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https://api.github.com/repos/huggingface/datasets/issues/2758 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2758/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2758/comments | https://api.github.com/repos/huggingface/datasets/issues/2758/events | https://github.com/huggingface/datasets/pull/2758 | 960,206,575 | MDExOlB1bGxSZXF1ZXN0NzAzMjQ5Nzky | 2,758 | Raise ManualDownloadError when loading a dataset that requires previous manual download | {
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} | This PR implements the raising of a `ManualDownloadError` when loading a dataset that requires previous manual download, and this is missing.
The `ManualDownloadError` is raised whether the dataset is loaded in normal or streaming mode.
Close #2749.
cc: @severo | {
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] | null | 2 | "2021-08-04T07:10:39Z" | "2021-08-04T16:01:24Z" | "2021-08-04T16:01:23Z" | NONE | null | null | null | ## Describe the bug
I am trying to concatenate two `Dataset` using `concatenate_datasets` but it turns out that after concatenation the features are casted from `torch.Tensor` to `list`.
It then leads to a weird tensors when trying to convert it to a `DataLoader`. However, if I use each `Dataset` separately everything behave as expected.
## Steps to reproduce the bug
```python
>>> featurized_teacher
Dataset({
features: ['t_labels', 't_input_ids', 't_token_type_ids', 't_attention_mask'],
num_rows: 502
})
>>> for f in featurized_teacher.features:
print(featurized_teacher[f].shape)
torch.Size([502])
torch.Size([502, 300])
torch.Size([502, 300])
torch.Size([502, 300])
>>> featurized_student
Dataset({
features: ['s_features', 's_labels'],
num_rows: 502
})
>>> for f in featurized_student.features:
print(featurized_student[f].shape)
torch.Size([502, 64])
torch.Size([502])
```
The shapes seem alright to me. Then the results after concatenation are as follow:
```python
>>> concat_dataset = datasets.concatenate_datasets([featurized_student, featurized_teacher], axis=1)
>>> type(concat_dataset["t_labels"])
<class 'list'>
```
One would expect to obtain the same type as the one before concatenation.
Am I doing something wrong here? Any idea on how to fix this unexpected behavior?
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-10.14.6-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 3.0.0
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} | Update metadata for wikihow dataset:
- Remove leading new line character in description and citation
- Update metadata JSON
- Remove no longer necessary `urls_checksums/checksums.txt` file
Related to #2748. | {
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] | null | 5 | "2021-08-03T10:47:04Z" | "2022-01-19T14:23:43Z" | "2022-01-19T14:19:05Z" | NONE | null | null | null | Hi,
I am need to concatenate my dataset with others several times, and after I concatenate it for the second time, the features of features (e.g. tags names) are collapsed. This hinders, for instance, the usage of tokenize function with `data.map`.
```
from datasets import load_dataset, concatenate_datasets
data = load_dataset('trec')['train']
concatenated = concatenate_datasets([data, data])
concatenated_2 = concatenate_datasets([concatenated, concatenated])
print('True features of features:', concatenated.features)
print('\nProduced features of features:', concatenated_2.features)
```
outputs
```
True features of features: {'label-coarse': ClassLabel(num_classes=6, names=['DESC', 'ENTY', 'ABBR', 'HUM', 'NUM', 'LOC'], names_file=None, id=None), 'label-fine': ClassLabel(num_classes=47, names=['manner', 'cremat', 'animal', 'exp', 'ind', 'gr', 'title', 'def', 'date', 'reason', 'event', 'state', 'desc', 'count', 'other', 'letter', 'religion', 'food', 'country', 'color', 'termeq', 'city', 'body', 'dismed', 'mount', 'money', 'product', 'period', 'substance', 'sport', 'plant', 'techmeth', 'volsize', 'instru', 'abb', 'speed', 'word', 'lang', 'perc', 'code', 'dist', 'temp', 'symbol', 'ord', 'veh', 'weight', 'currency'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}
Produced features of features: {'label-coarse': Value(dtype='int64', id=None), 'label-fine': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}
```
I am using `datasets` v.1.11.0 | {
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] | null | 2 | "2021-08-03T10:26:27Z" | "2021-08-09T08:53:35Z" | "2021-08-04T11:36:30Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
At least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reclor", streaming=True)
```
## Expected results
Ideally: raise a specific exception, something like `ManualDownloadError`.
Or at least give the reason in the message, as when we load in normal mode:
```python
from datasets import load_dataset
dataset = load_dataset("reclor")
```
```
AssertionError: The dataset reclor with config default requires manual data.
Please follow the manual download instructions: to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
.
Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path/to/manual/data>')
```
## Actual results
```
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-11.5-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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} | Closes #2663. I've tried adding multiprocessing in `to_json`. Here's some benchmarking I did to compare the timings of current version (say v1) and multi-proc version (say v2). I did this with `cpu_count` 4 (2015 Macbook Air)
1. Dataset name: `ascent_kb` - 8.9M samples (all samples were used, reporting this for a single run)
v1- ~225 seconds for converting whole dataset to json
v2- ~200 seconds for converting whole dataset to json
2. Dataset name: `lama` - 1.3M samples (all samples were used, reporting this for 2 runs)
v1- ~26 seconds for converting whole dataset to json
v2- ~23.6 seconds for converting whole dataset to json
I think it's safe to say that v2 is 10% faster as compared to v1. Timings may improve further with better configuration.
The only bottleneck I feel is writing to file from the output list. If we can improve that aspect then timings may improve further.
Let me know if any changes/improvements can be done in this @stas00, @lhoestq, @albertvillanova. @lhoestq even suggested to extend this work with other export methods as well like `csv` or `parquet`. | {
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] | closed | false | null | [] | null | 6 | "2021-08-02T22:18:57Z" | "2021-11-16T08:51:34Z" | "2021-08-03T19:45:43Z" | NONE | null | null | null | ## Describe the bug
Cannot load `few-nerd` dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('few-nerd', 'supervised')
```
## Actual results
Executing above code will give the following error:
```
Using the latest cached version of the module from /Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun 2 11:34:25 2021) since it couldn't be found locally at /Users/Mehrad/Documents/GitHub/genienlp/few-nerd/few-nerd.py, or remotely (FileNotFoundError).
Downloading and preparing dataset few_nerd/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/Mehrad/.cache/huggingface/datasets/few_nerd/supervised/0.0.0/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...
Traceback (most recent call last):
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split
disable=bool(logging.get_verbosity() == logging.NOTSET),
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53/few-nerd.py", line 196, in _generate_examples
with open(filepath, encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/Users/Mehrad/.cache/huggingface/datasets/downloads/supervised/train.json'
```
The bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https://github.com/nbroad1881/few-nerd/tree/main/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Python version: 3.8
- PyArrow version: 1.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2745 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2745/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2745/comments | https://api.github.com/repos/huggingface/datasets/issues/2745/events | https://github.com/huggingface/datasets/pull/2745 | 958,269,579 | MDExOlB1bGxSZXF1ZXN0NzAxNTc0Mjcz | 2,745 | added semeval18_emotion_classification dataset | {
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} | [] | closed | false | null | [] | null | 7 | "2021-08-02T15:39:55Z" | "2021-10-29T09:22:05Z" | "2021-09-21T09:48:35Z" | CONTRIBUTOR | null | 0 | {
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} | I added the data set of SemEval 2018 Task 1 (Subtask 5) for emotion detection in three languages.
```
datasets-cli test datasets/semeval18_emotion_classification/ --save_infos --all_configs
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_semeval18_emotion_classification
```
Both commands ran successfully.
I couldn't create the dummy data (the files are tsvs but have .txt ending, maybe that's the problem?) and therefore the test on the dummy data fails, maybe someone can help here.
I also formatted the code:
```
black --line-length 119 --target-version py36 datasets/semeval18_emotion_classification/
isort datasets/semeval18_emotion_classification/
flake8 datasets/semeval18_emotion_classification/
```
That's the publication for reference:
Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). SemEval-2018 task 1: Affect in tweets. Proceedings of the 12th International Workshop on Semantic Evaluation, 1β17. https://doi.org/10.18653/v1/S18-1001 | {
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] | null | 2 | "2021-08-02T13:01:26Z" | "2021-08-03T10:06:57Z" | "2021-08-03T09:25:33Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
The JSON file generated for https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/journalists_questions.py is https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/dataset_infos.json.
The only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead.
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Steps to reproduce the bug
Look at the files.
## Expected results
The first key should be `plain_text`:
```json
{
"plain_text": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Actual results
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
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] | null | 1 | "2021-08-02T12:55:09Z" | "2021-11-12T17:18:10Z" | "2021-11-12T17:18:10Z" | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
```python
from datasets import load_dataset_builder
from datasets.utils.streaming_download_manager import StreamingDownloadManager
builder = load_dataset_builder("journalists_questions", name="plain_text")
builder._split_generators(StreamingDownloadManager(base_path=builder.base_path))
```
raises
```
NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet
```
But the file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed:
```bash
curl --header "Range: bytes=0-100" -L https://drive.google.com/uc\?export\=download\&id\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U
506938088174940160 yes 1
302221719412830209 yes 1
289761704907268096 yes 1
513820885032378369 yes %
```
Yet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats.
**Describe the solution you'd like**
In the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text/html; charset=UTF-8`.
**Describe alternatives you've considered**
Add a variable in the dataset script to set the data format by hand.
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] | open | false | null | [] | null | 0 | "2021-08-02T10:06:50Z" | "2021-12-08T12:06:51Z" | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** Hypersim
- **Description:** photorealistic synthetic dataset for holistic indoor scene understanding
- **Paper:** *link to the dataset paper if available*
- **Data:** https://github.com/apple/ml-hypersim
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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} | Next `sacrebleu` release (v2.0.0) will remove `sacrebleu.DEFAULT_TOKENIZER`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15
This PR passes `tokenize` to `sacrebleu` only if explicitly passed by the user, otherwise it will not pass it (and `sacrebleu` will use its default, no matter where it is and how it is called).
Close: #2737. | {
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] | closed | false | null | [] | null | 5 | "2021-07-30T23:53:08Z" | "2021-09-22T10:47:41Z" | "2021-08-03T04:23:37Z" | NONE | null | null | null | With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2736/comments | https://api.github.com/repos/huggingface/datasets/issues/2736/events | https://github.com/huggingface/datasets/issues/2736 | 956,895,199 | MDU6SXNzdWU5NTY4OTUxOTk= | 2,736 | Add Microsoft Building Footprints dataset | {
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] | open | false | null | [] | null | 1 | "2021-07-30T16:17:08Z" | "2021-12-08T12:09:03Z" | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** Microsoft Building Footprints
- **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.microsoft.com/en-us/maps/building-footprints
- **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Reported by: @sashavor | {
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] | open | false | null | [] | null | 0 | "2021-07-30T16:08:39Z" | "2021-07-31T05:01:25Z" | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** Open Buildings
- **Description:** A dataset of building footprints to support social good applications.
Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. The project being based in Ghana, the current focus is on the continent of Africa.
See: "Mapping Africa's Buildings with Satellite Imagery" https://ai.googleblog.com/2021/07/mapping-africas-buildings-with.html
- **Paper:** https://arxiv.org/abs/2107.12283
- **Data:** https://sites.research.google/open-buildings/
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Reported by: @osanseviero | {
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https://api.github.com/repos/huggingface/datasets/issues/2734 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2734/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2734/comments | https://api.github.com/repos/huggingface/datasets/issues/2734/events | https://github.com/huggingface/datasets/pull/2734 | 956,844,874 | MDExOlB1bGxSZXF1ZXN0NzAwMzc4NjI4 | 2,734 | Update BibTeX entry | {
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https://api.github.com/repos/huggingface/datasets/issues/2733 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2733/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2733/comments | https://api.github.com/repos/huggingface/datasets/issues/2733/events | https://github.com/huggingface/datasets/pull/2733 | 956,725,476 | MDExOlB1bGxSZXF1ZXN0NzAwMjc1NDMy | 2,733 | Add missing parquet known extension | {
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```python
from datasets import load_dataset
base_url = "https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/"
data_files = {"train": base_url + "wikipedia-train.parquet"}
wiki = load_dataset("parquet", data_files=data_files, split="train", streaming=True)
```
It raises
```python
NotImplementedError: Extraction protocol for file at https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/wikipedia-train.parquet is not implemented yet
```
I added `parquet` to the list of known extensions
EDIT: added pickle, conllu, xml extensions as well | {
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https://api.github.com/repos/huggingface/datasets/issues/2732 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2732/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2732/comments | https://api.github.com/repos/huggingface/datasets/issues/2732/events | https://github.com/huggingface/datasets/pull/2732 | 956,676,360 | MDExOlB1bGxSZXF1ZXN0NzAwMjMzMzQy | 2,732 | Updated TTC4900 Dataset | {
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- Updated readme. | {
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} | Oh my **god** do not merge this yet, it's just a draft.
I've added a method (via a mixin) to the `arrow_dataset.Dataset` class that automatically converts our Dataset classes to TF Dataset classes ready for training. It hopefully has most of the features we want, including streaming from disk (no need to load the whole dataset in memory!), correct shuffling, variable-length batches to reduce compute, and correct support for unusual padding. It achieves that by calling the tokenizer `pad` method in the middle of a TF compute graph via a very hacky call to `tf.py_function`, which is heretical but seems to work.
A number of issues need to be resolved before it's ready to merge, though:
1) Is a MixIn the right way to do this? Do other classes besides `arrow_dataset.Dataset` need this method too?
2) Needs an argument to support constant-length batches for TPU training - this is easy to add and I'll do it soon.
3) Needs the user to supply the list of columns to drop from the arrow `Dataset`. Is there some automatic way to get the columns we want, or see which columns were added by the tokenizer?
4) Assumes the label column is always present and always called "label" - this is probably not great, but I'm not sure what the 'correct' thing to do here is. | {
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https://api.github.com/repos/huggingface/datasets/issues/2730 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2730/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2730/comments | https://api.github.com/repos/huggingface/datasets/issues/2730/events | https://github.com/huggingface/datasets/issues/2730 | 955,987,834 | MDU6SXNzdWU5NTU5ODc4MzQ= | 2,730 | Update CommonVoice with new release | {
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] | open | false | null | [] | null | 3 | "2021-07-29T15:59:59Z" | "2021-08-07T16:19:19Z" | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** CommonVoice mid-2021 release
- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).
- **Paper:** https://discourse.mozilla.org/t/common-voice-2021-mid-year-dataset-release/83812
- **Data:** https://commonvoice.mozilla.org/en/datasets
- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.
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/2729 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2729/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2729/comments | https://api.github.com/repos/huggingface/datasets/issues/2729/events | https://github.com/huggingface/datasets/pull/2729 | 955,920,489 | MDExOlB1bGxSZXF1ZXN0Njk5NTk5MjA4 | 2,729 | Fix IndexError while loading Arabic Billion Words dataset | {
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Close #2727. | {
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] | open | false | null | [] | null | 4 | "2021-07-29T14:18:38Z" | "2021-08-02T07:25:57Z" | null | CONTRIBUTOR | null | null | null | ## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
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https://api.github.com/repos/huggingface/datasets/issues/2727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2727/comments | https://api.github.com/repos/huggingface/datasets/issues/2727/events | https://github.com/huggingface/datasets/issues/2727 | 955,812,149 | MDU6SXNzdWU5NTU4MTIxNDk= | 2,727 | Error in loading the Arabic Billion Words Corpus | {
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] | null | 2 | "2021-07-29T12:53:09Z" | "2021-07-30T13:03:55Z" | "2021-07-30T13:03:55Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.
## Steps to reproduce the bug
```python
load_dataset("arabic_billion_words", "Techreen")
load_dataset("arabic_billion_words", "Almustaqbal")
```
## Expected results
The datasets load succefully.
## Actual results
```python
_extract_tags(self, sample, tag)
139 if len(out) > 0:
140 break
--> 141 return out[0]
142
143 def _clean_text(self, text):
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.7.11
- PyArrow version: 3.0.0
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] | null | 3 | "2021-07-28T14:24:23Z" | "2021-07-29T04:58:49Z" | "2021-07-28T16:38:01Z" | MEMBER | null | null | null | ## Describe the bug
When loading remote data files from a private repo, a 404 error is raised.
## Steps to reproduce the bug
```python
url = hf_hub_url("lewtun/asr-preds-test", "preds.jsonl", repo_type="dataset")
dset = load_dataset("json", data_files=url, use_auth_token=True)
# HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/datasets/lewtun/asr-preds-test/resolve/main/preds.jsonl
```
## Expected results
Load dataset.
## Actual results
404 Error.
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} | - Related to: #2682
We correct the values of `en` subset concerning the expected validation values (both `num_bytes` and `num_examples`.
Instead of having:
`{"name": "validation", "num_bytes": 828589180707, "num_examples": 364868892, "dataset_name": "c4"}`
We replace with correct values:
`{"name": "validation", "num_bytes": 825767266, "num_examples": 364608, "dataset_name": "c4"}`
There are still issues with validation with other subsets, but I can't download all the files, unzip to check for the correct number of bytes. (If you have a fast way to obtain those values for other subsets, I can do this in this PR ... otherwise I can't spend those resources)
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] | closed | false | null | [] | null | 2 | "2021-07-28T03:52:07Z" | "2022-03-21T08:27:51Z" | "2022-03-21T08:27:51Z" | NONE | null | null | null | Strangely missing cache file after I restart my program again.
`glue_dataset = datasets.load_dataset('glue', 'sst2')`
`FileNotFoundError: [Errno 2] No such file or directory: /Users/chris/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad/dataset_info.json'`
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} | This PR removes a check that's been added in #2684. My intention with this check was to capture an URL in the error message, but instead, it captures a substring of the previous regex match in the test function. Another option would be to replace this check with:
```python
m_paths = re.findall(r"\S*_dummy/_dummy.py\b", str(exc_info.value)) # on Linux this will match an URL as well as a local_path due to different os.sep, so take the last element (an URL always comes last in the list)
assert len(m_paths) > 0 and is_remote_url(m_paths[-1]) # is_remote_url comes from datasets.utils.file_utils
```
@lhoestq Let me know which one of these two approaches (delete or replace) do you prefer? | {
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] | open | false | null | [] | null | 0 | "2021-07-27T14:17:09Z" | "2021-10-22T09:36:08Z" | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
I want to cache data I generate from processing a dataset I've loaded in streaming mode, but I've currently no way to know if the remote data has been updated or not, thus I don't know when to invalidate my cache.
**Describe the solution you'd like**
Take the ETag of the data files into account and provide it (directly or through a hash) to give a signal that I can invalidate my cache.
**Describe alternatives you've considered**
None
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} | Organize Datasets documentation into four documentation types to improve clarity and discoverability of content.
**Content to add in the very short term (feel free to add anything I'm missing):**
- A discussion on why Datasets uses Arrow that includes some context and background about why we use Arrow. Would also be great to talk about Datasets speed and performance here, and if you can share any benchmarking/tests you did, that would be awesome! Finally, a discussion about how memory-mapping frees the user from RAM constraints would be very helpful.
- Explain why you would want to disable or override verifications when loading a dataset.
- If possible, include a code sample of when the number of elements in the field of an output dictionary arenβt the same as the other fields in the output dictionary (taken from the [note](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset) here). | {
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} | Made a very minor change to fix the issue#2716. Added the missing argument in the constructor call.
As discussed in the bug report, the change is made to prevent the `shuffle` method call from resetting the value of `batched` attribute in `MappedExamplesIterable`
Fix #2716. | {
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I did RCA on the dataset codebase, the problem is emerging from [this line of code](https://github.com/huggingface/datasets/blob/d25a0bf94d9f9a9aa6cabdf5b450b9c327d19729/src/datasets/iterable_dataset.py#L197) here as it is
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batch_size=self.batch_size`, as one can see it is missing batched argument, which means that the iterator fallsback to default constructor value, which in this case is `False`.
To remedy the problem we can change this line to
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batched=self.batched, batch_size=self.batch_size`
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] | open | false | null | [] | null | 1 | "2021-07-26T07:11:03Z" | "2021-07-26T09:16:25Z" | null | NONE | null | null | null | For the import into ELG, we would like a more precise description of the size of the dataset, instead of the current size categories. The size can be expressed in bytes, or any other preferred size unit. As suggested in the slack channel, perhaps this could be computed with a regex for existing datasets. | {
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https://api.github.com/repos/huggingface/datasets/issues/2713 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2713/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2713/comments | https://api.github.com/repos/huggingface/datasets/issues/2713/events | https://github.com/huggingface/datasets/pull/2713 | 952,515,256 | MDExOlB1bGxSZXF1ZXN0Njk2Njk3MzU0 | 2,713 | Enumerate all ner_tags values in WNUT 17 dataset | {
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} | This PR does:
- Enumerate all ner_tags in dataset card Data Fields section
- Add all metadata tags to dataset card
Close #2709. | {
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https://api.github.com/repos/huggingface/datasets/issues/2710 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2710/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2710/comments | https://api.github.com/repos/huggingface/datasets/issues/2710/events | https://github.com/huggingface/datasets/pull/2710 | 951,723,326 | MDExOlB1bGxSZXF1ZXN0Njk2MDYyNjAy | 2,710 | Update WikiANN data URL | {
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} | WikiANN data source URL is no longer accessible: 404 error from Dropbox.
We have decided to host it at Hugging Face. This PR updates the data source URL, the metadata JSON file and the dataset card.
Close #2691. | {
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https://api.github.com/repos/huggingface/datasets/issues/2709 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2709/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2709/comments | https://api.github.com/repos/huggingface/datasets/issues/2709/events | https://github.com/huggingface/datasets/issues/2709 | 951,534,757 | MDU6SXNzdWU5NTE1MzQ3NTc= | 2,709 | Missing documentation for wnut_17 (ner_tags) | {
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] | null | 1 | "2021-07-23T12:25:32Z" | "2021-07-26T09:30:55Z" | "2021-07-26T09:30:55Z" | CONTRIBUTOR | null | null | null | On the info page of the wnut_17 data set (https://huggingface.co/datasets/wnut_17), the model output of ner-tags is only documented for these 5 cases:
`ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).`
I trained a model with the data and it gives me 13 classes:
```
"id2label": {
"0": 0,
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9,
"10": 10,
"11": 11,
"12": 12
}
"label2id": {
"0": 0,
"1": 1,
"10": 10,
"11": 11,
"12": 12,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9
}
```
The paper (https://www.aclweb.org/anthology/W17-4418.pdf) explains those 6 categories, but the ordering does not match:
```
1. person
2. location (including GPE, facility)
3. corporation
4. product (tangible goods, or well-defined
services)
5. creative-work (song, movie, book and
so on)
6. group (subsuming music band, sports team,
and non-corporate organisations)
```
I would be very helpful for me, if somebody could clarify the model ouputs and explain the "B-" and "I-" prefixes to me.
Really great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner! | {
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https://api.github.com/repos/huggingface/datasets/issues/2708 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2708/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2708/comments | https://api.github.com/repos/huggingface/datasets/issues/2708/events | https://github.com/huggingface/datasets/issues/2708 | 951,092,660 | MDU6SXNzdWU5NTEwOTI2NjA= | 2,708 | QASC: incomplete training set | {
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] | closed | false | null | [] | null | 2 | "2021-07-22T21:59:44Z" | "2021-07-23T13:30:07Z" | "2021-07-23T13:30:07Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
The training instances are not loaded properly.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("qasc", script_version='1.10.2')
def load_instances(split):
instances = dataset[split]
print(f"split: {split} - size: {len(instances)}")
for x in instances:
print(json.dumps(x))
load_instances('test')
load_instances('validation')
load_instances('train')
```
## results
For test and validation, we can see the examples in the output (which is good!):
```
split: test - size: 920
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"}
...
```
However, only a few instances are loaded for the training split, which is not correct.
## Environment info
- `datasets` version: '1.10.2'
- Platform: MaxOS
- Python version:3.7
- PyArrow version: 3.0.0
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Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py` | {
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Unable to retreive wikiann English dataset
## Steps to reproduce the bug
```python
from datasets import list_datasets, load_dataset, list_metrics, load_metric
WIKIANN = load_dataset("wikiann","en")
```
## Expected results
Colab notebook should display successful download status
## Actual results
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
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} | The error message to tell which config name to load is not displayed.
This is because in the code it was considering the config kwargs to be non-empty, which is a special case for custom configs created on the fly. It appears after this change: https://github.com/huggingface/datasets/pull/2659
I fixed that by making the config kwargs empty by default, even if default parameters are passed
Fix https://github.com/huggingface/datasets/issues/2703 | {
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However in `datasets` 1.10.0 and 1.10.1 it doesn't show the right message:
```python
import datasets
datasets.load_dataset("glue")
```
raises
```python
AttributeError: 'BuilderConfig' object has no attribute 'text_features'
```
instead of
```python
ValueError: Config name is missing.
Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']
Example of usage:
`load_dataset('glue', 'cola')`
``` | {
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A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import Dataset
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: latest version as of 07/21/2021
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/2699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2699/comments | https://api.github.com/repos/huggingface/datasets/issues/2699/events | https://github.com/huggingface/datasets/issues/2699 | 950,221,226 | MDU6SXNzdWU5NTAyMjEyMjY= | 2,699 | cannot combine splits merging and streaming? | {
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] | open | false | null | [] | null | 1 | "2021-07-22T01:13:25Z" | "2021-07-22T08:27:47Z" | null | NONE | null | null | null | this does not work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`
with error:
`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`
these work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`
`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`
`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`
i could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits | {
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https://api.github.com/repos/huggingface/datasets/issues/2698 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2698/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2698/comments | https://api.github.com/repos/huggingface/datasets/issues/2698/events | https://github.com/huggingface/datasets/pull/2698 | 950,159,867 | MDExOlB1bGxSZXF1ZXN0Njk0NzUxMzMw | 2,698 | Ignore empty batch when writing | {
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} | This prevents an schema update with unknown column types, as reported in #2644.
This is my first attempt at fixing the issue. I tested the following:
- First batch returned by a batched map operation is empty.
- An intermediate batch is empty.
- `python -m unittest tests.test_arrow_writer` passes.
However, `arrow_writer` looks like a pretty generic interface, I'm not sure if there are other uses I may have overlooked. Let me know if that's the case, or if a better approach would be preferable. | {
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https://api.github.com/repos/huggingface/datasets/issues/2697 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2697/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2697/comments | https://api.github.com/repos/huggingface/datasets/issues/2697/events | https://github.com/huggingface/datasets/pull/2697 | 950,021,623 | MDExOlB1bGxSZXF1ZXN0Njk0NjMyODg0 | 2,697 | Fix import on Colab | {
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https://api.github.com/repos/huggingface/datasets/issues/2696 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2696/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2696/comments | https://api.github.com/repos/huggingface/datasets/issues/2696/events | https://github.com/huggingface/datasets/pull/2696 | 949,901,726 | MDExOlB1bGxSZXF1ZXN0Njk0NTMwODg3 | 2,696 | Add support for disable_progress_bar on Windows | {
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} | [] | closed | false | null | [] | null | 1 | "2021-07-21T16:34:53Z" | "2021-07-26T13:31:14Z" | "2021-07-26T09:38:37Z" | CONTRIBUTOR | null | 0 | {
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} | This PR is a continuation of #2667 and adds support for `utils.disable_progress_bar()` on Windows when using multiprocessing. This [answer](https://stackoverflow.com/a/6596695/14095927) on SO explains it nicely why the current approach (with calling `utils.is_progress_bar_enabled()` inside `Dataset._map_single`) would not work on Windows. | {
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https://api.github.com/repos/huggingface/datasets/issues/2695 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2695/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2695/comments | https://api.github.com/repos/huggingface/datasets/issues/2695/events | https://github.com/huggingface/datasets/issues/2695 | 949,864,823 | MDU6SXNzdWU5NDk4NjQ4MjM= | 2,695 | Cannot import load_dataset on Colab | {
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] | closed | false | null | [] | null | 5 | "2021-07-21T15:52:51Z" | "2021-07-22T07:26:25Z" | "2021-07-22T07:09:07Z" | NONE | null | null | null | ## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
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characters `[]` are special characters in bash | {
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https://api.github.com/repos/huggingface/datasets/issues/2693 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2693/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2693/comments | https://api.github.com/repos/huggingface/datasets/issues/2693/events | https://github.com/huggingface/datasets/pull/2693 | 949,797,014 | MDExOlB1bGxSZXF1ZXN0Njk0NDQ1ODAz | 2,693 | Fix OSCAR Esperanto | {
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} | The Esperanto part (original) of OSCAR has the wrong number of examples:
```python
from datasets import load_dataset
raw_datasets = load_dataset("oscar", "unshuffled_original_eo")
```
raises
```python
NonMatchingSplitsSizesError:
[{'expected': SplitInfo(name='train', num_bytes=314188336, num_examples=121171, dataset_name='oscar'),
'recorded': SplitInfo(name='train', num_bytes=314064514, num_examples=121168, dataset_name='oscar')}]
```
I updated the number of expected examples in dataset_infos.json
cc @sgugger | {
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https://api.github.com/repos/huggingface/datasets/issues/2691 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2691/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2691/comments | https://api.github.com/repos/huggingface/datasets/issues/2691/events | https://github.com/huggingface/datasets/issues/2691 | 949,758,379 | MDU6SXNzdWU5NDk3NTgzNzk= | 2,691 | xtreme / pan-x cannot be downloaded | {
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] | closed | false | null | [] | null | 5 | "2021-07-21T14:18:05Z" | "2021-07-26T09:34:22Z" | "2021-07-26T09:34:22Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2690 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2690/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2690/comments | https://api.github.com/repos/huggingface/datasets/issues/2690/events | https://github.com/huggingface/datasets/pull/2690 | 949,574,500 | MDExOlB1bGxSZXF1ZXN0Njk0MjU5MDc1 | 2,690 | Docs details | {
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} | Some comments here:
- the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch 'datasets[streaming]'` (maybe just link to https://huggingface.co/docs/datasets/installation.html + a one-liner that installs all the requirements / alternatively a requirements.txt file)
- "If youβd like to play with the examples, you must install it from source." in https://huggingface.co/docs/datasets/installation.html: it's not clear to me what this means (what are these "examples"?)
- in https://huggingface.co/docs/datasets/loading_datasets.html: "or AWS bucket if itβs not already stored in the library". It's the only place in the doc (aside from the docstring https://huggingface.co/docs/datasets/package_reference/loading_methods.html?highlight=aws bucket#datasets.list_datasets) where the "AWS bucket" is mentioned. It's not easy to understand what this means. Maybe explain more, and link to https://s3.amazonaws.com/datasets.huggingface.co and/or https://huggingface.co/docs/datasets/filesystems.html.
- example in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files is obsoleted by https://github.com/huggingface/datasets/pull/2326. Also: see https://github.com/huggingface/datasets/issues/2691 for a bug on this specific dataset.
- in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files the doc says "After youβve downloaded the files, you can point to the folder hosting them locally with the data_dir argument as follows:", but the following example does not show how to use `data_dir`
- in https://huggingface.co/docs/datasets/loading_datasets.html#csv-files, it would be nice to have an URL to the csv loader reference (but I'm not sure there is one in the API reference). This comment applies in many places in the doc: I would want the API reference to contain doc for all the code/functions/classes... and I would want a lot more links inside the doc pointing to the API entries.
- in the API reference (docstrings) I would prefer "SOURCE" to link to github instead of a copy of the code inside the docs site (eg. https://github.com/huggingface/datasets/blob/master/src/datasets/load.py#L711 instead of https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset)
- it seems like not all the API is exposed in the doc. For example, there is no doc for [`disable_progress_bar`](https://github.com/huggingface/datasets/search?q=disable_progress_bar), see https://huggingface.co/docs/datasets/search.html?q=disable_progress_bar, even if the code contains docstrings. Does it mean that the function is not officially supported? (otherwise, maybe it also deserves a mention in https://huggingface.co/docs/datasets/package_reference/logging_methods.html)
- in https://huggingface.co/docs/datasets/loading_datasets.html?highlight=most%20efficient%20format%20have%20json%20files%20consisting%20multiple%20json%20objects#json-files, "The most efficient format is to have JSON files consisting of multiple JSON objects, one per line, representing individual data rows:", maybe link to https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON and give it a name ("line-delimited JSON"? "JSON Lines" as in https://huggingface.co/docs/datasets/processing.html#exporting-a-dataset-to-csv-json-parquet-or-to-python-objects ?)
- in https://huggingface.co/docs/datasets/loading_datasets.html, for the local files sections, it would be nice to provide sample csv / json / text files to download, so that it's easier for the reader to try to load them (instead: they won't try)
- the doc explains how to shard a dataset, but does not explain why and when a dataset should be sharded (I have no idea... for [parallelizing](https://huggingface.co/docs/datasets/processing.html#multiprocessing)?). It does neither give an idea of the number of shards a dataset typically should have and why.
- the code example in https://huggingface.co/docs/datasets/processing.html#mapping-in-a-distributed-setting does not work, because `training_args` has not been defined before in the doc. | {
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] | closed | false | null | [] | null | 4 | "2021-07-21T08:13:40Z" | "2023-11-02T14:54:00Z" | "2021-07-21T13:11:04Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
If you use `rename_column` and do no other modification, you will be unable to save the dataset using `save_to_disk`
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
In [1]: from datasets import Dataset, load_from_disk
In [5]: dataset=Dataset.from_dict({'foo': [0]})
In [7]: dataset.save_to_disk('foo')
In [8]: dataset=load_from_disk('foo')
In [10]: dataset=dataset.rename_column('foo', 'bar')
In [11]: dataset.save_to_disk('foo')
---------------------------------------------------------------------------
PermissionError Traceback (most recent call last)
<ipython-input-11-a3bc0d4fc339> in <module>
----> 1 dataset.save_to_disk('foo')
/mnt/beegfs/projects/meerqat/anaconda3/envs/meerqat/lib/python3.7/site-packages/datasets/arrow_dataset.py in save_to_disk(self, dataset_path
, fs)
597 if Path(dataset_path, config.DATASET_ARROW_FILENAME) in cache_files_paths:
598 raise PermissionError(
--> 599 f"Tried to overwrite {Path(dataset_path, config.DATASET_ARROW_FILENAME)} but a dataset can't overwrite itself."
600 )
601 if Path(dataset_path, config.DATASET_INDICES_FILENAME) in cache_files_paths:
PermissionError: Tried to overwrite foo/dataset.arrow but a dataset can't overwrite itself.
```
N. B. I created the dataset from dict to enable easy reproduction but the same happens if you load an existing dataset (e.g. starting from `In [8]`)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-3.10.0-1160.11.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core
- Python version: 3.7.10
- PyArrow version: 3.0.0
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} | [] | closed | false | null | [] | null | 0 | "2021-07-20T17:43:23Z" | "2021-07-21T13:04:55Z" | "2021-07-21T13:04:55Z" | CONTRIBUTOR | null | 0 | {
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} | Currently, [Writing a dataset loading script](https://huggingface.co/docs/datasets/add_dataset.html) page has a small error. A link to `matinf` dataset in [_Dataset scripts of reference_](https://huggingface.co/docs/datasets/add_dataset.html#dataset-scripts-of-reference) section actually leads to `xsquad`, instead. This PR fixes that. | {
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} | `data_dir=None` was considered a dataset config parameter, hence creating a special config_id for all dataset being loaded.
Since the config_id is used to name the cache directories, this leaded to datasets being regenerated for users.
I fixed this by ignoring the value of `data_dir` when it's `None` when computing the config_id.
I also added a test to make sure the cache directories are not unexpectedly renamed in the future.
Fix https://github.com/huggingface/datasets/issues/2683 | {
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} | This PR:
- Update the JSON metadata file, which previously was raising a `NonMatchingSplitsSizesError`
- Fix the codec of the data files (`latin_1` instead of `utf-8`), which previously was raising ` UnicodeDecodeError` for some files
Close #2679. | {
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] | null | 0 | "2021-07-20T14:37:57Z" | "2021-07-20T16:27:15Z" | "2021-07-20T16:27:15Z" | MEMBER | null | null | null | Since #2659 I can see weird cache directory names with hashes in the config id, even though no additional config kwargs are passed. For example:
```python
from datasets import load_dataset_builder
c4_builder = load_dataset_builder("c4", "en")
print(c4_builder.cache_dir)
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en-174d3b7155eb68db/0.0.0/...
# instead of
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en/0.0.0/...
```
This issue could be annoying since it would simply ignore old cache directories for users, and regenerate datasets
cc @stas00 this is what you experienced a few days ago
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Fix https://github.com/huggingface/datasets/issues/2677 | {
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In 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are:
- https://paperswithcode.com/dataset/multinli -> https://huggingface.co/datasets/multi_nli and https://huggingface.co/datasets/multi_nli_mismatch
<img width="838" alt="Capture dβeΜcran 2021-07-20 aΜ 16 33 58" src="https://user-images.githubusercontent.com/1676121/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png">
- https://paperswithcode.com/dataset/squad -> https://huggingface.co/datasets/squad and https://huggingface.co/datasets/squad_v2
- https://paperswithcode.com/dataset/narrativeqa -> https://huggingface.co/datasets/narrativeqa and https://huggingface.co/datasets/narrativeqa_manual
- https://paperswithcode.com/dataset/hate-speech-and-offensive-language -> https://huggingface.co/datasets/hate_offensive and https://huggingface.co/datasets/hate_speech_offensive
- https://paperswithcode.com/dataset/newsph-nli -> https://huggingface.co/datasets/newsph and https://huggingface.co/datasets/newsph_nli
Possible solutions:
- don't fix (it works)
- for each pair of duplicate datasets, remove one, and create an alias to the other.
## Steps to reproduce the bug
Visit the Paperswithcode links, and look at the "Dataset Loaders" section
## Expected results
There should only be one reference to a Hugging Face dataset loader
## Actual results
Two Hugging Face dataset loaders
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https://paperswithcode.com/dataset/qasper | {
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] | null | 3 | "2021-07-20T10:13:20Z" | "2021-07-21T17:02:21Z" | "2021-07-21T13:11:58Z" | NONE | null | null | null | ## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2678 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2678/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2678/comments | https://api.github.com/repos/huggingface/datasets/issues/2678/events | https://github.com/huggingface/datasets/issues/2678 | 948,471,222 | MDU6SXNzdWU5NDg0NzEyMjI= | 2,678 | Import Error in Kaggle notebook | {
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] | closed | false | null | [] | null | 4 | "2021-07-20T09:28:38Z" | "2021-07-21T13:59:26Z" | "2021-07-21T13:03:02Z" | NONE | null | null | null | ## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2677 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2677/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2677/comments | https://api.github.com/repos/huggingface/datasets/issues/2677/events | https://github.com/huggingface/datasets/issues/2677 | 948,429,788 | MDU6SXNzdWU5NDg0Mjk3ODg= | 2,677 | Error when downloading C4 | {
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] | null | 3 | "2021-07-20T08:37:30Z" | "2021-07-20T14:41:31Z" | "2021-07-20T14:38:10Z" | NONE | null | null | null | Hi,
I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:
`datasets.load_dataset('c4', 'en')`
Is this a bug or do I have some configurations missing on my server?
Thanks!
<img width="1014" alt="Π‘Π½ΠΈΠΌΠΎΠΊ ΡΠΊΡΠ°Π½Π° 2021-07-20 Π² 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png"> | {
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https://api.github.com/repos/huggingface/datasets/issues/2676 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2676/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2676/comments | https://api.github.com/repos/huggingface/datasets/issues/2676/events | https://github.com/huggingface/datasets/pull/2676 | 947,734,909 | MDExOlB1bGxSZXF1ZXN0NjkyNjc2NTg5 | 2,676 | Increase json reader block_size automatically | {
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} | [] | closed | false | null | [] | null | 0 | "2021-07-19T14:51:14Z" | "2021-07-19T17:51:39Z" | "2021-07-19T17:51:38Z" | MEMBER | null | 0 | {
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} | Currently some files can't be read with the default parameters of the JSON lines reader.
For example this one:
https://huggingface.co/datasets/thomwolf/codeparrot/resolve/main/file-000000000006.json.gz
raises a pyarrow error:
```python
ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```
The block size that is used is the default one by pyarrow (related to this [jira issue](https://issues.apache.org/jira/browse/ARROW-9612)).
To fix this issue I changed the block_size to increase automatically if there is a straddling issue when parsing a batch of json lines.
By default the value is `chunksize // 32` in order to leverage multithreading, and it doubles every time a straddling issue occurs. The block_size is then reset for each file.
cc @thomwolf @albertvillanova | {
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} | Since https://github.com/huggingface/datasets/pull/2628 we use the ETag or the remote data files to compute the directory in the cache where a dataset is saved. This is useful in order to reload the dataset from the cache only if the remote files haven't changed.
In this I made the ETag requests parallel using multithreading. There is also a tqdm progress bar that shows up if there are more than 16 data files. | {
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- Fix parameter name: `smooth` to `smooth_method`.
- Improve kwargs description.
- Align docs on using a metric.
- Add example of passing additional arguments in using metrics.
Related to #2669. | {
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- Fix potential DiplicatedKeysError by ensuring keys are unique.
- Align examples in the docs with SQuAD code.
We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique). | {
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} | DONE:
- Fix unnecessary path join.
- Fix potential DiplicatedKeysError by ensuring keys are unique.
We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique). | {
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The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) development set has a total of 750 records.
The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records.
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] | open | false | null | [] | null | 0 | "2021-07-18T21:26:26Z" | "2021-10-07T13:33:25Z" | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
Creating an elasticsearch index on large dataset could be quite long and cannot be parallelized on shard (the index creation is colliding)
**Describe the solution you'd like**
When working on dataset shards, if an index already exists, its mapping should be checked and if compatible, the indexing process should continue with the shard data.
Additionally, at the end of the process, the `_indexes` dict should be send back to the original dataset object (from which the shards have been created) to allow to use the index for later filtering on the whole dataset.
**Describe alternatives you've considered**
Each dataset shard could created independent partial indices. then on the whole dataset level, indices should be all referred in `_indexes` dict and be used in querying through `get_nearest_examples()`. The drawback is that the scores will be computed independently on the partial indices leading to inconsistent values for most scoring based on corpus level statistics (tf/idf, BM25).
**Additional context**
The objectives is to parallelize the index creation to speed-up the process (ie surcharging the ES server which is fine to handle large load) while later enabling search on the whole dataset. | {
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] | null | 2 | "2021-07-18T08:32:31Z" | "2021-07-18T18:36:05Z" | "2021-07-18T11:19:04Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so.
## Steps to reproduce the bug
```python
import datasets
f1 = datasets.load_metric("f1", keep_in_memory=True, average="min")
f1.add_batch(predictions=[0,2,3], references=[1, 2, 3])
f1.compute()
```
## Expected results
No error, because `average="min"` should be passed correctly to f1_score in sklearn.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute
"f1": f1_score(
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score
return fbeta_score(y_true, y_pred, beta=1, labels=labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support
labels = _check_set_wise_labels(y_true, y_pred, average, labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels
raise ValueError("Target is %s but average='binary'. Please "
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1 | {
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This adds the [Russian SuperGLUE](https://russiansuperglue.com/) dataset. For the most part I reused the code for the original SuperGLUE, although there are some relatively minor differences in the structure that I accounted for. | {
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https://api.github.com/repos/huggingface/datasets/issues/2667 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2667/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2667/comments | https://api.github.com/repos/huggingface/datasets/issues/2667/events | https://github.com/huggingface/datasets/pull/2667 | 946,861,908 | MDExOlB1bGxSZXF1ZXN0NjkxOTYwNzc3 | 2,667 | Use tqdm from tqdm_utils | {
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} | This PR replaces `tqdm` from the `tqdm` lib with `tqdm` from `datasets.utils.tqdm_utils`. With this change, it's possible to disable progress bars just by calling `disable_progress_bar`. Note this doesn't work on Windows when using multiprocessing due to how global variables are shared between processes. Currently, there is no easy way to disable progress bars in a multiprocess setting on Windows (patching logging with `datasets.utils.logging.get_verbosity = lambda: datasets.utils.logging.NOTSET` doesn't seem to work as well), so adding support for this is a future goal. Additionally, this PR adds a unit ("ba" for batches) to the bar printed by `Dataset.to_json` (this change is motivated by https://github.com/huggingface/datasets/issues/2657). | {
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https://api.github.com/repos/huggingface/datasets/issues/2666 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2666/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2666/comments | https://api.github.com/repos/huggingface/datasets/issues/2666/events | https://github.com/huggingface/datasets/pull/2666 | 946,825,140 | MDExOlB1bGxSZXF1ZXN0NjkxOTMzMDM1 | 2,666 | Adds CodeClippy dataset [WIP] | {
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https://the-eye.eu/public/AI/training_data/code_clippy_data/ | {
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https://api.github.com/repos/huggingface/datasets/issues/2665 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2665/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2665/comments | https://api.github.com/repos/huggingface/datasets/issues/2665/events | https://github.com/huggingface/datasets/pull/2665 | 946,822,036 | MDExOlB1bGxSZXF1ZXN0NjkxOTMwNjky | 2,665 | Adds APPS dataset to the hub [WIP] | {
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https://api.github.com/repos/huggingface/datasets/issues/2663 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2663/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2663/comments | https://api.github.com/repos/huggingface/datasets/issues/2663/events | https://github.com/huggingface/datasets/issues/2663 | 946,552,273 | MDU6SXNzdWU5NDY1NTIyNzM= | 2,663 | [`to_json`] add multi-proc sharding support | {
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] | closed | false | null | [] | null | 2 | "2021-07-16T19:41:50Z" | "2021-09-13T13:56:37Z" | "2021-09-13T13:56:37Z" | CONTRIBUTOR | null | null | null | As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR.
I implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards?
I think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process.
I'm not sure whether the user should be responsible to concatenate the shards at the end or `datasets`, either way works for my needs.
The code I was using:
```
from multiprocessing import cpu_count, Process, Queue
[...]
filtered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count())
DATASET_NAME = "oscar"
SHARDS = 10
def process_shard(idx):
print(f"Sharding {idx}")
ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True)
# ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling
print(f"Saving {DATASET_NAME}-{idx}.jsonl")
ds_shard.to_json(f"{DATASET_NAME}-{idx}.jsonl", orient="records", lines=True, force_ascii=False)
queue = Queue()
processes = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)]
for p in processes:
p.start()
for p in processes:
p.join()
```
Thank you!
@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2662 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2662/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2662/comments | https://api.github.com/repos/huggingface/datasets/issues/2662/events | https://github.com/huggingface/datasets/pull/2662 | 946,470,815 | MDExOlB1bGxSZXF1ZXN0NjkxNjM5MjU5 | 2,662 | Load Dataset from the Hub (NO DATASET SCRIPT) | {
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} | ## Load the data from any Dataset repository on the Hub
This PR adds support for loading datasets from any dataset repository on the hub, without requiring any dataset script.
As a user it's now possible to create a repo and upload some csv/json/text/parquet files, and then be able to load the data in one line. Here is an example with the `allenai/c4` repository that contains a lot of compressed json lines files:
```python
from datasets import load_dataset
data_files = {"train": "en/c4-train.*.json.gz"}
c4 = load_dataset("allenai/c4", data_files=data_files, split="train", streaming=True)
print(c4.n_shards)
# 1024
print(next(iter(c4)))
# {'text': 'Beginners BBQ Class Takin...'}
```
By default it loads all the files, but as shown in the example you can choose the ones you want with unix style patterns.
Of course it's still possible to use dataset scripts since they offer the most flexibility.
## Implementation details
It uses `huggingface_hub` to list the files in a dataset repository.
If you provide a path to a local directory instead of a repository name, it works the same way but it uses `glob`.
Depending on the data files available, or passed in the `data_files` parameter, one of the available builders will be used among the csv, json, text and parquet builders.
Because of this, it's not possible to load both csv and json files at once. In this case you have to load them separately and then concatenate the two datasets for example.
## TODO
- [x] tests
- [x] docs
- [x] when huggingface_hub gets a new release, update the CI and the setup.py
Close https://github.com/huggingface/datasets/issues/2629 | {
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https://api.github.com/repos/huggingface/datasets/issues/2661 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2661/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2661/comments | https://api.github.com/repos/huggingface/datasets/issues/2661/events | https://github.com/huggingface/datasets/pull/2661 | 946,446,967 | MDExOlB1bGxSZXF1ZXN0NjkxNjE5MzAz | 2,661 | Add SD task for SUPERB | {
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} | Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
TODO:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [x] README: tags + description sections
- ~~Add DER metric~~ (we leave the DER metric for a follow-up PR)
Related to #2619.
Close #2653.
cc: @lewtun | {
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} | The goal of this PR is to remove duplicated checks in the `map` logic to execute them only once whenever possible (`fn_kwargs`, `input_columns`, ...). Additionally, this PR improves the consistency (to align it with `input_columns`) of the `remove_columns` check by adding support for a single string value, which is then wrapped into a list. | {
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https://api.github.com/repos/huggingface/datasets/issues/2659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2659/comments | https://api.github.com/repos/huggingface/datasets/issues/2659/events | https://github.com/huggingface/datasets/pull/2659 | 946,155,407 | MDExOlB1bGxSZXF1ZXN0NjkxMzcwNzU3 | 2,659 | Allow dataset config kwargs to be None | {
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} | Close https://github.com/huggingface/datasets/issues/2658
The dataset config kwargs that were set to None we simply ignored.
This was an issue when None has some meaning for certain parameters of certain builders, like the `sep` parameter of the "csv" builder that allows to infer to separator.
cc @SBrandeis | {
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] | null | 0 | "2021-07-16T10:05:44Z" | "2021-07-16T12:46:06Z" | "2021-07-16T12:46:06Z" | MEMBER | null | null | null | When doing `load_dataset("csv", sep=None)`, the `sep` passed to `pd.read_csv` is still the default `sep=","` instead, which makes it impossible to make the csv loader infer the separator.
Related to https://github.com/huggingface/datasets/pull/2656
cc @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/2657 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2657/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2657/comments | https://api.github.com/repos/huggingface/datasets/issues/2657/events | https://github.com/huggingface/datasets/issues/2657 | 945,822,829 | MDU6SXNzdWU5NDU4MjI4Mjk= | 2,657 | `to_json` reporting enhancements | {
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] | open | false | null | [] | null | 0 | "2021-07-15T23:32:18Z" | "2021-07-15T23:33:53Z" | null | CONTRIBUTOR | null | null | null | While using `to_json` 2 things came to mind that would have made the experience easier on the user:
1. Could we have a `desc` arg for the tqdm use and a fallback to just `to_json` so that it'd be clear to the user what's happening? Surely, one can just print the description before calling json, but I thought perhaps it'd help to have it self-identify like you did for other progress bars recently.
2. It took me a while to make sense of the reported numbers:
```
22%|βββ | 1536/7076 [12:30:57<44:09:42, 28.70s/it]
```
So iteration here happens to be 10K samples, and the total is 70M records. But the user does't know that, so the progress bar is perfect, but the numbers it reports are meaningless until one discovers that 1it=10K samples. And one still has to convert these in the head - so it's not quick. Not exactly sure what's the best way to approach this, perhaps it can be part of `desc`? or report M or K, so it'd be built-in if it were to print, e.g.:
```
22%|βββ | 15360K/70760K [12:30:57<44:09:42, 28.70s/it]
```
or
```
22%|βββ | 15.36M/70.76M [12:30:57<44:09:42, 28.70s/it]
```
(while of course remaining friendly to small datasets)
I forget if tqdm lets you add a magnitude identifier to the running count.
Thank you! | {
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https://api.github.com/repos/huggingface/datasets/issues/2656 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2656/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2656/comments | https://api.github.com/repos/huggingface/datasets/issues/2656/events | https://github.com/huggingface/datasets/pull/2656 | 945,421,790 | MDExOlB1bGxSZXF1ZXN0NjkwNzUzNjA3 | 2,656 | Change `from_csv` default arguments | {
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} | [] | closed | false | null | [] | null | 1 | "2021-07-15T14:09:06Z" | "2023-09-24T09:56:44Z" | "2021-07-16T10:23:26Z" | CONTRIBUTOR | null | 0 | {
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} | Passing `sep=None` to pandas's `read_csv` lets pandas guess the CSV file's separator
This PR allows users to use this pandas's feature by passing `sep=None` to `Dataset.from_csv`:
```python
Dataset.from_csv(
...,
sep=None
)
``` | {
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