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https://api.github.com/repos/huggingface/datasets/issues/3904 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3904/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3904/comments | https://api.github.com/repos/huggingface/datasets/issues/3904/events | https://github.com/huggingface/datasets/issues/3904 | 1,167,730,095 | I_kwDODunzps5FmiWv | 3,904 | CONLL2003 Dataset not available | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 4 | 2022-03-13 23:46:15+00:00 | 2023-06-28 18:08:16+00:00 | 2022-03-17 08:21:32+00:00 | NONE | null | null | null | null | ## Describe the bug
[CONLL2003](https://huggingface.co/datasets/conll2003) Dataset can no longer reach 'https://data.deepai.org/conll2003.zip'
## Steps to reproduce the bug
```python
from datasets import load_dataset
datasets = load_dataset("conll2003")
```
## Expected results
Download the conll2003 dataset.
## Actual results
Error: `ConnectionError: Couldn't reach https://data.deepai.org/conll2003.zip (error 502)`
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https://api.github.com/repos/huggingface/datasets/issues/3903 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3903/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3903/comments | https://api.github.com/repos/huggingface/datasets/issues/3903/events | https://github.com/huggingface/datasets/pull/3903 | 1,167,521,627 | PR_kwDODunzps40WnkI | 3,903 | Add Biwi Kinect Head Pose dataset. | {
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} | This PR adds the Biwi Kinect Head Pose dataset.
Dataset Request : Add Biwi Kinect Head Pose Database [#3822](https://github.com/huggingface/datasets/issues/3822)
The Biwi Kinect Head Pose Database is acquired with the Microsoft Kinect sensor, a structured IR light device.It contains 15K images of 20 people with 6 females and 14 males where 4 people were recorded twice.
For each frame, there is :
- a depth image, (.bin file)
- a corresponding rgb image (both 640x480 pixels),
- annotation ( present inside a .txt file)
The ground truth is the 3D location of the head and its rotation.
The dataset structure is as follows :
```
- 01.obj
- 01
- frame_00003_depth.bin
- frame_00003_pose.txt
- frame_00003_rgb.png
.
.
.
- 02.obj
- 02
- frame_00003_depth.bin
- frame_00003_pose.txt
- frame_00003_rgb.png
.
.
.
```
Preview of frame_00003_pose.txt :
```
0.988397 0.0731349 0.133128
-0.0441539 0.976945 -0.208876
-0.145334 0.200575 0.968838
126.665 40.4515 876.198
```
I have used the following dataset features :
```
features=datasets.Features(
{
"person_id": datasets.Value("string"),
"frame_number": datasets.Value("string"),
"depth_image": datasets.Value("string"),
"rgb_image": datasets.Image(),
"3D_head_center": datasets.Array2D(shape=(3, 3), dtype="float"),
"3D_head_rotation": datasets.Value("float"),
}
```
I am giving the path to the depth_image here.
I need some inputs for the following :
1. For each person, the dataset has the following additional information :
```
For each sequence, the corresponding .obj file represents a head template deformed to match the neutral face of that specific person. [*.obj file]
In each folder, two .cal files contain calibration information for the depth and the color camera, e.g., the intrinsic camera matrix of the depth camera and the global rotation and translation to the rgb camera.
```
Wanted to know how we can represent these features ?
2. For _generate_examples , do I parse the directories and fetch the required information ? This would mean reading the .txt file to obtain the "3D_head_center" and "3D_head_rotation" details. We could precompute the features information and have a metadata file and use the metadata file to yield information in _generate_examples ? Wanted your thoughts for the best approach for this ?
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https://api.github.com/repos/huggingface/datasets/issues/3902 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3902/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3902/comments | https://api.github.com/repos/huggingface/datasets/issues/3902/events | https://github.com/huggingface/datasets/issues/3902 | 1,167,403,377 | I_kwDODunzps5FlSlx | 3,902 | Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils' | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 5 | 2022-03-12 21:22:03+00:00 | 2023-02-09 14:53:49+00:00 | 2022-03-22 07:10:41+00:00 | NONE | null | null | null | null | ## Describe the bug
Unable to import datasets
## Steps to reproduce the bug
```python
from datasets import Dataset, DatasetDict
```
## Expected results
The import works without errors
## Actual results
```
AttributeError Traceback (most recent call last)
<ipython-input-37-c8cfcbe62127> in <module>
11 # from tqdm import tqdm
12 # import torch
---> 13 from datasets import Dataset
14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling
15 # from sentence_transformers import SentenceTransformer
~/.local/lib/python3.8/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in <module>
46 )
47
---> 48 import fsspec
49 import numpy as np
50 import pandas as pd
~/.local/lib/python3.8/site-packages/fsspec/__init__.py in <module>
10 from . import _version, caching
11 from .callbacks import Callback
---> 12 from .core import get_fs_token_paths, open, open_files, open_local
13 from .exceptions import FSTimeoutError
14 from .mapping import FSMap, get_mapper
~/.local/lib/python3.8/site-packages/fsspec/core.py in <module>
16 caches,
17 )
---> 18 from .compression import compr
19 from .registry import filesystem, get_filesystem_class
20 from .utils import (
~/.local/lib/python3.8/site-packages/fsspec/compression.py in <module>
68
69
---> 70 register_compression("zip", unzip, "zip")
71 register_compression("bz2", BZ2File, "bz2")
72
~/.local/lib/python3.8/site-packages/fsspec/compression.py in register_compression(name, callback, extensions, force)
44
45 for ext in extensions:
---> 46 if ext in fsspec.utils.compressions and not force:
47 raise ValueError(
48 "Duplicate compression file extension: %s (%s)" % (ext, name)
AttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Jupyter notebook
- Python version: 3.8.10
- PyArrow version: 7.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3901 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3901/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3901/comments | https://api.github.com/repos/huggingface/datasets/issues/3901/events | https://github.com/huggingface/datasets/issues/3901 | 1,167,339,773 | I_kwDODunzps5FlDD9 | 3,901 | Dataset viewer issue for IndicParaphrase- the preview doesn't show | {
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**Link:** *[IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase/viewer/hi/validation)*
*The preview of the dataset doesn't come up.
The error on the console is:
Status code: 400
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '/home/hf/datasets-preview-backend/hi_IndicParaphrase_v1.0.tar'*
Am I the one who added this dataset ? Yes
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https://api.github.com/repos/huggingface/datasets/issues/3900 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3900/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3900/comments | https://api.github.com/repos/huggingface/datasets/issues/3900/events | https://github.com/huggingface/datasets/pull/3900 | 1,167,224,903 | PR_kwDODunzps40VxRh | 3,900 | Add MetaShift dataset | {
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} | This PR adds the MetaShift dataset.
Dataset Request : Add MetaShift dataset [#3813](https://github.com/huggingface/datasets/issues/3813)
@lhoestq As discussed,
- I have copied the preprocessing script and modified it as required to not create new directories and folders and instead yield the images.
- I do the preprocessing in _split_generators to get the required data which is then passed to _generate_examples.
- Beyond the generated MetaShift dataset, the original preprocess script also generates the meta-graphs for each class, I have currently not included this part. [ Ref : [Link](https://github.com/Weixin-Liang/MetaShift#generate-full-metashift) ]
- There is a Bonus section, the authors share. I have currently not included this part. [ Ref : [Link](https://github.com/Weixin-Liang/MetaShift#bonus-generate-the-metashift-attributes-dataset-subsets-defined-by-subject-attributes) ]
- I had a basic test script which downloaded the dataset and tested the basic functionality. Things seems fine.
For real data, I performed the following test :
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_metashift
============================================== test session starts ===============================================
platform linux -- Python 3.7.11, pytest-7.0.1, pluggy-1.0.0
rootdir: ./datasets
plugins: hydra-core-1.1.1, datadir-1.3.1, forked-1.4.0, xdist-2.5.0
collected 1 item
tests/test_dataset_common.py . [100%]
========================================= 1 passed in 4821.25s (1:20:21) =========================================
```
- I couldn't get the dummy dataset. Need some inputs here.
Error as follows :
```
Using custom data configuration default
Dataset metashift with config None seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has to be created with less guidance. Make sure you create the file dummy_data/full-candidate-subsets.pkl.
for split in generator_splits:
UnboundLocalError: local variable 'generator_splits' referenced before assignment
```
To-Do :
- [x] Currently I am using the default _SELECTED_CLASSES. I need to use config option here as suggested
- [x] Complete fields in the Dataset Card.
- [x] Tagging the dataset using the Datasets Tagging app.
Need your help and suggestions for improvement. Thank you
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Note: Some of the tests have failed, but I wanted to make a PR anyway so that the rest of the code can be reviewed if anyone has time. I'll look into + work on fixing the failed tests when I'm back online after the weekend | {
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} | Proposing a draft WER metric card, @lhoestq I'm not very certain about "Values from popular papers" -- I don't know ASR very well, what do you think of the examples I found? | {
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* aligns the `tqdm` logic with Transformers (follows https://github.com/huggingface/transformers/pull/15167) by moving the code to `utils/logging.py`, adding `enable_progres_bar`/`disable_progres_bar` and removing `set_progress_bar_enabled` (a note for @lhoestq: I'm not adding `logging.tqdm` to the public namespace in this PR to avoid the situation where `from datasets import *; tqdm` would overshadow the standard `tqdm`
* aligns the cache control with the new `tqdm` logic by adding `enable_caching`/`disable_caching` to the public namespace and deprecating `set_caching_enabled` (not fully removing it because it's used more often than `set_progress_bar_enabled` and has a dedicated example in the old docs)
Fix #3586 | {
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{
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**Link:** https://huggingface.co/datasets/multi_news
```
Server error
Status code: 400
Exception: FileNotFoundError
Message: https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C/multi-news-original/train.src
```
Am I the one who added this dataset ? No
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cc @mariosasko | {
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} | Related to #3507 : dummy data for datasets created on the Hugging Face Hub are optional.
We can discuss later to make them optional for datasets in this repository as well | {
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https://api.github.com/repos/huggingface/datasets/issues/3893 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3893/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3893/comments | https://api.github.com/repos/huggingface/datasets/issues/3893/events | https://github.com/huggingface/datasets/pull/3893 | 1,166,551,684 | PR_kwDODunzps40TmxB | 3,893 | Add default branch for doc building | {
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} | Since other libraries use `main` as their default branch and it's now the standard default, you have to specify a different name in the doc config if you're using `master` like datasets (`doc-builder` tries to guess it, but in the job, we have weird checkout of merge commits so it doesn't always manage to get it right).
This PR makes sure it will always use master for the dev doc (until you decide to switchto main) | {
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} | Previous PR:
- #3796
introduced a side effect: `datasets-cli test` generates `dataset_infos.json` with `None` checksum values.
See:
- #3805
This PR introduces a way for `datasets-cli test` to force to record infos, even if `verify_infos=False`
Close #3848.
CC: @craffel | {
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} | Replace the old URLs with the Hub [URLs](https://huggingface.co/datasets/beans/tree/main/data).
Also reported by @stevhliu.
Fix #3889 | {
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"description": "A bug in a dataset script provided in the library",
"id": 2067388877,
"name": "dataset bug",
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
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] | closed | false | null | [] | null | 1 | 2022-03-10 16:34:08+00:00 | 2022-03-15 15:26:47+00:00 | 2022-03-15 15:26:47+00:00 | NONE | null | null | null | null | ## Describe the bug
The beans dataset is unavailable to download.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('beans')
```
## Expected results
The dataset would be downloaded with no issue.
## Actual results
```
ConnectionError: Couldn't reach https://storage.googleapis.com/ibeans/train.zip (error 403)
```
[It looks like the billing of this project has been disabled because it is associated with a delinquent account.](https://storage.googleapis.com/ibeans/train.zip )
## Environment info
Google Colab
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"following_url": "https://api.github.com/users/alvarobartt/following{/other_user}"... | null | 8 | 2022-03-10 16:19:12+00:00 | 2022-11-29 11:39:24+00:00 | NaT | MEMBER | null | null | 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 | null | {
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} | This PR adds the following improvements to the `imagefolder` dataset:
* skip the extract step for image files (as discussed in https://github.com/huggingface/datasets/pull/2830#discussion_r816817919)
* option to drop labels by setting `drop_labels=True` (useful for image pretraining cc @NielsRogge). This is faster than loading a dataset and removing the `label` column because we don't need to iterate over the files to infer class labels. | {
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- Implements a retry function
- Retries HfApi call inside `push_to_hub` when 504 error. To be agreed:
- max_retries = 2 (at 0.5 and 1 seconds)
Fix #3872. | {
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} | Following #3842 some docs were still outdated (with `buffer_size` as the first argument) | {
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https://api.github.com/repos/huggingface/datasets/issues/3883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3883/comments | https://api.github.com/repos/huggingface/datasets/issues/3883/events | https://github.com/huggingface/datasets/issues/3883 | 1,164,663,229 | I_kwDODunzps5Fa1m9 | 3,883 | The metric Meteor doesn't work for nltk ==3.6.4 | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 1 | 2022-03-10 02:28:27+00:00 | 2022-03-10 09:03:39+00:00 | 2022-03-10 09:03:39+00:00 | NONE | null | null | null | null | ## Describe the bug
Using the metric Meteor with nltk == 3.6.4 gives a TypeError:
TypeError: descriptor 'lower' for 'str' objects doesn't apply to a 'list' object
## Steps to reproduce the bug
```python
import datasets
metric = datasets.load_metric("meteor")
predictions = ["hello world"]
references = ["hello world"]
metric.compute(predictions=predictions, references=references)
```
## Expected results
TypeError: descriptor 'lower' for 'str' objects doesn't apply to a 'list' object
I think this TypeError exists because input sentences are tokenized into lists of tokens and the str.lower() is applied to this list of tokens.
## Actual results
No error but a meteor score
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: linux
- Python version: 3.8.12
- PyArrow version: 7.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3882 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3882/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3882/comments | https://api.github.com/repos/huggingface/datasets/issues/3882/events | https://github.com/huggingface/datasets/pull/3882 | 1,164,595,388 | PR_kwDODunzps40NKz7 | 3,882 | Image process doc | {
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} | This PR is a first draft of how to process image data. It adds:
- Load an image dataset with `image` and `path` (adds tip about `decode=False` param to access the path and bytes, thanks to @mariosasko).
- Load an image using the `ImageFolder` builder. I know there is an [example](https://huggingface.co/docs/datasets/master/en/loading#image-folders) of this already, but I also wanted to add it here so users don't miss it. This doc seems important for centralizing all of the image-related things so far. Datasets has grown so quickly 🚀 now that I think maybe splitting up the How-to guides by modality may be better since working with vision/audio data is slightly different from what users have seen up until now. This way we can continue to scale the docs to better accommodate vision/audio things.
- Add a data augmentation with `set_transform`. There is only 1 example here so far, but we can certainly add more.
Todo:
- [x] Couldn't figure out why my augmentation function works with `set_transform` but not `map` 🥲. Working with @mariosasko on this! | {
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```
load_dataset("imagefolder", data_dir="./my-dataset")
```
`https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/tmp/ipykernel_33/1648737256.py in <module>
----> 1 load_dataset("imagefolder", data_dir="./my-dataset")
/opt/conda/lib/python3.7/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, revision, use_auth_token, task, streaming, script_version, **config_kwargs)
1684 revision=revision,
1685 use_auth_token=use_auth_token,
-> 1686 **config_kwargs,
1687 )
1688
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)
1511 download_config.use_auth_token = use_auth_token
1512 dataset_module = dataset_module_factory(
-> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files
1514 )
1515
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)
1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
-> 1202 ) from None
1203 raise e1 from None
1204 else:
FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py
``` | {
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} | This PR updates the syntax of the framework-specific code samples. With this new syntax, you'll be able to:
- have paragraphs of text be framework-specific instead of just code samples
- have support for Flax code samples if you want.
This should be merged after https://github.com/huggingface/doc-builder/pull/63 and https://github.com/huggingface/doc-builder/pull/130 | {
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] | open | false | null | [] | null | 0 | 2022-03-09 16:12:25+00:00 | 2022-03-09 16:33:42+00:00 | NaT | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
Align to DCAT metadata to describe datasets
**Describe the solution you'd like**
Reuse terms and structure from DCAT in the metadata file, ideally generate a json-ld file dcat compliant
**Describe alternatives you've considered**
**Additional context**
DCAT is a W3C standard extended in Europe with DCAT-AP, an example is data.europa.eu publishing datasets metadata in DCAT-AP
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Before the fix, it was set to `bool` (default to `False`).
Also set properly its default value in all public functions. | {
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} | This is an attempt to make the user-facing `datasets`' submodule namespace cleaner:
In particular, this PR does the following:
* removes the unused `zip_nested` and `flatten_nest_dict` and their accompanying tests
* removes `pyarrow` from the top-level namespace
* properly uses `__all__` and the `from <module> import *` syntax to avoid importing the `<module>`'s submodules
* cleans up the `utils` namespace
* moves the `temp_seed` context manage from `datasets/utils/file_utils.py` to `datasets/utils/py_utils.py` | {
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} | Created a new pull request to resolve unrelated changes in PR caused due to rebasing.
Ref Older PR : [#3845](https://github.com/huggingface/datasets/pull/3845)
Feature request here : Add support for continuous metrics (RMSE, MAE) [#3608](https://github.com/huggingface/datasets/issues/3608) | {
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} | Proposal for a metrics card structure (with an example based on the SQuAD metric).
@thomwolf @lhoestq @douwekiela @lewtun -- feel free to comment on structure or content (it's an initial draft, so I realize there's stuff missing!). | {
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} | [] | closed | false | null | [] | null | 6 | 2022-03-09 12:03:37+00:00 | 2022-03-15 16:19:50+00:00 | 2022-03-15 16:19:50+00:00 | NONE | null | null | null | null | I am trying to push a large dataset(450000+) records with the help of `push_to_hub()`
While pushing, it gives some error like this.
```
Traceback (most recent call last):
File "data_split_speech.py", line 159, in <module>
data_new_2.push_to_hub("user-name/dataset-name",private=True)
File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub
repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub
api.upload_file(
File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file
raise err
File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file
r.raise_for_status()
File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet
```
Can anyone help me to resolve this issue.
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} | Pandas is a required packages and used quite a bit. I don't see any downside with adding its version to the `datasets-cli env` command. | {
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] | open | false | null | [] | null | 1 | 2022-03-09 03:06:18+00:00 | 2022-03-09 09:04:09+00:00 | NaT | NONE | null | null | null | null | Let's make Hugging Face Datasets the central hub for datasets in Portuguese :)
**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the Portuguese speaking community.
What are some datasets in Portuguese worth integrating into the Hugging Face hub?
Special thanks to @augusnunes for his collaboration on identifying the first ones:
- [NILC - USP](http://www.nilc.icmc.usp.br/nilc/index.php/tools-and-resources).
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
cc @osanseviero
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} | Following https://github.com/huggingface/datasets/pull/3860#issuecomment-1061756712 and https://github.com/huggingface/datasets/pull/3690 we need to update the code blocks to use markdown instead of sphinx | {
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https://api.github.com/repos/huggingface/datasets/issues/3862 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3862/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3862/comments | https://api.github.com/repos/huggingface/datasets/issues/3862/events | https://github.com/huggingface/datasets/pull/3862 | 1,162,753,733 | PR_kwDODunzps40HFht | 3,862 | Manipulate columns on IterableDataset (rename columns, cast, etc.) | {
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} | I added:
- add_column
- cast
- rename_column
- rename_columns
related to https://github.com/huggingface/datasets/issues/3444
TODO:
- [x] docs
- [x] tests | {
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https://api.github.com/repos/huggingface/datasets/issues/3861 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3861/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3861/comments | https://api.github.com/repos/huggingface/datasets/issues/3861/events | https://github.com/huggingface/datasets/issues/3861 | 1,162,702,044 | I_kwDODunzps5FTWzc | 3,861 | big_patent cased version | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 2 | 2022-03-08 14:08:55+00:00 | 2023-04-21 14:32:03+00:00 | 2023-04-21 14:32:03+00:00 | NONE | null | null | null | null | Hi! I am interested in working with the big_patent dataset.
In Tensorflow, there are a number of versions of the dataset:
- 1.0.0 : lower cased tokenized words
- 2.0.0 : Update to use cased raw strings
- 2.1.2 (default): Fix update to cased raw strings.
The version in the huggingface `datasets` library is the 1.0.0. I would be very interested in using the 2.1.2 cased version (used more, recently, for example in the Pegasus paper), but it does not seem to be supported (I tried using the `revision` parameter in `load_datasets`). Is there a way to already load it, or would it be possible to add that version? | {
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https://api.github.com/repos/huggingface/datasets/issues/3860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3860/comments | https://api.github.com/repos/huggingface/datasets/issues/3860/events | https://github.com/huggingface/datasets/pull/3860 | 1,162,623,329 | PR_kwDODunzps40GpzZ | 3,860 | Small doc fixes | {
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https://api.github.com/repos/huggingface/datasets/issues/3859 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3859/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3859/comments | https://api.github.com/repos/huggingface/datasets/issues/3859/events | https://github.com/huggingface/datasets/issues/3859 | 1,162,559,333 | I_kwDODunzps5FSz9l | 3,859 | Unable to dowload big_patent (FileNotFoundError) | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 1 | 2022-03-08 11:47:12+00:00 | 2022-03-08 13:04:09+00:00 | 2022-03-08 13:04:04+00:00 | NONE | null | null | null | null | ## Describe the bug
I am trying to download some splits of the big_patent dataset, using the following code:
`ds = load_dataset("big_patent", "g", split="validation", download_mode="force_redownload")
`
However, this leads to a FileNotFoundError.
FileNotFoundError Traceback (most recent call last)
[<ipython-input-3-8d8a745706a9>](https://localhost:8080/#) in <module>()
1 from datasets import load_dataset
----> 2 ds = load_dataset("big_patent", "g", split="validation", download_mode="force_redownload")
8 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)
1705 ignore_verifications=ignore_verifications,
1706 try_from_hf_gcs=try_from_hf_gcs,
-> 1707 use_auth_token=use_auth_token,
1708 )
1709
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) 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)
593 if not downloaded_from_gcs:
594 self._download_and_prepare(
--> 595 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
596 )
597 # Sync info
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
659 split_dict = SplitDict(dataset_name=self.name)
660 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 661 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
662
663 # Checksums verification
[/root/.cache/huggingface/modules/datasets_modules/datasets/big_patent/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c/big_patent.py](https://localhost:8080/#) in _split_generators(self, dl_manager)
123 split_types = ["train", "val", "test"]
124 extract_paths = dl_manager.extract(
--> 125 {k: os.path.join(dl_path, "bigPatentData", k + ".tar.gz") for k in split_types}
126 )
127 extract_paths = {k: os.path.join(extract_paths[k], k) for k in split_types}
[/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py](https://localhost:8080/#) in extract(self, path_or_paths, num_proc)
282 download_config.extract_compressed_file = True
283 extracted_paths = map_nested(
--> 284 partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False
285 )
286 path_or_paths = NestedDataStructure(path_or_paths)
[/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm)
260 mapped = [
261 _single_map_nested((function, obj, types, None, True))
--> 262 for obj in utils.tqdm(iterable, disable=disable_tqdm)
263 ]
264 else:
[/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <listcomp>(.0)
260 mapped = [
261 _single_map_nested((function, obj, types, None, True))
--> 262 for obj in utils.tqdm(iterable, disable=disable_tqdm)
263 ]
264 else:
[/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in _single_map_nested(args)
194 # Singleton first to spare some computation
195 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 196 return function(data_struct)
197
198 # Reduce logging to keep things readable in multiprocessing with tqdm
[/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py](https://localhost:8080/#) in cached_path(url_or_filename, download_config, **download_kwargs)
314 elif is_local_path(url_or_filename):
315 # File, but it doesn't exist.
--> 316 raise FileNotFoundError(f"Local file {url_or_filename} doesn't exist")
317 else:
318 # Something unknown
FileNotFoundError: Local file /root/.cache/huggingface/datasets/downloads/extracted/ad068abb3e11f9f2f5440b62e37eb2b03ee515df9de1637c55cd1793b68668b2/bigPatentData/train.tar.gz doesn't exist
I have tried this in a number of machines, including on Colab, so I think this is not environment dependent.
How do I load the bigPatent dataset? | {
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https://api.github.com/repos/huggingface/datasets/issues/3857 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3857/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3857/comments | https://api.github.com/repos/huggingface/datasets/issues/3857/events | https://github.com/huggingface/datasets/issues/3857 | 1,162,525,353 | I_kwDODunzps5FSrqp | 3,857 | Order of dataset changes due to glob.glob. | {
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After discussion with @lhoestq, just want to mention here that `glob.glob(...)` should always be used in combination with `sorted(...)` to make sure the list of files returned by `glob.glob(...)` doesn't change depending on the OS system.
There are currently multiple datasets that use `glob.glob()` without making use of `sorted(...)` even the streaming download manager (if I'm not mistaken):
https://github.com/huggingface/datasets/blob/c14bfeb4af89da14f870de5ddaa584b08aa08eeb/src/datasets/utils/streaming_download_manager.py#L483 | null | {
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} | This code currently raises an error because of the null image:
```python
import datasets
dataset_dict = { 'name': ['image001.jpg', 'image002.jpg'], 'image': ['cat.jpg', None] }
features = datasets.Features({
'name': datasets.Value('string'),
'image': datasets.Image(),
})
dataset = datasets.Dataset.from_dict(dataset_dict, features)
dataset.push_to_hub("username/dataset") # this line produces an error: 'NoneType' object is not subscriptable
```
I fixed this in this PR
TODO:
- [x] add a test | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 2 | 2022-03-08 09:55:17+00:00 | 2022-07-11 15:06:40+00:00 | 2022-07-11 15:06:40+00:00 | CONTRIBUTOR | null | null | null | null | ## Describe the bug
A pretty common behavior of an interaction between the Hub and datasets is the following.
An organization adds a dataset in private mode and wants to load it afterward.
```python
from transformers import load_dataset
ds = load_dataset("NewT5/dummy_data", "dummy")
```
This command then fails with:
```bash
FileNotFoundError: Couldn't find a dataset script at /home/patrick/NewT5/dummy_data/dummy_data.py or any data file in the same directory. Couldn't find 'NewT5/dummy_data' on the Hugging Face Hub either: FileNotFoundError: Dataset 'NewT5/dummy_data' doesn't exist on the Hub
```
**even though** the user has access to the website `NewT5/dummy_data` since she/he is part of the org.
We need to improve the error message here similar to how @sgugger, @LysandreJik and @julien-c have done it for transformers IMO.
## Steps to reproduce the bug
E.g. execute the following code to see the different error messages between `transformes` and `datasets`.
1. Transformers
```python
from transformers import BertModel
BertModel.from_pretrained("NewT5/dummy_model")
```
The error message is clearer here - it gives:
```
OSError: patrickvonplaten/gpt2-xl is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.
```
Let's maybe do the same for datasets? The PR was introduced to `transformers` here:
https://github.com/huggingface/transformers/pull/15261
## Expected results
Better error message
## 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: 1.18.4.dev0
- Platform: Linux-5.15.15-76051515-generic-x86_64-with-glibc2.34
- Python version: 3.9.7
- PyArrow version: 6.0.1
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 2 | 2022-03-08 09:40:52+00:00 | 2024-03-23 12:40:58+00:00 | 2022-03-09 08:13:33+00:00 | NONE | null | null | null | null | training_data = load_dataset("common_voice", "en",split='train[:250]+validation[:250]')
testing_data = load_dataset("common_voice", "en", split="test[:200]")
I'm trying to load only 8% of the English common voice data with accent == "England English." Can somebody assist me with this?
**Typical Voice Accent Proportions:**
- 24% United States English
- 8% England English
- 5% India and South Asia (India, Pakistan, Sri Lanka)
- 3% Australian English
- 3% Canadian English
- 2% Scottish English
- 1% Irish English
- 1% Southern African (South Africa, Zimbabwe, Namibia)
- 1% New Zealand English
Can we replicate this for Age as well?
**Age proportions of the common voice:-**
- 24% 19 - 29
- 14% 30 - 39
- 10% 40 - 49
- 6% < 19
- 4% 50 - 59
- 4% 60 - 69
- 1% 70 – 79 | {
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https://api.github.com/repos/huggingface/datasets/issues/3853 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3853/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3853/comments | https://api.github.com/repos/huggingface/datasets/issues/3853/events | https://github.com/huggingface/datasets/pull/3853 | 1,162,386,592 | PR_kwDODunzps40F3uN | 3,853 | add ontonotes_conll dataset | {
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} | # Introduction of the dataset
OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and used in the CoNLL-2012 shared task
, includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only).
This dataset is widely used in name entity recognition, coreference resolution, and semantic role labeling.
In dataset loading script, I modify and use the code of [AllenNLP/Ontonotes](https://docs.allennlp.org/models/main/models/common/ontonotes/#ontonotes) to read the special conll files while don't get extra package dependency.
# Some workarounds I did
1. task ids
I add tasks that I can't find anywhere `semantic-role-labeling`, `lemmatization`, and `word-sense-disambiguation` to the task category `structure-prediction`, because they are related to "syntax". I feel there is another good name for the task category since some tasks mentioned aren't related to structure, but I have no good idea.
2. `dl_manage.extract`
Since we'll get another zip after unzip the downloaded zip data, I have to use `dl_manager.extract` directly inside `_generate_examples`. But when testing dummy data, `dl_manager.extract` do nothing. So I make a conditional such that it manually extract data when testing dummy data.
# Help
Don't know how to fix the building doc error. | {
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} | [] | closed | false | null | [] | null | 1 | 2022-03-08 05:57:05+00:00 | 2022-03-08 16:54:36+00:00 | 2022-03-08 16:54:36+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | > Alternatively, you can follow the steps to [add a dataset](https://huggingface.co/docs/datasets/add_dataset.html) and [share a dataset](https://huggingface.co/docs/datasets/share_dataset.html) in the documentation.
The "add a dataset link" gives 404 Error, and the share_dataset link has changed. I feel this information is redundant/deprecated now since we have a more detailed guide for "How to add a dataset?". | {
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] | closed | false | null | [] | null | 8 | 2022-03-08 02:16:04+00:00 | 2022-09-27 12:13:55+00:00 | 2022-03-08 11:20:06+00:00 | NONE | null | null | null | null | ## Load audio dataset error
Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb,
```
from datasets import load_dataset, load_metric, Audio
raw_datasets = load_dataset("superb", "ks", split="train")
print(raw_datasets[0]["audio"])
```
following errors occur
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-169-3f8253239fa0> in <module>
----> 1 raw_datasets[0]["audio"]
/usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key)
1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
1925 return self._getitem(
-> 1926 key,
1927 )
1928
/usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)
1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
1910 formatted_output = format_table(
-> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
1912 )
1913 return formatted_output
/usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
/usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
/usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table)
310 row = self.python_arrow_extractor().extract_row(pa_table)
311 if self.decoded:
--> 312 row = self.python_features_decoder.decode_row(row)
313 return row
314
/usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row)
219
220 def decode_row(self, row: dict) -> dict:
--> 221 return self.features.decode_example(row) if self.features else row
222
223 def decode_column(self, column: list, column_name: str) -> list:
/usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example)
1320 else value
1321 for column_name, (feature, value) in utils.zip_dict(
-> 1322 {key: value for key, value in self.items() if key in example}, example
1323 )
1324 }
/usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0)
1319 if self._column_requires_decoding[column_name]
1320 else value
-> 1321 for column_name, (feature, value) in utils.zip_dict(
1322 {key: value for key, value in self.items() if key in example}, example
1323 )
/usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj)
1053 # Object with special decoding:
1054 elif isinstance(schema, (Audio, Image)):
-> 1055 return schema.decode_example(obj) if obj is not None else None
1056 return obj
1057
/usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value)
100 array, sampling_rate = self._decode_non_mp3_file_like(file)
101 else:
--> 102 array, sampling_rate = self._decode_non_mp3_path_like(path)
103 return {"path": path, "array": array, "sampling_rate": sampling_rate}
104
/usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path)
143
144 with xopen(path, "rb") as f:
--> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)
146 return array, sampling_rate
147
/usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)
110
111 y = []
--> 112 with audioread.audio_open(os.path.realpath(path)) as input_file:
113 sr_native = input_file.samplerate
114 n_channels = input_file.channels
/usr/lib/python3.6/posixpath.py in realpath(filename)
392 """Return the canonical path of the specified filename, eliminating any
393 symbolic links encountered in the path."""
--> 394 filename = os.fspath(filename)
395 path, ok = _joinrealpath(filename[:0], filename, {})
396 return abspath(path)
TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader
```
## Expected results
```
>>> raw_datasets[0]["audio"]
{'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347,
0.01623535, 0.01724243]),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav',
'sampling_rate': 16000}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3850 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3850/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3850/comments | https://api.github.com/repos/huggingface/datasets/issues/3850/events | https://github.com/huggingface/datasets/pull/3850 | 1,162,126,030 | PR_kwDODunzps40FBx9 | 3,850 | [feat] Add tqdm arguments | {
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https://api.github.com/repos/huggingface/datasets/issues/3849 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3849/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3849/comments | https://api.github.com/repos/huggingface/datasets/issues/3849/events | https://github.com/huggingface/datasets/pull/3849 | 1,162,091,075 | PR_kwDODunzps40E6sW | 3,849 | Add "Adversarial GLUE" dataset to datasets library | {
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} | Adds the Adversarial GLUE dataset: https://adversarialglue.github.io/
```python
>>> import datasets
>>> >>> datasets.load_dataset('adv_glue')
Using the latest cached version of the module from /home/jxm3/.cache/huggingface/modules/datasets_modules/datasets/adv_glue/26709a83facad2830d72d4419dd179c0be092f4ad3303ad0ebe815d0cdba5cb4 (last modified on Mon Mar 7 19:19:48 2022) since it couldn't be found locally at adv_glue., or remotely on the Hugging Face Hub.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jxm3/random/datasets/src/datasets/load.py", line 1657, in load_dataset
builder_instance = load_dataset_builder(
File "/home/jxm3/random/datasets/src/datasets/load.py", line 1510, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/jxm3/random/datasets/src/datasets/builder.py", line 1021, in __init__
super().__init__(*args, **kwargs)
File "/home/jxm3/random/datasets/src/datasets/builder.py", line 258, in __init__
self.config, self.config_id = self._create_builder_config(
File "/home/jxm3/random/datasets/src/datasets/builder.py", line 337, in _create_builder_config
raise ValueError(
ValueError: Config name is missing.
Please pick one among the available configs: ['adv_sst2', 'adv_qqp', 'adv_mnli', 'adv_mnli_mismatched', 'adv_qnli', 'adv_rte']
Example of usage:
`load_dataset('adv_glue', 'adv_sst2')`
>>> datasets.load_dataset('adv_glue', 'adv_sst2')['validation'][0]
Reusing dataset adv_glue (/home/jxm3/.cache/huggingface/datasets/adv_glue/adv_sst2/1.0.0/3719a903f606f2c96654d87b421bc01114c37084057cdccae65cd7bc24b10933)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 604.11it/s]
{'sentence': "it 's an uneven treat that bores fun at the democratic exercise while also examining its significance for those who take part .", 'label': 1, 'idx': 0}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3848/comments | https://api.github.com/repos/huggingface/datasets/issues/3848/events | https://github.com/huggingface/datasets/issues/3848 | 1,162,076,902 | I_kwDODunzps5FQ-Lm | 3,848 | NonMatchingChecksumError when checksum is None | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 7 | 2022-03-08 00:24:12+00:00 | 2022-03-15 14:37:26+00:00 | 2022-03-15 12:28:23+00:00 | CONTRIBUTOR | null | null | null | null | I ran into the following error when adding a new dataset:
```bash
expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}}
recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}
verification_name = 'dataset source files'
def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):
if expected_checksums is None:
logger.info("Unable to verify checksums.")
return
if len(set(expected_checksums) - set(recorded_checksums)) > 0:
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
if len(set(recorded_checksums) - set(expected_checksums)) > 0:
raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
for_verification_name = " for " + verification_name if verification_name is not None else ""
if len(bad_urls) > 0:
error_msg = "Checksums didn't match" + for_verification_name + ":\n"
> raise NonMatchingChecksumError(error_msg + str(bad_urls))
E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
E ['https://adversarialglue.github.io/dataset/dev.zip']
src/datasets/utils/info_utils.py:40: NonMatchingChecksumError
```
## Expected results
The dataset downloads correctly, and there is no error.
## Actual results
Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. | {
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] | open | false | null | [] | null | 28 | 2022-03-07 19:55:15+00:00 | 2025-05-19 11:58:55+00:00 | NaT | NONE | null | null | null | null | ## Describe the bug
For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory.
## Steps to reproduce the bug
Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example.
```python
from datasets import load_dataset
from transformers import AutoTokenizer
raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1")
# tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
text_column_name = "text"
column_names = raw_datasets["train"].column_names
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
remove_columns=column_names,
load_from_cache_file=True,
desc="Running tokenizer on every text in dataset",
)
```
## Expected results
No tokenization would be required after the 1st run. Everything should be loaded from the cache.
## Actual results
Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Ubuntu 18.04.6 LTS
- Python version: 3.6.9
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3846 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3846/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3846/comments | https://api.github.com/repos/huggingface/datasets/issues/3846/events | https://github.com/huggingface/datasets/pull/3846 | 1,161,810,226 | PR_kwDODunzps40D-uh | 3,846 | Update faiss device docstring | {
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} | This PR adds RMSE - Root Mean Squared Error and MAE - Mean Absolute Error to the metrics API.
Both implementations are based on usage of sciket-learn.
Feature request here : Add support for continuous metrics (RMSE, MAE) [#3608](https://github.com/huggingface/datasets/issues/3608)
Please suggest any changes if required. Thank you. | {
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Both implementations are based on usage of sciket-learn.
Feature request here : Add support for continuous metrics (RMSE, MAE) [#3608](https://github.com/huggingface/datasets/issues/3608)
Any suggestions and changes required will be helpful.
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Fix #3835
CC: @albertvillanova | {
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} | From #3444 , Dataset.shuffle can have the same API than IterableDataset.shuffle (i.e. in streaming mode).
Currently you can pass an optional seed to both if you want, BUT currently IterableDataset.shuffle always requires a buffer_size, used for approximate shuffling. I propose using a reasonable default value (maybe 1000) instead.
In this PR, I set the default `buffer_size` value to 1,000, and I reorder the `IterableDataset.shuffle` arguments to match `Dataset.shuffle`, i.e. making `seed` the first argument. | {
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] | closed | false | null | [] | null | 6 | 2022-03-07 10:24:04+00:00 | 2023-02-18 19:14:03+00:00 | 2023-02-13 13:48:41+00:00 | CONTRIBUTOR | null | null | null | null | ## Describe the bug
Pyright complains about module not exported.
## Steps to reproduce the bug
Use an editor/IDE with Pyright Language server with default configuration:
```python
from datasets import load_dataset
```
## Expected results
No complain from Pyright
## Actual results
Pyright complain below:
```
`load_dataset` is not exported from module "datasets"
Import from "datasets.load" instead [reportPrivateImportUsage]
```
Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation.
## Environment info
- `datasets` version: 1.18.3
- Platform: macOS-12.2.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3840 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3840/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3840/comments | https://api.github.com/repos/huggingface/datasets/issues/3840/events | https://github.com/huggingface/datasets/pull/3840 | 1,161,183,773 | PR_kwDODunzps40B8eu | 3,840 | Pin responses to fix CI for Windows | {
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See: https://app.circleci.com/pipelines/github/huggingface/datasets/10292/workflows/83de4a55-bff7-43ec-96f7-0c335af5c050/jobs/63355
Fix: #3839 | {
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https://api.github.com/repos/huggingface/datasets/issues/3839 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3839/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3839/comments | https://api.github.com/repos/huggingface/datasets/issues/3839/events | https://github.com/huggingface/datasets/issues/3839 | 1,161,183,482 | I_kwDODunzps5FNkD6 | 3,839 | CI is broken for Windows | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 0 | 2022-03-07 10:06:42+00:00 | 2022-05-20 14:13:43+00:00 | 2022-03-07 10:07:24+00:00 | MEMBER | null | null | null | null | ## Describe the bug
See: https://app.circleci.com/pipelines/github/huggingface/datasets/10292/workflows/83de4a55-bff7-43ec-96f7-0c335af5c050/jobs/63355
```
___________________ test_datasetdict_from_text_split[test] ____________________
[gw0] win32 -- Python 3.7.11 C:\tools\miniconda3\envs\py37\python.exe
split = 'test'
text_path = 'C:\\Users\\circleci\\AppData\\Local\\Temp\\pytest-of-circleci\\pytest-0\\popen-gw0\\data6\\dataset.txt'
tmp_path = WindowsPath('C:/Users/circleci/AppData/Local/Temp/pytest-of-circleci/pytest-0/popen-gw0/test_datasetdict_from_text_spl7')
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_datasetdict_from_text_split(split, text_path, tmp_path):
if split:
path = {split: text_path}
else:
split = "train"
path = {"train": text_path, "test": text_path}
cache_dir = tmp_path / "cache"
expected_features = {"text": "string"}
> dataset = TextDatasetReader(path, cache_dir=cache_dir).read()
tests\io\test_text.py:118:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\io\text.py:43: in read
use_auth_token=use_auth_token,
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\builder.py:588: in download_and_prepare
self._download_prepared_from_hf_gcs(dl_manager.download_config)
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\builder.py:630: in _download_prepared_from_hf_gcs
reader.download_from_hf_gcs(download_config, relative_data_dir)
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\arrow_reader.py:260: in download_from_hf_gcs
downloaded_dataset_info = cached_path(remote_dataset_info.replace(os.sep, "/"))
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\utils\file_utils.py:301: in cached_path
download_desc=download_config.download_desc,
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\utils\file_utils.py:560: in get_from_cache
headers=headers,
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\utils\file_utils.py:476: in http_head
max_retries=max_retries,
C:\tools\miniconda3\envs\py37\lib\site-packages\datasets\utils\file_utils.py:397: in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
C:\tools\miniconda3\envs\py37\lib\site-packages\requests\api.py:61: in request
return session.request(method=method, url=url, **kwargs)
C:\tools\miniconda3\envs\py37\lib\site-packages\requests\sessions.py:529: in request
resp = self.send(prep, **send_kwargs)
C:\tools\miniconda3\envs\py37\lib\site-packages\requests\sessions.py:645: in send
r = adapter.send(request, **kwargs)
C:\tools\miniconda3\envs\py37\lib\site-packages\responses\__init__.py:840: in unbound_on_send
return self._on_request(adapter, request, *a, **kwargs)
C:\tools\miniconda3\envs\py37\lib\site-packages\responses\__init__.py:780: in _on_request
match, match_failed_reasons = self._find_match(request)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <responses.RequestsMock object at 0x000002048AD70588>
request = <PreparedRequest [HEAD]>
def _find_first_match(self, request):
match_failed_reasons = []
> for i, match in enumerate(self._matches):
E AttributeError: 'RequestsMock' object has no attribute '_matches'
C:\tools\miniconda3\envs\py37\lib\site-packages\moto\core\models.py:289: AttributeError
```
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https://api.github.com/repos/huggingface/datasets/issues/3838 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3838/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3838/comments | https://api.github.com/repos/huggingface/datasets/issues/3838/events | https://github.com/huggingface/datasets/issues/3838 | 1,161,137,406 | I_kwDODunzps5FNYz- | 3,838 | Add a data type for labeled images (image segmentation) | {
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] | open | false | null | [] | null | 0 | 2022-03-07 09:38:15+00:00 | 2024-05-29 16:50:55+00:00 | NaT | COLLABORATOR | null | null | null | null | It might be a mix of Image and ClassLabel, and the color palette might be generated automatically.
---
### Example
every pixel in the images of the annotation column (in https://huggingface.co/datasets/scene_parse_150) has a value that gives its class, and the dataset itself is associated with a color palette (eg https://github.com/open-mmlab/mmsegmentation/blob/98a353b674c6052d319e7de4e5bcd65d670fcf84/mmseg/datasets/ade.py#L47) that maps every class with a color.
So we might want to render the image as a colored image instead of a black and white one.
<img width="785" alt="156741519-fbae6844-2606-4c28-837e-279d83d00865" src="https://user-images.githubusercontent.com/1676121/157005263-7058c584-2b70-465a-ad94-8a982f726cf4.png">
See https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/core/features/labeled_image.py for reference in Tensorflow | null | {
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**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/3832 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3832/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3832/comments | https://api.github.com/repos/huggingface/datasets/issues/3832/events | https://github.com/huggingface/datasets/issues/3832 | 1,160,503,446 | I_kwDODunzps5FK-CW | 3,832 | Making Hugging Face the place to go for Graph NNs datasets | {
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"color": "7AFCAA",... | open | false | null | [] | null | 4 | 2022-03-06 03:02:58+00:00 | 2022-03-14 07:45:38+00:00 | NaT | NONE | null | null | null | null | Let's make Hugging Face Datasets the central hub for GNN datasets :)
**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field.
What are some datasets worth integrating into the Hugging Face hub?
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Special thanks to @napoles-uach for his collaboration on identifying the first ones:
- [ ] [SNAP-Stanford OGB Datasets](https://github.com/snap-stanford/ogb).
- [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https://github.com/snap-stanford/pretrain-gnns).
- [ ] [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression)
cc @osanseviero
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https://api.github.com/repos/huggingface/datasets/issues/3831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3831/comments | https://api.github.com/repos/huggingface/datasets/issues/3831/events | https://github.com/huggingface/datasets/issues/3831 | 1,160,501,000 | I_kwDODunzps5FK9cI | 3,831 | when using to_tf_dataset with shuffle is true, not all completed batches are made | {
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when converting a dataset to tf_dataset by using to_tf_dataset with shuffle true, the remainder is not converted to one batch
## Steps to reproduce the bug
this is the sample code below
https://colab.research.google.com/drive/1_oRXWsR38ElO1EYF9ayFoCU7Ou1AAej4?usp=sharing
## Expected results
regardless of shuffle is true or not, 67 rows dataset should be 5 batches when batch size is 16.
## Actual results
4 batches
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3830 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3830/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3830/comments | https://api.github.com/repos/huggingface/datasets/issues/3830/events | https://github.com/huggingface/datasets/issues/3830 | 1,160,181,404 | I_kwDODunzps5FJvac | 3,830 | Got error when load cnn_dailymail dataset | {
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] | closed | false | null | [] | null | 2 | 2022-03-05 01:43:12+00:00 | 2022-03-07 06:53:41+00:00 | 2022-03-07 06:53:41+00:00 | NONE | null | null | null | null | When using datasets.load_dataset method to load cnn_dailymail dataset, got error as below:
- windows os: FileNotFoundError: [WinError 3] 系统找不到指定的路径。: 'D:\\SourceCode\\DataScience\\HuggingFace\\Data\\downloads\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\cnn\\stories'
- google colab: NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
The code is to load dataset:
windows os:
```
from datasets import load_dataset
dataset = load_dataset("cnn_dailymail", "3.0.0", cache_dir="D:\\SourceCode\\DataScience\\HuggingFace\\Data")
```
google colab:
```
import datasets
train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train")
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3829 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3829/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3829/comments | https://api.github.com/repos/huggingface/datasets/issues/3829/events | https://github.com/huggingface/datasets/issues/3829 | 1,160,154,352 | I_kwDODunzps5FJozw | 3,829 | [📄 Docs] Create a `datasets` performance guide. | {
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] | open | false | null | [] | null | 1 | 2022-03-05 00:28:06+00:00 | 2022-03-10 16:24:27+00:00 | NaT | NONE | null | null | null | null | ## Brief Overview
Downloading, saving, and preprocessing large datasets from the `datasets` library can often result in [performance bottlenecks](https://github.com/huggingface/datasets/issues/3735). These performance snags can be challenging to identify and to debug, especially for users who are less experienced with building deep learning experiments.
## Feature Request
Could we create a performance guide for using `datasets`, similar to:
* [Better performance with the `tf.data` API](https://github.com/huggingface/datasets/issues/3735)
* [Analyze `tf.data` performance with the TF Profiler](https://www.tensorflow.org/guide/data_performance_analysis)
This performance guide should detail practical options for improving performance with `datasets`, and enumerate any common best practices. It should also show how to use tools like the PyTorch Profiler or the TF Profiler to identify any performance bottlenecks (example below).
## Related Issues
* [wiki_dpr pre-processing performance #1670](https://github.com/huggingface/datasets/issues/1670)
* [Adjusting chunk size for streaming datasets #3499](https://github.com/huggingface/datasets/issues/3499)
* [how large datasets are handled under the hood #1004](https://github.com/huggingface/datasets/issues/1004)
* [using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? #1830](https://github.com/huggingface/datasets/issues/1830)
* [Best way to batch a large dataset? #315](https://github.com/huggingface/datasets/issues/315)
* [Saving processed dataset running infinitely #1911](https://github.com/huggingface/datasets/issues/1911) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/3828 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3828/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3828/comments | https://api.github.com/repos/huggingface/datasets/issues/3828/events | https://github.com/huggingface/datasets/issues/3828 | 1,160,064,029 | I_kwDODunzps5FJSwd | 3,828 | The Pile's _FEATURE spec seems to be incorrect | {
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] | closed | false | null | [] | null | 1 | 2022-03-04 21:25:32+00:00 | 2022-03-08 09:30:49+00:00 | 2022-03-08 09:30:48+00:00 | NONE | null | null | null | null | ## Describe the bug
If you look at https://huggingface.co/datasets/the_pile/blob/main/the_pile.py:
For "all"
* the pile_set_name is never set for data
* there's actually an id field inside of "meta"
For subcorpora pubmed_central and hacker_news:
* the meta is specified to be a string, but it's actually a dict with an id field inside.
## Steps to reproduce the bug
## Expected results
Feature spec should match the data I'd think?
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/3827 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3827/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3827/comments | https://api.github.com/repos/huggingface/datasets/issues/3827/events | https://github.com/huggingface/datasets/pull/3827 | 1,159,878,436 | PR_kwDODunzps4z95dj | 3,827 | Remove deprecated `remove_columns` param in `filter` | {
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} | A leftover from #3803. | {
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https://api.github.com/repos/huggingface/datasets/issues/3826 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3826/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3826/comments | https://api.github.com/repos/huggingface/datasets/issues/3826/events | https://github.com/huggingface/datasets/pull/3826 | 1,159,851,110 | PR_kwDODunzps4z90JU | 3,826 | Add IterableDataset.filter | {
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} | [] | closed | false | null | [] | null | 2 | 2022-03-04 16:57:23+00:00 | 2022-03-09 17:23:13+00:00 | 2022-03-09 17:23:11+00:00 | MEMBER | null | null | 0 | {
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} | _Needs https://github.com/huggingface/datasets/pull/3801 to be merged first_
I added `IterableDataset.filter` with an API that is a subset of `Dataset.filter`:
```python
def filter(self, function, batched=False, batch_size=1000, with_indices=false, input_columns=None):
```
TODO:
- [x] tests
- [x] docs
related to https://github.com/huggingface/datasets/issues/3444 and https://github.com/huggingface/datasets/issues/3753 | {
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https://api.github.com/repos/huggingface/datasets/issues/3823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3823/comments | https://api.github.com/repos/huggingface/datasets/issues/3823/events | https://github.com/huggingface/datasets/issues/3823 | 1,159,497,844 | I_kwDODunzps5FHIh0 | 3,823 | 500 internal server error when trying to open a dataset composed of Zarr stores | {
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] | closed | false | null | [] | null | 4 | 2022-03-04 10:37:14+00:00 | 2022-03-08 09:47:39+00:00 | 2022-03-08 09:47:39+00:00 | NONE | null | null | null | null | ## Describe the bug
The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code.
The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine.
In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format?
For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("openclimatefix/mrms")
```
## Expected results
The dataset should be downloaded or open up
## Actual results
A 500 internal server error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35
- Python version: 3.9.10
- PyArrow version: 7.0.0
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"gi... | null | 10 | 2022-03-04 08:48:39+00:00 | 2025-04-07 13:04:25+00:00 | 2022-06-01 13:00:47+00:00 | CONTRIBUTOR | null | null | null | null | ## Adding a Dataset
- **Name:** Biwi Kinect Head Pose Database
- **Description:** Over 15K images of 20 people recorded with a Kinect while turning their heads around freely. For each frame, depth and rgb images are provided, together with ground in the form of the 3D location of the head and its rotation angles.
- **Data:** [*link to the Github repository or current dataset location*](https://icu.ee.ethz.ch/research/datsets.html)
- **Motivation:** Useful pose estimation dataset
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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} | This PR combines all updates to Wikipedia dataset.
Once approved, this will be used to generate the pre-processed Wikipedia datasets.
Finally, this PR will be able to be merged into master:
- NOT using squash
- BUT a regular MERGE (or REBASE+MERGE), so that all commits are preserved
TODO:
- [x] #3435
- [x] #3789
- [x] #3825
- [x] Run to get the pre-processed data for big languages (backward compatibility)
- [x] #3958
CC: @geohci | {
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https://api.github.com/repos/huggingface/datasets/issues/3820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3820/comments | https://api.github.com/repos/huggingface/datasets/issues/3820/events | https://github.com/huggingface/datasets/issues/3820 | 1,159,106,603 | I_kwDODunzps5FFpAr | 3,820 | `pubmed_qa` checksum mismatch | {
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"descript... | closed | false | null | [] | null | 1 | 2022-03-04 00:28:08+00:00 | 2022-03-04 09:42:32+00:00 | 2022-03-04 09:42:32+00:00 | CONTRIBUTOR | null | null | null | null | ## Describe the bug
Loading [`pubmed_qa`](https://huggingface.co/datasets/pubmed_qa) results in a mismatched checksum error.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import datasets
try:
datasets.load_dataset("pubmed_qa", "pqa_labeled")
except Exception as e:
print(e)
try:
datasets.load_dataset("pubmed_qa", "pqa_unlabeled")
except Exception as e:
print(e)
try:
datasets.load_dataset("pubmed_qa", "pqa_artificial")
except Exception as e:
print(e)
```
## Expected results
Successful download.
## Actual results
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 665, in _download_and_prepare
verify_checksums(
File "/usr/local/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ', 'https://drive.google.com/uc?export=download&id=15v1x6aQDlZymaHGP7cZJZZYFfeJt2NdS']
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: macOS
- Python version: 3.8.1
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3818 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3818/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3818/comments | https://api.github.com/repos/huggingface/datasets/issues/3818/events | https://github.com/huggingface/datasets/issues/3818 | 1,158,788,545 | I_kwDODunzps5FEbXB | 3,818 | Support for "sources" parameter in the add() and add_batch() methods in datasets.metric - SARI | {
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The methods `add_batch` and `add` from the `Metric` [class](https://github.com/huggingface/datasets/blob/1675ad6a958435b675a849eafa8a7f10fe0f43bc/src/datasets/metric.py) does not work with [SARI](https://github.com/huggingface/datasets/blob/master/metrics/sari/sari.py) metric. This metric not only relies on the predictions and references, but also in the input.
For example, when the `add_batch` method is used, then the `compute()` method fails:
```
metric = load_metric("sari")
metric.add_batch(
predictions=["About 95 you now get in ."],
references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]])
metric.compute()
> TypeError: _compute() missing 1 required positional argument: 'sources'
```
Therefore, the `compute() `method can only be used standalone:
```
metric = load_metric("sari")
result = metric.compute(
sources=["About 95 species are currently accepted ."],
predictions=["About 95 you now get in ."],
references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]])
> {'sari': 26.953601953601954}
```
**Describe the solution you'd like**
Support for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class.
```
add_batch(*, sources=None, predictions=None, references=None, **kwargs)
add(*, sources=None, predictions=None, references=None, **kwargs)
compute()
```
**Describe alternatives you've considered**
I've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add) and [add_batch method](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add_batch).
**Additional context**
These methods are used in the transformers [pytorch examples](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py).
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} | In #3736 we introduced one method to generate examples when streaming, that is different from the one when not streaming.
In this PR I propose a new implementation which is simpler: it only has one function, based on `iter_archive`. And you still have access to local audio files when loading the dataset in non-streaming mode.
cc @patrickvonplaten @polinaeterna @anton-l @albertvillanova since this will become the template for many audio datasets to come.
This change can also trivially be applied to the other audio datasets that already exist. Using this line, you can get access to local files in non-streaming mode:
```python
local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None
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} | The `DownloadManager.iter_archive` method currently returns an iterator - which is **empty** once you iter over it once. This means you can't pass the same archive iterator to several splits.
To fix that, I changed the ouput of `DownloadManager.iter_archive` to be an iterable that you can iterate over several times, instead of a one-time-use iterator.
The `StreamingDownloadManager.iter_archive` already returns an appropriate iterable, and the code added in this PR is inspired from the one in `streaming_download_manager.py` | {
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} | This PR fixes an issue introduced by #3575 where `None` values stored in PyArrow arrays/structs would get ignored by `cast_storage` or by the `pa.array(cast_to_python_objects(..))` pattern. To fix the former, it also bumps the minimal PyArrow version to v5.0.0 to use the `mask` param in `pa.SturctArray`.
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"gi... | null | 7 | 2022-03-03 14:26:45+00:00 | 2022-04-10 13:39:59+00:00 | 2022-04-10 13:39:59+00:00 | CONTRIBUTOR | null | null | null | null | ## Adding a Dataset
- **Name:** MetaShift
- **Description:** collection of 12,868 sets of natural images across 410 classes-
- **Paper:** https://arxiv.org/abs/2202.06523v1
- **Data:** https://github.com/weixin-liang/metashift
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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} | # do not merge
## Hypothesis
packing data into a single zip archive could allow us not to care about splitting data into several tar archives for efficient streaming which is annoying (since data creators usually host the data in a single tar)
## Data
I host it [here](https://huggingface.co/datasets/polinaeterna/benchmark_dataset/)
## I checked three configurations:
1. All data in one zip archive, streaming only those files that exist in split metadata file (we can access them directrly with no need to iterate over full archive), see [this func](https://github.com/huggingface/datasets/compare/master...polinaeterna:benchmark-tar-zip?expand=1#diff-4f5200d4586aec5b2a89fcf34441c5f92156f9e9d408acc7e50666f9a1921ddcR196)
2. All data in three splits, the standart way to make streaming efficient, see [this func](https://github.com/huggingface/datasets/compare/master...polinaeterna:benchmark-tar-zip?expand=1#diff-4f5200d4586aec5b2a89fcf34441c5f92156f9e9d408acc7e50666f9a1921ddcR174)
3. All data in single tar, iterate over the full archive and take only files existing in split metadata file, see [this func](https://github.com/huggingface/datasets/compare/master...polinaeterna:benchmark-tar-zip?expand=1#diff-4f5200d4586aec5b2a89fcf34441c5f92156f9e9d408acc7e50666f9a1921ddcR150)
## Results
1. one zip
2. three tars
3. one tar
didn't check on the full data as it's time consuming but anyway it's pretty obvious that one-zip-way is not a good idea. here it's even worse than full iteration over tar containing all three splits (but that would depend on the case).
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https://api.github.com/repos/huggingface/datasets/issues/3810 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3810/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3810/comments | https://api.github.com/repos/huggingface/datasets/issues/3810/events | https://github.com/huggingface/datasets/pull/3810 | 1,158,202,093 | PR_kwDODunzps4z4WUW | 3,810 | Update version of xcopa dataset | {
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} | Note that there was a version update of the `xcopa` dataset: https://github.com/cambridgeltl/xcopa/releases
We updated our loading script, but we did not bump a new version number:
- #3254
This PR updates our loading script version from `1.0.0` to `1.1.0`. | {
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https://api.github.com/repos/huggingface/datasets/issues/3809 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3809/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3809/comments | https://api.github.com/repos/huggingface/datasets/issues/3809/events | https://github.com/huggingface/datasets/issues/3809 | 1,158,143,480 | I_kwDODunzps5FB934 | 3,809 | Checksums didn't match for datasets on Google Drive | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 1 | 2022-03-03 09:01:10+00:00 | 2022-03-03 09:24:58+00:00 | 2022-03-03 09:24:05+00:00 | NONE | null | null | null | null | ## Describe the bug
Datasets hosted on Google Drive do not seem to work right now.
Loading them fails with a checksum error.
## Steps to reproduce the bug
```python
from datasets import load_dataset
for dataset in ["head_qa", "yelp_review_full"]:
try:
load_dataset(dataset)
except Exception as exception:
print("Error", dataset, exception)
```
Here is a [colab](https://colab.research.google.com/drive/1wOtHBmL8I65NmUYakzPV5zhVCtHhi7uQ#scrollTo=cDzdCLlk-Bo4).
## Expected results
The datasets should be loaded.
## Actual results
```
Downloading and preparing dataset head_qa/es (download: 75.69 MiB, generated: 2.86 MiB, post-processed: Unknown size, total: 78.55 MiB) to /root/.cache/huggingface/datasets/head_qa/es/1.1.0/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9...
Error head_qa Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t']
Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /root/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43...
Error yelp_review_full Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0']
```
## Environment info
- `datasets` version: 1.18.3
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3808 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3808/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3808/comments | https://api.github.com/repos/huggingface/datasets/issues/3808/events | https://github.com/huggingface/datasets/issues/3808 | 1,157,650,043 | I_kwDODunzps5FAFZ7 | 3,808 | Pre-Processing Cache Fails when using a Factory pattern | {
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If you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time.
## Steps to reproduce the bug
```python
def preprocess_function_factory(augmentation=None):
def preprocess_function(examples):
# Tokenize the texts
if augmentation:
conversions1 = [
augmentation(example)
for example in examples[sentence1_key]
]
if sentence2_key is None:
args = (conversions1,)
else:
conversions2 = [
augmentation(example)
for example in examples[sentence2_key]
]
args = (conversions1, conversions2)
else:
args = (
(examples[sentence1_key],)
if sentence2_key is None
else (examples[sentence1_key], examples[sentence2_key])
)
result = tokenizer(
*args, padding=padding, max_length=max_seq_length, truncation=True
)
# Map labels to IDs (not necessary for GLUE tasks)
if label_to_id is not None and "label" in examples:
result["label"] = [
(label_to_id[l] if l != -1 else -1) for l in examples["label"]
]
return result
return preprocess_function
capitalize = lambda x: x.capitalize()
preprocess_function = preprocess_function_factory(augmentation=capitalize)
print(hash(preprocess_function)) # This will change on each run
raw_datasets = raw_datasets.map(
preprocess_function,
batched=True,
load_from_cache_file=True,
desc="Running transformation and tokenizer on dataset",
)
```
## Expected results
Running the code twice will cause the cache to be re-used.
## Actual results
Running the code twice causes the whole dataset to be re-processed
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https://api.github.com/repos/huggingface/datasets/issues/3807 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3807/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3807/comments | https://api.github.com/repos/huggingface/datasets/issues/3807/events | https://github.com/huggingface/datasets/issues/3807 | 1,157,531,812 | I_kwDODunzps5E_oik | 3,807 | NonMatchingChecksumError in xcopa dataset | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 6 | 2022-03-02 18:10:19+00:00 | 2022-05-20 06:00:42+00:00 | 2022-03-03 17:40:31+00:00 | NONE | null | null | null | null | ## Describe the bug
Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("xcopa", "it")
```
## Expected results
The dataset should be loaded correctly.
## Actual results
Fails with:
```python
in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/cambridgeltl/xcopa/archive/master.zip']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3, and 1.18.4.dev0
- Platform:
- Python version: 3.8
- PyArrow version:
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Previously, Spanish had the same URL as Vietnamese data file. | {
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