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I_kwDODunzps5B6X8e

Datasets streaming + map doesn't work for `Audio`

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MEMBER

## Describe the bug When using audio datasets in streaming mode, applying a `map(...)` before iterating leads to an error as the key `array` does not exist anymore. ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("common_voice", "en", streaming=True, split="train") def map_fn(batch): print("audio keys", batch["audio"].keys()) batch["audio"] = batch["audio"]["array"][:100] return batch ds = ds.map(map_fn) sample = next(iter(ds)) ``` I think the audio is somehow decoded before `.map(...)` is actually called. ## Expected results IMO, the above code snippet should work. ## Actual results ```bash audio keys dict_keys(['path', 'bytes']) Traceback (most recent call last): File "./run_audio.py", line 15, in <module> sample = next(iter(ds)) File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 192, in __iter__ yield key, self.function(example) File "./run_audio.py", line 9, in map_fn batch["input"] = batch["audio"]["array"][:100] KeyError: 'array' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.1.dev0 - Platform: Linux-5.3.0-64-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1

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I_kwDODunzps5B4H-Y

https://huggingface.co/datasets/huggingface/transformers-metadata

open

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_kwDODunzps5B3_CI

Add The Medical Segmentation Decathlon Dataset

open

NONE

## Adding a Dataset - **Name:** *The Medical Segmentation Decathlon Dataset* - **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. - **Paper:** [link to the dataset paper if available](https://arxiv.org/abs/2106.05735) - **Data:** http://medicaldecathlon.com/ - **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community. (cc @osanseviero @abidlabs ) 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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I_kwDODunzps5B2xb3

conll 2003 dataset source url is no longer valid

open

NONE

## Describe the bug Loading `conll2003` dataset fails because it was removed (just yesterday 1/14/2022) from the location it is looking for. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("conll2003") ``` ## Expected results The dataset should load. ## Actual results It is looking for the dataset at `https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt` but it was removed from there yesterday (see [commit](https://github.com/davidsbatista/NER-datasets/commit/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https://github.com/davidsbatista/NER-datasets/issues/8)). - We should replace this with an alternate valid location. - this is being referenced in the huggingface course chapter 7 [colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter7/section2_pt.ipynb), which is also broken. ```python FileNotFoundError Traceback (most recent call last) <ipython-input-4-27c956bec93c> in <module>() 1 from datasets import load_dataset 2 ----> 3 raw_datasets = load_dataset("conll2003") 11 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params) 610 ) 611 elif response is not None and response.status_code == 404: --> 612 raise FileNotFoundError(f"Couldn't find file at {url}") 613 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 614 if head_error is not None: FileNotFoundError: Couldn't find file at https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:

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I_kwDODunzps5B2sre

Unable to create a dataset from a parquet file in S3

open

NONE

## Describe the bug Trying to create a dataset from a parquet file in S3. ## Steps to reproduce the bug ```python import s3fs from datasets import Dataset s3 = s3fs.S3FileSystem(anon=False) with s3.open(PATH_LTR_TOY_CLEAN_DATASET, 'rb') as s3file: dataset = Dataset.from_parquet(s3file) ``` ## Expected results A new Dataset object ## Actual results ```AttributeError: 'S3File' object has no attribute 'decode'``` ``` AttributeError Traceback (most recent call last) <command-2452877612515691> in <module> 5 6 with s3.open(PATH_LTR_TOY_CLEAN_DATASET, 'rb') as s3file: ----> 7 dataset = Dataset.from_parquet(s3file) /databricks/python/lib/python3.8/site-packages/datasets/arrow_dataset.py in from_parquet(path_or_paths, split, features, cache_dir, keep_in_memory, columns, **kwargs) 907 from .io.parquet import ParquetDatasetReader 908 --> 909 return ParquetDatasetReader( 910 path_or_paths, 911 split=split, /databricks/python/lib/python3.8/site-packages/datasets/io/parquet.py in __init__(self, path_or_paths, split, features, cache_dir, keep_in_memory, **kwargs) 28 path_or_paths = path_or_paths if isinstance(path_or_paths, dict) else {self.split: path_or_paths} 29 hash = _PACKAGED_DATASETS_MODULES["parquet"][1] ---> 30 self.builder = Parquet( 31 cache_dir=cache_dir, 32 data_files=path_or_paths, /databricks/python/lib/python3.8/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, base_path, info, features, use_auth_token, namespace, data_files, data_dir, **config_kwargs) 246 247 if data_files is not None and not isinstance(data_files, DataFilesDict): --> 248 data_files = DataFilesDict.from_local_or_remote( 249 sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token 250 ) /databricks/python/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, /databricks/python/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token) 544 ) -> "DataFilesList": 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) /databricks/python/lib/python3.8/site-packages/datasets/data_files.py in resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) 191 data_files = [] 192 for pattern in patterns: --> 193 if is_remote_url(pattern): 194 data_files.append(Url(pattern)) 195 else: /databricks/python/lib/python3.8/site-packages/datasets/utils/file_utils.py in is_remote_url(url_or_filename) 115 116 def is_remote_url(url_or_filename: str) -> bool: --> 117 parsed = urlparse(url_or_filename) 118 return parsed.scheme in ("http", "https", "s3", "gs", "hdfs", "ftp") 119 /usr/lib/python3.8/urllib/parse.py in urlparse(url, scheme, allow_fragments) 370 Note that we don't break the components up in smaller bits 371 (e.g. netloc is a single string) and we don't expand % escapes.""" --> 372 url, scheme, _coerce_result = _coerce_args(url, scheme) 373 splitresult = urlsplit(url, scheme, allow_fragments) 374 scheme, netloc, url, query, fragment = splitresult /usr/lib/python3.8/urllib/parse.py in _coerce_args(*args) 122 if str_input: 123 return args + (_noop,) --> 124 return _decode_args(args) + (_encode_result,) 125 126 # Result objects are more helpful than simple tuples /usr/lib/python3.8/urllib/parse.py in _decode_args(args, encoding, errors) 106 def _decode_args(args, encoding=_implicit_encoding, 107 errors=_implicit_errors): --> 108 return tuple(x.decode(encoding, errors) if x else '' for x in args) 109 110 def _coerce_args(*args): /usr/lib/python3.8/urllib/parse.py in <genexpr>(.0) 106 def _decode_args(args, encoding=_implicit_encoding, 107 errors=_implicit_errors): --> 108 return tuple(x.decode(encoding, errors) if x else '' for x in args) 109 110 def _coerce_args(*args): AttributeError: 'S3File' object has no attribute 'decode' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.0 - Platform: Ubuntu 20.04.3 LTS - Python version: 3.8.10 - PyArrow version: 6.0.1

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I_kwDODunzps5B19LK

Bug in wiki bio load

open

NONE

wiki_bio is failing to load because of a failing drive link . Can someone fix this ?   a

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PR_kwDODunzps4xBmY4

Add Text2log Dataset

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CONTRIBUTOR

Adding the text2log dataset used for training FOL sentence translating models

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I_kwDODunzps5BxJkX

label information get lost after parquet serialization

open

NONE

## 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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I_kwDODunzps5BuFBh

Add The Mexican Emotional Speech Database (MESD)

open

NONE

## 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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PR_kwDODunzps4w8sUm

Add PASS dataset

open

CONTRIBUTOR

This PR adds the PASS dataset. Closes #3043

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PR_kwDODunzps4w8Usm

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

open

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 - [ ] fix current tests - [ ] add new tests - [ ] 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

open

NONE

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: Couldn't reach https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/wikiann-ner.tar.gz (error 403)

open

NONE

## 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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PR_kwDODunzps4w3fVQ

Add missing tasks to MuchoCine dataset

open

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)

open

NONE

New pull request of #3564. (for DKTC)

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I_kwDODunzps5BlnnX

Downloading Hugging Face Medical Dialog Dataset NonMatchingSplitsSizesError

open

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

CONTRIBUTOR

# 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

open

NONE

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’

open

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+

open

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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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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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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PR_kwDODunzps4wvALx

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

open

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'

open

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]) 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Requirement already satisfied: boto3<2.0.0,==1.16.34 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (1.16.34) Requirement already satisfied: Pint<0.17.0,>=0.16.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (0.16.1) Requirement already satisfied: GitPython>=2.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.18) Requirement already satisfied: backoff<2.0.0,>=1.10.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (1.11.1) Requirement already satisfied: ipython>=5.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sigopt->neural-compressor>=1.7->optimum[intel]) (7.16.1) Requirement already satisfied: docker<5.0.0,>=4.4.0 in 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python-socketio>=5.0.2->Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (4.3.0) Requirement already satisfied: bidict>=0.21.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from python-socketio>=5.0.2->Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (0.21.4) Requirement already satisfied: pyasn1>=0.1.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from rsa<5.0.0,>=4.7->sigopt->neural-compressor>=1.7->optimum[intel]) (0.4.8) Requirement already satisfied: smmap<6,>=3.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from gitdb<5,>=4.0.1->GitPython>=2.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (5.0.0) Requirement already satisfied: pyasn1-modules>=0.2.1 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]) (0.2.8) Requirement already satisfied: 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`

open

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

open

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

open

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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