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- #4259
was green, but after merging it to master, a code quality error appeared. | 2022-05-06T08:38:06Z | 4,270 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-05-03T15:21:34Z | https://api.github.com/repos/huggingface/datasets/issues/4270/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4270/timeline | Fix style in openbookqa dataset | https://api.github.com/repos/huggingface/datasets/issues/4270/events | null | {
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1,223,865,145 | https://api.github.com/repos/huggingface/datasets/issues/4269/labels{/name} | Update metadata of big_patent dataset with:
- license
- point of contact | 2022-05-06T08:38:09Z | 4,269 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-05-03T09:24:07Z | https://api.github.com/repos/huggingface/datasets/issues/4269/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4269/timeline | Add license and point of contact to big_patent dataset | https://api.github.com/repos/huggingface/datasets/issues/4269/events | null | {
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1,223,331,964 | https://api.github.com/repos/huggingface/datasets/issues/4268/labels{/name} | ## Describe the bug
Error generated when attempting to download dataset
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
ExpectedMoreDownloadedFiles Traceback (most recent call last)
[<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
3 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
| 2022-05-06T15:53:30Z | 4,268 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for [\"word\"](https://en.wiktionary.org/wiki/word),\r\n\r\nPronunciation\r\n([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wi... | {
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1,223,214,275 | https://api.github.com/repos/huggingface/datasets/issues/4267/labels{/name} | Replace data URL with one in the same repository. | 2022-05-06T08:38:13Z | 4,267 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-05-02T18:38:08Z | https://api.github.com/repos/huggingface/datasets/issues/4267/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4267/timeline | Replace data URL in SAMSum dataset within the same repository | https://api.github.com/repos/huggingface/datasets/issues/4267/events | null | {
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1,223,116,436 | https://api.github.com/repos/huggingface/datasets/issues/4266/labels{/name} | Adds the HF Speech Bench to Librispeech Dataset Card in place of the Papers With Code Leaderboard. Should improve usage and visibility of this leaderboard! Wondering whether this can also be done for [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) and [8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) through someone with permissions?
cc @patrickvonplaten: more leaderboard promotion! | 2022-05-05T08:47:20Z | 4,266 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-05-02T16:59:31Z | https://api.github.com/repos/huggingface/datasets/issues/4266/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4266/timeline | Add HF Speech Bench to Librispeech Dataset Card | https://api.github.com/repos/huggingface/datasets/issues/4266/events | null | {
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1,222,723,083 | https://api.github.com/repos/huggingface/datasets/issues/4263/labels{/name} | On the Hugging Face Hub, users refer to imagenet2012 (from #4178 ) as imagenet-1k in their model tags.
To correctly link models to imagenet, we should rename this dataset `imagenet-1k`.
Later we can add `imagenet-21k` as a new dataset if we want.
Once this one is merged we can delete the `imagenet2012` dataset repository on the Hub.
EDIT: to complete the rationale on why we should name it `imagenet-1k`:
If users specifically added the tag `imagenet-1k` , then it could be for two reasons (not sure which one is predominant), either they
- wanted to make it explicit that it’s not 21k -> the distinction is important for the community
- or they have been following this convention from other models -> the convention implicitly exists already | 2022-05-02T17:50:46Z | 4,263 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-05-02T10:26:21Z | https://api.github.com/repos/huggingface/datasets/issues/4263/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4263/timeline | Rename imagenet2012 -> imagenet-1k | https://api.github.com/repos/huggingface/datasets/issues/4263/events | null | {
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"> Later we can add imagenet-21k as a new dataset if we want.\r\n\r\nisn't it what models refer to as `imagenet` already?",
"> isn't it what models refer to as imagenet already?\r\n\r\nI wasn't sure, but it looks like it indeed. Therefore having a dataset `imagenet` for ImageNet 21k makes sense actually.\r\n\r\nE... | {
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1,222,130,749 | https://api.github.com/repos/huggingface/datasets/issues/4262/labels{/name} | 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. | 2022-05-03T14:27:33Z | 4,262 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-05-01T11:59:08Z | https://api.github.com/repos/huggingface/datasets/issues/4262/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4262/timeline | Add YAML tags to Dataset Card rotten tomatoes | https://api.github.com/repos/huggingface/datasets/issues/4262/events | null | {
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1,221,883,779 | https://api.github.com/repos/huggingface/datasets/issues/4261/labels{/name} | ## Describe the bug
Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results.
Furthermore, all splits contain duplicate samples.
## Steps to reproduce the bug
```python
from datasets import load_dataset
training = load_dataset("webis/conclugen", "base", split="train")
validation = load_dataset("webis/conclugen", "base", split="validation")
testing = load_dataset("webis/conclugen", "base", split="test")
# collect which sample id's are present in the training split
ids_validation = list()
ids_testing = list()
for train_sample in training:
train_argument = train_sample["argument"]
train_conclusion = train_sample["conclusion"]
train_id = train_sample["id"]
# test if current sample is in validation split
if train_argument in validation["argument"]:
for validation_sample in validation:
validation_argument = validation_sample["argument"]
validation_conclusion = validation_sample["conclusion"]
validation_id = validation_sample["id"]
if train_argument == validation_argument and train_conclusion == validation_conclusion:
ids_validation.append(validation_id)
# test if current sample is in test split
if train_argument in testing["argument"]:
for testing_sample in testing:
testing_argument = testing_sample["argument"]
testing_conclusion = testing_sample["conclusion"]
testing_id = testing_sample["id"]
if train_argument == testing_argument and train_conclusion == testing_conclusion:
ids_testing.append(testing_id)
```
## Expected results
Length of both lists `ids_validation` and `ids_testing` should be zero.
## Actual results
Length of `ids_validation` = `2556`
Length of `ids_testing` = `287`
Furthermore, there seems to be duplicate samples in (at least) the *training* split, since:
`print(len(set(ids_validation)))` = `950`
`print(len(set(ids_testing)))` = `101`
All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: macOS-12.3.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0 | 2022-05-03T06:04:26Z | 4,261 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi @xflashxx, thanks for reporting.\r\n\r\nPlease note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis\r\n\r\nWe are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.",
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1,221,830,292 | https://api.github.com/repos/huggingface/datasets/issues/4260/labels{/name} | Add the MR (Movie Review) dataset. The original dataset contains sentences from Rotten Tomatoes labeled as either "positive" or "negative".
Homepage: [https://www.cs.cornell.edu/people/pabo/movie-review-data/](https://www.cs.cornell.edu/people/pabo/movie-review-data/)
paperswithcode: [https://paperswithcode.com/dataset/mr](https://paperswithcode.com/dataset/mr)
- [ ] I was not able to generate dummy data, the original dataset files have ".pos" and ".neg" as file extensions so the auto-generator does not work. Is it fine like this or should dummy data be added?
| 2022-04-30T14:16:25Z | 4,260 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-30T13:19:33Z | https://api.github.com/repos/huggingface/datasets/issues/4260/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4260/timeline | Add mr_polarity movie review sentiment classification | https://api.github.com/repos/huggingface/datasets/issues/4260/events | null | {
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1,221,768,025 | https://api.github.com/repos/huggingface/datasets/issues/4259/labels{/name} | This PR fixes the Bug in the openbookqa dataset as mentioned in this issue #3550.
Fix #3550.
cc. @lhoestq @mariosasko | 2022-05-04T06:31:31Z | 4,259 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-30T07:41:39Z | https://api.github.com/repos/huggingface/datasets/issues/4259/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4259/timeline | Fix bug in choices labels in openbookqa dataset | https://api.github.com/repos/huggingface/datasets/issues/4259/events | null | {
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1,221,637,727 | https://api.github.com/repos/huggingface/datasets/issues/4258/labels{/name} | This pr fixes a couple bugs:
1) the perplexity was calculated with a 0 in the attention mask for the start token, which was causing high perplexity scores that were not correct
2) the documentation was not updated | 2022-05-02T16:33:20Z | 4,258 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-29T22:42:44Z | https://api.github.com/repos/huggingface/datasets/issues/4258/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4258/timeline | Fix/start token mask issue and update documentation | https://api.github.com/repos/huggingface/datasets/issues/4258/events | null | {
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1,221,142,899 | https://api.github.com/repos/huggingface/datasets/issues/4255/labels{/name} | I hosted the data files in https://huggingface.co/datasets/pubmed_qa. This is allowed because the data is under the MIT license.
cc @stas00 | 2022-04-29T16:24:55Z | 4,255 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-29T15:55:46Z | https://api.github.com/repos/huggingface/datasets/issues/4255/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4255/timeline | No google drive URL for pubmed_qa | https://api.github.com/repos/huggingface/datasets/issues/4255/events | null | {
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1,220,204,395 | https://api.github.com/repos/huggingface/datasets/issues/4254/labels{/name} | This PR replaces data URL in SAMSum dataset:
- original host (arxiv.org) does not allow HTTP Range requests
- we have hosted the data on the Hub (license: CC BY-NC-ND 4.0)
Moreover, it implements support for streaming.
Fix #4146.
Related to: #4236.
CC: @severo | 2022-05-06T08:38:16Z | 4,254 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-29T08:21:43Z | https://api.github.com/repos/huggingface/datasets/issues/4254/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4254/timeline | Replace data URL in SAMSum dataset and support streaming | https://api.github.com/repos/huggingface/datasets/issues/4254/events | null | {
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sorry for spamming you with review requests, @albertvillanova ! :hugs: | 2022-04-29T17:44:47Z | 4,253 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-28T20:58:27Z | https://api.github.com/repos/huggingface/datasets/issues/4253/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4253/timeline | Create metric cards for mean IOU | https://api.github.com/repos/huggingface/datasets/issues/4253/events | null | {
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1,219,093,830 | https://api.github.com/repos/huggingface/datasets/issues/4250/labels{/name} | Fixes #4152
This PR updates the PyArrow version to 6 in setup.py, CI job files .circleci/config.yaml and .github/workflows/benchmarks.yaml files.
This will fix ArrayND error which exists in pyarrow 5. | 2022-05-04T09:36:52Z | 4,250 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-28T18:10:50Z | https://api.github.com/repos/huggingface/datasets/issues/4250/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4250/timeline | Bump PyArrow Version to 6 | https://api.github.com/repos/huggingface/datasets/issues/4250/events | null | {
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1,218,524,424 | https://api.github.com/repos/huggingface/datasets/issues/4249/labels{/name} | Support streaming XGLUE dataset.
Fix #4247.
CC: @severo | 2022-05-06T08:38:21Z | 4,249 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-28T10:27:23Z | https://api.github.com/repos/huggingface/datasets/issues/4249/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4249/timeline | Support streaming XGLUE dataset | https://api.github.com/repos/huggingface/datasets/issues/4249/events | null | {
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1,218,460,444 | https://api.github.com/repos/huggingface/datasets/issues/4248/labels{/name} | ## Describe the bug
I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text.
Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ?
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
dataset = load_dataset("conll2003")
```
## Expected results
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
## Actual results
```python
print(dataset)
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 219554
})
test: Dataset({
features: ['text'],
num_rows: 50350
})
validation: Dataset({
features: ['text'],
num_rows: 55044
})
})
```
```python
for i in range(20):
print(dataset['train'][i])
{'text': '-DOCSTART- -X- -X- O'}
{'text': ''}
{'text': 'EU NNP B-NP B-ORG'}
{'text': 'rejects VBZ B-VP O'}
{'text': 'German JJ B-NP B-MISC'}
{'text': 'call NN I-NP O'}
{'text': 'to TO B-VP O'}
{'text': 'boycott VB I-VP O'}
{'text': 'British JJ B-NP B-MISC'}
{'text': 'lamb NN I-NP O'}
{'text': '. . O O'}
{'text': ''}
{'text': 'Peter NNP B-NP B-PER'}
{'text': 'Blackburn NNP I-NP I-PER'}
{'text': ''}
{'text': 'BRUSSELS NNP B-NP B-LOC'}
{'text': '1996-08-22 CD I-NP O'}
{'text': ''}
{'text': 'The DT B-NP O'}
{'text': 'European NNP I-NP B-ORG'}
```
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"Thanks for reporting @sue99.\r\n\r\nUnfortunately. I'm not able to reproduce your problem:\r\n```python\r\nIn [1]: import datasets\r\n ...: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"conll2003\")\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n fea... | {
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1,218,320,882 | https://api.github.com/repos/huggingface/datasets/issues/4247/labels{/name} | It seems that something wrong with the data previvew of XGLUE | 2022-04-29T08:23:28Z | 4,247 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-04-28T07:30:50Z | https://api.github.com/repos/huggingface/datasets/issues/4247/comments | {
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1,218,320,293 | https://api.github.com/repos/huggingface/datasets/issues/4246/labels{/name} | This PR implements support to load a dataset (w/o script) containing TSV files by passing only the dataset name (no need to pass `sep='\t'`):
```python
ds = load_dataset("dataset/name")
```
The refactoring allows for future builder kwargs customizations based on file extension.
Related to #4238. | 2022-05-06T08:38:28Z | 4,246 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-28T07:30:15Z | https://api.github.com/repos/huggingface/datasets/issues/4246/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4246/timeline | Support to load dataset with TSV files by passing only dataset name | https://api.github.com/repos/huggingface/datasets/issues/4246/events | null | {
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1,217,959,400 | https://api.github.com/repos/huggingface/datasets/issues/4245/labels{/name} | This PR adds code examples for `DatasetDict` in the API reference :) | 2022-04-29T18:19:34Z | 4,245 | null | https://api.github.com/repos/huggingface/datasets | false | [
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1,217,732,221 | https://api.github.com/repos/huggingface/datasets/issues/4244/labels{/name} | changed multi input text classification as task id instead of category | 2022-05-04T10:43:53Z | 4,244 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-27T18:28:14Z | https://api.github.com/repos/huggingface/datasets/issues/4244/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4244/timeline | task id update | https://api.github.com/repos/huggingface/datasets/issues/4244/events | null | {
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"Thanks for your contribution, @shayne-longpre.\r\n\r\nAre you still interested in adding this dataset? As we are transferring the dataset scripts from this GitHub repo, we would recommend you to add this to the Hugging Face Hub: https://huggingface.co/datasets"
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1,217,665,960 | https://api.github.com/repos/huggingface/datasets/issues/4242/labels{/name} | We don't need to use extraHeaders anymore for rate limits anymore. Anyway extraHeaders was not working with git LFS because it was passing the wrong auth to S3. | 2022-04-27T17:37:04Z | 4,242 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-27T17:22:31Z | https://api.github.com/repos/huggingface/datasets/issues/4242/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4242/timeline | Update auth when mirroring datasets on the hub | https://api.github.com/repos/huggingface/datasets/issues/4242/events | null | {
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1,217,423,686 | https://api.github.com/repos/huggingface/datasets/issues/4241/labels{/name} | ## Describe the bug
I am trying to download the GLUE dataset from the NLP module but get an error (see below).
## Steps to reproduce the bug
```python
import nlp
nlp.__version__ # '0.2.0'
nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
```
## Expected results
I expect the dataset to download without an error.
## Actual results
```
INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports.
INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue
INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py
INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json
INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0)
INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source
INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb
INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805
Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0...
Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s]
INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-7-669a8343dcc1> in <module>
----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
418 verify_infos = not save_infos and not ignore_verifications
419 self._download_and_prepare(
--> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
422 # Sync info
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
458 # Checksums verification
459 if verify_infos:
--> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
461 for split_generator in split_generators:
462 if str(split_generator.split_info.name).lower() == "all":
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)
34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
35 if len(bad_urls) > 0:
---> 36 raise NonMatchingChecksumError(str(bad_urls))
37 logger.info("All the checksums matched successfully.")
38
NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa
- Python version: 3.6.13
- PyArrow version: 6.0.1
- Pandas version: 1.1.5
| 2022-04-28T07:45:27Z | 4,241 | null | https://api.github.com/repos/huggingface/datasets | null | [
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] | 2022-04-27T14:14:21Z | https://api.github.com/repos/huggingface/datasets/issues/4241/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4241/timeline | NonMatchingChecksumError when attempting to download GLUE | https://api.github.com/repos/huggingface/datasets/issues/4241/events | null | {
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"Hi :)\r\n\r\nI think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with:\r\n\r\n```py\r\npip install -U datasets\r\n```\r\n\r\nThen you can download:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nds = load_data... | {
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1,217,287,594 | https://api.github.com/repos/huggingface/datasets/issues/4240/labels{/name} | Modified the `_generate_examples` function to consider all the turns for a chunk id as a single example
Modified the features accordingly
```
"turns": [
{
"names": datasets.features.Sequence(datasets.Value("string")),
"utterances": datasets.features.Sequence(datasets.Value("string")),
"number": datasets.Value("int32"),
}
],
}
```
I wasn't able to run `datasets-cli dummy_data datasets` command. Is there a workaround for this? | 2022-04-29T12:41:41Z | 4,240 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-27T12:31:36Z | https://api.github.com/repos/huggingface/datasets/issues/4240/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4240/timeline | Fix yield for crd3 | https://api.github.com/repos/huggingface/datasets/issues/4240/events | null | {
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"I don't think you need to generate new dummy data, since they're in the same format as the original data.\r\n\r\nThe CI is failing because of this error:\r\n```python\r\n> turn[\"names\"] = turn[\"NAMES\"]\r\nE TypeError: tuple indices must be integers or slices, not str... | {
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1,217,269,689 | https://api.github.com/repos/huggingface/datasets/issues/4239/labels{/name} | The list of use cases did not render on GitHub with the prepended spacing.
Additionally, some typo's we're fixed. | 2022-05-02T13:28:57Z | 4,239 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-27T12:15:50Z | https://api.github.com/repos/huggingface/datasets/issues/4239/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4239/timeline | Small fixes in ROC AUC docs | https://api.github.com/repos/huggingface/datasets/issues/4239/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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1,217,168,123 | https://api.github.com/repos/huggingface/datasets/issues/4238/labels{/name} | ## Describe the bug
I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error
```
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally:
```python
from datasets import load_dataset_builder
dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences")
print(dataset_builder.cache_dir)
print(dataset_builder.info.features)
print(dataset_builder.info.splits)
```
```
Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd
/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519
None
None
```
and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`.
Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it?
Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last.
Thank you.
## Steps to reproduce the bug
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset(
"loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
)
# You can make this part faster with num_proc=<some int>
sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
sentences = sentences.shuffle()
```
## Expected results
Properly tokenize dataset file `test.csv` without issues.
## Actual results
Specify the actual results or traceback.
```
Downloading data files: 100%
2/2 [00:16<00:00, 7.34s/it]
Downloading data: 100%
391M/391M [00:12<00:00, 36.6MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 40.0MB/s]
Extracting data files: 100%
2/2 [00:00<00:00, 47.66it/s]
Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.
100%
2/2 [00:00<00:00, 25.94it/s]
11%
942339/8256449 [01:55<13:11, 9245.85ex/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>()
12 )
13 # You can make this part faster with num_proc=<some int>
---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
15 sentences = sentences.shuffle()
10 frames
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
- ```
```
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.11.0+cu113 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
- ```
| 2022-04-27T16:29:25Z | 4,238 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi @loretoparisi, thanks for reporting.\r\n\r\nThere is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode=\"force_redownload\")`.\r\n\r\nPlease, let me know if this fixes your problem.\r\n\r\nI can confirm you that y... | {
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"Thanks for reporting. I understand it's an error in the dataset script. To reproduce:\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> split_names = ds.get_dataset_split_names(\"mozilla-foundation/common_voice_8_0\", use_auth_token=\"**********\")\r\nDownloading builder script: 100%|███████████████████████████... | {
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Moreover, this PR makes the dataset streamable.
Fix #4217. | 2022-06-10T08:10:55Z | 4,236 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-27T10:01:13Z | https://api.github.com/repos/huggingface/datasets/issues/4236/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4236/timeline | Replace data URL in big_patent dataset and support streaming | https://api.github.com/repos/huggingface/datasets/issues/4236/events | null | {
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1,216,952,640 | https://api.github.com/repos/huggingface/datasets/issues/4235/labels{/name} | ### System Info
```shell
I am using transformer trainer while meeting the issue.
The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency.
I wonder if there are any tricks like Sharding in huggingface trainer.
Looking forward to your reply.
```
### Who can help?
Trainer: @sgugger
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
None
### Expected behavior
```shell
I wonder if there are any tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html.
Thanks a lot!
```
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But on huggingface datasets:
Where is the German data? | 2023-07-25T15:10:15Z | 4,230 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,215,911,182 | https://api.github.com/repos/huggingface/datasets/issues/4221/labels{/name} | Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something?
Thank you in advance. | 2022-04-29T14:52:19Z | 4,221 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi @jordiae,\r\n\r\nInstead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:\r\n```python\r\n\"list_of_dict_feature\": [\r\n {\r\n \"key1_in_dict\": datasets.Value(\"string\"),\r\n \"key2_in_dict\": datasets.Value(\"int32\"),\r\n ...\r\n }\r\n... | {
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1,214,688,141 | https://api.github.com/repos/huggingface/datasets/issues/4217/labels{/name} | ## Dataset viewer issue for '*big_patent*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)*
*Unable to view because it says FileNotFound, also cannot download it through the python API*
Am I the one who added this dataset ? No
| 2022-05-26T06:29:43Z | 4,217 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive.\r\n\r\nSee related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com\r\n\r\nTo quote [@lhoestq](https://gith... | {
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1,214,614,029 | https://api.github.com/repos/huggingface/datasets/issues/4216/labels{/name} | I noticed this bug while answering [this question](https://discuss.huggingface.co/t/correct-way-to-create-a-dataset-from-a-csv-file/15686/11?u=mariosasko).
This code replicates the bug:
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": ex})
```
and this is the fix for it (before this PR):
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": dict(ex)})
```
Internally, this can be fixed by merging two dicts via dict unpacking (instead of `dict.update) `in `Dataset.map`, which avoids creating recursive dictionaries.
P.S. `{**a, **b}` is slightly more performant than `a.update(b)` in my bencmarks. | 2022-05-04T17:20:06Z | 4,216 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-25T14:40:32Z | https://api.github.com/repos/huggingface/datasets/issues/4216/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4216/timeline | Avoid recursion error in map if example is returned as dict value | https://api.github.com/repos/huggingface/datasets/issues/4216/events | null | {
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1,214,579,162 | https://api.github.com/repos/huggingface/datasets/issues/4215/labels{/name} | Addresses this comment: https://github.com/huggingface/datasets/pull/3801#pullrequestreview-901736921 | 2022-05-03T15:56:07Z | 4,215 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-25T14:15:19Z | https://api.github.com/repos/huggingface/datasets/issues/4215/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4215/timeline | Add `drop_last_batch` to `IterableDataset.map` | https://api.github.com/repos/huggingface/datasets/issues/4215/events | null | {
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1,214,572,430 | https://api.github.com/repos/huggingface/datasets/issues/4214/labels{/name} | Avoids having to set `ignore_verifications=True` in `load_dataset("imagefolder", ...)` to skip checksum verification and speed up loading.
The user can still pass `DownloadConfig(record_checksums=True)` to not skip this part. | 2022-05-03T15:28:32Z | 4,214 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-25T14:10:41Z | https://api.github.com/repos/huggingface/datasets/issues/4214/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4214/timeline | Skip checksum computation in Imagefolder by default | https://api.github.com/repos/huggingface/datasets/issues/4214/events | null | {
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1,214,510,010 | https://api.github.com/repos/huggingface/datasets/issues/4213/labels{/name} | Ready for review. | 2022-05-05T12:19:21Z | 4,213 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-25T13:26:18Z | https://api.github.com/repos/huggingface/datasets/issues/4213/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4213/timeline | ETT time series dataset | https://api.github.com/repos/huggingface/datasets/issues/4213/events | null | {
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1,214,498,582 | https://api.github.com/repos/huggingface/datasets/issues/4212/labels{/name} | `path` should be set to local path inside audio feature if exist so that bytes can correctly be deleted. | 2022-04-26T22:54:28Z | 4,212 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-25T13:18:26Z | https://api.github.com/repos/huggingface/datasets/issues/4212/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4212/timeline | [Common Voice] Make sure bytes are correctly deleted if `path` exists | https://api.github.com/repos/huggingface/datasets/issues/4212/events | null | {
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1,214,361,837 | https://api.github.com/repos/huggingface/datasets/issues/4211/labels{/name} | Hi there,
I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same.
Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli).
In short:
I have 3 feature mapping
```python
Tri_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
}
)
Ent_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]),
}
)
Con_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]),
}
)
```
Then I create different datasets
```python
dataset_splits = {}
for split in df["split"].unique():
print(split)
df_split = df.loc[df["split"] == split].copy()
if split in Tri_dataset:
df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
ds = Dataset.from_pandas(df_split, features=Tri_features)
elif split in Ent_bin_dataset:
df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1})
ds = Dataset.from_pandas(df_split, features=Ent_features)
elif split in Con_bin_dataset:
df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1})
ds = Dataset.from_pandas(df_split, features=Con_features)
else:
print("ERROR:", split)
dataset_splits[split] = ds
datasets = DatasetDict(dataset_splits)
```
I then push to hub
```python
datasets.push_to_hub("pietrolesci/robust_nli", token="<token>")
```
Finally, I load it from the hub
```python
datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli")
```
And I get that
```python
datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features
```
since
```python
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"])
```
gets remapped to
```python
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"])
``` | 2023-04-06T19:25:50Z | 4,211 | null | https://api.github.com/repos/huggingface/datasets | null | [
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} | https://api.github.com/repos/huggingface/datasets/issues/4211/timeline | DatasetDict containing Datasets with different features when pushed to hub gets remapped features | https://api.github.com/repos/huggingface/datasets/issues/4211/events | null | {
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... | null | completed | NONE | 2022-05-20T15:15:30Z | null | I_kwDODunzps5IYbDt | [
"Hi @pietrolesci, thanks for reporting.\r\n\r\nPlease note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different **split**.\r\n\r\nTo handle sub-datasets with different features, we u... | {
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1,214,089,130 | https://api.github.com/repos/huggingface/datasets/issues/4210/labels{/name} | ### System Info
```shell
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.10.0+cu111 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
```
### Who can help?
@LysandreJik
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset("loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
features = features
```
ERROR:
```
ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None)
Value(dtype='string', id=None)
Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39
Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...
Downloading data files: 100%
2/2 [00:18<00:00, 8.06s/it]
Downloading data: 100%
391M/391M [00:13<00:00, 35.3MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 36.5MB/s]
Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
15 frames
/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
ValueError: invalid literal for int() with base 10: 'cmn'
```
while loading without `features` it loads without errors
```
sentences = load_dataset("loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text']
)
```
but the `label` col seems to be wrong (without the `ClassLabel` object):
```
sentences['train'].features
{'label': Value(dtype='string', id=None),
'text': Value(dtype='string', id=None)}
```
The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences
Dataset format is:
```
ces Nechci vědět, co je tam uvnitř.
ces Kdo o tom chce slyšet?
deu Tom sagte, er fühle sich nicht wohl.
ber Mel-iyi-d anida-t tura ?
hun Gondom lesz rá rögtön.
ber Mel-iyi-d anida-tt tura ?
deu Ich will dich nicht reden hören.
```
### Expected behavior
```shell
correctly load train and test files.
``` | 2022-05-31T12:16:31Z | 4,210 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi! Casting class labels from strings is currently not supported in the CSV loader, but you can get the same result with an additional map as follows:\r\n```python\r\nfrom datasets import load_dataset,Features,Value,ClassLabel\r\nclass_names = [\"cmn\",\"deu\",\"rus\",\"fra\",\"eng\",\"jpn\",\"spa\",\"ita\",\"kor\... | {
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1,213,716,426 | https://api.github.com/repos/huggingface/datasets/issues/4208/labels{/name} | Resolves #3457
Dataset Request : Add CMU Graphics Lab Motion Capture dataset [#3457](https://github.com/huggingface/datasets/issues/3457)
This PR adds the CMU MoCap Dataset.
The authors didn't respond even after multiple follow ups, so I ended up crawling the website to get categories, subcategories and description information. Some of the subjects do not have category/subcategory/description as well. I am using a subject to categories, subcategories and description map (metadata file).
Currently the loading of the dataset works for "asf/amc" and "avi" formats since they have a single download link. But "c3d" and "mpg" have multiple download links (part archives) and dl_manager.download_and_extract() extracts the files to multiple paths, is there a way to extract these multiple archives into one folder ? Any other way to go about this ?
Any suggestions/inputs on this would be helpful. Thank you.
| 2022-10-03T09:38:24Z | 4,208 | null | https://api.github.com/repos/huggingface/datasets | false | [
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1,213,604,615 | https://api.github.com/repos/huggingface/datasets/issues/4207/labels{/name} | Typo: `datasets.DatsetDict` -> `datasets.DatasetDict` | 2022-05-05T13:17:47Z | 4,207 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-24T09:49:37Z | https://api.github.com/repos/huggingface/datasets/issues/4207/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4207/timeline | [Minor edit] Fix typo in class name | https://api.github.com/repos/huggingface/datasets/issues/4207/events | null | {
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1,212,715,581 | https://api.github.com/repos/huggingface/datasets/issues/4206/labels{/name} | This PR adds readme.md and ner_val.py to metrics.
Nerval is a python package that helps evaluate NER models. It creates classification report and confusion matrix at entity level. | 2023-07-11T09:34:56Z | 4,206 | null | https://api.github.com/repos/huggingface/datasets | false | [
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1,212,466,138 | https://api.github.com/repos/huggingface/datasets/issues/4205/labels{/name} | Minor change to fully align this function with the recent change in Transformers (https://github.com/huggingface/transformers/pull/16891) | 2022-05-03T15:28:42Z | 4,205 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-22T14:56:21Z | https://api.github.com/repos/huggingface/datasets/issues/4205/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4205/timeline | Fix `convert_file_size_to_int` for kilobits and megabits | https://api.github.com/repos/huggingface/datasets/issues/4205/events | null | {
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1,212,431,764 | https://api.github.com/repos/huggingface/datasets/issues/4204/labels{/name} | What this PR mainly does:
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Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!) | 2022-05-03T13:23:23Z | 4,204 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-22T14:24:26Z | https://api.github.com/repos/huggingface/datasets/issues/4204/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4204/timeline | Add Recall Metric Card | https://api.github.com/repos/huggingface/datasets/issues/4204/events | null | {
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1,212,431,067 | https://api.github.com/repos/huggingface/datasets/issues/4203/labels{/name} | What this PR mainly does:
- add metric card for precision metric
- update docs in precision python file
Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!) | 2022-05-03T14:23:40Z | 4,203 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-22T14:23:48Z | https://api.github.com/repos/huggingface/datasets/issues/4203/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4203/timeline | Add Precision Metric Card | https://api.github.com/repos/huggingface/datasets/issues/4203/events | null | {
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1,212,086,420 | https://api.github.com/repos/huggingface/datasets/issues/4201/labels{/name} | Update template to use new issue forms instead.
With this PR we can check if this new feature is useful for us.
Once validated, we can update the other templates.
CC: @severo | 2022-05-06T08:38:43Z | 4,201 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-22T09:34:44Z | https://api.github.com/repos/huggingface/datasets/issues/4201/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4201/timeline | Update GH template for dataset viewer issues | https://api.github.com/repos/huggingface/datasets/issues/4201/events | null | {
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1,211,980,110 | https://api.github.com/repos/huggingface/datasets/issues/4200/labels{/name} | This option was missing from the docs guide (it was only explained in the docstring of `load_dataset`). Although this is an infrequent use case, there might be some users interested in it.
Related to #4192
CC: @stevhliu | 2022-05-06T08:39:25Z | 4,200 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-22T08:08:25Z | https://api.github.com/repos/huggingface/datasets/issues/4200/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4200/timeline | Add to docs how to load from local script | https://api.github.com/repos/huggingface/datasets/issues/4200/events | null | {
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1,211,953,308 | https://api.github.com/repos/huggingface/datasets/issues/4199/labels{/name} | ## Describe the bug
It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch.
## Steps to reproduce the bug
Using the example provided in `red_caps` dataset.
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
import datasets
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": get_datasets_user_agent()},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"]))
return batch
def process_image_urls(batch):
processed_batch_image_urls = []
for image_url in batch["image_url"]:
processed_example_image_urls = []
image_url_splits = re.findall(r"http\S+", image_url)
for image_url_split in image_url_splits:
if "imgur" in image_url_split and "," in image_url_split:
for image_url_part in image_url_split.split(","):
if not image_url_part:
continue
image_url_part = image_url_part.strip()
root, ext = os.path.splitext(image_url_part)
if not root.startswith("http"):
root = "http://i.imgur.com/" + root
root = root.split("#")[0]
if not ext:
ext = ".jpg"
ext = re.split(r"[?%]", ext)[0]
image_url_part = root + ext
processed_example_image_urls.append(image_url_part)
else:
processed_example_image_urls.append(image_url_split)
processed_batch_image_urls.append(processed_example_image_urls)
batch["image_url"] = processed_batch_image_urls
return batch
dset = load_dataset("red_caps", "jellyfish")
dset = dset.map(process_image_urls, batched=True, num_proc=4)
features = dset["train"].features.copy()
features["image"] = datasets.Sequence(datasets.Image())
num_threads = 5
dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads})
```
Run this in an interpretor or as a script twice and see that the cache is missed the second time.
## Expected results
At reload there should not be any cache miss
## Actual results
Every time script is run, cache is missed and dataset is built from scratch.
## Environment info
- `datasets` version: 2.1.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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... | null | completed | CONTRIBUTOR | 2022-04-26T13:38:26Z | null | I_kwDODunzps5IPPCc | [
"Hi ! Maybe one of the objects in the function is not deterministic across sessions ? You can read more about it and how to investigate here: https://huggingface.co/docs/datasets/about_cache",
"Hi @apsdehal! Can you verify that replacing\r\n```python\r\ndef fetch_single_image(image_url, timeout=None, retries=0):\... | {
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1,211,456,559 | https://api.github.com/repos/huggingface/datasets/issues/4198/labels{/name} | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| 2022-05-03T11:29:05Z | 4,198 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-04-21T19:19:26Z | https://api.github.com/repos/huggingface/datasets/issues/4198/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4198/timeline | There is no dataset | https://api.github.com/repos/huggingface/datasets/issues/4198/events | null | {
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} | [] | null | completed | NONE | 2022-04-22T06:12:25Z | null | I_kwDODunzps5INVwv | [] | {
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1,211,342,558 | https://api.github.com/repos/huggingface/datasets/issues/4197/labels{/name} | This should fix all the issue we have with in place operations in mapping functions. This is crucial as where we do some weird things like:
```
def apply(batch):
batch_size = len(batch["id"])
batch["text"] = ["potato" for _ range(batch_size)]
return {}
# Columns are: {"id": int}
dset.map(apply, batched=True, remove_columns="text") # crashes because `text` is not in the original columns
dset.map(apply, batched=True) # mapped datasets has `text` column
```
In this PR we suggest to have `remove_columns=True` so that we ignore the input completely, and just use the output to generate mapped dataset. This means that inplace operations won't have any effects anymore. | 2023-09-24T10:02:32Z | 4,197 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-21T17:28:13Z | https://api.github.com/repos/huggingface/datasets/issues/4197/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4197/timeline | Add remove_columns=True | https://api.github.com/repos/huggingface/datasets/issues/4197/events | null | {
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"Any reason why we can't just do `[inputs.copy()]` in this line for in-place operations to not have effects anymore:\r\nhttps://github.com/huggingface/datasets/blob/bf432011ff9155a5bc16c03956bc63e514baf80d/src/datasets/arrow_dataset.p... | {
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1,211,271,261 | https://api.github.com/repos/huggingface/datasets/issues/4196/labels{/name} | Following https://github.com/huggingface/datasets/pull/4184, currently a dataset saved using `save_to_disk` doesn't actually contain the bytes of the image or audio files. Instead it stores the path to your local files.
Adding `embed_external_files` and set it to True by default to save_to_disk would be kind of a breaking change since some users will get bigger Arrow files when updating the lib, but the advantages are nice:
- the resulting dataset is self contained, in case you want to delete your cache for example or share it with someone else
- users also upload these Arrow files to cloud storage via the fs parameter, and in this case they would expect to upload a self-contained dataset
- consistency with push_to_hub
This can be implemented at the same time as sharding for `save_to_disk` for efficiency, and reuse the helpers from `push_to_hub` to embed the external files.
cc @mariosasko | 2022-12-14T18:22:59Z | 4,196 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-04-21T16:25:18Z | https://api.github.com/repos/huggingface/datasets/issues/4196/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4196/timeline | Embed image and audio files in `save_to_disk` | https://api.github.com/repos/huggingface/datasets/issues/4196/events | null | {
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1,210,958,602 | https://api.github.com/repos/huggingface/datasets/issues/4194/labels{/name} | Fix #4191.
CC: @SaulLu | 2022-05-12T15:16:34Z | 4,194 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-21T12:22:26Z | https://api.github.com/repos/huggingface/datasets/issues/4194/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4194/timeline | Support lists of multi-dimensional numpy arrays | https://api.github.com/repos/huggingface/datasets/issues/4194/events | null | {
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1,210,734,701 | https://api.github.com/repos/huggingface/datasets/issues/4193/labels{/name} | Following https://github.com/huggingface/datasets/pull/4187, I explained in the documentation of `save_to_disk` and `push_to_hub` how they handle image and audio data. | 2022-04-22T09:55:55Z | 4,193 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-21T09:04:36Z | https://api.github.com/repos/huggingface/datasets/issues/4193/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4193/timeline | Document save_to_disk and push_to_hub on images and audio files | https://api.github.com/repos/huggingface/datasets/issues/4193/events | null | {
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1,210,692,554 | https://api.github.com/repos/huggingface/datasets/issues/4192/labels{/name} |
Traceback (most recent call last):
File "/home/gs603/ahf/pretrained/model.py", line 48, in <module>
dataset = load_dataset("json",data_files="dataset/dataset_infos.json")
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset
**config_kwargs,
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder
data_files=data_files,
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory
download_mode=download_mode,
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module
data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote
if not isinstance(patterns_for_key, DataFilesList)
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote
data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls
for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained
the code is in the model.py,why I can't use the load_dataset function to load my local dataset? | 2022-04-25T16:51:57Z | 4,192 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi! :)\r\n\r\nI believe that should work unless `dataset_infos.json` isn't actually a dataset. For Hugging Face datasets, there is usually a file named `dataset_infos.json` which contains metadata about the dataset (eg. the dataset citation, license, description, etc). Can you double-check that `dataset_infos.json... | {
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1,210,028,090 | https://api.github.com/repos/huggingface/datasets/issues/4191/labels{/name} | **Is your feature request related to a problem? Please describe.**
It is possible to create an `Array2D` column from a list of arrays of dimension 1. Similarly, I think it might be nice to be able to create a `Array3D` column from a list of lists of arrays of dimension 1.
To illustrate my proposal, let's take the following toy dataset t:
```python
import numpy as np
from datasets import Dataset, features
data_map = {
1: np.array([[0.2, 0,4],[0.19, 0,3]]),
2: np.array([[0.1, 0,4],[0.19, 0,3]]),
}
def create_toy_ds():
my_dict = {"id":[1, 2]}
return Dataset.from_dict(my_dict)
ds = create_toy_ds()
```
The following 2D processing works without any errors raised:
```python
def prepare_dataset_2D(batch):
batch["pixel_values"] = [data_map[index] for index in batch["id"]]
return batch
ds_2D = ds.map(
prepare_dataset_2D,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array2D(shape=(2, 3), dtype="float32")})
)
```
The following 3D processing doesn't work:
```python
def prepare_dataset_3D(batch):
batch["pixel_values"] = [[data_map[index]] for index in batch["id"]]
return batch
ds_3D = ds.map(
prepare_dataset_3D,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3, dtype="float32")})
)
```
The error raised is:
```
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
[<ipython-input-6-676547e4cd41>](https://localhost:8080/#) in <module>()
3 batched=True,
4 remove_columns=ds.column_names,
----> 5 features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")})
6 )
12 frames
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1971 new_fingerprint=new_fingerprint,
1972 disable_tqdm=disable_tqdm,
-> 1973 desc=desc,
1974 )
1975 else:
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
518 self: "Dataset" = kwargs.pop("self")
519 # apply actual function
--> 520 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
521 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
522 for dataset in datasets:
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
485 }
486 # apply actual function
--> 487 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
488 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
489 # re-apply format to the output
[/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
456 # Call actual function
457
--> 458 out = func(self, *args, **kwargs)
459
460 # Update fingerprint of in-place transforms + update in-place history of transforms
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2354 writer.write_table(batch)
2355 else:
-> 2356 writer.write_batch(batch)
2357 if update_data and writer is not None:
2358 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_batch(self, batch_examples, writer_batch_size)
505 col_try_type = try_features[col] if try_features is not None and col in try_features else None
506 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 507 arrays.append(pa.array(typed_sequence))
508 inferred_features[col] = typed_sequence.get_inferred_type()
509 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in __arrow_array__(self, type)
175 storage = list_of_np_array_to_pyarrow_listarray(data, type=pa_type.value_type)
176 else:
--> 177 storage = pa.array(data, pa_type.storage_dtype)
178 return pa.ExtensionArray.from_storage(pa_type, storage)
179
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Can only convert 1-dimensional array values
```
**Describe the solution you'd like**
No error in the second scenario and an identical result to the following snippets.
**Describe alternatives you've considered**
There are other alternatives that work such as:
```python
def prepare_dataset_3D_bis(batch):
batch["pixel_values"] = [[data_map[index].tolist()] for index in batch["id"]]
return batch
ds_3D_bis = ds.map(
prepare_dataset_3D_bis,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")})
)
```
or
```python
def prepare_dataset_3D_ter(batch):
batch["pixel_values"] = [data_map[index][np.newaxis, :, :] for index in batch["id"]]
return batch
ds_3D_ter = ds.map(
prepare_dataset_3D_ter,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")})
)
```
But both solutions require the user to be aware that `data_map[index]` is an `np.array` type.
cc @lhoestq as we discuss this offline :smile: | 2022-05-12T15:08:40Z | 4,191 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | completed | CONTRIBUTOR | 2022-05-12T15:08:40Z | null | I_kwDODunzps5IH5A6 | [
"Hi @SaulLu, thanks for your proposal.\r\n\r\nJust I got a bit confused about the dimensions...\r\n- For the 2D case, you mention it is possible to create an `Array2D` from a list of arrays of dimension 1\r\n- However, you give an example of creating an `Array2D` from arrays of dimension 2:\r\n - the values of `da... | {
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1,209,901,677 | https://api.github.com/repos/huggingface/datasets/issues/4190/labels{/name} | This PR adds a `max_shard_size` param to `push_to_hub` and deprecates `shard_size` in favor of this new param to have a more descriptive name (a shard has at most the `shard_size` bytes in `push_to_hub`) for the param and to align the API with [Transformers](https://github.com/huggingface/transformers/blob/ff06b177917384137af2d9585697d2d76c40cdfc/src/transformers/modeling_utils.py#L1350).
| 2022-04-22T13:58:25Z | 4,190 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-20T16:08:01Z | https://api.github.com/repos/huggingface/datasets/issues/4190/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4190/timeline | Deprecate `shard_size` in `push_to_hub` in favor of `max_shard_size` | https://api.github.com/repos/huggingface/datasets/issues/4190/events | null | {
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1,209,881,351 | https://api.github.com/repos/huggingface/datasets/issues/4189/labels{/name} | Document how to use FAISS index for special operations, by accessing the index itself.
Close #4029. | 2022-05-06T08:43:10Z | 4,189 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-20T15:51:56Z | https://api.github.com/repos/huggingface/datasets/issues/4189/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4189/timeline | Document how to use FAISS index for special operations | https://api.github.com/repos/huggingface/datasets/issues/4189/events | null | {
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1,209,740,957 | https://api.github.com/repos/huggingface/datasets/issues/4188/labels{/name} | Support streaming cnn_dailymail dataset.
Fix #3969.
CC: @severo | 2022-05-11T13:39:06Z | 4,188 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-20T14:04:36Z | https://api.github.com/repos/huggingface/datasets/issues/4188/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4188/timeline | Support streaming cnn_dailymail dataset | https://api.github.com/repos/huggingface/datasets/issues/4188/events | null | {
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"Did you run the `datasets-cli` command before merging to make sure you generate all the examples ?"
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1,209,721,532 | https://api.github.com/repos/huggingface/datasets/issues/4187/labels{/name} | Right now if you pass both the `bytes` and a local `path` for audio or image data, then the `bytes` are unnecessarily written in the Arrow file, while we could just keep the local `path`.
This PR discards the `bytes` when the audio or image file exists locally.
In particular it's common for audio datasets builders to provide both the bytes and the local path in order to work for both streaming (using the bytes) and non-streaming mode (using a local file - which is often required for audio).
cc @patrickvonplaten | 2022-04-21T09:17:00Z | 4,187 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-20T13:50:37Z | https://api.github.com/repos/huggingface/datasets/issues/4187/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4187/timeline | Don't duplicate data when encoding audio or image | https://api.github.com/repos/huggingface/datasets/issues/4187/events | null | {
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"I'm not familiar with the concept of streaming vs non-streaming in HF datasets. I just wonder that you have the distinction here. Why doesn't it work to always make use of `bytes`? \"using a local file - which is often required for a... | {
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1,209,429,743 | https://api.github.com/repos/huggingface/datasets/issues/4185/labels{/name} | 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? | 2022-04-21T11:00:53Z | 4,185 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-04-20T09:35:55Z | https://api.github.com/repos/huggingface/datasets/issues/4185/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4185/timeline | Librispeech documentation, clarification on format | https://api.github.com/repos/huggingface/datasets/issues/4185/events | null | {
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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... | {
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1,208,592,669 | https://api.github.com/repos/huggingface/datasets/issues/4184/labels{/name} | Add `"all"` config to Librispeech
Closed #4179 | 2022-08-29T06:35:57Z | 4,184 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-19T16:27:56Z | https://api.github.com/repos/huggingface/datasets/issues/4184/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4184/timeline | [Librispeech] Add 'all' config | https://api.github.com/repos/huggingface/datasets/issues/4184/events | null | {
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"Fix https://github.com/huggingface/datasets/issues/4179",
"_The documentation is not available anymore as the PR was closed or merged._",
"Just that I understand: With this change, simply doing `load_dataset(\"librispeech_asr\")` is possible and returns the whole dataset?\r\n\r\nAnd to get the subsets, I do st... | {
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1,208,449,335 | https://api.github.com/repos/huggingface/datasets/issues/4183/labels{/name} | Added an example of how to load one config or the other | 2023-09-24T10:02:24Z | 4,183 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-19T14:26:59Z | https://api.github.com/repos/huggingface/datasets/issues/4183/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4183/timeline | Document librispeech configs | https://api.github.com/repos/huggingface/datasets/issues/4183/events | null | {
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"I think the main purpose of #4179 was how to be able to load both configs into one, so should we maybe add this part of the code: https://github.com/huggingface/datasets/issues/4179#issuecomment-1102383717 \r\n\r\nto the doc? \r\n\r\nActually @lhoestq would this work given that they have different split names: htt... | {
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1,208,285,235 | https://api.github.com/repos/huggingface/datasets/issues/4182/labels{/name} | ## Describe the bug
Source download_url from zenodo.org does not respond.
`_DOWNLOAD_URL = "https://zenodo.org/record/2787612/files/SICK.zip?download=1"`
Other datasets also use zenodo.org to store data and they cannot be downloaded as well.
It would be better to actually use more reliable way to store original data like s3 bucket.
## Steps to reproduce the bug
```python
load_dataset("sick")
```
## Expected results
Dataset should be downloaded.
## Actual results
ConnectionError: Couldn't reach https://zenodo.org/record/2787612/files/SICK.zip?download=1 (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out. (read timeout=100)")))
## Environment info
- `datasets` version: 2.1.0
- Platform: Darwin-21.4.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
| 2022-04-20T07:11:05Z | 4,182 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"[Off topic but related: Is the uptime of S3 provably better than Zenodo's?]",
"Hi @dkajtoch, please note that at HuggingFace we are not hosting this dataset: we are just using a script to download their data file and create a dataset from it.\r\n\r\nIt was the dataset owners decision to host their data at Zenodo... | {
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1,208,194,805 | https://api.github.com/repos/huggingface/datasets/issues/4181/labels{/name} | ## 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
| 2022-07-25T11:44:02Z | 4,181 | null | https://api.github.com/repos/huggingface/datasets | null | [
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} | [] | null | completed | CONTRIBUTOR | 2022-07-25T11:44:02Z | null | I_kwDODunzps5IA5b1 | [
"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: \... | {
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1,208,042,320 | https://api.github.com/repos/huggingface/datasets/issues/4180/labels{/name} | **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
Currently, `dataset["audio"]` will load EVERY element in the dataset in RAM, which can be quite big for an audio dataset.
Having an iterator (or sequence) type of object, would make inference with `transformers` 's `pipeline` easier to use and not so memory hungry.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
For a non breaking change:
```python
for audio in dataset.iterate("audio"):
# {"array": np.array(...), "sampling_rate":...}
```
For a breaking change solution (not necessary), changing the type of `dataset["audio"]` to a sequence type so that
```python
pipe = pipeline(model="...")
for out in pipe(dataset["audio"]):
# {"text":....}
```
could work
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
```python
def iterate(dataset, key):
for item in dataset:
yield dataset[key]
for out in pipeline(iterate(dataset, "audio")):
# {"array": ...}
```
This works but requires the helper function which feels slightly clunky.
**Additional context**
Add any other context about the feature request here.
The context is actually to showcase better integration between `pipeline` and `datasets` in the Quicktour demo: https://github.com/huggingface/transformers/pull/16723/files
@lhoestq
| 2022-04-21T10:30:58Z | 4,180 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Thanks for the suggestion ! I agree it would be nice to have something directly in `datasets` to do something as simple as that\r\n\r\ncc @albertvillanova @mariosasko @polinaeterna What do you think if we have something similar to pandas `Series` that wouldn't bring everything in memory when doing `dataset[\"audio... | {
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1,208,001,118 | https://api.github.com/repos/huggingface/datasets/issues/4179/labels{/name} | ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.
## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```
## Expected results
It should download and prepare the whole dataset (all subsets).
In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.
Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.
## Actual results
```
...
File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
locals:
archive_path = <not found>
dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
_DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
self.config.name = <local> 'default', len = 7
KeyError: 'default'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2
| 2022-07-27T16:10:00Z | 4,179 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"@patrickvonplaten Hi! I saw that you prepared this? :)",
"Another thing, but maybe this should be a separate issue: As I see from the code, it would try to use up to 16 simultaneous downloads? This is problematic for Librispeech or anything on OpenSLR. On [the homepage](https://www.openslr.org/), it says:\r\n\r\... | {
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1,207,787,073 | https://api.github.com/repos/huggingface/datasets/issues/4178/labels{/name} | To use the dataset download the tar file
[imagenet_object_localization_patched2019.tar.gz](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=imagenet_object_localization_patched2019.tar.gz) from Kaggle and then point the datasets library to it by using:
```py
from datasets import load_dataset
dataset = load_dataset("imagenet",
data_dir="/path/to/imagenet_object_localization_patched2019.tar.gz")
```
Currently train and validation splits are supported. | 2022-04-29T21:43:59Z | 4,178 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-19T06:01:35Z | https://api.github.com/repos/huggingface/datasets/issues/4178/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4178/timeline | [feat] Add ImageNet dataset | https://api.github.com/repos/huggingface/datasets/issues/4178/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the comments. I believe I have addressed all of them and also decreased the size of the dummy data file, so it should be ready for a re-review. I also made a change to allow adding synset mapping and valprep script in conf... | {
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1,207,535,920 | https://api.github.com/repos/huggingface/datasets/issues/4177/labels{/name} | This dataset for the [1st task of SemEval-2018](https://competitions.codalab.org/competitions/17751) competition was missing all subtasks except for subtask 5. I added another two subtasks (subtask 1 and 2), which are each comprised of 12 additional data subsets: for each language in En, Es, Ar, there are 4 datasets, broken down by emotions (anger, fear, joy, sadness).
## Remaining questions
I wasn't able to find any documentation about how one should make PRs to modify datasets. Because of that, I just did my best to integrate the new data into the code, and tested locally that this worked. I'm sorry if I'm not respecting your contributing guidelines – if they are documented somewhere, I'd appreciate if you could send a pointer!
Not sure how `dataset_infos.json` and `dummy` should be updated. My understanding is that they were automatically generated at the time of the original dataset creation? | 2022-10-05T10:38:16Z | 4,177 | null | https://api.github.com/repos/huggingface/datasets | false | [
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1,206,515,563 | https://api.github.com/repos/huggingface/datasets/issues/4176/labels{/name} | Hello, in the processing stage, I use two operations. The first one : map + filter, is very fast and it uses the full cores, while the socond step is very slow and did not use full cores.
Also, there is a significant lag between them. Am I missing something ?
```
raw_datasets = raw_datasets.map(split_func,
batched=False,
num_proc=args.preprocessing_num_workers,
load_from_cache_file=not args.overwrite_cache,
desc = "running split para ==>")\
.filter(lambda example: example['text1']!='' and example['text2']!='',
num_proc=args.preprocessing_num_workers, desc="filtering ==>")
processed_datasets = raw_datasets.map(
preprocess_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on dataset===>",
)
``` | 2022-04-18T00:03:00Z | 4,176 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,205,589,842 | https://api.github.com/repos/huggingface/datasets/issues/4175/labels{/name} | closes #2981 #2810
@nateraw @hassiahk I've listed you guys as co-author as you've contributed previously to this dataset | 2023-09-24T10:02:38Z | 4,175 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-15T13:42:32Z | https://api.github.com/repos/huggingface/datasets/issues/4175/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4175/timeline | Add WIT Dataset | https://api.github.com/repos/huggingface/datasets/issues/4175/events | null | {
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"Hi! Coming in late with some context.\r\n\r\nThere are two versions of the WIT dataset:\r\n1. The original source dataset managed by Wikimedia. It has more information, raw image representations, and each row corresponds to an image ... | {
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1,205,575,941 | https://api.github.com/repos/huggingface/datasets/issues/4174/labels{/name} | When `function` modifies input in-place, the guarantee that columns in `remove_columns` are contained in `input` doesn't hold true anymore. Therefore we need to relax way we pop elements by checking if that column exists. | 2022-04-15T14:52:07Z | 4,174 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-15T13:23:15Z | https://api.github.com/repos/huggingface/datasets/issues/4174/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4174/timeline | Fix when map function modifies input in-place | https://api.github.com/repos/huggingface/datasets/issues/4174/events | null | {
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1,204,657,114 | https://api.github.com/repos/huggingface/datasets/issues/4173/labels{/name} | As mentioned in https://github.com/huggingface/datasets/issues/4139 it's currently not possible to stream private/gated zipped images from the Hub.
This is because `Image.decode_example` does not handle authentication. Indeed decoding requires to access and download the file from the private repository.
In this PR I added authentication to `Image.decode_example` via a `token_per_repo_id` optional argument. I first wanted to just pass `use_auth_token` but a single `Image` instance can be responsible of decoding images from a combination of several datasets together (from `interleave_datasets` for example). Therefore I just used a dictionary `repo_id` -> `token` instead.
I'm getting the `repo_id` from the dataset builder (I replaced the `namepace` attribute with `repo_id`)
I did the same for `Audio.decode_example`
cc @SBrandeis @severo | 2022-05-05T14:05:54Z | 4,173 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-14T15:15:07Z | https://api.github.com/repos/huggingface/datasets/issues/4173/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4173/timeline | Stream private zipped images | https://api.github.com/repos/huggingface/datasets/issues/4173/events | null | {
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"oops looks like some tests are failing sorry, will fix them tomorrow\r\n\r\nEDIT: not today but asap hopefully",
"cc @mariosasko this is ready for review, let me know what you think !"
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1,204,433,160 | https://api.github.com/repos/huggingface/datasets/issues/4172/labels{/name} | Following comments in https://github.com/huggingface/datasets/issues/4003 we found that it was outdated and casing an error when loading the dataset | 2022-04-15T14:47:42Z | 4,172 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-14T11:53:06Z | https://api.github.com/repos/huggingface/datasets/issues/4172/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4172/timeline | Update assin2 dataset_infos.json | https://api.github.com/repos/huggingface/datasets/issues/4172/events | null | {
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1,204,413,620 | https://api.github.com/repos/huggingface/datasets/issues/4170/labels{/name} | This PR rewrites almost all of `to_tf_dataset()`, which makes it kind of hard to list all the changes, but the most critical ones are:
- Much better stability and no more dropping unexpected column names (Sorry @NielsRogge)
- Doesn't clobber custom transforms on the data (Sorry @NielsRogge again)
- Much better handling of the situation when the `collate_fn` adds columns that aren't in the dataset.
- Better inference of shapes and data types
- Lots of hacky special-casing code removed
- Can return string columns (as `tf.String`)
- Most arguments have default values, calling the method should be much simpler
- ~~Can accept a `model` argument and only return columns that are valid inputs to that model~~
- Drops the `dummy_labels` argument - this was a workaround for Keras issues that have been resolved by changes in `transformers`. Also remove it from tests and the Overview notebook.
I still have a couple of TODOs remaining and some testing to do, so don't merge yet, but it should be mostly ready for review at this point! | 2022-06-06T14:31:12Z | 4,170 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-14T11:30:58Z | https://api.github.com/repos/huggingface/datasets/issues/4170/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4170/timeline | to_tf_dataset rewrite | https://api.github.com/repos/huggingface/datasets/issues/4170/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"[Magic is now banned](https://www.youtube.com/watch?v=WIn58XoY728#t=36s) by decree of @sgugger. This is honestly much cleaner, and the functionality will make much more sense in `transformers` anyway!",
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1,203,995,869 | https://api.github.com/repos/huggingface/datasets/issues/4169/labels{/name} | ## Dataset viewer issue for '*timit_asr*'
**Link:** *https://huggingface.co/datasets/timit_asr*
Issue: The timit-asr dataset cannot be previewed recently.
Am I the one who added this dataset ? Yes-No
No | 2023-02-03T04:54:57Z | 4,169 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-04-14T03:28:31Z | https://api.github.com/repos/huggingface/datasets/issues/4169/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4169/timeline | Timit_asr dataset cannot be previewed recently | https://api.github.com/repos/huggingface/datasets/issues/4169/events | null | {
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"Thanks for reporting. The bug has already been detected, and we hope to fix it soon.",
"TIMIT is now a dataset that requires manual download, see #4145 \r\n\r\nTherefore it might take a bit more time to fix it",
"> TIMIT is now a dataset that requires manual download, see #4145\r\n> \r\n> Therefore it might ta... | {
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1,203,867,540 | https://api.github.com/repos/huggingface/datasets/issues/4168/labels{/name} | This PR adds code examples for functions related to the base Datasets class to highlight usage. Most of the examples use the `rotten_tomatoes` dataset since it is nice and small. Several things I would appreciate feedback on:
- Do you think it is clearer to make every code example fully reproducible so when users copy the code they can actually run it and get an output? This seems quite repetitive - maybe even unnecessary - but it is definitely clearer. Personally, I think we might be able to get away with not including this since users probably want to try the function on their own dataset. For example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> code example goes here
```
- Should we showcase a function with more than one parameter to highlight different use-cases (it's pretty basic right now, but I'd be happy to add more)?
- For the `class_encode_column` function, let me know if there is a simpler dataset with fewer columns (currently using `winograd_wsc`) so it is easier for users to see what changed.
- Where possible, I try to show the input before and the output after using a function like `flatten` for example. Do you think this is too much and just showing the usage (ie, `>>> ds.flatten()`) will be sufficient?
Thanks :) | 2022-04-27T18:53:37Z | 4,168 | null | https://api.github.com/repos/huggingface/datasets | false | [
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1,203,761,614 | https://api.github.com/repos/huggingface/datasets/issues/4167/labels{/name} | use http.extraHeader to avoid rate limit | 2022-04-13T20:56:41Z | 4,167 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-13T20:32:17Z | https://api.github.com/repos/huggingface/datasets/issues/4167/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4167/timeline | Avoid rate limit in update hub repositories | https://api.github.com/repos/huggingface/datasets/issues/4167/events | null | {
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1,203,758,004 | https://api.github.com/repos/huggingface/datasets/issues/4166/labels{/name} | Clarify docs and add clarifying example to the exact_match metric | 2022-05-03T12:23:31Z | 4,166 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-04-13T20:28:06Z | https://api.github.com/repos/huggingface/datasets/issues/4166/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4166/timeline | Fix exact match | https://api.github.com/repos/huggingface/datasets/issues/4166/events | null | {
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