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https://api.github.com/repos/huggingface/datasets/issues/3421 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3421/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3421/comments | https://api.github.com/repos/huggingface/datasets/issues/3421/events | https://github.com/huggingface/datasets/pull/3421 | 1,077,966,571 | PR_kwDODunzps4vuvJK | 3,421 | Adding mMARCO dataset | [
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] | closed | false | null | 7 | 2021-12-13T00:56:43Z | 2022-10-03T09:37:15Z | 2022-10-03T09:37:15Z | null | Adding mMARCO (v1.1) to HF datasets. | {
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"Hi @albertvillanova we've made a major overhaul of the loading script including all configurations we're making available. Could you please review it again?",
"@albertvillanova :ping_pong: ",
"Thanks @lhbonifacio for adding this dataset.\r\nHi there, i got an error about mmarco:\r\nConnectionError: Couldn't re... |
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 | 3 | 2021-12-16T18:51:13Z | 2022-02-17T14:16:27Z | 2022-02-17T14:16:27Z | 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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"Hi ! Indeed it says \"downloading and preparing\" but in your case it didn't need to download anything since you used local files (it would have thrown an error otherwise). I think we can improve the logging to make it clearer in this case",
"@lhoestq Thank you for explaining. I am sorry but I was not clear abou... |
https://api.github.com/repos/huggingface/datasets/issues/2645 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2645/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2645/comments | https://api.github.com/repos/huggingface/datasets/issues/2645/events | https://github.com/huggingface/datasets/issues/2645 | 944,374,284 | MDU6SXNzdWU5NDQzNzQyODQ= | 2,645 | load_dataset processing failed with OS error after downloading a dataset | [
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] | closed | false | null | 2 | 2021-07-14T12:23:53Z | 2021-07-15T09:34:02Z | 2021-07-15T09:34:02Z | null | ## Describe the bug
After downloading a dataset like opus100, there is a bug that
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```
## Expected results
there is no error when running load_dataset.
## Actual results
Specify the actual results or traceback.
Traceback (most recent call last):
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
example = cast_to_python_objects(example)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
return _cast_to_python_objects(obj)[0]
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
import torch
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
_load_global_deps()
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "download_hub_opus100.py", line 9, in <module>
this_dataset = load_dataset('opus100', language_pair)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
+ str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0
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} | https://api.github.com/repos/huggingface/datasets/issues/2645/timeline | null | completed | null | null | false | [
"Hi ! It looks like an issue with pytorch.\r\n\r\nCould you try to run `import torch` and see if it raises an error ?",
"> Hi ! It looks like an issue with pytorch.\r\n> \r\n> Could you try to run `import torch` and see if it raises an error ?\r\n\r\nIt works. Thank you!"
] |
https://api.github.com/repos/huggingface/datasets/issues/2928 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2928/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2928/comments | https://api.github.com/repos/huggingface/datasets/issues/2928/events | https://github.com/huggingface/datasets/pull/2928 | 997,941,506 | PR_kwDODunzps4r0yUb | 2,928 | Update BibTeX entry | [] | closed | false | null | 0 | 2021-09-16T08:39:20Z | 2021-09-16T12:35:34Z | 2021-09-16T12:35:34Z | null | Update BibTeX entry. | {
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https://api.github.com/repos/huggingface/datasets/issues/5825 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5825/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5825/comments | https://api.github.com/repos/huggingface/datasets/issues/5825/events | https://github.com/huggingface/datasets/issues/5825 | 1,697,327,483 | I_kwDODunzps5lKyl7 | 5,825 | FileNotFound even though exists | [] | open | false | null | 3 | 2023-05-05T09:49:55Z | 2023-05-07T17:43:46Z | null | null | ### Describe the bug
I'm trying to download https://huggingface.co/datasets/bigscience/xP3/resolve/main/ur/xp3_facebook_flores_spa_Latn-urd_Arab_devtest_ab-spa_Latn-urd_Arab.jsonl which works fine in my webbrowser, but somehow not with datasets. Am I doing sth wrong?
```
Downloading builder script: 100%
2.82k/2.82k [00:00<00:00, 64.2kB/s]
Downloading readme: 100%
12.6k/12.6k [00:00<00:00, 585kB/s]
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
[<ipython-input-2-4b45446a91d5>](https://localhost:8080/#) in <cell line: 4>()
2 lang = "ur"
3 fname = "xp3_facebook_flores_spa_Latn-urd_Arab_devtest_ab-spa_Latn-urd_Arab.jsonl"
----> 4 dataset = load_dataset("bigscience/xP3", data_files=f"{lang}/{fname}")
6 frames
[/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions)
291 if allowed_extensions is not None:
292 error_msg += f" with any supported extension {list(allowed_extensions)}"
--> 293 raise FileNotFoundError(error_msg)
294 return sorted(out)
295
FileNotFoundError: Unable to find 'https://huggingface.co/datasets/bigscience/xP3/resolve/main/ur/xp3_facebook_flores_spa_Latn-urd_Arab_devtest_ab-spa_Latn-urd_Arab.jsonl' at /content/https:/huggingface.co/datasets/bigscience/xP3/resolve/main
```
### Steps to reproduce the bug
```
!pip install -q datasets
from datasets import load_dataset
lang = "ur"
fname = "xp3_facebook_flores_spa_Latn-urd_Arab_devtest_ab-spa_Latn-urd_Arab.jsonl"
dataset = load_dataset("bigscience/xP3", data_files=f"{lang}/{fname}")
```
### Expected behavior
Correctly downloads
### Environment info
latest versions | {
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"Hi! \r\n\r\nThis would only work if `bigscience/xP3` was a no-code dataset, but it isn't (it has a Python builder script).\r\n\r\nBut this should work: \r\n```python\r\nload_dataset(\"json\", data_files=\"https://huggingface.co/datasets/bigscience/xP3/resolve/main/ur/xp3_facebook_flores_spa_Latn-urd_Arab_devtest_a... |
https://api.github.com/repos/huggingface/datasets/issues/3275 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3275/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3275/comments | https://api.github.com/repos/huggingface/datasets/issues/3275/events | https://github.com/huggingface/datasets/pull/3275 | 1,053,698,898 | PR_kwDODunzps4uiN9t | 3,275 | Force data files extraction if download_mode='force_redownload' | [] | closed | false | null | 0 | 2021-11-15T14:00:24Z | 2021-11-15T14:45:23Z | 2021-11-15T14:45:23Z | null | Avoids weird issues when redownloading a dataset due to cached data not being fully updated.
With this change, issues #3122 and https://github.com/huggingface/datasets/issues/2956 (not a fix, but a workaround) can be fixed as follows:
```python
dset = load_dataset(..., download_mode="force_redownload")
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https://api.github.com/repos/huggingface/datasets/issues/2047 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2047/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2047/comments | https://api.github.com/repos/huggingface/datasets/issues/2047/events | https://github.com/huggingface/datasets/pull/2047 | 830,626,430 | MDExOlB1bGxSZXF1ZXN0NTkyMTI2NzQ3 | 2,047 | Multilingual dIalogAct benchMark (miam) | [] | closed | false | null | 4 | 2021-03-12T23:02:55Z | 2021-03-23T10:36:34Z | 2021-03-19T10:47:13Z | null | My collaborators (@EmileChapuis, @PierreColombo) and I within the Affective Computing team at Telecom Paris would like to anonymously publish the miam dataset. It is assocated with a publication currently under review. We will update the dataset with full citations once the review period is over. | {
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"Hello. All aforementioned changes have been made. I've also re-run black on miam.py. :-)",
"I will run isort again. Hopefully it resolves the current check_code_quality test failure.",
"Once the review period is over, feel free to open a PR to add all the missing information ;)",
"Hi! I will follow up right ... |
https://api.github.com/repos/huggingface/datasets/issues/630 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/630/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/630/comments | https://api.github.com/repos/huggingface/datasets/issues/630/events | https://github.com/huggingface/datasets/issues/630 | 701,636,350 | MDU6SXNzdWU3MDE2MzYzNTA= | 630 | Text dataset not working with large files | [] | closed | false | null | 11 | 2020-09-15T06:02:36Z | 2020-09-25T22:21:43Z | 2020-09-25T22:21:43Z | null | ```
Traceback (most recent call last):
File "examples/language-modeling/run_language_modeling.py", line 333, in <module>
main()
File "examples/language-modeling/run_language_modeling.py", line 262, in main
get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
File "examples/language-modeling/run_language_modeling.py", line 144, in get_dataset
dataset = load_dataset("text", data_files=file_path, split='train+test')
File "/home/ksjae/.local/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/home/ksjae/.local/lib/python3.7/site-packages/datasets/builder.py", line 469, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/ksjae/.local/lib/python3.7/site-packages/datasets/builder.py", line 546, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ksjae/.local/lib/python3.7/site-packages/datasets/builder.py", line 888, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/home/ksjae/.local/lib/python3.7/site-packages/tqdm/std.py", line 1129, in __iter__
for obj in iterable:
File "/home/ksjae/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py", line 104, in _generate_tables
convert_options=self.config.convert_options,
File "pyarrow/_csv.pyx", line 714, in pyarrow._csv.read_csv
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
```
**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**
It gives the same message for both 200MB, 10GB .tx files but not for 700MB file.
Can't upload due to size & copyright problem. sorry. | {
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"Seems like it works when setting ```block_size=2100000000``` or something arbitrarily large though.",
"Can you give us some stats on the data files you use as inputs?",
"Basically ~600MB txt files(UTF-8) * 59. \r\ncontents like ```안녕하세요, 이것은 예제로 한번 말해보는 텍스트입니다. 그냥 이렇다고요.<|endoftext|>\\n```\r\n\r\nAlso, it gets... |
https://api.github.com/repos/huggingface/datasets/issues/1983 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1983/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1983/comments | https://api.github.com/repos/huggingface/datasets/issues/1983/events | https://github.com/huggingface/datasets/issues/1983 | 821,746,008 | MDU6SXNzdWU4MjE3NDYwMDg= | 1,983 | The size of CoNLL-2003 is not consistant with the official release. | [] | closed | false | null | 4 | 2021-03-04T04:41:34Z | 2022-10-05T13:13:26Z | 2022-10-05T13:13:26Z | null | Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.
The statistics of conll-2003 in this repo is :
\#train 14041 \#dev 3250 \#test 3453
While the official statistics is:
\#train 14987 \#dev 3466 \#test 3684
Wish for your reply~ | {
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"Hi,\r\n\r\nif you inspect the raw data, you can find there are 946 occurrences of `-DOCSTART- -X- -X- O` in the train split and `14041 + 946 = 14987`, which is exactly the number of sentences the authors report. `-DOCSTART-` is a special line that acts as a boundary between two different documents and is filtered ... |
https://api.github.com/repos/huggingface/datasets/issues/5894 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5894/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5894/comments | https://api.github.com/repos/huggingface/datasets/issues/5894/events | https://github.com/huggingface/datasets/pull/5894 | 1,724,774,910 | PR_kwDODunzps5RSjot | 5,894 | Force overwrite existing filesystem protocol | [] | closed | false | null | 2 | 2023-05-24T21:41:53Z | 2023-05-25T06:52:08Z | 2023-05-25T06:42:33Z | null | Fix #5876 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/3583 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3583/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3583/comments | https://api.github.com/repos/huggingface/datasets/issues/3583/events | https://github.com/huggingface/datasets/issues/3583 | 1,105,195,144 | I_kwDODunzps5B3_CI | 3,583 | Add The Medical Segmentation Decathlon Dataset | [
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"color": "bfdadc",... | open | false | null | 5 | 2022-01-16T21:42:25Z | 2022-03-18T10:44:42Z | null | null | ## Adding a Dataset
- **Name:** *The Medical Segmentation Decathlon Dataset*
- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects.
- **Paper:** [link to the dataset paper if available](https://arxiv.org/abs/2106.05735)
- **Data:** http://medicaldecathlon.com/
- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.
(cc @osanseviero @abidlabs )
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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"Hello! I have recently been involved with a medical image segmentation project myself and was going through the `The Medical Segmentation Decathlon Dataset` as well. \r\nI haven't yet had experience adding datasets to this repository yet but would love to get started. Should I take this issue?\r\nIf yes, I've got ... |
https://api.github.com/repos/huggingface/datasets/issues/817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/817/comments | https://api.github.com/repos/huggingface/datasets/issues/817/events | https://github.com/huggingface/datasets/issues/817 | 739,145,369 | MDU6SXNzdWU3MzkxNDUzNjk= | 817 | Add MRQA dataset | [
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] | closed | false | null | 1 | 2020-11-09T15:52:19Z | 2020-12-04T15:44:42Z | 2020-12-04T15:44:41Z | null | ## Adding a Dataset
- **Name:** MRQA
- **Description:** Collection of different (subsets of) QA datasets all converted to the same format to evaluate out-of-domain generalization (the datasets come from different domains, distributions, etc.). Some datasets are used for training and others are used for evaluation. This dataset was collected as part of MRQA 2019's shared task
- **Paper:** https://arxiv.org/abs/1910.09753
- **Data:** https://github.com/mrqa/MRQA-Shared-Task-2019
- **Motivation:** Out-of-domain generalization is becoming (has become) a de-factor evaluation for NLU systems
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html). | {
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"Done! cf #1117 and #1022"
] |
https://api.github.com/repos/huggingface/datasets/issues/915 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/915/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/915/comments | https://api.github.com/repos/huggingface/datasets/issues/915/events | https://github.com/huggingface/datasets/issues/915 | 753,118,481 | MDU6SXNzdWU3NTMxMTg0ODE= | 915 | Shall we change the hashing to encoding to reduce potential replicated cache files? | [
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"default": fals... | open | false | null | 2 | 2020-11-30T03:50:46Z | 2020-12-24T05:11:49Z | null | null | Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.
If you have interest in this, I'd love to help :). | {
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"This is an interesting idea !\r\nDo you have ideas about how to approach the decoding and the normalization ?",
"@lhoestq\r\nI think we first need to save the transformation chain to a list in `self._fingerprint`. Then we can\r\n- decode all the current saved datasets to see if there is already one that is equiv... |
https://api.github.com/repos/huggingface/datasets/issues/4327 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4327/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4327/comments | https://api.github.com/repos/huggingface/datasets/issues/4327/events | https://github.com/huggingface/datasets/issues/4327 | 1,233,840,020 | I_kwDODunzps5JiueU | 4,327 | `wikipedia` pre-processed datasets | [
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] | closed | false | null | 2 | 2022-05-12T11:25:42Z | 2022-08-31T08:26:57Z | 2022-08-31T08:26:57Z | null | ## Describe the bug
[Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", "20220301.en")
```
## Expected results
To load the dataset
## Actual results
Takes a very long time to load (after downloading)
After `Downloading data files: 100%`. It takes hours and gets killed.
Tried `wikipedia.simple` and it got processed after ~30mins. | {
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"Hi @vpj, thanks for reporting.\r\n\r\nI'm sorry, but I can't reproduce your bug: I load \"20220301.simple\"in 9 seconds:\r\n```shell\r\ntime python -c \"from datasets import load_dataset; load_dataset('wikipedia', '20220301.simple')\"\r\n\r\nDownloading and preparing dataset wikipedia/20220301.simple (download: 22... |
https://api.github.com/repos/huggingface/datasets/issues/1707 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1707/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1707/comments | https://api.github.com/repos/huggingface/datasets/issues/1707/events | https://github.com/huggingface/datasets/pull/1707 | 781,507,545 | MDExOlB1bGxSZXF1ZXN0NTUxMjE5MDk2 | 1,707 | Added generated READMEs for datasets that were missing one. | [] | closed | false | null | 1 | 2021-01-07T18:10:06Z | 2021-01-18T14:32:33Z | 2021-01-18T14:32:33Z | null | This is it: we worked on a generator with Yacine @yjernite , and we generated dataset cards for all missing ones (161), with all the information we could gather from datasets repository, and using dummy_data to generate examples when possible.
Code is available here for the moment: https://github.com/madlag/datasets_readme_generator .
We will move it to a Hugging Face repository and to https://huggingface.co/datasets/card-creator/ later.
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"Looks like we need to trim the ones with too many configs, will look into it tomorrow!"
] |
https://api.github.com/repos/huggingface/datasets/issues/3951 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3951/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3951/comments | https://api.github.com/repos/huggingface/datasets/issues/3951/events | https://github.com/huggingface/datasets/issues/3951 | 1,171,568,814 | I_kwDODunzps5F1Liu | 3,951 | Forked streaming datasets try to `open` data urls rather than use network | [
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] | closed | false | null | 1 | 2022-03-16T21:21:02Z | 2022-06-10T20:47:26Z | 2022-06-10T20:47:26Z | null | ## Describe the bug
Building on #3950, if you bypass the pickling problem you still can't use the dataset. Somehow something gets confused and the forked processes try to `open` urls rather than anything else.
## Steps to reproduce the bug
```python
from multiprocessing import freeze_support
import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets
import torch.utils.data
# work around #3950
class TorchIterableDataset(datasets.IterableDataset, torch.utils.data.IterableDataset):
pass
def _ensure_format(v: datasets.IterableDataset) -> datasets.IterableDataset:
return TorchIterableDataset(v._ex_iterable, v.info, v.split, "torch", v._shuffling)
if __name__ == '__main__':
freeze_support()
ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
ds = _ensure_format(ds)
model = AutoModelForCausalLM.from_pretrained("distilgpt2")
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
```
## Expected results
I'd expect the dataset to load the url correctly and produce examples.
## Actual results
```
warnings.warn(
***** Running training *****
Num examples = 8000
Num Epochs = 9223372036854775807
Instantaneous batch size per device = 8
Total train batch size (w. parallel, distributed & accumulation) = 8
Gradient Accumulation steps = 1
Total optimization steps = 1000
0%| | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last):
File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 22, in <module>
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train
for step, inputs in enumerate(epoch_iterator):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
data = self._next_data()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise
raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
for key, example in self._iter():
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 494, in _iter
yield from ex_iterable
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/Users/dlwh/.cache/huggingface/modules/datasets_modules/datasets/oscar/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2/oscar.py", line 358, in _generate_examples
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/en/en_part_1.txt.gz'
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 27, in poll
pid, sts = os.waitpid(self.pid, flag)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 6932) is killed by signal: Terminated: 15.
0%| | 0/1000 [00:02<?, ?it/s]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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"Thanks for reporting this second issue as well. We definitely want to make streaming datasets fully working in a distributed setup and with the best performance. Right now it only supports single process.\r\n\r\nIn this issue it seems that the streaming capabilities that we offer to dataset builders are not transf... |
https://api.github.com/repos/huggingface/datasets/issues/3007 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3007/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3007/comments | https://api.github.com/repos/huggingface/datasets/issues/3007/events | https://github.com/huggingface/datasets/pull/3007 | 1,014,775,450 | PR_kwDODunzps4sns-n | 3,007 | Correct a typo | [] | closed | false | null | 0 | 2021-10-04T06:15:47Z | 2021-10-04T09:27:57Z | 2021-10-04T09:27:57Z | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4560 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4560/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4560/comments | https://api.github.com/repos/huggingface/datasets/issues/4560/events | https://github.com/huggingface/datasets/pull/4560 | 1,283,558,873 | PR_kwDODunzps46TY9n | 4,560 | Add evaluation metadata to imagenet-1k | [
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"_The documentation is not available anymore as the PR was closed or merged._",
"As discussed with @lewtun, we are closing this PR, because it requires first the task names to be aligned between AutoTrain and datasets."
] |
https://api.github.com/repos/huggingface/datasets/issues/2282 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2282/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2282/comments | https://api.github.com/repos/huggingface/datasets/issues/2282/events | https://github.com/huggingface/datasets/pull/2282 | 870,900,332 | MDExOlB1bGxSZXF1ZXN0NjI2MDEyMzM3 | 2,282 | Initialize imdb dataset from don't stop pretraining paper | [] | closed | false | null | 0 | 2021-04-29T11:17:56Z | 2021-04-29T11:43:51Z | 2021-04-29T11:43:51Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4981 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4981/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4981/comments | https://api.github.com/repos/huggingface/datasets/issues/4981/events | https://github.com/huggingface/datasets/issues/4981 | 1,375,086,773 | I_kwDODunzps5R9ii1 | 4,981 | Can't create a dataset with `float16` features | [
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] | open | false | null | 7 | 2022-09-15T21:03:24Z | 2023-03-22T21:40:09Z | null | null | ## Describe the bug
I can't create a dataset with `float16` features.
I understand from the traceback that this is a `pyarrow` error, but I don't see anywhere in the `datasets` documentation about how to successfully do this. Is it actually supported? I've tried older versions of `pyarrow` as well with the same exact error.
The bug seems to arise from `datasets` casting the values to `double` and then `pyarrow` doesn't know how to convert those back to `float16`... does that sound right? Is there a way to bypass this since it's not necessary in the `numpy` and `torch` cases?
Thanks!
## Steps to reproduce the bug
All of the following raise the following error with the same exact (as far as I can tell) traceback:
```python
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
```python
from datasets import Dataset, Features, Value
Dataset.from_dict({"x": [0.0, 1.0, 2.0]}, features=Features(x=Value("float16")))
import numpy as np
Dataset.from_dict({"x": np.arange(3, dtype=np.float16)}, features=Features(x=Value("float16")))
import torch
Dataset.from_dict({"x": torch.arange(3).to(torch.float16)}, features=Features(x=Value("float16")))
```
## Expected results
A dataset with `float16` features is successfully created.
## Actual results
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
Cell In [14], line 1
----> 1 Dataset.from_dict({"x": [1.0, 2.0, 3.0]}, features=Features(x=Value("float16")))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py:870, in Dataset.from_dict(cls, mapping, features, info, split)
865 mapping = features.encode_batch(mapping)
866 mapping = {
867 col: OptimizedTypedSequence(data, type=features[col] if features is not None else None, col=col)
868 for col, data in mapping.items()
869 }
--> 870 pa_table = InMemoryTable.from_pydict(mapping=mapping)
871 if info.features is None:
872 info.features = Features({col: ts.get_inferred_type() for col, ts in mapping.items()})
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:750, in InMemoryTable.from_pydict(cls, *args, **kwargs)
734 @classmethod
735 def from_pydict(cls, *args, **kwargs):
736 """
737 Construct a Table from Arrow arrays or columns
738
(...)
748 :class:`datasets.table.Table`:
749 """
--> 750 return cls(pa.Table.from_pydict(*args, **kwargs))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:3648, in pyarrow.lib.Table.from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:5174, in pyarrow.lib._from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:343, in pyarrow.lib.asarray()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:231, in pyarrow.lib.array()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py:197, in TypedSequence.__arrow_array__(self, type)
192 # otherwise we can finally use the user's type
193 elif type is not None:
194 # We use cast_array_to_feature to support casting to custom types like Audio and Image
195 # Also, when trying type "string", we don't want to convert integers or floats to "string".
196 # We only do it if trying_type is False - since this is what the user asks for.
--> 197 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
198 return out
199 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1853, in cast_array_to_feature(array, feature, allow_number_to_str)
1851 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
1852 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1853 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
1854 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1762, in array_cast(array, pa_type, allow_number_to_str)
1760 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):
1761 raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
-> 1762 return array.cast(pa_type)
1763 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:919, in pyarrow.lib.Array.cast()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/compute.py:389, in cast(arr, target_type, safe, options)
387 else:
388 options = CastOptions.safe(target_type)
--> 389 return call_function("cast", [arr], options)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:560, in pyarrow._compute.call_function()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:355, in pyarrow._compute.Function.call()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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"Hi @dconathan, thanks for reporting.\r\n\r\nWe rely on Arrow as a backend, and as far as I know currently support for `float16` in Arrow is not fully implemented in Python (C++), hence the `ArrowNotImplementedError` you get.\r\n\r\nSee, e.g.: https://arrow.apache.org/docs/status.html?highlight=float16#data-types",... |
https://api.github.com/repos/huggingface/datasets/issues/3888 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3888/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3888/comments | https://api.github.com/repos/huggingface/datasets/issues/3888/events | https://github.com/huggingface/datasets/issues/3888 | 1,165,435,529 | I_kwDODunzps5FdyKJ | 3,888 | IterableDataset columns and feature types | [
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"color": "... | open | false | null | 8 | 2022-03-10T16:19:12Z | 2022-11-29T11:39:24Z | null | null | Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None`
However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models.
Here are a few cases that lead to `features` being `None`:
1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset
2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map`
3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map`
Things we can consider, for each point above:
1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features`
2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API)
2.b prefetch the first output value to infer the type
3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones
The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user
cc @mariosasko @albertvillanova | {
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"#self-assign",
"@alvarobartt I've assigned you the issue since I'm not actively working on it.",
"Cool thanks @mariosasko I'll try to fix it in the upcoming days, thanks!",
"@lhoestq so in order to address what’s not completed in this issue, do you think it makes sense to add a param `features` to `IterableD... |
https://api.github.com/repos/huggingface/datasets/issues/3562 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3562/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3562/comments | https://api.github.com/repos/huggingface/datasets/issues/3562/events | https://github.com/huggingface/datasets/pull/3562 | 1,098,341,351 | PR_kwDODunzps4wwa44 | 3,562 | Allow multiple task templates of the same type | [] | closed | false | null | 0 | 2022-01-10T20:32:07Z | 2022-01-11T14:16:47Z | 2022-01-11T14:16:47Z | null | Add support for multiple task templates of the same type. Fixes (partially) #2520.
CC: @lewtun | {
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https://api.github.com/repos/huggingface/datasets/issues/6030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6030/comments | https://api.github.com/repos/huggingface/datasets/issues/6030/events | https://github.com/huggingface/datasets/pull/6030 | 1,803,864,744 | PR_kwDODunzps5Vd0ZG | 6,030 | fixed typo in comment | [] | closed | false | null | 2 | 2023-07-13T22:49:57Z | 2023-07-14T14:21:58Z | 2023-07-14T14:13:38Z | null | This mistake was a bit confusing, so I thought it was worth sending a PR over. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/2896 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2896/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2896/comments | https://api.github.com/repos/huggingface/datasets/issues/2896/events | https://github.com/huggingface/datasets/pull/2896 | 993,613,113 | MDExOlB1bGxSZXF1ZXN0NzMxNzcwMTE3 | 2,896 | add multi-proc in `to_csv` | [] | closed | false | null | 2 | 2021-09-10T21:35:09Z | 2021-10-28T05:47:33Z | 2021-10-26T16:00:42Z | null | This PR extends the multi-proc method used in #2747 for`to_json` to `to_csv` as well.
Results on my machine post benchmarking on `ascent_kb` dataset (giving ~45% improvement when compared to num_proc = 1):
```
Time taken on 1 num_proc, 10000 batch_size 674.2055702209473
Time taken on 4 num_proc, 10000 batch_size 425.6553490161896
Time taken on 1 num_proc, 50000 batch_size 623.5897650718689
Time taken on 4 num_proc, 50000 batch_size 380.0402421951294
Time taken on 4 num_proc, 100000 batch_size 361.7168130874634
```
This is a WIP as writing tests is pending for this PR.
I'm also exploring [this](https://arrow.apache.org/docs/python/csv.html#incremental-writing) approach for which I'm using `pyarrow-5.0.0`.
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"I think you can just add a test `test_dataset_to_csv_multiproc` in `tests/io/test_csv.py` and we'll be good",
"Hi @lhoestq, \r\nI've added `test_dataset_to_csv` apart from `test_dataset_to_csv_multiproc` as no test was there to check generated CSV file when `num_proc=1`. Please let me know if anything is also re... |
https://api.github.com/repos/huggingface/datasets/issues/4350 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4350/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4350/comments | https://api.github.com/repos/huggingface/datasets/issues/4350/events | https://github.com/huggingface/datasets/pull/4350 | 1,235,505,104 | PR_kwDODunzps43zKIV | 4,350 | Add a new metric: CTC_Consistency | [] | closed | false | null | 1 | 2022-05-13T17:31:19Z | 2022-05-19T10:23:04Z | 2022-05-19T10:23:03Z | null | Add CTC_Consistency metric
Do I also need to modify the `test_metric_common.py` file to make it run on test? | {
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"Thanks for your contribution, @YEdenZ.\r\n\r\nPlease note that our old `metrics` module is in the process of being incorporated to a separate library called `evaluate`: https://github.com/huggingface/evaluate\r\n\r\nTherefore, I would ask you to transfer your PR to that repository. Thank you."
] |
https://api.github.com/repos/huggingface/datasets/issues/5413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5413/comments | https://api.github.com/repos/huggingface/datasets/issues/5413/events | https://github.com/huggingface/datasets/issues/5413 | 1,524,591,837 | I_kwDODunzps5a32zd | 5,413 | concatenate_datasets fails when two dataset with shards > 1 and unequal shard numbers | [] | closed | false | null | 1 | 2023-01-08T17:01:52Z | 2023-01-26T09:27:21Z | 2023-01-26T09:27:21Z | null | ### Describe the bug
When using `concatenate_datasets([dataset1, dataset2], axis = 1)` to concatenate two datasets with shards > 1, it fails:
```
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/combine.py", line 182, in concatenate_datasets
return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5499, in _concatenate_map_style_datasets
table = concat_tables([dset._data for dset in dsets], axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1778, in concat_tables
return ConcatenationTable.from_tables(tables, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1483, in from_tables
blocks = _extend_blocks(blocks, table_blocks, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1477, in _extend_blocks
result[i].extend(row_blocks)
IndexError: list index out of range
```
### Steps to reproduce the bug
dataset = concatenate_datasets([dataset1, dataset2], axis = 1)
### Expected behavior
The datasets are correctly concatenated.
### Environment info
datasets==2.8.0 | {
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"Hi ! Thanks for reporting :)\r\n\r\nI managed to reproduce the hub using\r\n```python\r\n\r\nfrom datasets import concatenate_datasets, Dataset, load_from_disk\r\n\r\nDataset.from_dict({\"a\": range(9)}).save_to_disk(\"tmp/ds1\")\r\nds1 = load_from_disk(\"tmp/ds1\")\r\nds1 = concatenate_datasets([ds1, ds1])\r\n\r\... |
https://api.github.com/repos/huggingface/datasets/issues/1797 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1797/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1797/comments | https://api.github.com/repos/huggingface/datasets/issues/1797/events | https://github.com/huggingface/datasets/issues/1797 | 797,357,901 | MDU6SXNzdWU3OTczNTc5MDE= | 1,797 | Connection error | [] | closed | false | null | 1 | 2021-01-30T07:32:45Z | 2021-08-04T18:09:37Z | 2021-08-04T18:09:37Z | null | Hi
I am hitting to the error, help me and thanks.
`train_data = datasets.load_dataset("xsum", split="train")`
`ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/xsum/xsum.py` | {
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"Hi ! For future references let me add a link to our discussion here : https://github.com/huggingface/datasets/issues/759#issuecomment-770684693\r\n\r\nLet me know if you manage to fix your proxy issue or if we can do something on our end to help you :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/4800 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4800/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4800/comments | https://api.github.com/repos/huggingface/datasets/issues/4800/events | https://github.com/huggingface/datasets/pull/4800 | 1,331,288,128 | PR_kwDODunzps48yIss | 4,800 | support LargeListArray in pyarrow | [] | open | false | null | 17 | 2022-08-08T03:58:46Z | 2022-10-20T16:34:04Z | null | null | ```python
import numpy as np
import datasets
a = np.zeros((5000000, 768))
res = datasets.Dataset.from_dict({'embedding': a})
'''
File '/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/arrow_writer.py', line 178, in __arrow_array__
out = numpy_to_pyarrow_listarray(data)
File "/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/features/features.py", line 1173, in numpy_to_pyarrow_listarray
offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32())
File "pyarrow/array.pxi", line 312, in pyarrow.lib.array
File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Integer value 2147483904 not in range: -2147483648 to 2147483647
'''
```
Loading a large numpy array currently raises the error above as the type of offsets is `int32`.
And pyarrow has supported [LargeListArray](https://arrow.apache.org/docs/python/generated/pyarrow.LargeListArray.html) for this case.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4800). All of your documentation changes will be reflected on that endpoint.",
"Hi, thanks for working on this! Can you run `make style` at the repo root to fix the code quality error in CI and add a test?",
"Hi, I have fixed... |
https://api.github.com/repos/huggingface/datasets/issues/4184 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4184/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4184/comments | https://api.github.com/repos/huggingface/datasets/issues/4184/events | https://github.com/huggingface/datasets/pull/4184 | 1,208,592,669 | PR_kwDODunzps42cB2j | 4,184 | [Librispeech] Add 'all' config | [] | closed | false | null | 27 | 2022-04-19T16:27:56Z | 2022-08-29T06:35:57Z | 2022-04-22T09:45:17Z | null | Add `"all"` config to Librispeech
Closed #4179 | {
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"Fix https://github.com/huggingface/datasets/issues/4179",
"_The documentation is not available anymore as the PR was closed or merged._",
"Just that I understand: With this change, simply doing `load_dataset(\"librispeech_asr\")` is possible and returns the whole dataset?\r\n\r\nAnd to get the subsets, I do st... |
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/1446 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1446/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1446/comments | https://api.github.com/repos/huggingface/datasets/issues/1446/events | https://github.com/huggingface/datasets/pull/1446 | 761,060,323 | MDExOlB1bGxSZXF1ZXN0NTM1Nzg1NDk1 | 1,446 | Add Bing Coronavirus Query Set | [] | closed | false | null | 0 | 2020-12-10T09:20:46Z | 2020-12-11T17:03:08Z | 2020-12-11T17:03:07Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5155 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5155/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5155/comments | https://api.github.com/repos/huggingface/datasets/issues/5155/events | https://github.com/huggingface/datasets/pull/5155 | 1,421,278,748 | PR_kwDODunzps5BcCYr | 5,155 | TextConfig: added "errors" | [] | closed | false | null | 3 | 2022-10-24T18:56:52Z | 2022-11-03T13:38:13Z | 2022-11-03T13:35:35Z | null | This patch adds the ability to set the `errors` option of `open` for loading text datasets. I needed it because some data I had scraped had bad bytes in it, so I needed `errors='ignore'`. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for adding this ! You can fix the CI by formatting your code using the `make style` command :)",
"[**@lhoestq**](https://github.com/lhoestq) commented on [Oct 27, 2022, 4:08 PM GMT+3:30](https://github.com/huggingface/datase... |
https://api.github.com/repos/huggingface/datasets/issues/2171 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2171/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2171/comments | https://api.github.com/repos/huggingface/datasets/issues/2171/events | https://github.com/huggingface/datasets/pull/2171 | 851,090,662 | MDExOlB1bGxSZXF1ZXN0NjA5NTY4MDcw | 2,171 | Fixed the link to wikiauto training data. | [] | closed | false | null | 3 | 2021-04-06T07:13:11Z | 2021-04-06T16:05:42Z | 2021-04-06T16:05:09Z | null | {
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"Also you can ignore the CI failing on `docs`, this has been fixed on master :)",
"@lhoestq I need to update other stuff on GEM later today too, so will merge this one and remove columns in the next PR!",
"Ok !"
] | |
https://api.github.com/repos/huggingface/datasets/issues/1357 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1357/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1357/comments | https://api.github.com/repos/huggingface/datasets/issues/1357/events | https://github.com/huggingface/datasets/pull/1357 | 760,023,525 | MDExOlB1bGxSZXF1ZXN0NTM0OTIzMzA4 | 1,357 | Youtube caption corrections | [] | closed | false | null | 10 | 2020-12-09T05:52:34Z | 2020-12-15T18:12:56Z | 2020-12-15T18:12:56Z | null | This PR adds a new dataset of YouTube captions, error and corrections. This dataset was created in just the last week, as inspired by this sprint! | {
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"Sorry about forgetting flake8.\r\nRather than use up the circleci resources on a new push with only formatting changes, I will wait to push until the results from all tests finish and/or any feedback comes in... probably tomorrow for me.",
"\r\nSo... my normal work is with mercurial and seem to have clearly fork... |
https://api.github.com/repos/huggingface/datasets/issues/3318 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3318/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3318/comments | https://api.github.com/repos/huggingface/datasets/issues/3318/events | https://github.com/huggingface/datasets/pull/3318 | 1,062,369,717 | PR_kwDODunzps4u9m-k | 3,318 | Finish transition to PyArrow 3.0.0 | [] | closed | false | null | 0 | 2021-11-24T12:30:14Z | 2021-11-24T15:35:05Z | 2021-11-24T15:35:04Z | null | Finish transition to PyArrow 3.0.0 that was started in #3098. | {
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https://api.github.com/repos/huggingface/datasets/issues/2609 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2609/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2609/comments | https://api.github.com/repos/huggingface/datasets/issues/2609/events | https://github.com/huggingface/datasets/pull/2609 | 939,616,682 | MDExOlB1bGxSZXF1ZXN0Njg1ODA3MTMz | 2,609 | Fix potential DuplicatedKeysError | [] | closed | false | {
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} | 1 | 2021-07-08T08:38:04Z | 2021-07-12T14:13:16Z | 2021-07-09T16:42:08Z | null | 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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"Finally, I'm splitting this PR."
] |
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"@lhoestq Thank you for suggesting changes. I fixed all the changes you suggested. Can you please review it again? ",
"@lhoestq Thank you for reviewing and suggesting changes. I made the requested changes. Can you please review it again?",
"Thanks @lhoestq for reviewing it again. I made the required changes. Ca... |
https://api.github.com/repos/huggingface/datasets/issues/1089 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1089/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1089/comments | https://api.github.com/repos/huggingface/datasets/issues/1089/events | https://github.com/huggingface/datasets/pull/1089 | 756,823,690 | MDExOlB1bGxSZXF1ZXN0NTMyMzA0MDM2 | 1,089 | add sharc_modified | [] | closed | false | null | 0 | 2020-12-04T05:49:49Z | 2020-12-04T10:41:30Z | 2020-12-04T10:31:44Z | null | Adding modified ShARC dataset https://github.com/nikhilweee/neural-conv-qa | {
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https://api.github.com/repos/huggingface/datasets/issues/2876 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2876/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2876/comments | https://api.github.com/repos/huggingface/datasets/issues/2876/events | https://github.com/huggingface/datasets/pull/2876 | 990,001,079 | MDExOlB1bGxSZXF1ZXN0NzI4NjU3MDc2 | 2,876 | Extend support for streaming datasets that use pathlib.Path.glob | [] | closed | false | null | 2 | 2021-09-07T13:43:45Z | 2021-09-10T09:50:49Z | 2021-09-10T09:50:48Z | null | This PR extends the support in streaming mode for datasets that use `pathlib`, by patching the method `pathlib.Path.glob`.
Related to #2874, #2866.
CC: @severo | {
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"I am thinking that ideally we should call `fs.glob()` instead...",
"Thanks, @lhoestq: the idea of adding the mock filesystem is to avoid network calls and reduce testing time ;) \r\n\r\nI have added `rglob` as well and fixed some bugs."
] |
https://api.github.com/repos/huggingface/datasets/issues/5994 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5994/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5994/comments | https://api.github.com/repos/huggingface/datasets/issues/5994/events | https://github.com/huggingface/datasets/pull/5994 | 1,776,829,004 | PR_kwDODunzps5UB1cA | 5,994 | Fix select_columns columns order | [] | closed | false | null | 4 | 2023-06-27T12:32:46Z | 2023-06-27T15:40:47Z | 2023-06-27T15:32:43Z | null | Fix the order of the columns in dataset.features when the order changes with `dataset.select_columns()`.
I also fixed the same issue for `dataset.flatten()`
Close https://github.com/huggingface/datasets/issues/5993 | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... |
https://api.github.com/repos/huggingface/datasets/issues/2984 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2984/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2984/comments | https://api.github.com/repos/huggingface/datasets/issues/2984/events | https://github.com/huggingface/datasets/issues/2984 | 1,010,484,326 | I_kwDODunzps48OsRm | 2,984 | Exceeded maximum rows when reading large files | [
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] | closed | false | null | 1 | 2021-09-29T04:49:22Z | 2021-10-12T06:05:42Z | 2021-10-12T06:05:42Z | null | ## Describe the bug
A clear and concise description of what the bug is.
When using `load_dataset` with json files, if the files are too large, there will be "Exceeded maximum rows" error.
## Steps to reproduce the bug
```python
dataset = load_dataset('json', data_files=data_files) # data files have 3M rows in a single file
```
## Expected results
No error
## Actual results
```
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
134 with open(file, encoding="utf-8") as f:
--> 135 dataset = json.load(f)
136 except json.JSONDecodeError:
~/anaconda3/envs/python/lib/python3.9/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
292 """
--> 293 return loads(fp.read(),
294 cls=cls, object_hook=object_hook,
~/anaconda3/envs/python/lib/python3.9/json/__init__.py in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
345 parse_constant is None and object_pairs_hook is None and not kw):
--> 346 return _default_decoder.decode(s)
347 if cls is None:
~/anaconda3/envs/python/lib/python3.9/json/decoder.py in decode(self, s, _w)
339 if end != len(s):
--> 340 raise JSONDecodeError("Extra data", s, end)
341 return obj
JSONDecodeError: Extra data: line 2 column 1 (char 20321)
During handling of the above exception, another exception occurred:
ArrowInvalid Traceback (most recent call last)
<ipython-input-20-ab3718a6482f> in <module>
----> 1 dataset = load_dataset('json', data_files=data_files)
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
841
842 # Download and prepare data
--> 843 builder_instance.download_and_prepare(
844 download_config=download_config,
845 download_mode=download_mode,
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
606 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
607 if not downloaded_from_gcs:
--> 608 self._download_and_prepare(
609 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
610 )
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
684 try:
685 # Prepare split will record examples associated to the split
--> 686 self._prepare_split(split_generator, **prepare_split_kwargs)
687 except OSError as e:
688 raise OSError(
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _prepare_split(self, split_generator)
1153 generator = self._generate_tables(**split_generator.gen_kwargs)
1154 with ArrowWriter(features=self.info.features, path=fpath) as writer:
-> 1155 for key, table in utils.tqdm(
1156 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)
1157 ):
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
135 dataset = json.load(f)
136 except json.JSONDecodeError:
--> 137 raise e
138 raise ValueError(
139 f"Not able to read records in the JSON file at {file}. "
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
114 while True:
115 try:
--> 116 pa_table = paj.read_json(
117 BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
118 )
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Exceeded maximum rows
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: 3.9
- PyArrow version: 4.0.1
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"Hi @zijwang, thanks for reporting this issue.\r\n\r\nYou did not mention which `datasets` version you are using, but looking at the code in the stack trace, it seems you are using an old version.\r\n\r\nCould you please update `datasets` (`pip install -U datasets`) and check if the problem persists?"
] |
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 | 3 | 2021-07-29T15:59:59Z | 2021-08-07T16:19:19Z | 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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"cc @patrickvonplaten?",
"Does anybody know if there is a bundled link, which would allow direct data download instead of manual? \r\nSomething similar to: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ab.tar.gz` ? cc @patil-suraj \r\n",
"Also see... |
https://api.github.com/repos/huggingface/datasets/issues/2806 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2806/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2806/comments | https://api.github.com/repos/huggingface/datasets/issues/2806/events | https://github.com/huggingface/datasets/pull/2806 | 971,625,449 | MDExOlB1bGxSZXF1ZXN0NzEzMzM5NDUw | 2,806 | Fix streaming tar files from canonical datasets | [] | closed | false | null | 5 | 2021-08-16T11:10:28Z | 2021-10-13T09:04:03Z | 2021-10-13T09:04:02Z | null | Previous PR #2800 implemented support to stream remote tar files when passing the parameter `data_files`: they required a glob string `"*"`.
However, this glob string creates an error when streaming canonical datasets (with a `join` after the `open`).
This PR fixes this issue and allows streaming tar files both from:
- canonical datasets scripts and
- data files.
This PR also adds support for compressed tar files: `.tar.gz`, `.tar.bz2`,...
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"In case it's relevant for this PR, I'm finding that I cannot stream the `bookcorpus` dataset (using the `master` branch of `datasets`), which is a `.tar.bz2` file:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nbooks_dataset_streamed = load_dataset(\"bookcorpus\", split=\"train\", streaming=True)\r\n... |
https://api.github.com/repos/huggingface/datasets/issues/1488 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1488/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1488/comments | https://api.github.com/repos/huggingface/datasets/issues/1488/events | https://github.com/huggingface/datasets/pull/1488 | 762,860,679 | MDExOlB1bGxSZXF1ZXN0NTM3MzY1ODUz | 1,488 | Adding NELL | [] | closed | false | null | 2 | 2020-12-11T20:25:25Z | 2021-01-07T08:37:07Z | 2020-12-21T14:45:00Z | null | NELL is a knowledge base and knowledge graph along with sentences used to create the KB. See http://rtw.ml.cmu.edu/rtw/ for more details. | {
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"hi @lhoestq, I wanted to push another change to this branch b/c I found a bug in the parsing. I need to swap arg1 and arg2. I tried to git push -u origin nell but it didn't work. So I tried to do git push --force -u origin nell which seems to work, but nothing is happening to this branch. I think this is because i... |
https://api.github.com/repos/huggingface/datasets/issues/1333 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1333/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1333/comments | https://api.github.com/repos/huggingface/datasets/issues/1333/events | https://github.com/huggingface/datasets/pull/1333 | 759,687,836 | MDExOlB1bGxSZXF1ZXN0NTM0NjQ4OTI4 | 1,333 | Add Tanzil Dataset | [] | closed | false | null | 0 | 2020-12-08T18:45:15Z | 2020-12-10T11:17:56Z | 2020-12-10T11:14:43Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/855 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/855/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/855/comments | https://api.github.com/repos/huggingface/datasets/issues/855/events | https://github.com/huggingface/datasets/pull/855 | 743,690,839 | MDExOlB1bGxSZXF1ZXN0NTIxNTQ2Njkx | 855 | Fix kor nli csv reader | [] | closed | false | null | 0 | 2020-11-16T09:53:41Z | 2020-11-16T13:59:14Z | 2020-11-16T13:59:12Z | null | The kor_nli dataset had an issue with the csv reader that was not able to parse the lines correctly. Some lines were merged together for some reason.
I fixed that by iterating through the lines directly instead of using a csv reader.
I also changed the feature names to match the other NLI datasets (i.e. use "premise", "hypothesis", "label" features)
Fix #821 | {
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https://api.github.com/repos/huggingface/datasets/issues/2847 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2847/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2847/comments | https://api.github.com/repos/huggingface/datasets/issues/2847/events | https://github.com/huggingface/datasets/pull/2847 | 981,589,693 | MDExOlB1bGxSZXF1ZXN0NzIxNjA3OTA0 | 2,847 | fix regex to accept negative timezone | [] | closed | false | null | 0 | 2021-08-27T20:54:05Z | 2021-09-13T20:39:50Z | 2021-09-07T09:34:23Z | null | fix #2846 | {
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https://api.github.com/repos/huggingface/datasets/issues/773 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/773/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/773/comments | https://api.github.com/repos/huggingface/datasets/issues/773/events | https://github.com/huggingface/datasets/issues/773 | 731,684,153 | MDU6SXNzdWU3MzE2ODQxNTM= | 773 | Adding CC-100: Monolingual Datasets from Web Crawl Data | [
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] | closed | false | null | 4 | 2020-10-28T18:20:41Z | 2022-01-26T13:22:54Z | 2020-12-14T10:20:07Z | null | ## Adding a Dataset
- **Name:** CC-100: Monolingual Datasets from Web Crawl Data
- **Description:** https://twitter.com/alex_conneau/status/1321507120848625665
- **Paper:** https://arxiv.org/abs/1911.02116
- **Data:** http://data.statmt.org/cc-100/
- **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how "Wikipedia-like" it is, hopefully helping avoid some of the worst parts of the common crawl.
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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"cc @aconneau ;) ",
"These dataset files are no longer available. https://data.statmt.org/cc-100/ files provided in this link are no longer available. Can anybody fix that issue?\r\n@abhishekkrthakur @yjernite ",
"Hi ! Can you open an issue to report this problem ? This will help keep track of the fix :)",
"... |
https://api.github.com/repos/huggingface/datasets/issues/3337 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3337/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3337/comments | https://api.github.com/repos/huggingface/datasets/issues/3337/events | https://github.com/huggingface/datasets/issues/3337 | 1,066,232,936 | I_kwDODunzps4_jWxo | 3,337 | Typing of Dataset.__getitem__ could be improved. | [
{
"color": "d73a4a",
"default": true,
"description": "Something isn't working",
"id": 1935892857,
"name": "bug",
"node_id": "MDU6TGFiZWwxOTM1ODkyODU3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/bug"
}
] | closed | false | null | 2 | 2021-11-29T16:20:11Z | 2021-12-14T10:28:54Z | 2021-12-14T10:28:54Z | null | ## Describe the bug
The newly added typing for Dataset.__getitem__ is Union[Dict, List]. This makes tools like mypy a bit awkward to use as we need to check the type manually. We could use type overloading to make this easier. [Documentation](https://docs.python.org/3/library/typing.html#typing.overload)
## Steps to reproduce the bug
Let's have a file `test.py`
```python
from typing import List, Dict, Any
from datasets import Dataset
ds = Dataset.from_dict({
'a': [1,2,3],
'b': ["1", "2", "3"]
})
one_colum: List[str] = ds['a']
some_index: Dict[Any, Any] = ds[1]
```
## Expected results
Running `mypy test.py` should not give any error.
## Actual results
```
test.py:10: error: Incompatible types in assignment (expression has type "Union[Dict[Any, Any], List[Any]]", variable has type "List[str]")
test.py:11: error: Incompatible types in assignment (expression has type "Union[Dict[Any, Any], List[Any]]", variable has type "Dict[Any, Any]")
Found 2 errors in 1 file (checked 1 source file)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.1
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} | https://api.github.com/repos/huggingface/datasets/issues/3337/timeline | null | completed | null | null | false | [
"Hi ! Thanks for the suggestion, I didn't know about this decorator.\r\n\r\nIf you are interesting in contributing, feel free to open a pull request to add the overload methods for each typing combination :) To assign you to this issue, you can comment `#self-assign` in this thread.\r\n\r\n`Dataset.__getitem__` is ... |
https://api.github.com/repos/huggingface/datasets/issues/6 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6/comments | https://api.github.com/repos/huggingface/datasets/issues/6/events | https://github.com/huggingface/datasets/issues/6 | 600,330,836 | MDU6SXNzdWU2MDAzMzA4MzY= | 6 | Error when citation is not given in the DatasetInfo | [] | closed | false | null | 3 | 2020-04-15T14:14:54Z | 2020-04-29T09:23:22Z | 2020-04-29T09:23:22Z | null | The following error is raised when the `citation` parameter is missing when we instantiate a `DatasetInfo`:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/dev/jplu/datasets/src/nlp/info.py", line 338, in __repr__
citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
AttributeError: 'NoneType' object has no attribute 'strip'
```
I propose to do the following change in the `info.py` file. The method:
```python
def __repr__(self):
splits_pprint = _indent("\n".join(["{"] + [
" '{}': {},".format(k, split.num_examples)
for k, split in sorted(self.splits.items())
] + ["}"]))
features_pprint = _indent(repr(self.features))
citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
return INFO_STR.format(
name=self.name,
version=self.version,
description=self.description,
total_num_examples=self.splits.total_num_examples,
features=features_pprint,
splits=splits_pprint,
citation=citation_pprint,
homepage=self.homepage,
supervised_keys=self.supervised_keys,
# Proto add a \n that we strip.
license=str(self.license).strip())
```
Becomes:
```python
def __repr__(self):
splits_pprint = _indent("\n".join(["{"] + [
" '{}': {},".format(k, split.num_examples)
for k, split in sorted(self.splits.items())
] + ["}"]))
features_pprint = _indent(repr(self.features))
## the strip is done only is the citation is given
citation_pprint = self.citation
if self.citation:
citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
return INFO_STR.format(
name=self.name,
version=self.version,
description=self.description,
total_num_examples=self.splits.total_num_examples,
features=features_pprint,
splits=splits_pprint,
citation=citation_pprint,
homepage=self.homepage,
supervised_keys=self.supervised_keys,
# Proto add a \n that we strip.
license=str(self.license).strip())
```
And now it is ok. @thomwolf are you ok with this fix? | {
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"Yes looks good to me.\r\nNote that we may refactor quite strongly the `info.py` to make it a lot simpler (it's very complicated for basically a dictionary of info I think)",
"No, problem ^^ It might just be a temporary fix :)",
"Fixed."
] |
https://api.github.com/repos/huggingface/datasets/issues/2748 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2748/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2748/comments | https://api.github.com/repos/huggingface/datasets/issues/2748/events | https://github.com/huggingface/datasets/pull/2748 | 958,889,041 | MDExOlB1bGxSZXF1ZXN0NzAyMDg4NTk4 | 2,748 | Generate metadata JSON for wikihow dataset | [] | closed | false | null | 0 | 2021-08-03T08:55:40Z | 2021-08-03T10:17:51Z | 2021-08-03T10:17:51Z | null | Related to #2743. | {
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https://api.github.com/repos/huggingface/datasets/issues/1205 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1205/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1205/comments | https://api.github.com/repos/huggingface/datasets/issues/1205/events | https://github.com/huggingface/datasets/pull/1205 | 757,942,403 | MDExOlB1bGxSZXF1ZXN0NTMzMjA4NDI1 | 1,205 | add lst20 with manual download | [] | closed | false | null | 2 | 2020-12-06T14:49:10Z | 2020-12-09T16:33:10Z | 2020-12-09T16:33:10Z | null | passed on local:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_lst20
```
Not sure how to test:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lst20
```
```
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
``` | {
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"The pytest suite doesn't allow manual downloads so we just make sure that the `datasets-cli test` command to run without errors instead",
"@lhoestq Changes made. Thank you for the review. I've made some same mistakes for https://github.com/huggingface/datasets/pull/1253 too. Will fix them before review."
] |
https://api.github.com/repos/huggingface/datasets/issues/5883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5883/comments | https://api.github.com/repos/huggingface/datasets/issues/5883/events | https://github.com/huggingface/datasets/pull/5883 | 1,719,527,597 | PR_kwDODunzps5RAkYi | 5,883 | Fix string-encoding, make `batch_size` optional, and minor improvements in `Dataset.to_tf_dataset` | [] | closed | false | null | 29 | 2023-05-22T11:51:07Z | 2023-06-08T11:09:03Z | 2023-06-06T16:49:15Z | null | ## What's in this PR?
This PR addresses some minor fixes and general improvements in the `to_tf_dataset` method of `datasets.Dataset`, to convert a 🤗HuggingFace Dataset as a TensorFlow Dataset.
The main bug solved in this PR comes with the string-encoding, since for safety purposes the internal conversion of `numpy.arrays` when `dtype` is unicode/string, is to convert it into `numpy.bytes`, more information in the docstring of https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#L210. That's triggered when using `tensorflow.numpy_function` as it's applying another type cast besides the one that `datasets` does, so the casting is applied at least twice per entry/batch. So this means that the definition of the `numpy.unicode_` dtype when the data in the batch is a string, is ignored, and replaced by `numpy.bytes_`.
Besides that, some other minor things have been fixed:
* Made `batch_size` an optional parameter in `to_tf_dataset`
* Map the `tensorflow` output dtypes just once, and not in every `tf.function` call during `map`
* Keep `numpy` formatting in the `datasets.Dataset` if already formatted like it, no need to format it again as `numpy`
* Docstring indentation in `dataset_to_tf` and `multiprocess_dataset_to_tf`
## What's missing in this PR?
I can include some integration tests if needed, to validate that `batch_size` is optional, and that the tensors in the TF-Dataset can be looped over with no issues as before. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n\r... |
https://api.github.com/repos/huggingface/datasets/issues/410 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/410/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/410/comments | https://api.github.com/repos/huggingface/datasets/issues/410/events | https://github.com/huggingface/datasets/pull/410 | 659,242,871 | MDExOlB1bGxSZXF1ZXN0NDUxMTEzMTI3 | 410 | 20newsgroup | [] | closed | false | null | 0 | 2020-07-17T13:07:57Z | 2020-07-20T07:05:29Z | 2020-07-20T07:05:28Z | null | Add 20Newsgroup dataset.
#353 | {
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https://api.github.com/repos/huggingface/datasets/issues/4835 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4835/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4835/comments | https://api.github.com/repos/huggingface/datasets/issues/4835/events | https://github.com/huggingface/datasets/pull/4835 | 1,336,994,835 | PR_kwDODunzps49FJg9 | 4,835 | Fix documentation card of ethos dataset | [] | closed | false | null | 1 | 2022-08-12T09:51:06Z | 2022-08-12T13:13:55Z | 2022-08-12T12:59:39Z | null | Fix documentation card of ethos dataset. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/6059 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6059/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6059/comments | https://api.github.com/repos/huggingface/datasets/issues/6059/events | https://github.com/huggingface/datasets/issues/6059 | 1,816,537,176 | I_kwDODunzps5sRihY | 6,059 | Provide ability to load label mappings from file | [
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] | open | false | null | 0 | 2023-07-22T02:04:19Z | 2023-07-22T02:04:19Z | null | null | ### Feature request
My task is classification of a dataset containing a large label set that includes a hierarchy. Even ignoring the hierarchy I'm not able to find an example using `datasets` where the label names aren't hard-coded. This works find for classification of a handful of labels but ideally there would be a way of loading the name/id mappings required for `datasets.features.ClassLabel` from a file.
It is possible to pass a file to ClassLabel but I cannot see an easy way of using this with `GeneratorBasedBuilder` since `self._info` is called before the `dl_manager` is constructed so even if my dataset contains say `label_mappings.json` there's no way of loading it in order to construct the `datasets.DatasetInfo`
I can see other uses to accessing the `download_manager` from `self._info` - i.e. if the files contain a schema (i.e. `arrow` or `parquet` files) the `datasets.DatasetInfo` could be inferred.
The workaround that was suggested in the forum is to generate a `.py` file from the `label_mappings.json` and import it.
```
class TestDatasetBuilder(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["label_1", "label_2"]),
}
),
task_templates=[TextClassification(text_column="text", label_column="label")],
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Generate AG News examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.DictReader(csv_file)
for id_, row in enumerate(csv_reader):
yield id_, row
```
### Motivation
Allow `datasets.DatasetInfo` to be generated based on the contents of the dataset.
### Your contribution
I'm willing to work on a PR with guidence. | {
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https://api.github.com/repos/huggingface/datasets/issues/1276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1276/comments | https://api.github.com/repos/huggingface/datasets/issues/1276/events | https://github.com/huggingface/datasets/pull/1276 | 758,965,936 | MDExOlB1bGxSZXF1ZXN0NTM0MDQyODYy | 1,276 | add One Million Posts Corpus | [] | closed | false | null | 1 | 2020-12-08T00:50:08Z | 2020-12-11T18:28:18Z | 2020-12-11T18:28:18Z | null | - **Name:** One Million Posts Corpus
- **Description:** The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language).
- **Paper:** https://dl.acm.org/doi/10.1145/3077136.3080711
- **Data:** https://github.com/OFAI/million-post-corpus
- **Motivation:** Big German (real-life) dataset containing different annotations around forum moderation with expert annotations.
### Checkbox
- [X] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [X] Fill the `_DESCRIPTION` and `_CITATION` variables
- [X] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [X] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [X] Generate the metadata file `dataset_infos.json` for all configurations
- [X] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [X] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [X] Both tests for the real data and the dummy data pass.
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"merging since the CI is fixed on master"
] |
https://api.github.com/repos/huggingface/datasets/issues/1100 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1100/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1100/comments | https://api.github.com/repos/huggingface/datasets/issues/1100/events | https://github.com/huggingface/datasets/pull/1100 | 756,998,433 | MDExOlB1bGxSZXF1ZXN0NTMyNDQ2ODc1 | 1,100 | Urdu fake news | [] | closed | false | null | 0 | 2020-12-04T10:41:20Z | 2020-12-04T11:19:00Z | 2020-12-04T11:19:00Z | null | Added Bend the Truth urdu fake news dataset. More inforation <a href="https://github.com/MaazAmjad/Datasets-for-Urdu-news">here</a>. | {
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https://api.github.com/repos/huggingface/datasets/issues/4969 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4969/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4969/comments | https://api.github.com/repos/huggingface/datasets/issues/4969/events | https://github.com/huggingface/datasets/pull/4969 | 1,369,334,740 | PR_kwDODunzps4-wPOk | 4,969 | Fix data URL and metadata of vivos dataset | [] | closed | false | null | 1 | 2022-09-12T06:12:34Z | 2022-09-12T07:16:15Z | 2022-09-12T07:14:19Z | null | After contacting the authors of the VIVOS dataset to report that their data server is down, we have received a reply from Hieu-Thi Luong that their data is now hosted on Zenodo: https://doi.org/10.5281/zenodo.7068130
This PR updates their data URL and some metadata (homepage, citation and license).
Fix #4936. | {
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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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"description": "New feature or request",
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"name": "enhancement",
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] | closed | false | null | 2 | 2021-07-16T19:41:50Z | 2021-09-13T13:56:37Z | 2021-09-13T13:56:37Z | 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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"Hi @stas00, \r\nI want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert pyarrow ... |
https://api.github.com/repos/huggingface/datasets/issues/2351 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2351/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2351/comments | https://api.github.com/repos/huggingface/datasets/issues/2351/events | https://github.com/huggingface/datasets/pull/2351 | 889,584,953 | MDExOlB1bGxSZXF1ZXN0NjQyNzI5NDIz | 2,351 | simpllify faiss index save | [] | closed | false | null | 0 | 2021-05-12T03:54:10Z | 2021-05-17T13:41:41Z | 2021-05-17T13:41:41Z | null | Fixes #2350
In some cases, Faiss GPU index objects do not have neither "device" nor "getDevice". Possibly this happens when some part of the index is computed on CPU.
In particular, this would happen with the index `OPQ16_128,IVF512,PQ32` (issue #2350). I did check it, but it is likely that `OPQ` or `PQ` transforms cause it.
I propose, instead of using the index object to get the device, to infer it form the `FaissIndex.device` field as it is done in `.add_vectors`. Here we assume that `.device` always corresponds to the index placement and it seems reasonable. | {
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https://api.github.com/repos/huggingface/datasets/issues/1268 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1268/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1268/comments | https://api.github.com/repos/huggingface/datasets/issues/1268/events | https://github.com/huggingface/datasets/pull/1268 | 758,871,252 | MDExOlB1bGxSZXF1ZXN0NTMzOTY0OTQ4 | 1,268 | new pr for Turkish NER | [] | closed | false | null | 3 | 2020-12-07T21:40:26Z | 2020-12-09T13:45:05Z | 2020-12-09T13:45:05Z | null | {
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"Can you run `make style` to fix the code format ?\r\n\r\nAlso it looks like the file `file_downloaded/TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.zip/TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.DUMP` is missing inside the dummy_data.zip\r\n\r\n\r\n(note that `TWNERTC_TC_Coarse... | |
https://api.github.com/repos/huggingface/datasets/issues/3271 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3271/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3271/comments | https://api.github.com/repos/huggingface/datasets/issues/3271/events | https://github.com/huggingface/datasets/pull/3271 | 1,053,482,919 | PR_kwDODunzps4uhgi1 | 3,271 | Decode audio from remote | [] | closed | false | null | 0 | 2021-11-15T10:25:56Z | 2021-11-16T11:35:58Z | 2021-11-16T11:35:58Z | null | Currently the Audio feature type can only decode local audio files, not remote files.
To fix this I replaced `open` with our `xopen` functoin that is compatible with remote files in audio.py
cc @albertvillanova @mariosasko | {
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https://api.github.com/repos/huggingface/datasets/issues/2725 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2725/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2725/comments | https://api.github.com/repos/huggingface/datasets/issues/2725/events | https://github.com/huggingface/datasets/pull/2725 | 955,020,776 | MDExOlB1bGxSZXF1ZXN0Njk4ODMwNjYw | 2,725 | Pass use_auth_token to request_etags | [] | closed | false | null | 0 | 2021-07-28T16:13:29Z | 2021-07-28T16:38:02Z | 2021-07-28T16:38:02Z | null | Fix #2724. | {
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https://api.github.com/repos/huggingface/datasets/issues/4663 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4663/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4663/comments | https://api.github.com/repos/huggingface/datasets/issues/4663/events | https://github.com/huggingface/datasets/pull/4663 | 1,299,298,693 | PR_kwDODunzps47H19n | 4,663 | Add text decorators | [] | closed | false | null | 1 | 2022-07-08T17:51:48Z | 2022-07-18T18:33:14Z | 2022-07-18T18:20:49Z | null | This PR adds some decoration to text about different modalities to make it more obvious separate guides exist for audio, vision, and text. The goal is to make it easier for users to discover these guides!
TODO:
- [x] Open PR to support new Tailwind classes | {
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https://api.github.com/repos/huggingface/datasets/issues/3478 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3478/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3478/comments | https://api.github.com/repos/huggingface/datasets/issues/3478/events | https://github.com/huggingface/datasets/pull/3478 | 1,087,860,180 | PR_kwDODunzps4wPMWq | 3,478 | Extend support for streaming datasets that use os.walk | [] | closed | false | null | 1 | 2021-12-23T16:42:55Z | 2021-12-24T10:50:20Z | 2021-12-24T10:50:19Z | null | This PR extends the support in streaming mode for datasets that use `os.walk`, by patching that function.
This PR adds support for streaming mode to datasets:
1. autshumato
1. code_x_glue_cd_code_to_text
1. code_x_glue_tc_nl_code_search_adv
1. nchlt
CC: @severo | {
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"Nice. I'll update the dataset viewer once merged, and test on these four datasets"
] |
https://api.github.com/repos/huggingface/datasets/issues/1429 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1429/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1429/comments | https://api.github.com/repos/huggingface/datasets/issues/1429/events | https://github.com/huggingface/datasets/pull/1429 | 760,737,818 | MDExOlB1bGxSZXF1ZXN0NTM1NTE5MjY5 | 1,429 | extract rar files | [] | closed | false | null | 0 | 2020-12-09T23:01:10Z | 2020-12-18T15:03:37Z | 2020-12-18T15:03:37Z | null | Unfortunately, I didn't find any native python libraries for extracting rar files. The user has to manually install `sudo apt-get install unrar`. Discussion with @yjernite is in the slack channel. | {
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https://api.github.com/repos/huggingface/datasets/issues/2463 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2463/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2463/comments | https://api.github.com/repos/huggingface/datasets/issues/2463/events | https://github.com/huggingface/datasets/pull/2463 | 915,454,788 | MDExOlB1bGxSZXF1ZXN0NjY1MjY3NTA2 | 2,463 | Fix proto_qa download link | [] | closed | false | null | 0 | 2021-06-08T20:23:16Z | 2021-06-10T12:49:56Z | 2021-06-10T08:31:10Z | null | Fixes #2459
Instead of updating the path, this PR fixes a commit hash as suggested by @lhoestq. | {
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https://api.github.com/repos/huggingface/datasets/issues/4174 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4174/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4174/comments | https://api.github.com/repos/huggingface/datasets/issues/4174/events | https://github.com/huggingface/datasets/pull/4174 | 1,205,575,941 | PR_kwDODunzps42SnJS | 4,174 | Fix when map function modifies input in-place | [] | closed | false | null | 1 | 2022-04-15T13:23:15Z | 2022-04-15T14:52:07Z | 2022-04-15T14:45:58Z | null | When `function` modifies input in-place, the guarantee that columns in `remove_columns` are contained in `input` doesn't hold true anymore. Therefore we need to relax way we pop elements by checking if that column exists. | {
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https://api.github.com/repos/huggingface/datasets/issues/191 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/191/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/191/comments | https://api.github.com/repos/huggingface/datasets/issues/191/events | https://github.com/huggingface/datasets/pull/191 | 624,394,936 | MDExOlB1bGxSZXF1ZXN0NDIyODI3MDMy | 191 | [Squad es] add dataset_infos | [] | closed | false | null | 0 | 2020-05-25T16:35:52Z | 2020-05-25T16:39:59Z | 2020-05-25T16:39:58Z | null | @mariamabarham - was still about to upload this. Should have waited with my comment a bit more :D | {
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https://api.github.com/repos/huggingface/datasets/issues/2143 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2143/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2143/comments | https://api.github.com/repos/huggingface/datasets/issues/2143/events | https://github.com/huggingface/datasets/pull/2143 | 844,313,228 | MDExOlB1bGxSZXF1ZXN0NjAzNTc0NjI0 | 2,143 | task casting via load_dataset | [] | closed | false | null | 0 | 2021-03-30T10:00:42Z | 2021-06-11T13:20:41Z | 2021-06-11T13:20:36Z | null | wip
not satisfied with the API, it means as a dataset implementer I need to write a function with boilerplate and write classes for each `<dataset><task>` "facet". | {
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https://api.github.com/repos/huggingface/datasets/issues/4048 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4048/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4048/comments | https://api.github.com/repos/huggingface/datasets/issues/4048/events | https://github.com/huggingface/datasets/issues/4048 | 1,183,804,576 | I_kwDODunzps5Gj2yg | 4,048 | Split size error on `amazon_us_reviews` / `PC_v1_00` dataset | [
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"descript... | closed | false | null | 3 | 2022-03-28T18:12:04Z | 2022-04-08T12:29:30Z | 2022-04-08T12:29:30Z | null | ## Describe the bug
When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m.
Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata.
Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first.
## Steps to reproduce the bug
```python
load_dataset('amazon_us_reviews', 'PC_v1_00')
```
## Expected results
Dataset is downloaded and extracted successfully.
## Actual results
An split size exception is thrown.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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"Follow-up: I have confirmed there are no duplicate lines via `sort amazon_reviews_us_PC_v1_00.tsv | uniq -cd` after extracting the raw file.",
"Hi @trentonstrong, thanks for reporting!\r\n\r\nI confirm that loading this dataset configuration throws a `NonMatchingSplitsSizesError`:\r\n```\r\nNonMatchingSplitsSize... |
https://api.github.com/repos/huggingface/datasets/issues/4512 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4512/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4512/comments | https://api.github.com/repos/huggingface/datasets/issues/4512/events | https://github.com/huggingface/datasets/pull/4512 | 1,273,378,129 | PR_kwDODunzps45xEDN | 4,512 | Add links to vision tasks scripts in ADD_NEW_DATASET template | [] | closed | false | null | 2 | 2022-06-16T10:35:35Z | 2022-07-08T14:07:50Z | 2022-07-08T13:56:23Z | null | Add links to vision dataset scripts in the ADD_NEW_DATASET template. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The CI failure is unrelated to the PR's changes. Merging."
] |
https://api.github.com/repos/huggingface/datasets/issues/3686 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3686/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3686/comments | https://api.github.com/repos/huggingface/datasets/issues/3686/events | https://github.com/huggingface/datasets/issues/3686 | 1,127,137,290 | I_kwDODunzps5DLsAK | 3,686 | `Translation` features cannot be `flatten`ed | [
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] | closed | false | null | 1 | 2022-02-08T11:33:48Z | 2022-03-18T17:28:13Z | 2022-03-18T17:28:13Z | null | ## Describe the bug
(`Dataset.flatten`)[https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L1265] fails for columns with feature (`Translation`)[https://github.com/huggingface/datasets/blob/3edbeb0ec6519b79f1119adc251a1a6b379a2c12/src/datasets/features/translation.py#L8]
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("europa_ecdc_tm", "en2fr", split="train[:10]")
print(dataset.features)
# {'translation': Translation(languages=['en', 'fr'], id=None)}
print(dataset[0])
# {'translation': {'en': 'Vaccination against hepatitis C is not yet available.', 'fr': 'Aucune vaccination contre l’hépatite C n’est encore disponible.'}}
dataset.flatten()
```
## Expected results
`dataset.flatten` should flatten the `Translation` column as if it were a dict of `Value("string")`
```python
dataset[0]
# {'translation.en': 'Vaccination against hepatitis C is not yet available.', 'translation.fr': 'Aucune vaccination contre l’hépatite C n’est encore disponible.' }
dataset.features
# {'translation.en': Value("string"), 'translation.fr': Value("string")}
```
## Actual results
```python
In [31]: dset.flatten()
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-31-bb88eb5276ee> in <module>
----> 1 dset.flatten()
[...]\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs)
411 # Call actual function
412
--> 413 out = func(self, *args, **kwargs)
414
415 # Update fingerprint of in-place transforms + update in-place history of transforms
[...]\site-packages\datasets\arrow_dataset.py in flatten(self, new_fingerprint, max_depth)
1294 break
1295 dataset.info.features = self.features.flatten(max_depth=max_depth)
-> 1296 dataset._data = update_metadata_with_features(dataset._data, dataset.features)
1297 logger.info(f'Flattened dataset from depth {depth} to depth {1 if depth + 1 < max_depth else "unknown"}.')
1298 dataset._fingerprint = new_fingerprint
[...]\site-packages\datasets\arrow_dataset.py in update_metadata_with_features(table, features)
534 def update_metadata_with_features(table: Table, features: Features):
535 """To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema."""
--> 536 features = Features({col_name: features[col_name] for col_name in table.column_names})
537 if table.schema.metadata is None or b"huggingface" not in table.schema.metadata:
538 pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features))
[...]\site-packages\datasets\arrow_dataset.py in <dictcomp>(.0)
534 def update_metadata_with_features(table: Table, features: Features):
535 """To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema."""
--> 536 features = Features({col_name: features[col_name] for col_name in table.column_names})
537 if table.schema.metadata is None or b"huggingface" not in table.schema.metadata:
538 pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features))
KeyError: 'translation.en'
```
## Environment info
- `datasets` version: 1.18.3
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.10
- PyArrow version: 3.0.0
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"Thanks for reporting, @SBrandeis! Some additional feature types that don't behave as expected when flattened: `Audio`, `Image` and `TranslationVariableLanguages`"
] |
https://api.github.com/repos/huggingface/datasets/issues/3835 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3835/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3835/comments | https://api.github.com/repos/huggingface/datasets/issues/3835/events | https://github.com/huggingface/datasets/issues/3835 | 1,161,029,205 | I_kwDODunzps5FM-ZV | 3,835 | The link given on the gigaword does not work | [
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] | closed | false | null | 0 | 2022-03-07T07:56:42Z | 2022-03-15T12:30:23Z | 2022-03-15T12:30:23Z | 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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https://api.github.com/repos/huggingface/datasets/issues/3668 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3668/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3668/comments | https://api.github.com/repos/huggingface/datasets/issues/3668/events | https://github.com/huggingface/datasets/issues/3668 | 1,122,261,736 | I_kwDODunzps5C5Fro | 3,668 | Couldn't cast array of type string error with cast_column | [
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] | closed | false | null | 5 | 2022-02-02T18:33:29Z | 2022-07-19T13:36:24Z | 2022-07-19T13:36:24Z | null | ## Describe the bug
In OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column("audio",Audio(sampling_rate=16_000))
method I get error
This was working with datasets version 1.17.1.dev0
but now with version 1.18.3 produces the error above.
## Steps to reproduce the bug
load dataset:
remove columns:
run my fix_path function.
This also creates the audio column that is referring to the absolute file path of the audio
Then I concatenate few other datasets and finally try the cast_column method
but get error:
## Expected results
A clear and concise description of the expected results.
## 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: 1.18.3
- Platform:
OVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn/ovh_huggingface)
- Python version: 3.8.8
- PyArrow version:
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"Hi ! I wasn't able to reproduce the error, are you still experiencing this ? I tried calling `cast_column` on a string column containing paths.\r\n\r\nIf you manage to share a reproducible code example that would be perfect",
"Hi,\r\n\r\nI think my team mate got this solved. Clolsing it for now and will reopen i... |
https://api.github.com/repos/huggingface/datasets/issues/3432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3432/comments | https://api.github.com/repos/huggingface/datasets/issues/3432/events | https://github.com/huggingface/datasets/pull/3432 | 1,079,910,769 | PR_kwDODunzps4v1NGS | 3,432 | Correctly indent builder config in dataset script docs | [] | closed | false | null | 0 | 2021-12-14T15:39:47Z | 2021-12-14T17:35:17Z | 2021-12-14T17:35:17Z | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4218 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4218/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4218/comments | https://api.github.com/repos/huggingface/datasets/issues/4218/events | https://github.com/huggingface/datasets/pull/4218 | 1,214,748,226 | PR_kwDODunzps42vTA0 | 4,218 | Make code for image downloading from image urls cacheable | [] | closed | false | null | 1 | 2022-04-25T16:17:59Z | 2022-04-26T17:00:24Z | 2022-04-26T13:38:26Z | null | Fix #4199 | {
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https://api.github.com/repos/huggingface/datasets/issues/242 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/242/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/242/comments | https://api.github.com/repos/huggingface/datasets/issues/242/events | https://github.com/huggingface/datasets/issues/242 | 631,733,683 | MDU6SXNzdWU2MzE3MzM2ODM= | 242 | UnicodeDecodeError when downloading GLUE-MNLI | [] | closed | false | null | 2 | 2020-06-05T16:30:01Z | 2020-06-09T16:06:47Z | 2020-06-08T08:45:03Z | null | When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 try:
-> 1129 for obj in iterable:
1130 yield obj
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\glue.py in _generate_examples(self, data_file, split, mrpc_files)
529
--> 530 for n, row in enumerate(reader):
531 if is_cola_non_test:
~\Miniconda3\envs\nlp\lib\csv.py in __next__(self)
110 self.fieldnames
--> 111 row = next(self.reader)
112 self.line_num = self.reader.line_num
~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final)
22 def decode(self, input, final=False):
---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0]
24
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>
```
Anyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay. | {
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"On Windows Python supports Unicode almost everywhere, but one of the notable exceptions is open() where it uses the locale encoding schema. So platform independent python scripts wou... |
https://api.github.com/repos/huggingface/datasets/issues/2613 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2613/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2613/comments | https://api.github.com/repos/huggingface/datasets/issues/2613/events | https://github.com/huggingface/datasets/pull/2613 | 940,759,852 | MDExOlB1bGxSZXF1ZXN0Njg2Nzg0MzY0 | 2,613 | Use ndarray.item instead of ndarray.tolist | [] | closed | false | {
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} | 0 | 2021-07-09T13:19:35Z | 2021-07-12T14:12:57Z | 2021-07-09T13:50:05Z | null | This PR follows up on #2612 to use `numpy.ndarray.item` instead of `numpy.ndarray.tolist` as the latter is somewhat confusing to the developer (even though it works).
Judging from the `numpy` docs, `ndarray.item` is closer to what we want: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html#numpy-ndarray-item
PS. Sorry for the duplicate work here. I should have read the numpy docs more carefully in #2612
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https://api.github.com/repos/huggingface/datasets/issues/1937 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1937/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1937/comments | https://api.github.com/repos/huggingface/datasets/issues/1937/events | https://github.com/huggingface/datasets/issues/1937 | 815,163,943 | MDU6SXNzdWU4MTUxNjM5NDM= | 1,937 | CommonGen dataset page shows an error OSError: [Errno 28] No space left on device | [
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] | closed | false | null | 2 | 2021-02-24T06:47:33Z | 2021-02-26T11:10:06Z | 2021-02-26T11:10:06Z | null | The page of the CommonGen data https://huggingface.co/datasets/viewer/?dataset=common_gen shows
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} | https://api.github.com/repos/huggingface/datasets/issues/1937/timeline | null | completed | null | null | false | [
"Facing the same issue for [Squad](https://huggingface.co/datasets/viewer/?dataset=squad) and [TriviaQA](https://huggingface.co/datasets/viewer/?dataset=trivia_qa) datasets as well.",
"We just fixed the issue, thanks for reporting !"
] |
https://api.github.com/repos/huggingface/datasets/issues/4247 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4247/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4247/comments | https://api.github.com/repos/huggingface/datasets/issues/4247/events | https://github.com/huggingface/datasets/issues/4247 | 1,218,320,882 | I_kwDODunzps5Inhny | 4,247 | The data preview of XGLUE | [] | closed | false | null | 3 | 2022-04-28T07:30:50Z | 2022-04-29T08:23:28Z | 2022-04-28T16:08:03Z | null | It seems that something wrong with the data previvew of XGLUE | {
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"\r\n",
"Thanks for reporting @czq1999.\r\n\r\nNote that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet.\r\n\r\nThat is the case for XGLUE dataset (... |
https://api.github.com/repos/huggingface/datasets/issues/1351 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1351/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1351/comments | https://api.github.com/repos/huggingface/datasets/issues/1351/events | https://github.com/huggingface/datasets/pull/1351 | 759,902,770 | MDExOlB1bGxSZXF1ZXN0NTM0ODI0NTcw | 1,351 | added craigslist_bargians | [] | closed | false | null | 0 | 2020-12-09T01:02:31Z | 2020-12-10T14:14:34Z | 2020-12-10T14:14:34Z | null | `craigslist_bargains` data set from [here](https://worksheets.codalab.org/worksheets/0x453913e76b65495d8b9730d41c7e0a0c/)
(Cleaned up version of #1278) | {
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https://api.github.com/repos/huggingface/datasets/issues/2761 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2761/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2761/comments | https://api.github.com/repos/huggingface/datasets/issues/2761/events | https://github.com/huggingface/datasets/issues/2761 | 961,568,287 | MDU6SXNzdWU5NjE1NjgyODc= | 2,761 | Error loading C4 realnewslike dataset | [
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] | closed | false | null | 4 | 2021-08-05T08:16:58Z | 2021-08-08T19:44:34Z | 2021-08-08T19:44:34Z | null | ## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/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='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1 | {
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} | https://api.github.com/repos/huggingface/datasets/issues/2761/timeline | null | completed | null | null | false | [
"Hi @danshirron, \r\n`c4` was updated few days back by @lhoestq. The new configs are `['en', 'en.noclean', 'en.realnewslike', 'en.webtextlike'].` You'll need to remove any older version of this dataset you previously downloaded and then run `load_dataset` again with new configuration.",
"@bhavitvyamalik @lhoestq ... |
https://api.github.com/repos/huggingface/datasets/issues/5524 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5524/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5524/comments | https://api.github.com/repos/huggingface/datasets/issues/5524/events | https://github.com/huggingface/datasets/pull/5524 | 1,580,219,454 | PR_kwDODunzps5JvbMw | 5,524 | [INVALID PR] | [] | closed | false | null | 1 | 2023-02-10T19:35:50Z | 2023-02-10T19:51:45Z | 2023-02-10T19:49:12Z | null | Hi to whoever is reading this! 🤗
## What's in this PR?
~~Basically, I've removed the 🤗`datasets` installation as `python -m pip install ".[quality]" in the `check_code_quality` job in `.github/workflows/ci.yaml`, as we don't need to install the whole package to run the CI, unless that's done on purpose e.g. to check that the Python package installation succeeds before running the tests over the matrix of os?~~
~~So I just wanted to check whether the time was reduced doing this (which I assume it will), plus whether this is something that can be improved, or just discarded in case you're also using that step to make sure that the package can be installed.~~
## What's missing?
~~I was just wondering whether you consider replacing `isort` and `flake8` with `ruff` (if possible), since it's way faster, more information at [`ruff`](https://github.com/charliermarsh/ruff). Before creating this PR the average time of the `check_code_quality` job was around 40s.~~
## Edit
Sorry for the inconvenience this may have caused, didn't realise that the config is defined in `setup.cfg` and `pyproject.toml`, so running those without installing the Python package leads to failure, my bad 😞
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/5541 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5541/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5541/comments | https://api.github.com/repos/huggingface/datasets/issues/5541/events | https://github.com/huggingface/datasets/issues/5541 | 1,588,633,555 | I_kwDODunzps5esJ_T | 5,541 | Flattening indices in selected datasets is extremely inefficient | [] | closed | false | null | 3 | 2023-02-17T01:52:24Z | 2023-02-22T13:15:20Z | 2023-02-17T11:12:33Z | null | ### Describe the bug
If we perform a `select` (or `shuffle`, `train_test_split`, etc.) operation on a dataset , we end up with a dataset with an `indices_table`. Currently, flattening such dataset consumes a lot of memory and the resulting flat dataset contains ChunkedArrays with as many chunks as there are rows. This is extremely inefficient and slows down the operations on the flat dataset, e.g., saving/loading the dataset to disk becomes really slow.
Perhaps more importantly, loading the dataset back from disk basically loads the whole table into RAM, as it cannot take advantage of memory mapping.
### Steps to reproduce the bug
The following script reproduces the issue:
```python
import gc
import os
import psutil
import tempfile
import time
from datasets import Dataset
DATASET_SIZE = 5000000
def profile(func):
def wrapper(*args, **kwargs):
mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
start = time.time()
# Run function here
out = func(*args, **kwargs)
end = time.time()
mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
print(f"{func.__name__} -- RAM memory used: {mem_after - mem_before} MB -- Total time: {end - start:.6f} s")
return out
return wrapper
def main():
ds = Dataset.from_list([{'col': i} for i in range(DATASET_SIZE)])
print(f"Num chunks for original ds: {ds.data['col'].num_chunks}")
with tempfile.TemporaryDirectory() as tmpdir:
path1 = os.path.join(tmpdir, 'ds1')
print("Original ds save/load")
profile(ds.save_to_disk)(path1)
ds_loaded = profile(Dataset.load_from_disk)(path1)
print(f"Num chunks for original ds after reloading: {ds_loaded.data['col'].num_chunks}")
print("")
ds_select = ds.select(reversed(range(len(ds))))
print(f"Num chunks for selected ds: {ds_select.data['col'].num_chunks}")
del ds
del ds_loaded
gc.collect()
# This would happen anyway when we call save_to_disk
ds_select = profile(ds_select.flatten_indices)()
print(f"Num chunks for selected ds after flattening: {ds_select.data['col'].num_chunks}")
print("")
path2 = os.path.join(tmpdir, 'ds2')
print("Selected ds save/load")
profile(ds_select.save_to_disk)(path2)
del ds_select
gc.collect()
ds_select_loaded = profile(Dataset.load_from_disk)(path2)
print(f"Num chunks for selected ds after reloading: {ds_select_loaded.data['col'].num_chunks}")
if __name__ == '__main__':
main()
```
Sample result:
```
Num chunks for original ds: 1
Original ds save/load
save_to_disk -- RAM memory used: 0.515625 MB -- Total time: 0.253888 s
load_from_disk -- RAM memory used: 42.765625 MB -- Total time: 0.015176 s
Num chunks for original ds after reloading: 5000
Num chunks for selected ds: 1
flatten_indices -- RAM memory used: 4852.609375 MB -- Total time: 46.116774 s
Num chunks for selected ds after flattening: 5000000
Selected ds save/load
save_to_disk -- RAM memory used: 1326.65625 MB -- Total time: 42.309825 s
load_from_disk -- RAM memory used: 2085.953125 MB -- Total time: 11.659137 s
Num chunks for selected ds after reloading: 5000000
```
### Expected behavior
Saving/loading the dataset should be much faster and consume almost no extra memory thanks to pyarrow memory mapping.
### Environment info
- `datasets` version: 2.9.1.dev0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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"Running the script above on the branch https://github.com/huggingface/datasets/pull/5542 results in the expected behaviour:\r\n```\r\nNum chunks for original ds: 1\r\nOriginal ds save/load\r\nsave_to_disk -- RAM memory used: 0.671875 MB -- Total time: 0.255265 s\r\nload_from_disk -- RAM memory used: 42.796875 MB -... |
https://api.github.com/repos/huggingface/datasets/issues/5385 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5385/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5385/comments | https://api.github.com/repos/huggingface/datasets/issues/5385/events | https://github.com/huggingface/datasets/issues/5385 | 1,508,535,532 | I_kwDODunzps5Z6mzs | 5,385 | Is `fs=` deprecated in `load_from_disk()` as well? | [] | closed | false | null | 3 | 2022-12-22T21:00:45Z | 2023-01-23T10:50:05Z | 2023-01-23T10:50:04Z | null | ### Describe the bug
The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec:
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340
Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ?
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779
### Steps to reproduce the bug
n/a
### Expected behavior
n/a
### Environment info
n/a | {
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"Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ",
"> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? I’ll look around for anywh... |
https://api.github.com/repos/huggingface/datasets/issues/2653 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2653/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2653/comments | https://api.github.com/repos/huggingface/datasets/issues/2653/events | https://github.com/huggingface/datasets/issues/2653 | 945,102,321 | MDU6SXNzdWU5NDUxMDIzMjE= | 2,653 | Add SD task for SUPERB | [
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} | 2 | 2021-07-15T07:51:40Z | 2021-08-04T17:03:52Z | 2021-08-04T17:03:52Z | null | 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).
Steps:
- [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
- [ ] README: tags + description sections
Related to #2619.
cc: @lewtun
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"Note that this subset requires us to:\r\n\r\n* generate the LibriMix corpus from LibriSpeech\r\n* prepare the corpus for diarization\r\n\r\nAs suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https://huggingface.co/datasets/superb/superb-data\r\n... |
https://api.github.com/repos/huggingface/datasets/issues/8 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8/comments | https://api.github.com/repos/huggingface/datasets/issues/8/events | https://github.com/huggingface/datasets/pull/8 | 601,783,243 | MDExOlB1bGxSZXF1ZXN0NDA0OTg0NDUz | 8 | Fix issue 6: error when the citation is missing in the DatasetInfo | [] | closed | false | null | 0 | 2020-04-17T08:04:26Z | 2020-04-29T09:27:11Z | 2020-04-20T13:24:12Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/1356 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1356/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1356/comments | https://api.github.com/repos/huggingface/datasets/issues/1356/events | https://github.com/huggingface/datasets/pull/1356 | 759,994,457 | MDExOlB1bGxSZXF1ZXN0NTM0ODk3OTQ1 | 1,356 | Add StackOverflow StackSample dataset | [] | closed | false | null | 5 | 2020-12-09T04:59:51Z | 2020-12-21T14:48:21Z | 2020-12-21T14:48:21Z | null | This PR adds the StackOverflow StackSample dataset from Kaggle: https://www.kaggle.com/stackoverflow/stacksample
Ran through all of the steps. However, since my dataset requires manually downloading the data, I was unable to run the pytest on the real dataset (the dummy data pytest passed). | {
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"@lhoestq Thanks for the review and suggestions! I've added your comments and pushed the changes. I'm having issues with the dummy data still. When I run the dummy data test\r\n\r\n```bash\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_so_stacksample\r\n```\r\nI g... |
https://api.github.com/repos/huggingface/datasets/issues/4694 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4694/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4694/comments | https://api.github.com/repos/huggingface/datasets/issues/4694/events | https://github.com/huggingface/datasets/issues/4694 | 1,306,958,380 | I_kwDODunzps5N5pos | 4,694 | Distributed data parallel training for streaming datasets | [
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] | open | false | null | 6 | 2022-07-17T01:29:43Z | 2023-04-26T18:21:09Z | null | null | ### Feature request
Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training?
### Motivation
Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation?
### Your contribution
Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do? | {
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} | https://api.github.com/repos/huggingface/datasets/issues/4694/timeline | null | null | null | null | false | [
"Hi ! According to https://huggingface.co/docs/datasets/use_with_pytorch#stream-data you can use the pytorch DataLoader with `num_workers>0` to distribute the shards across your workers (it uses `torch.utils.data.get_worker_info()` to get the worker ID and select the right subsets of shards to use)\r\n\r\n<s> EDIT:... |
https://api.github.com/repos/huggingface/datasets/issues/3129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3129/comments | https://api.github.com/repos/huggingface/datasets/issues/3129/events | https://github.com/huggingface/datasets/pull/3129 | 1,032,234,167 | PR_kwDODunzps4tezlA | 3,129 | Support Audio feature for TAR archives in sequential access | [] | closed | false | null | 7 | 2021-10-21T08:56:51Z | 2021-11-17T17:42:08Z | 2021-11-17T17:42:07Z | null | Add Audio feature support for TAR archived files in sequential access.
Fix #3128. | {
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"Also do you think we can adapt `cast_column` to keep the same value for this new parameter when the user only wants to change the sampling rate ?",
"Thanks for your comments, @lhoestq, I will address them afterwards.\r\n\r\nBut, I think it is more important/urgent first address the current blocking non-passing t... |
https://api.github.com/repos/huggingface/datasets/issues/4169 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4169/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4169/comments | https://api.github.com/repos/huggingface/datasets/issues/4169/events | https://github.com/huggingface/datasets/issues/4169 | 1,203,995,869 | I_kwDODunzps5Hw4Td | 4,169 | Timit_asr dataset cannot be previewed recently | [] | closed | false | null | 5 | 2022-04-14T03:28:31Z | 2023-02-03T04:54:57Z | 2022-05-06T16:06:51Z | null | ## Dataset viewer issue for '*timit_asr*'
**Link:** *https://huggingface.co/datasets/timit_asr*
Issue: The timit-asr dataset cannot be previewed recently.
Am I the one who added this dataset ? Yes-No
No | {
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"Thanks for reporting. The bug has already been detected, and we hope to fix it soon.",
"TIMIT is now a dataset that requires manual download, see #4145 \r\n\r\nTherefore it might take a bit more time to fix it",
"> TIMIT is now a dataset that requires manual download, see #4145\r\n> \r\n> Therefore it might ta... |
https://api.github.com/repos/huggingface/datasets/issues/4576 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4576/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4576/comments | https://api.github.com/repos/huggingface/datasets/issues/4576/events | https://github.com/huggingface/datasets/pull/4576 | 1,285,698,576 | PR_kwDODunzps46aSN_ | 4,576 | Include `metadata.jsonl` in resolved data files | [] | closed | false | null | 5 | 2022-06-27T12:01:29Z | 2022-07-01T12:44:55Z | 2022-06-30T10:15:32Z | null | Include `metadata.jsonl` in resolved data files.
Fix #4548
@lhoestq ~~https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 adds support for metadata files placed at the root, and https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 accounts for nested metadata files also, but this results in more complex code. Let me know which one of these two approaches you prefer.~~ Maybe https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 is good enough for now (for the sake of simplicity). https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 breaks the imagefolder tests due to duplicates in the resolved metadata files. One way to fix this would be to resolve the metadata pattern only on parent directories, but this adds even more logic to `_get_data_files_patterns`, so not sure if this is what we should do. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I still don't know if the way we implemented data files resolution could support the metadata.jsonl file without bad side effects for the other packaged builders. In particular here if you have a folder of csv/parquet/whatever files ... |
https://api.github.com/repos/huggingface/datasets/issues/4675 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4675/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4675/comments | https://api.github.com/repos/huggingface/datasets/issues/4675/events | https://github.com/huggingface/datasets/issues/4675 | 1,302,193,649 | I_kwDODunzps5NneXx | 4,675 | Unable to use dataset with PyTorch dataloader | [
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] | open | false | null | 1 | 2022-07-12T15:04:04Z | 2022-07-14T14:17:46Z | null | null | ## Describe the bug
When using `.with_format("torch")`, an arrow table is returned and I am unable to use it by passing it to a PyTorch DataLoader: please see the code below.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
ds = load_dataset(
"para_crawl",
name="enfr",
cache_dir="/tmp/test/",
split="train",
keep_in_memory=True,
)
dataloader = DataLoader(ds.with_format("torch"), num_workers=32)
print(next(iter(dataloader)))
```
Is there something I am doing wrong? The documentation does not say much about the behavior of `.with_format()` so I feel like I am a bit stuck here :-/
Thanks in advance for your help!
## Expected results
The code should run with no error
## Actual results
```
AttributeError: 'str' object has no attribute 'dtype'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.18.0-348.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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"Hi! `para_crawl` has a single column of type `Translation`, which stores translation dictionaries. These dictionaries can be stored in a NumPy array but not in a PyTorch tensor since PyTorch only supports numeric types. In `datasets`, the conversion to `torch` works as follows: \r\n1. convert PyArrow table to NumP... |
https://api.github.com/repos/huggingface/datasets/issues/5454 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5454/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5454/comments | https://api.github.com/repos/huggingface/datasets/issues/5454/events | https://github.com/huggingface/datasets/issues/5454 | 1,552,890,419 | I_kwDODunzps5cjzoz | 5,454 | Save and resume the state of a DataLoader | [
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"default": fals... | open | false | null | 2 | 2023-01-23T10:58:54Z | 2023-01-24T01:45:48Z | null | null | It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)
What I have in mind (but lmk if you have other ideas or comments):
For map-style datasets, this requires to have a PyTorch Sampler state that can be saved and reloaded per node and worker.
For iterable datasets, this requires to save the state of the dataset iterator, which includes:
- the current shard idx and row position in the current shard
- the epoch number
- the rng state
- the shuffle buffer
Right now you can already resume the data loading of an iterable dataset by using `IterableDataset.skip` but it takes a lot of time because it re-iterates on all the past data until it reaches the resuming point.
cc @stas00 @sgugger | {
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"Something that'd be nice to have is \"manual update of state\". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy.",
"Your outline spec is very sound and clear, @lhoestq - thank you!\r\n\r\n@thomasw21, indeed that would be a wonderful extra fe... |
https://api.github.com/repos/huggingface/datasets/issues/5811 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5811/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5811/comments | https://api.github.com/repos/huggingface/datasets/issues/5811/events | https://github.com/huggingface/datasets/issues/5811 | 1,689,919,046 | I_kwDODunzps5kuh5G | 5,811 | load_dataset: TypeError: 'NoneType' object is not callable, on local dataset filename changes | [] | open | false | null | 1 | 2023-04-30T13:27:17Z | 2023-05-05T17:44:03Z | null | null | ### Describe the bug
I've adapted Databrick's [train_dolly.py](/databrickslabs/dolly/blob/master/train_dolly.py) to train using a local dataset, which has been working. Upon changing the filenames of the `.json` & `.py` files in my local dataset directory, `dataset = load_dataset(path_or_dataset)["train"]` throws the error:
```python
2023-04-30 09:10:52 INFO [training.trainer] Loading dataset from dushowxa-characters
Traceback (most recent call last):
File "/data/dushowxa-dolly/train_dushowxa.py", line 26, in <module>
load_training_dataset()
File "/data/dushowxa-dolly/training/trainer.py", line 89, in load_training_dataset
dataset = load_dataset(path_or_dataset)["train"]
File "/data/dushowxa-dolly/.venv/lib/python3.10/site-packages/datasets/load.py", line 1773, in load_dataset
builder_instance = load_dataset_builder(
File "/data/dushowxa-dolly/.venv/lib/python3.10/site-packages/datasets/load.py", line 1528, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
TypeError: 'NoneType' object is not callable
```
The local dataset filenames were of the form `dushowxa-characters/expanse-dushowxa-characters.json` and are now of the form `dushowxa-characters/dushowxa-characters.json` (the word `expanse-` was removed from the filenames). Is this perhaps a dataset caching issue?
I have attempted to manually clear caches, but to no effect:
```sh
rm -rfv ~/.cache/huggingface/datasets/*
rm -rfv ~/.cache/huggingface/modules/*
```
### Steps to reproduce the bug
Run `python3 train_dushowxa.py` (adapted from Databrick's [train_dolly.py](/databrickslabs/dolly/blob/master/train_dolly.py)).
### Expected behavior
Training succeeds as before local dataset filenames were changed.
### Environment info
Ubuntu 22.04, Python 3.10.6, venv
```python
accelerate>=0.16.0,<1
click>=8.0.4,<9
datasets>=2.10.0,<3
deepspeed>=0.9.0,<1
transformers[torch]>=4.28.1,<5
langchain>=0.0.139
``` | {
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"This error means a `DatasetBuilder` subclass that generates the dataset could not be found inside the script, so make sure `dushowxa-characters/dushowxa-characters.py `is a valid dataset script (assuming `path_or_dataset` is `dushowxa-characters`)\r\n\r\nAlso, we should improve the error to make it more obvious wh... |
https://api.github.com/repos/huggingface/datasets/issues/2302 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2302/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2302/comments | https://api.github.com/repos/huggingface/datasets/issues/2302/events | https://github.com/huggingface/datasets/pull/2302 | 873,961,435 | MDExOlB1bGxSZXF1ZXN0NjI4NjIzMDQ3 | 2,302 | Add SubjQA dataset | [] | closed | false | null | 4 | 2021-05-02T14:51:20Z | 2021-05-10T09:21:19Z | 2021-05-10T09:21:19Z | null | Hello datasetters 🙂!
Here's an interesting dataset about extractive question-answering on _subjective_ product / restaurant reviews. It's quite challenging for models fine-tuned on SQuAD and provides a nice example of domain adaptation (i.e. fine-tuning a SQuAD model on this domain gives better performance).
I found a bug in the start/end indices that I've proposed a fix for here: https://github.com/megagonlabs/SubjQA/pull/2
Unfortunately, the dataset creators are unresponsive, so for now I am using my fork as the source. Will update the URL if/when the creators respond. | {
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"I'm not sure why the windows test fails, but looking at the logs it looks like some caching issue on one of the metrics ... maybe re-run and 🤞 ?",
"Hi @lewtun, thanks for adding this dataset!\r\n\r\nIf the dataset is going to be referenced heavily, I think it's worth spending some time to make the dataset card ... |
https://api.github.com/repos/huggingface/datasets/issues/2147 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2147/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2147/comments | https://api.github.com/repos/huggingface/datasets/issues/2147/events | https://github.com/huggingface/datasets/pull/2147 | 844,687,831 | MDExOlB1bGxSZXF1ZXN0NjAzOTA3NjM4 | 2,147 | Render docstring return type as inline | [
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] | closed | false | null | 0 | 2021-03-30T14:55:43Z | 2021-03-31T13:11:05Z | 2021-03-31T13:11:05Z | null | This documentation setting will avoid having the return type in a separate line under `Return type`.
See e.g. current docs for `Dataset.to_csv`. | {
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