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Some notes:
* the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here
* I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`)
* this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427
TODOs:
- [x] dataset card
- [ ] dummy data
cc @merveenoyan
Closes #2526 | {
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} | When doing `push_to_hub`, the feature types are lost (see issue https://github.com/huggingface/datasets/issues/3394).
This PR fixes this by also pushing a `dataset_infos.json` file to the Hub, that stores the feature types.
Other minor changes:
- renamed the `___` separator to `--`, since `--` is now disallowed in a name in the back-end.
I tested this feature with datasets like conll2003 that has feature types like `ClassLabel` that were previously lost.
Close https://github.com/huggingface/datasets/issues/3394
I would like to include this in today's release (though not mandatory), so feel free to comment/suggest changes | {
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I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)
```
## Expected results
Expecting to load 'cnn_dailymail' dataset.
## Actual results
`NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
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] | open | false | null | [] | null | 2 | 2021-12-21T03:29:01Z | 2022-11-21T19:55:11Z | null | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
using latest datasets=datasets-1.16.1-py3-none-any.whl
process my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:
then I get this error:
I have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux docker
- Python version: 3.6
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Fix #3462.
Close bigscience-workshop/data_tooling#107 | {
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] | null | 0 | 2021-12-20T17:44:01Z | 2021-12-21T06:24:02Z | 2021-12-21T06:24:01Z | MEMBER | null | null | null | Please note also: the HuggingFace version at https://huggingface.co/datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203.
## Adding a Dataset
- **Name:** swahili_news
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Related to:
- bigscience-workshop/data_tooling#107
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} | Remove Markdown syntax for links in metrics description, as it is not properly rendered.
Related to #3437. | {
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} | Following https://github.com/huggingface/datasets/pull/3456#event-5792250497 it looks like `datasets` can silently convert lists to strings using `str()`, instead of raising an error.
This PR fixes this and should fix the issue with WER showing low values if the input format is not right. | {
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] | closed | false | null | [] | null | 2 | 2021-12-20T16:16:49Z | 2021-12-20T16:34:57Z | 2021-12-20T16:34:57Z | NONE | null | null | null | ## Describe the bug
When using dataset.select to select a subset of a dataset, dataset._indices are set to indicate which elements are now considered in the dataset.
The same thing happens when you shuffle the dataset; dataset._indices are set to indicate what the new order of the data is.
However, if you then use a dataset.filter, that filter interacts with those dataset._indices values in a non-intuitive manner.
https://huggingface.co/docs/datasets/_modules/datasets/arrow_dataset.html#Dataset.filter
Effectively, it looks like the original set of _indices were discared and overwritten by the set created during the filter operation.
I think this is actually an issue with how the map function handles dataset._indices. Ideally it should use the _indices it gets passed, and then return an updated _indices which reflect the map transformation applied to the starting _indices.
## Steps to reproduce the bug
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print("initial 10 elements")
print(dataset['label']) # -> [1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
print("filtered 10 elements looking for label 0")
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1]
```
## Actual results
```
$ python indices_bug.py
initial 10 elements
[1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
filtered 10 elements looking for label 0
[1, 1, 1, 1, 1, 1]
```
This code block first shuffles the dataset (to get a mix of label 0 and label 1).
Then it selects just the first 10 elements (the number of elements does not matter, 10 is just easy to visualize). The important part is that you select some subset of the dataset.
Finally, a filter is applied to pull out just the elements with `label == 0`.
The bug is that you cannot combine any dataset operation which sets the dataset._indices with filter.
In this case I have 2, shuffle and subset.
If you just use a single dataset._indices operation (in this case shuffle) the bug still shows up.
The shuffle sets the dataset._indices and then filter uses those indices in the map, then overwrites dataset._indices with the filter results.
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
```
## Expected results
In an ideal world, the dataset filter would respect any dataset._indices values which had previously been set.
If you use dataset.filter with the base dataset (where dataset._indices has not been set) then the filter command works as expected.
## Environment info
Here are the commands required to rebuild the conda environment from scratch.
```
# create a virtual environment
conda create -n dataset_indices python=3.8 -y
# activate the virtual environment
conda activate dataset_indices
# install huggingface datasets
conda install datasets
```
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 3.0.0
### Full Conda Environment
```
$ conda env export
name: dasaset_indices
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- abseil-cpp=20210324.2=h2531618_0
- aiohttp=3.8.1=py38h7f8727e_0
- aiosignal=1.2.0=pyhd3eb1b0_0
- arrow-cpp=3.0.0=py38h6b21186_4
- attrs=21.2.0=pyhd3eb1b0_0
- aws-c-common=0.4.57=he6710b0_1
- aws-c-event-stream=0.1.6=h2531618_5
- aws-checksums=0.1.9=he6710b0_0
- aws-sdk-cpp=1.8.185=hce553d0_0
- bcj-cffi=0.5.1=py38h295c915_0
- blas=1.0=mkl
- boost-cpp=1.73.0=h27cfd23_11
- bottleneck=1.3.2=py38heb32a55_1
- brotli=1.0.9=he6710b0_2
- brotli-python=1.0.9=py38heb0550a_2
- brotlicffi=1.0.9.2=py38h295c915_0
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.17.1=h27cfd23_0
- ca-certificates=2021.10.26=h06a4308_2
- certifi=2021.10.8=py38h06a4308_0
- cffi=1.14.6=py38h400218f_0
- conllu=4.4.1=pyhd3eb1b0_0
- cryptography=36.0.0=py38h9ce1e76_0
- dataclasses=0.8=pyh6d0b6a4_7
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- et_xmlfile=1.1.0=py38h06a4308_0
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- intel-openmp=2021.4.0=h06a4308_3561
- krb5=1.19.2=hac12032_0
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- libboost=1.73.0=h3ff78a5_11
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- ncurses=6.3=h7f8727e_2
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- setuptools=58.0.4=py38h06a4308_0
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- sqlite=3.36.0=hc218d9a_0
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- tk=8.6.11=h1ccaba5_0
- typing_extensions=3.10.0.2=pyh06a4308_0
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- wheel=0.37.0=pyhd3eb1b0_1
- xxhash=0.8.0=h7f8727e_3
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- zipp=3.6.0=pyhd3eb1b0_0
- zlib=1.2.11=h7f8727e_4
- zstd=1.4.9=haebb681_0
- pip:
- async-timeout==4.0.2
- charset-normalizer==2.0.9
- datasets==1.16.1
- fsspec==2021.11.1
- huggingface-hub==0.2.1
- multidict==5.2.0
- pandas==1.3.5
- pyarrow==6.0.1
- pytz==2021.3
- pyyaml==6.0
- tqdm==4.62.3
- typing-extensions==4.0.1
- urllib3==1.26.7
- yarl==1.7.2
```
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] | open | false | null | [] | null | 3 | 2021-12-20T14:34:39Z | 2022-03-16T16:53:09Z | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** CMU Graphics Lab Motion Capture database
- **Description:** The database contains free motions which you can download and use.
- **Data:** http://mocap.cs.cmu.edu/
- **Motivation:** Nice motion capture dataset
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | {
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} | Currently we have the following problem when using the WER. When the input format to the WER metric is wrong, instead of throwing an error message a word-error-rate is computed which is incorrect. E.g. when doing the following:
```python
from datasets import load_metric
wer = load_metric("wer")
target_str = ["hello this is nice", "hello the weather is bloomy"]
pred_str = [["hello it's nice"], ["hello it's the weather"]]
print("Wrong:", wer.compute(predictions=pred_str, references=target_str))
print("Correct", wer.compute(predictions=[x[0] for x in pred_str], references=target_str))
```
We get:
```
Wrong: 1.0
Correct 0.5555555555555556
```
meaning that we get a word-error rate for incorrectly passed input formats. We should raise an error here instead so that people don't spent hours fixing a model while it's their incorrect evaluation metric is the problem for a low WER. | {
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] | closed | false | null | [] | null | 2 | 2021-12-20T10:10:43Z | 2023-07-25T15:36:14Z | 2023-07-25T15:36:14Z | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
It requires a lot of effort to improve a datasheet.
**Describe the solution you'd like**
UI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.)
**Describe alternatives you've considered**
The current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.
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} | This PR:
- Adds tests to DownloadManager and StreamingDownloadManager `iter_archive` for both path and file inputs
- Fixes bugs in `iter_archive` introduced in:
- #3443
Fix #3453. | {
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After the merge of:
- #3443
the method `iter_archive` throws a ValueError:
```
ValueError: read of closed file
```
## Steps to reproduce the bug
```python
for path, file in dl_manager.iter_archive(archive_path):
pass
```
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is there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset. | {
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} | Let's have a script that updates the dataset repositories on staging for now. This way we can make sure it works fine before going in prod.
Related to https://github.com/huggingface/datasets/issues/3341
The script runs on each commit on `master`. It checks the datasets that were changed, and it pushes the changes to the corresponding repositories on the Hub.
If there's a new dataset, then a new repository is created.
If the commit is a new release of `datasets`, it also pushes the tag to all the repositories. | {
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] | closed | false | null | [] | null | 1 | 2021-12-17T17:00:39Z | 2023-07-25T15:38:47Z | 2023-07-25T15:38:47Z | NONE | null | null | null | ## Describe the bug
I observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter')
## Steps to reproduce the bug
```
from datasets import Dataset
import pandas as pd
dic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']}
df = pd.DataFrame.from_dict(dic)
dataset = Dataset.from_pandas(df)
split_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]
eval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0)
print( eval_dataset['x'])
print(eval_dataset_2['x'])
```
One observes that elements in eval_dataset2 are actually coming from the training dataset...
## Expected results
The expected results would be that the filtered eval dataset would only contain elements from the original eval dataset.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows 10
- Python version: 3.7
- PyArrow version: 5.0.0
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] | closed | false | null | [] | null | 2 | 2021-12-17T15:29:11Z | 2023-07-25T15:33:57Z | 2023-07-25T15:33:56Z | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
No.
**Describe the solution you'd like**
I would like to be able to concatenate datasets as follows:
```python
>>> dataset["train"] += dataset["validation"]
```
... instead of using `concatenate_datasets()`:
```python
>>> raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], raw_datasets["validation"]])
>>> del raw_datasets["validation"]
```
**Describe alternatives you've considered**
Well, I have considered `concatenate_datasets()` 😀
**Additional context**
N.a.
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**Link:** https://huggingface.co/datasets/IGESML/pubmed_neg
I am getting the error:
Status code: 400
Exception: JSONDecodeError
Message: Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)
I have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.
Am I the one who added this dataset ? Yes
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https://api.github.com/repos/huggingface/datasets/issues/3447 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3447/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3447/comments | https://api.github.com/repos/huggingface/datasets/issues/3447/events | https://github.com/huggingface/datasets/issues/3447 | 1,082,539,790 | I_kwDODunzps5Ahj8O | 3,447 | HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading | {
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] | closed | false | null | [] | null | 3 | 2021-12-16T18:51:13Z | 2022-02-17T14:16:27Z | 2022-02-17T14:16:27Z | NONE | null | null | null | ## Describe the bug
According to https://huggingface.co/docs/datasets/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to "run in full offline mode". It didn't work for me. At the very beginning, datasets still tried to download "custom data configuration" for JSON, despite I have run the program once and cached all data into the same --cache_dir.
"Downloading" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers "FileNotFound" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.
## Steps to reproduce the bug
```
export HF_DATASETS_OFFLINE=1
python run_clm.py --model_name_or_path=models/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache/trainpy.v2
```
## Expected results
datasets should stop all "downloading" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, "default-471372bed4b51b53", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.
## Actual results
The logging shows datasets are still downloading into "datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426".
```
12/16/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53
12/16/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)
Downloading and preparing dataset json/default to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|██████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 17623.13it/s]
12/16/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min
12/16/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min
100%|███████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1206.99it/s]
12/16/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.
12/16/2021 10:26:00 - INFO - datasets.builder - Generating split train
12/16/2021 10:26:01 - INFO - datasets.builder - Generating split validation
12/16/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.
Dataset json downloaded and prepared to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.
100%|█████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 53.54it/s]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux
- Python version: 3.8.10
- PyArrow version: 6.0.1
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} | Remove the redundant path information in the audio/image dataset as discussed in https://github.com/huggingface/datasets/pull/3430#issuecomment-994734828
TODOs:
* [ ] merge https://github.com/huggingface/datasets/pull/3430
* [ ] merge https://github.com/huggingface/datasets/pull/3364
* [ ] re-generate the info files of the updated audio datasets
cc: @patrickvonplaten @anton-l @nateraw (I expect this to break the audio/vision examples in Transformers; after this change you'll be able to access underlying paths as follows `dset = dset.cast_column("audio", Audio(..., decode=False)); path = dset[0]["audio"]`)
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] | closed | false | null | [] | null | 1 | 2021-12-16T15:57:00Z | 2022-01-03T10:09:00Z | 2022-01-03T10:09:00Z | NONE | null | null | null | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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] | null | 8 | 2021-12-16T11:26:11Z | 2023-08-16T09:28:17Z | null | MEMBER | null | null | null | ## Intro
items marked like <s>this</s> are done already :)
Currently the two classes have two distinct API for processing:
### The `.map()` method
Both have those parameters in common: function, batched, batch_size
- IterableDataset is missing those parameters:
<s>with_indices</s>, with_rank, <s>input_columns</s>, <s>drop_last_batch</s>, <s>remove_columns</s>, features, disable_nullable, fn_kwargs, num_proc
- Dataset also has additional parameters that are exclusive, due to caching:
keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint
- <s>There is also an important difference in terms of behavior:
**Dataset.map adds new columns** (with dict.update)
BUT
**IterableDataset discards previous columns** (it overwrites the dict)
IMO the two methods should have the same behavior. This would be an important breaking change though.</s>
- Dataset.map is eager while IterableDataset.map is lazy
### The `.shuffle()` method
- <s>Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.</s>
- <s>IterableDataset is missing the parameter generator</s>
- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint
### The `.with_format()` method
- IterableDataset only supports "torch" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs
- other methods like `set_format`, `reset_format` or `formatted_as` are also missing
### Other methods
- Both have the same `remove_columns` method
- IterableDataset is missing: <s>cast</s>, <s>cast_column</s>, <s>filter</s>, <s>rename_column</s>, <s>rename_columns</s>, class_encode_column, flatten, prepare_for_task, train_test_split, shard
- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform
- And others don't really make sense for an iterable dataset: select, sort, add_column, add_item
- Dataset is missing skip and take, that IterableDataset implements.
## Questions
I think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.
1. What should be aligned and what shouldn't between those two APIs ?
IMO the minimum is to align the main processing methods.
It would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters. DONE ✅
It would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard. WIP 🟠
2. What are the breaking changes for IterableDataset ?
The main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them. DONE ✅
3. Shall we also do some changes for regular datasets ?
I agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:
- keep the eager Dataset.map with caching
- keep the with_transform method for lazy processing
- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)
We could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.
For information, TFDS does lazy map by default, and has an additional `.cache()` method.
## Opinions ?
I'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.
cc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger | {
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With this feature, we can iterate over a tar file inside another tar file. | {
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} | Add `config.row` option to `text` module to allow yielding lines (default, current case), paragraphs or documents.
Feel free to comment on the name of the config parameter `row`:
- Currently, the docs state datasets are made of rows and columns
- Other names I considered: `example`, `item` | {
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] | open | false | null | [] | null | 1 | 2021-12-15T22:26:19Z | 2021-12-28T15:17:05Z | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** QuALITY
- **Description:** A challenging question answering with very long contexts (Twitter [thread](https://twitter.com/sleepinyourhat/status/1471225421794529281?s=20))
- **Paper:** No ArXiv link yet, but draft is [here](https://github.com/nyu-mll/quality/blob/main/quality_preprint.pdf)
- **Data:** GitHub repo [here](https://github.com/nyu-mll/quality)
- **Motivation:** This dataset would serve as a nice way to benchmark long-range Transformer models like BigBird, Longformer and their descendants. In particular, it would be very interesting to see how the S4 model fares on this given it's impressive performance on the Long Range Arena
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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Hi,
I am trying to avoid dataset library using cached files, I get the following bug when this tried to read the cached files. I tried to do the followings:
```
from datasets import set_caching_enabled
set_caching_enabled(False)
```
also force redownlaod:
```
download_mode='force_redownload'
```
but none worked so far, this is on a cluster and on some of the machines this reads from the cached files, I really appreciate any idea on how to fully remove caching @lhoestq
many thanks
```
File "run_clm.py", line 496, in <module>
main()
File "run_clm.py", line 419, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 943, in train
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/transformers/trainer.py", line 1445, in _maybe_log_save_evaluate
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 172, in evaluate
output = self.eval_loop(
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 241, in eval_loop
metrics = self.compute_pet_metrics(eval_datasets, model, self.extra_info[metric_key_prefix], task=task)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 268, in compute_pet_metrics
centroids = self._compute_per_token_train_centroids(model, task=task)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 353, in _compute_per_token_train_centroids
data = get_label_samples(self.get_per_task_train_dataset(task), label)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 350, in get_label_samples
return dataset.filter(lambda example: int(example['labels']) == label)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper
out = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2519, in filter
indices = self.map(
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2036, in map
return self._map_single(
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 503, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper
out = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2248, in _map_single
return Dataset.from_file(cache_file_name, info=info, split=self.split)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 654, in from_file
return cls(
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 593, in __init__
self.info.features = self.info.features.reorder_fields_as(inferred_features)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1092, in reorder_fields_as
return Features(recursive_reorder(self, other))
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1081, in recursive_reorder
raise ValueError(f"Keys mismatch: between {source} and {target}" + stack_position)
ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'candidates_ids': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'labels': Value(dtype='int64', id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'extra_fields': {}, 'task': Value(dtype='string', id=None)}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux
- Python version: 3.8.12
- PyArrow version: 6.0.1
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cc: @patrickvonplaten | {
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} | * More structured approach to detecting redirects
* Remove redundant template filter code (covered by strip_code)
* Add language-specific lists of additional media namespace aliases for filtering
* Add language-specific lists of category namespace aliases for new link text cleaning step
* Remove magic words (parser directions like __TOC__ that occasionally occur in text)
Fix #3400
With support from @albertvillanova
CC @yjernite | {
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] | closed | false | null | [] | null | 1 | 2021-12-15T11:21:21Z | 2023-02-28T16:22:29Z | 2023-02-28T16:22:28Z | CONTRIBUTOR | null | null | null | ## Adding a Dataset
- **Name:** The People's Speech
- **Description:** a massive English-language dataset of audio transcriptions of full sentences.
- **Paper:** https://openreview.net/pdf?id=R8CwidgJ0yT
- **Data:** https://mlcommons.org/en/peoples-speech/
- **Motivation:** With over 30,000 hours of speech, this dataset is the largest and most diverse freely available English speech recognition corpus today.
[The article](https://thegradient.pub/new-datasets-to-democratize-speech-recognition-technology-2/) which may be useful when working on the dataset.
cc: @anton-l
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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] | closed | false | null | [] | null | 0 | 2021-12-15T11:14:44Z | 2022-02-22T10:03:53Z | 2022-02-22T10:03:53Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** Multilingual Spoken Words
- **Description:** Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken words in 50 languages for academic research and commercial applications in keyword spotting and spoken term search, licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords, totaling 23.4 million 1-second spoken examples (over 6,000 hours).
Read more: https://mlcommons.org/en/news/spoken-words-blog/
- **Paper:** https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/fe131d7f5a6b38b23cc967316c13dae2-Paper-round2.pdf
- **Data:** https://mlcommons.org/en/multilingual-spoken-words/
- **Motivation:**
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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" Unable to resolve any data file that matches '['**train*']' at /data2/whr/lzy/open_domain_data/retrieval/wiki_dpr with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'zip']", so how could i deal with this problem?
thx.
And below is my code .
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https://api.github.com/repos/huggingface/datasets/issues/3430 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3430/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3430/comments | https://api.github.com/repos/huggingface/datasets/issues/3430/events | https://github.com/huggingface/datasets/pull/3430 | 1,079,811,124 | PR_kwDODunzps4v033w | 3,430 | Make decoding of Audio and Image feature optional | {
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} | Add the `decode` argument (`True` by default) to the `Audio` and the `Image` feature to make it possible to toggle on/off decoding of these features.
Even though we've discussed that on Slack, I'm not removing the `_storage_dtype` argument of the Audio feature in this PR to avoid breaking the Audio feature tests. | {
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} | `cast` currently emits the following warning when called on Windows:
```
Parameter 'function'=<function Dataset.cast.<locals>.<lambda> at 0x000001C930571EA0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting
and caching to work. If you reuse this transform, the caching mechanism will consider it to be different
from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
```
It seems like the issue stems from the `config.PYARROW_VERSION` object not being serializable on Windows (tested with `dumps(lambda: config.PYARROW_VERSION)`), so I'm fixing this by capturing `config.PYARROW_VERSION.major` before the lambda definition. | {
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} | Add:
- Enron Emails subset of The Pile: "enron_emails" config
Close bigscience-workshop/data_tooling#310.
CC: @StellaAthena | {
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## Steps to reproduce the bug
```python
from datasets import get_dataset_config_names
get_dataset_config_names("allenai/c4")
```
## Expected results
I would expect to get the answer quickly, at least in less than 10s
## Actual results
It takes about 45s on my environment
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.11.0-1022-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
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} | [] | closed | false | null | [] | null | 2 | 2021-12-13T13:38:13Z | 2022-01-12T14:13:16Z | 2022-01-12T14:13:15Z | CONTRIBUTOR | null | 0 | {
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} | Add the RedCaps dataset. I'm not adding the generated `dataset_infos.json` file for now due to its size (11 MB).
TODOs:
- [x] dummy data
- [x] dataset card
Close #3316 | {
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] | closed | false | null | [] | null | 14 | 2021-12-13T03:43:17Z | 2022-12-14T16:04:22Z | 2022-12-14T16:04:22Z | NONE | null | null | null | ## Describe the bug
The data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import pandas as pd
import numpy as np
import os
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
import shutil
NUM_OF_USER = 1000000
NUM_OF_ACTION = 50000
NUM_OF_SEQUENCE = 10000
NUM_OF_FILES = 32
NUM_OF_WORKERS = 16
if __name__ == "__main__":
shutil.rmtree("./dataset")
for i in range(NUM_OF_FILES):
sequence_data = pd.DataFrame(
{
"imei": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),
"sequence": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)
}
)
if not os.path.exists("./dataset"):
os.makedirs("./dataset")
sequence_data.to_csv(f"./dataset/sequence_data_{i}.csv",
index=False)
dataset = load_dataset("csv",
data_files=[os.path.join("./dataset",file) for file in os.listdir("./dataset") if file.endswith(".csv")],
split="train",
streaming=True).with_format("torch")
data_loader = DataLoader(dataset,
batch_size=1024,
num_workers=NUM_OF_WORKERS)
result = pd.DataFrame()
for i, batch in tqdm(enumerate(data_loader)):
result = pd.concat([result,
pd.DataFrame(batch)],
axis=0)
result.to_csv(f"num_work_{NUM_OF_WORKERS}.csv", index=False)
```
## Expected results
data do not duplicate
## Actual results
data duplicate NUM_OF_WORKERS = 16
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:datasets==1.14.0
- Platform:transformers==4.11.3
- Python version:3.8
- PyArrow version:
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] | closed | false | null | [] | null | 1 | 2021-12-13T02:49:51Z | 2022-01-07T14:06:47Z | 2022-01-07T14:06:47Z | NONE | null | null | null | ## Describe the bug
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1371, in load_metric
metric = metric_cls(
TypeError: 'NoneType' object is not callable
## Steps to reproduce the bug
```python
metric = load_metric("glue", "sst2")
```
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 6.0.1
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} | This pull request adds a categorized Long-form question answering dataset `ELI5_Category`. It's a new variant of the [ELI5](https://huggingface.co/datasets/eli5) dataset that uses the Reddit tags to alleviate the training/validation overlapping in the origin ELI5 dataset.
A [report](https://celeritasml.netlify.app/posts/2021-12-01-eli5c/)(Section 2) on this dataset. | {
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] | open | false | null | [] | null | 6 | 2021-12-11T01:36:31Z | 2021-12-21T15:49:07Z | null | CONTRIBUTOR | null | null | null | ## Describe the bug
Saving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
original = load_dataset("squad", split="train")
original.to_json("from_original.json") # Takes 0 seconds
selected_subset1 = original.select([i for i in range(len(original))])
selected_subset1.to_json("from_select1.json") # Takes 212 seconds
selected_subset2 = original.select([i for i in range(int(len(original) / 2))])
selected_subset2.to_json("from_select2.json") # Takes 90 seconds
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master (https://github.com/huggingface/datasets/commit/6090f3cfb5c819f441dd4a4bb635e037c875b044)
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27
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} | Add loading script for Wikisource dataset.
Fix #3399.
CC: @geohci, @yjernite | {
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https://api.github.com/repos/huggingface/datasets/issues/3417 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3417/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3417/comments | https://api.github.com/repos/huggingface/datasets/issues/3417/events | https://github.com/huggingface/datasets/pull/3417 | 1,076,943,343 | PR_kwDODunzps4vrwd7 | 3,417 | Fix type of bridge field in QED | {
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} | [] | closed | false | null | [] | null | 0 | 2021-12-10T15:07:21Z | 2021-12-14T14:39:06Z | 2021-12-14T14:39:05Z | CONTRIBUTOR | null | 0 | {
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} | Use `Value("string")` instead of `Value("bool")` for the feature type of the `"bridge"` field in the QED dataset. If the value is `False`, set to `None`.
The following paragraph in the QED repo explains the purpose of this field:
>Each annotation in referential_equalities is a pair of spans, the question_reference and the sentence_reference, corresponding to an entity mention in the question and the selected_sentence respectively. As described in the paper, sentence_references can be "bridged in", in which case they do not correspond with any actual span in the selected_sentence. Hence, sentence_reference spans contain an additional field, bridge, which is a prepositional phrase when a reference is bridged, and is False otherwise. Prepositional phrases serve to link bridged references to an anchoring phrase in the selected_sentence. In the case a sentence_reference is bridged, the start and end, as well as the span string, map to such an anchoring phrase in the selected_sentence.
Fix #3346
cc @VictorSanh | {
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] | closed | false | null | [] | null | 1 | 2021-12-10T13:49:17Z | 2021-12-14T14:38:29Z | 2021-12-14T14:38:29Z | NONE | null | null | null | ## Dataset viewer issue for '* disaster_response_messages*'
**Link:** https://huggingface.co/datasets/disaster_response_messages
Dataset unavailable. Link dead: https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv
Am I the one who added this dataset ?No
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] | closed | false | null | [] | null | 2 | 2021-12-10T06:08:59Z | 2022-03-31T16:38:51Z | 2022-03-31T16:38:51Z | MEMBER | null | null | null | ## Describe the bug
Some CI tests fail randomly.
1. In https://github.com/huggingface/datasets/pull/3375/commits/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =
```
2. After re-running the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test (one on Linux and a different one on Windows):
- On Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =
```
- On Windows:
```
=========================== short test summary info ===========================
FAILED tests/test_load.py::test_load_dataset_builder_for_community_dataset_without_script
= 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =
```
The test `tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.
3. After re-running again the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/39f32f2119cf91b86867216bb5c356c586503c6a, ALL the tests passed.
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https://api.github.com/repos/huggingface/datasets/issues/3414 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3414/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3414/comments | https://api.github.com/repos/huggingface/datasets/issues/3414/events | https://github.com/huggingface/datasets/pull/3414 | 1,076,028,998 | PR_kwDODunzps4voyaq | 3,414 | Skip None encoding (line deleted by accident in #3195) | {
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} | Return the line deleted by accident in #3195 while [resolving merge conflicts](https://github.com/huggingface/datasets/pull/3195/commits/8b0ed15be08559056b817836a07d47acda0c4510).
Fix #3181 (finally :))
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https://api.github.com/repos/huggingface/datasets/issues/3413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3413/comments | https://api.github.com/repos/huggingface/datasets/issues/3413/events | https://github.com/huggingface/datasets/pull/3413 | 1,075,854,325 | PR_kwDODunzps4voNZv | 3,413 | Add WIDER FACE dataset | {
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} | Adds the WIDER FACE face detection benchmark.
TODOs:
* [x] dataset card
* [x] dummy data | {
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https://api.github.com/repos/huggingface/datasets/issues/3412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3412/comments | https://api.github.com/repos/huggingface/datasets/issues/3412/events | https://github.com/huggingface/datasets/pull/3412 | 1,075,846,368 | PR_kwDODunzps4voLs4 | 3,412 | Fix flaky test again for s3 serialization | {
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} | Following https://github.com/huggingface/datasets/pull/3388 that wasn't enough (see CI error [here](https://app.circleci.com/pipelines/github/huggingface/datasets/9080/workflows/b971fb27-ff20-4220-9416-c19acdfdf6f4/jobs/55985)) | {
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] | open | false | null | [] | null | 2 | 2021-12-09T17:54:35Z | 2021-12-22T11:21:33Z | null | NONE | null | null | null | ## Describe the bug
Model I am using (Bert, XLNet ...): bert-base-chinese
The problem arises when using:
* [https://github.com/huggingface/transformers/blob/master/examples/research_projects/mlm_wwm/run_mlm_wwm.py] the official example scripts: `rum_mlm_wwm.py`
The tasks I am working on is: pretraining whole word masking with my own dataset and ref.json file
I tried follow the run_mlm_wwm.py procedure to do whole word masking on pretraining task. my file is in .txt form, where one line represents one sample, with `9,264,784` chinese lines in total. the ref.json file is also contains 9,264,784 lines of whole word masking reference data for my chinese corpus. but when I try to adapt the run_mlm_wwm.py script, it shows that somehow after
`datasets["train"] = load_dataset(...`
`len(datasets["train"])` returns `9,265,365`
then, after `tokenized_datasets = datasets.map(...`
`len(tokenized_datasets["train"])` returns `9,265,279`
I'm really confused and tried to trace code by myself but can't know what happened after a week trial.
I want to know what happened in the `load_dataset()` function and `datasets.map` here and how did I get more lines of data than I input. so I'm here to ask.
## To reproduce
Sorry that I can't provide my data here since it did not belong to me. but I'm sure I remove the blank lines.
## Expected behavior
I expect the code run as it should. but the AssertionError in line 167 keeps raise as the line of reference json and datasets['train'] differs.
Thanks for your patient reading!
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 3.0.0
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} | Following https://github.com/huggingface/datasets/pull/3045
Currently one can pass `new_fingerprint` to `.map()` to use a custom fingerprint instead of the one computed by hashing the map transform. However it's ignored if `num_proc>1`.
In this PR I fixed that by passing `new_fingerprint` to `._map_single()` when `num_proc>1`.
More specifically, `new_fingerprint` with a suffix based on the process `rank` is passed, so that each process has a different `new_fingerprint`
cc @TevenLeScao @vlievin | {
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**Link:** *link to the dataset viewer page*
*short description of the issue*
When creating an empty dataset repo, the Dataset Preview provides a helpful message that no files were found. There is a tiny typo in that message: "ressource" should be "resource"
Am I the one who added this dataset ?
N/A
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} | When inferring the module for datasets without script, set a maximum number of iterations over data files.
This PR fixes the issue of taking too long when hundred of data files present.
Please, feel free to agree on both numbers:
```
# Datasets without script
DATA_FILES_MAX_NUMBER = 10
ARCHIVED_DATA_FILES_MAX_NUMBER = 5
```
Fix #3404. | {
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} | Fix module inference for an archive file that contains files within a directory.
Fix #3405. | {
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] | null | 0 | 2021-12-08T12:32:15Z | 2021-12-08T13:03:29Z | 2021-12-08T13:03:29Z | MEMBER | null | null | null | ## Describe the bug
When a zipped file contains archived files within a directory, the function `infer_module_for_data_files_in_archives` does not work.
It only works for files located in the root directory of the ZIP file.
## Steps to reproduce the bug
```python
infer_module_for_data_files_in_archives(["path/to/zip/file.zip"], False)
``` | {
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] | null | 0 | 2021-12-07T18:44:49Z | 2021-12-14T17:08:41Z | 2021-12-14T17:08:41Z | MEMBER | null | null | null | **Is your feature request related to a problem? Please describe.**
When hundreds of ZIP files are present in a dataset, format inference takes too long.
See: https://github.com/bigscience-workshop/data_tooling/issues/232#issuecomment-986685497
**Describe the solution you'd like**
Iterate over a maximum number of files.
CC: @lhoestq
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] | closed | false | null | [] | null | 4 | 2021-12-07T17:57:59Z | 2021-12-17T07:00:35Z | 2021-12-17T07:00:35Z | CONTRIBUTOR | null | null | null | ## Describe the bug
Cannot seem to import datasets when running run_summarizer.py script on a VM set up on ovhcloud
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
No error
## Actual results
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/conda/lib/python3.7/site-packages/datasets/__init__.py", line 34, in <module>
from .arrow_dataset import Dataset, concatenate_datasets
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 48, in <module>
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_writer.py", line 27, in <module>
from .features import (
File "/opt/conda/lib/python3.7/site-packages/datasets/features/__init__.py", line 2, in <module>
from .audio import Audio
File "/opt/conda/lib/python3.7/site-packages/datasets/features/audio.py", line 8, in <module>
from ..utils.streaming_download_manager import xopen
File "/opt/conda/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 16, in <module>
from ..filesystems import COMPRESSION_FILESYSTEMS
File "/opt/conda/lib/python3.7/site-packages/datasets/filesystems/__init__.py", line 13, in <module>
from .s3filesystem import S3FileSystem # noqa: F401
File "/opt/conda/lib/python3.7/site-packages/datasets/filesystems/s3filesystem.py", line 1, in <module>
import s3fs
File "/opt/conda/lib/python3.7/site-packages/s3fs/__init__.py", line 1, in <module>
from .core import S3FileSystem, S3File
File "/opt/conda/lib/python3.7/site-packages/s3fs/core.py", line 11, in <module>
from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper, maybe_sync
ImportError: cannot import name 'maybe_sync' from 'fsspec.asyn' (/opt/conda/lib/python3.7/site-packages/fsspec/asyn.py)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.0
- Platform: OVH Cloud Tesla V100 Machine
- Python version: 3.7.9
- PyArrow version: 6.0.1
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] | null | 1 | 2021-12-07T17:33:19Z | 2023-11-23T07:56:29Z | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** Add pre-processed data to:
- *wikimedia/wikipedia*: https://huggingface.co/datasets/wikimedia/wikipedia
- *wikimedia/wikisource*: https://huggingface.co/datasets/wikimedia/wikisource
- **Description:** Add pre-processed data to the Hub for all languages
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** This will be very useful for the NLP community, as the pre-processing has a high cost for lot of researchers (both in computation and in knowledge)
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
CC: @geohci, @yjernite | {
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- _extract_content(filepath):
- Replace .startswith("#redirect") with more structured approach: if elem.find(f"./{namespace}redirect") is None: continue
- _parse_and_clean_wikicode(raw_content, parser):
- Remove rm_template from cleaning -- this is redundant with .strip_code() from mwparserformhell
- Build a language-specific list of namespace prefixes to filter out per below get_namespace_prefixes
- Optional: strip prefixes like categories -- e.g., Category:Towns in Tianjin becomes Towns in Tianjin
- Optional: strip magic words
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] | null | 1 | 2021-12-07T17:21:31Z | 2021-12-10T17:26:26Z | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** *wikisource*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** Additional high quality textual data, besides Wikipedia.
Add loading script as "canonical" dataset (as it is the case for ""wikipedia").
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
CC: @geohci, @yjernite | {
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This should be done for all pre-processed datasets under "wikimedia" org in the Hub: https://huggingface.co/wikimedia
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} | This pull request adds the BNL's [processed newspaper collections](https://data.bnl.lu/data/historical-newspapers/) as a dataset. This is partly done to support BigScience see: https://github.com/bigscience-workshop/data_tooling/issues/192.
The Datacard is more sparse than I would like but I plan to make a separate pull request to try and make this more complete at a later date.
I had to manually add the `dummy_data` but I believe I've done this correctly (the tests pass locally).
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] | null | 5 | 2021-12-07T15:11:36Z | 2022-04-25T16:12:22Z | 2022-04-25T16:12:01Z | MEMBER | null | null | null | ## Dataset viewer issue for '*openslr*', '*projecte-aina/parlament_parla*'
**Link:** *https://huggingface.co/datasets/openslr*
**Link:** *https://huggingface.co/datasets/projecte-aina/parlament_parla*
Error:
```
Status code: 400
Exception: ImportError
Message: To support decoding audio files, please install 'librosa'.
```
Am I the one who added this dataset ? Yes-No
- openslr: No
- projecte-aina/parlament_parla: Yes
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] | open | false | null | [] | null | 0 | 2021-12-07T10:37:02Z | 2021-12-09T15:56:03Z | null | NONE | null | null | null | ## Adding a Dataset
- **Name:** *Common Voice Belarusian Dataset*
- **Description:** *[commonvoice.mozilla.org/be](https://commonvoice.mozilla.org/be)*
- **Data:** *[commonvoice.mozilla.org/be/datasets](https://commonvoice.mozilla.org/be/datasets)*
- **Motivation:** *It has more than 7GB of data, so it will be great to have it in this package so anyone can try to train something for Belarusian language.*
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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] | closed | false | null | [] | null | 1 | 2021-12-07T08:41:01Z | 2021-12-07T14:04:28Z | 2021-12-07T14:04:28Z | CONTRIBUTOR | null | null | null | ## Dataset viewer issue for `dansbecker/hackernews_hiring_posts`
**Link:** https://huggingface.co/datasets/dansbecker/hackernews_hiring_posts
*short description of the issue*
Dataset preview not showing for uploaded DatasetDict. See https://discuss.huggingface.co/t/dataset-preview-not-showing-for-uploaded-datasetdict/12603
Am I the one who added this dataset ?
No -> @dansbecker | {
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* There is currently no way to select some columns of a dataset. In pandas, one can use `df[['col1', 'col2']]` to select columns, but in `datasets`, it results in error.
**Describe the solution you'd like**
* A new method that can be used to create a new dataset with only a list of specified columns.
**Describe alternatives you've considered**
`.remove_columns(self, columns: Union[str, List[str]], inverse: bool = False)`
Or
`.select(self, indices: Iterable = None, columns: List[str] = None)`
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] | closed | false | null | [] | null | 1 | 2021-12-06T18:22:49Z | 2021-12-06T20:22:05Z | 2021-12-06T20:22:05Z | NONE | null | null | null | ## Describe the bug
I have prepared dataset to datasets and now I am trying to load it back Finnish-NLP/voxpopuli_fi
I get "KeyError: 'Field "builder_name" does not exist in table schema'"
My dataset folder and files should be like @patrickvonplaten has here https://huggingface.co/datasets/flax-community/german-common-voice-processed
How my voxpopuli dataset looks like:
Part of the processing (path column is the absolute path to audio files)
```
def add_audio_column(example):
example['audio'] = example['path']
return example
voxpopuli = voxpopuli.map(add_audio_column)
voxpopuli.cast_column("audio", Audio())
voxpopuli["audio"] <-- to my knowledge this does load the local files and prepares those arrays
voxpopuli = voxpopuli.cast_column("audio", Audio(sampling_rate=16_000)) resampling 16kHz
```
I have then saved it to disk_
`voxpopuli.save_to_disk('/asr_disk/datasets_processed_new/voxpopuli')`
and made folder structure same as @patrickvonplaten
I also get same error while trying to load_dataset from his repo:
## Steps to reproduce the bug
```python
dataset = load_dataset("Finnish-NLP/voxpopuli_fi")
```
## Expected results
Dataset is loaded correctly and looks like in the first picture
## Actual results
Loading throws keyError:
KeyError: 'Field "builder_name" does not exist in table schema'
Resources I have been trying to follow:
https://huggingface.co/docs/datasets/audio_process.html
https://huggingface.co/docs/datasets/share_dataset.html
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.2.dev0
- Platform: Ubuntu 20.04.2 LTS
- Python version: 3.8.12
- PyArrow version: 6.0.1
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- **Name:** EDGAR Database
- **Description:** https://www.sec.gov/edgar/about EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system, is the primary system for companies and others submitting documents under the Securities Act of 1933, the Securities Exchange Act of 1934, the Trust Indenture Act of 1939, and the Investment Company Act of 1940. Containing millions of company and individual filings, EDGAR benefits investors, corporations, and the U.S. economy overall by increasing the efficiency, transparency, and fairness of the securities markets. The system processes about 3,000 filings per day, serves up 3,000 terabytes of data to the public annually, and accommodates 40,000 new filers per year on average. EDGAR® and EDGARLink® are registered trademarks of the SEC.
- **Data:** https://www.sec.gov/os/accessing-edgar-data
- **Motivation:** Enabling and improving FSI (Financial Services Industry) datasets to increase ease of use
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://github.com/huggingface/datasets/commit/73ed6615b4b3eb74d5311684f7b9e05cdb76c989
I fixed that by not checking the content of the random part of the temporary directory name | {
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} | Create Language Modeling task to be able to specify the input "text" column in a dataset.
This can be useful for datasets which are not exclusively used for language modeling and have more than one column:
- for text classification datasets (with columns "review" and "rating", for example), the Language Modeling task can be used to specify the "text" column ("review" in this case).
TODO:
- [ ] Add the LanguageModeling task to all dataset scripts which can be used for language modeling | {
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- Fix typos in dataset cards
- Fix Papers With Code ID for:
- Bilingual Corpus of Arabic-English Parallel Tweets
- Tweets Hate Speech Detection
- Add pretty name tags | {
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] | open | false | null | [] | null | 3 | 2021-12-06T05:20:54Z | 2022-01-17T15:25:01Z | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
A `torch.utils.data.Dataset.__getitem__` operates on a single example.
But 🤗 `Datasets.with_transform` doesn't seem to allow non-batched transform.
**Describe the solution you'd like**
Have a `batched=True` argument in `Datasets.with_transform`
**Describe alternatives you've considered**
* Convert a non-batched transform function to batched one myself.
* Wrap a 🤗 Dataset with torch Dataset, and add a `__getitem__`. 🙄
* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change. | {
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https://api.github.com/repos/huggingface/datasets/issues/3384 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3384/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3384/comments | https://api.github.com/repos/huggingface/datasets/issues/3384/events | https://github.com/huggingface/datasets/pull/3384 | 1,071,594,165 | PR_kwDODunzps4vaNwL | 3,384 | Adding mMARCO dataset | {
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This way, all the languages covered in the translation are available in a easy way. | {
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} | update cc100 dataset to support loading Georgian (ka) data which is originally available in CC100 dataset source.
All tests are passed.
Dummy data generated.
metadata generated. | {
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} | Add typing overloads to Dataset.__getitem__ for mypy
Fixes #3337
**Iterable**
Iterable from `collections` cannot have a type, so you can't do `Iterable[int]` for example. `typing` has a Generic version that builds upon the one from `collections`.
**Flake8**
I had to add `# noqa: F811`, this is a bug from Flake8.
datasets uses flake8==3.7.9 which released in October 2019 if I update flake8 (4.0.1), I no longer get these errors, but I did not want to make the update without your approval. (It also triggers other errors like no args in f-strings.) | {
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https://api.github.com/repos/huggingface/datasets/issues/3381 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3381/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3381/comments | https://api.github.com/repos/huggingface/datasets/issues/3381/events | https://github.com/huggingface/datasets/issues/3381 | 1,071,283,879 | I_kwDODunzps4_2n6n | 3,381 | Unable to load audio_features from common_voice dataset | {
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] | closed | false | null | [] | null | 3 | 2021-12-04T19:59:11Z | 2021-12-06T17:52:42Z | 2021-12-06T17:52:42Z | NONE | null | null | null | ## Describe the bug
I am not able to load audio features from common_voice dataset
## Steps to reproduce the bug
```
from datasets import load_dataset
import torchaudio
test_dataset = load_dataset("common_voice", "hi", split="test[:2%]")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
```
## Expected results
This piece of code should return test_dataset after loading audio features.
## Actual results
Reusing dataset common_voice (/home/jovyan/.cache/huggingface/datasets/common_voice/hi/6.1.0/b879a355caa529b11f2249400b61cadd0d9433f334d5c60f8c7216ccedfecfe1)
/opt/conda/lib/python3.7/site-packages/transformers/configuration_utils.py:341: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 "
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
0%| | 0/3 [00:00<?, ?ex/s]formats: can't open input file `common_voice_hi_23795358.mp3': No such file or directory
0%| | 0/3 [00:00<?, ?ex/s]
Traceback (most recent call last):
File "demo_file.py", line 23, in <module>
test_dataset = test_dataset.map(speech_file_to_array_fn)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2036, in map
desc=desc,
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 518, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 485, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py", line 411, in wrapper
out = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2368, in _map_single
example = apply_function_on_filtered_inputs(example, i, offset=offset)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2277, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1978, in decorated
result = f(decorated_item, *args, **kwargs)
File "demo_file.py", line 19, in speech_file_to_array_fn
speech_array, sampling_rate = torchaudio.load(batch["path"])
File "/opt/conda/lib/python3.7/site-packages/torchaudio/backend/sox_io_backend.py", line 154, in load
filepath, frame_offset, num_frames, normalize, channels_first, format)
RuntimeError: Error loading audio file: failed to open file common_voice_hi_23795358.mp3
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-4.14.243 with-debian-bullseye-sid
- Python version: 3.7.9
- PyArrow version: 6.0.1
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} | [] | closed | false | null | [] | null | 0 | 2021-12-04T09:18:33Z | 2022-01-11T12:29:53Z | 2022-01-11T12:29:53Z | MEMBER | null | null | null | Thanks to all of you, `datasets` will pass 11.5k stars :star2: this week!
If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts:
[**hf.co/oss-survey**](https://hf.co/oss-survey)
(please reply in the above feedback form rather than to this thread)
Thank you all on behalf of the HuggingFace team! 🤗 | {
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} | Hello @lhoestq , thank you for your detailed answer on previous PR !
I made this new PR because I misused git on the previous one #3347.
Related issue #3272.
# Comments :
* For extension check I used the `_get_extraction_protocol` function in **download_manager.py** with a slight change and called it `_get_extraction_protocol_local`:
**I removed this part :**
```python
elif path.endswith(".tar.gz") or path.endswith(".tgz"):
raise NotImplementedError(
f"Extraction protocol for TAR archives like '{urlpath}' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead."
)
```
**And also changed :**
```diff
- extension = path.split(".")[-1]
+ extension = "tar" if path.endswith(".tar.gz") else path.split(".")[-1]
```
The reason for this is a compression like **.tar.gz** will be considered a **.gz** which is handled with **zipfile**, though **tar.gz** can only be opened using **tarfile**.
Please tell me if there's anything to change.
# Tasks :
- [x] download_manager.py
- [x] streaming_download_manager.py | {
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} | Add The Pile subsets:
- pubmed
- ubuntu_irc
- europarl
- hacker_news
- nih_exporter
Close bigscience-workshop/data_tooling#301.
CC: @StellaAthena | {
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} | This is a draft PR since I ran into few small problems.
I referred to this TFDS code: https://github.com/tensorflow/datasets/blob/2538a08c184d53b37bfcf52cc21dd382572a88f4/tensorflow_datasets/object_detection/coco.py
cc: @mariosasko | {
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} | Proposed solution:
- I have added the method `iter_files` to DownloadManager and StreamingDownloadManager
- I use this in modules: "csv", "json", "text"
- I test for CSV/JSONL/TXT zipped (and non-zipped) files, both in streaming and non-streaming modes
Fix #3373. | {
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] | null | 2 | 2021-12-03T10:10:54Z | 2021-12-08T14:14:41Z | 2021-12-08T14:14:41Z | NONE | null | null | null | Hi, it seems like there are updates in cluewsc2020, chid, c3 and tnews, since i could not load them due to the checksum error. | {
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] | null | 0 | 2021-12-03T09:48:24Z | 2021-12-16T18:03:31Z | 2021-12-16T18:03:31Z | MEMBER | null | null | null | Given a community 🤗 dataset repository containing only a zipped CSV file (only raw data, no loading script), I would like to load it in streaming mode without passing `data_files`:
```
ds_name = "bigscience-catalogue-data/vietnamese_poetry_from_fsoft_ai_lab"
ds = load_dataset(ds_name, split="train", streaming=True, use_auth_token=True)
item = next(iter(ds))
```
Currently, it gives a `FileNotFoundError` because there is no glob (no "\*" after "zip://": "zip://*") in the passed URL:
```
'zip://::https://huggingface.co/datasets/bigscience-catalogue-data/vietnamese_poetry_from_fsoft_ai_lab/resolve/e5d45f1bd9a8a798cc14f0a45ebc1ce91907c792/poems_dataset.zip'
```
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] | closed | false | null | [] | null | 0 | 2021-12-02T20:21:07Z | 2022-03-18T09:36:48Z | 2022-03-18T09:36:48Z | CONTRIBUTOR | null | null | null | Some people who host datasets on github seem to include a table of metadata at the end of their README.md to make the dataset indexable by [Google Dataset Search](https://datasetsearch.research.google.com/) (See [here](https://github.com/google-research/google-research/tree/master/goemotions#dataset-metadata) and [here](https://github.com/cvdfoundation/google-landmark#dataset-metadata)). This could be a useful addition to canonical datasets; perhaps even community datasets.
I'll include a screenshot (as opposed to markdown) as an example so as not to have a github issue indexed as a dataset:
> 
**_PS: It might very well be the case that this is already covered by some other markdown magic I'm not aware of._**
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https://api.github.com/repos/huggingface/datasets/issues/3371 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3371/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3371/comments | https://api.github.com/repos/huggingface/datasets/issues/3371/events | https://github.com/huggingface/datasets/pull/3371 | 1,069,821,335 | PR_kwDODunzps4vUnbp | 3,371 | New: Americas NLI dataset | {
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} | This PR adds the [Americas NLI](https://arxiv.org/abs/2104.08726) dataset, extension of XNLI to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika.
One odd thing (not sure) is that I had to set
`datasets-cli dummy_data ./datasets/americas_nli/ --auto_generate --n_lines 7500`
`n_lines` very large to successfully generate the dummy files for all the subsets. Happy to get some guidance here.
Otherwise, I hope everything is in order :)
e: missed a step, onto fixing the tests
e2: there you go -- hope it's ok to have added more languages with their ISO codes to `languages.json`, need those tests to pass :laughing: | {
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https://api.github.com/repos/huggingface/datasets/issues/3370 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3370/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3370/comments | https://api.github.com/repos/huggingface/datasets/issues/3370/events | https://github.com/huggingface/datasets/pull/3370 | 1,069,735,423 | PR_kwDODunzps4vUVA3 | 3,370 | Document a training loop for streaming dataset | {
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} | [] | closed | false | null | [] | null | 0 | 2021-12-02T16:17:00Z | 2021-12-03T13:34:35Z | 2021-12-03T13:34:34Z | MEMBER | null | 0 | {
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} | I added some docs about streaming dataset. In particular I added two subsections:
- one on how to use `map` for preprocessing
- one on how to use a streaming dataset in a pytorch training loop
cc @patrickvonplaten @stevhliu if you have some comments
cc @Rocketknight1 later we can add the one for TF and I might need your help ^^' | {
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https://api.github.com/repos/huggingface/datasets/issues/3369 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3369/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3369/comments | https://api.github.com/repos/huggingface/datasets/issues/3369/events | https://github.com/huggingface/datasets/issues/3369 | 1,069,587,674 | I_kwDODunzps4_wJza | 3,369 | [Audio] Allow resampling for audio datasets in streaming mode | {
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] | null | 2 | 2021-12-02T14:04:57Z | 2021-12-16T15:55:19Z | 2021-12-16T15:55:19Z | MEMBER | null | null | null | Many audio datasets like Common Voice always need to be resampled. This can very easily be done in non-streaming mode as follows:
```python
from datasets import load_dataset
ds = load_dataset("common_voice", "ab", split="test")
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
```
However in streaming mode it fails currently:
```python
from datasets import load_dataset
ds = load_dataset("common_voice", "ab", split="test", streaming=True)
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
```
with the following error:
```
AttributeError: 'IterableDataset' object has no attribute 'cast_column'
```
It would be great if we could add such a feature (I'm not 100% sure though how complex this would be) | {
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