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https://api.github.com/repos/huggingface/datasets/issues/3579 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3579/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3579/comments | https://api.github.com/repos/huggingface/datasets/issues/3579/events | https://github.com/huggingface/datasets/pull/3579 | 1,103,451,118 | PR_kwDODunzps4xBmY4 | 3,579 | Add Text2log Dataset | {
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] | open | false | null | [] | null | [] | 1,642,155,038,000 | 1,642,155,038,000 | null | NONE | null | null | null | ## Describe the bug
In *dataset_info.json* file, information about the label get lost after the dataset serialization.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# normal save
dataset = load_dataset('glue', 'sst2', split='train')
dataset.save_to_disk("normal_save")
# save after parquet serialization
dataset.to_parquet("glue-sst2-train.parquet")
dataset = load_dataset("parquet", data_files='glue-sst2-train.parquet')
dataset.save_to_disk("save_after_parquet")
```
## Expected results
I expected to keep label information in *dataset_info.json* file even after parquet serialization
## Actual results
In the normal serialization i got
```json
"label": {
"num_classes": 2,
"names": [
"negative",
"positive"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
```
And after parquet serialization i got
```json
"label": {
"dtype": "int64",
"id": null,
"_type": "Value"
},
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: ubuntu 20.04
- Python version: 3.8.10
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3577 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3577/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3577/comments | https://api.github.com/repos/huggingface/datasets/issues/3577/events | https://github.com/huggingface/datasets/issues/3577 | 1,102,598,241 | I_kwDODunzps5BuFBh | 3,577 | Add The Mexican Emotional Speech Database (MESD) | {
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] | open | false | null | [] | null | [] | 1,642,117,776,000 | 1,642,117,776,000 | null | NONE | null | null | null | ## Adding a Dataset
- **Name:** *The Mexican Emotional Speech Database (MESD)*
- **Description:** *Contains 864 voice recordings with six different prosodies: anger, disgust, fear, happiness, neutral, and sadness. Furthermore, three voice categories are included: female adult, male adult, and child. *
- **Paper:** *[Paper](https://ieeexplore.ieee.org/abstract/document/9629934/authors#authors)*
- **Data:** *[link to the Github repository or current dataset location](https://data.mendeley.com/datasets/cy34mh68j9/3)*
- **Motivation:** *Would add Spanish speech data to the HF datasets :) *
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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Closes #3043 | {
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https://api.github.com/repos/huggingface/datasets/issues/3575 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3575/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3575/comments | https://api.github.com/repos/huggingface/datasets/issues/3575/events | https://github.com/huggingface/datasets/pull/3575 | 1,101,947,955 | PR_kwDODunzps4w8Usm | 3,575 | Add Arrow type casting to struct for Image and Audio + Support nested casting | {
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"Regarding the tests I'm just missing the FixedSizeListType type casting for ListArray objects, will to it tomorrow as well as adding new tests + docstrings\r\n\r\nand also adding soundfile in the CI",
"While writing some tests I noticed that the ExtensionArray can't be directly concatenated - maybe we can get rid of the extension types/arrays and only keep their storages in native arrow types.\r\n\r\nIn this case the `cast_storage` functions should be the responsibility of the Image and Audio classes directly. And therefore we would need two never cast to a pyarrow type again but to a HF feature - since they'd end up being the one able to tell what's castable or not. This is fine in my opinion but let me know what you think. I can take care of this on monday I think"
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} | ## Intro
1. Currently, it's not possible to have nested features containing Audio or Image.
2. Moreover one can keep an Arrow array as a StringArray to store paths to images, but such arrays can't be directly concatenated to another image array if it's stored an another Arrow type (typically, a StructType).
3. Allowing several Arrow types for a single HF feature type also leads to bugs like this one #3497
4. Issues like #3247 are quite frequent and happen when Arrow fails to reorder StructArrays.
5. Casting Audio feature type is blocking preparation for the ASR task template: https://github.com/huggingface/datasets/pull/3364
All those issues are linked together by the fact that:
- we are limited by the Arrow type casting which is lacking features for nested types.
- and especially for Audio and Image: they are not robust enough for concatenation and feature inference.
## Proposed solution
To fix 1 and 4 I implemented nested array type casting (which is missing in PyArrow).
To fix 2, 3 and 5 while having a simple implementation for nested array type casting, I changed the storage type of Audio and Image to always be a StructType. Also casting from StringType is directly implemented via a new function `cast_storage` that is defined individually for Audio and Image. I also added nested decoding.
## Implementation details
### I. Better Arrow data type casting for nested data structures
I implemented new functions `array_cast` and `table_cast` that do the exact same as `pyarrow.Array.cast` or `pyarrow.Table.cast` but support nested struct casting and array re-ordering.
These functions can be used on PyArrow objects, and are already integrated in our own `datasets.table.Table.cast` functions. So one can do `my_dataset.data.cast(pyarrow_schema_with_custom_hf_types)` directly.
### II. New image and audio extension types with custom casting
I used PyArrow extension types to be able to define what casting is allowed or not. For example both StringType->ImageExtensionType and StructType->ImageExtensionType are allowed, via the `cast_storage` method.
I factorized all the PyArrow + Pandas extension stuff in the `base_extension.py` file. This aims at separating the front-facing API code of `datasets` from the Arrow back-end which requires advanced knowledge.
### III. Nested feature decoding
I added a new function `decode_nested_example` to decode image and audio data in nested data structures. For optimization's sake, this function is only called if a column has at least one feature that requires decoding.
## Alternative considered
The casting to struct type could have been done directly with python objects using some Audio and Image methods, but bringing arrow data to python objects is expensive. The Audio and Image types could also have been able to convert the arrow data directly, but this is not convenient to use when casting a full Arrow Table with nested fields. Therefore I decided to keep the Arrow data casting logic in Arrow extension types.
## Future work
This work can be used to allow the ArrayND feature types to be nested too (see issue #887)
## TODO
- [ ] fix current tests
- [ ] add new tests
- [ ] docstrings/comments | {
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} | Add support for the [Mauve](https://github.com/krishnap25/mauve) metric introduced in this [paper](https://arxiv.org/pdf/2102.01454.pdf) (Neurips, 2021). | {
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https://api.github.com/repos/huggingface/datasets/issues/3572 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3572/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3572/comments | https://api.github.com/repos/huggingface/datasets/issues/3572/events | https://github.com/huggingface/datasets/issues/3572 | 1,100,634,244 | I_kwDODunzps5BmliE | 3,572 | ConnectionError: Couldn't reach https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/wikiann-ner.tar.gz (error 403) | {
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] | open | false | null | [] | null | [] | 1,642,010,376,000 | 1,642,010,376,000 | null | NONE | null | null | null | ## Adding a Dataset
- **Name:**IndicGLUE**
- **Description:** *natural language understanding benchmark for Indian languages*
- **Paper:** *(https://indicnlp.ai4bharat.org/home/)*
- **Data:** *https://huggingface.co/datasets/indic_glue#data-fields*
- **Motivation:** *I am trying to train my model on Indian languages*
While I am trying to load dataset it is giving me with the above error. | {
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https://api.github.com/repos/huggingface/datasets/issues/3571 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3571/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3571/comments | https://api.github.com/repos/huggingface/datasets/issues/3571/events | https://github.com/huggingface/datasets/pull/3571 | 1,100,519,604 | PR_kwDODunzps4w3fVQ | 3,571 | Add missing tasks to MuchoCine dataset | {
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} | Addresses the 2nd bullet point in #2520.
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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"I reflect your comment! @lhoestq ",
"Wait, the format of the data just changed, so I'll take it into consideration and commit it.",
"I update the code according to the dataset structure change."
] | 1,642,001,489,000 | 1,642,161,049,000 | null | NONE | null | false | {
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} | New pull request of #3564. (for DKTC)
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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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} | [] | open | false | null | [] | null | [] | 1,641,996,224,000 | 1,641,996,224,000 | null | NONE | null | null | 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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} | [] | open | false | null | [] | null | [] | 1,641,991,378,000 | 1,641,994,141,000 | null | CONTRIBUTOR | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/3567",
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} | # 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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} | [] | open | false | null | [] | null | [] | 1,641,982,892,000 | 1,642,189,448,000 | null | NONE | null | true | {
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} | 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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"> \r\n\r\nI did `make style` and it affected over 500 files\r\n\r\n```\r\nAll done! ✨ 🍰 ✨\r\n575 files reformatted, 372 files left unchanged.\r\nisort tests src benchmarks datasets/**/*.py metri\r\n```\r\n\r\n(result)\r\n\r\n",
"Nvm I was using wrong black version"
] | 1,641,914,797,000 | 1,642,003,887,000 | null | CONTRIBUTOR | null | false | {
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} | 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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"I reflect your review. cc. @lhoestq ",
"Ah sorry, I missed KMWP comment, wait.",
"I request 2 new pull requests. #3569 #3570"
] | 1,641,910,448,000 | 1,642,001,629,000 | null | NONE | null | false | {
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} | Add the DKTC dataset.
- https://github.com/tunib-ai/DKTC | {
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https://api.github.com/repos/huggingface/datasets/issues/3563 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3563/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3563/comments | https://api.github.com/repos/huggingface/datasets/issues/3563/events | https://github.com/huggingface/datasets/issues/3563 | 1,099,070,368 | I_kwDODunzps5Bgnug | 3,563 | Dataset.from_pandas preserves useless index | {
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"Hi! That makes sense. Sure, feel free to open a PR! Just a small suggestion: let's make `preserve_index` a parameter of `Dataset.from_pandas` (which we then pass to `InMemoryTable.from_pandas`) with `None` as a default value to not have this as a breaking change. "
] | 1,641,902,827,000 | 1,642,003,887,000 | null | CONTRIBUTOR | null | null | 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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} | Add support for multiple task templates of the same type. Fixes (partially) #2520.
CC: @lewtun | {
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https://api.github.com/repos/huggingface/datasets/issues/3561 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3561/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3561/comments | https://api.github.com/repos/huggingface/datasets/issues/3561/events | https://github.com/huggingface/datasets/issues/3561 | 1,098,328,870 | I_kwDODunzps5Bdysm | 3,561 | Cannot load ‘bookcorpusopen’ | {
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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."
] | 1,641,845,838,000 | 1,641,965,808,000 | null | NONE | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/3560 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3560/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3560/comments | https://api.github.com/repos/huggingface/datasets/issues/3560/events | https://github.com/huggingface/datasets/pull/3560 | 1,098,280,652 | PR_kwDODunzps4wwOMf | 3,560 | Run pyupgrade for Python 3.6+ | {
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"Hi ! Thanks for the change :)\r\nCould it be possible to only run it for the code in `src/` ? We try to not change the code in the `datasets/` directory too often since it refreshes the users cache when they upgrade `datasets`.",
"> Hi ! Thanks for the change :)\r\n> Could it be possible to only run it for the code in `src/` ? We try to not change the code in the `datasets/` directory too often since it refreshes the users cache when they upgrade `datasets`.\r\n\r\nI reverted the changes in `datasets/` instead of changing only `src/`. Does it sound good?"
] | 1,641,842,453,000 | 1,642,000,307,000 | null | CONTRIBUTOR | null | false | {
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} | Run the command:
```bash
pyupgrade $(find . -name "*.py" -type f) --py36-plus
```
Which mainly avoids unnecessary lists creations and also removes unnecessary code for Python 3.6+.
It was originally part of #3489.
Tip for reviewing faster: use the CLI (`git diff`) and scroll. | {
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https://api.github.com/repos/huggingface/datasets/issues/3559 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3559/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3559/comments | https://api.github.com/repos/huggingface/datasets/issues/3559/events | https://github.com/huggingface/datasets/pull/3559 | 1,098,178,222 | PR_kwDODunzps4wv420 | 3,559 | Fix `DuplicatedKeysError` and improve card in `tweet_qa` | {
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https://api.github.com/repos/huggingface/datasets/issues/3558 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3558/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3558/comments | https://api.github.com/repos/huggingface/datasets/issues/3558/events | https://github.com/huggingface/datasets/issues/3558 | 1,098,025,866 | I_kwDODunzps5BcouK | 3,558 | Integrate Milvus (pymilvus) library | {
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https://api.github.com/repos/huggingface/datasets/issues/3557 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3557/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3557/comments | https://api.github.com/repos/huggingface/datasets/issues/3557/events | https://github.com/huggingface/datasets/pull/3557 | 1,097,946,034 | PR_kwDODunzps4wvIHl | 3,557 | Fix bug in `ImageClassifcation` task template | {
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"The CI failures are unrelated to the changes in this PR.",
"> The CI failures are unrelated to the changes in this PR.\r\n\r\nIt seems that some of the failures are due to the tests on the dataset cards (e.g. CIFAR, MNIST, FASHION_MNIST). Perhaps it's worth addressing those in this PR to avoid confusing downstream developers who branch off `master` and suddenly have a failing CI?",
"@lewtun We only run these tests against the modified datasets on the PR branch, so this will not lead to errors after merging."
] | 1,641,823,799,000 | 1,641,916,072,000 | null | CONTRIBUTOR | null | false | {
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} | Fixes a bug in the `ImageClassification` task template which requires specifying class labels twice in dataset scripts. Additionally, this PR refactors the API around the classification task templates for cleaner `labels` handling.
CC: @lewtun @nateraw | {
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https://api.github.com/repos/huggingface/datasets/issues/3556 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3556/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3556/comments | https://api.github.com/repos/huggingface/datasets/issues/3556/events | https://github.com/huggingface/datasets/pull/3556 | 1,097,907,724 | PR_kwDODunzps4wvALx | 3,556 | Preserve encoding/decoding with features in `Iterable.map` call | {
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} | As described in https://github.com/huggingface/datasets/issues/3505#issuecomment-1004755657, this PR uses a generator expression to encode/decode examples with `features` (which are set to None in `map`) before applying a map transform.
Fix #3505 | {
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https://api.github.com/repos/huggingface/datasets/issues/3555 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3555/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3555/comments | https://api.github.com/repos/huggingface/datasets/issues/3555/events | https://github.com/huggingface/datasets/issues/3555 | 1,097,736,982 | I_kwDODunzps5BbiMW | 3,555 | DuplicatedKeysError when loading tweet_qa dataset | {
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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```"
] | 1,641,811,991,000 | 1,642,000,653,000 | null | NONE | null | null | 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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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)
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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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https://api.github.com/repos/huggingface/datasets/issues/3553 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3553/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3553/comments | https://api.github.com/repos/huggingface/datasets/issues/3553/events | https://github.com/huggingface/datasets/issues/3553 | 1,097,252,275 | I_kwDODunzps5BZr2z | 3,553 | set_format("np") no longer works for Image data | {
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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"
] | 1,641,748,693,000 | 1,642,081,166,000 | null | NONE | null | null | 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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} | Add the KMWP & DKTC dataset.
Additional notes:
- Both datasets will be released on January 10 through the GitHub link below.
- https://github.com/tunib-ai/DKTC
- https://github.com/tunib-ai/KMWP
- So it doesn't work as a link at the moment, but the code will work soon (after it is released on January 10). | {
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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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"@lhoestq, I looked into how to compress with `zipfile` for which few methods exist, let me know which one looks good:\r\n1. create the file in normal `wb` mode and then zip it separately\r\n2. use `ZipFile.write_str` to write file into the archive. For this we'll need to change how we're writing files from `_write` method \r\n\r\nHow `pandas` handles it is that they have created a wrapper for standard library class `ZipFile` and allow the returned file-like handle to accept byte strings via `write` method instead of `write_str` (purpose was to change the name of function by creating that wrapper)",
"1. sounds not ideal since it creates an intermediary file.\r\nI like pandas' approach. Is it possible to implement 2. using the pandas class ? Or maybe we can have something similar ?"
] | 1,641,579,902,000 | 1,642,168,983,000 | null | CONTRIBUTOR | null | true | {
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} | This PR adds `bz2`, `xz`, and `zip` (WIP) for `to_json`. I also plan to add `infer` like how `pandas` does it | {
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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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] | null | [] | 1,641,576,777,000 | 1,641,917,717,000 | null | CONTRIBUTOR | null | null | 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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Currently if I upload a CSV with paths to audio files, the column type is string instead of Audio.
**Describe the solution you'd like**
I'd like to be able to specify the types of the column, so that when loading the dataset I directly get the features types I want.
The feature types could read from the `dataset_infos.json` for example.
**Describe alternatives you've considered**
Create a dataset script to specify the features, but that seems complicated for a simple thing.
cc @abidlabs | {
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"Thanks for reporting. I think this can be fixed by improving the `CachedDatasetModuleFactory` and making it look into the `parquet` cache directory (datasets from push_to_hub are loaded with the parquet dataset builder). I'll look into it"
] | 1,641,568,345,000 | 1,641,811,484,000 | null | MEMBER | null | null | null | ## Describe the bug
In offline mode, one can still access previously-cached datasets. This fails with datasets created with `push_to_hub`.
## Steps to reproduce the bug
in Python:
```
import datasets
mpwiki = datasets.load_dataset("teven/matched_passages_wikidata")
```
in bash:
```
export HF_DATASETS_OFFLINE=1
```
in Python:
```
import datasets
mpwiki = datasets.load_dataset("teven/matched_passages_wikidata")
```
## Expected results
`datasets` should find the previously-cached dataset.
## Actual results
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'teven/matched_passages_wikidata': Offline mode is enabled
## Environment info
- `datasets` version: 1.16.2.dev0
- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 3.0.0
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"The CI failures are unrelated to the changes."
] | 1,641,565,824,000 | 1,641,578,956,000 | null | CONTRIBUTOR | null | false | {
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"Currently, it does not work with https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0/blob/main/common_voice_7_0.py#L146 (which was the goal), because `dl_manager.download_config.use_auth_token` is ignored, and the authentication is required to be use `huggingface-cli login`.\r\nIn my use case (dataset viewer), I'd prefer to use a specific \"User Token Access\", with only the \"read\" role (https://huggingface.co/settings/token).\r\n\r\nSee https://github.com/huggingface/datasets-preview-backend/issues/74#issuecomment-1007316853 for the context",
"> Simply passing download_config is ok :)\r\n\r\nhmm, I prefer only passing use_auth_token. But the question is more: is it correct, in the (convoluted) case if `download_config.use_auth_token` exists and is different from `use_auth_token`? Which one should be used?",
"If both are passed, `use_auth_token` should have the priority (more specific parameters have the higher priority)"
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] | open | false | null | [] | null | [] | 1,641,510,145,000 | 1,641,510,145,000 | null | CONTRIBUTOR | null | null | null | Hello,
**Is your feature request related to a problem? Please describe.**
My use case is that I have one writer that adds columns and multiple workers reading the same `Dataset`. Each worker should have access to columns added by the writer when they reload the dataset.
I understand that we shouldn't overwrite an arrow file as this could cause Segfault and so on. Before 1.16, I was able to overwrite the dataset and that would work most of the time with some retries.
**Describe the solution you'd like**
I was thinking that if we could append `Dataset._data_files`, when the workers reload the Dataset, they would get the new columns.
**Describe alternatives you've considered**
I currently need to
1. Save multiple "versions" of the dataset and load the latest.
2. Try working with cache files to get the latest columns.
**Additional context**
I think this would be a great addition to HFDataset as Parquet supports multi-files input out of the box!
I can make a PR myself with some pointers as needed :) | {
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"Hi ! Thanks for your message :) This is a great idea indeed. We haven't started working on this yet though. For now I guess you can host your metric on the Hub (either with your model or your dataset) and use `hf_hub_download` to download it (docs [here](https://github.com/huggingface/huggingface_hub/blob/main/docs/hub/how-to-downstream.md#cached_download))",
"This is a great solution in the meantime, thanks!",
"Here's the code I used, in case it can be of help to someone else:\r\n```python\r\nimport os, shutil\r\nfrom huggingface_hub import hf_hub_download\r\ndef download_metric(repo_id, file_path):\r\n # repo_id: for models \"username/model_name\", for datasets \"datasets/username/model_name\"\r\n local_metric_path = hf_hub_download(repo_id=repo_id, filename=file_path)\r\n updated_local_metric_path = (os.path.dirname(local_metric_path) + os.path.basename(local_metric_path).replace(\".\", \"_\") + \".py\")\r\n shutil.copy(local_metric_path, updated_local_metric_path)\r\n return updated_local_metric_path\r\n\r\nmetric = load_metric(download_metric(REPO_ID, FILE_PATH))\r\n```"
] | 1,641,468,386,000 | 1,641,760,093,000 | null | CONTRIBUTOR | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/3541 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3541/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3541/comments | https://api.github.com/repos/huggingface/datasets/issues/3541/events | https://github.com/huggingface/datasets/issues/3541 | 1,095,033,828 | I_kwDODunzps5BROPk | 3,541 | Support 7-zip compressed data files | {
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] | open | false | null | [] | null | [] | 1,641,453,063,000 | 1,641,453,063,000 | null | MEMBER | null | null | null | **Is your feature request related to a problem? Please describe.**
We should support 7-zip compressed data files:
- in `extract`
- in `iter_archive`
both in streaming and non-streaming modes.
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https://api.github.com/repos/huggingface/datasets/issues/3540 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3540/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3540/comments | https://api.github.com/repos/huggingface/datasets/issues/3540/events | https://github.com/huggingface/datasets/issues/3540 | 1,094,900,336 | I_kwDODunzps5BQtpw | 3,540 | How to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset? | {
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] | open | false | null | [] | null | [] | 1,641,435,222,000 | 1,641,435,459,000 | null | NONE | null | null | null | Hi,
I use torch.utils.data.Dataset to define my own data, but I need to use the 'map' function of datasets.arrow_dataset.Dataset later, so I hope to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset.
Here is an example.
```
from torch.utils.data import Dataset
from datasets.arrow_dataset import Dataset as HFDataset
class ADataset(Dataset):
def __init__(self, data):
super().__init__()
self.data = data
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return self.len
class MDataset():
def __init__(self, tokenizer: AutoTokenizer, data_args, training_args):
self.train_dataset = ADataset(data_args)
self.tokenizer = tokenizer
self.data_args = data_args
self.train_dataset = self.train_dataset.map(
self.process_function,
batched=True,
remove_columns=column_names,
load_from_cache_file=True,
desc="Running tokenizer on train dataset",
)
def process_function(self, examples):
sentences = [" ".join(sample[0][3]) for sample in examples]
tokenized = self.tokenizer(
sentences,
max_length=self.max_seq_len,
padding=self.padding,
truncation=True)
```
But it would raise an ERROR, AttributeError: 'ADataset' object has no attribute 'map'.
so how to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?
Thanks in advance!
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"The CI failure is about some missing tags or sections in the dataset cards, and is unrelated to the part about non commercial use of this PR. Merging"
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} | Noticing that the recent commit throws a lot of errors in the automatic checks. It looks to me that those errors are simply errors that were already there (metadata issues), unrelated to what I've just changed, but worth another look to make sure. | {
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https://api.github.com/repos/huggingface/datasets/issues/3537 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3537/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3537/comments | https://api.github.com/repos/huggingface/datasets/issues/3537/events | https://github.com/huggingface/datasets/pull/3537 | 1,094,738,734 | PR_kwDODunzps4wlH1d | 3,537 | added PII statements and license links to data cards | {
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multilingual_librispeech
openslr
speech commands
superb
timit_asr
vctk | {
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"Pushed the lastest changes!"
] | 1,641,408,305,000 | 1,642,028,386,000 | null | CONTRIBUTOR | null | false | {
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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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