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https://api.github.com/repos/huggingface/datasets/issues/3566 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3566/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3566/comments | https://api.github.com/repos/huggingface/datasets/issues/3566/events | https://github.com/huggingface/datasets/pull/3566 | 1,100,155,902 | PR_kwDODunzps4w2Tcc | 3,566 | Add initial electricity time series dataset | {
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"@kashif Some commits on the PR branch are not authored by you, so could you please open a new PR and not use rebase this time :)? You can copy and paste the dataset dir to the new branch. \r\n\r\n",
"making a new PR"
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"> \r\n\r\nI did `make style` and it affected over 500 files\r\n\r\n```\r\nAll done! ✨ 🍰 ✨\r\n575 files reformatted, 372 files left unchanged.\r\nisort tests src benchmarks datasets/**/*.py metri\r\n```\r\n\r\n(result)\r\n\r\n",
"Nvm I was using wrong black version"
] | 1,641,914,797,000 | 1,642,003,887,000 | 1,642,003,887,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/3564 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3564/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3564/comments | https://api.github.com/repos/huggingface/datasets/issues/3564/events | https://github.com/huggingface/datasets/pull/3564 | 1,099,214,403 | PR_kwDODunzps4wzSOL | 3,564 | Add the KMWP & DKTC dataset. | {
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"I reflect your review. cc. @lhoestq ",
"Ah sorry, I missed KMWP comment, wait.",
"I request 2 new pull requests. #3569 #3570"
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- https://github.com/tunib-ai/DKTC | {
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"Hi! That makes sense. Sure, feel free to open a PR! Just a small suggestion: let's make `preserve_index` a parameter of `Dataset.from_pandas` (which we then pass to `InMemoryTable.from_pandas`) with `None` as a default value to not have this as a breaking change. "
] | 1,641,902,827,000 | 1,642,003,887,000 | 1,642,003,887,000 | CONTRIBUTOR | null | null | {
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} | ## Describe the bug
Let's say that you want to create a Dataset object from pandas dataframe. Most likely you will write something like this:
```
import pandas as pd
from datasets import Dataset
df = pd.read_csv('some_dataset.csv')
# Some DataFrame preprocessing code...
dataset = Dataset.from_pandas(df)
```
If your preprocessing code contain indexing operations like this:
```
df = df[df.col1 == some_value]
```
then your df.index can be changed from (default) ```RangeIndex(start=0, stop=16590, step=1)``` to something like this ```Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8,
9,
...
83979, 83980, 83981, 83982, 83983, 83984, 83985, 83986, 83987,
83988],
dtype='int64', length=16590)```
In this case, PyArrow (by default) will preserve this non-standard index. In the result, your dataset object will have the extra field that you likely don't want to have: '__index_level_0__'.
You can easily fix this by just adding extra argument ```preserve_index=False``` to call of ```InMemoryTable.from_pandas``` in ```arrow_dataset.py```.
If you approve that this isn't desirable behavior, I can make a PR fixing that.
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.11.0-44-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 6.0.1
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} | Add support for multiple task templates of the same type. Fixes (partially) #2520.
CC: @lewtun | {
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"The host of this copy of the dataset (https://the-eye.eu) is down and has been down for a good amount of time ([potentially months](https://www.reddit.com/r/Roms/comments/q82s15/theeye_downdied/))\r\n\r\nFinding this dataset is a little esoteric, as the original authors took down the official BookCorpus dataset some time ago.\r\n\r\nThere are community-created versions of BookCorpus, such as the files hosted in the link below.\r\nhttps://battle.shawwn.com/sdb/bookcorpus/\r\n\r\nAnd more discussion here:\r\nhttps://github.com/soskek/bookcorpus\r\n\r\nDo we want to remove this dataset entirely? There's a fair argument for this, given that the official BookCorpus dataset was taken down by the authors. If not, perhaps can open a PR with the link to the community-created tar above and updated dataset description.",
"Hi! The `bookcorpusopen` dataset is not working for the same reason as explained in this comment: https://github.com/huggingface/datasets/issues/3504#issuecomment-1004564980",
"Hi @HUIYINXUE, it should work now that the data owners created a mirror server with all data, and we updated the URL in our library."
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} | ## Describe the bug
Cannot load 'bookcorpusopen'
## Steps to reproduce the bug
```python
dataset = load_dataset('bookcorpusopen')
```
or
```python
dataset = load_dataset('bookcorpusopen',script_version='master')
```
## Actual results
ConnectionError: Couldn't reach https://the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz
## Environment info
- `datasets` version: 1.9.0
- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64
- Python version: 3.6.13
- PyArrow version: 6.0.1
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"Hi ! Thanks for the change :)\r\nCould it be possible to only run it for the code in `src/` ? We try to not change the code in the `datasets/` directory too often since it refreshes the users cache when they upgrade `datasets`.",
"> Hi ! Thanks for the change :)\r\n> Could it be possible to only run it for the code in `src/` ? We try to not change the code in the `datasets/` directory too often since it refreshes the users cache when they upgrade `datasets`.\r\n\r\nI reverted the changes in `datasets/` instead of changing only `src/`. Does it sound good?",
"I just resolved some conflicts with the master branch. If the CI is green we can merge :)"
] | 1,641,842,453,000 | 1,643,636,329,000 | 1,643,621,854,000 | CONTRIBUTOR | null | false | {
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} | Run the command:
```bash
pyupgrade $(find . -name "*.py" -type f) --py36-plus
```
Which mainly avoids unnecessary lists creations and also removes unnecessary code for Python 3.6+.
It was originally part of #3489.
Tip for reviewing faster: use the CLI (`git diff`) and scroll. | {
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"The CI failures are unrelated to the changes in this PR.",
"> The CI failures are unrelated to the changes in this PR.\r\n\r\nIt seems that some of the failures are due to the tests on the dataset cards (e.g. CIFAR, MNIST, FASHION_MNIST). Perhaps it's worth addressing those in this PR to avoid confusing downstream developers who branch off `master` and suddenly have a failing CI?",
"@lewtun We only run these tests against the modified datasets on the PR branch, so this will not lead to errors after merging."
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} | Fixes a bug in the `ImageClassification` task template which requires specifying class labels twice in dataset scripts. Additionally, this PR refactors the API around the classification task templates for cleaner `labels` handling.
CC: @lewtun @nateraw | {
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} | As described in https://github.com/huggingface/datasets/issues/3505#issuecomment-1004755657, this PR uses a generator expression to encode/decode examples with `features` (which are set to None in `map`) before applying a map transform.
Fix #3505 | {
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"Hi, we've just merged the PR with the fix. The fixed version of the dataset can be downloaded as follows:\r\n```python\r\nimport datasets\r\ndset = datasets.load_dataset(\"tweet_qa\", revision=\"master\")\r\n```"
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} | When loading the tweet_qa dataset with `load_dataset('tweet_qa')`, the following error occurs:
`DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 2a167f9e016ba338e1813fed275a6a1e
Keys should be unique and deterministic in nature
`
Might be related to issues #2433 and #2333
- `datasets` version: 1.17.0
- Python version: 3.8.5
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https://api.github.com/repos/huggingface/datasets/issues/3554 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3554/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3554/comments | https://api.github.com/repos/huggingface/datasets/issues/3554/events | https://github.com/huggingface/datasets/issues/3554 | 1,097,711,367 | I_kwDODunzps5Bbb8H | 3,554 | ImportError: cannot import name 'is_valid_waiter_error' | {
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"Hi! I can't reproduce this error in Colab, but I'm assuming you are using Amazon SageMaker Studio Notebooks (you mention the `conda_pytorch_p36` kernel), so maybe @philschmid knows more about what might be causing this issue? ",
"Hey @mariosasko. Yes, I am using **Amazon SageMaker Studio Jupyter Labs**. However, I no longer need this notebook; but it would be nice to have this problem solved for others. So don't stress too much if you two can't reproduce error.",
"Hey @danielbellhv, \r\n\r\nThis issue might be related to Studio probably not having an up to date `botocore` and `boto3` version. I ran into this as well a while back. My workaround was \r\n```python\r\n# using older dataset due to incompatibility of sagemaker notebook & aws-cli with > s3fs and fsspec to >= 2021.10\r\n!pip install \"datasets==1.13\" --upgrade\r\n```\r\n\r\nIn `datasets` we use the latest `s3fs` and `fsspec` but aws-cli and notebook is not supporting this. You could also update the `aws-cli` and associated packages to get the latest `datasets` version\r\n"
] | 1,641,810,724,000 | 1,644,831,357,000 | 1,644,831,357,000 | NONE | null | null | {
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} | Based on [SO post](https://stackoverflow.com/q/70606147/17840900).
I'm following along to this [Notebook][1], cell "**Loading the dataset**".
Kernel: `conda_pytorch_p36`.
I run:
```
! pip install datasets transformers optimum[intel]
```
Output:
```
Requirement already satisfied: datasets in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (1.17.0)
Requirement already satisfied: transformers in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (4.15.0)
Requirement already satisfied: optimum[intel] in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (0.1.3)
Requirement already satisfied: numpy>=1.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (1.19.5)
Requirement already satisfied: dill in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.3.4)
Requirement already satisfied: tqdm>=4.62.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (4.62.3)
Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.2.1)
Requirement already satisfied: packaging in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (21.3)
Requirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (6.0.1)
Requirement already satisfied: pandas in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (1.1.5)
Requirement already satisfied: xxhash in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2.0.2)
Requirement already satisfied: aiohttp in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (3.8.1)
Requirement already satisfied: fsspec[http]>=2021.05.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2021.11.1)
Requirement already satisfied: dataclasses in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.8)
Requirement already satisfied: multiprocess in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.70.12.2)
Requirement already satisfied: importlib-metadata in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (4.5.0)
Requirement already satisfied: requests>=2.19.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2.25.1)
Requirement already satisfied: pyyaml>=5.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (5.4.1)
Requirement already satisfied: regex!=2019.12.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (2021.4.4)
Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (0.10.3)
Requirement already satisfied: filelock in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (3.0.12)
Requirement already satisfied: sacremoses in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (0.0.46)
Requirement already satisfied: torch>=1.9 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.10.1)
Requirement already satisfied: sympy in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.8)
Requirement already satisfied: coloredlogs in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (15.0.1)
Requirement already satisfied: pycocotools in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (2.0.3)
Requirement already satisfied: neural-compressor>=1.7 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.9)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.10.0.0)
Requirement already satisfied: sigopt in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.2.0)
Requirement already satisfied: opencv-python in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (4.5.1.48)
Requirement already satisfied: cryptography in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.4.7)
Requirement already satisfied: py-cpuinfo in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.0.0)
Requirement already satisfied: gevent in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (21.1.2)
Requirement already satisfied: schema in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.7.5)
Requirement already satisfied: psutil in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.8.0)
Requirement already satisfied: gevent-websocket in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.10.1)
Requirement already satisfied: hyperopt in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.2.7)
Requirement already satisfied: Flask in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.0.1)
Requirement already satisfied: prettytable in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.5.0)
Requirement already satisfied: Flask-SocketIO in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.1.1)
Requirement already satisfied: scikit-learn in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.24.2)
Requirement already satisfied: Pillow in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.4.0)
Requirement already satisfied: Flask-Cors in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.0.10)
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```
---
**Cell:**
```python
from datasets import load_dataset, load_metric
```
OR
```python
import datasets
```
**Traceback:**
```
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-7-34fb7ba3338d> in <module>
----> 1 from datasets import load_dataset, load_metric
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/__init__.py in <module>
32 )
33
---> 34 from .arrow_dataset import Dataset, concatenate_datasets
35 from .arrow_reader import ArrowReader, ReadInstruction
36 from .arrow_writer import ArrowWriter
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/arrow_dataset.py in <module>
59 from . import config, utils
60 from .arrow_reader import ArrowReader
---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper
63 from .filesystems import extract_path_from_uri, is_remote_filesystem
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/arrow_writer.py in <module>
26
27 from . import config, utils
---> 28 from .features import (
29 Features,
30 ImageExtensionType,
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/features/__init__.py in <module>
1 # flake8: noqa
----> 2 from .audio import Audio
3 from .features import *
4 from .features import (
5 _ArrayXD,
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/features/audio.py in <module>
5 import pyarrow as pa
6
----> 7 from ..utils.streaming_download_manager import xopen
8
9
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/utils/streaming_download_manager.py in <module>
16
17 from .. import config
---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS
19 from .download_manager import DownloadConfig, map_nested
20 from .file_utils import (
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/filesystems/__init__.py in <module>
11
12 if _has_s3fs:
---> 13 from .s3filesystem import S3FileSystem # noqa: F401
14
15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/filesystems/s3filesystem.py in <module>
----> 1 import s3fs
2
3
4 class S3FileSystem(s3fs.S3FileSystem):
5 """
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/s3fs/__init__.py in <module>
----> 1 from .core import S3FileSystem, S3File
2 from .mapping import S3Map
3
4 from ._version import get_versions
5
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/s3fs/core.py in <module>
12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper
13
---> 14 import aiobotocore
15 import botocore
16 import aiobotocore.session
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/__init__.py in <module>
----> 1 from .session import get_session, AioSession
2
3 __all__ = ['get_session', 'AioSession']
4 __version__ = '1.3.0'
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/session.py in <module>
4 from botocore import retryhandler, translate
5 from botocore.exceptions import PartialCredentialsError
----> 6 from .client import AioClientCreator, AioBaseClient
7 from .hooks import AioHierarchicalEmitter
8 from .parsers import AioResponseParserFactory
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/client.py in <module>
11 from .args import AioClientArgsCreator
12 from .utils import AioS3RegionRedirector
---> 13 from . import waiter
14
15 history_recorder = get_global_history_recorder()
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/waiter.py in <module>
4 from botocore.exceptions import ClientError
5 from botocore.waiter import WaiterModel # noqa: F401, lgtm[py/unused-import]
----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \
7 NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error
8 from botocore.docs.docstring import WaiterDocstring
ImportError: cannot import name 'is_valid_waiter_error'
```
Please let me know if there's anything else I can add to post.
[1]: https://github.com/huggingface/notebooks/blob/master/examples/text_classification_quantization_inc.ipynb | {
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"A quick fix for now is doing this:\r\n\r\n```python\r\nX_train = np.stack(dataset[\"train\"][\"image\"])[..., None]",
"This error also propagates to jax and is even trickier to fix, since `.with_format(type='jax')` will use numpy conversion internally (and fail). For a three line failure:\r\n\r\n```python\r\ndataset = datasets.load_dataset(\"mnist\")\r\ndataset.set_format(\"jax\")\r\nX_train = dataset[\"train\"][\"image\"]\r\n```",
"Hi! We've recently introduced a new Image feature that yields PIL Images (and caches transforms on them) instead of arrays.\r\n\r\nHowever, this feature requires a custom transform to yield np arrays directly:\r\n```python\r\nddict = datasets.load_dataset(\"mnist\")\r\n\r\ndef pil_image_to_array(batch):\r\n return {\"image\": [np.array(img) for img in batch[\"image\"]]} # or jnp.array(img) for Jax\r\n\r\nddict.set_transform(pil_image_to_array, columns=\"image\", output_all_columns=True)\r\n```\r\n\r\n[Docs](https://huggingface.co/docs/datasets/master/process.html#format-transform) on `set_transform`.\r\n\r\nAlso, the approach proposed by @cgarciae is not the best because it loads the entire column in memory.\r\n\r\n@albertvillanova @lhoestq WDYT? The Audio and the Image feature currently don't support the TF/Jax/PT Formatters, but for the Numpy Formatter maybe it makes more sense to return np arrays (and not a dict in the case of the Audio feature or a PIL Image object in the case of the Image feature).",
"Yes I agree it should return arrays and not a PIL image (and possible an array instead of a dict for audio data).\r\nI'm currently finishing some code refactoring of the image and audio and opening a PR today. Maybe we can look into that after the refactoring"
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} | ## Describe the bug
`dataset.set_format("np")` no longer works for image data, previously you could load the MNIST like this:
```python
dataset = load_dataset("mnist")
dataset.set_format("np")
X_train = dataset["train"]["image"][..., None] # <== No longer a numpy array
```
but now it doesn't work, `set_format("np")` seems to have no effect and the dataset just returns a list/array of PIL images instead of numpy arrays as requested.
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} | Add the KMWP & DKTC dataset.
Additional notes:
- Both datasets will be released on January 10 through the GitHub link below.
- https://github.com/tunib-ai/DKTC
- https://github.com/tunib-ai/KMWP
- So it doesn't work as a link at the moment, but the code will work soon (after it is released on January 10). | {
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"@lhoestq, I looked into how to compress with `zipfile` for which few methods exist, let me know which one looks good:\r\n1. create the file in normal `wb` mode and then zip it separately\r\n2. use `ZipFile.write_str` to write file into the archive. For this we'll need to change how we're writing files from `_write` method \r\n\r\nHow `pandas` handles it is that they have created a wrapper for standard library class `ZipFile` and allow the returned file-like handle to accept byte strings via `write` method instead of `write_str` (purpose was to change the name of function by creating that wrapper)",
"1. sounds not ideal since it creates an intermediary file.\r\nI like pandas' approach. Is it possible to implement 2. using the pandas class ? Or maybe we can have something similar ?",
"Definitely, @lhoestq! I've adapted that from original code and turns out it is faster than `gz` compression. Apart from that I've also added `infer` option to automatically infer compression type from `path_or_buf` given",
"One small thing, currently I'm assuming that user will provide compression extension in `path_or_buf`. Is it this also possible?\r\n`dataset.to_json(\"from_dataset.json\", compression=\"zip\")`? \r\nShould I put an `assert` to ensure the file name provided always has a compression extension?",
"Thanks !\r\n\r\n> One small thing, currently I'm assuming that user will provide compression extension in path_or_buf. Is it this also possible?\r\n>dataset.to_json(\"from_dataset.json\", compression=\"zip\")?\r\n>Should I put an assert to ensure the file name provided always has a compression extension?\r\n\r\nI think it's fine as it is right now :) No need to check the extension of the filename passed to `path_or_buf`.\r\n",
"> turns out it is faster than gz compression\r\n\r\nI think the default compression level of `gzip` is 9 in python, which is very slow. Maybe we can switch to compression level 6 instead which is faster, like the `gzip` command on unix",
"I found that `fsspec` has something that may interest you: [fsspec.open(..., compression=...)](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.open). I don't remember if we've already mentioned it or not\r\n\r\nIt also has `zip` if I understand correctly ! see https://github.com/fsspec/filesystem_spec/blob/master/fsspec/compression.py#L70\r\n\r\nSince `fsspec` is a dependency of `datasets` we can use all this :)\r\n\r\nLet me know if you prefer using `fsspec` instead (I haven't tested this yet to write compressed files). IMO it sounds pretty easy to use and it would make the code base simpler",
"Just tried `fsspec` but I'm not able to write compressed `zip` files :/\r\n`gzip`, `xz`, `bz2` are all working fine and it's really simple (no need for `FileWriteHandler` now!)"
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} | ## Describe the bug
Dataset entries contains a typo.
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> obqa = load_dataset('openbookqa', 'main')
>>> obqa['train'][0]
```
## Expected results
```python
{'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['A', 'B', 'C', 'D']}, 'answerKey': 'D'}
```
## Actual results
```python
{'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting']}, 'answerKey': 'D'}
```
The bug is present in all configs and all splits.
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-5.4.0-1057-aws-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 4.0.1
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"Hi ! Thanks for pushing this :)\r\n\r\nIt seems that you created this PR from an old version of `datasets` that didn't have the sem_eval_2018_task_1.py file.\r\n\r\nCan you try merging `master` into your branch ? Or re-create your PR from a branch that comes from a more recent version of `datasets` ?\r\n\r\nAnd sorry for the late response !",
"Hi! No problem! I made the new branch like you said and opened https://github.com/huggingface/datasets/pull/3643 for it. I will close this one."
] | 1,641,569,872,000 | 1,643,298,723,000 | 1,643,298,723,000 | CONTRIBUTOR | null | false | {
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} | This changes the download location of sem_eval_2018_task_1 files to include the test set labels as discussed in https://github.com/huggingface/datasets/issues/2745#issuecomment-954588500_ with @lhoestq. | {
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"After looking into this, discovered that this is already supported if the `dataset_infos.json` file is configured correctly! Here is a working example: https://huggingface.co/datasets/abidlabs/test-audio-13\r\n\r\nThis should be probably be documented, though. "
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} | **Is your feature request related to a problem? Please describe.**
Currently if I upload a CSV with paths to audio files, the column type is string instead of Audio.
**Describe the solution you'd like**
I'd like to be able to specify the types of the column, so that when loading the dataset I directly get the features types I want.
The feature types could read from the `dataset_infos.json` for example.
**Describe alternatives you've considered**
Create a dataset script to specify the features, but that seems complicated for a simple thing.
cc @abidlabs | {
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"Thanks for reporting. I think this can be fixed by improving the `CachedDatasetModuleFactory` and making it look into the `parquet` cache directory (datasets from push_to_hub are loaded with the parquet dataset builder). I'll look into it"
] | 1,641,568,345,000 | 1,641,811,484,000 | null | MEMBER | null | null | {
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} | ## Describe the bug
In offline mode, one can still access previously-cached datasets. This fails with datasets created with `push_to_hub`.
## Steps to reproduce the bug
in Python:
```
import datasets
mpwiki = datasets.load_dataset("teven/matched_passages_wikidata")
```
in bash:
```
export HF_DATASETS_OFFLINE=1
```
in Python:
```
import datasets
mpwiki = datasets.load_dataset("teven/matched_passages_wikidata")
```
## Expected results
`datasets` should find the previously-cached dataset.
## Actual results
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'teven/matched_passages_wikidata': Offline mode is enabled
## Environment info
- `datasets` version: 1.16.2.dev0
- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 3.0.0
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"The CI failures are unrelated to the changes."
] | 1,641,565,824,000 | 1,641,578,956,000 | 1,641,578,955,000 | CONTRIBUTOR | null | false | {
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"Currently, it does not work with https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0/blob/main/common_voice_7_0.py#L146 (which was the goal), because `dl_manager.download_config.use_auth_token` is ignored, and the authentication is required to be use `huggingface-cli login`.\r\nIn my use case (dataset viewer), I'd prefer to use a specific \"User Token Access\", with only the \"read\" role (https://huggingface.co/settings/token).\r\n\r\nSee https://github.com/huggingface/datasets-preview-backend/issues/74#issuecomment-1007316853 for the context",
"> Simply passing download_config is ok :)\r\n\r\nhmm, I prefer only passing use_auth_token. But the question is more: is it correct, in the (convoluted) case if `download_config.use_auth_token` exists and is different from `use_auth_token`? Which one should be used?",
"If both are passed, `use_auth_token` should have the priority (more specific parameters have the higher priority)"
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**Is your feature request related to a problem? Please describe.**
My use case is that I have one writer that adds columns and multiple workers reading the same `Dataset`. Each worker should have access to columns added by the writer when they reload the dataset.
I understand that we shouldn't overwrite an arrow file as this could cause Segfault and so on. Before 1.16, I was able to overwrite the dataset and that would work most of the time with some retries.
**Describe the solution you'd like**
I was thinking that if we could append `Dataset._data_files`, when the workers reload the Dataset, they would get the new columns.
**Describe alternatives you've considered**
I currently need to
1. Save multiple "versions" of the dataset and load the latest.
2. Try working with cache files to get the latest columns.
**Additional context**
I think this would be a great addition to HFDataset as Parquet supports multi-files input out of the box!
I can make a PR myself with some pointers as needed :) | {
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"Hi ! Thanks for your message :) This is a great idea indeed. We haven't started working on this yet though. For now I guess you can host your metric on the Hub (either with your model or your dataset) and use `hf_hub_download` to download it (docs [here](https://github.com/huggingface/huggingface_hub/blob/main/docs/hub/how-to-downstream.md#cached_download))",
"This is a great solution in the meantime, thanks!",
"Here's the code I used, in case it can be of help to someone else:\r\n```python\r\nimport os, shutil\r\nfrom huggingface_hub import hf_hub_download\r\ndef download_metric(repo_id, file_path):\r\n # repo_id: for models \"username/model_name\", for datasets \"datasets/username/model_name\"\r\n local_metric_path = hf_hub_download(repo_id=repo_id, filename=file_path)\r\n updated_local_metric_path = (os.path.dirname(local_metric_path) + os.path.basename(local_metric_path).replace(\".\", \"_\") + \".py\")\r\n shutil.copy(local_metric_path, updated_local_metric_path)\r\n return updated_local_metric_path\r\n\r\nmetric = load_metric(download_metric(REPO_ID, FILE_PATH))\r\n```"
] | 1,641,468,386,000 | 1,641,760,093,000 | null | CONTRIBUTOR | null | null | {
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} | **Is your feature request related to a problem? Please describe.**
Currently, I can load a metric implemented by me by providing the local path to the file in `load_metric`.
However, there is no option to do it with the metric uploaded to the hub.
This means that if I want to allow other users to use it, they must download it first which makes the usage less smooth.
**Describe the solution you'd like**
Load metrics from the hub just like datasets are loaded.
In order to not break stuff, the convention can be to put the metric file in a "metrics" folder in the hub.
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* more details about the data structure
* this dataset does not contain annotations | {
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"This should also resolve: https://github.com/huggingface/datasets/issues/3185."
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} | **Is your feature request related to a problem? Please describe.**
We should support 7-zip compressed data files:
- in `extract`
- in `iter_archive`
both in streaming and non-streaming modes.
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} | Hi,
I use torch.utils.data.Dataset to define my own data, but I need to use the 'map' function of datasets.arrow_dataset.Dataset later, so I hope to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset.
Here is an example.
```
from torch.utils.data import Dataset
from datasets.arrow_dataset import Dataset as HFDataset
class ADataset(Dataset):
def __init__(self, data):
super().__init__()
self.data = data
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return self.len
class MDataset():
def __init__(self, tokenizer: AutoTokenizer, data_args, training_args):
self.train_dataset = ADataset(data_args)
self.tokenizer = tokenizer
self.data_args = data_args
self.train_dataset = self.train_dataset.map(
self.process_function,
batched=True,
remove_columns=column_names,
load_from_cache_file=True,
desc="Running tokenizer on train dataset",
)
def process_function(self, examples):
sentences = [" ".join(sample[0][3]) for sample in examples]
tokenized = self.tokenizer(
sentences,
max_length=self.max_seq_len,
padding=self.padding,
truncation=True)
```
But it would raise an ERROR, AttributeError: 'ADataset' object has no attribute 'map'.
so how to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?
Thanks in advance!
| {
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"The CI failure is about some missing tags or sections in the dataset cards, and is unrelated to the part about non commercial use of this PR. Merging"
] | 1,641,423,698,000 | 1,641,495,500,000 | 1,641,495,499,000 | CONTRIBUTOR | null | false | {
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} | Noticing that the recent commit throws a lot of errors in the automatic checks. It looks to me that those errors are simply errors that were already there (metadata issues), unrelated to what I've just changed, but worth another look to make sure. | {
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} | Updates for the following datacards:
multilingual_librispeech
openslr
speech commands
superb
timit_asr
vctk | {
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"Pushed the lastest changes!"
] | 1,641,408,305,000 | 1,642,028,386,000 | 1,642,028,385,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/3535 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3535/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3535/comments | https://api.github.com/repos/huggingface/datasets/issues/3535/events | https://github.com/huggingface/datasets/pull/3535 | 1,094,633,214 | PR_kwDODunzps4wkxv0 | 3,535 | Add SVHN dataset | {
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Additional notes:
* compared to the TFDS implementation, exposes additional the "full numbers" config
* adds the streaming support for `os.path.splitext` and `scipy.io.loadmat`
* adds `h5py` to the requirements list for the dummy data test | {
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Close #3510. | {
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"known issue due to https://github.com/huggingface/datasets/pull/2362 (and [internal](https://github.com/huggingface/moon-landing/issues/946)) , will be solved soon"
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} | When I want to look at all datasets of the category: `speech-processing` *i.e.* https://huggingface.co/datasets?task_categories=task_categories:speech-processing&sort=downloads , then the following dataset doesn't show up for some reason:
- https://huggingface.co/datasets/speech_commands
even thought it's task tags seem correct:
https://raw.githubusercontent.com/huggingface/datasets/master/datasets/speech_commands/README.md | {
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"this is great, maybe just put all of it in one line?\r\n\r\n> TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging"
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CC: @julien-c @VictorSanh | {
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} | ## Describe the bug
As reported by @julien-c, many community datasets contain the line `YAML tags:` at the top of the YAML section in the header of the README file. See e.g.: https://huggingface.co/datasets/bigscience/P3/commit/a03bea08cf4d58f268b469593069af6aeb15de32
Maybe we should give clearer instruction/hints in the README template.
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Correct license type in metadata. | {
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"Thanks, @meg-huggingface for the updates!\r\n\r\nThanks also for setting me as a reviewer, but I'm totally not a specialist of the datasets themselves, so I prefer to just lurk and let @lhoestq @albertvillanova @mariosasko or @patrickvonplaten review these changes (https://github.com/huggingface/datasets/pulls/assigned/meg-huggingface).",
"The MIT license seems to be for the crawling code, no ? Then maybe we can also redirect users to the [terms of smashwords.com](https://www.smashwords.com/about/tos) regarding copyrights, in particular the paragraph 10 for end-users. In particular it seems that end users can download and use the content \"for their personal enjoyment in any reasonable non-commercial manner in compliance with copyright law\" and the smashwords end-users agreement.\r\n\r\nIt should be the same for https://github.com/huggingface/datasets/pull/3526 as well"
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} | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("wmt19", 'zh-en')
```
## Expected results
The dataset should download.
## Actual results
`ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip`
## Environment info
- `datasets` version: 1.15.1
- Platform: Linux
- Python version: 3.8
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"I'm not sure that we want to change the tags at the top of the cards by hand. Those are used to create the tags in the hub. Although looking at all the tags now, we might want to normalize the current tags again (hyphens or no, \".0\" or no). Maybe we could add a binary tag for public domain or not?",
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} | Without this change the following script (with any lang parameter) consistently fails. After changing to the HTTPS URL, the script works as expected.
```python
from datasets import load_dataset
dataset = load_dataset("cc100", lang="en")
```
This is the error produced by the previous script:
```sh
Using custom data configuration en-lang=en
Downloading and preparing dataset cc100/en to /home/antti/.cache/huggingface/datasets/cc100/en-lang=en/0.0.0/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b...
Traceback (most recent call last):
File "/home/antti/tmp/cc100/cc100.py", line 3, in <module>
dataset = load_dataset("cc100", lang="en")
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/load.py", line 1694, in load_dataset
builder_instance.download_and_prepare(
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 595, in download_and_prepare
self._download_and_prepare(
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/antti/.cache/huggingface/modules/datasets_modules/datasets/cc100/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b/cc100.py", line 117, in _split_generators
path = dl_manager.download_and_extract(download_url)
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 308, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 251, in map_nested
return function(data_struct)
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach http://data.statmt.org/cc-100/en.txt.xz (error 503)
```
Note that I get the same behavior also using curl on the command line. The plain HTTP "curl -L http://data.statmt.org/cc-100/en.txt.xz" fails with "503 Service unavailable", but the with the HTTPS version of the URL curl starts downloading the file.
My guess is that the server does overly aggressive rate-limitting. When a client requests an HTTP URL, it (sensibly) gets redirected to the HTTPS equivalent, but now the server notices two requests coming from the same client (the original HTTP and the redirected HTTPS) during a brief time windows and rate-limitter kicks in and blocks the second request! If the client initally uses the HTTPS URL there's only one incoming request which the rate-limitter allows. | {
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"In the framework of BigScience:\r\n- bigscience-workshop/data_tooling#121\r\n\r\nI have created this dataset as a community dataset: https://huggingface.co/datasets/albertvillanova/pmc_open_access\r\n\r\nHowever, I was wondering that it may be more appropriate to move it under an org namespace: `pubmed_central` or `pmc`\r\nThis way, we could add other datasets I'm also working on: Author Manuscript Dataset, Historical OCR Dataset, LitArch Open Access Subset.\r\n\r\nWhat do you think? @lhoestq @mariosasko ",
"Why not ! Having them under such namespaces would also help people searching for this kind of datasets.\r\nWe can also invite people from pubmed at one point",
"DONE: https://huggingface.co/datasets/pmc/open_access"
] | 1,641,279,275,000 | 1,642,433,157,000 | 1,642,433,157,000 | MEMBER | null | null | {
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} | ## Adding a Dataset
- **Name:** PubMed Central Open Access
- **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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"Thanks so much, @mariosasko and @lhoestq , much appreciated!"
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"Thanks for reporting @VictorSanh.\r\n\r\nI'm looking at it... "
] | 1,641,225,518,000 | 1,644,844,903,000 | 1,644,844,903,000 | MEMBER | null | null | {
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} | ## Describe the bug
I am trying to load a dataset called `evidence_infer_treatment`. The first subset (`1.1`) works fine but the second returns an error (`2.0`). It downloads a file but crashes during the checksums.
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> load_dataset("evidence_infer_treatment", "2.0")
Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset
use_auth_token=use_auth_token,
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 664, in _download_and_prepare
self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 33, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'http://evidence-inference.ebm-nlp.com/v2.0.tar.gz'}
```
I did try to pass the argument `ignore_verifications=True` but run into an error when trying to build the dataset:
```python
>>> load_dataset("evidence_infer_treatment", "2.0", ignore_verifications=True, download_mode="force_redownload")
Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24...
Downloading: 164MB [00:23, 6.98MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset
use_auth_token=use_auth_token,
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 681, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 1080, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 1032, in encode_example
return encode_nested_example(self, example)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in encode_nested_example
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in encode_nested_example
list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in <listcomp>
list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 828, in encode_nested_example
for k, dict_tuples in utils.zip_dict(schema.feature, *obj):
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in zip_dict
yield key, tuple(d[key] for d in dicts)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in <genexpr>
yield key, tuple(d[key] for d in dicts)
KeyError: ''
```
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.0.0-1020-gcp-x86_64-with-debian-buster-sid
- Python version: 3.7.11
- PyArrow version: 6.0.1
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"Hi, which dataset is giving you an error?"
] | 1,641,202,871,000 | 1,642,425,965,000 | 1,642,425,965,000 | NONE | null | 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/3511 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3511/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3511/comments | https://api.github.com/repos/huggingface/datasets/issues/3511/events | https://github.com/huggingface/datasets/issues/3511 | 1,092,170,411 | I_kwDODunzps5BGTKr | 3,511 | Dataset | {
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"Can you reopen with the correct dataset name (if relevant)?\r\n\r\nThanks",
"The dataset viewer was down tonight. It works again."
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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/3510 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3510/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3510/comments | https://api.github.com/repos/huggingface/datasets/issues/3510/events | https://github.com/huggingface/datasets/issues/3510 | 1,091,997,004 | I_kwDODunzps5BFo1M | 3,510 | `wiki_dpr` details for Open Domain Question Answering tasks | {
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"Hi ! According to the DPR paper, the wikipedia dump is the one from Dec. 20, 2018.\r\nEach instance contains a paragraph of at most 100 word, as well as the title of the wikipedia page it comes from and the DPR embedding (a 768-d vector).",
"Closed by:\r\n- #3534"
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} | Hey guys!
Thanks for creating the `wiki_dpr` dataset!
I am currently trying to use the dataset for context retrieval using DPR on NQ questions and need details about what each of the files and data instances mean, which version of the Wikipedia dump it uses, etc. Please respond at your earliest convenience regarding the same! Thanks a ton!
P.S.: (If one of @thomwolf @lewtun @lhoestq could respond, that would be even better since they have the first-hand details of the dataset. If anyone else has those, please reach out! Thanks!) | {
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https://api.github.com/repos/huggingface/datasets/issues/3507 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3507/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3507/comments | https://api.github.com/repos/huggingface/datasets/issues/3507/events | https://github.com/huggingface/datasets/issues/3507 | 1,091,214,808 | I_kwDODunzps5BCp3Y | 3,507 | Discuss whether support canonical datasets w/o dataset_infos.json and/or dummy data | {
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"IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work), so that for datasets that have a working dataset preview, we can remove the dummy data IMO. On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data.\r\n\r\nI don't really have an opinion regarding the JSON metadata as I don't know enough about it.\r\n\r\n",
"I don't know all the details, but generally I'd be in favor of unifying the metadata formats into YAML inside .md (and so deprecating the dataset_infos.json) \r\n\r\n(Ultimately the CI can run on \"HuggingFace Actions\" instead of on GitHub)",
"The dataset_infos.json file currently has these useful infos for each dataset configuration, that I think can be moved to the dataset tags:\r\n- Size of the dataset in MB: download size, arrow file size, and total size (sum of download + arrow)\r\n- Size of each split in MB and number of examples. Again this can be moved to the dataset tags\r\n- Feature type of each column\r\n- supported task templates (it defines what columns correspond to the features and labels for example)\r\n\r\nBut it also has this, which I'm not sure if it should be in the tags or not:\r\n- Checksums of the downloaded files for integrity verifications\r\n\r\nSo ultimately this file could probably be deprecated in favor of having the infos in the tags.\r\n\r\n> Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nTo get the exact number of examples and size in MB of the dataset, one needs to download and generate it completely. IMO these infos are very important when someone considers using a dataset. Though using streaming we could do some extrapolation to have approximate values instead.\r\n\r\nFor the integrity verifications we also need the number of examples and the checksums of the downloaded files, so it requires the dataset to be fully downloaded once. This can be optional though.\r\n\r\n> IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work)\r\n\r\nI agree with this. Usually if a dataset works in streaming mode, then it works in non-streaming mode (the other way around is not true though).\r\n\r\n> On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data.\r\n\r\nYes indeed, or at least make sure that it was tested on the true data.",
"(note that if we wanted to display sizes, etc we could also pretty easily parse the `dataset_infos.json` on the hub side)",
"I agree that we can move the relevant parts of `dataset_infos.json` to the YAML tags.\r\n\r\n> On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data. <\r\n> > Yes indeed, or at least make sure that it was tested on the true data.\r\n\r\nI like the idea of testing streaming and falling back to the dummy data test if streaming does not work. Generating dummy data can be very tedious, so this would be a nice incentive for the contributors to make their datasets streamable. ",
"CC: @severo ",
"About dummy data, please see e.g. this PR: https://github.com/huggingface/datasets/pull/3692/commits/62368daac0672041524a471386d5e78005cf357a\r\n- I updated the previous dummy data: I just had to rename the file and its directory\r\n - the dummy data zip contains only a single file: `pubmed22n0001.xml.gz`\r\n\r\nThen I discover it fails: https://app.circleci.com/pipelines/github/huggingface/datasets/9800/workflows/173a4433-8feb-4fc6-ab9e-59762084e3e1/jobs/60437\r\n```\r\nNo such file or directory: '.../dummy_data/pubmed22n0002.xml.gz'\r\n```\r\n- it needs dummy data for all the 1114 files: \r\n `_URLs = [f\"ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed22n{i:04d}.xml.gz\" for i in range(1, 1115)]`\r\n- this confirms me that it never passed the test: these dummy data files were not present before my PR\r\n- therefore, is it really useful the data test if we just ignore it when it does not pass?\r\n\r\nIn relation with JSON metadata, I'm generating the file for `pubmed` (see above) in a GCP instance: it's running for more than 3 hours and only 9 million examples generated so far (before my PR, it had 32 million, now it has more).",
"I mention in https://github.com/huggingface/datasets-server/wiki/Preliminary-design that the future \"datasets server\" could be in charge of generating both the dummy data and the dataset-info.json file if required (or their equivalent).",
"Hi ! I think dummy data generation is out of scope for the datasets server, since it's about generating the original data files.\r\n\r\nThat would be amazing to have it generate the dataset_infos.json though !",
"From some offline discussion with @mariosasko and especially for vision datasets, we'll probably not require dummy data anymore and use streaming instead :) This will make adding a new dataset much easier.\r\nThis should also make sure that streaming works as expected directly in the CI, without having to check the dataset viewer once the PR is merged",
"OK. I removed the \"dummy data\" item from the services of the dataset server",
"It seems that migration from dataset-info.json to dataset card YAML has been acted.\r\n\r\nProbably it's a good idea, but I didn't find the pros and cons of this decision, so I put some I could think of:\r\n\r\npros:\r\n- only one file to parse, share, sync\r\n- it gives a hint to the users that if you write your dataset card, you should also specify the metadata\r\n\r\ncons:\r\n- the metadata header might be very long, before reaching the start of the README/dataset card. It might be surprising when you edit the file because the metadata is not shown on top when the dataset card is rendered (dataset page). It also somewhat prevents including large strings like the checksums\r\n- YAML vs JSON: not sure which one is easier for users to fill and maintain\r\n- two concepts are mixed in the same file (metadata and documentation). This means that if you're interested only in one of them, you still have to know how to parse the whole file.\r\n- [low priority] besides the JSON file, we might want to support yaml or toml file if the user prefers (as [prettier](https://prettier.io/docs/en/configuration.html) and others do for their config files, for example). Inside the md, I understand that only YAML is allowed",
"> the metadata header might be very long, before reaching the start of the README/dataset card. It might be surprising when you edit the file because the metadata is not shown on top when the dataset card is rendered (dataset page). It also somewhat prevents including large strings like the checksums\r\n\r\nNote that we could simply not have the checksums in the YAML metadata at all, or maybe at one point have a pointer to another file instead.\r\n\r\nWe can also choose to hide (collapse) certain sections in the YAML by default when we open the dataset card editor.\r\n\r\n> two concepts are mixed in the same file (metadata and documentation). This means that if you're interested only in one of them, you still have to know how to parse the whole file.\r\n\r\nI think it's fine for now. Later if we really end up with too many YAML sections we can see if we need to tweak the API endpoints or the `datasets`/`huggingface_hub` tools\r\n\r\n> YAML vs JSON: not sure which one is easier for users to fill and maintain\r\n\r\nRegarding YAML vs JSON: I think YAML is easier to write by hand, and I also think that it's better for consistency - i.e. we're using more and more YAML to configure models/datasets/spaces",
"I didn't know the decision was already taken. Good to know. 😅",
"> the metadata header might be very long, before reaching the start of the README/dataset card. It might be surprising when you edit the file because the metadata is not shown on top when the dataset card is rendered (dataset page). It also somewhat prevents including large strings like the checksums\r\n\r\nWe can definitely work on this on the hub side to make the UX better"
] | 1,640,883,865,000 | 1,645,466,953,000 | null | MEMBER | null | null | {
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} | I open this PR to have a public discussion about this topic and make a decision.
As previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?
On the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:
- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)
- we are migrating canonical datasets to the Hub
Do we really need to continue testing them in out CI?
Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).
Feel free to ping other people for the discussion.
CC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw | {
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} | - Related to: #3244
- Fixes: #3503
We extends `.filter( ... batched: bool)` support to DatasetDict. | {
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"Hi! This is probably due to the fact that `IterableDataset.map` sets `features` to `None` before mapping examples. We can fix the issue by passing the old `features` dict to the map generator and performing encoding/decoding there (before calling the map transform function)."
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} | ## Describe the bug
I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only.
I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset, Audio
from transformers import Wav2Vec2Processor
def encode(batch, processor):
print("Audio: ",batch['audio'])
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
return batch
def print_ds(ds):
iterator = iter(ds)
for d in iterator:
print("Data: ",d)
break
processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path)
dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'},
data_dir="data", streaming=True, split="train")
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.map(lambda x: encode(x,processor))
print("Features: ",dataset.features)
print_ds(dataset)
```
## Expected results
map function not printing Audio type features be used with processor function and getting error in processor call due to this.
## Actual results
# after load_dataset call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'}
# after cast_column call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ...,
1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}}
# after map call
Features: None
Audio: data/0116_003.wav
Traceback (most recent call last):
File "demo2.py", line 36, in <module>
print_ds(dataset)
File "demo2.py", line 11, in print_ds
for d in iterator:
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__
yield key, self.function(example)
File "demo2.py", line 32, in <lambda>
dataset = dataset.map(lambda x: batch_encode(x,processor))
File "demo2.py", line 6, in batch_encode
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
TypeError: string indices must be integers
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-4.14.243 with-debian-bullseye-sid
- Python version: 3.7.9
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3504 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3504/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3504/comments | https://api.github.com/repos/huggingface/datasets/issues/3504/events | https://github.com/huggingface/datasets/issues/3504 | 1,090,682,230 | I_kwDODunzps5BAn12 | 3,504 | Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst | {
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"Hi @ToddMorrill, thanks for reporting.\r\n\r\nThree weeks ago I contacted the team who created the Pile dataset to report this issue with their data host server: https://the-eye.eu\r\n\r\nThey told me that unfortunately, the-eye was heavily affected by the recent tornado catastrophe in the US. They hope to have their data back online asap.",
"Hi @ToddMorrill, people from the Pile team have mirrored their data in a new host server: https://mystic.the-eye.eu\r\n\r\nSee:\r\n- #3627\r\n\r\nIt should work if you update your URL.\r\n\r\nWe should also update the URL in our course material.",
"The old URL is still present in the HuggingFace course here: \r\nhttps://huggingface.co/course/chapter5/4?fw=pt\r\n\r\nI have created a PR for the Notebook here: https://github.com/huggingface/notebooks/pull/148\r\nNot sure if the HTML is in a public repo. I wasn't able to find it. ",
"Fixed the other two URLs here: \r\nhttps://github.com/mwunderlich/notebooks/pull/1"
] | 1,640,802,200,000 | 1,645,170,540,000 | 1,645,110,265,000 | NONE | null | null | {
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} | ## Describe the bug
I am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https://huggingface.co/course/chapter5/4?fw=pt).
https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
I also tried with `wget` as follows.
```
wget https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
```
## Expected results
I expect to be able to download this file.
## Actual results
Traceback
```
---------------------------------------------------------------------------
timeout Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
158 try:
--> 159 conn = connection.create_connection(
160 (self._dns_host, self.port), self.timeout, **extra_kw
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
83 if err is not None:
---> 84 raise err
85
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
73 sock.bind(source_address)
---> 74 sock.connect(sa)
75 return sock
timeout: timed out
During handling of the above exception, another exception occurred:
ConnectTimeoutError Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
664 # Make the request on the httplib connection object.
--> 665 httplib_response = self._make_request(
666 conn,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
375 try:
--> 376 self._validate_conn(conn)
377 except (SocketTimeout, BaseSSLError) as e:
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
995 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
--> 996 conn.connect()
997
/usr/lib/python3/dist-packages/urllib3/connection.py in connect(self)
313 # Add certificate verification
--> 314 conn = self._new_conn()
315 hostname = self.host
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
163 except SocketTimeout:
--> 164 raise ConnectTimeoutError(
165 self,
ConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')
During handling of the above exception, another exception occurred:
MaxRetryError Traceback (most recent call last)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
438 if not chunked:
--> 439 resp = conn.urlopen(
440 method=request.method,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
718
--> 719 retries = retries.increment(
720 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
/usr/lib/python3/dist-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
435 if new_retry.is_exhausted():
--> 436 raise MaxRetryError(_pool, url, error or ResponseError(cause))
437
MaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
During handling of the above exception, another exception occurred:
ConnectTimeout Traceback (most recent call last)
/tmp/ipykernel_15104/606583593.py in <module>
3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)
4 data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
----> 5 pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
6 pubmed_dataset
~/.local/lib/python3.8/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)
1655
1656 # Create a dataset builder
-> 1657 builder_instance = load_dataset_builder(
1658 path=path,
1659 name=name,
~/.local/lib/python3.8/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)
1492 download_config = download_config.copy() if download_config else DownloadConfig()
1493 download_config.use_auth_token = use_auth_token
-> 1494 dataset_module = dataset_module_factory(
1495 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files
1496 )
~/.local/lib/python3.8/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)
1116 # Try packaged
1117 if path in _PACKAGED_DATASETS_MODULES:
-> 1118 return PackagedDatasetModuleFactory(
1119 path, data_files=data_files, download_config=download_config, download_mode=download_mode
1120 ).get_module()
~/.local/lib/python3.8/site-packages/datasets/load.py in get_module(self)
773 else get_patterns_locally(str(Path().resolve()))
774 )
--> 775 data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)
776 module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]
777 builder_kwargs = {"hash": hash, "data_files": data_files}
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
576 for key, patterns_for_key in patterns.items():
577 out[key] = (
--> 578 DataFilesList.from_local_or_remote(
579 patterns_for_key,
580 base_path=base_path,
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
545 base_path = base_path if base_path is not None else str(Path().resolve())
546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
--> 547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)
548 return cls(data_files, origin_metadata)
549
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)
492 data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None
493 ) -> Tuple[str]:
--> 494 return thread_map(
495 partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),
496 data_files,
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)
92 """
93 from concurrent.futures import ThreadPoolExecutor
---> 94 return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
95
96
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)
74 map_args.update(chunksize=chunksize)
75 with PoolExecutor(**pool_kwargs) as ex:
---> 76 return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))
77
78
~/.local/lib/python3.8/site-packages/tqdm/notebook.py in __iter__(self)
252 def __iter__(self):
253 try:
--> 254 for obj in super(tqdm_notebook, self).__iter__():
255 # return super(tqdm...) will not catch exception
256 yield obj
~/.local/lib/python3.8/site-packages/tqdm/std.py in __iter__(self)
1171 # (note: keep this check outside the loop for performance)
1172 if self.disable:
-> 1173 for obj in iterable:
1174 yield obj
1175 return
/usr/lib/python3.8/concurrent/futures/_base.py in result_iterator()
617 # Careful not to keep a reference to the popped future
618 if timeout is None:
--> 619 yield fs.pop().result()
620 else:
621 yield fs.pop().result(end_time - time.monotonic())
/usr/lib/python3.8/concurrent/futures/_base.py in result(self, timeout)
442 raise CancelledError()
443 elif self._state == FINISHED:
--> 444 return self.__get_result()
445 else:
446 raise TimeoutError()
/usr/lib/python3.8/concurrent/futures/_base.py in __get_result(self)
387 if self._exception:
388 try:
--> 389 raise self._exception
390 finally:
391 # Break a reference cycle with the exception in self._exception
/usr/lib/python3.8/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)
483 if isinstance(data_file, Url):
484 data_file = str(data_file)
--> 485 return (request_etag(data_file, use_auth_token=use_auth_token),)
486 else:
487 data_file = str(data_file.resolve())
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in request_etag(url, use_auth_token)
489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:
490 headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)
--> 491 response = http_head(url, headers=headers, max_retries=3)
492 response.raise_for_status()
493 etag = response.headers.get("ETag") if response.ok else None
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
474 headers = copy.deepcopy(headers) or {}
475 headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent"))
--> 476 response = _request_with_retry(
477 method="HEAD",
478 url=url,
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
408 if tries > max_retries:
--> 409 raise err
410 else:
411 logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]")
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
403 tries += 1
404 try:
--> 405 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
406 success = True
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/usr/lib/python3/dist-packages/requests/api.py in request(method, url, **kwargs)
58 # cases, and look like a memory leak in others.
59 with sessions.Session() as session:
---> 60 return session.request(method=method, url=url, **kwargs)
61
62
/usr/lib/python3/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
531 }
532 send_kwargs.update(settings)
--> 533 resp = self.send(prep, **send_kwargs)
534
535 return resp
/usr/lib/python3/dist-packages/requests/sessions.py in send(self, request, **kwargs)
644
645 # Send the request
--> 646 r = adapter.send(request, **kwargs)
647
648 # Total elapsed time of the request (approximately)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
502 # TODO: Remove this in 3.0.0: see #2811
503 if not isinstance(e.reason, NewConnectionError):
--> 504 raise ConnectTimeout(e, request=request)
505
506 if isinstance(e.reason, ResponseError):
ConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
```
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
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} | I hope this is really a bug, I could not find it among the open issues
## Describe the bug
using `batched=False` in DataSet.filter throws error
```python
TypeError: filter() got an unexpected keyword argument 'batched'
```
but in the docs it is lister as an argument.
## Steps to reproduce the bug
```python
task = "mnli"
max_length = 128
tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/")
dataset = load_dataset("glue", task)
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mnli-mm": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
##### tokenization_parameters
sentence1_key, sentence2_key = task_to_keys[task]
def preprocess_function(examples, max_length):
if sentence2_key is None:
return tokenizer(
examples[sentence1_key], truncation=True, max_length=max_length
)
return tokenizer(
examples[sentence1_key],
examples[sentence2_key],
truncation=False,
padding="max_length",
max_length=max_length,
)
encoded_dataset = dataset.map(
lambda x: preprocess_function(x, max_length=max_length), batched=False
)
encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1, 1.17.0
- Platform: ubuntu
- Python version: 3.8.12
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"Hi ! Data streaming uses `fsspec` to read the data files progressively. IIRC the block size for buffering is 5MiB by default. So every time you finish iterating over a block, it downloads the next one. You can still try to increase the `fsspec` block size for buffering if it can help. To do so you just need to increase `fsspec.spec.AbstractBufferedFile.DEFAULT_BLOCK_SIZE `\r\n\r\nCurrently this is unfortunately done in a single thread, so it blocks the processing to download and uncompress the next block. At one point it would be nice to be able to do that in parallel !",
"Hi! Thanks for the help, I will try it :)"
] | 1,640,726,273,000 | 1,641,569,961,000 | null | CONTRIBUTOR | null | null | {
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} | **Is your feature request related to a problem? Please describe.**
I want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing.
**Describe the solution you'd like**
I would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.
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} | [] | 1,640,721,007,000 | 1,641,408,503,000 | 1,641,400,701,000 | CONTRIBUTOR | null | false | {
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} | I made a script some time back to fetch `pretty_names` from `papers_with_code` dataset along with some other rules incase that dataset wasn't available on `papers_with_code`. Updating them in the `README` of `datasets`. Took only the first 200 datasets into consideration and after some eyeballing, most of them were looking good to me! | {
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https://api.github.com/repos/huggingface/datasets/issues/3497 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3497/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3497/comments | https://api.github.com/repos/huggingface/datasets/issues/3497/events | https://github.com/huggingface/datasets/issues/3497 | 1,090,050,148 | I_kwDODunzps5A-Nhk | 3,497 | Changing sampling rate in audio dataset and subsequently mapping with `num_proc > 1` leads to weird bug | {
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"Same error occures when using max samples with https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py",
"I'm seeing this too, when using preprocessing_num_workers with \r\nhttps://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py"
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} | Running:
```python
from datasets import load_dataset, DatasetDict
import datasets
from transformers import AutoFeatureExtractor
raw_datasets = DatasetDict()
raw_datasets["train"] = load_dataset("common_voice", "ab", split="train")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
raw_datasets = raw_datasets.cast_column(
"audio", datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
)
num_workers = 16
def prepare_dataset(batch):
sample = batch["audio"]
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
batch["input_values"] = inputs.input_values[0]
batch["input_length"] = len(batch["input_values"])
return batch
raw_datasets.map(
prepare_dataset,
remove_columns=next(iter(raw_datasets.values())).column_names,
num_proc=16,
desc="preprocess datasets",
)
```
gives
```bash
File "/home/patrick/experiments/run_bug.py", line 25, in <module>
raw_datasets.map(
File "/home/patrick/python_bin/datasets/dataset_dict.py", line 492, in map
{
File "/home/patrick/python_bin/datasets/dataset_dict.py", line 493, in <dictcomp>
k: dataset.map(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2139, in map
shards = [
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2140, in <listcomp>
self.shard(num_shards=num_proc, index=rank, contiguous=True, keep_in_memory=keep_in_memory)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 3164, in shard
return self.select(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 485, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/patrick/python_bin/datasets/fingerprint.py", line 411, in wrapper
out = func(self, *args, **kwargs)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2756, in select
return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2667, in _new_dataset_with_indices
return Dataset(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 659, in __init__
raise ValueError(
ValueError: External features info don't match the dataset:
Got
{'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)}
with type
struct<client_id: string, path: string, audio: string, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string>
but expected something like
{'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)}
with type
struct<client_id: string, path: string, audio: struct<path: string, bytes: binary>, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string>
```
Versions:
```python
- `datasets` version: 1.16.2.dev0
- Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 6.0.1
```
and `transformers`:
```
- `transformers` version: 4.16.0.dev0
- Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33
- Python version: 3.9.7
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3496 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3496/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3496/comments | https://api.github.com/repos/huggingface/datasets/issues/3496/events | https://github.com/huggingface/datasets/pull/3496 | 1,089,989,155 | PR_kwDODunzps4wV1_w | 3,496 | Update version of pib dataset and make it streamable | {
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"It seems like there is still an error: `Message: 'TarContainedFile' object has no attribute 'readable'`\r\n\r\nhttps://huggingface.co/datasets/pib/viewer",
"@severo I was wondering about that...\r\n\r\nIt works fine when I run it in streaming mode in my terminal:\r\n```python\r\nIn [3]: from datasets import load_dataset; ds = load_dataset(\"pib\", \"gu-pa\", split=\"train\", streaming=True); item = next(iter(ds))\r\n\r\nIn [4]: item\r\nOut[4]: \r\n{'translation': {'gu': 'એવો નિર્ણય લેવાયો હતો કે ખંતપૂર્વકની કામગીરી હાથ ધરવા, કાયદેસર અને ટેકનિકલ મૂલ્યાંકન કરવા, વેન્ચર કેપિટલ ઇન્વેસ્ટમેન્ટ સમિતિની બેઠક યોજવા વગેરે એઆઇએફને કરવામાં આવેલ પ્રતિબદ્ધતાના 0.50 ટકા સુધી અને બાકીની રકમ એફએફએસને પૂર્ણ કરવામાં આવશે.',\r\n 'pa': 'ਇਹ ਵੀ ਫੈਸਲਾ ਕੀਤਾ ਗਿਆ ਕਿ ਐੱਫਆਈਆਈ ਅਤੇ ਬਕਾਏ ਲਈ ਕੀਤੀਆਂ ਗਈਆਂ ਵਚਨਬੱਧਤਾਵਾਂ ਦੇ 0.50 % ਦੀ ਸੀਮਾ ਤੱਕ ਐੱਫਈਐੱਸ ਨੂੰ ਮਿਲਿਆ ਜਾਏਗਾ, ਇਸ ਨਾਲ ਉੱਦਮ ਪੂੰਜੀ ਨਿਵੇਸ਼ ਕਮੇਟੀ ਦੀ ਬੈਠਕ ਦਾ ਆਯੋਜਨ ਉਚਿਤ ਸਾਵਧਾਨੀ, ਕਾਨੂੰਨੀ ਅਤੇ ਤਕਨੀਕੀ ਮੁੱਲਾਂਕਣ ਲਈ ਸੰਚਾਲਨ ਖਰਚ ਆਦਿ ਦੀ ਪੂਰਤੀ ਹੋਵੇਗੀ।'}}\r\n```",
"OK, it works now!\r\n\r\n<img width=\"794\" alt=\"Capture d’écran 2022-01-03 à 15 41 44\" src=\"https://user-images.githubusercontent.com/1676121/147943676-6199d1a9-f288-4350-af96-a7c297ebb743.png\">\r\n"
] | 1,640,707,315,000 | 1,641,220,948,000 | 1,640,767,377,000 | MEMBER | null | false | {
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} | This PR:
- Updates version of pib dataset: from 0.0.0 to 1.3.0
- Makes the dataset streamable
Fix #3491.
CC: @severo | {
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} | ## Adding a Dataset
- **Name:** VoxLingua107
- **Description:** VoxLingua107 is a speech dataset for training spoken language identification models.
- **Paper:** https://arxiv.org/abs/2011.12998
- **Data:** http://bark.phon.ioc.ee/voxlingua107/
- **Motivation:** 107 languages, totaling 6628 hours for the train split.
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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"Good catch !!",
"The CI fail is unrelated to this PR and fixed on master, merging :)"
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By cloning the full repository we can properly detect a new release, and tag all the dataset repositories accordingly
cc @SBrandeis | {
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} | (Partially) closes #3480. I have added `gzip` compression for `to_json`. I realised we can run into this compression problem with `to_csv` as well. `IOHandler` can be used for `to_csv` too. Please let me know if any changes are required. | {
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Related to bigscience-workshop/data_tooling#130
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"Hi ! `datasets` currently supports reading local files or files over HTTP. We may add support for other filesystems (cloud storages, hdfs...) at one point though :)"
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so I wander does datasets support read data from hdfs? | {
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"@bryant1410 Thanks for working on this. Could you please split the PR into 4 or 5 smaller PRs (ideally one PR for each bullet point from your description) because it's not practical to review such a large PR, especially if the changes are not interrelated?"
] | 1,640,629,256,000 | 1,641,320,371,000 | null | CONTRIBUTOR | null | false | {
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} | Like in `join([... for s in ...])`. Also changed other things that I saw:
* Use a `with` statement for many `open` that missed them, so the files don't remain open.
* Remove unused variables.
* Many HTTP links converted into HTTPS (verified).
* Remove unnecessary "r" mode arg in `open` (double-checked it was actually the default in each case).
* Remove Python 2 style of using `super`.
* Run `pyupgrade $(find . -name "*.py" -type f) --py36-plus` (which already does some of the previous points).
* Run `dos2unix $(find . -name "*.py" -type f)` (CRLF to LF line endings).
* Fix typos. | {
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"I think the test passes because it simply ignore what's after `gzip://`.\r\n\r\nThe returned urlpath is expected to look like `gzip://filename::url`, and the filename is currently considered to be what's after the final `/`, hence the result.\r\n\r\nWe can decide to change this and simply have `gzip://::url`, this way we don't need to guess the filename, what do you think ?"
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} | ## Describe the bug
There is an ssue with `StreamingDownloadManager._extract`.
I don't know how the test `test_streaming_gg_drive_gzipped` passes:
For
```python
TEST_GG_DRIVE_GZIPPED_URL = "https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz"
urlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL)
```
gives `urlpath`:
```python
'gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz'
```
The gzip path makes no sense: `gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz`
## Steps to reproduce the bug
```python
from datasets.utils.streaming_download_manager import StreamingDownloadManager
dl_manager = StreamingDownloadManager()
urlpath = dl_manager.extract("https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz")
print(urlpath)
```
## Expected results
The query parameters should not be set as part of the path.
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"You can add columns that you wish to set into `torch` format using `dataset.set_format(\"torch\", ['id', 'abc'])` so that input batch of the transform only contains those columns",
"Sorry, I miss `output_all_columns` args and thought after `dataset.set_format(\"torch\", columns=columns)` I can only get specific columns I assigned."
] | 1,640,589,595,000 | 1,640,596,027,000 | 1,640,596,027,000 | NONE | null | null | {
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} | **Is your feature request related to a problem? Please describe.**
When using `dataset.set_format("torch")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns.
**Describe the solution you'd like**
skip columns which cannot set to specific format when set_format instead of raise an error.
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"Hi! \r\n\r\nYes, this makes sense for numeric values, but first I have to finish https://github.com/huggingface/datasets/pull/3336 because currently ArrayXD only allows the first dimension to be dynamic."
] | 1,640,571,362,000 | 1,641,390,843,000 | null | NONE | null | null | {
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} | As describe in https://github.com/huggingface/datasets/issues/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added. | {
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"The CI failure is unrelated to this PR and fixed on master, merging !"
] | 1,640,529,433,000 | 1,641,411,896,000 | 1,641,400,452,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/3482 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3482/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3482/comments | https://api.github.com/repos/huggingface/datasets/issues/3482/events | https://github.com/huggingface/datasets/pull/3482 | 1,088,317,921 | PR_kwDODunzps4wQqE1 | 3,482 | Fix duplicate keys in NewsQA | {
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"Flaky tests?"
] | 1,640,343,719,000 | 1,640,536,193,000 | null | CONTRIBUTOR | null | false | {
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} | * Fix duplicate keys in NewsQA when loading from CSV files.
* Fix s/narqa/newsqa/ in the download manually error message.
* Make the download manually error message show nicely when printed. Otherwise, is hard to read due to spacing issues.
* Fix the format of the license text.
* Reformat the code to make it simpler. | {
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} | Previously, `BaseCompressedFileFileSystem.info` was overridden and transformed from function to dict.
This generated a bug for filesystem methods that use `self.info()`, like e.g. `fs.isfile()`.
This PR:
- Adds tests for `fs.isfile` (that use `fs.info`).
- Fixes custom `BaseCompressedFileFileSystem.info` by removing its overriding. | {
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"Thanks for reporting @SaulLu.\r\n\r\nAt first sight I think the problem is caused because `pandas` only takes into account the `compression` parameter if called with a non-null file path or buffer. And in our implementation, we call pandas `to_json` with `None` `path_or_buf`.\r\n\r\nWe should fix this:\r\n- either handling directly the `compression` parameter ourselves\r\n- or refactoring to pass non-null path or buffer to pandas\r\n\r\nCC: @lhoestq",
"I was thinking if we can handle the `compression` parameter by ourselves? Compression types will be similar to what `pandas` offer. Initially, we can try this with 2-3 compression types and see how good/bad it is? Let me know if it sounds good, I can raise a PR for this next week",
"Hi ! Thanks for your help @bhavitvyamalik :)\r\nMaybe let's start with `gzip` ? I think it's the most common use case, then if we're fine with it we can add other compression methods"
] | 1,640,337,831,000 | 1,641,387,815,000 | 1,641,387,815,000 | NONE | null | null | {
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} | ## Describe the bug
In the documentation of the `to_json` method, it is stated in the parameters that
> **to_json_kwargs – Parameters to pass to pandas’s pandas.DataFrame.to_json.
however when we pass for example `compression="gzip"`, the saved file is not compressed.
Would you also have expected compression to be applied? :relaxed:
## Steps to reproduce the bug
```python
my_dict = {"a": [1, 2, 3], "b": [1, 2, 3]}
```
### Result with datasets
```python
from datasets import Dataset
dataset = Dataset.from_dict(my_dict)
dataset.to_json("dic_with_datasets.jsonl.gz", compression="gzip")
!cat dic_with_datasets.jsonl.gz
```
output
```
{"a":1,"b":1}
{"a":2,"b":2}
{"a":3,"b":3}
```
Note: I would expected to see binary data here
### Result with pandas
```python
import pandas as pd
df = pd.DataFrame(my_dict)
df.to_json("dic_with_pandas.jsonl.gz", lines=True, orient="records", compression="gzip")
!cat dic_with_pandas.jsonl.gz
```
output
```
4��a�dic_with_pandas.jsonl��VJT�2�QJ��\� ��g��yƵ���������)���
```
Note: It looks like binary data
## Expected results
I would have expected that the saved result with datasets would also be a binary file
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.11
- PyArrow version: 5.0.0
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"You're right, we have an issue today with the datasets preview. We're investigating.",
"It should be fixed now. Thanks for reporting.",
"Down again. ",
"Fixed for good."
] | 1,640,333,928,000 | 1,640,356,066,000 | 1,640,356,066,000 | NONE | null | null | {
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} | ## Dataset viewer issue for '*french_book_reviews*'
**Link:** https://huggingface.co/datasets/Abirate/french_book_reviews
**short description of the issue**
For my dataset, the dataset preview is no longer functional (it used to work: The dataset had been added the day before and it was fine...)
And, after looking over the datasets, I discovered that this issue affects all Hugging Face datasets (as of yesterday, December 23, 2021, around 10 p.m. (CET)).
**Am I the one who added this dataset** : Yes
**Note**: here a screenshot showing the issue
**And here for glue dataset :**
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"Nice. I'll update the dataset viewer once merged, and test on these four datasets"
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} | This PR extends the support in streaming mode for datasets that use `os.walk`, by patching that function.
This PR adds support for streaming mode to datasets:
1. autshumato
1. code_x_glue_cd_code_to_text
1. code_x_glue_tc_nl_code_search_adv
1. nchlt
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/3477 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3477/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3477/comments | https://api.github.com/repos/huggingface/datasets/issues/3477/events | https://github.com/huggingface/datasets/pull/3477 | 1,087,850,253 | PR_kwDODunzps4wPKPX | 3,477 | Use `iter_files` instead of `str(Path(...)` in image dataset | {
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"`iter_archive` is about to support ZIP archives. I think we should use this no ?\r\n\r\nsee #3347 https://github.com/huggingface/datasets/pull/3379",
"I was interested in the support for isfile/dir in remote.\r\n\r\nAnyway, `iter_files` will be available for community users.",
"I'm not a big fan of having two functions that do the same thing. What do you think ?",
"They do not do the same thing:\r\n- One iterates over files in a directory\r\n- The other I guess will iterate over the members of an archive",
"Makes sense ! Sounds good then - sorry for my misunderstanding\r\n\r\nNote that `iter_archive` will be more performant for data streaming that `iter_files` thanks to the buffering so maybe in the future we can `iter_archive` for some of these datasets",
"Yes, @lhoestq I agree with you: once `iter_archive` supports zip files, it will be more suitable than `iter_files` for these 2 datasets.\r\n\r\nAnyway, this PR also implements `isfile`/`isdir` in streaming mode, besides fixing `iter_files`. And I'm interested in having those in master.\r\n\r\nMaybe, could we merge this PR into master and take note to refactor the datasets to use `iter_archive` once zip is supported?\r\nOther option could be to split this PR into 2..."
] | 1,640,276,815,000 | 1,640,704,502,000 | 1,640,704,502,000 | CONTRIBUTOR | null | false | {
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} | Use `iter_files` in the `beans` and the `cats_vs_dogs` dataset scripts as suggested by @albertvillanova.
Additional changes:
* Fix `iter_files` in `MockDownloadManager` (see this [CI error](https://app.circleci.com/pipelines/github/huggingface/datasets/9247/workflows/2657ff8a-b531-4fd9-a9fc-6541a72e8d83/jobs/57028))
* Add support for `os.path.isdir` and `os.path.isfile` in streaming (`os.path.isfile` is needed in `StreamingDownloadManager`'s `iter_files` to make `cats_vs_dogs` streamable)
TODO:
- [ ] add tests for `xisdir` and `xisfile` | {
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This PR adds support for streaming mode to datasets:
1. ami
1. assin
1. assin2
1. counter
1. enriched_web_nlg
1. europarl_bilingual
1. hyperpartisan_news_detection
1. polsum
1. qa4mre
1. quail
1. ted_talks_iwslt
1. udhr
1. web_nlg
1. winograd_wsc
CC: @severo | {
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"Hi @puzzler10, thanks for reporting.\r\n\r\nPlease note this dataset is not hosted on Hugging Face Hub. See: \r\nhttps://github.com/huggingface/datasets/blob/c8f914473b041833fd47178fa4373cdcb56ac522/datasets/rotten_tomatoes/rotten_tomatoes.py#L42\r\n\r\nIf there are issues with the source data of a dataset, you should contact the data owners/creators instead. In the homepage associated with this dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/), you can find the authors of the dataset and how to contact them:\r\n> If you have any questions or comments regarding this site, please send email to Bo Pang or Lillian Lee.\r\n\r\nP.S.: Please also note that the example you gave of non-English review is in Portuguese (not Spanish). ;)",
"Maybe best to just put a quick sentence in the dataset description that highlights this? "
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} | ## Describe the bug
See title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user.
## Steps to reproduce the bug
Go to the [dataset viewer](https://huggingface.co/datasets/viewer/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that.
## Expected results
English movie reviews only.
## Actual results
Example of a Spanish movie review (4173):
> "É uma pena que , mais tarde , o próprio filme abandone o tom de paródia e passe a utilizar os mesmos clichês que havia satirizado "
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} | If I iterate over a vision dataset, the images are not decoded, and the dictionary with the bytes is returned.
This PR enables image decoding in `Dataset.__iter__`
Close https://github.com/huggingface/datasets/issues/3473 | {
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"As discussed, I remember I set `decoded=False` here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed.",
"> I set decoded=False here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed\r\n\r\nhttps://github.com/huggingface/datasets/pull/3430 will add more control to decoding, so I think it's OK to enable decoding in `__iter__` for now. After we merge the linked PR, the user can easily disable it again.",
"@mariosasko I wonder why there is no issue in `Audio` feature with decoding disabled in `__iter__`, whereas there is in `Image` feature.\r\n\r\nEnabling decoding in `__iter__` will make fail Audio regressions tests: https://github.com/huggingface/datasets/runs/4608657230?check_suite_focus=true\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_not_decoded\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_decoded\r\n========================= 2 failed, 15 passed in 8.37s =========================",
"Please also note that the regression tests were implemented in accordance with the specifications:\r\n- when doing a `map` (wich calls `__iter__`) of a function that doesn't access the audio field, the decoding should be disabled; this is why the decoding is disabled in `__iter__` (and only enabled in `__getitem__`).",
"> I wonder why there is no issue in Audio feature with decoding disabled in __iter__, whereas there is in Image feature.\r\n\r\n@albertvillanova Not sure if I understand this part. Currently, both the Image and the Audio feature don't decode data in `__iter__`, so their behavior is aligned there.\r\n",
"Therefore, this is not an issue, neither for Audio nor Image feature.\r\n\r\nCould you please elaborate more on the expected use case? @lhoestq @NielsRogge \r\n\r\nThe expected use cases (in accordance with the specs: see #2324):\r\n- decoding should be enabled when accessing a specific item (`__getitem__`)\r\n- decoding should be disabled while iterating (`__iter__`) to allow preprocessing of non-audio/image features (like label or text, for example) using `.map`\r\n- decoding should be enabled in a `.map` only if the `.map` function accesses the audio/image feature (implemented using `LazyDict`)",
"For me it's not an issue, actually. I just (mistakenly) tried to iterate over a PyTorch Dataset instead of a PyTorch DataLoader, \r\n\r\ni.e. I did this:\r\n\r\n`batch = next(iter(train_ds)) `\r\n\r\nwhereas I actually wanted to do\r\n\r\n`batch = next(iter(train_dataloader))`\r\n\r\nand then it turned out that in the first case, the image was a string of bytes rather than a Pillow image, hence Quentin opened an issue.",
"Thanks @NielsRogge for the context.\r\n\r\nSo IMO everything is working as expected.\r\n\r\nI'm closing this issue. Feel free to reopen it again if further changes of the specs should be addressed.",
"Thanks for the details :)\r\n\r\nI still think that it's unexpected to get different results when doing\r\n```python\r\nfor i in range(len(dataset)):\r\n sample = dataset[i]\r\n```\r\nand\r\n```python\r\nfor sample in dataset:\r\n pass\r\n```\r\neven though I understand that if you don't need to decode the data, then decoding image or audio data when iterating is a waste of time and resources.\r\n\r\nBut in this case users can still drop the column that need decoding to get the full speed back no ?"
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} | ## Describe the bug
If I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.
## Steps to reproduce the bug
```python
from datasets import load_dataset
import PIL
mnist = load_dataset("mnist", split="train")
first_image = mnist[0]["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # passes
first_image = next(iter(mnist))["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # fails
```
## Expected results
The image should be decoded, as a PIL Image
## Actual results
We get a dictionary
```
{'bytes': b'\x89PNG\r\n\x1a\n\x00..., 'path': None}
```
## Environment info
- `datasets` version: 1.17.1.dev0
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.2
- PyArrow version: 6.0.0
The bug also exists in 1.17.0
## Investigation
I think the issue is that decoding is disabled in `__iter__`:
https://github.com/huggingface/datasets/blob/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6/src/datasets/arrow_dataset.py#L1651-L1661
Do you remember why it was disabled in the first place @albertvillanova ?
Also cc @mariosasko @NielsRogge
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- Fix path for Windows
- Adds license
- Adds more info in dataset card
Close bigscience-workshop/data_tooling#279 | {
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Currently, `ClassLabel` docstring rendering is not right. | {
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"I also modified the doctest call to raise the exception that doctest may catch, instead of `doctest.UnexpectedException`.\r\nThis will make debugging easier if it happens again"
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This should fix the CI on master | {
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"The CI failures other than a missing dummy data file and missing fields in the card are unrelated to this PR. ",
"Thanks a lot for this great work and fixing TFDS based script @mariosasko 🤗 will generate the dummy dataset and write the model card tomorrow!",
"@mariosasko I added the dataset card, I'm on the dummy data rn. ",
"@merveenoyan Let me know if you need any help with the dummy data.\r\n\r\nI plan to split the current script/dataset into 4 smaller scripts/datasets to make sure they are properly indexed by Papers With Code later on. In this format:\r\n* the `*_image_captioning` configs will form the [COCO Captions](https://paperswithcode.com/sota/image-captioning-on-coco-captions) dataset (also present in TFDS, but only the 2017 version)\r\n* the `stuff_segmentation` config will form the [COCO Stuff](https://paperswithcode.com/dataset/coco-stuff) dataset\r\n* the `desnepose` config will form the [DensePose-COCO](https://paperswithcode.com/dataset/densepose) dataset\r\n* the rest will be [COCO](https://paperswithcode.com/dataset/coco) (+ will add the `minival` and the `valminusminival` splits to COCO 2014)\r\n\r\nAlso, if I find the time, I'll add preprocessing examples that rely on `pycocotools` to the README files."
] | 1,640,095,670,000 | 1,641,219,726,000 | null | CONTRIBUTOR | null | true | {
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} | This PR adds the MS COCO dataset. Compared to the [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/object_detection/coco.py) script, this implementation adds 8 additional configs to cover the tasks other than object detection.
Some notes:
* the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here
* I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`)
* this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427
TODOs:
- [x] dataset card
- [ ] dummy data
cc @merveenoyan
Closes #2526 | {
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"The change from `___` to `--` was allowed by https://github.com/huggingface/moon-landing/pull/1657"
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} | When doing `push_to_hub`, the feature types are lost (see issue https://github.com/huggingface/datasets/issues/3394).
This PR fixes this by also pushing a `dataset_infos.json` file to the Hub, that stores the feature types.
Other minor changes:
- renamed the `___` separator to `--`, since `--` is now disallowed in a name in the back-end.
I tested this feature with datasets like conll2003 that has feature types like `ClassLabel` that were previously lost.
Close https://github.com/huggingface/datasets/issues/3394
I would like to include this in today's release (though not mandatory), so feel free to comment/suggest changes | {
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"Hi Albert,\r\nThank you for your comments.\r\nI hope I have uploaded my local git repo to include the dummy files and style reworkings.\r\nAdded YAML in Readme as well.\r\n\r\nPlease check again.\r\n\r\nHope it works now :)"
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"Hi @talha1503, thanks for reporting.\r\n\r\nIt seems there is an issue with one of the data files hosted at Google Drive:\r\n```\r\nGoogle Drive - Quota exceeded\r\n\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nAs you probably know, Hugging Face does not host the data, and in this case the data owner decided to host their data at Google Drive, which has quota limits.\r\n\r\nIs there anything we could do, @lhoestq @mariosasko?",
"This looks related to https://github.com/huggingface/datasets/issues/996",
"It seems that [this](https://huggingface.co/datasets/ccdv/cnn_dailymail) copy of the dataset has fixed the problem"
] | 1,640,057,541,000 | 1,645,107,237,000 | 1,645,107,237,000 | NONE | null | null | {
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} | ## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)
```
## Expected results
Expecting to load 'cnn_dailymail' dataset.
## Actual results
`NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
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