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"@sahoodib, thanks for reporting.\r\n\r\nIndeed, none of the data links appearing in the IndicGLUE website are working, e.g.: https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/soham-articles.tar.gz\r\n```\r\n<Error>\r\n<Code>UserProjectAccountProblem</Code>\r\n<Message>User project billing account not in good standing.</Message>\r\n<Details>\r\nThe billing account for the owning project is disabled in state delinquent\r\n</Details>\r\n</Error>\r\n```\r\n\r\nWe have contacted the data owners to inform them about their issue and ask them if they plan to fix it.",
"Yesterday I resent a reminder email with more AI4Bharat-related people in the loop.\r\n\r\nI also opened an issue in their repos:\r\n- https://github.com/AI4Bharat/indicnlp_corpus/issues/14\r\n- https://github.com/AI4Bharat/ai4bharat.org/issues/71",
"We have received a reply from the authors reporting they have updated the URLs of their data files and opened a PR. See:\r\n- #4978 "
] | "2022-01-12T17:59:36" | "2022-09-15T21:57:34" | "2022-09-15T21:57:34" | NONE | null | While I am trying to load IndicGLUE dataset (https://huggingface.co/datasets/indic_glue) it is giving me with the error:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/wikiann-ner.tar.gz (error 403) | {
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I'm also removing the licensing information, because I couldn't verify that it is correct. | {
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https://api.github.com/repos/huggingface/datasets/issues/3570 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3570/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3570/comments | https://api.github.com/repos/huggingface/datasets/issues/3570/events | https://github.com/huggingface/datasets/pull/3570 | 1,100,480,791 | PR_kwDODunzps4w3Xez | 3,570 | Add the KMWP dataset (extension of #3564) | {
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https://api.github.com/repos/huggingface/datasets/issues/3569 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3569/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3569/comments | https://api.github.com/repos/huggingface/datasets/issues/3569/events | https://github.com/huggingface/datasets/pull/3569 | 1,100,478,994 | PR_kwDODunzps4w3XGo | 3,569 | Add the DKTC dataset (Extension of #3564) | {
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https://api.github.com/repos/huggingface/datasets/issues/3568 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3568/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3568/comments | https://api.github.com/repos/huggingface/datasets/issues/3568/events | https://github.com/huggingface/datasets/issues/3568 | 1,100,380,631 | I_kwDODunzps5BlnnX | 3,568 | Downloading Hugging Face Medical Dialog Dataset NonMatchingSplitsSizesError | {
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"Hi @fabianslife, thanks for reporting.\r\n\r\nI think you were using an old version of `datasets` because this bug was already fixed in version `1.13.0` (13 Oct 2021):\r\n- Fix: 55fd140a63b8f03a0e72985647e498f1fc799d3f\r\n- PR: #3046\r\n- Issue: #2969 \r\n\r\nPlease, feel free to update the library: `pip install -U datasets`."
] | "2022-01-12T14:03:44" | "2022-02-14T09:32:34" | "2022-02-14T09:32:34" | NONE | null | I wanted to download the Nedical Dialog Dataset from huggingface, using this github link:
https://github.com/huggingface/datasets/tree/master/datasets/medical_dialog
After downloading the raw datasets from google drive, i unpacked everything and put it in the same folder as the medical_dialog.py which is:
```
import copy
import os
import re
import datasets
_CITATION = """\
@article{chen2020meddiag,
title={MedDialog: a large-scale medical dialogue dataset},
author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},
journal={arXiv preprint arXiv:2004.03329},
year={2020}
}
"""
_DESCRIPTION = """\
The MedDialog dataset (English) contains conversations (in English) between doctors and patients.\
It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. \
The raw dialogues are from healthcaremagic.com and icliniq.com.\
All copyrights of the data belong to healthcaremagic.com and icliniq.com.
"""
_HOMEPAGE = "https://github.com/UCSD-AI4H/Medical-Dialogue-System"
_LICENSE = ""
class MedicalDialog(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="en", description="The dataset of medical dialogs in English.", version=VERSION),
datasets.BuilderConfig(name="zh", description="The dataset of medical dialogs in Chinese.", version=VERSION),
]
@property
def manual_download_instructions(self):
return """\
\n For English:\nYou need to go to https://drive.google.com/drive/folders/1g29ssimdZ6JzTST6Y8g6h-ogUNReBtJD?usp=sharing,\
and manually download the dataset from Google Drive. Once it is completed,
a file named Medical-Dialogue-Dataset-English-<timestamp-info>.zip will appear in your Downloads folder(
or whichever folder your browser chooses to save files to). Unzip the folder to obtain
a folder named "Medical-Dialogue-Dataset-English" several text files.
Now, you can specify the path to this folder for the data_dir argument in the
datasets.load_dataset(...) option.
The <path/to/folder> can e.g. be "/Downloads/Medical-Dialogue-Dataset-English".
The data can then be loaded using the below command:\
datasets.load_dataset("medical_dialog", name="en", data_dir="/Downloads/Medical-Dialogue-Dataset-English")`.
\n For Chinese:\nFollow the above process. Change the 'name' to 'zh'.The download link is https://drive.google.com/drive/folders/1r09_i8nJ9c1nliXVGXwSqRYqklcHd9e2
**NOTE**
- A caution while downloading from drive. It is better to download single files since creating a zip might not include files <500 MB. This has been observed mutiple times.
- After downloading the files and adding them to the appropriate folder, the path of the folder can be given as input tu the data_dir path.
"""
datasets.load_dataset("medical_dialog", name="en", data_dir="Medical-Dialogue-Dataset-English")
def _info(self):
if self.config.name == "zh":
features = datasets.Features(
{
"file_name": datasets.Value("string"),
"dialogue_id": datasets.Value("int32"),
"dialogue_url": datasets.Value("string"),
"dialogue_turns": datasets.Sequence(
{
"speaker": datasets.ClassLabel(names=["病人", "医生"]),
"utterance": datasets.Value("string"),
}
),
}
)
if self.config.name == "en":
features = datasets.Features(
{
"file_name": datasets.Value("string"),
"dialogue_id": datasets.Value("int32"),
"dialogue_url": datasets.Value("string"),
"dialogue_turns": datasets.Sequence(
{
"speaker": datasets.ClassLabel(names=["Patient", "Doctor"]),
"utterance": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
features=features,
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(path_to_manual_file):
raise FileNotFoundError(
f"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('medical_dialog', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})"
)
filepaths = [
os.path.join(path_to_manual_file, txt_file_name)
for txt_file_name in sorted(os.listdir(path_to_manual_file))
if txt_file_name.endswith("txt")
]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": filepaths})]
def _generate_examples(self, filepaths):
"""Yields examples. Iterates over each file and give the creates the corresponding features.
NOTE:
- The code makes some assumption on the structure of the raw .txt file.
- There are some checks to separate different id's. Hopefully, should not cause further issues later when more txt files are added.
"""
data_lang = self.config.name
id_ = -1
for filepath in filepaths:
with open(filepath, encoding="utf-8") as f_in:
# Parameters to just "sectionize" the raw data
last_part = ""
last_dialog = {}
last_list = []
last_user = ""
check_list = []
# These flags are present to have a single function address both chinese and english data
# English data is a little hahazard (i.e. the sentences spans multiple different lines),
# Chinese is compact with one line for doctor and patient.
conv_flag = False
des_flag = False
while True:
line = f_in.readline()
if not line:
break
# Extracting the dialog id
if line[:2] == "id": # Hardcode alert!
# Handling ID references that may come in the description
# These were observed in the Chinese dataset and were not
# followed by numbers
try:
dialogue_id = int(re.findall(r"\d+", line)[0])
except IndexError:
continue
# Extracting the url
if line[:4] == "http": # Hardcode alert!
dialogue_url = line.rstrip()
# Extracting the patient info from description.
if line[:11] == "Description": # Hardcode alert!
last_part = "description"
last_dialog = {}
last_list = []
last_user = ""
last_conv = {"speaker": "", "utterance": ""}
while True:
line = f_in.readline()
if (not line) or (line in ["\n", "\n\r"]):
break
else:
if data_lang == "zh": # Condition in chinese
if line[:5] == "病情描述:": # Hardcode alert!
last_user = "病人"
sen = f_in.readline().rstrip()
des_flag = True
if data_lang == "en":
last_user = "Patient"
sen = line.rstrip()
des_flag = True
if des_flag:
if sen == "":
continue
if sen in check_list:
last_conv["speaker"] = ""
last_conv["utterance"] = ""
else:
last_conv["speaker"] = last_user
last_conv["utterance"] = sen
check_list.append(sen)
des_flag = False
break
# Extracting the conversation info from dialogue.
elif line[:8] == "Dialogue": # Hardcode alert!
if last_part == "description" and len(last_conv["utterance"]) > 0:
last_part = "dialogue"
if data_lang == "zh":
last_user = "病人"
if data_lang == "en":
last_user = "Patient"
while True:
line = f_in.readline()
if (not line) or (line in ["\n", "\n\r"]):
conv_flag = False
last_user = ""
last_list.append(copy.deepcopy(last_conv))
# To ensure close of conversation, only even number of sentences
# are extracted
last_turn = len(last_list)
if int(last_turn / 2) > 0:
temp = int(last_turn / 2)
id_ += 1
last_dialog["file_name"] = filepath
last_dialog["dialogue_id"] = dialogue_id
last_dialog["dialogue_url"] = dialogue_url
last_dialog["dialogue_turns"] = last_list[: temp * 2]
yield id_, last_dialog
break
if data_lang == "zh":
if line[:3] == "病人:" or line[:3] == "医生:": # Hardcode alert!
user = line[:2] # Hardcode alert!
line = f_in.readline()
conv_flag = True
# The elif block is to ensure that multi-line sentences are captured.
# This has been observed only in english.
if data_lang == "en":
if line.strip() == "Patient:" or line.strip() == "Doctor:": # Hardcode alert!
user = line.replace(":", "").rstrip()
line = f_in.readline()
conv_flag = True
elif line[:2] != "id": # Hardcode alert!
conv_flag = True
# Continues till the next ID is parsed
if conv_flag:
sen = line.rstrip()
if sen == "":
continue
if user == last_user:
last_conv["utterance"] = last_conv["utterance"] + sen
else:
last_user = user
last_list.append(copy.deepcopy(last_conv))
last_conv["utterance"] = sen
last_conv["speaker"] = user
```
running this code gives me the error:
```
File "C:\Users\Fabia\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\utils\info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=292801173, num_examples=229674, dataset_name='medical_dialog')}]
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3567 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3567/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3567/comments | https://api.github.com/repos/huggingface/datasets/issues/3567/events | https://github.com/huggingface/datasets/pull/3567 | 1,100,296,696 | PR_kwDODunzps4w2xDl | 3,567 | Fix push to hub to allow individual split push | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-12T12:42:58" | "2022-07-27T12:11:12" | "2022-07-27T12:11:11" | MEMBER | null | # Description of the issue
If one decides to push a split on a datasets repo, he uploads the dataset and overrides the config. However previous config splits end up being lost despite still having the dataset necessary.
The new flow is the following:
- query the old config from the repo
- update into a new config (add/overwrite new split for example)
- push the new config
# Side fix
- `repo_id` in HfFileSystem was wrongly typed.
- I've added `indent=2` as it becomes much easier to read now.
| {
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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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} | [] | closed | false | null | [] | null | [] | "2022-01-12T10:21:32" | "2022-02-15T13:31:48" | "2022-02-15T13:31:48" | CONTRIBUTOR | null | Here is an initial prototype time series dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/3565 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3565/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3565/comments | https://api.github.com/repos/huggingface/datasets/issues/3565/events | https://github.com/huggingface/datasets/pull/3565 | 1,099,296,693 | PR_kwDODunzps4wzjhH | 3,565 | Add parameter `preserve_index` to `from_pandas` | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-11T15:26:37" | "2022-01-12T16:11:27" | "2022-01-12T16:11:27" | CONTRIBUTOR | null | Added optional parameter, so that user can get rid of useless index preserving. [Issue](https://github.com/huggingface/datasets/issues/3563) | {
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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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} | [] | closed | false | null | [] | null | [] | "2022-01-11T14:14:08" | "2022-01-12T15:33:49" | "2022-01-12T15:33:28" | NONE | null | Add the DKTC dataset.
- https://github.com/tunib-ai/DKTC | {
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] | closed | false | null | [] | null | [
"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. "
] | "2022-01-11T12:07:07" | "2022-01-12T16:11:27" | "2022-01-12T16:11:27" | CONTRIBUTOR | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/3562 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3562/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3562/comments | https://api.github.com/repos/huggingface/datasets/issues/3562/events | https://github.com/huggingface/datasets/pull/3562 | 1,098,341,351 | PR_kwDODunzps4wwa44 | 3,562 | Allow multiple task templates of the same type | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-10T20:32:07" | "2022-01-11T14:16:47" | "2022-01-11T14:16:47" | CONTRIBUTOR | null | 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."
] | "2022-01-10T20:17:18" | "2022-02-14T09:19:27" | "2022-02-14T09:18:47" | NONE | null | ## 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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```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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CC: @lewtun @nateraw | {
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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```"
] | "2022-01-10T10:53:11" | "2022-01-12T15:17:33" | "2022-01-12T15:13:56" | NONE | null | 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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"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"
] | "2022-01-10T10:32:04" | "2022-02-14T09:35:57" | "2022-02-14T09:35:57" | NONE | null | 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)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from packaging->datasets) (2.4.7)
Requirement already satisfied: chardet<5,>=3.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (4.0.0)
Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (2021.5.30)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (1.26.5)
Requirement already satisfied: idna<3,>=2.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (2.10)
Requirement already satisfied: yarl<2.0,>=1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.6.3)
Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (2.0.9)
Requirement already satisfied: attrs>=17.3.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (21.2.0)
Requirement already satisfied: asynctest==0.13.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (0.13.0)
Requirement already satisfied: idna-ssl>=1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.1.0)
Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (4.0.1)
Requirement already satisfied: aiosignal>=1.1.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.2.0)
Requirement already satisfied: frozenlist>=1.1.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.2.0)
Requirement already satisfied: multidict<7.0,>=4.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (5.1.0)
Requirement already satisfied: humanfriendly>=9.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from coloredlogs->optimum[intel]) (10.0)
Requirement already satisfied: zipp>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from importlib-metadata->datasets) (3.4.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pandas->datasets) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pandas->datasets) (2021.1)
Requirement already satisfied: matplotlib>=2.1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (3.3.4)
Requirement already satisfied: cython>=0.27.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (0.29.23)
Requirement already satisfied: setuptools>=18.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (52.0.0.post20210125)
Requirement already satisfied: joblib in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (1.0.1)
Requirement already satisfied: click in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (8.0.1)
Requirement already satisfied: six in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (1.16.0)
Requirement already satisfied: mpmath>=0.19 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sympy->optimum[intel]) (1.2.1)
Requirement already satisfied: kiwisolver>=1.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from matplotlib>=2.1.0->pycocotools->optimum[intel]) (1.3.1)
Requirement already satisfied: cycler>=0.10 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib>=2.1.0->pycocotools->optimum[intel]) (0.10.0)
Requirement already satisfied: cffi>=1.12 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from cryptography->neural-compressor>=1.7->optimum[intel]) (1.14.5)
Requirement already satisfied: Werkzeug>=2.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from Flask->neural-compressor>=1.7->optimum[intel]) (2.0.2)
Requirement already satisfied: Jinja2>=3.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from Flask->neural-compressor>=1.7->optimum[intel]) (3.0.1)
Requirement already satisfied: itsdangerous>=2.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from Flask->neural-compressor>=1.7->optimum[intel]) (2.0.1)
Requirement already satisfied: python-socketio>=5.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (5.5.0)
Requirement already satisfied: zope.event in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from gevent->neural-compressor>=1.7->optimum[intel]) (4.5.0)
Requirement already satisfied: greenlet<2.0,>=0.4.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from gevent->neural-compressor>=1.7->optimum[intel]) (1.1.0)
Requirement already satisfied: zope.interface in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from gevent->neural-compressor>=1.7->optimum[intel]) (5.4.0)
Requirement already satisfied: future in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (0.18.2)
Requirement already satisfied: cloudpickle in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (1.6.0)
Requirement already satisfied: networkx>=2.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (2.5)
Requirement already satisfied: scipy in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (1.5.3)
Requirement already satisfied: py4j in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (0.10.7)
Requirement already satisfied: wcwidth in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from prettytable->neural-compressor>=1.7->optimum[intel]) (0.2.5)
Requirement already satisfied: contextlib2>=0.5.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from schema->neural-compressor>=1.7->optimum[intel]) (0.6.0.post1)
Requirement already satisfied: threadpoolctl>=2.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from scikit-learn->neural-compressor>=1.7->optimum[intel]) (2.1.0)
Requirement already satisfied: pyOpenSSL>=20.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (20.0.1)
Requirement already satisfied: pypng>=0.0.20 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (0.0.21)
Requirement already satisfied: kubernetes<13.0.0,>=12.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (12.0.1)
Requirement already satisfied: rsa<5.0.0,>=4.7 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (4.7.2)
Requirement already satisfied: boto3<2.0.0,==1.16.34 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (1.16.34)
Requirement already satisfied: Pint<0.17.0,>=0.16.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (0.16.1)
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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",
"This has been fixed in https://github.com/huggingface/datasets/pull/5072, which is included in the latest release of `datasets`."
] | "2022-01-09T17:18:13" | "2022-10-14T12:03:55" | "2022-10-14T12:03:54" | NONE | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/3552 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3552/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3552/comments | https://api.github.com/repos/huggingface/datasets/issues/3552/events | https://github.com/huggingface/datasets/pull/3552 | 1,096,985,204 | PR_kwDODunzps4wsM29 | 3,552 | Add the KMWP & DKTC dataset. | {
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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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https://api.github.com/repos/huggingface/datasets/issues/3551 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3551/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3551/comments | https://api.github.com/repos/huggingface/datasets/issues/3551/events | https://github.com/huggingface/datasets/pull/3551 | 1,096,561,111 | PR_kwDODunzps4wq_AO | 3,551 | Add more compression types for `to_json` | {
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https://api.github.com/repos/huggingface/datasets/issues/3550 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3550/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3550/comments | https://api.github.com/repos/huggingface/datasets/issues/3550/events | https://github.com/huggingface/datasets/issues/3550 | 1,096,522,377 | I_kwDODunzps5BW5qJ | 3,550 | Bug in `openbookqa` dataset | {
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"Closed by:\r\n- #4259"
] | "2022-01-07T17:32:57" | "2022-05-04T06:33:00" | "2022-05-04T06:32:19" | CONTRIBUTOR | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/3549 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3549/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3549/comments | https://api.github.com/repos/huggingface/datasets/issues/3549/events | https://github.com/huggingface/datasets/pull/3549 | 1,096,426,996 | PR_kwDODunzps4wqkGt | 3,549 | Fix sem_eval_2018_task_1 download location | {
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https://api.github.com/repos/huggingface/datasets/issues/3548 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3548/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3548/comments | https://api.github.com/repos/huggingface/datasets/issues/3548/events | https://github.com/huggingface/datasets/issues/3548 | 1,096,409,512 | I_kwDODunzps5BWeGo | 3,548 | Specify the feature types of a dataset on the Hub without needing a dataset script | {
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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. "
] | "2022-01-07T15:17:06" | "2022-01-20T14:48:38" | "2022-01-20T14:48:38" | MEMBER | null | **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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} | [] | closed | false | null | [] | null | [] | "2022-01-07T14:30:24" | "2022-01-07T18:09:16" | "2022-01-07T18:09:15" | CONTRIBUTOR | null | This is a second time I'm removing print statements in our datasets, so I've added a test to avoid these issues in the future. | {
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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```",
"Solved with https://github.com/huggingface/evaluate 🤗 ",
"Yay!! cc @lvwerra @sashavor @douwekiela \r\n\r\nPlease share your feedback @eladsegal =)"
] | "2022-01-06T11:26:26" | "2022-05-31T20:59:14" | "2022-05-31T20:53:37" | CONTRIBUTOR | null | **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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] | null | [] | "2022-01-05T21:26:28" | "2022-01-05T23:34:25" | "2022-01-05T23:24:15" | CONTRIBUTOR | null | 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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multilingual_librispeech
openslr
speech commands
superb
timit_asr
vctk | {
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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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CC: @julien-c @VictorSanh | {
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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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Adding link to license.
Correct license type in metadata. | {
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https://api.github.com/repos/huggingface/datasets/issues/3522 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3522/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3522/comments | https://api.github.com/repos/huggingface/datasets/issues/3522/events | https://github.com/huggingface/datasets/issues/3522 | 1,093,807,586 | I_kwDODunzps5BMi3i | 3,522 | wmt19 is broken (zh-en) | {
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"This issue is not reproducible."
] | "2022-01-04T22:33:45" | "2022-05-06T16:27:37" | "2022-05-06T16:27:37" | NONE | null | ## 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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```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"
] | "2022-01-04T06:54:35" | "2022-01-17T15:25:57" | "2022-01-17T15:25:57" | MEMBER | null | ## 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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Before, the download was failing | {
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"Thanks for reporting @VictorSanh.\r\n\r\nI'm looking at it... "
] | "2022-01-03T15:58:38" | "2022-02-14T13:21:43" | "2022-02-14T13:21:43" | MEMBER | null | ## 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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} | [] | closed | false | null | [] | null | [] | "2022-01-03T15:31:39" | "2022-01-05T18:52:48" | "2022-01-05T18:52:48" | CONTRIBUTOR | null | Fix the `to_tf_dataset` references in the docs. The currently failing example of usage will be fixed by #3338. | {
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https://api.github.com/repos/huggingface/datasets/issues/3513 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3513/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3513/comments | https://api.github.com/repos/huggingface/datasets/issues/3513/events | https://github.com/huggingface/datasets/pull/3513 | 1,092,569,802 | PR_kwDODunzps4weGWl | 3,513 | Add desc parameter to filter | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-03T14:44:18" | "2022-01-05T18:31:25" | "2022-01-05T18:31:25" | CONTRIBUTOR | null | Fix #3317 | {
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https://api.github.com/repos/huggingface/datasets/issues/3512 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3512/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3512/comments | https://api.github.com/repos/huggingface/datasets/issues/3512/events | https://github.com/huggingface/datasets/issues/3512 | 1,092,359,973 | I_kwDODunzps5BHBcl | 3,512 | No Data format found | {
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"Hi, which dataset is giving you an error?"
] | "2022-01-03T09:41:11" | "2022-01-17T13:26:05" | "2022-01-17T13:26:05" | NONE | null | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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https://api.github.com/repos/huggingface/datasets/issues/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."
] | "2022-01-03T02:03:23" | "2022-01-03T08:41:26" | "2022-01-03T08:23:07" | NONE | null | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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https://api.github.com/repos/huggingface/datasets/issues/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"
] | "2022-01-02T11:04:01" | "2022-02-17T13:46:20" | "2022-02-17T13:46:20" | NONE | null | 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",
"Tensorflow Datasets catalog includes a community catalog where you can find and use HF datasets (see [here](https://www.tensorflow.org/datasets/community_catalog/huggingface)).\r\n\r\nFYI I noticed today that they are using the exported dataset_infos.json files from github to get the metadata (see their code [here](https://github.com/tensorflow/datasets/blob/a482f01c036a10496f5e22e69a2ef81b707cc418/tensorflow_datasets/scripts/documentation/build_community_catalog.py#L261))",
"Metadata is now stored as YAML, and dummy data is deprecated, so I think we can close this issue."
] | "2021-12-30T17:04:25" | "2022-11-04T15:31:38" | "2022-11-04T15:31:37" | MEMBER | null | 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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https://api.github.com/repos/huggingface/datasets/issues/3506 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3506/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3506/comments | https://api.github.com/repos/huggingface/datasets/issues/3506/events | https://github.com/huggingface/datasets/pull/3506 | 1,091,166,595 | PR_kwDODunzps4wZpot | 3,506 | Allows DatasetDict.filter to have batching option | {
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- 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)."
] | "2021-12-30T14:52:01" | "2022-01-18T19:54:07" | "2022-01-18T19:54:07" | NONE | null | ## 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"
] | "2021-12-29T18:23:20" | "2022-02-18T07:49:00" | "2022-02-17T15:04:25" | NONE | null | ## 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
- PyArrow version: 6.0.1 | {
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] | null | [] | "2021-12-29T12:01:04" | "2022-01-04T10:24:27" | "2022-01-04T10:24:27" | CONTRIBUTOR | null | 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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] | closed | false | null | [] | null | [] | "2021-12-29T10:58:46" | "2022-10-03T09:36:14" | "2022-10-03T09:36:14" | CONTRIBUTOR | null | Fixes #3441. | {
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https://api.github.com/repos/huggingface/datasets/issues/3501 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3501/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3501/comments | https://api.github.com/repos/huggingface/datasets/issues/3501/events | https://github.com/huggingface/datasets/pull/3501 | 1,090,413,758 | PR_kwDODunzps4wXM8H | 3,501 | Update pib dataset card | {
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https://api.github.com/repos/huggingface/datasets/issues/3500 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3500/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3500/comments | https://api.github.com/repos/huggingface/datasets/issues/3500/events | https://github.com/huggingface/datasets/pull/3500 | 1,090,406,133 | PR_kwDODunzps4wXLTB | 3,500 | Docs: Add VCTK dataset description | {
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} | [] | closed | false | null | [] | null | [] | "2021-12-29T10:02:05" | "2022-01-04T10:46:02" | "2022-01-04T10:25:09" | CONTRIBUTOR | null | This PR is a very minor followup to #1837, with only docs changes (single comment string). | {
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https://api.github.com/repos/huggingface/datasets/issues/3499 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3499/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3499/comments | https://api.github.com/repos/huggingface/datasets/issues/3499/events | https://github.com/huggingface/datasets/issues/3499 | 1,090,132,618 | I_kwDODunzps5A-hqK | 3,499 | Adjusting chunk size for streaming datasets | {
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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 :)"
] | "2021-12-28T21:17:53" | "2022-05-06T16:29:05" | "2022-05-06T16:29:05" | CONTRIBUTOR | null | **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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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"
] | "2021-12-28T18:03:49" | "2022-01-21T13:22:27" | "2022-01-21T13:22:27" | MEMBER | null | 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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} | [] | closed | false | null | [] | null | [] | "2021-12-28T16:01:55" | "2022-01-03T14:42:28" | "2021-12-29T08:42:57" | MEMBER | null | 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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https://api.github.com/repos/huggingface/datasets/issues/3494 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3494/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3494/comments | https://api.github.com/repos/huggingface/datasets/issues/3494/events | https://github.com/huggingface/datasets/pull/3494 | 1,089,983,103 | PR_kwDODunzps4wV0vB | 3,494 | Clone full repo to detect new tags when mirroring datasets on the Hub | {
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} | [] | closed | false | null | [] | null | [] | "2021-12-28T15:50:47" | "2021-12-28T16:07:21" | "2021-12-28T16:07:20" | MEMBER | null | The new releases of `datasets` were not detected because the shallow clone in the CI wasn't getting the git tags.
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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https://api.github.com/repos/huggingface/datasets/issues/3493 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3493/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3493/comments | https://api.github.com/repos/huggingface/datasets/issues/3493/events | https://github.com/huggingface/datasets/pull/3493 | 1,089,967,286 | PR_kwDODunzps4wVxfr | 3,493 | Fix VCTK encoding | {
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} | [] | closed | false | null | [] | null | [] | "2021-12-28T15:23:36" | "2021-12-28T15:48:18" | "2021-12-28T15:48:17" | MEMBER | null | utf-8 encoding was missing in the VCTK dataset builder added in #3351 | {
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} | [] | closed | false | null | [] | null | [] | "2021-12-28T15:01:11" | "2022-07-10T14:36:52" | "2022-01-05T13:03:36" | CONTRIBUTOR | null | (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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"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."
] | "2021-12-27T07:19:55" | "2021-12-27T09:07:07" | "2021-12-27T09:07:07" | NONE | null | **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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* 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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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"
] | "2021-12-24T09:23:51" | "2022-01-05T13:03:35" | "2022-01-05T13:03:35" | NONE | null | ## 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."
] | "2021-12-24T08:18:48" | "2021-12-24T14:27:46" | "2021-12-24T14:27:46" | NONE | null | ## 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
![Dataset preview is not available for my dataset](https://user-images.githubusercontent.com/66887439/147333078-60734578-420d-4e91-8691-a90afeaa8948.jpg)
**And here for glue dataset :**
![Dataset preview is not available for other Hugging Face datasets(glue)](https://user-images.githubusercontent.com/66887439/147333492-26fa530c-befd-4992-8361-70c51397a25a.jpg)
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} | [] | closed | false | null | [] | null | [] | "2021-12-23T16:42:55" | "2021-12-24T10:50:20" | "2021-12-24T10:50:19" | MEMBER | null | This PR extends the support in streaming mode for datasets that use `os.walk`, by patching that function.
This PR adds support for streaming mode to datasets:
1. autshumato
1. code_x_glue_cd_code_to_text
1. code_x_glue_tc_nl_code_search_adv
1. nchlt
CC: @severo | {
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} | [] | closed | false | null | [] | null | [] | "2021-12-23T16:26:55" | "2021-12-28T15:15:02" | "2021-12-28T15:15:02" | CONTRIBUTOR | null | 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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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 ?"
] | "2021-12-22T15:26:32" | "2021-12-27T14:13:21" | "2021-12-23T15:21:57" | MEMBER | null | ## 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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- Yields stripped text
- 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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This should fix the CI on master | {
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Some notes:
* the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here
* I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`)
* this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427
TODOs:
- [x] dataset card
- [ ] dummy data
cc @merveenoyan
Closes #2526 | {
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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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https://api.github.com/repos/huggingface/datasets/issues/3465 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3465/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3465/comments | https://api.github.com/repos/huggingface/datasets/issues/3465/events | https://github.com/huggingface/datasets/issues/3465 | 1,085,400,432 | I_kwDODunzps5AseVw | 3,465 | Unable to load 'cnn_dailymail' dataset | {
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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"
] | "2021-12-21T03:32:21" | "2022-02-17T14:13:57" | "2022-02-17T14:13:57" | NONE | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/3463 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3463/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3463/comments | https://api.github.com/repos/huggingface/datasets/issues/3463/events | https://github.com/huggingface/datasets/pull/3463 | 1,085,078,795 | PR_kwDODunzps4wGB4P | 3,463 | Update swahili_news dataset | {
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} | [] | closed | false | null | [] | null | [] | "2021-12-20T18:20:20" | "2021-12-21T06:24:03" | "2021-12-21T06:24:02" | MEMBER | null | Update dataset with latest verion data files.
Fix #3462.
Close bigscience-workshop/data_tooling#107 | {
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] | null | [] | "2021-12-20T17:44:01" | "2021-12-21T06:24:02" | "2021-12-21T06:24:01" | MEMBER | null | Please note also: the HuggingFace version at https://huggingface.co/datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203.
## Adding a Dataset
- **Name:** swahili_news
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Related to:
- bigscience-workshop/data_tooling#107
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https://api.github.com/repos/huggingface/datasets/issues/3461 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3461/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3461/comments | https://api.github.com/repos/huggingface/datasets/issues/3461/events | https://github.com/huggingface/datasets/pull/3461 | 1,085,007,346 | PR_kwDODunzps4wFzDP | 3,461 | Fix links in metrics description | {
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} | [] | closed | false | null | [] | null | [] | "2021-12-20T16:56:19" | "2021-12-20T17:14:52" | "2021-12-20T17:14:51" | MEMBER | null | Remove Markdown syntax for links in metrics description, as it is not properly rendered.
Related to #3437. | {
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"I think this is a duplicate of [#3190](https://github.com/huggingface/datasets/issues/3190)?",
"Upgrading the datasets version as per #3190 fixes this bug. \r\nI'm Marking as closed."
] | "2021-12-20T16:16:49" | "2021-12-20T16:34:57" | "2021-12-20T16:34:57" | NONE | null | ## Describe the bug
When using dataset.select to select a subset of a dataset, dataset._indices are set to indicate which elements are now considered in the dataset.
The same thing happens when you shuffle the dataset; dataset._indices are set to indicate what the new order of the data is.
However, if you then use a dataset.filter, that filter interacts with those dataset._indices values in a non-intuitive manner.
https://huggingface.co/docs/datasets/_modules/datasets/arrow_dataset.html#Dataset.filter
Effectively, it looks like the original set of _indices were discared and overwritten by the set created during the filter operation.
I think this is actually an issue with how the map function handles dataset._indices. Ideally it should use the _indices it gets passed, and then return an updated _indices which reflect the map transformation applied to the starting _indices.
## Steps to reproduce the bug
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print("initial 10 elements")
print(dataset['label']) # -> [1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
print("filtered 10 elements looking for label 0")
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1]
```
## Actual results
```
$ python indices_bug.py
initial 10 elements
[1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
filtered 10 elements looking for label 0
[1, 1, 1, 1, 1, 1]
```
This code block first shuffles the dataset (to get a mix of label 0 and label 1).
Then it selects just the first 10 elements (the number of elements does not matter, 10 is just easy to visualize). The important part is that you select some subset of the dataset.
Finally, a filter is applied to pull out just the elements with `label == 0`.
The bug is that you cannot combine any dataset operation which sets the dataset._indices with filter.
In this case I have 2, shuffle and subset.
If you just use a single dataset._indices operation (in this case shuffle) the bug still shows up.
The shuffle sets the dataset._indices and then filter uses those indices in the map, then overwrites dataset._indices with the filter results.
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
```
## Expected results
In an ideal world, the dataset filter would respect any dataset._indices values which had previously been set.
If you use dataset.filter with the base dataset (where dataset._indices has not been set) then the filter command works as expected.
## Environment info
Here are the commands required to rebuild the conda environment from scratch.
```
# create a virtual environment
conda create -n dataset_indices python=3.8 -y
# activate the virtual environment
conda activate dataset_indices
# install huggingface datasets
conda install datasets
```
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 3.0.0
### Full Conda Environment
```
$ conda env export
name: dasaset_indices
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- abseil-cpp=20210324.2=h2531618_0
- aiohttp=3.8.1=py38h7f8727e_0
- aiosignal=1.2.0=pyhd3eb1b0_0
- arrow-cpp=3.0.0=py38h6b21186_4
- attrs=21.2.0=pyhd3eb1b0_0
- aws-c-common=0.4.57=he6710b0_1
- aws-c-event-stream=0.1.6=h2531618_5
- aws-checksums=0.1.9=he6710b0_0
- aws-sdk-cpp=1.8.185=hce553d0_0
- bcj-cffi=0.5.1=py38h295c915_0
- blas=1.0=mkl
- boost-cpp=1.73.0=h27cfd23_11
- bottleneck=1.3.2=py38heb32a55_1
- brotli=1.0.9=he6710b0_2
- brotli-python=1.0.9=py38heb0550a_2
- brotlicffi=1.0.9.2=py38h295c915_0
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.17.1=h27cfd23_0
- ca-certificates=2021.10.26=h06a4308_2
- certifi=2021.10.8=py38h06a4308_0
- cffi=1.14.6=py38h400218f_0
- conllu=4.4.1=pyhd3eb1b0_0
- cryptography=36.0.0=py38h9ce1e76_0
- dataclasses=0.8=pyh6d0b6a4_7
- dill=0.3.4=pyhd3eb1b0_0
- double-conversion=3.1.5=he6710b0_1
- et_xmlfile=1.1.0=py38h06a4308_0
- filelock=3.4.0=pyhd3eb1b0_0
- frozenlist=1.2.0=py38h7f8727e_0
- gflags=2.2.2=he6710b0_0
- glog=0.5.0=h2531618_0
- gmp=6.2.1=h2531618_2
- grpc-cpp=1.39.0=hae934f6_5
- huggingface_hub=0.0.17=pyhd3eb1b0_0
- icu=58.2=he6710b0_3
- idna=3.3=pyhd3eb1b0_0
- importlib-metadata=4.8.2=py38h06a4308_0
- importlib_metadata=4.8.2=hd3eb1b0_0
- intel-openmp=2021.4.0=h06a4308_3561
- krb5=1.19.2=hac12032_0
- ld_impl_linux-64=2.35.1=h7274673_9
- libboost=1.73.0=h3ff78a5_11
- libcurl=7.80.0=h0b77cf5_0
- libedit=3.1.20210910=h7f8727e_0
- libev=4.33=h7f8727e_1
- libevent=2.1.8=h1ba5d50_1
- libffi=3.3=he6710b0_2
- libgcc-ng=9.3.0=h5101ec6_17
- libgomp=9.3.0=h5101ec6_17
- libnghttp2=1.46.0=hce63b2e_0
- libprotobuf=3.17.2=h4ff587b_1
- libssh2=1.9.0=h1ba5d50_1
- libstdcxx-ng=9.3.0=hd4cf53a_17
- libthrift=0.14.2=hcc01f38_0
- libxml2=2.9.12=h03d6c58_0
- libxslt=1.1.34=hc22bd24_0
- lxml=4.6.3=py38h9120a33_0
- lz4-c=1.9.3=h295c915_1
- mkl=2021.4.0=h06a4308_640
- mkl-service=2.4.0=py38h7f8727e_0
- mkl_fft=1.3.1=py38hd3c417c_0
- mkl_random=1.2.2=py38h51133e4_0
- multiprocess=0.70.12.2=py38h7f8727e_0
- multivolumefile=0.2.3=pyhd3eb1b0_0
- ncurses=6.3=h7f8727e_2
- numexpr=2.7.3=py38h22e1b3c_1
- numpy=1.21.2=py38h20f2e39_0
- numpy-base=1.21.2=py38h79a1101_0
- openpyxl=3.0.9=pyhd3eb1b0_0
- openssl=1.1.1l=h7f8727e_0
- orc=1.6.9=ha97a36c_3
- packaging=21.3=pyhd3eb1b0_0
- pip=21.2.4=py38h06a4308_0
- py7zr=0.16.1=pyhd3eb1b0_1
- pycparser=2.21=pyhd3eb1b0_0
- pycryptodomex=3.10.1=py38h27cfd23_1
- pyopenssl=21.0.0=pyhd3eb1b0_1
- pyparsing=3.0.4=pyhd3eb1b0_0
- pyppmd=0.16.1=py38h295c915_0
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.12=h12debd9_0
- python-dateutil=2.8.2=pyhd3eb1b0_0
- python-xxhash=2.0.2=py38h7f8727e_0
- pyzstd=0.14.4=py38h7f8727e_3
- re2=2020.11.01=h2531618_1
- readline=8.1=h27cfd23_0
- requests=2.26.0=pyhd3eb1b0_0
- setuptools=58.0.4=py38h06a4308_0
- six=1.16.0=pyhd3eb1b0_0
- snappy=1.1.8=he6710b0_0
- sqlite=3.36.0=hc218d9a_0
- texttable=1.6.4=pyhd3eb1b0_0
- tk=8.6.11=h1ccaba5_0
- typing_extensions=3.10.0.2=pyh06a4308_0
- uriparser=0.9.3=he6710b0_1
- utf8proc=2.6.1=h27cfd23_0
- wheel=0.37.0=pyhd3eb1b0_1
- xxhash=0.8.0=h7f8727e_3
- xz=5.2.5=h7b6447c_0
- zipp=3.6.0=pyhd3eb1b0_0
- zlib=1.2.11=h7f8727e_4
- zstd=1.4.9=haebb681_0
- pip:
- async-timeout==4.0.2
- charset-normalizer==2.0.9
- datasets==1.16.1
- fsspec==2021.11.1
- huggingface-hub==0.2.1
- multidict==5.2.0
- pandas==1.3.5
- pyarrow==6.0.1
- pytz==2021.3
- pyyaml==6.0
- tqdm==4.62.3
- typing-extensions==4.0.1
- urllib3==1.26.7
- yarl==1.7.2
```
| {
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https://api.github.com/repos/huggingface/datasets/issues/3458 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3458/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3458/comments | https://api.github.com/repos/huggingface/datasets/issues/3458/events | https://github.com/huggingface/datasets/pull/3458 | 1,084,926,025 | PR_kwDODunzps4wFiRb | 3,458 | Fix duplicated tag in wikicorpus dataset card | {
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https://api.github.com/repos/huggingface/datasets/issues/3456 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3456/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3456/comments | https://api.github.com/repos/huggingface/datasets/issues/3456/events | https://github.com/huggingface/datasets/pull/3456 | 1,084,687,973 | PR_kwDODunzps4wEwXz | 3,456 | [WER] Better error message for wer | {
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} | [] | closed | false | null | [] | null | [] | "2021-12-20T11:38:40" | "2021-12-20T16:53:37" | "2021-12-20T16:53:36" | MEMBER | null | Currently we have the following problem when using the WER. When the input format to the WER metric is wrong, instead of throwing an error message a word-error-rate is computed which is incorrect. E.g. when doing the following:
```python
from datasets import load_metric
wer = load_metric("wer")
target_str = ["hello this is nice", "hello the weather is bloomy"]
pred_str = [["hello it's nice"], ["hello it's the weather"]]
print("Wrong:", wer.compute(predictions=pred_str, references=target_str))
print("Correct", wer.compute(predictions=[x[0] for x in pred_str], references=target_str))
```
We get:
```
Wrong: 1.0
Correct 0.5555555555555556
```
meaning that we get a word-error rate for incorrectly passed input formats. We should raise an error here instead so that people don't spent hours fixing a model while it's their incorrect evaluation metric is the problem for a low WER. | {
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