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https://api.github.com/repos/huggingface/datasets/issues/1698 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1698/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1698/comments | https://api.github.com/repos/huggingface/datasets/issues/1698/events | https://github.com/huggingface/datasets/pull/1698 | 781,152,561 | MDExOlB1bGxSZXF1ZXN0NTUwOTI0ODQ3 | 1,698 | Update Coached Conv Pref DatasetCard | {
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"Really cool!\r\n\r\nCan you add some task tags for `dialogue-modeling` (under `sequence-modeling`) and `parsing` (under `structured-prediction`)?"
] | 2021-01-07T09:07:16Z | 2021-01-08T17:04:33Z | 2021-01-08T17:04:32Z | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-09-29T16:22:31Z | 2023-10-16T16:03:18Z | 2023-10-16T13:30:46Z | CONTRIBUTOR | null | 0 | {
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} | Reduces the number of commits in `push_to_hub` by using the `preupload` API from https://github.com/huggingface/huggingface_hub/pull/1699. Each commit contains a maximum of 50 uploaded files.
A shard's fingerprint no longer needs to be added as a suffix to support resuming an upload, meaning the shards' naming scheme is the same as the initial one.
Also, it adds support for the following params: `create_pr`, `commit_message` and `revision` (`branch` deprecated; unlike the previous implementation, this one creates a branch if the branch does not exist to be consistent with `transformers`).
(Nit) This implementation keeps the markdown section of the generated README.md empty to enable importing the card template (when the card is accessed on the Hub).
Fixes https://github.com/huggingface/datasets/issues/5492, fixes https://github.com/huggingface/datasets/issues/6257, fixes https://github.com/huggingface/datasets/issues/5045, fixes https://github.com/huggingface/datasets/issues/6271
TODO:
- [x] set the minimal version to the next `hfh` release (once it's published) | {
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https://api.github.com/repos/huggingface/datasets/issues/6226 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6226/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6226/comments | https://api.github.com/repos/huggingface/datasets/issues/6226/events | https://github.com/huggingface/datasets/pull/6226 | 1,887,462,591 | PR_kwDODunzps5Z3arq | 6,226 | Add push_to_hub with multiple configs docs | {
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"_The documentation is not available anymore as the PR was closed or merged._",
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https://api.github.com/repos/huggingface/datasets/issues/4597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4597/comments | https://api.github.com/repos/huggingface/datasets/issues/4597/events | https://github.com/huggingface/datasets/issues/4597 | 1,288,672,007 | I_kwDODunzps5Mz5MH | 4,597 | Streaming issue for financial_phrasebank | {
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"cc @huggingface/datasets: it seems like https://www.researchgate.net/ is flaky for datasets hosting (I put the \"hosted-on-google-drive\" tag since it's the same kind of issue I think)",
"Let's see if their license allows hosting their data on the Hub.",
"License is Creative Commons Attribution-NonCommercial-S... | 2022-06-29T12:45:43Z | 2022-07-01T09:29:36Z | 2022-07-01T09:29:36Z | MEMBER | null | null | null | ### Link
https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train
### Description
As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset:
```
Server error
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No | {
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https://api.github.com/repos/huggingface/datasets/issues/1615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1615/comments | https://api.github.com/repos/huggingface/datasets/issues/1615/events | https://github.com/huggingface/datasets/issues/1615 | 771,641,088 | MDU6SXNzdWU3NzE2NDEwODg= | 1,615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | {
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"Hi @SapirWeissbuch,\r\nWhen you are saying it freezes, at that time it is unzipping the file from the zip file it downloaded. Since it's a very heavy file it'll take some time. It was taking ~11GB after unzipping when it started reading examples for me. Hope that helps!\r\n![Screenshot 2020-12-21 at 23 40 52](http... | 2020-12-20T17:27:38Z | 2021-06-25T13:11:33Z | null | NONE | null | null | null | Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
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https://api.github.com/repos/huggingface/datasets/issues/2421 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2421/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2421/comments | https://api.github.com/repos/huggingface/datasets/issues/2421/events | https://github.com/huggingface/datasets/pull/2421 | 905,549,756 | MDExOlB1bGxSZXF1ZXN0NjU2NjIwMTM3 | 2,421 | doc: fix typo HF_MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES | {
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https://api.github.com/repos/huggingface/datasets/issues/3594 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3594/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3594/comments | https://api.github.com/repos/huggingface/datasets/issues/3594/events | https://github.com/huggingface/datasets/pull/3594 | 1,107,174,619 | PR_kwDODunzps4xN3Kk | 3,594 | fix multiple language downloading in mC4 | {
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"The CI failure is unrelated to your PR and fixed on master, merging :)"
] | 2022-01-18T17:25:19Z | 2022-01-19T11:22:57Z | 2022-01-18T19:10:22Z | CONTRIBUTOR | null | 0 | {
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} | If we try to access multiple languages of the [mC4 dataset](https://github.com/huggingface/datasets/tree/master/datasets/mc4), it will throw an error. For example, if we do
```python
mc4_subset_two_langs = load_dataset("mc4", languages=["st", "su"])
```
we got
```
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/multilingual/c4-st+su.tfrecord-00000-of-00002.json.gz
```
Now it should work. Check it (from the root dir of a project):
```python
mc4_subset_two_langs = load_dataset("./datasets/mc4/", languages=["st", "su"])
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1326 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1326/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1326/comments | https://api.github.com/repos/huggingface/datasets/issues/1326/events | https://github.com/huggingface/datasets/pull/1326 | 759,611,784 | MDExOlB1bGxSZXF1ZXN0NTM0NTg2ODY4 | 1,326 | TEP: Tehran English-Persian parallel corpus | {
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more info : http://opus.nlpl.eu/TEP.php | {
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https://api.github.com/repos/huggingface/datasets/issues/6173 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6173/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6173/comments | https://api.github.com/repos/huggingface/datasets/issues/6173/events | https://github.com/huggingface/datasets/issues/6173 | 1,863,422,065 | I_kwDODunzps5vEZBx | 6,173 | Fix CI for pyarrow 13.0.0 | {
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```
FAILED tests/test_formatting.py::ArrowExtractorTest::test_pandas_extractor - AssertionError: Attributes of Series are different
Attribute "dtype" are different
[left]: datetime64[us, UTC]
[right]: datetime64[ns, UTC]
```
```
FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type
fixed_size_list<item: int32>[3]
to
Sequence(feature=Value(dtype='int64', id=None), length=3, id=None)
```
e.g. in https://github.com/huggingface/datasets/actions/runs/5952253963/job/16143847230
first error may be related to https://github.com/apache/arrow/issues/33321
second one maybe because `feature.length * len(array) == len(array_values)` is not satisfied anymore somehow ? | {
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https://api.github.com/repos/huggingface/datasets/issues/5738 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5738/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5738/comments | https://api.github.com/repos/huggingface/datasets/issues/5738/events | https://github.com/huggingface/datasets/issues/5738 | 1,663,477,690 | I_kwDODunzps5jJqe6 | 5,738 | load_dataset("text","dataset.txt") loads the wrong dataset! | {
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"You need to provide a text file as `data_files`, not as a configuration:\r\n\r\n```python\r\nmy_dataset = load_dataset(\"text\", data_files=\"TextFile.txt\")\r\n```\r\n\r\nOtherwise, since `data_files` is `None`, it picks up Colab's sample datasets from the `content` dir."
] | 2023-04-12T01:07:46Z | 2023-04-19T12:08:27Z | 2023-04-19T12:08:27Z | NONE | null | null | null | ### Describe the bug
I am trying to load my own custom text dataset using the load_dataset function. My dataset is a bunch of ordered text, think along the lines of shakespeare plays. However, after I load the dataset and I inspect it, the dataset is a table with a bunch of latitude and longitude values! What in the world??
### Steps to reproduce the bug
my_dataset = load_dataset("text","TextFile.txt")
my_dataset
### Expected behavior
I expected the dataset to contain the actual data from the text document that I used.
### Environment info
Google Colab | {
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https://api.github.com/repos/huggingface/datasets/issues/3377 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3377/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3377/comments | https://api.github.com/repos/huggingface/datasets/issues/3377/events | https://github.com/huggingface/datasets/pull/3377 | 1,070,562,907 | PR_kwDODunzps4vXCHn | 3,377 | COCO 🥥 on the 🤗 Hub? | {
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"@mariosasko I fixed couple of bugs",
"TO-DO: \r\n- [x] Add unlabeled 2017 splits, train and validation splits of 2015\r\n- [x] Add Class Labels as list instead",
"@mariosasko added fine & coarse grained labels, will fix the bugs (currently getting set up with VM, my internet is too slow to run the tests and do... | 2021-12-03T12:55:27Z | 2021-12-20T14:14:01Z | 2021-12-20T14:14:00Z | CONTRIBUTOR | null | 1 | {
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} | This is a draft PR since I ran into few small problems.
I referred to this TFDS code: https://github.com/tensorflow/datasets/blob/2538a08c184d53b37bfcf52cc21dd382572a88f4/tensorflow_datasets/object_detection/coco.py
cc: @mariosasko | {
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https://api.github.com/repos/huggingface/datasets/issues/5038 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5038/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5038/comments | https://api.github.com/repos/huggingface/datasets/issues/5038/events | https://github.com/huggingface/datasets/issues/5038 | 1,389,631,122 | I_kwDODunzps5S1BaS | 5,038 | `Dataset.unique` showing wrong output after filtering | {
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"Hi! It seems like `flatten_indices` (called in `unique`) doesn't know how to handle empty indices mappings. I'm working on the fix.",
"Thanks, that was fast!"
] | 2022-09-28T16:20:35Z | 2022-09-30T15:44:25Z | 2022-09-30T15:44:25Z | CONTRIBUTOR | null | null | null | ## Describe the bug
After filtering a dataset, and if no samples remain, `Dataset.unique` will return the unique values of the unfiltered dataset.
## Steps to reproduce the bug
```python
from datasets import Dataset
dataset = Dataset.from_dict({'id': [0]})
dataset = dataset.filter(lambda _: False)
print(dataset.unique('id'))
```
## Expected results
The above code should return an empty list since the dataset is empty.
## Actual results
```bash
[0]
```
## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-5.18.19-100.fc35.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.14
- PyArrow version: 7.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/3256 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3256/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3256/comments | https://api.github.com/repos/huggingface/datasets/issues/3256/events | https://github.com/huggingface/datasets/pull/3256 | 1,052,000,613 | PR_kwDODunzps4udTqg | 3,256 | asserts replaced by exception for text classification task with test. | {
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"Haha it looks like you got the chance of being reviewed twice at the same time and got the same suggestion twice x)\r\nAnyway it's all good now so we can merge !",
"Thanks for the feedback. "
] | 2021-11-12T14:05:36Z | 2021-11-12T15:09:33Z | 2021-11-12T14:59:32Z | CONTRIBUTOR | null | 0 | {
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} | I have replaced only a single assert in text_classification.py along with a unit test to verify an exception is raised based on https://github.com/huggingface/datasets/issues/3171 .
I would like to first understand the code contribution workflow. So keeping the change to a single file rather than making too many changes. Once this gets approved, I will look into the rest.
Thanks. | {
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https://api.github.com/repos/huggingface/datasets/issues/1485 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1485/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1485/comments | https://api.github.com/repos/huggingface/datasets/issues/1485/events | https://github.com/huggingface/datasets/pull/1485 | 762,774,822 | MDExOlB1bGxSZXF1ZXN0NTM3Mjg4MTg0 | 1,485 | Re-added wiki_movies dataset due to previous PR having changes from m… | {
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https://api.github.com/repos/huggingface/datasets/issues/1447 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1447/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1447/comments | https://api.github.com/repos/huggingface/datasets/issues/1447/events | https://github.com/huggingface/datasets/pull/1447 | 761,067,955 | MDExOlB1bGxSZXF1ZXN0NTM1NzkxODk1 | 1,447 | Update step-by-step guide for windows | {
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"Hi @thomwolf, for simplification purposes, I think you could remove the \"`pip install ...`\" steps from this commit, 'cause these deps (black, isort, flake8) are already installed on `pip install -e \".[dev]\"` on the [Start by preparing your environment](https://github.com/huggingface/datasets/blob/704107f924e74... | 2020-12-10T09:30:59Z | 2020-12-10T12:18:47Z | 2020-12-10T09:31:14Z | MEMBER | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4554 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4554/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4554/comments | https://api.github.com/repos/huggingface/datasets/issues/4554/events | https://github.com/huggingface/datasets/pull/4554 | 1,283,369,453 | PR_kwDODunzps46Sv_f | 4,554 | Fix WMT dataset loading issue and docs update (Re-opened) | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-24T07:26:16Z | 2022-07-08T15:39:20Z | 2022-07-08T15:27:44Z | CONTRIBUTOR | null | 0 | {
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} | This PR is a fix for #4354
Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets.
Let me know, if any additional changes are required.
Thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/5012 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5012/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5012/comments | https://api.github.com/repos/huggingface/datasets/issues/5012/events | https://github.com/huggingface/datasets/issues/5012 | 1,382,851,096 | I_kwDODunzps5SbKIY | 5,012 | Force JSON format regardless of file naming on S3 | {
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"Hi ! Support for URIs like `s3://...` is not implemented yet in `data_files=`. You can use the HTTP URL instead if your data is public in the meantime",
"Hi,\r\nI want to make sure I understand this response. I have a set of files on S3 that are private for security reasons. Because they are not public files I ... | 2022-09-22T18:28:15Z | 2023-08-16T09:58:36Z | 2023-08-16T09:58:36Z | NONE | null | null | null | I have a file on S3 created by Data Version Control, it looks like `s3://dvc/ac/badff5b134382a0f25248f1b45d7b2` but contains a json file. If I run
```python
dataset = load_dataset(
"json",
data_files='s3://dvc/ac/badff5b134382a0f25248f1b45d7b2'
)
```
It gives me
```
InvalidSchema: No connection adapters were found for 's3://dvc/ac/badff5b134382a0f25248f1b45d7b2'
```
However, I cannot go ahead and change the names of the s3 file. Is there a way to "force" load a S3 url with certain decoder (JSON, CSV, etc.) regardless of s3 URL naming? | {
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https://api.github.com/repos/huggingface/datasets/issues/5767 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5767/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5767/comments | https://api.github.com/repos/huggingface/datasets/issues/5767/events | https://github.com/huggingface/datasets/issues/5767 | 1,672,433,979 | I_kwDODunzps5jr1E7 | 5,767 | How to use Distill-BERT with different datasets? | {
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"Closing this one in favor of the same issue opened in the `transformers` repo."
] | 2023-04-18T06:25:12Z | 2023-04-20T16:52:05Z | 2023-04-20T16:52:05Z | NONE | null | null | null | ### Describe the bug
- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyTorch version (GPU?): 1.12.0+cu102 (True)
- Tensorflow version (GPU?): 2.10.0 (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>
### Steps to reproduce the bug
I recently read [this](https://huggingface.co/docs/transformers/quicktour#train-with-tensorflow:~:text=The%20most%20important%20thing%20to%20remember%20is%20you%20need%20to%20instantiate%20a%20tokenizer%20with%20the%20same%20model%20name%20to%20ensure%20you%E2%80%99re%20using%20the%20same%20tokenization%20rules%20a%20model%20was%20pretrained%20with.) and was wondering how to use distill-BERT (which is pre-trained with imdb dataset) with a different dataset (for eg. [this](https://huggingface.co/datasets/yhavinga/imdb_dutch) dataset)?
### Expected behavior
Distill-BERT should work with different datasets.
### Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 11.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3748 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3748/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3748/comments | https://api.github.com/repos/huggingface/datasets/issues/3748/events | https://github.com/huggingface/datasets/pull/3748 | 1,142,128,763 | PR_kwDODunzps4zCEyM | 3,748 | Add tqdm arguments | {
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1. It is able to show the progress bar by adding the length of the iterator.
2. Pass in tqdm_kwargs so that can enable more feasibility for the control of tqdm library. | {
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https://api.github.com/repos/huggingface/datasets/issues/3696 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3696/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3696/comments | https://api.github.com/repos/huggingface/datasets/issues/3696/events | https://github.com/huggingface/datasets/pull/3696 | 1,129,764,534 | PR_kwDODunzps4yXXgH | 3,696 | Force unique keys in newsqa dataset | {
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Fix #3630. | {
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"It's a first attempt at solving https://github.com/huggingface/datasets/issues/3013.",
"I only kept these ones:\r\n```\r\n path: str,\r\n data_files: Optional[Union[Dict, List, str]] = None,\r\n download_config: Optional[DownloadConfig] = None,\r\n download_mode: Optional[GenerateMode] = None,\r\n ... | 2022-02-02T22:11:56Z | 2022-02-21T15:57:11Z | 2022-02-21T15:57:10Z | CONTRIBUTOR | null | 0 | {
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} | in get_dataset_infos(). Also: add the `use_auth_token` parameter, and create get_dataset_config_info()
✅ Closes: #3013 | {
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"Could you also run `make style` to fix the CI check on code formatting ?",
"@lhoestq completed and resolved all comments."
] | 2021-02-05T09:30:54Z | 2021-02-15T09:57:39Z | 2021-02-15T09:57:39Z | CONTRIBUTOR | null | 0 | {
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} | # Dataset Card for Hindi Discourse Analysis Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : https://www.aclweb.org/anthology/2020.aacl-main.71
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. Hindi Discourse Analysis (HDA) Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Premise and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Entailed means that hypotheis can be inferred from premise and not-entailed means vice versa
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
- Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- train, test and dev files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह एक वर्णनात्मक कथन है।', 'label': 1, 'premise': 'जैसे उस का सारा चेहरा अपना हो और आँखें किसी दूसरे की जो चेहरे पर पपोटों के पीछे महसूर कर दी गईं।', 'topic': 1}
```
### Data Fields
- Each row contatins 4 columns - premise, hypothesis, label and topic.
### Data Splits
- Train : 31892
- Valid : 9460
- Test : 9970
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available Hindi Discourse Analysis classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper https://www.aclweb.org/anthology/2020.aacl-main.71
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- Initial Data was collected by members of MIDAS Lab from Hindi Websites. They crowd sourced the data annotation process and selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode.
- Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
- The Discourse is further classified into "Argumentative" , "Descriptive" , "Dialogic" , "Informative" and "Narrative" - 5 Clases.
#### Who are the source language producers?
Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically by machine and corresponding recasting process.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
No known bias exist in the dataset.
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations . Size of data may not be enough to train large models
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/midas-research/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
### Contributions
Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset.
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https://api.github.com/repos/huggingface/datasets/issues/3096 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3096/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3096/comments | https://api.github.com/repos/huggingface/datasets/issues/3096/events | https://github.com/huggingface/datasets/pull/3096 | 1,027,535,685 | PR_kwDODunzps4tQblQ | 3,096 | Fix Audio feature mp3 resampling | {
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} | Issue #3095 is related to mp3 resampling, not to `cast_column`.
This PR fixes Audio feature mp3 resampling.
Fix #3095. | {
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https://api.github.com/repos/huggingface/datasets/issues/2386 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2386/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2386/comments | https://api.github.com/repos/huggingface/datasets/issues/2386/events | https://github.com/huggingface/datasets/issues/2386 | 897,560,049 | MDU6SXNzdWU4OTc1NjAwNDk= | 2,386 | Accessing Arrow dataset cache_files | {
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"Thanks @bhavitvyamalik for referencing the workaround. Setting `keep_in_memory=False` is working."
] | 2021-05-20T23:57:43Z | 2021-05-21T19:18:03Z | 2021-05-21T19:18:03Z | NONE | null | null | null | ## Describe the bug
In datasets 1.5.0 the following code snippet would have printed the cache_files:
```
train_data = load_dataset('conll2003', split='train', cache_dir='data')
print(train_data.cache_files[0]['filename'])
```
However, in the newest release (1.6.1), it prints an empty list.
I also tried loading the dataset with `keep_in_memory=True` argument but still `cache_files` is empty.
Was wondering if this is a bug or I need to pass additional arguments so I can access the cache_files.
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https://api.github.com/repos/huggingface/datasets/issues/4379 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4379/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4379/comments | https://api.github.com/repos/huggingface/datasets/issues/4379/events | https://github.com/huggingface/datasets/issues/4379 | 1,243,175,854 | I_kwDODunzps5KGVuu | 4,379 | Latest dill release raises exception | {
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"Fixed by:\r\n- #4380 ",
"Just an additional insight, the latest dill (either 0.3.5 or 0.3.5.1) also broke the hashing/fingerprinting of any mapping function.\r\n\r\nFor example:\r\n```\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"rotten_tomatoes\")\r\nd.map(lambda x: x)\r\n```\r\n\r\nReturns th... | 2022-05-20T13:48:36Z | 2022-05-21T15:53:26Z | 2022-05-20T17:06:27Z | MEMBER | null | null | null | ## Describe the bug
As reported by @sgugger, latest dill release is breaking things with Datasets.
```
______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________
self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None
def get(self, timeout=None):
self.wait(timeout)
if not self.ready():
raise TimeoutError
if self._success:
return self._value
else:
> raise self._value
E TypeError: '>' not supported between instances of 'NoneType' and 'float'
```
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"@lhoestq\r\n\r\nShould I remove the scripts? or atleast remove running them from the CircleCI config?\r\n\r\nAlso, I hope it is okay that the combined method (metadata+content) is only a slow test, and for the Circle CI, I assume only non-slow tests are run? If yes, this would mean separate tests for content and m... | 2021-05-11T17:14:27Z | 2021-05-21T12:10:47Z | 2021-05-21T12:10:47Z | CONTRIBUTOR | null | 0 | {
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} | Adding tests for dataset cards
This PR will potentially remove the scripts being used for dataset tags and readme validation.
Additionally, this will allow testing dataset readmes by providing the name as follows:
```bash
pytest tests/test_dataset_cards.py::test_dataset_tags[fashion_mnist]
```
and
```bash
pytest tests/test_dataset_cards.py::test_readme_content[fashion_mnist]
```
or a combined test as:
```bash
pytest tests/test_dataset_cards.py::test_dataset_card[fashion_mnist]
```
@lhoestq | {
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"Hi @lhoestq @yjernite, requesting you to review this for any changes needed. Thanks! :)",
"Hi @lhoestq , I have updated the PR"
] | 2020-12-12T12:17:53Z | 2020-12-15T18:32:38Z | 2020-12-15T18:32:38Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3193 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3193/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3193/comments | https://api.github.com/repos/huggingface/datasets/issues/3193/events | https://github.com/huggingface/datasets/issues/3193 | 1,041,971,117 | I_kwDODunzps4-Gzet | 3,193 | Update link to datasets-tagging app | {
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- huggingface/datasets-tagging#22
We should update the link in Datasets. | {
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https://api.github.com/repos/huggingface/datasets/issues/3305 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3305/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3305/comments | https://api.github.com/repos/huggingface/datasets/issues/3305/events | https://github.com/huggingface/datasets/pull/3305 | 1,059,161,000 | PR_kwDODunzps4uzZWv | 3,305 | asserts replaced with exception for ``fingerprint.py``, ``search.py``, ``arrow_writer.py`` and ``metric.py`` | {
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} | Addresses #3171
Fixes exception for ``fingerprint.py``, ``search.py``, ``arrow_writer.py`` and ``metric.py`` and modified tests | {
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https://api.github.com/repos/huggingface/datasets/issues/6216 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6216/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6216/comments | https://api.github.com/repos/huggingface/datasets/issues/6216/events | https://github.com/huggingface/datasets/pull/6216 | 1,883,492,703 | PR_kwDODunzps5Zp8al | 6,216 | Release: 2.13.2 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-09-06T08:15:32Z | 2023-09-06T08:52:18Z | 2023-09-06T08:22:43Z | MEMBER | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5831/comments | https://api.github.com/repos/huggingface/datasets/issues/5831/events | https://github.com/huggingface/datasets/issues/5831 | 1,701,813,835 | I_kwDODunzps5lb55L | 5,831 | [Bug]504 Server Error when loading dataset which was already cached | {
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"I am experiencing the same problem with the following environment:\r\n\r\n* `datasets` version: 2.11.0\r\n* Platform: `Linux 5.19.0-41-generic x86_64 GNU/Linux`\r\n* Python version: `3.8.5`\r\n* Huggingface_hub version: 0.13.3\r\n* PyArrow version: `11.0.0`\r\n* Pandas version: `1.5.3`\r\n\r\nTrying to get some di... | 2023-05-09T10:31:07Z | 2023-05-10T01:48:20Z | null | NONE | null | null | null | ### Describe the bug
I have already cached the dataset using:
```
dataset = load_dataset("databricks/databricks-dolly-15k",
cache_dir="/mnt/data/llm/datasets/databricks-dolly-15k")
```
After that, I tried to load it again using the same machine, I got this error:
```
Traceback (most recent call last):
File "/mnt/home/llm/pythia/train.py", line 16, in <module>
dataset = load_dataset("databricks/databricks-dolly-15k",
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1773, in load_dataset
builder_instance = load_dataset_builder(
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1502, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1219, in dataset_module_factory
raise e1 from None
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1186, in dataset_module_factory
raise e
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1160, in dataset_module_factory
dataset_info = hf_api.dataset_info(
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 120, in _inner_fn
return fn(*args, **kwargs)
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 1667, in dataset_info
hf_raise_for_status(r)
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py", line 301, in hf_raise_for_status
raise HfHubHTTPError(str(e), response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/databricks/databricks-dolly-15k
```
### Steps to reproduce the bug
1. cache the databrick-dolly-15k dataset using load_dataset, setting a cache_dir
2. use load_dataset again, setting the same cache_dir
### Expected behavior
Dataset loaded succuessfully.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-4.18.0-372.16.1.el8_6.x86_64-x86_64-with-glibc2.27
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/2257 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2257/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2257/comments | https://api.github.com/repos/huggingface/datasets/issues/2257/events | https://github.com/huggingface/datasets/pull/2257 | 866,755,203 | MDExOlB1bGxSZXF1ZXN0NjIyNTkwMDQw | 2,257 | added metrics for CUAD | {
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"> For now I've added F1, AUPR, Precision at 80% recall, and Precision at 90%. Last 3 metrics were reported in the [paper](https://arxiv.org/pdf/2103.06268.pdf). Please let me know if we require `exact_match` metric too here\r\n\r\n@bhavitvyamalik I guess the mentioned metrics are enough but it would be better if ... | 2021-04-24T14:09:54Z | 2021-04-29T09:53:38Z | 2021-04-27T16:16:32Z | CONTRIBUTOR | null | 0 | {
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} | For now I've added F1, AUPR, Precision at 80% recall, and Precision at 90%. Last 3 metrics were reported in the [paper](https://arxiv.org/pdf/2103.06268.pdf). Please let me know if we require `exact_match` metric too here | {
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https://api.github.com/repos/huggingface/datasets/issues/3579 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3579/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3579/comments | https://api.github.com/repos/huggingface/datasets/issues/3579/events | https://github.com/huggingface/datasets/pull/3579 | 1,103,451,118 | PR_kwDODunzps4xBmY4 | 3,579 | Add Text2log Dataset | {
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"The CI fails are unrelated to your PR and fixed on master, I think we can merge now !"
] | 2022-01-14T10:45:01Z | 2022-01-20T17:09:44Z | 2022-01-20T17:09:44Z | CONTRIBUTOR | null | 0 | {
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"I have a question: why not using a tmp dir instead, like the DummyDataGeneratorDownloadManager does?",
"Hi @lhoestq, I am trying to understand better the logic...\r\n\r\nWhy do we have a `dynamic_module_path` besides the modules cache path?\r\n```python\r\nDYNAMIC_MODULES_PATH = os.path.join(HF_MODULES_CACHE, \"... | 2020-12-09T10:59:59Z | 2022-07-06T15:19:47Z | null | MEMBER | null | 0 | {
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} | When passed `--cache_dir` arg:
```shell
python datasets-cli test datasets/<my-dataset-folder> --save_infos --all_configs --cache_dir <my-cache-dir>
```
it is not used for caching the modules, which are cached in the default location at `.cache/huggingface/modules`.
With this fix, the modules will be cached at `<my-cache-dir>/modules`. | {
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https://api.github.com/repos/huggingface/datasets/issues/2897 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2897/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2897/comments | https://api.github.com/repos/huggingface/datasets/issues/2897/events | https://github.com/huggingface/datasets/pull/2897 | 993,798,386 | MDExOlB1bGxSZXF1ZXN0NzMxOTA0ODk4 | 2,897 | Add OpenAI's HumanEval dataset | {
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"I just fixed the class name, and added `[More Information Needed]` in empty sections in case people want to complete the dataset card :)"
] | 2021-09-11T09:37:47Z | 2021-09-16T15:02:11Z | 2021-09-16T15:02:11Z | MEMBER | null | 0 | {
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} | This PR adds OpenAI's [HumanEval](https://github.com/openai/human-eval) dataset. The dataset consists of 164 handcrafted programming problems with solutions and unittests to verify solution. This dataset is useful to evaluate code generation models. | {
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https://api.github.com/repos/huggingface/datasets/issues/3171 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3171/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3171/comments | https://api.github.com/repos/huggingface/datasets/issues/3171/events | https://github.com/huggingface/datasets/issues/3171 | 1,037,728,059 | I_kwDODunzps492nk7 | 3,171 | Raise exceptions instead of using assertions for control flow | {
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"Adding the remaining tasks for this issue to help new code contributors. \r\n$ cd src/datasets && ack assert -lc \r\n- [x] commands/convert.py:1\r\n- [x] arrow_reader.py:3\r\n- [x] load.py:7\r\n- [x] utils/py_utils.py:2\r\n- [x] features/features.py:9\r\n- [x] arrow_writer.py:7\r\n- [x] search.py:6\r\n- [x] table... | 2021-10-27T18:26:52Z | 2021-12-23T16:40:37Z | 2021-12-23T16:40:37Z | CONTRIBUTOR | null | null | null | Motivated by https://github.com/huggingface/transformers/issues/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.
Currently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR. | {
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https://api.github.com/repos/huggingface/datasets/issues/1996 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1996/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1996/comments | https://api.github.com/repos/huggingface/datasets/issues/1996/events | https://github.com/huggingface/datasets/issues/1996 | 823,573,410 | MDU6SXNzdWU4MjM1NzM0MTA= | 1,996 | Error when exploring `arabic_speech_corpus` | {
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"descrip... | closed | false | null | [] | null | [
"Thanks for reporting! We'll fix that as soon as possible",
"Actually soundfile is not a dependency of this dataset.\r\nThe error comes from a bug that was fixed in this commit: https://github.com/huggingface/datasets/pull/1767/commits/c304e63629f4453367de2fd42883a78768055532\r\nBasically the library used to cons... | 2021-03-06T05:55:20Z | 2022-10-05T13:24:26Z | 2022-10-05T13:24:26Z | NONE | null | null | null | Navigate to https://huggingface.co/datasets/viewer/?dataset=arabic_speech_corpus
Error:
```
ImportError: To be able to use this dataset, you need to install the following dependencies['soundfile'] using 'pip install soundfile' for instance'
Traceback:
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/script_runner.py", line 332, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 233, in <module>
configs = get_confs(option)
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py", line 604, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py", line 588, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp-viewer/run.py", line 145, in get_confs
module_path = nlp.load.prepare_module(path, dataset=True
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/datasets/load.py", line 342, in prepare_module
f"To be able to use this {module_type}, you need to install the following dependencies"
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6235 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6235/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6235/comments | https://api.github.com/repos/huggingface/datasets/issues/6235/events | https://github.com/huggingface/datasets/issues/6235 | 1,893,337,083 | I_kwDODunzps5w2gf7 | 6,235 | Support multiprocessing for download/extract nestedly | {
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] | open | false | null | [] | null | [] | 2023-09-12T21:51:08Z | 2023-09-12T21:51:08Z | null | NONE | null | null | null | ### Feature request
Current multiprocessing for download/extract is not done nestedly. For example, when processing SlimPajama, there is only 3 processes (for train/test/val), while there are many files inside these 3 folders
```
Downloading data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #1: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #2: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #1: 0%| | 0/1 [00:00<?, ?obj/s][A
Extracting data files #2: 0%| | 0/1 [00:00<?, ?obj/s][A[A
```
### Motivation
speedup dataset loading
### Your contribution
I can help test the feature | {
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https://api.github.com/repos/huggingface/datasets/issues/4772 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4772/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4772/comments | https://api.github.com/repos/huggingface/datasets/issues/4772/events | https://github.com/huggingface/datasets/issues/4772 | 1,322,693,123 | I_kwDODunzps5O1rID | 4,772 | AssertionError when using label_cols in to_tf_dataset | {
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"cc @Rocketknight1 ",
"Hi @lehrig, this is caused by the data collator renaming \"label\" to \"labels\". If you set `label_cols=[\"labels\"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames co... | 2022-07-29T21:32:12Z | 2022-09-12T11:24:46Z | 2022-09-12T11:24:46Z | NONE | null | null | null | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
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https://api.github.com/repos/huggingface/datasets/issues/3723 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3723/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3723/comments | https://api.github.com/repos/huggingface/datasets/issues/3723/events | https://github.com/huggingface/datasets/pull/3723 | 1,138,789,493 | PR_kwDODunzps4y3RuI | 3,723 | Fix flatten of complex feature types | {
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"Apparently the merge brought back some tests that use `flatten_()` that we removed recently",
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-02-15T14:45:33Z | 2022-03-18T17:32:26Z | 2022-03-18T17:28:14Z | CONTRIBUTOR | null | 0 | {
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} | Fix `flatten` for the following feature types: Image/Audio, Translation, and TranslationVariableLanguages.
Inspired by `cast`/`table_cast`, I've introduced a `table_flatten` function to handle the Image/Audio types.
CC: @SBrandeis
Fix #3686.
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https://api.github.com/repos/huggingface/datasets/issues/3279 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3279/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3279/comments | https://api.github.com/repos/huggingface/datasets/issues/3279/events | https://github.com/huggingface/datasets/pull/3279 | 1,054,711,852 | PR_kwDODunzps4ulVHe | 3,279 | Minor Typo Fix - Precision to Recall | {
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https://api.github.com/repos/huggingface/datasets/issues/2183 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2183/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2183/comments | https://api.github.com/repos/huggingface/datasets/issues/2183/events | https://github.com/huggingface/datasets/pull/2183 | 852,518,411 | MDExOlB1bGxSZXF1ZXN0NjEwNzU3MjUz | 2,183 | Fix s3fs tests for py36 and py37+ | {
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} | Recently several changes happened:
1. latest versions of `fsspec` require python>3.7 for async features
2. `s3fs` added a dependency on `aiobotocore`, which is not compatible with the `moto` s3 mock context manager
This PR fixes both issues, by pinning `fsspec` and `s3fs` for python 3.6, and by using `moto` in server mode to support running the tests on python>=3.7 with the latest version of `fsspec` and `s3fs`.
cc @philschmid | {
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https://api.github.com/repos/huggingface/datasets/issues/3013 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3013/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3013/comments | https://api.github.com/repos/huggingface/datasets/issues/3013/events | https://github.com/huggingface/datasets/issues/3013 | 1,014,960,419 | I_kwDODunzps48fxEj | 3,013 | Improve `get_dataset_infos`? | {
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"default": ... | closed | false | null | [] | null | [
"To keeps things simple maybe we should use `load_dataset_builder` in `get_dataset_infos`.\r\n`load_dataset_builder` instantiates a builder and runs the _infos() method in order to give you the most up-to-date infos, even if the dataset_infos.json is outdated or missing."
] | 2021-10-04T09:47:04Z | 2022-02-21T15:57:10Z | 2022-02-21T15:57:10Z | CONTRIBUTOR | null | null | null | Using the dedicated function `get_dataset_infos` on a dataset that has no dataset-info.json file returns an empty info:
```
>>> from datasets import get_dataset_infos
>>> get_dataset_infos('wit')
{}
```
While it's totally possible to get it (regenerate it) with:
```
>>> from datasets import load_dataset_builder
>>> builder = load_dataset_builder('wit')
>>> builder.info
DatasetInfo(description='Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set\n of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its\n size enables WIT to be used as a pretraining dataset for multimodal machine learning models.\n', citation='@article{srinivasan2021wit,\n title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},\n author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},\n journal={arXiv preprint arXiv:2103.01913},\n year={2021}\n}\n', homepage='https://github.com/google-research-datasets/wit', license='', features={'b64_bytes': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'image_url': Value(dtype='string', id=None), 'metadata_url': Value(dtype='string', id=None), 'original_height': Value(dtype='int32', id=None), 'original_width': Value(dtype='int32', id=None), 'mime_type': Value(dtype='string', id=None), 'caption_attribution_description': Value(dtype='string', id=None), 'wit_features': Sequence(feature={'language': Value(dtype='string', id=None), 'page_url': Value(dtype='string', id=None), 'attribution_passes_lang_id': Value(dtype='string', id=None), 'caption_alt_text_description': Value(dtype='string', id=None), 'caption_reference_description': Value(dtype='string', id=None), 'caption_title_and_reference_description': Value(dtype='string', id=None), 'context_page_description': Value(dtype='string', id=None), 'context_section_description': Value(dtype='string', id=None), 'hierarchical_section_title': Value(dtype='string', id=None), 'is_main_image': Value(dtype='string', id=None), 'page_changed_recently': Value(dtype='string', id=None), 'page_title': Value(dtype='string', id=None), 'section_title': Value(dtype='string', id=None)}, length=-1, id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='wit', config_name='default', version=0.0.0, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None)
```
Should we test if info is empty, and in that case regenerate it? Or always generate it? | {
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https://api.github.com/repos/huggingface/datasets/issues/1785 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1785/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1785/comments | https://api.github.com/repos/huggingface/datasets/issues/1785/events | https://github.com/huggingface/datasets/issues/1785 | 795,458,856 | MDU6SXNzdWU3OTU0NTg4NTY= | 1,785 | Not enough disk space (Needed: Unknown size) when caching on a cluster | {
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"Hi ! \r\n\r\nWhat do you mean by \"disk_usage(\".\").free` can't compute on the cluster's shared disk\" exactly ?\r\nDoes it return 0 ?",
"Yes, that's right. It shows 0 free space even though there is. I suspect it might have to do with permissions on the shared disk.\r\n\r\n```python\r\n>>> disk_usage(\".\")\r\... | 2021-01-27T21:30:59Z | 2022-11-07T16:33:03Z | 2021-01-30T01:07:56Z | CONTRIBUTOR | null | null | null | I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path")
OSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)
```
[`utils.has_sufficient_disk_space`](https://github.com/huggingface/datasets/blob/8a03ab7d123a76ee744304f21ce868c75f411214/src/datasets/utils/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(".").free` can't compute on the cluster's shared disk).
This is exactly where the error gets thrown:
https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L502
```python
if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):
raise IOError(
"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})".format(
utils.size_str(self.info.size_in_bytes or 0),
utils.size_str(self.info.download_size or 0),
utils.size_str(self.info.dataset_size or 0),
utils.size_str(self.info.post_processing_size or 0),
)
)
```
What would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal.
Would it be possible to pass a flag to skip this check on disk space? | {
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https://api.github.com/repos/huggingface/datasets/issues/1727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1727/comments | https://api.github.com/repos/huggingface/datasets/issues/1727/events | https://github.com/huggingface/datasets/issues/1727 | 784,435,131 | MDU6SXNzdWU3ODQ0MzUxMzE= | 1,727 | BLEURT score calculation raises UnrecognizedFlagError | {
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"Upgrading tensorflow to version 2.4.0 solved the issue.",
"I still have the same error even with TF 2.4.0.",
"And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!",
"I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.co... | 2021-01-12T17:27:02Z | 2022-06-01T16:06:02Z | 2022-06-01T16:06:02Z | NONE | null | null | null | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2962 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2962/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2962/comments | https://api.github.com/repos/huggingface/datasets/issues/2962/events | https://github.com/huggingface/datasets/issues/2962 | 1,006,557,666 | I_kwDODunzps47_tni | 2,962 | Enable splits during streaming the dataset | {
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] | open | false | null | [] | null | [] | 2021-09-24T15:01:29Z | 2021-09-24T15:01:29Z | null | CONTRIBUTOR | null | null | null | ## Describe the Problem
I'd like to stream only a specific percentage or part of the dataset.
I want to do splitting when I'm streaming dataset as well.
## Solution
Enabling splits when `streaming = True` as well.
`e.g. dataset = load_dataset('dataset', split='train[:100]', streaming = True)`
## Alternatives
Below is the alternative of doing it.
`dataset = load_dataset("dataset", split='train', streaming = True).take(100)`
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https://api.github.com/repos/huggingface/datasets/issues/4334 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4334/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4334/comments | https://api.github.com/repos/huggingface/datasets/issues/4334/events | https://github.com/huggingface/datasets/pull/4334 | 1,234,103,477 | PR_kwDODunzps43uguB | 4,334 | Adding eval metadata for billsum | {
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https://api.github.com/repos/huggingface/datasets/issues/2978 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2978/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2978/comments | https://api.github.com/repos/huggingface/datasets/issues/2978/events | https://github.com/huggingface/datasets/issues/2978 | 1,009,521,419 | I_kwDODunzps48LBML | 2,978 | Run CI tests against non-production server | {
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"Hey @albertvillanova could you provide more context, including extracts from the discussion we had ?\r\n\r\nLet's ping @Pierrci @julien-c and @n1t0 for their opinion about that",
"@julien-c increased the huggingface.co production workers in order to see if it solve [the 502 you had this morning](https://app.circ... | 2021-09-28T09:41:26Z | 2021-09-28T15:23:50Z | null | MEMBER | null | null | null | Currently, the CI test suite performs requests to the HF production server.
As discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`. | {
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https://api.github.com/repos/huggingface/datasets/issues/5833 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5833/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5833/comments | https://api.github.com/repos/huggingface/datasets/issues/5833/events | https://github.com/huggingface/datasets/issues/5833 | 1,702,280,682 | I_kwDODunzps5ldr3q | 5,833 | Unable to push dataset - `create_pr` problem | {
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"Thanks for reporting, @agombert.\r\n\r\nIn this case, I think the root issue is authentication: before pushing to Hub, you should authenticate. See our docs: https://huggingface.co/docs/datasets/upload_dataset#upload-with-python\r\n> 2. To upload a dataset on the Hub in Python, you need to log in to your Hugging F... | 2023-05-09T15:32:55Z | 2023-10-11T08:52:06Z | null | NONE | null | null | null | ### Describe the bug
I can't upload to the hub the dataset I manually created locally (Image dataset). I have a problem when using the method `.push_to_hub` which asks for a `create_pr` attribute which is not compatible.
### Steps to reproduce the bug
here what I have:
```python
dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts")
```
Output:
```python
Pushing split train to the Hub.
Pushing dataset shards to the dataset hub: 0%| | 0/2 [00:00<?, ?it/s]
Creating parquet from Arrow format: 0%| | 0/3 [00:00<?, ?ba/s]
Creating parquet from Arrow format: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 12.70ba/s]
Pushing dataset shards to the dataset hub: 0%| | 0/2 [00:01<?, ?it/s]
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py:259, in hf_raise_for_status(response, endpoint_name)
258 try:
--> 259 response.raise_for_status()
260 except HTTPError as e:
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/agomberto/FrenchCensus-handwritten-texts/commit/main
The above exception was the direct cause of the following exception:
HfHubHTTPError Traceback (most recent call last)
Cell In[7], line 1
----> 1 dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts")
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/dataset_dict.py:1583, in DatasetDict.push_to_hub(self, repo_id, private, token, branch, max_shard_size, num_shards, embed_external_files)
1581 logger.warning(f"Pushing split {split} to the Hub.")
1582 # The split=key needs to be removed before merging
-> 1583 repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(
1584 repo_id,
1585 split=split,
1586 private=private,
1587 token=token,
1588 branch=branch,
1589 max_shard_size=max_shard_size,
1590 num_shards=num_shards.get(split),
1591 embed_external_files=embed_external_files,
1592 )
1593 total_uploaded_size += uploaded_size
1594 total_dataset_nbytes += dataset_nbytes
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/arrow_dataset.py:5275, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, num_shards, embed_external_files)
5273 shard.to_parquet(buffer)
5274 uploaded_size += buffer.tell()
-> 5275 _retry(
5276 api.upload_file,
5277 func_kwargs={
5278 "path_or_fileobj": buffer.getvalue(),
5279 "path_in_repo": shard_path_in_repo,
5280 "repo_id": repo_id,
5281 "token": token,
5282 "repo_type": "dataset",
5283 "revision": branch,
5284 },
5285 exceptions=HTTPError,
5286 status_codes=[504],
5287 base_wait_time=2.0,
5288 max_retries=5,
5289 max_wait_time=20.0,
5290 )
5291 shards_path_in_repo.append(shard_path_in_repo)
5293 # Cleanup to remove unused files
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/utils/file_utils.py:285, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
283 except exceptions as err:
284 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
--> 285 raise err
286 else:
287 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/utils/file_utils.py:282, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
280 while True:
281 try:
--> 282 return func(*func_args, **func_kwargs)
283 except exceptions as err:
284 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:120, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
117 if check_use_auth_token:
118 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 120 return fn(*args, **kwargs)
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/hf_api.py:2998, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, commit_message, commit_description, create_pr, parent_commit)
2990 commit_message = (
2991 commit_message if commit_message is not None else f"Upload {path_in_repo} with huggingface_hub"
2992 )
2993 operation = CommitOperationAdd(
2994 path_or_fileobj=path_or_fileobj,
2995 path_in_repo=path_in_repo,
2996 )
-> 2998 commit_info = self.create_commit(
2999 repo_id=repo_id,
3000 repo_type=repo_type,
3001 operations=[operation],
3002 commit_message=commit_message,
3003 commit_description=commit_description,
3004 token=token,
3005 revision=revision,
3006 create_pr=create_pr,
3007 parent_commit=parent_commit,
3008 )
3010 if commit_info.pr_url is not None:
3011 revision = quote(_parse_revision_from_pr_url(commit_info.pr_url), safe="")
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:120, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
117 if check_use_auth_token:
118 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 120 return fn(*args, **kwargs)
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/hf_api.py:2548, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit)
2546 try:
2547 commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params)
-> 2548 hf_raise_for_status(commit_resp, endpoint_name="commit")
2549 except RepositoryNotFoundError as e:
2550 e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py:301, in hf_raise_for_status(response, endpoint_name)
297 raise BadRequestError(message, response=response) from e
299 # Convert `HTTPError` into a `HfHubHTTPError` to display request information
300 # as well (request id and/or server error message)
--> 301 raise HfHubHTTPError(str(e), response=response) from e
HfHubHTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/agomberto/FrenchCensus-handwritten-texts/commit/main (Request ID: Root=1-645a66bf-255ad91602a6404e6cb70fba)
Forbidden: pass `create_pr=1` as a query parameter to create a Pull Request
```
And then when I do
```python
dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts", create_pr=1)
```
I get
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[8], line 1
----> 1 dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts", create_pr=1)
TypeError: push_to_hub() got an unexpected keyword argument 'create_pr'
```
### Expected behavior
I would like to have the dataset updloaded [here](https://huggingface.co/datasets/agomberto/FrenchCensus-handwritten-texts).
### Environment info
```bash
- `datasets` version: 2.12.0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.8.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 1.5.3
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6147 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6147/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6147/comments | https://api.github.com/repos/huggingface/datasets/issues/6147/events | https://github.com/huggingface/datasets/issues/6147 | 1,848,914,830 | I_kwDODunzps5uNDOO | 6,147 | ValueError when running BeamBasedBuilder with GCS path in cache_dir | {
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"The cause of the error seems to be that `datasets` adds \"gcs://\" as a schema, while `beam` checks only \"gs://\".\r\n\r\ndatasets: https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/builder.py#L822\r\n\r\nbeam: [link](https://github.com/apache/beam/blob/25e1a64641b... | 2023-08-14T03:11:34Z | 2023-08-14T03:19:43Z | null | NONE | null | null | null | ### Describe the bug
When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs:
```
ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite']
```
Same error occurs after running `pip install apache-beam[gcp]` as instructed.
### Steps to reproduce the bug
Put `my_beam_dataset.py`:
```python
import datasets
class MyBeamDataset(datasets.BeamBasedBuilder):
def _info(self):
features = datasets.Features({"value": datasets.Value("int64")})
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager, pipeline):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})]
def _build_pcollection(self, pipeline):
import apache_beam as beam
return pipeline | beam.Create([{"value": i} for i in range(10)])
```
Run:
```bash
datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner"
```
### Expected behavior
Running the BeamBasedBuilder with a GCS cache path without any errors.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.4-arm64-arm-64bit
- Python version: 3.9.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 9.0.0
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/2749 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2749/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2749/comments | https://api.github.com/repos/huggingface/datasets/issues/2749/events | https://github.com/huggingface/datasets/issues/2749 | 958,968,748 | MDU6SXNzdWU5NTg5Njg3NDg= | 2,749 | Raise a proper exception when trying to stream a dataset that requires to manually download files | {
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"Hi @severo, thanks for reporting.\r\n\r\nAs discussed, datasets requiring manual download should be:\r\n- programmatically identifiable\r\n- properly handled with more clear error message when trying to load them with streaming\r\n\r\nIn relation with programmatically identifiability, note that for datasets requir... | 2021-08-03T10:26:27Z | 2021-08-09T08:53:35Z | 2021-08-04T11:36:30Z | CONTRIBUTOR | null | null | null | ## Describe the bug
At least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reclor", streaming=True)
```
## Expected results
Ideally: raise a specific exception, something like `ManualDownloadError`.
Or at least give the reason in the message, as when we load in normal mode:
```python
from datasets import load_dataset
dataset = load_dataset("reclor")
```
```
AssertionError: The dataset reclor with config default requires manual data.
Please follow the manual download instructions: to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
.
Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path/to/manual/data>')
```
## Actual results
```
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-11.5-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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"For training the multilabel classifier, I would combine the labels into a list, for example for the English dataset:\r\n\r\n```\r\ndfpre=pd.read_csv(path+\"2018-E-c-En-train.txt\",sep=\"\\t\")\r\ndfpre['list'] = dfpre[dfpre.columns[2:]].values.tolist()\r\ndf = dfpre[['Tweet', 'list']].copy()\r\ndf.rename(columns={... | 2021-08-02T15:39:55Z | 2021-10-29T09:22:05Z | 2021-09-21T09:48:35Z | CONTRIBUTOR | null | 0 | {
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} | I added the data set of SemEval 2018 Task 1 (Subtask 5) for emotion detection in three languages.
```
datasets-cli test datasets/semeval18_emotion_classification/ --save_infos --all_configs
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_semeval18_emotion_classification
```
Both commands ran successfully.
I couldn't create the dummy data (the files are tsvs but have .txt ending, maybe that's the problem?) and therefore the test on the dummy data fails, maybe someone can help here.
I also formatted the code:
```
black --line-length 119 --target-version py36 datasets/semeval18_emotion_classification/
isort datasets/semeval18_emotion_classification/
flake8 datasets/semeval18_emotion_classification/
```
That's the publication for reference:
Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). SemEval-2018 task 1: Affect in tweets. Proceedings of the 12th International Workshop on Semantic Evaluation, 1–17. https://doi.org/10.18653/v1/S18-1001 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5979). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-06-22T18:32:14Z | 2023-06-22T18:42:22Z | 2023-06-22T18:32:22Z | MEMBER | null | 0 | {
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"this is great, maybe just put all of it in one line?\r\n\r\n> TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging"
] | 2022-01-05T06:47:52Z | 2022-01-17T15:54:37Z | 2022-01-17T15:54:36Z | MEMBER | null | 0 | {
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CC: @julien-c @VictorSanh | {
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"Merging since the CI error is unrelated to this PR and has been fixed on master",
"Thank you for taking a look at the CI error - I was a bit confused with that. Thanks!"
] | 2021-05-27T14:53:19Z | 2021-06-28T13:46:14Z | 2021-06-28T13:04:56Z | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-20T08:45:16Z | 2022-09-20T16:11:27Z | 2022-09-20T13:08:29Z | CONTRIBUTOR | null | 0 | {
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} | This PR updates features.py to avoid casting tuples to lists when reading the results of Dataset.map as suggested by @lhoestq [here](https://github.com/huggingface/datasets/issues/4676#issuecomment-1187371367) in https://github.com/huggingface/datasets/issues/4676.
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"It looks to be the same issue as the one reported in https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0.\r\n\r\nCan you check the length of `train_dataset` before the `train_sampler = self._get_train_sampler()` (and after `_remove_unused_columns`) line?"
] | 2023-10-11T09:59:38Z | 2023-10-17T11:24:06Z | 2023-10-17T11:24:06Z | NONE | null | null | null | ### Describe the bug
I am encountering an `IndexError` when trying to access data from a DataLoader which wraps around a dataset I've loaded using the `datasets` library. The error suggests that the dataset size is `0`, but when I check the length and print the dataset, it's clear that it has `1166` entries.
### Steps to reproduce the bug
1. Load a dataset with `1166` entries.
2. Create a DataLoader using this dataset.
3. Try iterating over the DataLoader.
code:
```python
def get_train_dataloader(self) -> DataLoader:
if self.train_dataset is None:
raise ValueError("Trainer: training requires a train_dataset.")
train_dataset = self.train_dataset
data_collator = self.data_collator
print(len(train_dataset))
print(train_dataset)
if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
train_dataset = self._remove_unused_columns(train_dataset, description="training")
else:
data_collator = self._get_collator_with_removed_columns(data_collator, description="training")
train_sampler = self._get_train_sampler()
dl = DataLoader(
train_dataset,
batch_size=self._train_batch_size,
sampler=train_sampler,
collate_fn=data_collator,
drop_last=self.args.dataloader_drop_last,
num_workers=self.args.dataloader_num_workers,
pin_memory=self.args.dataloader_pin_memory,
worker_init_fn=seed_worker,
)
print(dl)
print(len(dl))
for i in dl:
print(i)
break
return dl
```
output :
```
1166
Dataset({
features: ['input_ids', 'special_tokens_mask'],
num_rows: 1166
})
<torch.utils.data.dataloader.DataLoader object ...>
146
```
Error:
```
Traceback (most recent call last):
File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 266, in <module>
train()
File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 260, in train
trainer.train()
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/transformers/trainer.py", line 1506, in train
return inner_training_loop(
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/transformers/trainer.py", line 1520, in _inner_training_loop
train_dataloader = self.get_train_dataloader()
File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 80, in get_train_dataloader
for i in dl:
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 630, in __next__
data = self._next_data()
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 674, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__
batch = self.__getitem__(keys)
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__
return self._getitem(key)
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table
_check_valid_index_key(key, size)
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key
_check_valid_index_key(int(max(key)), size=size)
File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 1116 is out of bounds for size 0
```
### Expected behavior
I expect to be able to iterate over the DataLoader without encountering an IndexError since the dataset is populated.
### Environment info
- `datasets` library version: [2.14.5]
- Platform: [Linux]
- Python version: 3.10
- Other libraries involved: HuggingFace Transformers | {
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https://api.github.com/repos/huggingface/datasets/issues/3860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3860/comments | https://api.github.com/repos/huggingface/datasets/issues/3860/events | https://github.com/huggingface/datasets/pull/3860 | 1,162,623,329 | PR_kwDODunzps40GpzZ | 3,860 | Small doc fixes | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3860). All of your documentation changes will be reflected on that endpoint.",
"There are still some `.. code-block:: python` (e.g. see [this](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#datase... | 2022-03-08T12:55:39Z | 2022-03-08T17:37:13Z | 2022-03-08T17:37:13Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/2855 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2855/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2855/comments | https://api.github.com/repos/huggingface/datasets/issues/2855/events | https://github.com/huggingface/datasets/pull/2855 | 983,858,229 | MDExOlB1bGxSZXF1ZXN0NzIzMzcxMTIy | 2,855 | Fix windows CI CondaError | {
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} | From this thread: https://github.com/conda/conda/issues/6057
We can fix the conda error
```
CondaError: Cannot link a source that does not exist.
C:\Users\...\Anaconda3\Scripts\conda.exe
```
by doing
```bash
conda update conda
```
before doing any install in the windows CI | {
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https://api.github.com/repos/huggingface/datasets/issues/3061 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3061/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3061/comments | https://api.github.com/repos/huggingface/datasets/issues/3061/events | https://github.com/huggingface/datasets/issues/3061 | 1,023,103,119 | I_kwDODunzps48-1CP | 3,061 | Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?) | {
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"@lhoestq, @albertvillanova can we have `**tqdm_kwargs` in `map`? If there are any fields that are important to our tqdm (like iterable or unit), we can pop them before initialising the tqdm object so as to avoid duplicity.",
"Hi ! Sounds like a good idea :)\r\n\r\nAlso I think it would be better to have this as ... | 2021-10-11T20:49:49Z | 2021-10-22T09:34:10Z | null | NONE | null | null | null | **A clear and concise description of what you want to happen.**
It would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions.
**Describe alternatives you've considered**
By the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish?
**Additional context**
Furthering tqdm integration #2374 and huggingface/transformers#11797 solutioned by huggingface/transformers#12226 provided with tqdm description as `desc=`
@sgugger @bhavitvyamalik | {
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https://api.github.com/repos/huggingface/datasets/issues/2636 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2636/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2636/comments | https://api.github.com/repos/huggingface/datasets/issues/2636/events | https://github.com/huggingface/datasets/pull/2636 | 943,044,514 | MDExOlB1bGxSZXF1ZXN0Njg4NzEyMTY4 | 2,636 | Streaming for the Pandas loader | {
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} | It was not using open in the builder. Therefore pd.read_pickle could fail when streaming from a private repo for example.
Indeed, when streaming, open is extended to support reading from remote files and handles authentication to the HF Hub | {
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https://api.github.com/repos/huggingface/datasets/issues/2448 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2448/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2448/comments | https://api.github.com/repos/huggingface/datasets/issues/2448/events | https://github.com/huggingface/datasets/pull/2448 | 912,360,109 | MDExOlB1bGxSZXF1ZXN0NjYyNTI2NjA3 | 2,448 | Fix flores download link | {
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"Hi! Replacing `nlp.<obj>` with `datasets.<obj>` in the script should fix the problem. `nlp` has been renamed to `datasets` more than a year ago, so please use `datasets` instead to avoid weird issues.",
"Thank You! Was able to solve with the help of this.",
"But I request you to please fix the same in the data... | 2022-03-24T09:21:51Z | 2022-03-26T09:48:21Z | 2022-03-26T03:35:43Z | NONE | null | null | null | ## Dataset viewer issue for 'squad_multitask*'
**Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask
*short description of the issue*
I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine.
I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format')
Error:
Status code: 400
Exception: TypeError
Message: argument of type 'Value' is not iterable
Kindly advice.
| {
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https://api.github.com/repos/huggingface/datasets/issues/4276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4276/comments | https://api.github.com/repos/huggingface/datasets/issues/4276/events | https://github.com/huggingface/datasets/issues/4276 | 1,224,949,252 | I_kwDODunzps5JAz4E | 4,276 | OpenBookQA has missing and inconsistent field names | {
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"Thanks for reporting, @vblagoje.\r\n\r\nIndeed, I noticed some of these issues while reviewing this PR:\r\n- #4259 \r\n\r\nThis is in my TODO list. ",
"Ok, awesome @albertvillanova How about #4275 ?",
"On the other hand, I am not sure if we should always preserve the original nested structure. I think we shoul... | 2022-05-04T05:51:52Z | 2022-10-11T17:11:53Z | 2022-10-05T13:50:03Z | CONTRIBUTOR | null | null | null | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanScore'],
- 'clarity': row['clarity'],
- 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
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"I'm getting an error when I try to create the dummy data:\r\n```python\r\naclifton@pop-os:~/data/hf_datasets_sprint/datasets$ python datasets-cli dummy_data ./datasets/logiqa_en/ --auto_generate \r\n2021-01-07 10:50:12.024791: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic l... | 2020-12-17T15:42:00Z | 2022-10-03T09:38:30Z | 2022-10-03T09:38:30Z | CONTRIBUTOR | null | 0 | {
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"Hi @klyuhang9,\r\n\r\nI'm sorry but I can't reproduce your problem:\r\n```python\r\nIn [2]: metric = load_metric('glue', 'rte')\r\nDownloading builder script: 5.76kB [00:00, 2.40MB/s]\r\n```\r\n\r\nCould you please, retry to load the metric? Sometimes there are temporary connectivity issues.\r\n\r\nFeel free to re... | 2022-03-29T07:43:08Z | 2022-03-29T14:06:01Z | 2022-03-29T14:06:01Z | NONE | null | null | null | Hi, friend. I meet a problem.
When I run the code:
`metric = load_metric('glue', 'rte')`
There is a problem raising:
`metric = metric_cls(
TypeError: 'NoneType' object is not callable `
I don't know why. Thanks for your help!
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https://api.github.com/repos/huggingface/datasets/issues/4363 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4363/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4363/comments | https://api.github.com/repos/huggingface/datasets/issues/4363/events | https://github.com/huggingface/datasets/issues/4363 | 1,238,897,652 | I_kwDODunzps5J2BP0 | 4,363 | The dataset preview is not available for this split. | {
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"Hi! A dataset has to be streamable to work with the viewer. I did a quick test, and yours is, so this might be a bug in the viewer. cc @severo \r\n",
"Looking at it. The message is now:\r\n\r\n```\r\nMessage: cannot cache function '__shear_dense': no locator available for file '/src/services/worker/.venv/... | 2022-05-17T16:34:43Z | 2022-06-08T12:32:10Z | 2022-06-08T09:26:56Z | NONE | null | null | null | I have uploaded the corpus developed by our lab in the speech domain to huggingface [datasets](https://huggingface.co/datasets/Roh/ryanspeech). You can read about the companion paper accepted in interspeech 2021 [here](https://arxiv.org/abs/2106.08468). The dataset works fine but I can't make the dataset preview work. It gives me the following error that I don't understand. Can you help me to begin debugging it?
```
Status code: 400
Exception: AttributeError
Message: 'NoneType' object has no attribute 'split'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2613 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2613/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2613/comments | https://api.github.com/repos/huggingface/datasets/issues/2613/events | https://github.com/huggingface/datasets/pull/2613 | 940,759,852 | MDExOlB1bGxSZXF1ZXN0Njg2Nzg0MzY0 | 2,613 | Use ndarray.item instead of ndarray.tolist | {
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} | [] | 2021-07-09T13:19:35Z | 2021-07-12T14:12:57Z | 2021-07-09T13:50:05Z | MEMBER | null | 0 | {
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} | This PR follows up on #2612 to use `numpy.ndarray.item` instead of `numpy.ndarray.tolist` as the latter is somewhat confusing to the developer (even though it works).
Judging from the `numpy` docs, `ndarray.item` is closer to what we want: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html#numpy-ndarray-item
PS. Sorry for the duplicate work here. I should have read the numpy docs more carefully in #2612
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https://api.github.com/repos/huggingface/datasets/issues/3800 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3800/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3800/comments | https://api.github.com/repos/huggingface/datasets/issues/3800/events | https://github.com/huggingface/datasets/pull/3800 | 1,155,620,761 | PR_kwDODunzps4zvkjA | 3,800 | Added computer vision tasks | {
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} | Previous PR was in my fork so thought it'd be easier if I do it from a branch. Added computer vision task datasets according to HF tasks. | {
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https://api.github.com/repos/huggingface/datasets/issues/1229 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1229/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1229/comments | https://api.github.com/repos/huggingface/datasets/issues/1229/events | https://github.com/huggingface/datasets/pull/1229 | 758,100,707 | MDExOlB1bGxSZXF1ZXN0NTMzMzI2OTgw | 1,229 | Muchocine - Spanish movie reviews dataset | {
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"Hi @mapmeld !\r\nhave you had a chance to take a look at my suggestions ?\r\n\r\nFeel free to ping me if you have questions or when you're ready for a review",
"@lhoestq unfortunately I don't have any more information about where the dataset comes from",
"It's fine, you can just add the sections titles back an... | 2020-12-07T02:23:29Z | 2020-12-21T10:09:09Z | 2020-12-21T10:09:09Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/2245 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2245/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2245/comments | https://api.github.com/repos/huggingface/datasets/issues/2245/events | https://github.com/huggingface/datasets/pull/2245 | 863,191,655 | MDExOlB1bGxSZXF1ZXN0NjE5NjQzMjQ3 | 2,245 | Add `key` type and duplicates verification with hashing | {
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"@lhoestq The tests for key type and duplicate keys have been added and verified successfully.\r\nAfter generating with an intentionally faulty `mnist` script, when there is an incompatible key type, it shows:\r\n\r\n```\r\nDownloading and preparing dataset mnist/mnist (download: 11.06 MiB, generated: 60.62 MiB, po... | 2021-04-20T20:03:19Z | 2021-05-10T18:04:37Z | 2021-05-10T17:31:22Z | CONTRIBUTOR | null | 0 | {
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} | Closes #2230
There is currently no verification for the data type and the uniqueness of the keys yielded by the `dataset_builder`.
This PR is currently a work in progress with the following goals:
- [x] Adding `hash_salt` to `ArrowWriter` so that the keys belonging to different splits have different hash
- [x] Add `key` arrtibute to `ArrowWriter.write()` for hashing
- [x] Add a hashing class which takes an input key of certain type (`str`/`int`/anything convertible to string) and produces a 128-bit hash using `hashlib.md5`
- [x] Creating a function giving a custom error message when non-unique keys are found
**[This will take care of type-checking for keys]**
- [x] Checking for duplicate keys in `writer.write()` for each batch
[**NOTE**: This PR is currently concerned with `GeneratorBasedBuilder` only, for simplification. A subsequent PR will be made in future for `ArrowBasedBuilder`]
@lhoestq Thank you for the feedback. It would be great to have your guidance on this! | {
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https://api.github.com/repos/huggingface/datasets/issues/2698 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2698/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2698/comments | https://api.github.com/repos/huggingface/datasets/issues/2698/events | https://github.com/huggingface/datasets/pull/2698 | 950,159,867 | MDExOlB1bGxSZXF1ZXN0Njk0NzUxMzMw | 2,698 | Ignore empty batch when writing | {
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} | This prevents an schema update with unknown column types, as reported in #2644.
This is my first attempt at fixing the issue. I tested the following:
- First batch returned by a batched map operation is empty.
- An intermediate batch is empty.
- `python -m unittest tests.test_arrow_writer` passes.
However, `arrow_writer` looks like a pretty generic interface, I'm not sure if there are other uses I may have overlooked. Let me know if that's the case, or if a better approach would be preferable. | {
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https://api.github.com/repos/huggingface/datasets/issues/6188 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6188/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6188/comments | https://api.github.com/repos/huggingface/datasets/issues/6188/events | https://github.com/huggingface/datasets/issues/6188 | 1,870,987,640 | I_kwDODunzps5vhQF4 | 6,188 | [Feature Request] Check the length of batch before writing so that empty batch is allowed | {
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"I think this error means you filter all examples within an (input) batch by deleting its columns. In that case, to avoid the error, you can set the column value to an empty list (`input_batch[\"col\"] = []`) instead."
] | 2023-08-29T06:37:34Z | 2023-09-19T21:55:38Z | 2023-09-19T21:55:37Z | NONE | null | null | null | ### Use Case
I use `dataset.map(process_fn, batched=True)` to process the dataset, with data **augmentations or filtering**. However, when all examples within a batch is filtered out, i.e. **an empty batch is returned**, the following error will be thrown:
```
ValueError: Schema and number of arrays unequal
```
This is because the empty batch does not comply with the schema of other batches. I think an empty batch should be allowed to facilitate coding (one does not need to assign an empty list manually for all keys.)
A simple fix is to check the length of `batch` before writing:
```
if len(batch):
writer.write_batch(batch)
```
instead of
https://github.com/huggingface/datasets/blob/74d60213dcbd7c99484c62ce1d3dfd90a1df0770/src/datasets/arrow_dataset.py#L3493
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https://api.github.com/repos/huggingface/datasets/issues/4957 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4957/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4957/comments | https://api.github.com/repos/huggingface/datasets/issues/4957/events | https://github.com/huggingface/datasets/pull/4957 | 1,366,532,849 | PR_kwDODunzps4-nGIk | 4,957 | Add `Dataset.from_generator` | {
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"I restarted the builder PR job just in case",
"_The documentation is not available anymore as the PR was closed or merged._",
"CI is now green. https://github.com/huggingface/doc-builder/pull/296 explains why it failed."
] | 2022-09-08T15:08:25Z | 2022-09-16T14:46:35Z | 2022-09-16T14:44:18Z | CONTRIBUTOR | null | 0 | {
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} | Add `Dataset.from_generator` to the API to allow creating datasets from data larger than RAM. The implementation relies on a packaged module not exposed in `load_dataset` to tie this method with `datasets`' caching mechanism.
Closes https://github.com/huggingface/datasets/issues/4417 | {
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https://api.github.com/repos/huggingface/datasets/issues/2389 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2389/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2389/comments | https://api.github.com/repos/huggingface/datasets/issues/2389/events | https://github.com/huggingface/datasets/pull/2389 | 897,822,270 | MDExOlB1bGxSZXF1ZXN0NjQ5Nzc3MDMz | 2,389 | Insert task templates for text classification | {
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"Update: found a few datasets that slipped through the net. Adding them shortly!",
"You might have thought about this already, but would it make sense to use the `datasets.features.ClassLabel` values when possible instead of declaring the list once for the `feature` and once for the `template`?",
"> You might h... | 2021-05-21T08:36:26Z | 2021-05-28T15:28:58Z | 2021-05-28T15:26:28Z | MEMBER | null | 0 | {
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} | This PR inserts text-classification templates for datasets with the following properties:
* Only one config
* At most two features of `(Value, ClassLabel)` type
Note that this misses datasets like `sentiment140` which only has `Value` type features - these will be handled in a separate PR | {
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https://api.github.com/repos/huggingface/datasets/issues/5615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5615/comments | https://api.github.com/repos/huggingface/datasets/issues/5615/events | https://github.com/huggingface/datasets/issues/5615 | 1,612,552,653 | I_kwDODunzps5gHZnN | 5,615 | IterableDataset.add_column is unable to accept another IterableDataset as a parameter. | {
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"Hi! You can use `concatenate_datasets([ids1, ids2], axis=1)` to do this."
] | 2023-03-07T01:52:00Z | 2023-03-09T15:24:05Z | 2023-03-09T15:23:54Z | NONE | null | null | null | ### Describe the bug
`IterableDataset.add_column` occurs an exception when passing another `IterableDataset` as a parameter.
The method seems to accept only eager evaluated values.
https://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/iterable_dataset.py#L1388-L1391
I wrote codes below to make it.
```py
def add_column(dataset: IterableDataset, name: str, add_dataset: IterableDataset, key: str) -> IterableDataset:
iter_add_dataset = iter(add_dataset)
def add_column_fn(example):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: next(iter_add_dataset)[key]}
return dataset.map(add_column_fn)
```
Is there other way to do it? Or is it intended?
### Steps to reproduce the bug
Thie codes below occurs `NotImplementedError`
```py
from datasets import IterableDataset
def gen(num):
yield {f"col{num}": 1}
yield {f"col{num}": 2}
yield {f"col{num}": 3}
ids1 = IterableDataset.from_generator(gen, gen_kwargs={"num": 1})
ids2 = IterableDataset.from_generator(gen, gen_kwargs={"num": 2})
new_ids = ids1.add_column("new_col", ids1)
for row in new_ids:
print(row)
```
### Expected behavior
`IterableDataset.add_column` is able to task `IterableDataset` and lazy evaluated values as a parameter since IterableDataset is lazy evalued.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.7
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/2177 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2177/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2177/comments | https://api.github.com/repos/huggingface/datasets/issues/2177/events | https://github.com/huggingface/datasets/pull/2177 | 852,065,307 | MDExOlB1bGxSZXF1ZXN0NjEwMzc5MDYx | 2,177 | add social thumbnial | {
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"diff_url": "https://github.com/huggingface/datasets/pull/2177.diff",
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} | # What does this PR do?
I added OpenGraph/ Twitter Card support to the docs to create nice social thumbnails.
To be able to add these I needed to install `sphinxext-opengraph`. I came across this [issue](https://github.com/readthedocs/readthedocs.org/issues/1758) on the readthedocs repo saying that since someone has built this plugin they are not integrating and providing documentation to it. That's why I added it for creating the documentation. The repository can be found [here](https://github.com/wpilibsuite/sphinxext-opengraph/tree/main).
P.S. It seemed that `make style` never ran for `docs/` i hope the changes are okay otherwise I'll revert it. | {
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https://api.github.com/repos/huggingface/datasets/issues/5426 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5426/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5426/comments | https://api.github.com/repos/huggingface/datasets/issues/5426/events | https://github.com/huggingface/datasets/issues/5426 | 1,535,158,555 | I_kwDODunzps5bgKkb | 5,426 | CI tests are broken: SchemaInferenceError | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | [] | 2023-01-16T16:02:07Z | 2023-06-02T06:40:32Z | 2023-01-16T16:49:04Z | MEMBER | null | null | null | CI test (unit, ubuntu-latest, deps-minimum) is broken, raising a `SchemaInferenceError`: see https://github.com/huggingface/datasets/actions/runs/3930901593/jobs/6721492004
```
FAILED tests/test_beam.py::BeamBuilderTest::test_download_and_prepare_sharded - datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
```
Stack trace:
```
______________ BeamBuilderTest.test_download_and_prepare_sharded _______________
[gw1] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
self = <tests.test_beam.BeamBuilderTest testMethod=test_download_and_prepare_sharded>
@require_beam
def test_download_and_prepare_sharded(self):
import apache_beam as beam
original_write_parquet = beam.io.parquetio.WriteToParquet
expected_num_examples = len(get_test_dummy_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
with patch("apache_beam.io.parquetio.WriteToParquet") as write_parquet_mock:
write_parquet_mock.side_effect = partial(original_write_parquet, num_shards=2)
> builder.download_and_prepare()
tests/test_beam.py:97:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:864: in download_and_prepare
**download_and_prepare_kwargs,
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:1976: in _download_and_prepare
num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter))
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:694: in finalize
shard_num_bytes, _ = parquet_to_arrow(source, destination)
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:740: in parquet_to_arrow
num_bytes, num_examples = writer.finalize()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <datasets.arrow_writer.ArrowWriter object at 0x7f6dcbb3e810>
close_stream = True
def finalize(self, close_stream=True):
self.write_rows_on_file()
# In case current_examples < writer_batch_size, but user uses finalize()
if self._check_duplicates:
self.check_duplicate_keys()
# Re-intializing to empty list for next batch
self.hkey_record = []
self.write_examples_on_file()
# If schema is known, infer features even if no examples were written
if self.pa_writer is None and self.schema:
self._build_writer(self.schema)
if self.pa_writer is not None:
self.pa_writer.close()
self.pa_writer = None
if close_stream:
self.stream.close()
else:
if close_stream:
self.stream.close()
> raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:593: SchemaInferenceError
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5747 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5747/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5747/comments | https://api.github.com/repos/huggingface/datasets/issues/5747/events | https://github.com/huggingface/datasets/pull/5747 | 1,667,270,412 | PR_kwDODunzps5ORtBF | 5,747 | [WIP] Add Dataset.to_spark | {
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https://api.github.com/repos/huggingface/datasets/issues/3276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3276/comments | https://api.github.com/repos/huggingface/datasets/issues/3276/events | https://github.com/huggingface/datasets/pull/3276 | 1,053,793,063 | PR_kwDODunzps4uihih | 3,276 | Update KILT metadata JSON | {
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https://api.github.com/repos/huggingface/datasets/issues/3962 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3962/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3962/comments | https://api.github.com/repos/huggingface/datasets/issues/3962/events | https://github.com/huggingface/datasets/pull/3962 | 1,173,482,291 | PR_kwDODunzps40psq2 | 3,962 | Fix flatten of Sequence feature type | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-03-18T11:27:42Z | 2022-03-21T14:40:47Z | 2022-03-21T14:36:12Z | MEMBER | null | 0 | {
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} | The `Sequence` features type is not correctly flattened if it contains a dictionary. This PR fixes this, and I added a test case for this.
Close https://github.com/huggingface/datasets/issues/3795 | {
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https://api.github.com/repos/huggingface/datasets/issues/4699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4699/comments | https://api.github.com/repos/huggingface/datasets/issues/4699/events | https://github.com/huggingface/datasets/pull/4699 | 1,307,555,592 | PR_kwDODunzps47jA6Z | 4,699 | Fix Authentification Error while streaming | {
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"Hi, thanks for working on this, but the fix for this has already been merged in https://github.com/huggingface/datasets/pull/4608."
] | 2022-07-18T08:03:41Z | 2022-07-20T13:10:44Z | 2022-07-20T13:10:43Z | NONE | null | 0 | {
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} | I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub.
```
from datasets import load_dataset
dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True)
for d in dataset['train']:
print(d)
break # this is for checking
```
This code is an example for streaming private datasets.
when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this,
```
/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)
1007 status=self.status,
1008 message=self.reason,
→ 1009 headers=self.headers,
1010 )
1011
ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet')
```
(this is an example on the dataset has `parquet` extenstion)
It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~".
so I add three lines.
With this change, there is no error anymore(but this code is ad-hoc). | {
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https://api.github.com/repos/huggingface/datasets/issues/5799 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5799/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5799/comments | https://api.github.com/repos/huggingface/datasets/issues/5799/events | https://github.com/huggingface/datasets/issues/5799 | 1,686,334,572 | I_kwDODunzps5kg2xs | 5,799 | Files downloaded to cache do not respect umask | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | [] | 2023-04-27T08:06:05Z | 2023-04-27T09:30:17Z | 2023-04-27T09:30:17Z | MEMBER | null | null | null | As reported by @stas00, files downloaded to the cache do not respect umask:
```bash
$ ls -l /path/to/cache/datasets/downloads/
-rw------- 1 uername username 150M Apr 25 16:41 5e646c1d600f065adaeb134e536f6f2f296a6d804bd1f0e1fdcd20ee28c185c6
```
Related to:
- #2065 | {
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https://api.github.com/repos/huggingface/datasets/issues/5094 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5094/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5094/comments | https://api.github.com/repos/huggingface/datasets/issues/5094/events | https://github.com/huggingface/datasets/issues/5094 | 1,403,214,950 | I_kwDODunzps5To1xm | 5,094 | Multiprocessing with `Dataset.map` and `PyTorch` results in deadlock | {
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] | closed | false | null | [] | null | [
"Hi ! Could it be an Out of Memory issue that could have killed one of the processes ? can you check your memory ?",
"Hi! I don't think it is a memory issue. I'm monitoring the main and spawn python processes and threads with `htop` and the memory does not peak. Besides, the example I've posted above should not b... | 2022-10-10T13:50:56Z | 2023-07-24T15:29:13Z | 2023-07-24T15:29:13Z | NONE | null | null | null | ## Describe the bug
There seems to be an issue with using multiprocessing with `datasets.Dataset.map` (i.e. setting `num_proc` to a value greater than one) combined with a function that uses `torch` under the hood. The subprocesses that `datasets.Dataset.map` spawns [a this step](https://github.com/huggingface/datasets/blob/1b935dab9d2f171a8c6294269421fe967eb55e34/src/datasets/arrow_dataset.py#L2663) go into wait mode forever.
## Steps to reproduce the bug
The below code goes into deadlock when `NUMBER_OF_PROCESSES` is greater than one.
```python
NUMBER_OF_PROCESSES = 2
from transformers import AutoTokenizer, AutoModel
from datasets import load_dataset
dataset = load_dataset("glue", "mrpc", split="train")
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model.to("cpu")
def cls_pooling(model_output):
return model_output.last_hidden_state[:, 0]
def generate_embeddings_batched(examples):
sentences_batch = list(examples['sentence1'])
encoded_input = tokenizer(
sentences_batch, padding=True, truncation=True, return_tensors="pt"
)
encoded_input = {k: v.to("cpu") for k, v in encoded_input.items()}
model_output = model(**encoded_input)
embeddings = cls_pooling(model_output)
examples['embeddings'] = embeddings.detach().cpu().numpy() # 64, 384
return examples
embeddings_dataset = dataset.map(
generate_embeddings_batched,
batched=True,
batch_size=10,
num_proc=NUMBER_OF_PROCESSES
)
```
While debugging it I've seen that it gets "stuck" when calling `torch.nn.Embedding.forward` but some testing shows that the same happens with other functions from `torch.nn`.
## Environment info
- Platform: Linux-5.14.0-1052-oem-x86_64-with-glibc2.31
- Python version: 3.9.14
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
Not sure if this is a HF problem, a PyTorch problem or something I'm doing wrong..
Thanks!
| {
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https://api.github.com/repos/huggingface/datasets/issues/4552 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4552/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4552/comments | https://api.github.com/repos/huggingface/datasets/issues/4552/events | https://github.com/huggingface/datasets/pull/4552 | 1,282,615,646 | PR_kwDODunzps46QSHV | 4,552 | Tell users to upload on the hub directly | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks ! I updated the two remaining files"
] | 2022-06-23T15:47:52Z | 2022-06-26T15:49:46Z | 2022-06-26T15:39:11Z | MEMBER | null | 0 | {
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} | As noted in https://github.com/huggingface/datasets/pull/4534, it is still not clear that it is recommended to add datasets on the Hugging Face Hub directly instead of GitHub, so I updated some docs.
Moreover since users won't be able to get reviews from us on the Hub, I added a paragraph to tell users that they can open a discussion and tag `datasets` maintainers for reviews.
Finally I removed the _previous good reasons_ to add a dataset on GitHub to only keep this one:
> In some rare cases it makes more sense to open a PR on GitHub. For example when you are not the author of the dataset and there is no clear organization / namespace that you can put the dataset under.
Does it sound good to you @albertvillanova @julien-c ? | {
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https://api.github.com/repos/huggingface/datasets/issues/2098 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2098/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2098/comments | https://api.github.com/repos/huggingface/datasets/issues/2098/events | https://github.com/huggingface/datasets/issues/2098 | 838,447,959 | MDU6SXNzdWU4Mzg0NDc5NTk= | 2,098 | SQuAD version | {
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"Hi ! This is 1.1 as specified by the download urls here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/squad/squad.py#L50-L55",
"Got it. Thank you~"
] | 2021-03-23T07:47:54Z | 2021-03-26T09:48:54Z | 2021-03-26T09:48:54Z | NONE | null | null | null | Hi~
I want train on squad dataset. What's the version of the squad? Is it 1.1 or 1.0? I'm new in QA, I don't find some descriptions about it. | {
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https://api.github.com/repos/huggingface/datasets/issues/4063 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4063/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4063/comments | https://api.github.com/repos/huggingface/datasets/issues/4063/events | https://github.com/huggingface/datasets/pull/4063 | 1,186,611,368 | PR_kwDODunzps41UiDm | 4,063 | Increase max retries for GitHub metrics | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-03-30T15:12:48Z | 2022-03-31T14:42:52Z | 2022-03-31T14:37:47Z | MEMBER | null | 0 | {
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} | As GitHub recurrently raises connectivity issues, this PR increases the number of max retries to request GitHub metrics.
Related to:
- #3134
Also related to:
- #4059 | {
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https://api.github.com/repos/huggingface/datasets/issues/4168 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4168/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4168/comments | https://api.github.com/repos/huggingface/datasets/issues/4168/events | https://github.com/huggingface/datasets/pull/4168 | 1,203,867,540 | PR_kwDODunzps42NL6F | 4,168 | Add code examples to API docs | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> 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.\r... | 2022-04-13T23:03:38Z | 2022-04-27T18:53:37Z | 2022-04-27T18:48:34Z | MEMBER | null | 0 | {
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} | 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 :) | {
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https://api.github.com/repos/huggingface/datasets/issues/2337 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2337/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2337/comments | https://api.github.com/repos/huggingface/datasets/issues/2337/events | https://github.com/huggingface/datasets/issues/2337 | 881,610,567 | MDU6SXNzdWU4ODE2MTA1Njc= | 2,337 | NonMatchingChecksumError for web_of_science dataset | {
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"I've raised a PR for this. Should work with `dataset = load_dataset(\"web_of_science\", \"WOS11967\", ignore_verifications=True)`once it gets merged into the main branch. Thanks for reporting this! "
] | 2021-05-09T02:02:02Z | 2021-05-10T13:35:53Z | 2021-05-10T13:35:53Z | NONE | null | null | null | NonMatchingChecksumError when trying to download the web_of_science dataset.
>NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1']
Setting `ignore_verfications=True` results in OSError.
>OSError: Cannot find data file.
Original error:
[Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/37ab2c42f50d553c1d0ea432baca3e9e11fedea4aeec63a81e6b7e25dd10d4e7/WOS5736/X.txt'
```python
dataset = load_dataset('web_of_science', 'WOS5736')
```
There are 3 data instances and they all don't work. 'WOS5736', 'WOS11967', 'WOS46985'
datasets 1.6.2
python 3.7.10
Ubuntu 18.04.5 LTS | {
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https://api.github.com/repos/huggingface/datasets/issues/2808 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2808/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2808/comments | https://api.github.com/repos/huggingface/datasets/issues/2808/events | https://github.com/huggingface/datasets/issues/2808 | 971,882,320 | MDU6SXNzdWU5NzE4ODIzMjA= | 2,808 | Enable streaming for Wikipedia corpora | {
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"Closing as this has been addressed in https://github.com/huggingface/datasets/pull/5689."
] | 2021-08-16T15:59:12Z | 2023-07-20T13:45:30Z | 2023-07-20T13:45:30Z | MEMBER | null | null | null | **Is your feature request related to a problem? Please describe.**
Several of the [Wikipedia corpora](https://huggingface.co/datasets?search=wiki) on the Hub involve quite large files that would be a good candidate for streaming. Currently it is not possible to stream these corpora:
```python
from datasets import load_dataset
# Throws ValueError: Builder wikipedia is not streamable.
wiki_dataset_streamed = load_dataset("wikipedia", "20200501.en", split="train", streaming=True)
```
Given that these corpora are derived from Wikipedia dumps in XML format which are then processed with Apache Beam, I am not sure whether streaming is possible in principle. The goal of this issue is to discuss whether this feature even makes sense :)
**Describe the solution you'd like**
It would be nice to be able to stream Wikipedia corpora from the Hub with something like
```python
from datasets import load_dataset
wiki_dataset_streamed = load_dataset("wikipedia", "20200501.en", split="train", streaming=True)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2707 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2707/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2707/comments | https://api.github.com/repos/huggingface/datasets/issues/2707/events | https://github.com/huggingface/datasets/issues/2707 | 950,812,945 | MDU6SXNzdWU5NTA4MTI5NDU= | 2,707 | 404 Not Found Error when loading LAMA dataset | {
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"Hi @dwil2444! I was able to reproduce your error when I downgraded to v1.1.2. Updating to the latest version of Datasets fixed the error for me :)",
"Hi @dwil2444, thanks for reporting.\r\n\r\nCould you please confirm which `datasets` version you were using and if the problem persists after you update it to the ... | 2021-07-22T15:52:33Z | 2021-07-26T14:29:07Z | 2021-07-26T14:29:07Z | NONE | null | null | null | The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py` | {
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https://api.github.com/repos/huggingface/datasets/issues/3498 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3498/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3498/comments | https://api.github.com/repos/huggingface/datasets/issues/3498/events | https://github.com/huggingface/datasets/pull/3498 | 1,090,096,332 | PR_kwDODunzps4wWL5U | 3,498 | update `pretty_name` for first 200 datasets | {
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} | [] | closed | false | null | [] | null | [] | 2021-12-28T19:50:07Z | 2022-07-10T14:36:53Z | 2022-01-05T16:38:21Z | CONTRIBUTOR | null | 0 | {
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} | I made a script some time back to fetch `pretty_names` from `papers_with_code` dataset along with some other rules incase that dataset wasn't available on `papers_with_code`. Updating them in the `README` of `datasets`. Took only the first 200 datasets into consideration and after some eyeballing, most of them were looking good to me! | {
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https://api.github.com/repos/huggingface/datasets/issues/1776 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1776/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1776/comments | https://api.github.com/repos/huggingface/datasets/issues/1776/events | https://github.com/huggingface/datasets/issues/1776 | 792,755,249 | MDU6SXNzdWU3OTI3NTUyNDk= | 1,776 | [Question & Bug Report] Can we preprocess a dataset on the fly? | {
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"We are very actively working on this. How does your dataset look like in practice (number/size/type of files)?",
"It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm... | 2021-01-24T09:28:24Z | 2021-05-20T04:15:58Z | 2021-05-20T04:15:58Z | NONE | null | null | null | I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache?
BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:
https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532 | {
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https://api.github.com/repos/huggingface/datasets/issues/5937 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5937/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5937/comments | https://api.github.com/repos/huggingface/datasets/issues/5937/events | https://github.com/huggingface/datasets/pull/5937 | 1,749,388,597 | PR_kwDODunzps5SmLIs | 5,937 | Avoid parallel redownload in cache | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-06-09T08:18:36Z | 2023-06-14T12:30:59Z | 2023-06-14T12:23:57Z | MEMBER | null | 0 | {
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} | Avoid parallel redownload in cache by retrying inside the lock if path exists. | {
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https://api.github.com/repos/huggingface/datasets/issues/5449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5449/comments | https://api.github.com/repos/huggingface/datasets/issues/5449/events | https://github.com/huggingface/datasets/pull/5449 | 1,550,801,453 | PR_kwDODunzps5INgD9 | 5,449 | Support fsspec 2023.1.0 in CI | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-20T12:53:17Z | 2023-01-20T13:32:50Z | 2023-01-20T13:26:03Z | MEMBER | null | 0 | {
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} | Support fsspec 2023.1.0 in CI.
In the 2023.1.0 fsspec release, they replaced the type of `fsspec.registry`:
- from `ReadOnlyRegistry`, with an attribute called `target`
- to `MappingProxyType`, without that attribute
Consequently, we need to change our `mock_fsspec` fixtures, that were using the `target` attribute.
Fix #5448. | {
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https://api.github.com/repos/huggingface/datasets/issues/1980 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1980/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1980/comments | https://api.github.com/repos/huggingface/datasets/issues/1980/events | https://github.com/huggingface/datasets/pull/1980 | 821,312,810 | MDExOlB1bGxSZXF1ZXN0NTg0MTI1OTUy | 1,980 | Loading all answers from drop | {
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"Nice thanks for the change !\r\nThis looks all good to me\r\n\r\nBefore we merge can you just update the dataset_infos.json file of drop ? You can do it by running\r\n```\r\ndatasets-cli test ./datasets/drop --all_configs --save_infos --ignore_verifications\r\n```",
"Done!"
] | 2021-03-03T17:13:07Z | 2021-03-15T11:27:26Z | 2021-03-15T11:27:26Z | CONTRIBUTOR | null | 0 | {
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} | Hello all,
I propose this change to the DROP loading script so that all answers are loaded no matter their type. Currently, only "span" answers are loaded, which excludes a significant amount of answers from drop (i.e. "number" and "date").
I updated the script with the version I use for my work. However, I couldn't find a way to verify that all is working when integrated with the datasets repo, since the `load_dataset` method seems to always download the script from github and not local files.
Note that 9 items from the train set have no answers, as well as 1 from the validation set. The script I propose simply do not load them.
Let me know if there is anything else I can do,
Clément | {
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https://api.github.com/repos/huggingface/datasets/issues/4482 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4482/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4482/comments | https://api.github.com/repos/huggingface/datasets/issues/4482/events | https://github.com/huggingface/datasets/pull/4482 | 1,269,237,447 | PR_kwDODunzps45jS_c | 4,482 | Test that TensorFlow is not imported on startup | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Should we close this PR?",
"I'm closing this PR. Feel free to reopen it if necessary."
] | 2022-06-13T10:33:49Z | 2023-10-12T06:31:39Z | 2023-10-11T09:11:56Z | MEMBER | null | 1 | {
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} | TF takes some time to be imported, and also uses some GPU memory.
I just added a test to make sure that in the future it's never imported by default when
```python
import datasets
```
is called.
Right now this fails because `huggingface_hub` does import tensorflow (though this is fixed now on their `main` branch)
I'll mark this PR as ready for review once `huggingface_hub` has a new release | {
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https://api.github.com/repos/huggingface/datasets/issues/5107 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5107/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5107/comments | https://api.github.com/repos/huggingface/datasets/issues/5107/events | https://github.com/huggingface/datasets/pull/5107 | 1,406,736,710 | PR_kwDODunzps5ArjCZ | 5,107 | Multiprocessed dataset builder | {
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"I would also like to add a test, but am not sure whether it should go into `test_builder` (more natural imo) or `test_load` (which already contains a lot of the things I have to import to run my current testing setup). For reference, what I run to test that it works looks like:\r\n\r\n```\r\nimport os\r\nfrom path... | 2022-10-12T19:59:17Z | 2022-12-01T15:37:09Z | 2022-11-09T17:11:43Z | CONTRIBUTOR | null | 0 | {
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} | This PR adds the multiprocessing part of #2650 (but not the caching of already-computed arrow files). On the other side, loading of sharded arrow files still needs to be implemented (sharded parquet files can already be loaded). | {
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https://api.github.com/repos/huggingface/datasets/issues/6025 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6025/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6025/comments | https://api.github.com/repos/huggingface/datasets/issues/6025/events | https://github.com/huggingface/datasets/issues/6025 | 1,801,852,601 | I_kwDODunzps5rZha5 | 6,025 | Using a dataset for a use other than it was intended for. | {
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"I've opened a PR with a fix. In the meantime, you can avoid the error by deleting `task_templates` with `dataset.info.task_templates = None` before the `interleave_datasets` call.\r\n` "
] | 2023-07-12T22:33:17Z | 2023-07-13T13:57:36Z | 2023-07-13T13:57:36Z | NONE | null | null | null | ### Describe the bug
Hi, I want to use the rotten tomatoes dataset but for a task other than classification, but when I interleave the dataset, it throws ```'ValueError: Column label is not present in features.'```. It seems that the label_col must be there in the dataset for some reason?
Here is the full stacktrace
```
File "/home/suryahari/Vornoi/tryage-handoff-other-datasets.py", line 276, in create_dataloaders
dataset = interleave_datasets(dsfold, stopping_strategy="all_exhausted")
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py", line 134, in interleave_datasets
return _interleave_iterable_datasets(
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1833, in _interleave_iterable_datasets
info = DatasetInfo.from_merge([d.info for d in datasets])
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in from_merge
dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None]
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in <listcomp>
dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None]
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 378, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 208, in __post_init__
self.task_templates = [
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 209, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/tasks/text_classification.py", line 20, in align_with_features
raise ValueError(f"Column {self.label_column} is not present in features.")
ValueError: Column label is not present in features.
```
### Steps to reproduce the bug
Delete the column `labels` from the `rotten_tomatoes` dataset. Try to interleave it with other datasets.
### Expected behavior
Should let me use the dataset with just the `text` field
### Environment info
latest datasets library? I don't think this was an issue in earlier versions. | {
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