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https://api.github.com/repos/huggingface/datasets/issues/3850 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3850/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3850/comments | https://api.github.com/repos/huggingface/datasets/issues/3850/events | https://github.com/huggingface/datasets/pull/3850 | 1,162,126,030 | PR_kwDODunzps40FBx9 | 3,850 | [feat] Add tqdm arguments | {
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https://api.github.com/repos/huggingface/datasets/issues/3849 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3849/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3849/comments | https://api.github.com/repos/huggingface/datasets/issues/3849/events | https://github.com/huggingface/datasets/pull/3849 | 1,162,091,075 | PR_kwDODunzps40E6sW | 3,849 | Add "Adversarial GLUE" dataset to datasets library | {
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} | [] | closed | false | null | [] | null | 5 | 2022-03-08T00:47:11Z | 2022-03-28T11:17:14Z | 2022-03-28T11:12:04Z | CONTRIBUTOR | null | null | null | Adds the Adversarial GLUE dataset: https://adversarialglue.github.io/
```python
>>> import datasets
>>> >>> datasets.load_dataset('adv_glue')
Using the latest cached version of the module from /home/jxm3/.cache/huggingface/modules/datasets_modules/datasets/adv_glue/26709a83facad2830d72d4419dd179c0be092f4ad3303ad0ebe815d0cdba5cb4 (last modified on Mon Mar 7 19:19:48 2022) since it couldn't be found locally at adv_glue., or remotely on the Hugging Face Hub.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jxm3/random/datasets/src/datasets/load.py", line 1657, in load_dataset
builder_instance = load_dataset_builder(
File "/home/jxm3/random/datasets/src/datasets/load.py", line 1510, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/jxm3/random/datasets/src/datasets/builder.py", line 1021, in __init__
super().__init__(*args, **kwargs)
File "/home/jxm3/random/datasets/src/datasets/builder.py", line 258, in __init__
self.config, self.config_id = self._create_builder_config(
File "/home/jxm3/random/datasets/src/datasets/builder.py", line 337, in _create_builder_config
raise ValueError(
ValueError: Config name is missing.
Please pick one among the available configs: ['adv_sst2', 'adv_qqp', 'adv_mnli', 'adv_mnli_mismatched', 'adv_qnli', 'adv_rte']
Example of usage:
`load_dataset('adv_glue', 'adv_sst2')`
>>> datasets.load_dataset('adv_glue', 'adv_sst2')['validation'][0]
Reusing dataset adv_glue (/home/jxm3/.cache/huggingface/datasets/adv_glue/adv_sst2/1.0.0/3719a903f606f2c96654d87b421bc01114c37084057cdccae65cd7bc24b10933)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 604.11it/s]
{'sentence': "it 's an uneven treat that bores fun at the democratic exercise while also examining its significance for those who take part .", 'label': 1, 'idx': 0}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3848/comments | https://api.github.com/repos/huggingface/datasets/issues/3848/events | https://github.com/huggingface/datasets/issues/3848 | 1,162,076,902 | I_kwDODunzps5FQ-Lm | 3,848 | NonMatchingChecksumError when checksum is None | {
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} | I ran into the following error when adding a new dataset:
```bash
expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}}
recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}
verification_name = 'dataset source files'
def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):
if expected_checksums is None:
logger.info("Unable to verify checksums.")
return
if len(set(expected_checksums) - set(recorded_checksums)) > 0:
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
if len(set(recorded_checksums) - set(expected_checksums)) > 0:
raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
for_verification_name = " for " + verification_name if verification_name is not None else ""
if len(bad_urls) > 0:
error_msg = "Checksums didn't match" + for_verification_name + ":\n"
> raise NonMatchingChecksumError(error_msg + str(bad_urls))
E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
E ['https://adversarialglue.github.io/dataset/dev.zip']
src/datasets/utils/info_utils.py:40: NonMatchingChecksumError
```
## Expected results
The dataset downloads correctly, and there is no error.
## Actual results
Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. | {
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} | ## Describe the bug
For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory.
## Steps to reproduce the bug
Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example.
```python
from datasets import load_dataset
from transformers import AutoTokenizer
raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1")
# tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
text_column_name = "text"
column_names = raw_datasets["train"].column_names
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
remove_columns=column_names,
load_from_cache_file=True,
desc="Running tokenizer on every text in dataset",
)
```
## Expected results
No tokenization would be required after the 1st run. Everything should be loaded from the cache.
## Actual results
Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Ubuntu 18.04.6 LTS
- Python version: 3.6.9
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3846 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3846/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3846/comments | https://api.github.com/repos/huggingface/datasets/issues/3846/events | https://github.com/huggingface/datasets/pull/3846 | 1,161,810,226 | PR_kwDODunzps40D-uh | 3,846 | Update faiss device docstring | {
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} | [] | closed | false | null | [] | null | 1 | 2022-03-07T19:06:59Z | 2022-03-07T19:21:23Z | 2022-03-07T19:21:22Z | MEMBER | null | null | null | Following https://github.com/huggingface/datasets/pull/3721 I updated the docstring of the `device` argument of the FAISS related methods of `Dataset` | {
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} | [] | closed | false | null | [] | null | 6 | 2022-03-07T17:53:24Z | 2022-03-09T16:50:03Z | 2022-03-09T16:50:03Z | CONTRIBUTOR | null | null | null | This PR adds RMSE - Root Mean Squared Error and MAE - Mean Absolute Error to the metrics API.
Both implementations are based on usage of sciket-learn.
Feature request here : Add support for continuous metrics (RMSE, MAE) [#3608](https://github.com/huggingface/datasets/issues/3608)
Please suggest any changes if required. Thank you. | {
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} | [] | closed | false | null | [] | null | 2 | 2022-03-07T17:06:38Z | 2022-03-07T17:24:32Z | 2022-03-07T17:15:06Z | CONTRIBUTOR | null | null | null | This PR adds RMSE - Root Mean Squared Error and MAE - Mean Absolute Error to the metrics API.
Both implementations are based on usage of sciket-learn.
Feature request here : Add support for continuous metrics (RMSE, MAE) [#3608](https://github.com/huggingface/datasets/issues/3608)
Any suggestions and changes required will be helpful.
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https://api.github.com/repos/huggingface/datasets/issues/3843 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3843/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3843/comments | https://api.github.com/repos/huggingface/datasets/issues/3843/events | https://github.com/huggingface/datasets/pull/3843 | 1,161,397,812 | PR_kwDODunzps40Cm0D | 3,843 | Fix Google Drive URL to avoid Virus scan warning in streaming mode | {
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} | [] | closed | false | null | [] | null | 2 | 2022-03-07T13:09:19Z | 2022-03-15T12:30:25Z | 2022-03-15T12:30:23Z | COLLABORATOR | null | null | null | The streaming version of https://github.com/huggingface/datasets/pull/3787.
Fix #3835
CC: @albertvillanova | {
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} | [] | closed | false | null | [] | null | 3 | 2022-03-07T12:10:46Z | 2022-03-07T19:03:43Z | 2022-03-07T19:03:42Z | MEMBER | null | null | null | From #3444 , Dataset.shuffle can have the same API than IterableDataset.shuffle (i.e. in streaming mode).
Currently you can pass an optional seed to both if you want, BUT currently IterableDataset.shuffle always requires a buffer_size, used for approximate shuffling. I propose using a reasonable default value (maybe 1000) instead.
In this PR, I set the default `buffer_size` value to 1,000, and I reorder the `IterableDataset.shuffle` arguments to match `Dataset.shuffle`, i.e. making `seed` the first argument. | {
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] | closed | false | null | [] | null | 6 | 2022-03-07T10:24:04Z | 2023-02-18T19:14:03Z | 2023-02-13T13:48:41Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ## Describe the bug
Pyright complains about module not exported.
## Steps to reproduce the bug
Use an editor/IDE with Pyright Language server with default configuration:
```python
from datasets import load_dataset
```
## Expected results
No complain from Pyright
## Actual results
Pyright complain below:
```
`load_dataset` is not exported from module "datasets"
Import from "datasets.load" instead [reportPrivateImportUsage]
```
Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation.
## Environment info
- `datasets` version: 1.18.3
- Platform: macOS-12.2.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3840 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3840/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3840/comments | https://api.github.com/repos/huggingface/datasets/issues/3840/events | https://github.com/huggingface/datasets/pull/3840 | 1,161,183,773 | PR_kwDODunzps40B8eu | 3,840 | Pin responses to fix CI for Windows | {
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See: https://app.circleci.com/pipelines/github/huggingface/datasets/10292/workflows/83de4a55-bff7-43ec-96f7-0c335af5c050/jobs/63355
Fix: #3839 | {
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https://api.github.com/repos/huggingface/datasets/issues/3839 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3839/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3839/comments | https://api.github.com/repos/huggingface/datasets/issues/3839/events | https://github.com/huggingface/datasets/issues/3839 | 1,161,183,482 | I_kwDODunzps5FNkD6 | 3,839 | CI is broken for Windows | {
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} | ## Describe the bug
See: https://app.circleci.com/pipelines/github/huggingface/datasets/10292/workflows/83de4a55-bff7-43ec-96f7-0c335af5c050/jobs/63355
```
___________________ test_datasetdict_from_text_split[test] ____________________
[gw0] win32 -- Python 3.7.11 C:\tools\miniconda3\envs\py37\python.exe
split = 'test'
text_path = 'C:\\Users\\circleci\\AppData\\Local\\Temp\\pytest-of-circleci\\pytest-0\\popen-gw0\\data6\\dataset.txt'
tmp_path = WindowsPath('C:/Users/circleci/AppData/Local/Temp/pytest-of-circleci/pytest-0/popen-gw0/test_datasetdict_from_text_spl7')
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_datasetdict_from_text_split(split, text_path, tmp_path):
if split:
path = {split: text_path}
else:
split = "train"
path = {"train": text_path, "test": text_path}
cache_dir = tmp_path / "cache"
expected_features = {"text": "string"}
> dataset = TextDatasetReader(path, cache_dir=cache_dir).read()
tests\io\test_text.py:118:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\io\text.py:43: in read
use_auth_token=use_auth_token,
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\builder.py:588: in download_and_prepare
self._download_prepared_from_hf_gcs(dl_manager.download_config)
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\builder.py:630: in _download_prepared_from_hf_gcs
reader.download_from_hf_gcs(download_config, relative_data_dir)
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\arrow_reader.py:260: in download_from_hf_gcs
downloaded_dataset_info = cached_path(remote_dataset_info.replace(os.sep, "/"))
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\utils\file_utils.py:301: in cached_path
download_desc=download_config.download_desc,
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\utils\file_utils.py:560: in get_from_cache
headers=headers,
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\utils\file_utils.py:476: in http_head
max_retries=max_retries,
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\utils\file_utils.py:397: in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
C:\tools\miniconda3\envs\py37\lib\site-packages\requests\api.py:61: in request
return session.request(method=method, url=url, **kwargs)
C:\tools\miniconda3\envs\py37\lib\site-packages\requests\sessions.py:529: in request
resp = self.send(prep, **send_kwargs)
C:\tools\miniconda3\envs\py37\lib\site-packages\requests\sessions.py:645: in send
r = adapter.send(request, **kwargs)
C:\tools\miniconda3\envs\py37\lib\site-packages\responses\__init__.py:840: in unbound_on_send
return self._on_request(adapter, request, *a, **kwargs)
C:\tools\miniconda3\envs\py37\lib\site-packages\responses\__init__.py:780: in _on_request
match, match_failed_reasons = self._find_match(request)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <responses.RequestsMock object at 0x000002048AD70588>
request = <PreparedRequest [HEAD]>
def _find_first_match(self, request):
match_failed_reasons = []
> for i, match in enumerate(self._matches):
E AttributeError: 'RequestsMock' object has no attribute '_matches'
C:\tools\miniconda3\envs\py37\lib\site-packages\moto\core\models.py:289: AttributeError
```
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https://api.github.com/repos/huggingface/datasets/issues/3838 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3838/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3838/comments | https://api.github.com/repos/huggingface/datasets/issues/3838/events | https://github.com/huggingface/datasets/issues/3838 | 1,161,137,406 | I_kwDODunzps5FNYz- | 3,838 | Add a data type for labeled images (image segmentation) | {
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} | It might be a mix of Image and ClassLabel, and the color palette might be generated automatically.
---
### Example
every pixel in the images of the annotation column (in https://huggingface.co/datasets/scene_parse_150) has a value that gives its class, and the dataset itself is associated with a color palette (eg https://github.com/open-mmlab/mmsegmentation/blob/98a353b674c6052d319e7de4e5bcd65d670fcf84/mmseg/datasets/ade.py#L47) that maps every class with a color.
So we might want to render the image as a colored image instead of a black and white one.
<img width="785" alt="156741519-fbae6844-2606-4c28-837e-279d83d00865" src="https://user-images.githubusercontent.com/1676121/157005263-7058c584-2b70-465a-ad94-8a982f726cf4.png">
See https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/core/features/labeled_image.py for reference in Tensorflow | null | {
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https://api.github.com/repos/huggingface/datasets/issues/3837 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3837/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3837/comments | https://api.github.com/repos/huggingface/datasets/issues/3837/events | https://github.com/huggingface/datasets/pull/3837 | 1,161,109,031 | PR_kwDODunzps40BwE1 | 3,837 | Release: 1.18.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/3835 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3835/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3835/comments | https://api.github.com/repos/huggingface/datasets/issues/3835/events | https://github.com/huggingface/datasets/issues/3835 | 1,161,029,205 | I_kwDODunzps5FM-ZV | 3,835 | The link given on the gigaword does not work | {
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} | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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https://api.github.com/repos/huggingface/datasets/issues/3834 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3834/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3834/comments | https://api.github.com/repos/huggingface/datasets/issues/3834/events | https://github.com/huggingface/datasets/pull/3834 | 1,160,657,937 | PR_kwDODunzps40ATVw | 3,834 | Fix dead dataset scripts creation link. | {
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https://api.github.com/repos/huggingface/datasets/issues/3833 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3833/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3833/comments | https://api.github.com/repos/huggingface/datasets/issues/3833/events | https://github.com/huggingface/datasets/pull/3833 | 1,160,543,713 | PR_kwDODunzps4z_99t | 3,833 | Small typos in How-to-train tutorial. | {
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https://api.github.com/repos/huggingface/datasets/issues/3832 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3832/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3832/comments | https://api.github.com/repos/huggingface/datasets/issues/3832/events | https://github.com/huggingface/datasets/issues/3832 | 1,160,503,446 | I_kwDODunzps5FK-CW | 3,832 | Making Hugging Face the place to go for Graph NNs datasets | {
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} | Let's make Hugging Face Datasets the central hub for GNN datasets :)
**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field.
What are some datasets worth integrating into the Hugging Face hub?
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Special thanks to @napoles-uach for his collaboration on identifying the first ones:
- [ ] [SNAP-Stanford OGB Datasets](https://github.com/snap-stanford/ogb).
- [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https://github.com/snap-stanford/pretrain-gnns).
- [ ] [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression)
cc @osanseviero
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} | ## Describe the bug
when converting a dataset to tf_dataset by using to_tf_dataset with shuffle true, the remainder is not converted to one batch
## Steps to reproduce the bug
this is the sample code below
https://colab.research.google.com/drive/1_oRXWsR38ElO1EYF9ayFoCU7Ou1AAej4?usp=sharing
## Expected results
regardless of shuffle is true or not, 67 rows dataset should be 5 batches when batch size is 16.
## Actual results
4 batches
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3830 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3830/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3830/comments | https://api.github.com/repos/huggingface/datasets/issues/3830/events | https://github.com/huggingface/datasets/issues/3830 | 1,160,181,404 | I_kwDODunzps5FJvac | 3,830 | Got error when load cnn_dailymail dataset | {
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} | When using datasets.load_dataset method to load cnn_dailymail dataset, got error as below:
- windows os: FileNotFoundError: [WinError 3] 系统找不到指定的路径。: 'D:\\SourceCode\\DataScience\\HuggingFace\\Data\\downloads\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\cnn\\stories'
- google colab: NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
The code is to load dataset:
windows os:
```
from datasets import load_dataset
dataset = load_dataset("cnn_dailymail", "3.0.0", cache_dir="D:\\SourceCode\\DataScience\\HuggingFace\\Data")
```
google colab:
```
import datasets
train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train")
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3829 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3829/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3829/comments | https://api.github.com/repos/huggingface/datasets/issues/3829/events | https://github.com/huggingface/datasets/issues/3829 | 1,160,154,352 | I_kwDODunzps5FJozw | 3,829 | [📄 Docs] Create a `datasets` performance guide. | {
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} | ## Brief Overview
Downloading, saving, and preprocessing large datasets from the `datasets` library can often result in [performance bottlenecks](https://github.com/huggingface/datasets/issues/3735). These performance snags can be challenging to identify and to debug, especially for users who are less experienced with building deep learning experiments.
## Feature Request
Could we create a performance guide for using `datasets`, similar to:
* [Better performance with the `tf.data` API](https://github.com/huggingface/datasets/issues/3735)
* [Analyze `tf.data` performance with the TF Profiler](https://www.tensorflow.org/guide/data_performance_analysis)
This performance guide should detail practical options for improving performance with `datasets`, and enumerate any common best practices. It should also show how to use tools like the PyTorch Profiler or the TF Profiler to identify any performance bottlenecks (example below).
## Related Issues
* [wiki_dpr pre-processing performance #1670](https://github.com/huggingface/datasets/issues/1670)
* [Adjusting chunk size for streaming datasets #3499](https://github.com/huggingface/datasets/issues/3499)
* [how large datasets are handled under the hood #1004](https://github.com/huggingface/datasets/issues/1004)
* [using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? #1830](https://github.com/huggingface/datasets/issues/1830)
* [Best way to batch a large dataset? #315](https://github.com/huggingface/datasets/issues/315)
* [Saving processed dataset running infinitely #1911](https://github.com/huggingface/datasets/issues/1911) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/3828 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3828/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3828/comments | https://api.github.com/repos/huggingface/datasets/issues/3828/events | https://github.com/huggingface/datasets/issues/3828 | 1,160,064,029 | I_kwDODunzps5FJSwd | 3,828 | The Pile's _FEATURE spec seems to be incorrect | {
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} | ## Describe the bug
If you look at https://huggingface.co/datasets/the_pile/blob/main/the_pile.py:
For "all"
* the pile_set_name is never set for data
* there's actually an id field inside of "meta"
For subcorpora pubmed_central and hacker_news:
* the meta is specified to be a string, but it's actually a dict with an id field inside.
## Steps to reproduce the bug
## Expected results
Feature spec should match the data I'd think?
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/3827 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3827/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3827/comments | https://api.github.com/repos/huggingface/datasets/issues/3827/events | https://github.com/huggingface/datasets/pull/3827 | 1,159,878,436 | PR_kwDODunzps4z95dj | 3,827 | Remove deprecated `remove_columns` param in `filter` | {
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https://api.github.com/repos/huggingface/datasets/issues/3826 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3826/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3826/comments | https://api.github.com/repos/huggingface/datasets/issues/3826/events | https://github.com/huggingface/datasets/pull/3826 | 1,159,851,110 | PR_kwDODunzps4z90JU | 3,826 | Add IterableDataset.filter | {
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I added `IterableDataset.filter` with an API that is a subset of `Dataset.filter`:
```python
def filter(self, function, batched=False, batch_size=1000, with_indices=false, input_columns=None):
```
TODO:
- [x] tests
- [x] docs
related to https://github.com/huggingface/datasets/issues/3444 and https://github.com/huggingface/datasets/issues/3753 | {
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https://api.github.com/repos/huggingface/datasets/issues/3825 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3825/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3825/comments | https://api.github.com/repos/huggingface/datasets/issues/3825/events | https://github.com/huggingface/datasets/pull/3825 | 1,159,802,345 | PR_kwDODunzps4z9p4b | 3,825 | Update version and date in Wikipedia dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/3824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3824/comments | https://api.github.com/repos/huggingface/datasets/issues/3824/events | https://github.com/huggingface/datasets/pull/3824 | 1,159,574,186 | PR_kwDODunzps4z85SO | 3,824 | Allow not specifying feature cols other than `predictions`/`references` in `Metric.compute` | {
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https://api.github.com/repos/huggingface/datasets/issues/3823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3823/comments | https://api.github.com/repos/huggingface/datasets/issues/3823/events | https://github.com/huggingface/datasets/issues/3823 | 1,159,497,844 | I_kwDODunzps5FHIh0 | 3,823 | 500 internal server error when trying to open a dataset composed of Zarr stores | {
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] | closed | false | null | [] | null | 4 | 2022-03-04T10:37:14Z | 2022-03-08T09:47:39Z | 2022-03-08T09:47:39Z | NONE | null | null | {
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} | ## Describe the bug
The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code.
The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine.
In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format?
For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("openclimatefix/mrms")
```
## Expected results
The dataset should be downloaded or open up
## Actual results
A 500 internal server error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35
- Python version: 3.9.10
- PyArrow version: 7.0.0
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} | ## Adding a Dataset
- **Name:** Biwi Kinect Head Pose Database
- **Description:** Over 15K images of 20 people recorded with a Kinect while turning their heads around freely. For each frame, depth and rgb images are provided, together with ground in the form of the 3D location of the head and its rotation angles.
- **Data:** [*link to the Github repository or current dataset location*](https://icu.ee.ethz.ch/research/datsets.html)
- **Motivation:** Useful pose estimation dataset
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Once approved, this will be used to generate the pre-processed Wikipedia datasets.
Finally, this PR will be able to be merged into master:
- NOT using squash
- BUT a regular MERGE (or REBASE+MERGE), so that all commits are preserved
TODO:
- [x] #3435
- [x] #3789
- [x] #3825
- [x] Run to get the pre-processed data for big languages (backward compatibility)
- [x] #3958
CC: @geohci | {
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https://api.github.com/repos/huggingface/datasets/issues/3820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3820/comments | https://api.github.com/repos/huggingface/datasets/issues/3820/events | https://github.com/huggingface/datasets/issues/3820 | 1,159,106,603 | I_kwDODunzps5FFpAr | 3,820 | `pubmed_qa` checksum mismatch | {
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] | closed | false | null | [] | null | 1 | 2022-03-04T00:28:08Z | 2022-03-04T09:42:32Z | 2022-03-04T09:42:32Z | CONTRIBUTOR | null | null | {
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} | ## Describe the bug
Loading [`pubmed_qa`](https://huggingface.co/datasets/pubmed_qa) results in a mismatched checksum error.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import datasets
try:
datasets.load_dataset("pubmed_qa", "pqa_labeled")
except Exception as e:
print(e)
try:
datasets.load_dataset("pubmed_qa", "pqa_unlabeled")
except Exception as e:
print(e)
try:
datasets.load_dataset("pubmed_qa", "pqa_artificial")
except Exception as e:
print(e)
```
## Expected results
Successful download.
## Actual results
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 665, in _download_and_prepare
verify_checksums(
File "/usr/local/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ', 'https://drive.google.com/uc?export=download&id=15v1x6aQDlZymaHGP7cZJZZYFfeJt2NdS']
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: macOS
- Python version: 3.8.1
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3819 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3819/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3819/comments | https://api.github.com/repos/huggingface/datasets/issues/3819/events | https://github.com/huggingface/datasets/pull/3819 | 1,158,848,288 | PR_kwDODunzps4z6fvn | 3,819 | Fix typo in doc build yml | {
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https://api.github.com/repos/huggingface/datasets/issues/3818 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3818/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3818/comments | https://api.github.com/repos/huggingface/datasets/issues/3818/events | https://github.com/huggingface/datasets/issues/3818 | 1,158,788,545 | I_kwDODunzps5FEbXB | 3,818 | Support for "sources" parameter in the add() and add_batch() methods in datasets.metric - SARI | {
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] | closed | false | null | [] | null | 3 | 2022-03-03T18:57:54Z | 2022-03-04T18:04:21Z | 2022-03-04T18:04:21Z | NONE | null | null | {
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} | **Is your feature request related to a problem? Please describe.**
The methods `add_batch` and `add` from the `Metric` [class](https://github.com/huggingface/datasets/blob/1675ad6a958435b675a849eafa8a7f10fe0f43bc/src/datasets/metric.py) does not work with [SARI](https://github.com/huggingface/datasets/blob/master/metrics/sari/sari.py) metric. This metric not only relies on the predictions and references, but also in the input.
For example, when the `add_batch` method is used, then the `compute()` method fails:
```
metric = load_metric("sari")
metric.add_batch(
predictions=["About 95 you now get in ."],
references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]])
metric.compute()
> TypeError: _compute() missing 1 required positional argument: 'sources'
```
Therefore, the `compute() `method can only be used standalone:
```
metric = load_metric("sari")
result = metric.compute(
sources=["About 95 species are currently accepted ."],
predictions=["About 95 you now get in ."],
references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]])
> {'sari': 26.953601953601954}
```
**Describe the solution you'd like**
Support for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class.
```
add_batch(*, sources=None, predictions=None, references=None, **kwargs)
add(*, sources=None, predictions=None, references=None, **kwargs)
compute()
```
**Describe alternatives you've considered**
I've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add) and [add_batch method](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add_batch).
**Additional context**
These methods are used in the transformers [pytorch examples](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py).
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https://api.github.com/repos/huggingface/datasets/issues/3817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3817/comments | https://api.github.com/repos/huggingface/datasets/issues/3817/events | https://github.com/huggingface/datasets/pull/3817 | 1,158,592,335 | PR_kwDODunzps4z5pQ7 | 3,817 | Simplify Common Voice code | {
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} | [] | closed | false | null | [] | null | 1 | 2022-03-03T16:01:21Z | 2022-03-04T14:51:48Z | 2022-03-04T12:39:23Z | MEMBER | null | null | null | In #3736 we introduced one method to generate examples when streaming, that is different from the one when not streaming.
In this PR I propose a new implementation which is simpler: it only has one function, based on `iter_archive`. And you still have access to local audio files when loading the dataset in non-streaming mode.
cc @patrickvonplaten @polinaeterna @anton-l @albertvillanova since this will become the template for many audio datasets to come.
This change can also trivially be applied to the other audio datasets that already exist. Using this line, you can get access to local files in non-streaming mode:
```python
local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3816 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3816/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3816/comments | https://api.github.com/repos/huggingface/datasets/issues/3816/events | https://github.com/huggingface/datasets/pull/3816 | 1,158,589,913 | PR_kwDODunzps4z5owP | 3,816 | Doc new UI test workflows2 | {
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https://api.github.com/repos/huggingface/datasets/issues/3815 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3815/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3815/comments | https://api.github.com/repos/huggingface/datasets/issues/3815/events | https://github.com/huggingface/datasets/pull/3815 | 1,158,589,512 | PR_kwDODunzps4z5oq- | 3,815 | Fix iter_archive getting reset | {
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} | [] | closed | false | null | [] | null | 0 | 2022-03-03T15:58:52Z | 2022-03-03T18:06:37Z | 2022-03-03T18:06:13Z | MEMBER | null | null | null | The `DownloadManager.iter_archive` method currently returns an iterator - which is **empty** once you iter over it once. This means you can't pass the same archive iterator to several splits.
To fix that, I changed the ouput of `DownloadManager.iter_archive` to be an iterable that you can iterate over several times, instead of a one-time-use iterator.
The `StreamingDownloadManager.iter_archive` already returns an appropriate iterable, and the code added in this PR is inspired from the one in `streaming_download_manager.py` | {
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https://api.github.com/repos/huggingface/datasets/issues/3814 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3814/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3814/comments | https://api.github.com/repos/huggingface/datasets/issues/3814/events | https://github.com/huggingface/datasets/pull/3814 | 1,158,518,995 | PR_kwDODunzps4z5Zk4 | 3,814 | Handle Nones in PyArrow struct | {
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https://api.github.com/repos/huggingface/datasets/issues/3813 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3813/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3813/comments | https://api.github.com/repos/huggingface/datasets/issues/3813/events | https://github.com/huggingface/datasets/issues/3813 | 1,158,474,859 | I_kwDODunzps5FDOxr | 3,813 | Add MetaShift dataset | {
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] | null | 7 | 2022-03-03T14:26:45Z | 2022-04-10T13:39:59Z | 2022-04-10T13:39:59Z | CONTRIBUTOR | null | null | {
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} | ## Adding a Dataset
- **Name:** MetaShift
- **Description:** collection of 12,868 sets of natural images across 410 classes-
- **Paper:** https://arxiv.org/abs/2202.06523v1
- **Data:** https://github.com/weixin-liang/metashift
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/3812 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3812/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3812/comments | https://api.github.com/repos/huggingface/datasets/issues/3812/events | https://github.com/huggingface/datasets/pull/3812 | 1,158,369,995 | PR_kwDODunzps4z46C4 | 3,812 | benchmark streaming speed with tar vs zip archives | {
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} | [] | closed | false | null | [] | null | 1 | 2022-03-03T12:48:41Z | 2022-03-03T14:55:34Z | 2022-03-03T14:55:33Z | CONTRIBUTOR | null | null | null | # do not merge
## Hypothesis
packing data into a single zip archive could allow us not to care about splitting data into several tar archives for efficient streaming which is annoying (since data creators usually host the data in a single tar)
## Data
I host it [here](https://huggingface.co/datasets/polinaeterna/benchmark_dataset/)
## I checked three configurations:
1. All data in one zip archive, streaming only those files that exist in split metadata file (we can access them directrly with no need to iterate over full archive), see [this func](https://github.com/huggingface/datasets/compare/master...polinaeterna:benchmark-tar-zip?expand=1#diff-4f5200d4586aec5b2a89fcf34441c5f92156f9e9d408acc7e50666f9a1921ddcR196)
2. All data in three splits, the standart way to make streaming efficient, see [this func](https://github.com/huggingface/datasets/compare/master...polinaeterna:benchmark-tar-zip?expand=1#diff-4f5200d4586aec5b2a89fcf34441c5f92156f9e9d408acc7e50666f9a1921ddcR174)
3. All data in single tar, iterate over the full archive and take only files existing in split metadata file, see [this func](https://github.com/huggingface/datasets/compare/master...polinaeterna:benchmark-tar-zip?expand=1#diff-4f5200d4586aec5b2a89fcf34441c5f92156f9e9d408acc7e50666f9a1921ddcR150)
## Results
1. one zip
2. three tars
3. one tar
didn't check on the full data as it's time consuming but anyway it's pretty obvious that one-zip-way is not a good idea. here it's even worse than full iteration over tar containing all three splits (but that would depend on the case).
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https://api.github.com/repos/huggingface/datasets/issues/3811 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3811/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3811/comments | https://api.github.com/repos/huggingface/datasets/issues/3811/events | https://github.com/huggingface/datasets/pull/3811 | 1,158,234,407 | PR_kwDODunzps4z4dHS | 3,811 | Update dev doc gh workflows | {
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https://api.github.com/repos/huggingface/datasets/issues/3810 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3810/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3810/comments | https://api.github.com/repos/huggingface/datasets/issues/3810/events | https://github.com/huggingface/datasets/pull/3810 | 1,158,202,093 | PR_kwDODunzps4z4WUW | 3,810 | Update version of xcopa dataset | {
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We updated our loading script, but we did not bump a new version number:
- #3254
This PR updates our loading script version from `1.0.0` to `1.1.0`. | {
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} | ## Describe the bug
Datasets hosted on Google Drive do not seem to work right now.
Loading them fails with a checksum error.
## Steps to reproduce the bug
```python
from datasets import load_dataset
for dataset in ["head_qa", "yelp_review_full"]:
try:
load_dataset(dataset)
except Exception as exception:
print("Error", dataset, exception)
```
Here is a [colab](https://colab.research.google.com/drive/1wOtHBmL8I65NmUYakzPV5zhVCtHhi7uQ#scrollTo=cDzdCLlk-Bo4).
## Expected results
The datasets should be loaded.
## Actual results
```
Downloading and preparing dataset head_qa/es (download: 75.69 MiB, generated: 2.86 MiB, post-processed: Unknown size, total: 78.55 MiB) to /root/.cache/huggingface/datasets/head_qa/es/1.1.0/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9...
Error head_qa Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t']
Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /root/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43...
Error yelp_review_full Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0']
```
## Environment info
- `datasets` version: 1.18.3
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3808 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3808/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3808/comments | https://api.github.com/repos/huggingface/datasets/issues/3808/events | https://github.com/huggingface/datasets/issues/3808 | 1,157,650,043 | I_kwDODunzps5FAFZ7 | 3,808 | Pre-Processing Cache Fails when using a Factory pattern | {
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} | ## Describe the bug
If you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time.
## Steps to reproduce the bug
```python
def preprocess_function_factory(augmentation=None):
def preprocess_function(examples):
# Tokenize the texts
if augmentation:
conversions1 = [
augmentation(example)
for example in examples[sentence1_key]
]
if sentence2_key is None:
args = (conversions1,)
else:
conversions2 = [
augmentation(example)
for example in examples[sentence2_key]
]
args = (conversions1, conversions2)
else:
args = (
(examples[sentence1_key],)
if sentence2_key is None
else (examples[sentence1_key], examples[sentence2_key])
)
result = tokenizer(
*args, padding=padding, max_length=max_seq_length, truncation=True
)
# Map labels to IDs (not necessary for GLUE tasks)
if label_to_id is not None and "label" in examples:
result["label"] = [
(label_to_id[l] if l != -1 else -1) for l in examples["label"]
]
return result
return preprocess_function
capitalize = lambda x: x.capitalize()
preprocess_function = preprocess_function_factory(augmentation=capitalize)
print(hash(preprocess_function)) # This will change on each run
raw_datasets = raw_datasets.map(
preprocess_function,
batched=True,
load_from_cache_file=True,
desc="Running transformation and tokenizer on dataset",
)
```
## Expected results
Running the code twice will cause the cache to be re-used.
## Actual results
Running the code twice causes the whole dataset to be re-processed
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"completed": 0,
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} | ## Describe the bug
Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("xcopa", "it")
```
## Expected results
The dataset should be loaded correctly.
## Actual results
Fails with:
```python
in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/cambridgeltl/xcopa/archive/master.zip']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3, and 1.18.4.dev0
- Platform:
- Python version: 3.8
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/3806 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3806/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3806/comments | https://api.github.com/repos/huggingface/datasets/issues/3806/events | https://github.com/huggingface/datasets/pull/3806 | 1,157,505,826 | PR_kwDODunzps4z2FeI | 3,806 | Fix Spanish data file URL in wiki_lingua dataset | {
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} | [] | closed | false | null | [] | null | 0 | 2022-03-02T17:43:42Z | 2022-03-03T08:38:17Z | 2022-03-03T08:38:16Z | MEMBER | null | null | null | This PR fixes the URL for Spanish data file.
Previously, Spanish had the same URL as Vietnamese data file. | {
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https://api.github.com/repos/huggingface/datasets/issues/3805 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3805/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3805/comments | https://api.github.com/repos/huggingface/datasets/issues/3805/events | https://github.com/huggingface/datasets/pull/3805 | 1,157,454,884 | PR_kwDODunzps4z16os | 3,805 | Remove decode: true for image feature in head_qa | {
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https://api.github.com/repos/huggingface/datasets/issues/3804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3804/comments | https://api.github.com/repos/huggingface/datasets/issues/3804/events | https://github.com/huggingface/datasets/issues/3804 | 1,157,297,278 | I_kwDODunzps5E-vR- | 3,804 | Text builder with custom separator line boundaries | {
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} | **Is your feature request related to a problem? Please describe.**
The current [Text](https://github.com/huggingface/datasets/blob/207be676bffe9d164740a41a883af6125edef135/src/datasets/packaged_modules/text/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted.
**Describe the solution you'd like**
```python
if self.config.sample_by == "line":
batch_idx = 0
while True:
batch = f.read(self.config.chunksize)
if not batch:
break
batch += f.readline() # finish current line
if self.config.custom_newline is None:
batch = batch.splitlines(keepends=self.config.keep_linebreaks)
else:
batch = batch.split(self.config.custom_newline)[:-1]
pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema)
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), pa_table
batch_idx += 1
```
**A clear and concise description of what you want to happen.**
Creating the dataset rows with a subset of the `splitlines()` line boundaries. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/3803 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3803/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3803/comments | https://api.github.com/repos/huggingface/datasets/issues/3803/events | https://github.com/huggingface/datasets/pull/3803 | 1,157,271,679 | PR_kwDODunzps4z1T48 | 3,803 | Remove deprecated methods/params (preparation for v2.0) | {
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* `Dataset.cast_`/`DatasetDict.cast_`
* `Dataset.dictionary_encode_column_`/`DatasetDict.dictionary_encode_column_`
* `Dataset.remove_columns_`/`DatasetDict.remove_columns_`
* `Dataset.rename_columns_`/`DatasetDict.rename_columns_`
* `prepare_module`
* param `script_version` in `load_dataset`/`load_metric`
* param `version` in `hf_github_url`
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https://api.github.com/repos/huggingface/datasets/issues/3802 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3802/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3802/comments | https://api.github.com/repos/huggingface/datasets/issues/3802/events | https://github.com/huggingface/datasets/pull/3802 | 1,157,009,964 | PR_kwDODunzps4z0biM | 3,802 | Release of FairLex dataset | {
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} | [] | closed | false | null | [] | null | 11 | 2022-03-02T10:40:18Z | 2022-03-02T15:21:10Z | 2022-03-02T15:18:54Z | CONTRIBUTOR | null | null | null |
**FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing**
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
*Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Letizia, Sebastian Felix Schwemer, Anders Søgaard. FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing. 2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*
Note: Please review this initial commit, and I'll update the publication link, once I'll have the ArXived version. Thanks!
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https://api.github.com/repos/huggingface/datasets/issues/3801 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3801/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3801/comments | https://api.github.com/repos/huggingface/datasets/issues/3801/events | https://github.com/huggingface/datasets/pull/3801 | 1,155,649,279 | PR_kwDODunzps4zvqjN | 3,801 | [Breaking] Align `map` when streaming: update instead of overwrite + add missing parameters | {
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} | [] | closed | false | null | [] | null | 1 | 2022-03-01T18:06:43Z | 2022-03-07T16:30:30Z | 2022-03-07T16:30:29Z | MEMBER | null | null | null | Currently the datasets in streaming mode and in non-streaming mode have two distinct API for `map` processing.
In this PR I'm aligning the two by changing `map` in streamign mode. This includes a **major breaking change** and will require a major release of the library: **Datasets 2.0**
In particular, `Dataset.map` adds new columns (with dict.update) BUT `IterableDataset.map` used to discard previous columns (it overwrites the dict). In this PR I'm chaning the `IterableDataset.map` to behave the same way as `Dataset.map`: it will update the examples instead of overwriting them.
I'm also adding those missing parameters to streaming `map`: with_indices, input_columns, remove_columns
### TODO
- [x] tests
- [x] docs
Related to https://github.com/huggingface/datasets/issues/3444 | {
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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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} | [] | closed | false | null | [] | null | 0 | 2022-03-01T17:37:46Z | 2022-03-04T07:15:55Z | 2022-03-04T07:15:55Z | CONTRIBUTOR | null | null | null | 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/3799 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3799/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3799/comments | https://api.github.com/repos/huggingface/datasets/issues/3799/events | https://github.com/huggingface/datasets/pull/3799 | 1,155,356,102 | PR_kwDODunzps4zus9R | 3,799 | Xtreme-S Metrics | {
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} | [] | closed | false | null | [] | null | 3 | 2022-03-01T13:42:28Z | 2022-03-16T14:40:29Z | 2022-03-16T14:40:26Z | CONTRIBUTOR | null | null | null | **Added datasets (TODO)**:
- [x] MLS
- [x] Covost2
- [x] Minds-14
- [x] Voxpopuli
- [x] FLoRes (need data)
**Metrics**: Done | {
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https://api.github.com/repos/huggingface/datasets/issues/3798 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3798/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3798/comments | https://api.github.com/repos/huggingface/datasets/issues/3798/events | https://github.com/huggingface/datasets/pull/3798 | 1,154,411,066 | PR_kwDODunzps4zrl5Y | 3,798 | Fix error message in CSV loader for newer Pandas versions | {
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} | [] | closed | false | null | [] | null | 0 | 2022-02-28T18:24:10Z | 2022-02-28T18:51:39Z | 2022-02-28T18:51:38Z | COLLABORATOR | null | null | null | Fix the error message in the CSV loader for `Pandas >= 1.4`. To fix this, I directly print the current file name in the for-loop. An alternative would be to use a check similar to this:
```python
csv_file_reader.handle.handle if datasets.config.PANDAS_VERSION >= version.parse("1.4") else csv_file_reader.f
```
CC: @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/3797 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3797/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3797/comments | https://api.github.com/repos/huggingface/datasets/issues/3797/events | https://github.com/huggingface/datasets/pull/3797 | 1,154,383,063 | PR_kwDODunzps4zrgAD | 3,797 | Reddit dataset card contribution | {
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} | [] | closed | false | null | [] | null | 0 | 2022-02-28T16:28:45Z | 2022-02-28T17:03:46Z | 2022-02-28T17:03:46Z | COLLABORATOR | null | null | null | This will speed up the loading of the datasets where the number of data files is large (can easily happen with `imagefoler`, for instance) | {
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] | null | 2 | 2022-02-27T13:57:40Z | 2022-03-21T14:36:12Z | 2022-03-21T14:36:12Z | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
after downloading the natural_questions dataset, can not flatten the dataset considering there are `long answer` and `short answer` in `annotations`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('natural_questions',cache_dir = 'data/dataset_cache_dir')
dataset['train'].flatten()
```
## Expected results
a dataset with `long_answer` as features
## Actual results
Traceback (most recent call last):
File "temp.py", line 5, in <module>
dataset['train'].flatten()
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/fingerprint.py", line 413, in wrapper
out = func(self, *args, **kwargs)
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1296, in flatten
dataset._data = update_metadata_with_features(dataset._data, dataset.features)
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 536, in update_metadata_with_features
features = Features({col_name: features[col_name] for col_name in table.column_names})
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 536, in <dictcomp>
features = Features({col_name: features[col_name] for col_name in table.column_names})
KeyError: 'annotations.long_answer'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.13
- Platform: MBP
- Python version: 3.8
- PyArrow version: 6.0.1
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In this PR I implement the metric in a simple way with the help of numpy only.
Similar to the [MAUVE implementation](https://github.com/huggingface/datasets/blob/master/metrics/mauve/mauve.py), we can make this metric accept texts as input and encode them with a featurize model, if that is desirable. | {
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{
"color": "E5583E",
"default": false,
"description": "Related to the dataset viewer on huggingface.co",
"id": 3470211881,
"name": "dataset-viewer",
"node_id": "LA_kwDODunzps7O1zsp",
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] | closed | false | null | [] | null | 26 | 2022-02-25T19:55:09Z | 2024-03-13T12:25:08Z | 2022-02-28T08:44:18Z | NONE | null | null | {
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} | ## Dataset viewer issue for 'wiki_lingua*'
**Link:** *link to the dataset viewer page*
`data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]")
`
*short description of the issue*
```
[NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()
```
Am I the one who added this dataset ? No
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} | [] | closed | false | null | [] | null | 0 | 2022-02-25T18:26:35Z | 2022-03-01T13:10:43Z | 2022-03-01T13:10:42Z | COLLABORATOR | null | null | null | As discussed in https://github.com/huggingface/datasets/pull/2830#issuecomment-1048989764, this PR adds a QOL improvement to easily reference the files inside a directory in `load_dataset` using the `data_dir` param (very handy for ImageFolder because it avoids globbing, but also useful for the other loaders). Additionally, it fixes the issue with `HfFileSystem.isdir`, which would previously always return `False`, and aligns the path-handling logic in `HfFileSystem` with `fsspec.GitHubFileSystem`. | {
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https://api.github.com/repos/huggingface/datasets/issues/3790 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3790/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3790/comments | https://api.github.com/repos/huggingface/datasets/issues/3790/events | https://github.com/huggingface/datasets/pull/3790 | 1,150,646,899 | PR_kwDODunzps4zedMa | 3,790 | Add doc builder scripts | {
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} | [] | closed | false | null | [] | null | 3 | 2022-02-25T16:38:47Z | 2022-03-01T15:55:42Z | 2022-03-01T15:55:41Z | MEMBER | null | null | null | I added the three scripts:
- build_dev_documentation.yml
- build_documentation.yml
- delete_dev_documentation.yml
I got them from `transformers` and did a few changes:
- I removed the `transformers`-specific dependencies
- I changed all the paths to be "datasets" instead of "transformers"
- I passed the `--library_name datasets` arg to the `doc-builder build` command (according to https://github.com/huggingface/doc-builder/pull/94/files#diff-bcc33cf7c223511e498776684a9a433810b527a0a38f483b1487e8a42b6575d3R26)
cc @LysandreJik @mishig25 | {
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https://api.github.com/repos/huggingface/datasets/issues/3789 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3789/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3789/comments | https://api.github.com/repos/huggingface/datasets/issues/3789/events | https://github.com/huggingface/datasets/pull/3789 | 1,150,587,404 | PR_kwDODunzps4zeQpx | 3,789 | Add URL and ID fields to Wikipedia dataset | {
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} | [] | closed | false | null | [] | null | 3 | 2022-02-25T15:34:37Z | 2022-03-04T08:24:24Z | 2022-03-04T08:24:23Z | MEMBER | null | null | null | This PR adds the URL field, so that we conform to proper attribution, required by their license: provide credit to the authors by including a hyperlink (where possible) or URL to the page or pages you are re-using.
About the conversion from title to URL, I found that apart from replacing blanks with underscores, some other special character must also be percent-encoded (e.g. `"` to `%22`): https://meta.wikimedia.org/wiki/Help:URL
Therefore, I have finally used `urllib.parse.quote` function. This additionally percent-encodes non-ASCII characters, but Wikimedia docs say these are equivalent:
> For the other characters either the code or the character can be used in internal and external links, they are equivalent. The system does a conversion when needed.
> [[%C3%80_propos_de_M%C3%A9ta]]
> is rendered as [À_propos_de_Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), almost like [À propos de Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), which leads to this page on Meta with in the address bar the URL
> [http://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta)
> while [http://meta.wikipedia.org/wiki/À_propos_de_Méta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta) leads to the same.
Fix #3398.
CC: @geohci | {
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} | ## Describe the bug
As reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/3787 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3787/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3787/comments | https://api.github.com/repos/huggingface/datasets/issues/3787/events | https://github.com/huggingface/datasets/pull/3787 | 1,150,235,569 | PR_kwDODunzps4zdE7b | 3,787 | Fix Google Drive URL to avoid Virus scan warning | {
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Fix #3786, fix #3784. | {
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} | ## Describe the bug
Recently, some issues were reported with URLs from Google Drive, where we were downloading the Virus scan warning page instead of the data file itself.
See:
- #3758
- #3773
- #3784
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So, if the original link looked like https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ
The new link now looks like https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ&confirm=t
Fixes #3784 | {
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} | ## Describe the bug
I am unable to download the CNN-Dailymail dataset. Upon closer investigation, I realised why this was happening:
- The dataset sits in Google Drive, and both the CNN and DM datasets are large.
- Google is unable to scan the folder for viruses, **so the link which would originally download the dataset, now downloads the source code of this web page:**
- **This leads to the following error**:
```python
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
## Steps to reproduce the bug
```python
import datasets
dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train")
```
## Expected results
That the dataset is downloaded and processed just like other datasets.
## Actual results
Hit with this error:
```python
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3782/comments | https://api.github.com/repos/huggingface/datasets/issues/3782/events | https://github.com/huggingface/datasets/pull/3782 | 1,148,994,022 | PR_kwDODunzps4zY-Xb | 3,782 | Error of writing with different schema, due to nonpreservation of nullability | {
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```
dataset.map(
batched=True,
disable_nullable=True,
)
```
will get the following error at here https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/arrow_writer.py#L516
`pyarrow.lib.ArrowInvalid: Tried to write record batch with different schema`
## 2. Debugging
### 2.1 tracing
During `_map_single`, the following are called
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/arrow_dataset.py#L2523
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/arrow_writer.py#L511
### 2.2. Observation
The problem is, even after `table_cast`, `pa_table.schema != self._schema`
`pa_table.schema` (before/after `table_cast`)
```
input_ids: list<item: int32>
child 0, item: int32
```
`self._schema`
```
input_ids: list<item: int32> not null
child 0, item: int32
```
### 2.3. Reason
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/table.py#L1121
Here we lose nullability stored in `schema` because it seems that `Features` is always nullable and don't store nullability.
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/table.py#L1103
So, casting to schema from such `Features` loses nullability, and eventually causes error of writing with different schema
## 3. Solution
1. Let `Features` stores nullability.
2. Directly cast table with original schema but not schema from converted `Features`. (this PR)
3. Don't `cast_table` when `write_table` | {
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} | [] | closed | false | null | [] | null | 1 | 2022-02-23T21:29:16Z | 2022-02-28T18:00:40Z | 2022-02-28T11:21:14Z | CONTRIBUTOR | null | null | null | The changes proposed are based on the "TL;DR: Mining Reddit to Learn Automatic Summarization" paper & https://zenodo.org/record/1043504#.YhaKHpbQC38
It is a Reddit dataset indeed, but the name given to the dataset by the authors is Webis-TLDR-17 (corpus), so perhaps it should be modified as well.
The task at which the dataset is aimed is abstractive summarization.
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} | Hello,
I tried to download the **newsroom** dataset but it didn't work out for me. it said me to **download it manually**!
For manually, Link is also didn't work! It is sawing some ad or something!
If anybody has solved this issue please help me out or if somebody has this dataset please share your google drive link, it would be a great help!
Thanks
Darshan Tank | {
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Indeed this makes the documentation confusing and we don't want this distinction anymore in the future. Ideally users should share their datasets on the Hub directly.
### Changes
- the documentation about dataset loading mentions the datasets on the Hub (no difference between canonical and community, since they all have their own repository now)
- the documentation about adding a dataset doesn't explain the technical differences between canonical and community anymore, and only presents how to add a community dataset. There is still a small section at the bottom that mentions the datasets that are still on GitHub and redirects to the `ADD_NEW_DATASET.md` guide on GitHub about how to contribute a dataset to the `datasets` library
- the code source doesn't mention "canonical" anymore anywhere. There is still a `GitHubDatasetModuleFactory` class that is left, but I updated the docstring to say that it will be eventually removed in favor of the `HubDatasetModuleFactory` classes that already exist
Would love to have your feedbacks on this !
cc @julien-c @thomwolf @SBrandeis | {
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} | **Is your feature request related to a problem? Please describe.**
The Wikipedia dataset can be really big. This is a problem if you want to use it locally in a laptop with the Apache Beam `DirectRunner`. Even if your laptop have a considerable amount of memory (e.g. 32gb).
**Describe the solution you'd like**
I would like to use the `data_files` argument in the `load_dataset` function to define which file in the wikipedia dataset I would like to download. Thus, I can work with the dataset in a smaller machine using the Apache Beam `DirectRunner`.
**Describe alternatives you've considered**
I've tried to use the `simple` Wikipedia dataset. But it's in English and I would like to use Portuguese texts in my model.
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https://api.github.com/repos/huggingface/datasets/issues/3775 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3775/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3775/comments | https://api.github.com/repos/huggingface/datasets/issues/3775/events | https://github.com/huggingface/datasets/pull/3775 | 1,146,849,454 | PR_kwDODunzps4zSEd4 | 3,775 | Update gigaword card and info | {
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} | ## Describe the bug
A checksum occurs when downloading the reddit_tifu data (both long & short).
## Steps to reproduce the bug
reddit_tifu_dataset = load_dataset('reddit_tifu', 'long')
## Expected results
The expected result is for the dataset to be downloaded and cached locally.
## Actual results
File "/.../lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 7.0.0
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] | null | 1 | 2022-02-22T00:43:33Z | 2022-02-22T08:12:39Z | 2022-02-22T08:12:39Z | NONE | null | null | {
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} | ### Describe the bug
Failure to generate dataset msr_sqa because of duplicate keys.
### Steps to reproduce the bug
```
from datasets import load_dataset
load_dataset("msr_sqa")
```
### Expected results
The examples keys should be unique.
**Actual results**
```
>>> load_dataset("msr_sqa")
Downloading:
6.72k/? [00:00<00:00, 148kB/s]
Downloading:
2.93k/? [00:00<00:00, 53.8kB/s]
Using custom data configuration default
Downloading and preparing dataset msr_sqa/default (download: 4.57 MiB, generated: 26.25 MiB, post-processed: Unknown size, total: 30.83 MiB) to /root/.cache/huggingface/datasets/msr_sqa/default/0.0.0/70b2a497bd3cc8fc960a3557d2bad1eac5edde824505e15c9c8ebe4c260fd4d1...
Downloading: 100%
4.80M/4.80M [00:00<00:00, 7.49MB/s]
---------------------------------------------------------------------------
DuplicatedKeysError Traceback (most recent call last)
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator)
1080 example = self.info.features.encode_example(record)
-> 1081 writer.write(example, key)
1082 finally:
8 frames
DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: nt-639
Keys should be unique and deterministic in nature
During handling of the above exception, another exception occurred:
DuplicatedKeysError Traceback (most recent call last)
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in check_duplicate_keys(self)
449 for hash, key in self.hkey_record:
450 if hash in tmp_record:
--> 451 raise DuplicatedKeysError(key)
452 else:
453 tmp_record.add(hash)
DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: nt-639
Keys should be unique and deterministic in nature
```
### Environment info
datasets version: 1.18.3
Platform: Google colab notebook
Python version: 3.7
PyArrow version: 6.0.1
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} | ## Describe the bug
assigning the resulted dataset to original dataset causes lost of the faiss index
## Steps to reproduce the bug
`my_dataset` is a regular loaded dataset. It's a part of a customed dataset structure
```python
self.dataset.add_faiss_index('embeddings')
self.dataset.list_indexes()
# ['embeddings']
dataset2 = my_dataset.map(
lambda x: self._get_nearest_examples_batch(x['text']), batch=True
)
# the unexpected result:
dataset2.list_indexes()
# []
self.dataset.list_indexes()
# ['embeddings']
```
in case something wrong with my `_get_nearest_examples_batch()`, it's like this
```python
def _get_nearest_examples_batch(self, examples, k=5):
queries = embed(examples)
scores_batch, retrievals_batch = self.dataset.get_nearest_examples_batch(self.faiss_column, queries, k)
return {
'neighbors': [batch['text'] for batch in retrievals_batch],
'scores': scores_batch
}
```
## Expected results
`map` shouldn't drop the indexes, in another word, indexes should be carried to the generated dataset
## Actual results
map drops the indexes
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.8.12
- PyArrow version: 7.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3767 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3767/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3767/comments | https://api.github.com/repos/huggingface/datasets/issues/3767/events | https://github.com/huggingface/datasets/pull/3767 | 1,146,036,648 | PR_kwDODunzps4zPahh | 3,767 | Expose method and fix param | {
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} | [] | closed | false | null | [] | null | 0 | 2022-02-21T16:57:47Z | 2022-02-22T08:35:03Z | 2022-02-22T08:35:02Z | COLLABORATOR | null | null | null | A fix + expose a new method, following https://github.com/huggingface/datasets/pull/3670 | {
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https://api.github.com/repos/huggingface/datasets/issues/3766 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3766/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3766/comments | https://api.github.com/repos/huggingface/datasets/issues/3766/events | https://github.com/huggingface/datasets/pull/3766 | 1,145,829,289 | PR_kwDODunzps4zOujH | 3,766 | Fix head_qa data URL | {
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https://api.github.com/repos/huggingface/datasets/issues/3765 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3765/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3765/comments | https://api.github.com/repos/huggingface/datasets/issues/3765/events | https://github.com/huggingface/datasets/pull/3765 | 1,145,126,881 | PR_kwDODunzps4zMdIL | 3,765 | Update URL for tagging app | {
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https://api.github.com/repos/huggingface/datasets/issues/3764 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3764/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3764/comments | https://api.github.com/repos/huggingface/datasets/issues/3764/events | https://github.com/huggingface/datasets/issues/3764 | 1,145,107,050 | I_kwDODunzps5EQPJq | 3,764 | ! | {
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"color": "E5583E",
"default": false,
"description": "Related to the dataset viewer on huggingface.co",
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"completed": 0,
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} | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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https://api.github.com/repos/huggingface/datasets/issues/3763 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3763/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3763/comments | https://api.github.com/repos/huggingface/datasets/issues/3763/events | https://github.com/huggingface/datasets/issues/3763 | 1,145,099,878 | I_kwDODunzps5EQNZm | 3,763 | It's not possible download `20200501.pt` dataset | {
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} | ## Describe the bug
The dataset `20200501.pt` is broken.
The available datasets: https://dumps.wikimedia.org/ptwiki/
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("wikipedia", "20200501.pt", beam_runner='DirectRunner')
```
## Expected results
I expect to download the dataset locally.
## Actual results
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("wikipedia", "20200501.pt", beam_runner='DirectRunner')
Downloading and preparing dataset wikipedia/20200501.pt to /home/jvanz/.cache/huggingface/datasets/wikipedia/20200501.pt/1.0.0/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475...
/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/apache_beam/__init__.py:79: UserWarning: This version of Apache Beam has not been sufficiently tested on Python 3.9. You may encounter bugs or missing features.
warnings.warn(
0%| | 0/1 [00:00<?, ?it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset
builder_instance.download_and_prepare(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 1245, in _download_and_prepare
super()._download_and_prepare(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/jvanz/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475/wikipedia.py", line 420, in _split_generators
downloaded_files = dl_manager.download_and_extract({"info": info_url})
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 307, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 195, in download
downloaded_path_or_paths = map_nested(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 260, in map_nested
mapped = [
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 261, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 196, in _single_map_nested
return function(data_struct)
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 216, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 612, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/ptwiki/20200501/dumpstatus.json
```
## Environment info
```
- `datasets` version: 1.18.3
- Platform: Linux-5.3.18-150300.59.49-default-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 6.0.1
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] | closed | false | null | [] | null | 3 | 2022-02-19T21:21:45Z | 2022-02-21T12:16:35Z | 2022-02-21T12:16:35Z | CONTRIBUTOR | null | null | {
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} | I can make a PR, just wanted approval before starting.
**Is your feature request related to a problem? Please describe.**
It is often the case that classes are not ordered in alphabetical order. Current `class_encode_column` sort the classes before indexing.
https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L1235
**Describe the solution you'd like**
I would like to add a **optional** parameter `class_names` to `class_encode_column` that would be used for the mapping instead of sorting the unique values.
**Describe alternatives you've considered**
One can use map instead. I find it harder to read.
```python
CLASS_NAMES = ['apple', 'orange', 'potato']
ds = ds.map(lambda item: CLASS_NAMES.index(item[label_column]))
# Proposition
ds = ds.class_encode_column(label_column, CLASS_NAMES)
```
**Additional context**
I can make the PR if this feature is accepted.
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} | **Is your feature request related to a problem? Please describe.**
Would be great to see be able to understand datasets with the goal of improving data quality, and helping mitigate fairness and bias issues.
**Describe the solution you'd like**
Something like https://knowyourdata.withgoogle.com/ for HF hub | {
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"completed": 0,
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} | ## Dataset viewer issue for '*savtadepth-flags*'
**Link:** *[savtadepth-flags](https://huggingface.co/datasets/kingabzpro/savtadepth-flags)*
*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https://huggingface.co/spaces/kingabzpro/savtadepth.*
Am I the one who added this dataset ? Yes
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- Renames `GenerateMode` to `DownloadMode`
- Implements `DeprecatedEnum`
- Deprecates `GenerateMode`
Close #769. | {
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] | null | 2 | 2022-02-18T16:32:43Z | 2022-02-28T14:29:18Z | 2022-02-21T14:39:19Z | COLLABORATOR | null | null | {
"completed": 0,
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} | ## Describe the bug
A file for the `head_qa` dataset is missing (https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t/HEAD_EN/train_HEAD_EN.json)
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> load_dataset("head_qa", name="en")
```
## Expected results
The dataset should be loaded
## Actual results
```
Downloading and preparing dataset head_qa/en (download: 75.69 MiB, generated: 2.69 MiB, post-processed: Unknown size, total: 78.38 MiB) to /home/slesage/.cache/huggingface/datasets/head_qa/en/1.1.0/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9...
Downloading data: 2.21kB [00:00, 2.05MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py", line 1729, in load_dataset
builder_instance.download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 665, in _download_and_prepare
verify_checksums(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t']
```
## Environment info
- `datasets` version: 1.18.4.dev0
- Platform: Linux-5.11.0-1028-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 6.0.1
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This code differs from the code in [this](https://huggingface.co/docs/transformers/perplexity) HF blog post because the blogpost code fails in at least the following circumstances:
- returns nans whenever the stride = 1
- hits a runtime error when the stride is significantly larger than the max model length (e.g. if max_model_length = 512 and stride = 1024)
Note that:
- As it is, it only works for causal models. Pseudoperplexity can be added later as another metric to work with masked language models.
- It takes in a list of strings so that it can be dataset independent. This does mean that it doesn't currently batch inputs, and is therefore relatively slow.
- It overwrites the metrics compute() function for a specific perplexity compute() function. This is because the current general metrics compute() function requires model-generated predictions, which doesn't make sense in the context of perplexity | {
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] | null | 2 | 2022-02-18T15:35:38Z | 2022-12-13T16:59:06Z | 2022-12-13T16:59:06Z | NONE | null | null | {
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} | ## Describe the bug
The `datasets.features.Image` feature class decodes image data by default. Expectedly, when indexing a dataset or using the `map()` method, images are returned as PIL Image instances.
However, when calling `map()` and setting a specific data column with the `input_columns` argument, the image data is passed as raw byte representation to the mapping function.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from torchvision import transforms
from PIL.Image import Image
dataset = load_dataset('mnist', split='train')
def transform_all_columns(example):
# example['image'] is encoded as PIL Image
assert isinstance(example['image'], Image)
return example
def transform_image_column(image):
# image is decoded here and represented as raw bytes
assert isinstance(image, Image)
return image
# single-sample dataset for debugging purposes
dev = dataset.select([0])
dev.map(transform_all_columns)
dev.map(transform_image_column, input_columns='image')
```
## Expected results
Image data should be passed in decoded form, i.e. as PIL Image objects to the mapping function unless the `decode` attribute on the image feature is set to `False`.
## Actual results
The mapping function receives images as raw byte data.
## Environment info
- `datasets` version: 1.18.3
- Platform: Linux-5.11.0-49-generic-x86_64-with-glibc2.32
- Python version: 3.8.0b4
- PyArrow version: 7.0.0
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} | ## Dataset viewer issue for '*rubrix/news*'
**Link:https://huggingface.co/datasets/rubrix/news** *link to the dataset viewer page*
Cannot see the dataset preview:
```
Status code: 400
Exception: Status400Error
Message: Not found. Cache is waiting to be refreshed.
```
Am I the one who added this dataset ? No
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https://api.github.com/repos/huggingface/datasets/issues/3754 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3754/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3754/comments | https://api.github.com/repos/huggingface/datasets/issues/3754/events | https://github.com/huggingface/datasets/issues/3754 | 1,142,886,536 | I_kwDODunzps5EHxCI | 3,754 | Overflowing indices in `select` | {
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] | closed | false | null | [] | null | 2 | 2022-02-18T11:30:52Z | 2022-02-18T11:38:23Z | 2022-02-18T11:38:23Z | MEMBER | null | null | {
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} | ## Describe the bug
The `Dataset.select` function seems to accept indices that are larger than the dataset size and seems to effectively use `index %len(ds)`.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"test": [1,2,3]})
ds = ds.select(range(5))
print(ds)
print()
print(ds["test"])
```
Result:
```python
Dataset({
features: ['test'],
num_rows: 5
})
[1, 2, 3, 1, 2]
```
This behaviour is not documented and can lead to unexpected behaviour when for example taking a sample larger than the dataset and thus creating a lot of duplicates.
## Expected results
It think this should throw an error or at least a very big warning:
```python
IndexError: Invalid key: 5 is out of bounds for size 3
```
## Environment info
- `datasets` version: 1.18.3
- Platform: macOS-12.0.1-x86_64-i386-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0 | {
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] | open | false | null | [] | null | 8 | 2022-02-18T10:45:41Z | 2025-03-19T14:50:14Z | null | MEMBER | null | null | {
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} | Some ideas for a few features that could be useful when working with large datasets in streaming mode.
## `filter` for `IterableDataset`
Adding filtering to streaming datasets would be useful in several scenarios:
- filter a dataset with many languages for a subset of languages
- filter a dataset for specific licenses
- other custom logic to get a subset
The only way to achieve this at the moment is I think through writing a custom loading script and implementing filters there.
## `IterableDataset` to `Dataset` conversion
In combination with the above filter a functionality to "play" the whole stream would be useful. The motivation is that often one might filter the dataset to get a manageable size for experimentation. In that case streaming mode is no longer necessary as the filtered dataset is small enough and it would be useful to be able to play through the whole stream to create a normal `Dataset` with all its benefits.
```python
ds = load_dataset("some_large_dataset", streaming=True)
ds_filter = ds.filter(lambda x: x["lang"]="fr")
ds_filter = ds_filter.stream() # here the `IterableDataset` is converted to a `Dataset`
```
Naturally, this could be expanded with `stream(n=1000)` which creates a `Dataset` with the first `n` elements similar to `take`.
## Stream to the Hub
While streaming allows to use a dataset as is without saving the whole dataset on the local machine it is currently not possible to process a dataset and add it to the hub. The only way to do this is by downloading the full dataset and saving the processed dataset again before pushing them to the hub. The API could looks something like:
```python
ds = load_dataset("some_large_dataset", streaming=True)
ds_filter = ds.filter(some_filter_func)
ds_processed = ds_filter.map(some_processing_func)
ds_processed.push_to_hub("new_better_dataset", batch_size=100_000)
```
Under the hood this could be done by processing and aggregating `batch_size` elements and then pushing that batch as a single file to the hub. With this functionality one could process and create TB scale datasets while only requiring size of `batch_size` local disk space.
cc @lhoestq @albertvillanova | null | {
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https://api.github.com/repos/huggingface/datasets/issues/3752 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3752/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3752/comments | https://api.github.com/repos/huggingface/datasets/issues/3752/events | https://github.com/huggingface/datasets/pull/3752 | 1,142,627,889 | PR_kwDODunzps4zD1D9 | 3,752 | Update metadata JSON for cats_vs_dogs dataset | {
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} | [] | closed | false | null | [] | null | 0 | 2022-02-18T08:32:53Z | 2022-02-18T14:56:12Z | 2022-02-18T14:56:11Z | MEMBER | null | null | null | Note that the number of examples in the train split was already fixed in the dataset card.
Fix #3750. | {
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https://api.github.com/repos/huggingface/datasets/issues/3751 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3751/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3751/comments | https://api.github.com/repos/huggingface/datasets/issues/3751/events | https://github.com/huggingface/datasets/pull/3751 | 1,142,609,327 | PR_kwDODunzps4zDw9_ | 3,751 | Fix typo in train split name | {
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```
| Tain | Valid | Test |
```
This PR:
- fixes the typo in the train split name
- fixes the column alignment of the split tables
in the README guide and in all datasets. | {
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