Checksum Computation took 0.0 min
Unable to verify checksums.
Generating split train
Traceback (most recent call last):
File "/home/bram/Python/projects/dutch-simplification/utils.py", line 45, in prepare_data
dataset = load_dataset("text", data_files=dataset_f)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/load.py", line 608, in load_dataset
builder_instance.download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 468, in download_and_prepare
self._download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 546, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 888, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/tqdm/std.py", line 1130, in __iter__
for obj in iterable:
File "/home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py", line 100, in _generate_tables
pa_table = pac.read_csv(
File "pyarrow/_csv.pyx", line 714, in pyarrow._csv.read_csv
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: CSV parse error: Expected 1 columns, got 2
```
Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:
```
Checking C:\Users\bramv\.cache\huggingface\datasets\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.json
Using custom data configuration default
```
| {
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"Can you give us more information on your os and pip environments (pip list)?",
"@thomwolf Sure. I'll try downgrading to 3.7 now even though Arrow say they support >=3.5.\r\n\r\nLinux (Ubuntu 18.04) - Python 3.8\r\n======================\r\nPackage - Version\r\n---------------------\r\ncertifi 2... |
https://api.github.com/repos/huggingface/datasets/issues/4547 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4547/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4547/comments | https://api.github.com/repos/huggingface/datasets/issues/4547/events | https://github.com/huggingface/datasets/pull/4547 | 1,282,160,517 | PR_kwDODunzps46Ot5u | 4,547 | [CI] Fix some warnings | [] | closed | false | null | 4 | 2022-06-23T10:10:49Z | 2022-06-28T14:10:57Z | 2022-06-28T13:59:54Z | null | There are some warnings in the CI that are annoying, I tried to remove most of them | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"There is a CI failure only related to the missing content of the universal_dependencies dataset card, we can ignore this failure in this PR",
"good catch, I thought I resolved them all sorry",
"Alright it should be good now"
] |
https://api.github.com/repos/huggingface/datasets/issues/1282 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1282/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1282/comments | https://api.github.com/repos/huggingface/datasets/issues/1282/events | https://github.com/huggingface/datasets/pull/1282 | 759,208,335 | MDExOlB1bGxSZXF1ZXN0NTM0MjQ4NzI5 | 1,282 | add thaiqa_squad | [] | closed | false | null | 0 | 2020-12-08T08:14:38Z | 2020-12-08T18:36:18Z | 2020-12-08T18:36:18Z | null | Example format is a little different from SQuAD since `thaiqa` always have one answer per question so I added a check to convert answers to lists if they are not already one to future-proof additional questions that might have multiple answers.
`thaiqa_squad` is an open-domain, extractive question answering dataset (4,000 questions in `train` and 74 questions in `dev`) in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, originally created by [NECTEC](https://www.nectec.or.th/en/) from Wikipedia articles and adapted to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format by [PyThaiNLP](https://github.com/PyThaiNLP/). | {
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https://api.github.com/repos/huggingface/datasets/issues/5365 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5365/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5365/comments | https://api.github.com/repos/huggingface/datasets/issues/5365/events | https://github.com/huggingface/datasets/pull/5365 | 1,498,422,466 | PR_kwDODunzps5Fi6ZD | 5,365 | fix: image array should support other formats than uint8 | [] | closed | false | null | 4 | 2022-12-15T13:17:50Z | 2023-01-26T18:46:45Z | 2023-01-26T18:39:36Z | null | Currently images that are provided as ndarrays, but not in `uint8` format are going to loose data. Namely, for example in a depth image where the data is in float32 format, the type-casting to uint8 will basically make the whole image blank.
`PIL.Image.fromarray` [does support mode `F`](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes).
although maybe some further metadata could be supplied via the [Image](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/main_classes#datasets.Image) object. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi, thanks for working on this! \r\n\r\nI agree that the current type-casting (always cast to `np.uint8` as Tensorflow Datasets does) is a bit too harsh. However, not all dtypes are supported in `Image.fromarray` (e.g. np.int64), so ... |
https://api.github.com/repos/huggingface/datasets/issues/4185 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4185/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4185/comments | https://api.github.com/repos/huggingface/datasets/issues/4185/events | https://github.com/huggingface/datasets/issues/4185 | 1,209,429,743 | I_kwDODunzps5IFm7v | 4,185 | Librispeech documentation, clarification on format | [] | open | false | null | 8 | 2022-04-20T09:35:55Z | 2022-04-21T11:00:53Z | null | null | https://github.com/huggingface/datasets/blob/cd3ce34ab1604118351e1978d26402de57188901/datasets/librispeech_asr/librispeech_asr.py#L53
> Note that in order to limit the required storage for preparing this dataset, the audio
> is stored in the .flac format and is not converted to a float32 array. To convert, the audio
> file to a float32 array, please make use of the `.map()` function as follows:
>
> ```python
> import soundfile as sf
> def map_to_array(batch):
> speech_array, _ = sf.read(batch["file"])
> batch["speech"] = speech_array
> return batch
> dataset = dataset.map(map_to_array, remove_columns=["file"])
> ```
Is this still true?
In my case, `ds["train.100"]` returns:
```
Dataset({
features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'],
num_rows: 28539
})
```
and taking the first instance yields:
```
{'file': '374-180298-0000.flac',
'audio': {'path': '374-180298-0000.flac',
'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ...,
-2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
'sampling_rate': 16000},
'text': 'CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED',
'speaker_id': 374,
'chapter_id': 180298,
'id': '374-180298-0000'}
```
The `audio` `array` seems to be already decoded. So such convert/decode code as mentioned in the doc is wrong?
But I wonder, is it actually stored as flac on disk, and the decoding is done on-the-fly? Or was it decoded already during the preparation and is stored as raw samples on disk?
Note that I also used `datasets.load_dataset("librispeech_asr", "clean").save_to_disk(...)` and then `datasets.load_from_disk(...)` in this example. Does this change anything on how it is stored on disk?
A small related question: Actually I would prefer to even store it as mp3 or ogg on disk. Is this easy to convert? | {
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"(@patrickvonplaten )",
"Also cc @lhoestq here",
"The documentation in the code is definitely outdated - thanks for letting me know, I'll remove it in https://github.com/huggingface/datasets/pull/4184 .\r\n\r\nYou're exactly right `audio` `array` already decodes the audio file to the correct waveform. This is d... |
https://api.github.com/repos/huggingface/datasets/issues/3754 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3754/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3754/comments | https://api.github.com/repos/huggingface/datasets/issues/3754/events | https://github.com/huggingface/datasets/issues/3754 | 1,142,886,536 | I_kwDODunzps5EHxCI | 3,754 | Overflowing indices in `select` | [
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] | closed | false | null | 2 | 2022-02-18T11:30:52Z | 2022-02-18T11:38:23Z | 2022-02-18T11:38:23Z | null | ## Describe the bug
The `Dataset.select` function seems to accept indices that are larger than the dataset size and seems to effectively use `index %len(ds)`.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"test": [1,2,3]})
ds = ds.select(range(5))
print(ds)
print()
print(ds["test"])
```
Result:
```python
Dataset({
features: ['test'],
num_rows: 5
})
[1, 2, 3, 1, 2]
```
This behaviour is not documented and can lead to unexpected behaviour when for example taking a sample larger than the dataset and thus creating a lot of duplicates.
## Expected results
It think this should throw an error or at least a very big warning:
```python
IndexError: Invalid key: 5 is out of bounds for size 3
```
## Environment info
- `datasets` version: 1.18.3
- Platform: macOS-12.0.1-x86_64-i386-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0 | {
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"Fixed on master (see https://github.com/huggingface/datasets/pull/3719).",
"Awesome, I did not find that one! Thanks."
] |
https://api.github.com/repos/huggingface/datasets/issues/4045 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4045/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4045/comments | https://api.github.com/repos/huggingface/datasets/issues/4045/events | https://github.com/huggingface/datasets/pull/4045 | 1,183,661,091 | PR_kwDODunzps41KtfV | 4,045 | Fix CLI dummy data generation | [] | closed | false | null | 1 | 2022-03-28T16:09:15Z | 2022-03-31T15:04:12Z | 2022-03-31T14:59:06Z | null | PR:
- #3868
broke the CLI dummy data generation.
Fix #4044. | {
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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/5130 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5130/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5130/comments | https://api.github.com/repos/huggingface/datasets/issues/5130/events | https://github.com/huggingface/datasets/pull/5130 | 1,413,435,000 | PR_kwDODunzps5BBxXX | 5,130 | Avoid extra cast in `class_encode_column` | [] | closed | false | null | 1 | 2022-10-18T15:31:24Z | 2022-10-19T11:53:02Z | 2022-10-19T11:50:46Z | null | Pass the updated features to `map` to avoid the `cast` in `class_encode_column`. | {
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] |
https://api.github.com/repos/huggingface/datasets/issues/2027 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2027/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2027/comments | https://api.github.com/repos/huggingface/datasets/issues/2027/events | https://github.com/huggingface/datasets/pull/2027 | 828,490,444 | MDExOlB1bGxSZXF1ZXN0NTkwMjkzNDA1 | 2,027 | Update format columns in Dataset.rename_columns | [] | closed | false | null | 0 | 2021-03-10T23:50:59Z | 2021-03-11T14:38:40Z | 2021-03-11T14:38:40Z | null | Fixes #2026 | {
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https://api.github.com/repos/huggingface/datasets/issues/3104 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3104/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3104/comments | https://api.github.com/repos/huggingface/datasets/issues/3104/events | https://github.com/huggingface/datasets/issues/3104 | 1,029,080,412 | I_kwDODunzps49VoVc | 3,104 | Missing Zenodo 1.13.3 release | [
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] | closed | false | null | 1 | 2021-10-18T12:57:18Z | 2021-10-22T13:22:25Z | 2021-10-22T13:22:24Z | null | After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305
TODO:
- [x] Contact Zenodo support
- [x] Check it is fixed | {
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} | https://api.github.com/repos/huggingface/datasets/issues/3104/timeline | null | completed | null | null | false | [
"Zenodo has fixed on their side the 1.13.3 release: https://zenodo.org/record/5589150"
] |
https://api.github.com/repos/huggingface/datasets/issues/455 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/455/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/455/comments | https://api.github.com/repos/huggingface/datasets/issues/455/events | https://github.com/huggingface/datasets/pull/455 | 668,037,965 | MDExOlB1bGxSZXF1ZXN0NDU4NTk4NTUw | 455 | Add bleurt | [] | closed | false | null | 4 | 2020-07-29T18:08:32Z | 2020-07-31T13:56:14Z | 2020-07-31T13:56:14Z | null | This PR adds the BLEURT metric to the library.
The BLEURT `Metric` downloads a TF checkpoint corresponding to its `config_name` at creation (in the `_info` function). Default is set to `bleurt-base-128`.
Note that the default in the original package is `bleurt-tiny-128`, but they throw a warning and recommend using `bleurt-base-128` instead. I think it's safer to have our users have a functioning metric when they call the default behavior, we'll address discrepancies in the issues/discussions if it comes up.
In addition to the BLEURT file, `load.py` was changed so we can ask users to pip install the required packages from git when they have a `setup.py` but are not on PyPL
cc @ankparikh @tsellam | {
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"Sorry one nit: Could we use named arguments for the call to BLEURT?\r\n\r\ni.e. \r\n scores = self.scorer.score(references=references, candidates=predictions)\r\n\r\n(i.e. so it is less bug prone)\r\n",
"Following up on Ankur's comment---we are going to drop support for\npositional (not named) arguments i... |
https://api.github.com/repos/huggingface/datasets/issues/4668 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4668/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4668/comments | https://api.github.com/repos/huggingface/datasets/issues/4668/events | https://github.com/huggingface/datasets/issues/4668 | 1,299,735,893 | I_kwDODunzps5NeGVV | 4,668 | Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed | [
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] | closed | false | null | 1 | 2022-07-09T18:04:13Z | 2022-07-11T07:47:47Z | 2022-07-11T07:47:47Z | null | ### Link
https://huggingface.co/hungnm/multilingual-amazon-review-sentiment
### Description
_No response_
### Owner
Yes | {
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"It seems like a private dataset. The viewer is currently not supported on the private datasets."
] |
https://api.github.com/repos/huggingface/datasets/issues/3622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3622/comments | https://api.github.com/repos/huggingface/datasets/issues/3622/events | https://github.com/huggingface/datasets/issues/3622 | 1,112,831,661 | I_kwDODunzps5CVHat | 3,622 | Extend support for streaming datasets that use os.path.relpath | [
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] | closed | false | null | 0 | 2022-01-24T15:58:23Z | 2022-02-04T14:03:54Z | 2022-02-04T14:03:54Z | null | Extend support for streaming datasets that use `os.path.relpath`.
This feature will also be useful to yield the relative path of audio or image files.
| {
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https://api.github.com/repos/huggingface/datasets/issues/1413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1413/comments | https://api.github.com/repos/huggingface/datasets/issues/1413/events | https://github.com/huggingface/datasets/pull/1413 | 760,615,090 | MDExOlB1bGxSZXF1ZXN0NTM1NDE4MDY2 | 1,413 | Add OffComBR | [] | closed | false | null | 3 | 2020-12-09T19:38:08Z | 2020-12-14T18:06:45Z | 2020-12-14T16:51:10Z | null | Add [OffComBR](https://github.com/rogersdepelle/OffComBR) from [Offensive Comments in the Brazilian Web: a dataset and baseline results](https://sol.sbc.org.br/index.php/brasnam/article/view/3260/3222) paper.
But I'm having a hard time generating dummy data since the original dataset extion is `.arff` and the [_create_dummy_data function](https://github.com/huggingface/datasets/blob/a4aeaf911240057286a01bff1b1d75a89aedd57b/src/datasets/commands/dummy_data.py#L185) doesn't allow it. | {
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"Hello @hugoabonizio, thanks for the contribution.\r\nRegarding the fake data, you can generate it manually.\r\nRunning the `python datasets-cli dummy_data datasets/offcombr` should give you instructions on how to manually create the dummy data.\r\nFor reference, here is a spec for `.arff` files : https://www.cs.wa... |
https://api.github.com/repos/huggingface/datasets/issues/4100 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4100/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4100/comments | https://api.github.com/repos/huggingface/datasets/issues/4100/events | https://github.com/huggingface/datasets/pull/4100 | 1,193,393,959 | PR_kwDODunzps41q4ce | 4,100 | Improve RedCaps dataset card | [] | closed | false | null | 2 | 2022-04-05T15:57:14Z | 2022-04-13T14:08:54Z | 2022-04-13T14:02:26Z | null | This PR modifies the RedCaps card to:
* fix the formatting of the Point of Contact fields on the Hub
* speed up the image fetching logic (aligns it with the [img2dataset](https://github.com/rom1504/img2dataset) tool) and make it more robust (return None if **any** exception is thrown) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I find this preprocessing a bit too specific to add it as a method to `datasets` as it's only useful in the context of CV (and we support multiple modalities). However, I agree it would be great to move this code to another lib to av... |
https://api.github.com/repos/huggingface/datasets/issues/1722 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1722/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1722/comments | https://api.github.com/repos/huggingface/datasets/issues/1722/events | https://github.com/huggingface/datasets/pull/1722 | 783,921,679 | MDExOlB1bGxSZXF1ZXN0NTUzMTk3MTg4 | 1,722 | Added unfiltered versions of the Wiki-Auto training data for the GEM simplification task. | [] | closed | false | null | 1 | 2021-01-12T05:26:04Z | 2021-01-12T18:14:53Z | 2021-01-12T17:35:57Z | null | {
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"The current version of Wiki-Auto dataset contains a filtered version of the aligned dataset. The commit adds unfiltered versions of the data that can be useful the GEM task participants."
] | |
https://api.github.com/repos/huggingface/datasets/issues/3580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3580/comments | https://api.github.com/repos/huggingface/datasets/issues/3580/events | https://github.com/huggingface/datasets/issues/3580 | 1,104,663,242 | I_kwDODunzps5B19LK | 3,580 | Bug in wiki bio load | [
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"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
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] | closed | false | null | 4 | 2022-01-15T10:04:33Z | 2022-01-31T08:38:09Z | 2022-01-31T08:38:09Z | null |
wiki_bio is failing to load because of a failing drive link . Can someone fix this ?
a | {
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"+1, here's the error I got: \r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>>\r\n>>> load_dataset(\"wiki_bio\")\r\nDownloading: 7.58kB [00:00, 4.42MB/s]\r\nDownloading: 2.71kB [00:00, 1.30MB/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio/default (download: 318... |
https://api.github.com/repos/huggingface/datasets/issues/4181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4181/comments | https://api.github.com/repos/huggingface/datasets/issues/4181/events | https://github.com/huggingface/datasets/issues/4181 | 1,208,194,805 | I_kwDODunzps5IA5b1 | 4,181 | Support streaming FLEURS dataset | [
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] | closed | false | null | 9 | 2022-04-19T11:09:56Z | 2022-07-25T11:44:02Z | 2022-07-25T11:44:02Z | null | ## Dataset viewer issue for '*name of the dataset*'
https://huggingface.co/datasets/google/fleurs
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://storage.googleapis.com/xtreme_translations/FLEURS/af_za.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
Am I the one who added this dataset ? Yes
Can I fix this somehow in the script? @lhoestq @severo
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} | https://api.github.com/repos/huggingface/datasets/issues/4181/timeline | null | completed | null | null | false | [
"Yes, you just have to use `dl_manager.iter_archive` instead of `dl_manager.download_and_extract`.\r\n\r\nThat's because `download_and_extract` doesn't support TAR archives in streaming mode.",
"Tried to make it streamable, but I don't think it's really possible. @lhoestq @polinaeterna maybe you guys can check: \... |
https://api.github.com/repos/huggingface/datasets/issues/5963 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5963/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5963/comments | https://api.github.com/repos/huggingface/datasets/issues/5963/events | https://github.com/huggingface/datasets/issues/5963 | 1,762,774,457 | I_kwDODunzps5pEc25 | 5,963 | Got an error _pickle.PicklingError use Dataset.from_spark. | [] | closed | false | null | 5 | 2023-06-19T05:30:35Z | 2023-07-24T11:55:46Z | 2023-07-24T11:55:46Z | null | python 3.9.2
Got an error _pickle.PicklingError use Dataset.from_spark.
Did the dataset import load data from spark dataframe using multi-node Spark cluster
df = spark.read.parquet(args.input_data).repartition(50)
ds = Dataset.from_spark(df, keep_in_memory=True,
cache_dir="/pnc-data/data/nuplan/t5_spark/cache_data")
ds.save_to_disk(args.output_data)
Error :
_pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforma
tion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.
23/06/16 21:17:20 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.)
_Originally posted by @yanzia12138 in https://github.com/huggingface/datasets/issues/5701#issuecomment-1594674306_
W
Traceback (most recent call last):
File "/home/work/main.py", line 100, in <module>
run(args)
File "/home/work/main.py", line 80, in run
ds = Dataset.from_spark(df1, keep_in_memory=True,
File "/home/work/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1281, in from_spark
return SparkDatasetReader(
File "/home/work/.local/lib/python3.9/site-packages/datasets/io/spark.py", line 53, in read
self.builder.download_and_prepare(
File "/home/work/.local/lib/python3.9/site-packages/datasets/builder.py", line 909, in download_and_prepare
self._download_and_prepare(
File "/home/work/.local/lib/python3.9/site-packages/datasets/builder.py", line 1004, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/work/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py", line 254, in _prepare_split
self._validate_cache_dir()
File "/home/work/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py", line 122, in _validate_cache_dir
self._spark.sparkContext.parallelize(range(1), 1).mapPartitions(create_cache_and_write_probe).collect()
File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 950, in collect
sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2951, in _jrdd
wrapped_func = _wrap_function(self.ctx, self.func, self._prev_jrdd_deserializer,
File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2830, in _wrap_function
pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command)
File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2816, in _prepare_for_python_RDD
pickled_command = ser.dumps(command)
File "/home/work/.local/lib/python3.9/site-packages/pyspark/serializers.py", line 447, in dumps
raise pickle.PicklingError(msg)
_pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. S
parkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.
23/06/19 13:51:21 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.)
| {
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"i got error using method from_spark when using multi-node Spark cluster. seems could only use \"from_spark\" in local?",
"@lhoestq ",
"cc @maddiedawson it looks like there an issue with `_validate_cache_dir` ?\r\n\r\nIt looks like the function passed to mapPartitions has a reference to the Spark dataset build... |
https://api.github.com/repos/huggingface/datasets/issues/5129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5129/comments | https://api.github.com/repos/huggingface/datasets/issues/5129/events | https://github.com/huggingface/datasets/issues/5129 | 1,413,031,664 | I_kwDODunzps5UOSbw | 5,129 | unexpected `cast` or `class_encode_column` result after `rename_column` | [
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] | closed | false | null | 4 | 2022-10-18T11:15:24Z | 2022-10-19T03:02:26Z | 2022-10-19T03:02:26Z | null | ## Describe the bug
When invoke `cast` or `class_encode_column` to a colunm renamed by `rename_column` , it will convert all the variables in this column into one variable. I also run this script in version 2.5.2, this bug does not appear. So I switched to the older version.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("amazon_reviews_multi", "en")
data = dataset['train']
data = data.remove_columns(
[
"review_id",
"product_id",
"reviewer_id",
"review_title",
"language",
"product_category",
]
)
data = data.rename_column("review_body", "text")
data1 = data.class_encode_column("stars")
print(set(data1.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
data = data.rename_column("stars", "label")
print(set(data.data.columns[0]))
# output: {<pyarrow.Int32Scalar: 5>, <pyarrow.Int32Scalar: 4>, <pyarrow.Int32Scalar: 1>, <pyarrow.Int32Scalar: 3>, <pyarrow.Int32Scalar: 2>}
data2 = data.class_encode_column("label")
print(set(data2.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 0>}
```
## Expected results
the last print should be:
{<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
## Actual results
but it output:
{<pyarrow.Int64Scalar: 0>}
## Environment info
- `datasets` version: 2.6.1
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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"Hi! Unfortunately, I can't reproduce this issue locally (in Python 3.7/3.10) or in Colab. I would assume this is due to a bug we fixed in the latest release, but your version is up-to-date, so I'm not sure if there is something we can do to help...",
"Hi, 方子东. I tried running the code with exact the same configu... |
https://api.github.com/repos/huggingface/datasets/issues/4736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4736/comments | https://api.github.com/repos/huggingface/datasets/issues/4736/events | https://github.com/huggingface/datasets/issues/4736 | 1,314,931,996 | I_kwDODunzps5OYEUc | 4,736 | Dataset Viewer issue for deepklarity/huggingface-spaces-dataset | [
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] | closed | false | null | 1 | 2022-07-22T12:14:18Z | 2022-07-22T13:46:38Z | 2022-07-22T13:46:38Z | null | ### Link
https://huggingface.co/datasets/deepklarity/huggingface-spaces-dataset/viewer/deepklarity--huggingface-spaces-dataset/train
### Description
Hi Team,
I'm getting the following error on a uploaded dataset. I'm getting the same status for a couple of hours now. The dataset size is `<1MB` and the format is csv, so I'm not sure if it's supposed to take this much time or not.
```
Status code: 400
Exception: Status400Error
Message: The split is being processed. Retry later.
```
Is there any explicit step to be taken to get the viewer to work?
### Owner
Yes | {
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"Thanks for reporting. You're right, workers were under-provisioned due to a manual error, and the job queue was full. It's fixed now."
] |
https://api.github.com/repos/huggingface/datasets/issues/3738 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3738/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3738/comments | https://api.github.com/repos/huggingface/datasets/issues/3738/events | https://github.com/huggingface/datasets/issues/3738 | 1,140,164,253 | I_kwDODunzps5D9Yad | 3,738 | For data-only datasets, streaming and non-streaming don't behave the same | [
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] | open | false | null | 9 | 2022-02-16T15:20:57Z | 2022-02-21T14:24:55Z | null | null | See https://huggingface.co/datasets/huggingface/transformers-metadata: it only contains two JSON files.
In streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:
```python
import datasets as ds
iterable_dataset = ds.load_dataset("huggingface/transformers-metadata", split="train", streaming=True);
rows = list(iterable_dataset.take(100))
rows[0]
# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}
rows[99]
# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}
```
In normal mode, an exception is thrown:
```python
import datasets as ds
dataset = ds.load_dataset("huggingface/transformers-metadata", split="train");
```
```
ValueError: Couldn't cast
model_class: string
pipeline_tag: string
auto_class: string
to
{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}
because column names don't match
``` | {
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"Note that we might change the heuristic and create a different config per file, at least in that case.",
"Hi @severo, thanks for reporting.\r\n\r\nYes, this happens because when non-streaming, a cast of all data is done in order to \"concatenate\" it all into a single dataset (thus the error), while this casting... |
https://api.github.com/repos/huggingface/datasets/issues/1611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1611/comments | https://api.github.com/repos/huggingface/datasets/issues/1611/events | https://github.com/huggingface/datasets/issues/1611 | 771,486,456 | MDU6SXNzdWU3NzE0ODY0NTY= | 1,611 | shuffle with torch generator | [
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] | closed | false | null | 8 | 2020-12-20T00:57:14Z | 2022-06-01T15:30:13Z | 2022-06-01T15:30:13Z | null | Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq | {
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"Is there a way one can convert the two generator? not sure overall what alternatives I could have to shuffle the datasets with a torch generator, thanks ",
"@lhoestq let me please expalin in more details, maybe you could help me suggesting an alternative to solve the issue for now, I have multiple large dataset... |
https://api.github.com/repos/huggingface/datasets/issues/4055 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4055/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4055/comments | https://api.github.com/repos/huggingface/datasets/issues/4055/events | https://github.com/huggingface/datasets/pull/4055 | 1,184,976,292 | PR_kwDODunzps41PGF1 | 4,055 | [DO NOT MERGE] Test doc-builder | [] | closed | false | null | 2 | 2022-03-29T14:39:02Z | 2022-03-30T12:31:14Z | 2022-03-30T12:25:52Z | null | This is a test PR to ensure the changes in https://github.com/huggingface/doc-builder/pull/164 don't break anything in `datasets` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Docs built successfully, so closing this."
] |
https://api.github.com/repos/huggingface/datasets/issues/1537 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1537/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1537/comments | https://api.github.com/repos/huggingface/datasets/issues/1537/events | https://github.com/huggingface/datasets/pull/1537 | 765,095,210 | MDExOlB1bGxSZXF1ZXN0NTM4ODY1NzIz | 1,537 | added ohsumed | [] | closed | false | null | 0 | 2020-12-13T06:58:23Z | 2020-12-17T18:28:16Z | 2020-12-17T18:28:16Z | null | UPDATE2: PR passed all tests. Now waiting for review.
UPDATE: pushed a new version. cross fingers that it should complete all the tests! :)
If it passes all tests then it's not a draft version.
This is a draft version | {
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https://api.github.com/repos/huggingface/datasets/issues/4371 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4371/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4371/comments | https://api.github.com/repos/huggingface/datasets/issues/4371/events | https://github.com/huggingface/datasets/pull/4371 | 1,241,500,906 | PR_kwDODunzps44GzSZ | 4,371 | Add missing language tags for udhr dataset | [] | closed | false | null | 1 | 2022-05-19T09:34:10Z | 2022-06-08T12:03:24Z | 2022-05-20T09:43:10Z | null | Related to #4362. | {
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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/3317 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3317/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3317/comments | https://api.github.com/repos/huggingface/datasets/issues/3317/events | https://github.com/huggingface/datasets/issues/3317 | 1,062,284,447 | I_kwDODunzps4_USyf | 3,317 | Add desc parameter to Dataset filter method | [
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] | closed | false | null | 4 | 2021-11-24T11:01:36Z | 2022-01-05T18:31:24Z | 2022-01-05T18:31:24Z | null | **Is your feature request related to a problem? Please describe.**
As I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?
**Describe the solution you'd like**
Add desc parameter to Dataset filter method
**Describe alternatives you've considered**
N/A
**Additional context**
N/A
| {
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"Hi,\r\n\r\n`Dataset.map` allows more generic transforms compared to `Dataset.filter`, which purpose is very specific (to filter examples based on a condition). That's why I don't think we need the `desc` parameter there for consistency. #3196 has added descriptions to the `Dataset` methods that call `.map` intern... |
https://api.github.com/repos/huggingface/datasets/issues/342 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/342/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/342/comments | https://api.github.com/repos/huggingface/datasets/issues/342/events | https://github.com/huggingface/datasets/issues/342 | 651,333,194 | MDU6SXNzdWU2NTEzMzMxOTQ= | 342 | Features should be updated when `map()` changes schema | [] | closed | false | null | 1 | 2020-07-06T08:03:23Z | 2020-07-23T10:15:16Z | 2020-07-23T10:15:16Z | null | `dataset.map()` can change the schema and column names.
We should update the features in this case (with what is possible to infer). | {
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"`dataset.column_names` are being updated but `dataset.features` aren't indeed..."
] |
https://api.github.com/repos/huggingface/datasets/issues/1871 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1871/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1871/comments | https://api.github.com/repos/huggingface/datasets/issues/1871/events | https://github.com/huggingface/datasets/pull/1871 | 807,697,671 | MDExOlB1bGxSZXF1ZXN0NTcyODk5Nzgz | 1,871 | Add newspop dataset | [] | closed | false | null | 1 | 2021-02-13T07:31:23Z | 2021-03-08T10:12:45Z | 2021-03-08T10:12:45Z | null | {
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"Thanks for the changes :)\r\nmerging"
] | |
https://api.github.com/repos/huggingface/datasets/issues/4386 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4386/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4386/comments | https://api.github.com/repos/huggingface/datasets/issues/4386/events | https://github.com/huggingface/datasets/issues/4386 | 1,243,965,532 | I_kwDODunzps5KJWhc | 4,386 | Bug for wiki_auto_asset_turk from GEM | [
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] | closed | false | null | 7 | 2022-05-21T12:31:30Z | 2022-05-24T05:55:52Z | 2022-05-23T10:29:55Z | null | ## Describe the bug
The script of wiki_auto_asset_turk for GEM may be out of date.
## Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('gem', 'wiki_auto_asset_turk')
```
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1731, in load_dataset
builder_instance.download_and_prepare(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 640, in download_and_prepare
self._download_and_prepare(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 1158, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 707, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/tangtianyi/.cache/huggingface/modules/datasets_modules/datasets/gem/982a54473b12c6a6e40d4356e025fb7172a5bb2065e655e2c1af51f2b3cf4ca1/gem.py", line 538, in _split_generators
dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 416, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 294, in download
downloaded_path_or_paths = map_nested(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 351, in map_nested
mapped = [
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 352, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 288, in _single_map_nested
return function(data_struct)
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 320, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 234, in cached_path
output_path = get_from_cache(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 579, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://github.com/facebookresearch/asset/raw/master/dataset/asset.test.orig
``` | {
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"Thanks for reporting, @StevenTang1998.\r\n\r\nI'm looking into it. ",
"Hi @StevenTang1998,\r\n\r\nWe have fixed the issue:\r\n- #4389\r\n\r\nThe fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by installing `datasets` from our GitHub repo:\r\n```\r\npip... |
https://api.github.com/repos/huggingface/datasets/issues/2259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2259/comments | https://api.github.com/repos/huggingface/datasets/issues/2259/events | https://github.com/huggingface/datasets/pull/2259 | 866,880,092 | MDExOlB1bGxSZXF1ZXN0NjIyNjc2ODA0 | 2,259 | Add support for Split.ALL | [] | closed | false | null | 1 | 2021-04-25T01:45:42Z | 2021-06-28T08:21:27Z | 2021-06-28T08:21:27Z | null | The title says it all. | {
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"Honestly, I think we should fix some other issues in Split API before this change. E. g. currently the following will not work, even though it should:\r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"sst\", split=datasets.Split.TRAIN+datasets.Split.TEST) # AssertionError\r\n```\r\n\r\nEDIT:\r\nActually,... |
https://api.github.com/repos/huggingface/datasets/issues/330 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/330/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/330/comments | https://api.github.com/repos/huggingface/datasets/issues/330/events | https://github.com/huggingface/datasets/pull/330 | 648,525,720 | MDExOlB1bGxSZXF1ZXN0NDQyMzIxMjEw | 330 | Doc red | [] | closed | false | null | 0 | 2020-06-30T22:05:31Z | 2020-07-06T12:10:39Z | 2020-07-05T12:27:29Z | null | Adding [DocRED](https://github.com/thunlp/DocRED) - a relation extraction dataset which tests document-level RE. A few implementation notes:
- There are 2 separate versions of the training set - *annotated* and *distant*. Instead of `nlp.Split.Train` I've used the splits `"train_annotated"` and `"train_distant"` to reflect this.
- As well as the relation id, the full relation name is mapped from `rel_info.json`
- I renamed the 'h', 'r', 't' keys to 'head', 'relation' and 'tail' to make them more readable.
- Used the fix from #319 to allow nested sequences of dicts. | {
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https://api.github.com/repos/huggingface/datasets/issues/4281 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4281/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4281/comments | https://api.github.com/repos/huggingface/datasets/issues/4281/events | https://github.com/huggingface/datasets/pull/4281 | 1,225,556,939 | PR_kwDODunzps43TNBm | 4,281 | Remove a copy-paste sentence in dataset cards | [] | closed | false | null | 2 | 2022-05-04T15:41:55Z | 2022-05-06T08:38:03Z | 2022-05-04T18:33:16Z | null | Remove the following copy-paste sentence from dataset cards:
```
We show detailed information for up to 5 configurations of the dataset.
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The non-passing tests have nothing to do with this PR."
] |
https://api.github.com/repos/huggingface/datasets/issues/654 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/654/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/654/comments | https://api.github.com/repos/huggingface/datasets/issues/654/events | https://github.com/huggingface/datasets/pull/654 | 705,511,058 | MDExOlB1bGxSZXF1ZXN0NDkwMjI1Nzk3 | 654 | Allow empty inputs in metrics | [] | closed | false | null | 0 | 2020-09-21T11:26:36Z | 2020-10-06T03:51:48Z | 2020-09-21T16:13:38Z | null | There was an arrow error when trying to compute a metric with empty inputs. The error was occurring when reading the arrow file, before calling metric._compute. | {
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https://api.github.com/repos/huggingface/datasets/issues/977 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/977/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/977/comments | https://api.github.com/repos/huggingface/datasets/issues/977/events | https://github.com/huggingface/datasets/pull/977 | 754,839,594 | MDExOlB1bGxSZXF1ZXN0NTMwNjY2ODg3 | 977 | Add ROPES dataset | [] | closed | false | null | 0 | 2020-12-02T00:52:10Z | 2020-12-02T10:58:36Z | 2020-12-02T10:58:35Z | null | ROPES dataset
Reasoning over paragraph effects in situations - testing a system's ability to apply knowledge from a passage of text to a new situation. The task is framed into a reading comprehension task following squad-style extractive qa.
One thing to note: labels of the test set are hidden (leaderboard submission) so I encoded that as an empty list (ropes.py:L125) | {
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https://api.github.com/repos/huggingface/datasets/issues/3613 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3613/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3613/comments | https://api.github.com/repos/huggingface/datasets/issues/3613/events | https://github.com/huggingface/datasets/issues/3613 | 1,110,684,015 | I_kwDODunzps5CM7Fv | 3,613 | Files not updating in dataset viewer | [
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] | closed | false | null | 2 | 2022-01-21T16:47:20Z | 2022-01-22T08:13:13Z | 2022-01-22T08:13:13Z | null | ## Dataset viewer issue for '*name of the dataset*'
**Link:**
Some examples:
* https://huggingface.co/datasets/abidlabs/crowdsourced-speech4
* https://huggingface.co/datasets/abidlabs/test-audio-13
*short description of the issue*
It seems that the dataset viewer is reading a cached version of the dataset and it is not updating to reflect new files that are added to the dataset. I get this error:
Am I the one who added this dataset? Yes | {
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"Yes. The jobs queue is full right now, following an upgrade... Back to normality in the next hours hopefully. I'll look at your datasets to be sure the dataset viewer works as expected on them.",
"Should have been fixed now."
] |
https://api.github.com/repos/huggingface/datasets/issues/6022 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6022/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6022/comments | https://api.github.com/repos/huggingface/datasets/issues/6022/events | https://github.com/huggingface/datasets/issues/6022 | 1,800,092,589 | I_kwDODunzps5rSzut | 6,022 | Batch map raises TypeError: '>=' not supported between instances of 'NoneType' and 'int' | [] | closed | false | null | 1 | 2023-07-12T03:20:17Z | 2023-07-12T16:18:06Z | 2023-07-12T16:18:05Z | null | ### Describe the bug
When mapping some datasets with `batched=True`, datasets may raise an exeception:
```python
Traceback (most recent call last):
File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1328, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3483, in _map_single
writer.write_batch(batch)
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_writer.py", line 549, in write_batch
array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 2063, in cast_array_to_feature
return feature.cast_storage(array)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/features/features.py", line 1098, in cast_storage
if min_max["max"] >= self.num_classes:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: '>=' not supported between instances of 'NoneType' and 'int'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/codingl2k1/Work/datasets/t1.py", line 33, in <module>
ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 850, in map
{
File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 851, in <dictcomp>
k: dataset.map(
^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 577, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 542, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3179, in map
for rank, done, content in iflatmap_unordered(
File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in iflatmap_unordered
[async_result.get(timeout=0.05) for async_result in async_results]
File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in <listcomp>
[async_result.get(timeout=0.05) for async_result in async_results]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 774, in get
raise self._value
TypeError: '>=' not supported between instances of 'NoneType' and 'int'
```
### Steps to reproduce the bug
1. Checkout the latest main of datasets.
2. Run the code:
```python
from datasets import load_dataset
def transforms(examples):
# examples["pixel_values"] = [image.convert("RGB").resize((100, 100)) for image in examples["image"]]
return examples
ds = load_dataset("scene_parse_150")
ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5)
print(ds)
```
### Expected behavior
map without exception.
### Environment info
Datasets: https://github.com/huggingface/datasets/commit/b8067c0262073891180869f700ebef5ac3dc5cce
Python: 3.11.4
System: Macos | {
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"Thanks for reporting! I've opened a PR with a fix."
] |
https://api.github.com/repos/huggingface/datasets/issues/1928 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1928/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1928/comments | https://api.github.com/repos/huggingface/datasets/issues/1928/events | https://github.com/huggingface/datasets/pull/1928 | 813,793,434 | MDExOlB1bGxSZXF1ZXN0NTc3ODgyMDM4 | 1,928 | Updating old cards | [] | closed | false | null | 0 | 2021-02-22T19:26:04Z | 2021-02-23T18:19:25Z | 2021-02-23T18:19:25Z | null | Updated the cards for [Allocine](https://github.com/mcmillanmajora/datasets/tree/updating-old-cards/datasets/allocine), [CNN/DailyMail](https://github.com/mcmillanmajora/datasets/tree/updating-old-cards/datasets/cnn_dailymail), and [SNLI](https://github.com/mcmillanmajora/datasets/tree/updating-old-cards/datasets/snli). For the most part, the information was just rearranged or rephrased, but the social impact statements are new. | {
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https://api.github.com/repos/huggingface/datasets/issues/202 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/202/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/202/comments | https://api.github.com/repos/huggingface/datasets/issues/202/events | https://github.com/huggingface/datasets/issues/202 | 625,493,983 | MDU6SXNzdWU2MjU0OTM5ODM= | 202 | Mistaken `_KWARGS_DESCRIPTION` for XNLI metric | [] | closed | false | null | 1 | 2020-05-27T08:34:42Z | 2020-05-28T13:22:36Z | 2020-05-28T13:22:36Z | null | Hi!
The [`_KWARGS_DESCRIPTION`](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/xnli/xnli.py#L45) for the XNLI metric uses `Args` and `Returns` text from [BLEU](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/bleu/bleu.py#L58) metric:
```
_KWARGS_DESCRIPTION = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: list of translations to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
'bleu': bleu score,
'precisions': geometric mean of n-gram precisions,
'brevity_penalty': brevity penalty,
'length_ratio': ratio of lengths,
'translation_length': translation_length,
'reference_length': reference_length
"""
```
But it should be something like:
```
_KWARGS_DESCRIPTION = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
'accuracy': accuracy
``` | {
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"Indeed, good catch ! thanks\r\nFixing it right now"
] |
https://api.github.com/repos/huggingface/datasets/issues/2193 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2193/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2193/comments | https://api.github.com/repos/huggingface/datasets/issues/2193/events | https://github.com/huggingface/datasets/issues/2193 | 853,725,707 | MDU6SXNzdWU4NTM3MjU3MDc= | 2,193 | Filtering/mapping on one column is very slow | [
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] | closed | false | null | 12 | 2021-04-08T18:16:14Z | 2021-04-26T16:13:59Z | 2021-04-26T16:13:59Z | null | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that— I'm not very familiar with the pyarrow API.
I know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.
PS: This is definitely not a "dataset request." I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. | {
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"Hi ! Yes we are working on making `filter` significantly faster. You can look at related PRs here: #2060 #2178 \r\n\r\nI think you can expect to have the fast version of `filter` available next week.\r\n\r\nWe'll make it only select one column, and we'll also make the overall filtering operation way faster by avoi... |
https://api.github.com/repos/huggingface/datasets/issues/2249 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2249/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2249/comments | https://api.github.com/repos/huggingface/datasets/issues/2249/events | https://github.com/huggingface/datasets/pull/2249 | 865,257,826 | MDExOlB1bGxSZXF1ZXN0NjIxMzU1MzE3 | 2,249 | Allow downloading/processing/caching only specific splits | [
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} | 2 | 2021-04-22T17:51:44Z | 2022-07-06T15:19:48Z | null | null | Allow downloading/processing/caching only specific splits without downloading/processing/caching the other splits.
This PR implements two steps to handle only specific splits:
- it allows processing/caching only specific splits into Arrow files
- for some simple cases, it allows downloading only specific splits (which is more intricate as it depends on the user-defined method `_split_generators`)
This PR makes several assumptions:
- `DownloadConfig` contains the configuration settings for downloading
- the parameter `split` passed to `load_dataset` is just a parameter for loading (from cache), not for downloading | {
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"> If you pass a dictionary like this:\r\n> \r\n> ```\r\n> {\"main_metadata\": url_to_main_data,\r\n> \"secondary_metadata\": url_to_sec_data,\r\n> \"train\": url_train_data,\r\n> \"test\": url_test_data}\r\n> ```\r\n> \r\n> then only the train or test keys will be kept, which I feel not intuitive.\r\n> \r\n> For e... |
https://api.github.com/repos/huggingface/datasets/issues/2913 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2913/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2913/comments | https://api.github.com/repos/huggingface/datasets/issues/2913/events | https://github.com/huggingface/datasets/issues/2913 | 996,436,368 | I_kwDODunzps47ZGmQ | 2,913 | timit_asr dataset only includes one text phrase | [
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] | closed | false | null | 2 | 2021-09-14T21:06:07Z | 2021-09-15T08:05:19Z | 2021-09-15T08:05:18Z | null | ## Describe the bug
The dataset 'timit_asr' only includes one text phrase. It only includes the transcription "Would such an act of refusal be useful?" multiple times rather than different phrases.
## Steps to reproduce the bug
Note: I am following the tutorial https://huggingface.co/blog/fine-tune-wav2vec2-english
1. Install the dataset and other packages
```python
!pip install datasets>=1.5.0
!pip install transformers==4.4.0
!pip install soundfile
!pip install jiwer
```
2. Load the dataset
```python
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
```
3. Remove columns that we don't want
```python
timit = timit.remove_columns(["phonetic_detail", "word_detail", "dialect_region", "id", "sentence_type", "speaker_id"])
```
4. Write a short function to display some random samples of the dataset.
```python
from datasets import ClassLabel
import random
import pandas as pd
from IPython.display import display, HTML
def show_random_elements(dataset, num_examples=10):
assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset."
picks = []
for _ in range(num_examples):
pick = random.randint(0, len(dataset)-1)
while pick in picks:
pick = random.randint(0, len(dataset)-1)
picks.append(pick)
df = pd.DataFrame(dataset[picks])
display(HTML(df.to_html()))
show_random_elements(timit["train"].remove_columns(["file"]))
```
## Expected results
10 random different transcription phrases.
## Actual results
10 of the same transcription phrase "Would such an act of refusal be useful?"
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.4.1
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: not listed
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"Hi @margotwagner, \r\nThis bug was fixed in #1995. Upgrading the datasets should work (min v1.8.0 ideally)",
"Hi @margotwagner,\r\n\r\nYes, as @bhavitvyamalik has commented, this bug was fixed in `datasets` version 1.5.0. You need to update it, as your current version is 1.4.1:\r\n> Environment info\r\n> - `data... |
https://api.github.com/repos/huggingface/datasets/issues/1214 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1214/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1214/comments | https://api.github.com/repos/huggingface/datasets/issues/1214/events | https://github.com/huggingface/datasets/pull/1214 | 758,002,786 | MDExOlB1bGxSZXF1ZXN0NTMzMjUyNTgx | 1,214 | adding medical-questions-pairs dataset | [] | closed | false | null | 0 | 2020-12-06T19:30:12Z | 2020-12-09T14:42:53Z | 2020-12-09T14:42:53Z | null | This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors.
Dataset : https://github.com/curai/medical-question-pair-dataset
Paper : https://drive.google.com/file/d/1CHPGBXkvZuZc8hpr46HeHU6U6jnVze-s/view | {
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https://api.github.com/repos/huggingface/datasets/issues/4158 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4158/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4158/comments | https://api.github.com/repos/huggingface/datasets/issues/4158/events | https://github.com/huggingface/datasets/pull/4158 | 1,202,376,843 | PR_kwDODunzps42ITg3 | 4,158 | Add AUC ROC Metric | [] | closed | false | null | 1 | 2022-04-12T20:53:28Z | 2022-04-26T19:41:50Z | 2022-04-26T19:35:22Z | null | null | {
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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/101 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/101/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/101/comments | https://api.github.com/repos/huggingface/datasets/issues/101/events | https://github.com/huggingface/datasets/pull/101 | 618,111,651 | MDExOlB1bGxSZXF1ZXN0NDE3ODk5OTQ2 | 101 | [Reddit] add reddit | [] | closed | false | null | 0 | 2020-05-14T10:25:02Z | 2020-05-14T10:27:25Z | 2020-05-14T10:27:24Z | null | - Everything worked fine @mariamabarham. Made my computer nearly crash, but all seems to be working :-) | {
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https://api.github.com/repos/huggingface/datasets/issues/1736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1736/comments | https://api.github.com/repos/huggingface/datasets/issues/1736/events | https://github.com/huggingface/datasets/pull/1736 | 785,433,854 | MDExOlB1bGxSZXF1ZXN0NTU0NDYyNjYw | 1,736 | Adjust BrWaC dataset features name | [] | closed | false | null | 0 | 2021-01-13T20:39:04Z | 2021-01-14T10:29:38Z | 2021-01-14T10:29:38Z | null | I added this dataset some days ago, and today I used it to train some models and realized that the names of the features aren't so good.
Looking at the current features hierarchy, we have "paragraphs" with a list of "sentences" with a list of "sentences?!". But the actual hierarchy is a "text" with a list of "paragraphs" with a list of "sentences".
I confused myself trying to use the dataset with these names. So I think it's better to change it. | {
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https://api.github.com/repos/huggingface/datasets/issues/1952 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1952/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1952/comments | https://api.github.com/repos/huggingface/datasets/issues/1952/events | https://github.com/huggingface/datasets/pull/1952 | 817,428,160 | MDExOlB1bGxSZXF1ZXN0NTgwOTIyNjQw | 1,952 | Handle timeouts | [] | closed | false | null | 4 | 2021-02-26T15:02:07Z | 2021-03-01T14:29:24Z | 2021-03-01T14:29:24Z | null | As noticed in https://github.com/huggingface/datasets/issues/1939, timeouts were not properly handled when loading a dataset.
This caused the connection to hang indefinitely when working in a firewalled environment cc @stas00
I added a default timeout, and included an option to our offline environment for tests to be able to simulate both connection errors and timeout errors (previously it was simulating connection errors only).
Now networks calls don't hang indefinitely.
The default timeout is set to 10sec (we might reduce it). | {
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"I never said the calls were hanging indefinitely, what we need is quite different - in the firewalled env with a network, there should be no network calls or they should fail instantly.\r\n\r\nTo make this work I suppose on top of this PR we need:\r\n1. `DATASETS_OFFLINE` env var to force set timeout to 0 globally... |
https://api.github.com/repos/huggingface/datasets/issues/1289 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1289/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1289/comments | https://api.github.com/repos/huggingface/datasets/issues/1289/events | https://github.com/huggingface/datasets/pull/1289 | 759,333,684 | MDExOlB1bGxSZXF1ZXN0NTM0MzU2ODUw | 1,289 | Jigsaw toxicity classification dataset added | [] | closed | false | null | 0 | 2020-12-08T10:38:51Z | 2020-12-08T15:17:48Z | 2020-12-08T15:17:48Z | null | The dataset requires manually downloading data from Kaggle. | {
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https://api.github.com/repos/huggingface/datasets/issues/5869 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5869/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5869/comments | https://api.github.com/repos/huggingface/datasets/issues/5869/events | https://github.com/huggingface/datasets/issues/5869 | 1,711,990,003 | I_kwDODunzps5mCuTz | 5,869 | Image Encoding Issue when submitting a Parquet Dataset | [
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] | closed | false | null | 16 | 2023-05-16T09:42:58Z | 2023-06-16T12:48:38Z | 2023-06-16T09:30:48Z | null | ### Describe the bug
Hello,
I'd like to report an issue related to pushing a dataset represented as a Parquet file to a dataset repository using Dask. Here are the details:
We attempted to load an example dataset in Parquet format from the Hugging Face (HF) filesystem using Dask with the following code snippet:
```
import dask.dataframe as dd
df = dd.read_parquet("hf://datasets/lambdalabs/pokemon-blip-captions",index=False)
```
In this dataset, the "image" column is represented as a dictionary/struct with the format:
```
df = df.compute()
df["image"].iloc[0].keys()
-> dict_keys(['bytes', 'path'])
```
I think this is the format encoded by the [`Image`](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Image) feature extractor from datasets to format suitable for Arrow.
The next step was to push the dataset to a repository that I created:
```
dd.to_parquet(dask_df, path = "hf://datasets/philippemo/dummy_dataset/data")
```
However, after pushing the dataset using Dask, the "image" column is now represented as the encoded dictionary `(['bytes', 'path'])`, and the images are not properly visualized. You can find the dataset here: [Link to the problematic dataset](https://huggingface.co/datasets/philippemo/dummy_dataset).
It's worth noting that both the original dataset and the one submitted with Dask have the same schema with minor alterations related to metadata:
**[ Schema of original dummy example.](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/blob/main/data/train-00000-of-00001-566cc9b19d7203f8.parquet)**
```
image: struct<bytes: binary, path: null>
child 0, bytes: binary
child 1, path: null
text: string
```
**[ Schema of pushed dataset with dask](https://huggingface.co/datasets/philippemo/dummy_dataset/blob/main/data/part.0.parquet)**
```
image: struct<bytes: binary, path: null>
child 0, bytes: binary
child 1, path: null
text: string
```
This issue seems to be related to an encoding type that occurs when pushing a model to the hub. Normally, models should be represented as an HF dataset before pushing, but we are working with an example where we need to push large datasets using Dask.
Could you please provide clarification on how to resolve this issue?
Thank you!
### Reproduction
To get the schema I downloaded the parquet files and used pyarrow.parquet to read the schema
```
import pyarrow.parquet
pyarrow.parquet.read_schema(<path_to_parquet>, memory_map=True)
```
### Logs
_No response_
### System info
```shell
- huggingface_hub version: 0.14.1
- Platform: Linux-5.19.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /home/philippe/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: philippemo
- Configured git credential helpers: cache
- FastAI: N/A
- Tensorflow: N/A
- Torch: N/A
- Jinja2: 3.1.2
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.4.0
- hf_transfer: N/A
- gradio: N/A
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /home/philippe/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /home/philippe/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/philippe/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
```
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"Hi @PhilippeMoussalli thanks for opening a detailed issue. It seems the issue is more related to the `datasets` library so I'll ping @lhoestq @mariosasko on this one :) \n\n(edit: also can one of you move the issue to the datasets repo? Thanks in advance 🙏)",
"Hi ! The `Image()` info is stored in the **schema m... |
https://api.github.com/repos/huggingface/datasets/issues/4262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4262/comments | https://api.github.com/repos/huggingface/datasets/issues/4262/events | https://github.com/huggingface/datasets/pull/4262 | 1,222,130,749 | PR_kwDODunzps43IOye | 4,262 | Add YAML tags to Dataset Card rotten tomatoes | [] | closed | false | null | 1 | 2022-05-01T11:59:08Z | 2022-05-03T14:27:33Z | 2022-05-03T14:20:35Z | null | The dataset card for the rotten tomatoes / MR movie review dataset had some missing YAML tags. Hopefully, this also improves the visibility of this dataset now that paperswithcode and huggingface link to eachother. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/5981 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5981/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5981/comments | https://api.github.com/repos/huggingface/datasets/issues/5981/events | https://github.com/huggingface/datasets/issues/5981 | 1,770,310,087 | I_kwDODunzps5phMnH | 5,981 | Only two cores are getting used in sagemaker with pytorch 3.10 kernel | [] | closed | false | null | 3 | 2023-06-22T19:57:31Z | 2023-07-24T11:54:52Z | 2023-07-24T11:54:52Z | null | ### Describe the bug
When using the newer pytorch 3.10 kernel, only 2 cores are being used by huggingface filter and map functions. The Pytorch 3.9 kernel would use as many cores as specified in the num_proc field.
We have solved this in our own code by placing the following snippet in the code that is called inside subprocesses:
```os.sched_setaffinity(0, {i for i in range(1000)})```
The problem, as near as we can tell, us that once upon a time, cpu affinity was set using a bitmask ("0xfffff" and the like), and affinity recently changed to a list of processors rather than to using the mask. As such, only processors 1 and 17 are shown to be working in htop.
When running functions via `map`, the above resetting of affinity works to spread across the cores. When using `filter`, however, only two cores are active.
### Steps to reproduce the bug
Repro steps:
1. Create an aws sagemaker instance
2. use the pytorch 3_10 kernel
3. Load a dataset
4. run a filter operation
5. watch as only 2 cores are used when num_proc > 2
6. run a map operation
7. watch as only 2 cores are used when num_proc > 2
8. run a map operation with processor affinity reset inside the function called via map
9. Watch as all cores run
### Expected behavior
All specified cores are used via the num_proc argument.
### Environment info
AWS sagemaker with the following init script run in the terminal after instance creation:
conda init bash
bash
conda activate pytorch_p310
pip install Wand PyPDF pytesseract datasets seqeval pdfplumber transformers pymupdf sentencepiece timm donut-python accelerate optimum xgboost
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
sudo yum -y install htop
sudo yum -y update
sudo yum -y install wget libstdc++ autoconf automake libtool autoconf-archive pkg-config gcc gcc-c++ make libjpeg-devel libpng-devel libtiff-devel zlib-devel | {
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"I think it's more likely that this issue is related to PyTorch than Datasets, as PyTorch (on import) registers functions to execute when forking a process. Maybe this is the culprit: https://github.com/pytorch/pytorch/issues/99625",
"From reading that ticket, it may be down in mkl? Is it worth hotfixing in the ... |
https://api.github.com/repos/huggingface/datasets/issues/2790 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2790/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2790/comments | https://api.github.com/repos/huggingface/datasets/issues/2790/events | https://github.com/huggingface/datasets/pull/2790 | 967,772,181 | MDExOlB1bGxSZXF1ZXN0NzA5OTI3NjM2 | 2,790 | Fix typo in test_dataset_common | [] | closed | false | null | 0 | 2021-08-12T01:10:29Z | 2021-08-12T11:31:29Z | 2021-08-12T11:31:29Z | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5322 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5322/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5322/comments | https://api.github.com/repos/huggingface/datasets/issues/5322/events | https://github.com/huggingface/datasets/pull/5322 | 1,471,502,162 | PR_kwDODunzps5EEeQP | 5,322 | Raise error for `.tar` archives in the same way as for `.tar.gz` and `.tgz` in `_get_extraction_protocol` | [] | closed | false | null | 1 | 2022-12-01T15:19:28Z | 2022-12-14T16:37:16Z | 2022-12-14T16:33:30Z | null | Currently `download_and_extract` doesn't throw an error when it is used with files with `.tar` extension in streaming mode because `_get_extraction_protocol` doesn't do it (like it does for `tar.gz` and `tgz`). `_get_extraction_protocol` returns formatted url as if we support tar protocol but we don't.
That means that in dataset scripts `.tar` files would be attempted to load and fail during examples generation (after `download_and_extract` execution). So this PR raises error for `tar` files too.
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] |
https://api.github.com/repos/huggingface/datasets/issues/2968 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2968/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2968/comments | https://api.github.com/repos/huggingface/datasets/issues/2968/events | https://github.com/huggingface/datasets/issues/2968 | 1,007,209,488 | I_kwDODunzps48CMwQ | 2,968 | `DatasetDict` cannot be exported to parquet if the splits have different features | [
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] | closed | false | null | 9 | 2021-09-25T22:18:39Z | 2021-10-07T22:47:42Z | 2021-10-07T22:47:26Z | null | ## Describe the bug
I'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly.
For `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.
## Steps to reproduce the bug
The following works as expected:
```python
from datasets import load_dataset
ds = load_dataset("lhoestq/custom_squad")
ds['train'].to_parquet("./ds/train/split.parquet")
ds['validation'].to_parquet("./ds/validation/split.parquet")
brand_new_dataset = load_dataset("ds")
```
Modifying a single split to add a new feature ends up in a crash:
```python
from datasets import load_dataset
ds = load_dataset("lhoestq/custom_squad")
def identical_answers(e):
e['identical_answers'] = len(set(e['answers']['text'])) == 1
return e
ds['validation'] = ds['validation'].map(identical_answers)
ds['train'].to_parquet("./ds/train/split.parquet")
ds['validation'].to_parquet("./ds/validation/split.parquet")
brand_new_dataset = load_dataset("ds")
```
```
File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 26, in <module>
brand_new_dataset = load_dataset("ds")
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1151, in load_dataset
builder_instance.download_and_prepare(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 642, in download_and_prepare
self._download_and_prepare(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 732, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 1194, in _prepare_split
writer.write_table(table)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in <listcomp>
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1257, in pyarrow.lib.Table.__getitem__
File "pyarrow/table.pxi", line 1833, in pyarrow.lib.Table.column
File "pyarrow/table.pxi", line 1808, in pyarrow.lib.Table._ensure_integer_index
KeyError: 'Field "identical_answers" does not exist in table schema'
```
It does work, however, to use the `save_to_disk` and `load_from_disk` methods:
```py
from datasets import load_from_disk
ds = load_dataset("lhoestq/custom_squad")
def identical_answers(e):
e['identical_answers'] = len(set(e['answers']['text'])) == 1
return e
ds['validation'] = ds['validation'].map(identical_answers)
ds.save_to_disk("local_path")
brand_new_dataset = load_from_disk("local_path")
```
## Expected results
The saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.
If it's helpful, I've traced a possible patch to the `write_table` method here:
https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L424-L425
The writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:
https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L254-L255
but the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:
https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/builder.py#L1190
Updating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:
```
File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 27, in <module>
brand_new_dataset = load_dataset("ds")
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1163, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 819, in as_dataset
datasets = utils.map_nested(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 207, in map_nested
mapped = [
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 208, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 143, in _single_map_nested
return function(data_struct)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 850, in _build_single_dataset
ds = self._as_dataset(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 920, in _as_dataset
dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 217, in read
return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 238, in read_files
pa_table = self._read_files(files, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 173, in _read_files
pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 308, in _get_table_from_filename
table = ArrowReader.read_table(filename, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 327, in read_table
return table_cls.from_file(filename)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 458, in from_file
table = _memory_mapped_arrow_table_from_file(filename)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 45, in _memory_mapped_arrow_table_from_file
pa_table = opened_stream.read_all()
File "pyarrow/ipc.pxi", line 563, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow/error.pxi", line 114, in pyarrow.lib.check_status
OSError: Header-type of flatbuffer-encoded Message is not RecordBatch.
```
## Environment info
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
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"This is because you have to specify which split corresponds to what file:\r\n```python\r\ndata_files = {\"train\": \"train/split.parquet\", \"validation\": \"validation/split.parquet\"}\r\nbrand_new_dataset_2 = load_dataset(\"ds\", data_files=data_files)\r\n```\r\n\r\nOtherwise it tries to concatenate the two spli... |
https://api.github.com/repos/huggingface/datasets/issues/3813 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3813/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3813/comments | https://api.github.com/repos/huggingface/datasets/issues/3813/events | https://github.com/huggingface/datasets/issues/3813 | 1,158,474,859 | I_kwDODunzps5FDOxr | 3,813 | Add MetaShift dataset | [
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"description": "Requesting to add a new dataset",
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"name": "dataset request",
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"color": "bfdadc",... | closed | false | null | 7 | 2022-03-03T14:26:45Z | 2022-04-10T13:39:59Z | 2022-04-10T13:39:59Z | null | ## Adding a Dataset
- **Name:** MetaShift
- **Description:** collection of 12,868 sets of natural images across 410 classes-
- **Paper:** https://arxiv.org/abs/2202.06523v1
- **Data:** https://github.com/weixin-liang/metashift
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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"I would like to take this up and give it a shot. Any image specific - dataset guidelines to keep in mind ? Thank you.",
"#self-assign",
"I've started working on adding this dataset. I require some inputs on the following : \r\n\r\nRef for the initial draft [here](https://github.com/dnaveenr/datasets/blob/add_m... |
https://api.github.com/repos/huggingface/datasets/issues/5258 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5258/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5258/comments | https://api.github.com/repos/huggingface/datasets/issues/5258/events | https://github.com/huggingface/datasets/issues/5258 | 1,453,516,636 | I_kwDODunzps5Woudc | 5,258 | Restore order of split names in dataset_info for canonical datasets | [
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"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20contribution"
}
] | closed | false | null | 3 | 2022-11-17T15:13:15Z | 2023-02-16T09:49:05Z | 2022-11-19T06:51:37Z | null | After a bulk edit of canonical datasets to create the YAML `dataset_info` metadata, the split names were accidentally sorted alphabetically. See for example:
- https://huggingface.co/datasets/bc2gm_corpus/commit/2384629484401ecf4bb77cd808816719c424e57c
Note that this order is the one appearing in the preview of the datasets.
I'm making a bulk edit to align the order of the splits appearing in the metadata info with the order appearing in the loading script.
Related to:
- #5202 | {
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"The bulk edit is running...\r\n\r\nSee for example: \r\n- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2\r\n- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1",
"TODO: Add \"dataset_info\" YAML metadata to:\r\n- [x] \"chr_en\" has no metadata JSON fil... |
https://api.github.com/repos/huggingface/datasets/issues/1006 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1006/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1006/comments | https://api.github.com/repos/huggingface/datasets/issues/1006/events | https://github.com/huggingface/datasets/pull/1006 | 755,362,766 | MDExOlB1bGxSZXF1ZXN0NTMxMDg3NTIy | 1,006 | add yahoo_answers_topics | [] | closed | false | null | 1 | 2020-12-02T15:16:13Z | 2020-12-03T16:44:38Z | 2020-12-02T18:01:32Z | null | This PR adds yahoo answers topic classification dataset.
More info:
https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset
cc @joeddav, @yjernite | {
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"feel free to merge/ping me to merge if there're no more changes to do"
] |
https://api.github.com/repos/huggingface/datasets/issues/2421 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2421/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2421/comments | https://api.github.com/repos/huggingface/datasets/issues/2421/events | https://github.com/huggingface/datasets/pull/2421 | 905,549,756 | MDExOlB1bGxSZXF1ZXN0NjU2NjIwMTM3 | 2,421 | doc: fix typo HF_MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES | [] | closed | false | null | 0 | 2021-05-28T14:52:10Z | 2021-06-04T09:52:45Z | 2021-06-04T09:52:45Z | null | MAX_MEMORY_DATASET_SIZE_IN_BYTES should be HF_MAX_MEMORY_DATASET_SIZE_IN_BYTES | {
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https://api.github.com/repos/huggingface/datasets/issues/4889 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4889/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4889/comments | https://api.github.com/repos/huggingface/datasets/issues/4889/events | https://github.com/huggingface/datasets/issues/4889 | 1,349,758,525 | I_kwDODunzps5Qc649 | 4,889 | torchaudio 11.0 yields different results than torchaudio 12.1 when loading MP3 | [
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] | closed | false | null | 5 | 2022-08-24T16:54:43Z | 2023-03-02T15:33:05Z | 2023-03-02T15:33:04Z | null | ## Describe the bug
When loading Common Voice with torchaudio 0.11.0 the results are different to 0.12.1 which leads to problems in transformers see: https://github.com/huggingface/transformers/pull/18749
## Steps to reproduce the bug
If you run the following code once with `torchaudio==0.11.0+cu102` and `torchaudio==0.12.1+cu102` you can see that the tensors differ. This is a pretty big breaking change and makes some integration tests fail in Transformers.
```python
#!/usr/bin/env python3
from datasets import load_dataset
import datasets
import numpy as np
import torch
import torchaudio
print("torch vesion", torch.__version__)
print("torchaudio vesion", torchaudio.__version__)
save_audio = True
load_audios = False
if save_audio:
ds = load_dataset("common_voice", "en", split="train", streaming=True)
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
ds_iter = iter(ds)
sample = next(ds_iter)
np.save(f"audio_sample_{torch.__version__}", sample["audio"]["array"])
print(sample["audio"]["array"])
if load_audios:
array_torch_11 = np.load("/home/patrick/audio_sample_1.11.0+cu102.npy")
print("Array 11 Shape", array_torch_11.shape)
print("Array 11 abs sum", np.sum(np.abs(array_torch_11)))
array_torch_12 = np.load("/home/patrick/audio_sample_1.12.1+cu102.npy")
print("Array 12 Shape", array_torch_12.shape)
print("Array 12 abs sum", np.sum(np.abs(array_torch_12)))
```
Having saved the tensors the print output yields:
```
torch vesion 1.12.1+cu102
torchaudio vesion 0.12.1+cu102
Array 11 Shape (122880,)
Array 11 abs sum 1396.4988
Array 12 Shape (123264,)
Array 12 abs sum 1396.5193
```
## Expected results
torchaudio 11.0 and 12.1 should yield same results.
## Actual results
See above.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.1.dev0
- Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.34
- Python version: 3.9.7
- PyArrow version: 6.0.1
- Pandas version: 1.4.2
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} | https://api.github.com/repos/huggingface/datasets/issues/4889/timeline | null | completed | null | null | false | [
"Maybe we can just pass this along to torchaudio @lhoestq @albertvillanova ? It be great if you could investigate if the errors lies in datasets or in torchaudio.",
"torchaudio did a change in [0.12](https://github.com/pytorch/audio/releases/tag/v0.12.0) on MP3 decoding (which affects common voice):\r\n> MP3 deco... |
https://api.github.com/repos/huggingface/datasets/issues/4259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4259/comments | https://api.github.com/repos/huggingface/datasets/issues/4259/events | https://github.com/huggingface/datasets/pull/4259 | 1,221,768,025 | PR_kwDODunzps43HHGc | 4,259 | Fix bug in choices labels in openbookqa dataset | [] | closed | false | null | 1 | 2022-04-30T07:41:39Z | 2022-05-04T06:31:31Z | 2022-05-03T15:14:21Z | null | This PR fixes the Bug in the openbookqa dataset as mentioned in this issue #3550.
Fix #3550.
cc. @lhoestq @mariosasko | {
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https://api.github.com/repos/huggingface/datasets/issues/109 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/109/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/109/comments | https://api.github.com/repos/huggingface/datasets/issues/109/events | https://github.com/huggingface/datasets/pull/109 | 618,508,359 | MDExOlB1bGxSZXF1ZXN0NDE4MjI0MDYw | 109 | [Reclor] fix reclor | [] | closed | false | null | 0 | 2020-05-14T20:16:26Z | 2020-05-14T20:19:09Z | 2020-05-14T20:19:08Z | null | - That's probably one me. Could have made the manual data test more flexible. @mariamabarham | {
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https://api.github.com/repos/huggingface/datasets/issues/1564 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1564/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1564/comments | https://api.github.com/repos/huggingface/datasets/issues/1564/events | https://github.com/huggingface/datasets/pull/1564 | 766,266,609 | MDExOlB1bGxSZXF1ZXN0NTM5MzQzMjAy | 1,564 | added saudinewsnet | [] | closed | false | null | 9 | 2020-12-14T10:35:09Z | 2020-12-22T09:51:04Z | 2020-12-22T09:51:04Z | null | I'm having issues in creating the dummy data. I'm still investigating how to fix it. I'll close the PR if I couldn't find a solution | {
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"Hi @abdulelahsm - This is an interesting dataset! But there are multiple issues with the PR. Some of them are listed below: \r\n- default builder config is not defined. There should be atleast one builder config \r\n- URL is incorrectly constructed so the data files are not being downloaded \r\n- dataset_info.jso... |
https://api.github.com/repos/huggingface/datasets/issues/1026 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1026/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1026/comments | https://api.github.com/repos/huggingface/datasets/issues/1026/events | https://github.com/huggingface/datasets/issues/1026 | 755,689,195 | MDU6SXNzdWU3NTU2ODkxOTU= | 1,026 | Lío o | [] | closed | false | null | 0 | 2020-12-02T23:32:25Z | 2020-12-03T16:42:47Z | 2020-12-03T16:42:47Z | null | ````l`````````
```
O
```
`````
Ño
```
````
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5666 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5666/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5666/comments | https://api.github.com/repos/huggingface/datasets/issues/5666/events | https://github.com/huggingface/datasets/issues/5666 | 1,637,675,062 | I_kwDODunzps5hnPA2 | 5,666 | Support tensorflow 2.12.0 in CI | [
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] | closed | false | null | 0 | 2023-03-23T14:37:51Z | 2023-03-23T16:14:54Z | 2023-03-23T16:14:54Z | null | Once we find out the root cause of:
- #5663
we should revert the temporary pin on tensorflow introduced by:
- #5664 | {
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https://api.github.com/repos/huggingface/datasets/issues/1831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1831/comments | https://api.github.com/repos/huggingface/datasets/issues/1831/events | https://github.com/huggingface/datasets/issues/1831 | 802,868,854 | MDU6SXNzdWU4MDI4Njg4NTQ= | 1,831 | Some question about raw dataset download info in the project . | [] | closed | false | null | 4 | 2021-02-07T05:33:36Z | 2021-02-25T14:10:18Z | 2021-02-25T14:10:18Z | null | Hi , i review the code in
https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py
in the _split_generators function is the truly logic of download raw datasets with dl_manager
and use Conll2003 cls by use import_main_class in load_dataset function
My question is that , with this logic it seems that i can not have the raw dataset download location
in variable in downloaded_files in _split_generators.
If someone also want use huggingface datasets as raw dataset downloader,
how can he retrieve the raw dataset download path from attributes in
datasets.dataset_dict.DatasetDict ? | {
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"Hi ! The `dl_manager` is a `DownloadManager` object and is responsible for downloading the raw data files.\r\nIt is used by dataset builders in their `_split_generators` method to download the raw data files that are necessary to build the datasets splits.\r\n\r\nThe `Conll2003` class is a dataset builder, and so ... |
https://api.github.com/repos/huggingface/datasets/issues/1635 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1635/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1635/comments | https://api.github.com/repos/huggingface/datasets/issues/1635/events | https://github.com/huggingface/datasets/issues/1635 | 774,524,492 | MDU6SXNzdWU3NzQ1MjQ0OTI= | 1,635 | Persian Abstractive/Extractive Text Summarization | [
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] | closed | false | null | 0 | 2020-12-24T17:47:12Z | 2021-01-04T15:11:04Z | 2021-01-04T15:11:04Z | null | Assembling datasets tailored to different tasks and languages is a precious target. This would be great to have this dataset included.
## Adding a Dataset
- **Name:** *pn-summary*
- **Description:** *A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.*
- **Paper:** *https://arxiv.org/abs/2012.11204*
- **Data:** *https://github.com/hooshvare/pn-summary/#download*
- **Motivation:** *It is the first Persian abstractive/extractive Text summarization dataset (like cnn_dailymail for English)!*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| {
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https://api.github.com/repos/huggingface/datasets/issues/2942 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2942/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2942/comments | https://api.github.com/repos/huggingface/datasets/issues/2942/events | https://github.com/huggingface/datasets/pull/2942 | 1,000,309,765 | PR_kwDODunzps4r7tY6 | 2,942 | Add SEDE dataset | [] | closed | false | null | 4 | 2021-09-19T13:11:24Z | 2021-09-24T10:39:55Z | 2021-09-24T10:39:54Z | null | This PR adds the SEDE dataset for the task of realistic Text-to-SQL, following the instructions of how to add a database and a dataset card.
Please see our paper for more details: https://arxiv.org/abs/2106.05006 | {
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"Thanks @albertvillanova for your great suggestions! I just pushed a new commit with the necessary fixes. For some reason, the test `test_metric_common` failed for `meteor` metric, which doesn't have any connection to this PR, so I'm trying to rebase and see if it helps.",
"Hi @Hazoom,\r\n\r\nYou were right: the ... |
https://api.github.com/repos/huggingface/datasets/issues/1521 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1521/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1521/comments | https://api.github.com/repos/huggingface/datasets/issues/1521/events | https://github.com/huggingface/datasets/pull/1521 | 764,320,841 | MDExOlB1bGxSZXF1ZXN0NTM4NDQzOTgz | 1,521 | Atomic | [] | closed | false | null | 1 | 2020-12-12T20:18:08Z | 2020-12-12T22:56:48Z | 2020-12-12T22:56:48Z | null | This is the ATOMIC common sense dataset. More info can be found here:
* README.md still to be created. | {
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"I had to create a new PR to fix git errors. See: https://github.com/huggingface/datasets/pull/1525\r\n\r\nI'm closing this PR. "
] |
https://api.github.com/repos/huggingface/datasets/issues/542 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/542/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/542/comments | https://api.github.com/repos/huggingface/datasets/issues/542/events | https://github.com/huggingface/datasets/pull/542 | 688,555,036 | MDExOlB1bGxSZXF1ZXN0NDc1NzkyNTY0 | 542 | Add TensorFlow example | [] | closed | false | null | 0 | 2020-08-29T15:39:27Z | 2020-08-31T09:49:20Z | 2020-08-31T09:49:19Z | null | Update the Quick Tour documentation in order to add the TensorFlow equivalent source code for the classification example. Now it is possible to select either the code in PyTorch or in TensorFlow in the Quick tour. | {
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https://api.github.com/repos/huggingface/datasets/issues/4385 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4385/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4385/comments | https://api.github.com/repos/huggingface/datasets/issues/4385/events | https://github.com/huggingface/datasets/pull/4385 | 1,243,921,287 | PR_kwDODunzps44OwXF | 4,385 | Test dill | [] | closed | false | null | 4 | 2022-05-21T08:57:43Z | 2022-05-25T08:30:13Z | 2022-05-25T08:21:48Z | null | Regression test for future releases of `dill`.
Related to #4379. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I should point out that the hash will be the same if computed twice with the same code on the same version of dill (after adding huggingface's code that removes line numbers and file names, and sorts globals.) My changes in dill 0.3.... |
https://api.github.com/repos/huggingface/datasets/issues/1436 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1436/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1436/comments | https://api.github.com/repos/huggingface/datasets/issues/1436/events | https://github.com/huggingface/datasets/pull/1436 | 760,873,132 | MDExOlB1bGxSZXF1ZXN0NTM1NjI1MDM0 | 1,436 | add ALT | [] | closed | false | null | 1 | 2020-12-10T04:17:21Z | 2020-12-13T16:14:18Z | 2020-12-11T15:52:41Z | null | ALT dataset -- https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ | {
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"The errors in de CI are fixed on master so it's fine"
] |
https://api.github.com/repos/huggingface/datasets/issues/2690 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2690/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2690/comments | https://api.github.com/repos/huggingface/datasets/issues/2690/events | https://github.com/huggingface/datasets/pull/2690 | 949,574,500 | MDExOlB1bGxSZXF1ZXN0Njk0MjU5MDc1 | 2,690 | Docs details | [] | closed | false | null | 1 | 2021-07-21T10:43:14Z | 2021-07-27T18:40:54Z | 2021-07-27T18:40:54Z | null | Some comments here:
- the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch 'datasets[streaming]'` (maybe just link to https://huggingface.co/docs/datasets/installation.html + a one-liner that installs all the requirements / alternatively a requirements.txt file)
- "If you’d like to play with the examples, you must install it from source." in https://huggingface.co/docs/datasets/installation.html: it's not clear to me what this means (what are these "examples"?)
- in https://huggingface.co/docs/datasets/loading_datasets.html: "or AWS bucket if it’s not already stored in the library". It's the only place in the doc (aside from the docstring https://huggingface.co/docs/datasets/package_reference/loading_methods.html?highlight=aws bucket#datasets.list_datasets) where the "AWS bucket" is mentioned. It's not easy to understand what this means. Maybe explain more, and link to https://s3.amazonaws.com/datasets.huggingface.co and/or https://huggingface.co/docs/datasets/filesystems.html.
- example in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files is obsoleted by https://github.com/huggingface/datasets/pull/2326. Also: see https://github.com/huggingface/datasets/issues/2691 for a bug on this specific dataset.
- in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files the doc says "After you’ve downloaded the files, you can point to the folder hosting them locally with the data_dir argument as follows:", but the following example does not show how to use `data_dir`
- in https://huggingface.co/docs/datasets/loading_datasets.html#csv-files, it would be nice to have an URL to the csv loader reference (but I'm not sure there is one in the API reference). This comment applies in many places in the doc: I would want the API reference to contain doc for all the code/functions/classes... and I would want a lot more links inside the doc pointing to the API entries.
- in the API reference (docstrings) I would prefer "SOURCE" to link to github instead of a copy of the code inside the docs site (eg. https://github.com/huggingface/datasets/blob/master/src/datasets/load.py#L711 instead of https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset)
- it seems like not all the API is exposed in the doc. For example, there is no doc for [`disable_progress_bar`](https://github.com/huggingface/datasets/search?q=disable_progress_bar), see https://huggingface.co/docs/datasets/search.html?q=disable_progress_bar, even if the code contains docstrings. Does it mean that the function is not officially supported? (otherwise, maybe it also deserves a mention in https://huggingface.co/docs/datasets/package_reference/logging_methods.html)
- in https://huggingface.co/docs/datasets/loading_datasets.html?highlight=most%20efficient%20format%20have%20json%20files%20consisting%20multiple%20json%20objects#json-files, "The most efficient format is to have JSON files consisting of multiple JSON objects, one per line, representing individual data rows:", maybe link to https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON and give it a name ("line-delimited JSON"? "JSON Lines" as in https://huggingface.co/docs/datasets/processing.html#exporting-a-dataset-to-csv-json-parquet-or-to-python-objects ?)
- in https://huggingface.co/docs/datasets/loading_datasets.html, for the local files sections, it would be nice to provide sample csv / json / text files to download, so that it's easier for the reader to try to load them (instead: they won't try)
- the doc explains how to shard a dataset, but does not explain why and when a dataset should be sharded (I have no idea... for [parallelizing](https://huggingface.co/docs/datasets/processing.html#multiprocessing)?). It does neither give an idea of the number of shards a dataset typically should have and why.
- the code example in https://huggingface.co/docs/datasets/processing.html#mapping-in-a-distributed-setting does not work, because `training_args` has not been defined before in the doc. | {
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"Thanks for all the comments and for the corrections in the docs !\r\n\r\nAbout all the points you mentioned:\r\n\r\n> * the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch ... |
https://api.github.com/repos/huggingface/datasets/issues/2727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2727/comments | https://api.github.com/repos/huggingface/datasets/issues/2727/events | https://github.com/huggingface/datasets/issues/2727 | 955,812,149 | MDU6SXNzdWU5NTU4MTIxNDk= | 2,727 | Error in loading the Arabic Billion Words Corpus | [
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] | closed | false | null | 2 | 2021-07-29T12:53:09Z | 2021-07-30T13:03:55Z | 2021-07-30T13:03:55Z | null | ## Describe the bug
I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.
## Steps to reproduce the bug
```python
load_dataset("arabic_billion_words", "Techreen")
load_dataset("arabic_billion_words", "Almustaqbal")
```
## Expected results
The datasets load succefully.
## Actual results
```python
_extract_tags(self, sample, tag)
139 if len(out) > 0:
140 break
--> 141 return out[0]
142
143 def _clean_text(self, text):
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.7.11
- PyArrow version: 3.0.0
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"I modified the dataset loading script to catch the `IndexError` and inspect the records at which the error is happening, and I found this:\r\nFor the `Techreen` config, the error happens in 36 records when trying to find the `Text` or `Dateline` tags. All these 36 records look something like:\r\n```\r\n<Techreen>\... |
https://api.github.com/repos/huggingface/datasets/issues/4503 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4503/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4503/comments | https://api.github.com/repos/huggingface/datasets/issues/4503/events | https://github.com/huggingface/datasets/pull/4503 | 1,272,367,055 | PR_kwDODunzps45twLR | 4,503 | Refactor and add metadata to fever dataset | [] | closed | false | null | 5 | 2022-06-15T14:59:47Z | 2022-07-06T11:54:15Z | 2022-07-06T11:41:30Z | null | Related to: #4452 and #3792. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"But this is somehow fever v3 dataset (see this link https://fever.ai/ under the dropdown menu called Datasets). Our fever dataset already contains v1 and v2 configs. Then, I added this as if v3 config (but named feverous instead of v... |
https://api.github.com/repos/huggingface/datasets/issues/1183 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1183/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1183/comments | https://api.github.com/repos/huggingface/datasets/issues/1183/events | https://github.com/huggingface/datasets/pull/1183 | 757,806,570 | MDExOlB1bGxSZXF1ZXN0NTMzMTEwOTY4 | 1,183 | add mkb dataset | [] | closed | false | null | 3 | 2020-12-05T23:44:33Z | 2020-12-09T09:38:50Z | 2020-12-09T09:38:50Z | null | This PR will add Mann Ki Baat dataset (parallel data for Indian languages). | {
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"Could you update the languages tags before we merge @VasudevGupta7 ?",
"done.",
"thanks !"
] |
https://api.github.com/repos/huggingface/datasets/issues/777 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/777/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/777/comments | https://api.github.com/repos/huggingface/datasets/issues/777/events | https://github.com/huggingface/datasets/pull/777 | 732,376,648 | MDExOlB1bGxSZXF1ZXN0NTEyMzI2ODM2 | 777 | Better error message for uninitialized metric | [] | closed | false | null | 0 | 2020-10-29T14:42:50Z | 2020-10-29T15:18:26Z | 2020-10-29T15:18:24Z | null | When calling `metric.compute()` without having called `metric.add` or `metric.add_batch` at least once, the error was quite cryptic. I added a better error message
Fix #729 | {
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https://api.github.com/repos/huggingface/datasets/issues/3595 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3595/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3595/comments | https://api.github.com/repos/huggingface/datasets/issues/3595/events | https://github.com/huggingface/datasets/pull/3595 | 1,107,260,527 | PR_kwDODunzps4xOIxH | 3,595 | Add ImageNet toy datasets from fastai | [
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] | closed | false | null | 1 | 2022-01-18T19:03:35Z | 2022-09-30T14:39:35Z | 2022-09-30T14:39:35Z | null | Adds the ImageNet toy datasets from FastAI: Imagenette, Imagewoof and Imagewang.
TODOs:
* [ ] add dummy data
* [ ] add dataset card
* [ ] generate `dataset_info.json` | {
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"Thanks for your contribution, @mariosasko. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us i... |
https://api.github.com/repos/huggingface/datasets/issues/5305 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5305/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5305/comments | https://api.github.com/repos/huggingface/datasets/issues/5305/events | https://github.com/huggingface/datasets/issues/5305 | 1,465,627,826 | I_kwDODunzps5XW7Sy | 5,305 | Dataset joelito/mc4_legal does not work with multiple files | [] | closed | false | null | 2 | 2022-11-28T00:16:16Z | 2022-11-28T07:22:42Z | 2022-11-28T07:22:42Z | null | ### Describe the bug
The dataset https://huggingface.co/datasets/joelito/mc4_legal works for languages like bg with a single data file, but not for languages with multiple files like de. It shows zero rows for the de dataset.
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main) [1]> python test_mc4_legal.py (debug)
Found cached dataset mc4_legal (/Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/de/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f)
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 0
})
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main)> python test_mc4_legal.py (debug)
Downloading and preparing dataset mc4_legal/bg to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1240.55it/s]
Dataset mc4_legal downloaded and prepared to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f. Subsequent calls will reuse this data.
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 204
})
### Steps to reproduce the bug
import datasets
from datasets import load_dataset, get_dataset_config_names
language = "bg"
test = load_dataset("joelito/mc4_legal", language, split='train')
### Expected behavior
It should display the correct number of rows for the de dataset which should be a large number (thousands or more).
### Environment info
Package Version
------------------------ --------------
absl-py 1.3.0
aiohttp 3.8.1
aiosignal 1.2.0
astunparse 1.6.3
async-timeout 4.0.2
attrs 22.1.0
beautifulsoup4 4.11.1
blinker 1.4
blis 0.7.8
Bottleneck 1.3.4
brotlipy 0.7.0
cachetools 5.2.0
catalogue 2.0.7
certifi 2022.5.18.1
cffi 1.15.1
chardet 4.0.0
charset-normalizer 2.1.0
click 8.0.4
conllu 4.5.2
cryptography 38.0.1
cymem 2.0.6
datasets 2.6.1
dill 0.3.5.1
docker-pycreds 0.4.0
fasttext 0.9.2
fasttext-langdetect 1.0.3
filelock 3.0.12
flatbuffers 20210226132247
frozenlist 1.3.0
fsspec 2022.5.0
gast 0.4.0
gcloud 0.18.3
gitdb 4.0.9
GitPython 3.1.27
google-auth 2.9.0
google-auth-oauthlib 0.4.6
google-pasta 0.2.0
googleapis-common-protos 1.57.0
grpcio 1.47.0
h5py 3.7.0
httplib2 0.21.0
huggingface-hub 0.8.1
idna 3.4
importlib-metadata 4.12.0
Jinja2 3.1.2
joblib 1.0.1
keras 2.9.0
Keras-Preprocessing 1.1.2
langcodes 3.3.0
lxml 4.9.1
Markdown 3.3.7
MarkupSafe 2.1.1
mkl-fft 1.3.1
mkl-random 1.2.2
mkl-service 2.4.0
multidict 6.0.2
multiprocess 0.70.13
murmurhash 1.0.7
numexpr 2.8.1
numpy 1.22.3
oauth2client 4.1.3
oauthlib 3.2.1
opt-einsum 3.3.0
packaging 21.3
pandas 1.4.2
pathtools 0.1.2
pathy 0.6.1
pip 21.1.2
preshed 3.0.6
promise 2.3
protobuf 4.21.9
psutil 5.9.1
pyarrow 8.0.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pybind11 2.9.2
pycountry 22.3.5
pycparser 2.21
pydantic 1.8.2
PyJWT 2.4.0
pylzma 0.5.0
pyOpenSSL 22.0.0
pyparsing 3.0.4
PySocks 1.7.1
python-dateutil 2.8.2
pytz 2021.3
PyYAML 6.0
regex 2021.4.4
requests 2.28.1
requests-oauthlib 1.3.1
responses 0.18.0
rsa 4.8
sacremoses 0.0.45
scikit-learn 1.1.1
scipy 1.8.1
sentencepiece 0.1.96
sentry-sdk 1.6.0
setproctitle 1.2.3
setuptools 65.5.0
shortuuid 1.0.9
six 1.16.0
smart-open 5.2.1
smmap 5.0.0
soupsieve 2.3.2.post1
spacy 3.3.1
spacy-legacy 3.0.9
spacy-loggers 1.0.2
srsly 2.4.3
tabulate 0.8.9
tensorboard 2.9.1
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
tensorflow 2.9.1
tensorflow-estimator 2.9.0
termcolor 2.1.0
thinc 8.0.17
threadpoolctl 3.1.0
tokenizers 0.12.1
torch 1.13.0
tqdm 4.64.0
transformers 4.20.1
typer 0.4.1
typing-extensions 4.3.0
Unidecode 1.3.6
urllib3 1.26.12
wandb 0.12.20
wasabi 0.9.1
web-anno-tsv 0.0.1
Werkzeug 2.1.2
wget 3.2
wheel 0.35.1
wrapt 1.14.1
xxhash 3.0.0
yarl 1.8.1
zipp 3.8.0
Python 3.8.10
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"Thanks for reporting @JoelNiklaus.\r\n\r\nPlease note that since we moved all dataset loading scripts to the Hub, the issues and pull requests relative to specific datasets are directly handled on the Hub, in their Community tab. I'm transferring this issue there: https://huggingface.co/datasets/joelito/mc4_legal/... |
https://api.github.com/repos/huggingface/datasets/issues/5030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5030/comments | https://api.github.com/repos/huggingface/datasets/issues/5030/events | https://github.com/huggingface/datasets/pull/5030 | 1,388,061,340 | PR_kwDODunzps4_tfBO | 5,030 | Fast dataset iter | [] | closed | false | null | 2 | 2022-09-27T16:44:51Z | 2022-09-29T15:50:44Z | 2022-09-29T15:48:17Z | null | Use `pa.Table.to_reader` to make iteration over examples/batches faster in `Dataset.{__iter__, map}`
TODO:
* [x] benchmarking (the only benchmark for now - iterating over (single) examples of `bookcorpus` (75 mil examples) in Colab is approx. 2.3x faster)
* [x] check if iterating over bigger chunks + slicing to fetch individual examples in `_iter` yields better performance
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"_The documentation is not available anymore as the PR was closed or merged._",
"I ran some benchmarks (focused on the data fetching part of `__iter__`) and it seems like the combination `table.to_reader(batch_size)` + `RecordBatch.slice` performs the best ([script](https://gist.github.com/mariosasko/0248288a2e3a... |
https://api.github.com/repos/huggingface/datasets/issues/227 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/227/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/227/comments | https://api.github.com/repos/huggingface/datasets/issues/227/events | https://github.com/huggingface/datasets/issues/227 | 629,845,704 | MDU6SXNzdWU2Mjk4NDU3MDQ= | 227 | Should we still have to force to install apache_beam to download wikipedia ? | [] | closed | false | null | 3 | 2020-06-03T09:33:20Z | 2020-06-03T15:25:41Z | 2020-06-03T15:25:41Z | null | Hi, first thanks to @lhoestq 's revolutionary work, I successfully downloaded processed wikipedia according to the doc. 😍😍😍
But at the first try, it tell me to install `apache_beam` and `mwparserfromhell`, which I thought wouldn't be used according to #204 , it was kind of confusing me at that time.
Maybe we should not force users to install these ? Or we just add them to`nlp`'s dependency ? | {
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"Thanks for your message 😊 \r\nIndeed users shouldn't have to install those dependencies",
"Got it, feel free to close this issue when you think it’s resolved.",
"It should be good now :)"
] |
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