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https://github.com/huggingface/datasets/pull/3576

PR_kwDODunzps4w8sUm

Add PASS dataset

closed

CONTRIBUTOR

This PR adds the PASS dataset. Closes #3043

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https://github.com/huggingface/datasets/pull/3575

PR_kwDODunzps4w8Usm

Add Arrow type casting to struct for Image and Audio + Support nested casting

closed

MEMBER

## 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 - [x] fix current tests - [x] add new tests - [x] docstrings/comments

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PR_kwDODunzps4w7vu6

Fix qa4mre tags

closed

MEMBER

The YAML tags were invalid. I also fixed the dataset mirroring logging that failed because of this issue [here](https://github.com/huggingface/datasets/actions/runs/1690109581)

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PR_kwDODunzps4w5oE_

Add Mauve metric

closed

CONTRIBUTOR

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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I_kwDODunzps5BmliE

ConnectionError in IndicGLUE dataset

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NONE

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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PR_kwDODunzps4w3fVQ

Add missing tasks to MuchoCine dataset

closed

CONTRIBUTOR

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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PR_kwDODunzps4w3Xez

Add the KMWP dataset (extension of #3564)

open

NONE

New pull request of #3564 (Add the KMWP dataset)

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PR_kwDODunzps4w3XGo

Add the DKTC dataset (Extension of #3564)

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NONE

New pull request of #3564. (for DKTC)

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I_kwDODunzps5BlnnX

Downloading Hugging Face Medical Dialog Dataset NonMatchingSplitsSizesError

closed

NONE

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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PR_kwDODunzps4w2xDl

Fix push to hub to allow individual split push

open

MEMBER

# 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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PR_kwDODunzps4w2Tcc

Add initial electricity time series dataset

closed

CONTRIBUTOR

Here is an initial prototype time series dataset

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PR_kwDODunzps4wzjhH

Add parameter `preserve_index` to `from_pandas`

closed

CONTRIBUTOR

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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PR_kwDODunzps4wzSOL

Add the KMWP & DKTC dataset.

closed

NONE

Add the DKTC dataset. - https://github.com/tunib-ai/DKTC

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I_kwDODunzps5Bgnug

Dataset.from_pandas preserves useless index

closed

CONTRIBUTOR

## 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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PR_kwDODunzps4wwa44

Allow multiple task templates of the same type

closed

CONTRIBUTOR

Add support for multiple task templates of the same type. Fixes (partially) #2520. CC: @lewtun

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I_kwDODunzps5Bdysm

Cannot load ‘bookcorpusopen’

closed

NONE

## 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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PR_kwDODunzps4wwOMf

Run pyupgrade for Python 3.6+

closed

CONTRIBUTOR

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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PR_kwDODunzps4wv420

Fix `DuplicatedKeysError` and improve card in `tweet_qa`

closed

CONTRIBUTOR

Fix #3555

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https://github.com/huggingface/datasets/issues/3558

I_kwDODunzps5BcouK

Integrate Milvus (pymilvus) library

open

CONTRIBUTOR

Milvus is a popular open-source vector database. We should add a new vector index to support this project.

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https://github.com/huggingface/datasets/pull/3557

PR_kwDODunzps4wvIHl

Fix bug in `ImageClassifcation` task template

closed

CONTRIBUTOR

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://github.com/huggingface/datasets/pull/3556

PR_kwDODunzps4wvALx

Preserve encoding/decoding with features in `Iterable.map` call

closed

CONTRIBUTOR

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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I_kwDODunzps5BbiMW

DuplicatedKeysError when loading tweet_qa dataset

closed

NONE

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_kwDODunzps5Bbb8H

ImportError: cannot import name 'is_valid_waiter_error'

closed

NONE

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) 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cachetools<5.0,>=2.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (4.2.2) Requirement already satisfied: parso<0.8.0,>=0.7.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from jedi>=0.10->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.1) Requirement already satisfied: ipython-genutils in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from traitlets>=4.2->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0) Requirement already satisfied: ptyprocess>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pexpect->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.0) Requirement already satisfied: oauthlib>=3.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests-oauthlib->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.1) ``` --- **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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I_kwDODunzps5BZr2z

set_format("np") no longer works for Image data

open

NONE

## 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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PR_kwDODunzps4wsM29

Add the KMWP & DKTC dataset.

closed

NONE

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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PR_kwDODunzps4wq_AO

Add more compression types for `to_json`

closed

CONTRIBUTOR

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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I_kwDODunzps5BW5qJ

Bug in `openbookqa` dataset

open

CONTRIBUTOR

## 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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PR_kwDODunzps4wqkGt

Fix sem_eval_2018_task_1 download location

closed

CONTRIBUTOR

This changes the download location of sem_eval_2018_task_1 files to include the test set labels as discussed in https://github.com/huggingface/datasets/issues/2745#issuecomment-954588500_ with @lhoestq.

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I_kwDODunzps5BWeGo

Specify the feature types of a dataset on the Hub without needing a dataset script

closed

MEMBER

**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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I_kwDODunzps5BWdIL

Datasets created with `push_to_hub` can't be accessed in offline mode

open

MEMBER

## 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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PR_kwDODunzps4wqYIV

Remove print statements in datasets

closed

CONTRIBUTOR

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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PR_kwDODunzps4wpziv

fix: 🐛 pass token when retrieving the split names

closed

CONTRIBUTOR

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I_kwDODunzps5BUFjp

Ability to split a dataset in multiple files.

open

CONTRIBUTOR

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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I_kwDODunzps5BR9RG

Allow loading community metrics from the hub, just like datasets

open

CONTRIBUTOR

**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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PR_kwDODunzps4wmPIP

Update the CC-100 dataset card

closed

CONTRIBUTOR

* summary from the dataset homepage * more details about the data structure * this dataset does not contain annotations

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I_kwDODunzps5BROPk

Support 7-zip compressed data files

open

MEMBER

**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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I_kwDODunzps5BQtpw

How to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?

open

NONE

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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https://github.com/huggingface/datasets/pull/3539

PR_kwDODunzps4wlXU4

Research wording for nc licenses

closed

CONTRIBUTOR

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PR_kwDODunzps4wlLmD

Readme usage update

closed

CONTRIBUTOR

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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PR_kwDODunzps4wlH1d

added PII statements and license links to data cards

closed

CONTRIBUTOR

Updates for the following datacards: multilingual_librispeech openslr speech commands superb timit_asr vctk

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PR_kwDODunzps4wk0Yb

update `pretty_name` for all datasets

closed

CONTRIBUTOR

This PR updates `pretty_name` for all datasets. Previous PR #3498 had done this for only first 200 datasets

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PR_kwDODunzps4wkxv0

Add SVHN dataset

closed

CONTRIBUTOR

Add the SVHN dataset. 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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PR_kwDODunzps4wj3LE

Update wiki_dpr README.md

closed

MEMBER

Some infos of wiki_dpr were missing as noted in https://github.com/huggingface/datasets/issues/3510, I added them and updated the tags and the examples Close #3510.

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I_kwDODunzps5BN39z

Task search function on hub not working correctly

open

MEMBER

When I want to look at all datasets of the category: `speech-processing` *i.e.* https://huggingface.co/datasets?task_categories=task_categories:speech-processing&sort=downloads , then the following dataset doesn't show up for some reason: - https://huggingface.co/datasets/speech_commands even thought it's task tags seem correct: https://raw.githubusercontent.com/huggingface/datasets/master/datasets/speech_commands/README.md

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PR_kwDODunzps4wi1ft

Give clearer instructions to add the YAML tags

closed

MEMBER

Fix #3531. CC: @julien-c @VictorSanh

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I_kwDODunzps5BNZ-A

Give clearer instructions to add the YAML tags

closed

MEMBER

## Describe the bug As reported by @julien-c, many community datasets contain the line `YAML tags:` at the top of the YAML section in the header of the README file. See e.g.: https://huggingface.co/datasets/bigscience/P3/commit/a03bea08cf4d58f268b469593069af6aeb15de32 Maybe we should give clearer instruction/hints in the README template.

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PR_kwDODunzps4wiZCw

Update README.md

closed

CONTRIBUTOR

Removing reference to "Common Voice" in Personal and Sensitive Information section. Adding link to license. Correct license type in metadata.

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PR_kwDODunzps4wiPA9

Update README.md

closed

CONTRIBUTOR

Updating licensing information & personal and sensitive information.

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PR_kwDODunzps4wiOqH

Update README.md

closed

CONTRIBUTOR

Updating license with appropriate capitalization & a link. Updating Personal and Sensitive Information to address PII concern.

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PR_kwDODunzps4wiN1w

Update README.md

closed

CONTRIBUTOR

Adding licensing information.

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PR_kwDODunzps4wiMaQ

Update README.md

open

CONTRIBUTOR

Not entirely sure, following the links here, but it seems the relevant license is at https://github.com/soskek/bookcorpus/blob/master/LICENSE

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PR_kwDODunzps4wiL8p

Adding license information for Openbookcorpus

open

CONTRIBUTOR

Not entirely sure, following the links here, but it seems the relevant license is at https://github.com/soskek/bookcorpus/blob/master/LICENSE

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PR_kwDODunzps4wiK_v

Adding link to license.

closed

CONTRIBUTOR

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PR_kwDODunzps4wiJc2

Added links to licensing and PII message in vctk dataset

closed

CONTRIBUTOR

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I_kwDODunzps5BMi3i

wmt19 is broken (zh-en)

open

NONE

## 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:[email protected]/parallel/casia2015.zip` ## Environment info - `datasets` version: 1.15.1 - Platform: Linux - Python version: 3.8

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PR_kwDODunzps4wiFCs

Vivos license update

closed

CONTRIBUTOR

Updated the license information with the link to the license text

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https://github.com/huggingface/datasets/pull/3520

PR_kwDODunzps4wh6oD

Audio datacard update - first pass

closed

CONTRIBUTOR

Filling out data card "Personal and Sensitive Information" for speech datasets to note PII concerns

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PR_kwDODunzps4whnXH

CC100: Using HTTPS for the data source URL fixes load_dataset()

closed

CONTRIBUTOR

Without this change the following script (with any lang parameter) consistently fails. After changing to the HTTPS URL, the script works as expected. ```python from datasets import load_dataset dataset = load_dataset("cc100", lang="en") ``` This is the error produced by the previous script: ```sh Using custom data configuration en-lang=en Downloading and preparing dataset cc100/en to /home/antti/.cache/huggingface/datasets/cc100/en-lang=en/0.0.0/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b... Traceback (most recent call last): File "/home/antti/tmp/cc100/cc100.py", line 3, in <module> dataset = load_dataset("cc100", lang="en") File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/load.py", line 1694, in load_dataset builder_instance.download_and_prepare( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 595, in download_and_prepare self._download_and_prepare( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/antti/.cache/huggingface/modules/datasets_modules/datasets/cc100/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b/cc100.py", line 117, in _split_generators path = dl_manager.download_and_extract(download_url) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 308, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 251, in map_nested return function(data_struct) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach http://data.statmt.org/cc-100/en.txt.xz (error 503) ``` Note that I get the same behavior also using curl on the command line. The plain HTTP "curl -L http://data.statmt.org/cc-100/en.txt.xz" fails with "503 Service unavailable", but the with the HTTPS version of the URL curl starts downloading the file. My guess is that the server does overly aggressive rate-limitting. When a client requests an HTTP URL, it (sensibly) gets redirected to the HTTPS equivalent, but now the server notices two requests coming from the same client (the original HTTP and the redirected HTTPS) during a brief time windows and rate-limitter kicks in and blocks the second request! If the client initally uses the HTTPS URL there's only one incoming request which the rate-limitter allows.

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I_kwDODunzps5BJtMf

Add PubMed Central Open Access dataset

closed

MEMBER

## 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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PR_kwDODunzps4wemwU

Add CPPE-5 dataset

closed

CONTRIBUTOR

Adds the recently released CPPE-5 dataset.

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PR_kwDODunzps4weYhE

dataset `asset` - change to raw.githubusercontent.com URLs

closed

MEMBER

Changed the URLs to the ones it was automatically re-directing. Before, the download was failing

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I_kwDODunzps5BICE3

`ExpectedMoreDownloadedFiles` for `evidence_infer_treatment`

closed

MEMBER

## 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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https://github.com/huggingface/datasets/pull/3514

PR_kwDODunzps4weN9W

Fix to_tf_dataset references in docs

closed

CONTRIBUTOR

Fix the `to_tf_dataset` references in the docs. The currently failing example of usage will be fixed by #3338.

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PR_kwDODunzps4weGWl

Add desc parameter to filter

closed

CONTRIBUTOR

Fix #3317

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I_kwDODunzps5BHBcl

No Data format found

closed

NONE

## 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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I_kwDODunzps5BGTKr

Dataset

closed

NONE

## 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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I_kwDODunzps5BFo1M

`wiki_dpr` details for Open Domain Question Answering tasks

closed

NONE

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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I_kwDODunzps5BCp3Y

Discuss whether support canonical datasets w/o dataset_infos.json and/or dummy data

open

MEMBER

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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PR_kwDODunzps4wZpot

Allows DatasetDict.filter to have batching option

closed

MEMBER

- Related to: #3244 - Fixes: #3503 We extends `.filter( ... batched: bool)` support to DatasetDict.

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I_kwDODunzps5BCaPk

cast_column function not working with map function in streaming mode for Audio features

closed

NONE

## 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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I_kwDODunzps5BAn12

Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst

closed

NONE

## 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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https://github.com/huggingface/datasets/issues/3503

I_kwDODunzps5A_0sf

Batched in filter throws error

closed

NONE

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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https://github.com/huggingface/datasets/pull/3502

PR_kwDODunzps4wXSLi

Add QuALITY

open

MEMBER

Fixes #3441.

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https://github.com/huggingface/datasets/pull/3501

PR_kwDODunzps4wXM8H

Update pib dataset card

closed

MEMBER

Related to #3496

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