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* There's another raw version of the dataset available from tfds. However, they're quite large so I don't have the resources to fully test all the configs for that version yet. We'll try to add it later. | {
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} | - **Name:** *Cyberbullying Detection Task*
- **Description:** *The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.*
- **Data:** *https://github.com/ptaszynski/cyberbullying-Polish*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.* | {
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WIP
TODO:
- scenario_kbs (bug with pyarrow conversion)
- download from codalab checksums bug | {
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} | - **Name**: *cdsc (domains: cdsc-e & cdsc-r)*
- **Description**: *Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (2017) for a detailed description of the resource.*
- **Data**: *http://2019.poleval.pl/index.php/tasks/*
- **Motivation**: *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.* | {
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} | https://github.com/mhardalov/exams-qa
`multilingual` configs have all languages mixed together
`crosslingual` mixes the languages for test but separates them for train and dec, so I've made one config per language for train/dev data and one config with the joint test set | {
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} | Add news topic classification dataset in Myanmar / Burmese languagess
This data was collected in 2017 by Aye Hninn Khine , and published on GitHub with a GPL license
https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
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} | Adds the KELM dataset.
- Webpage/repo: https://github.com/google-research-datasets/KELM-corpus
- Paper: https://arxiv.org/pdf/2010.12688.pdf | {
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} | This PR adds the full set of 176 languages from the balanced train/dev/test splits of WikiANN / PAN-X from: https://github.com/afshinrahimi/mmner
Until now, only 40 of these languages were available in `datasets` as part of the XTREME benchmark
Courtesy of the dataset author, we can now download this dataset from a Dropbox URL without needing a manual download anymore 🥳, so at some point it would be worth updating the PAN-X subset of XTREME as well 😄
Link to gist with some snippets for producing dummy data: https://gist.github.com/lewtun/5b93294ab6dbcf59d1493dbe2cfd6bb9
P.S. @yjernite I think I was confused about needing to generate a set of YAML tags per config, so ended up just adding a single one in the README. | {
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- **Description:** *The NKJP-NER is based on a human-annotated part of NKJP. We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.*
- **Data:** *https://klejbenchmark.com/tasks/*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.*
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I linearized most of the dictionnaries to lists.
Referenced to the authors' datasheet for the dataset card.
🦆🦆🦆
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it might be because it is not a direct link (it returns 302 and redirects to aws that returns 403 for head requests).
```
r.head("https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz", allow_redirects=True)
# <Response [403]>
``` | {
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**Note**: The MNLI tsv file is broken, so this code currently excludes the file. Please suggest other alternative if any @lhoestq
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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https://api.github.com/repos/huggingface/datasets/issues/1060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1060/comments | https://api.github.com/repos/huggingface/datasets/issues/1060/events | https://github.com/huggingface/datasets/pull/1060 | 756,349,001 | MDExOlB1bGxSZXF1ZXN0NTMxOTA4MTgx | 1,060 | Fix squad V2 metric script | {
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} | The current squad v2 metric doesn't work with the squad (v1 or v2) datasets. The script is copied from `squad_evaluate` in transformers that requires the labels (with multiple answers) to be like this:
```
references = [{'id': 'a', 'answers': [
{'text': 'Denver Broncos', 'answer_start': 177},
{'text': 'Denver Broncos', 'answer_start': 177}
]}]
```
while the dataset had references like this:
```
references = [{'id': 'a', 'answers':
{'text': ['Denver Broncos' 'Denver Broncos'], 'answer_start': [177, 177]}
}]
```
Using one or the other format fails with the current squad v2 metric:
```
from datasets import load_metric
metric = load_metric("squad_v2")
predictions = [{'id': 'a', 'prediction_text': 'Denver Broncos', 'no_answer_probability': 0.0}]
references = [{'id': 'a', 'answers': [
{'text': 'Denver Broncos', 'answer_start': 177},
{'text': 'Denver Broncos', 'answer_start': 177}
]}]
metric.compute(predictions=predictions, references=references)
```
fails as well as
```
from datasets import load_metric
metric = load_metric("squad_v2")
predictions = [{'id': 'a', 'prediction_text': 'Denver Broncos', 'no_answer_probability': 0.0}]
references = [{'id': 'a', 'answers':
{'text': ['Denver Broncos' 'Denver Broncos'], 'answer_start': [177, 177]}
}]
metric.compute(predictions=predictions, references=references)
```
This is because arrow reformats the references behind the scene.
With this PR (tested locally), both the snippets up there work and return proper results. | {
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|
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} | Add deal_or_no_dialog Dataset
github: https://github.com/facebookresearch/end-to-end-negotiator
Paper: [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://arxiv.org/abs/1706.05125) | {
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https://api.github.com/repos/huggingface/datasets/issues/1054 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1054/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1054/comments | https://api.github.com/repos/huggingface/datasets/issues/1054/events | https://github.com/huggingface/datasets/pull/1054 | 756,265,688 | MDExOlB1bGxSZXF1ZXN0NTMxODM3NzQ0 | 1,054 | Add dataset - SemEval 2014 - Task 1 | {
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} | Adding the dataset of SemEval 2014 Task 1
Found the dataset under the shared Google Sheet > Recurring Task Datasets
Task Homepage - https://alt.qcri.org/semeval2014/task1
Thank you! | {
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} | # Why I did
When I use "social_bias_frames" datasets in this library, I got 404 Errors.
So, I fixed this error and another some problems that I faced to use the dataset.
# What I did
* Modify this dataset URL
* Modify this dataset file names
* Add a "dataSource" column
Thank you! | {
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More info:
https://sharc-data.github.io/index.html | {
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} | Adds the GoEmotions dataset, a nice emotion classification dataset with 27 (multi-)label annotations on reddit comments. Includes both a large raw version and a narrowed version with predefined train/test/val splits, which I've included as separate configs with the latter as a default.
- Webpage/repo: https://github.com/google-research/google-research/tree/master/goemotions
- Paper: https://arxiv.org/abs/2005.00547 | {
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} | Added Korean NLU datasets. The link to the dataset can be found [here](https://github.com/kakaobrain/KorNLUDatasets) and the paper can be found [here](https://arxiv.org/abs/2004.03289)
**Note**: The MNLI tsv file is broken, so this code currently excludes the file. Please suggest other alternative if any @lhoestq
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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} | [] | closed | false | null | [] | null | [] | 2020-12-03T11:43:46Z | 2022-10-05T12:12:41Z | 2022-10-05T12:12:41Z | NONE | null | null | null | I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!
```import datasets
import torch
from datasets import load_dataset
print("version datasets", datasets.__version__)
dataset = load_dataset("snli", split='train[0:50]')
def tokenizer_fn(example):
# actually uses a tokenizer which does something like:
return {'input_ids': torch.tensor([[0, 1, 2]])}
print("First item in dataset:\n", dataset[0])
tokenized = tokenizer_fn(dataset[0])
print("Tokenized hyp:\n", tokenized)
dataset_tok = dataset.map(tokenizer_fn, batched=False,
remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
dataset_tok = dataset.map(tokenizer_fn, batched=False,
remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
```
The output is:
```
version datasets 1.1.3
Reusing dataset snli (/home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)
First item in dataset:
{'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}
Tokenized hyp:
{'input_ids': tensor([[0, 1, 2]])}
Loading cached processed dataset at /home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c/cache-fe38f449fe9ac46f.arrow
Tokenized using map:
{'input_ids': [[0, 1, 2]]}
<class 'torch.Tensor'> <class 'list'>
```
Or am I doing something wrong?
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paper: https://www.aclweb.org/anthology/W17-1307/ | {
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} | - More info on the dataset: https://evasharma.github.io/bigpatent/
- There's another raw version of the dataset available from tfds. However, they're quite large so I don't have the resources to fully test all the configs for that version yet. We'll try to add it later.
- ~Currently, there are no dummy data for this dataset yet as I'm facing some problems with generating them. I'm trying to add them later.~ | {
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} | Universal declaration of human rights with translations in 464 languages and dialects.
- UN page: https://www.ohchr.org/EN/UDHR/Pages/UDHRIndex.aspx
- Raw data source: https://unicode.org/udhr/index.html
Each instance of the dataset corresponds to one translation of the document. Since there's only one instance per language (and because there are 500 languages so the dummy data would be messy), I opted to just include them all under the same single config. I wasn't able to find any kind of license so I just copied the copyright notice.
I was pretty careful careful generating the language tags so they _should_ all be correct & consistent BCP-47 codes per the docs. | {
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More info:
http://qangaroo.cs.ucl.ac.uk/index.html | {
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More info:
http://qangaroo.cs.ucl.ac.uk/index.html
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https://api.github.com/repos/huggingface/datasets/issues/1034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1034/comments | https://api.github.com/repos/huggingface/datasets/issues/1034/events | https://github.com/huggingface/datasets/pull/1034 | 755,936,327 | MDExOlB1bGxSZXF1ZXN0NTMxNTY0MjA0 | 1,034 | add scb_mt_enth_2020 | {
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} | ## scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
The primary objective of our work is to build a large-scale English-Thai dataset for machine translation.
We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources,
namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents.
Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner.
We train machine translation models based on this dataset. Our models' performance are comparable to that of
Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is
included in the training data for both Thai-English and English-Thai translation.
The dataset, pre-trained models, and source code to reproduce our work are available for public use. | {
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Also support filenames with additional compression extension: ".txm.gz". | {
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more info : http://www.cfilt.iitb.ac.in/iitb_parallel/ | {
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} | This PR adds CrowS-Pairs datasets.
More info:
https://github.com/nyu-mll/crows-pairs/
https://arxiv.org/pdf/2010.00133.pdf | {
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- **Description:** *Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review).*
- **Data:** *https://github.com/allegro/klejbenchmark-allegroreviews*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.* | {
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} | Adding the ASSET dataset from https://github.com/facebookresearch/asset
One config for the simplification data, one for the human ratings of quality.
The README.md borrows from that written by @juand-r | {
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- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **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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````
``` | {
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- Webpage: https://allenai.org/data/zest
- Paper: https://arxiv.org/abs/2011.08115
The nature of this dataset made the supported task tags tricky if you wouldn't mind giving any feedback @yjernite. Also let me know if you think we should have a `other-task-generalization` or something like that... | {
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} | This PR adds the Schema Guided Dialogue dataset created for the DSTC8 challenge
- https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
A bit simpler than MultiWOZ, the only tricky thing is the sequence of dictionaries that had to be linearized. There is a config for the data proper, and a config for the schemas. | {
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} | MRQA (shared task 2019)
out of distribution generalization
Framed as extractive question answering
Dataset is the concatenation (of subsets) of existing QA datasets processed to match the SQuAD format | {
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Added the `README.md` card with tags to the best of my knowledge
Multi-target summaries or TLDRs of Scientific Documents
Continued from #986 | {
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- [x] Ran the tests successfully
- [x] Created the dummy data | {
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More info:
https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset
cc @joeddav, @yjernite | {
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https://api.github.com/repos/huggingface/datasets/issues/1004 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1004/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1004/comments | https://api.github.com/repos/huggingface/datasets/issues/1004/events | https://github.com/huggingface/datasets/issues/1004 | 755,325,368 | MDU6SXNzdWU3NTUzMjUzNjg= | 1,004 | how large datasets are handled under the hood | {
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} | [] | closed | false | null | [] | null | [] | 2020-12-02T14:32:40Z | 2022-10-05T12:13:29Z | 2022-10-05T12:13:29Z | NONE | null | null | null | Hi
I want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks | {
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} | Add Multi-XScience Dataset.
github repo: https://github.com/yaolu/Multi-XScience
paper: [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://arxiv.org/abs/2010.14235) | {
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