Upload batch 93 (20 files, last=huggingface_dataset/Dataset_Card/kor_sae.md)
Browse files- huggingface_dataset/Dataset_Card/Lo_adapt-pre-trained-VL-models-to-text-data-Wikipedia.md +12 -0
- huggingface_dataset/Dataset_Card/NYTK_HuRC.md +172 -0
- huggingface_dataset/Dataset_Card/alt.md +430 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-vi-f50546-2087567167.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__41-inverse-scaling__41-e36c9c-1692459560.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__examplehsd-raw-ff3db7-1730160389.md +34 -0
- huggingface_dataset/Dataset_Card/georeactor_reddit_one_ups_2014.md +78 -0
- huggingface_dataset/Dataset_Card/gigaword.md +220 -0
- huggingface_dataset/Dataset_Card/hope_edi.md +267 -0
- huggingface_dataset/Dataset_Card/huggingnft_azuki.md +175 -0
- huggingface_dataset/Dataset_Card/indic_glue.md +1796 -0
- huggingface_dataset/Dataset_Card/johnowhitaker_vqgan1024_encs_sf.md +11 -0
- huggingface_dataset/Dataset_Card/jordanparker6_publaynet.md +32 -0
- huggingface_dataset/Dataset_Card/julien-c_reactiongif.md +57 -0
- huggingface_dataset/Dataset_Card/kor_sae.md +181 -0
- huggingface_dataset/Dataset_Card/multi_woz_v22.md +453 -0
- huggingface_dataset/Dataset_Card/rocca_top-reddit-posts.md +13 -0
- huggingface_dataset/Dataset_Card/ruanchaves_stan_small.md +106 -0
- huggingface_dataset/Dataset_Card/saibo_bookcorpus_compact_1024_test.md +30 -0
- huggingface_dataset/Dataset_Card/wikihow.md +53 -0
huggingface_dataset/Dataset_Card/Lo_adapt-pre-trained-VL-models-to-text-data-Wikipedia.md
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---
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language:
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- en
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license:
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- cc-by-sa-3.0
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multilinguality:
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- monolingual
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---
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The Wikipedia train data used to train BERT-base baselines and adapt vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?".
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The data has been created from the "20200501.en" revision of the [wikipedia dataset](https://huggingface.co/datasets/wikipedia) on Huggingface.
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huggingface_dataset/Dataset_Card/NYTK_HuRC.md
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---
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YAML tags:
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annotations_creators:
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- crowdsourced
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language_creators:
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- found
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- expert-generated
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language:
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- hu
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license:
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- cc-by-4.0
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multilinguality:
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- monolingual
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pretty_name: HuRC
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size_categories:
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- unknown
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source_datasets:
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- extended|other
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task_categories:
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- question-answering
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task_ids:
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- extractive-qa
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- abstractive-qa
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---
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# Dataset Card for HuRC
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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| 34 |
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| 35 |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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| 36 |
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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| 42 |
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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| 66 |
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| 67 |
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- [Dataset Curators](#dataset-curators)
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| 68 |
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- [Licensing Information](#licensing-information)
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| 71 |
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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| 76 |
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- **Homepage:**
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| 77 |
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- **Repository:**
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| 78 |
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[HuRC dataset](https://github.com/nytud/HuRC)
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| 79 |
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- **Paper:**
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| 80 |
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- **Leaderboard:**
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| 81 |
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- **Point of Contact:**
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| 82 |
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[lnnoemi](mailto:ligeti-nagy.noemi@nytud.hu)
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| 83 |
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### Dataset Summary
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| 85 |
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| 86 |
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This is the dataset card for the Hungarian Corpus for Reading Comprehension with Commonsense Reasoning (HuRC), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU.
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| 87 |
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The dataset contains 80 614 instances. Each instance is composed of a lead, a passage and a cloze-style query with a masked entity. The task is to select the named entity that is being masked in the query.
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| 88 |
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The data was automatically collected from the online news of Népszabadság online (nol.hu).
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| 89 |
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### Languages
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The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.
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## Dataset Structure
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| 95 |
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### Data Instances
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| 97 |
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For each instance, there is an id, a lead, a passage, a query and a MASK.
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An example:
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| 100 |
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```
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| 101 |
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{
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| 102 |
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"id": "1",
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| 103 |
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"lead": ["A Közigazgatási és Igazságügyi Minisztérium szerint a Bárka Színház esetében felmerült a felelőtlen gazdálkodás gyanúja, egyes értesülések szerint pedig ebben \"a színház igazgatójának és gazdasági vezetőjének felelőssége is felmerül\""],
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"passage": [
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"A teátrumnak Navracsics Tibor közigazgatási és igazságügyi miniszterhez és Kocsis Máté VIII. kerületi polgármesterhez",
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"reagálva a tárca azt írta, hogy a felelőtlen gazdálkodás gyanújában \"egyes értesülések szerint a színház igazgatójának és gazdasági vezetőjének felelőssége is felmerül\". A KIM \"éppen ezért nagyon várja az Állami Számvevőszék készülő jelentését, hogy tiszta képet kaphasson a színház működéséről\".",
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"A minisztérium hangsúlyozta, hogy az elmúlt évben is mindent elkövetett azért, hogy a Bárka Színház \"valós, rangos művészeti térként\" működjön, és a továbbiakban is ez a szándéka, de jelenleg a társulat működtetését a minisztérium fenntartói támogatás formájában jogszerűen még nem tudja megoldani.",
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"A teátrum az átadás-átvétel elhúzódásának okát keresve tette közzé nyílt levelét, amelyben elmaradó fizetésekre, előadásokra és bemutatókra hívta fel a figyelmet, és jelezte, hogy várja a helyzet megoldását.",
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"A színház átadás-átvétele jelenleg zajlik, a folyamat végeztével a Bárka a józsefvárosi önkormányzattól állami tulajdonba, a tervek szerint a Közigazgatási és Igazságügyi Minisztérium fenntartásába kerül."
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],
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"query": "A KIM 2014-es költségvetésében szerepel a Bárka Színház, de amíg nem a minisztérium a [MASK] fenntartója, addig ez a költségvetési keret nem nyitható meg.",
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"MASK": "Bárka",
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| 113 |
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}
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```
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| 115 |
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| 116 |
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### Data Fields
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| 117 |
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| 118 |
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- id: unique id of the instances;
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| 119 |
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- lead: a short summary of the article as it was extracted from the source texts;
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| 120 |
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- passage: 3-6 paragraphs of texts as the body of the article;
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| 121 |
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- query: the last paragraph of an article, some kind of summary or conclusion, with a named entity masked (with [MASK]) in it;
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| 122 |
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- MASK: the masked named entity.
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| 123 |
+
|
| 124 |
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### Data Splits
|
| 125 |
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HuRC has 3 splits: *train*, *validation* and *test*.
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| 126 |
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| 127 |
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| Dataset split | Number of instances in the split | Proportion of the split
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| 128 |
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|---------------|----------------------------------| ---------|
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| train | 64614 | 80%|
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| 130 |
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| validation | 8000 |10%|
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| 131 |
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| test | 8000 |10%|
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| 132 |
+
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| 133 |
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The test data is distributed without the MASK fields. To evaluate your model, please [contact us](mailto:ligeti-nagy.noemi@nytud.hu), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment).
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| 134 |
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| 135 |
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## Dataset Creation
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| 136 |
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| 137 |
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### Source Data
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| 138 |
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| 139 |
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#### Initial Data Collection and Normalization
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| 140 |
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| 141 |
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To produce the Hungarian material, we used the daily articles from Népszabadság Online which had titles and summaries as well. We selected 3-6 paragraphs from each article from the ones which contain proper nouns both in the main part and the summary as well. We trained a NER model using huBERT (Nemeskey 2021) for recognizing proper nouns. NerKor (Simon és Vadász 2021) and Huggingface’s token-level classification library were used to fine-tune the model. Our model achieved an F-score of 90.18 on the test material. As a final step, we found pairs of proper names which are present both in the main article and the summary. Multiple articles contained more than one such pairs so we used those more than once. This resulted in a database of 88655 instances (from 49782 articles).
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The quantitative properties of our corpus are as follows: Number of articles: 88655 Number of different articles (type): 49782 Token: 27703631 Type: 1115.260 Average length of text (token): 249.42 (median: 229) Average question length (token): 63.07 (median: 56). We fine-tuned the corpus by hand.
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| 144 |
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One annotator per 100 unit checked and validated the dataset for which we provided our own demo interface. Automatic masking and the previous occurrence of the entity was checked. This resulted in a database of 80 614 validated entries.
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| 146 |
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| 147 |
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## Additional Information
|
| 148 |
+
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| 149 |
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### Licensing Information
|
| 150 |
+
|
| 151 |
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HuRC is released under the cc-by-4.0 license.
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| 152 |
+
|
| 153 |
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### Citation Information
|
| 154 |
+
|
| 155 |
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If you use this resource or any part of its documentation, please refer to:
|
| 156 |
+
|
| 157 |
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Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press)
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| 158 |
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|
| 159 |
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```
|
| 160 |
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|
| 161 |
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@inproceedings{ligetinagy2022hulu,
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| 162 |
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title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
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| 163 |
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author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Váradi, T.},
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| 164 |
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booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
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| 165 |
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year={2022}
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| 166 |
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}
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| 167 |
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```
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| 168 |
+
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| 169 |
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|
| 170 |
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### Contributions
|
| 171 |
+
|
| 172 |
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Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset.
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|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- bn
|
| 8 |
+
- en
|
| 9 |
+
- fil
|
| 10 |
+
- hi
|
| 11 |
+
- id
|
| 12 |
+
- ja
|
| 13 |
+
- km
|
| 14 |
+
- lo
|
| 15 |
+
- ms
|
| 16 |
+
- my
|
| 17 |
+
- th
|
| 18 |
+
- vi
|
| 19 |
+
- zh
|
| 20 |
+
license:
|
| 21 |
+
- cc-by-4.0
|
| 22 |
+
multilinguality:
|
| 23 |
+
- multilingual
|
| 24 |
+
- translation
|
| 25 |
+
size_categories:
|
| 26 |
+
- 100K<n<1M
|
| 27 |
+
- 10K<n<100K
|
| 28 |
+
source_datasets:
|
| 29 |
+
- original
|
| 30 |
+
task_categories:
|
| 31 |
+
- translation
|
| 32 |
+
- token-classification
|
| 33 |
+
task_ids:
|
| 34 |
+
- parsing
|
| 35 |
+
paperswithcode_id: alt
|
| 36 |
+
pretty_name: Asian Language Treebank
|
| 37 |
+
configs:
|
| 38 |
+
- alt-en
|
| 39 |
+
- alt-jp
|
| 40 |
+
- alt-km
|
| 41 |
+
- alt-my
|
| 42 |
+
- alt-my-transliteration
|
| 43 |
+
- alt-my-west-transliteration
|
| 44 |
+
- alt-parallel
|
| 45 |
+
dataset_info:
|
| 46 |
+
- config_name: alt-parallel
|
| 47 |
+
features:
|
| 48 |
+
- name: SNT.URLID
|
| 49 |
+
dtype: string
|
| 50 |
+
- name: SNT.URLID.SNTID
|
| 51 |
+
dtype: string
|
| 52 |
+
- name: url
|
| 53 |
+
dtype: string
|
| 54 |
+
- name: translation
|
| 55 |
+
dtype:
|
| 56 |
+
translation:
|
| 57 |
+
languages:
|
| 58 |
+
- bg
|
| 59 |
+
- en
|
| 60 |
+
- en_tok
|
| 61 |
+
- fil
|
| 62 |
+
- hi
|
| 63 |
+
- id
|
| 64 |
+
- ja
|
| 65 |
+
- khm
|
| 66 |
+
- lo
|
| 67 |
+
- ms
|
| 68 |
+
- my
|
| 69 |
+
- th
|
| 70 |
+
- vi
|
| 71 |
+
- zh
|
| 72 |
+
splits:
|
| 73 |
+
- name: train
|
| 74 |
+
num_bytes: 68462384
|
| 75 |
+
num_examples: 18094
|
| 76 |
+
- name: validation
|
| 77 |
+
num_bytes: 3712980
|
| 78 |
+
num_examples: 1004
|
| 79 |
+
- name: test
|
| 80 |
+
num_bytes: 3815633
|
| 81 |
+
num_examples: 1019
|
| 82 |
+
download_size: 21285784
|
| 83 |
+
dataset_size: 75990997
|
| 84 |
+
- config_name: alt-en
|
| 85 |
+
features:
|
| 86 |
+
- name: SNT.URLID
|
| 87 |
+
dtype: string
|
| 88 |
+
- name: SNT.URLID.SNTID
|
| 89 |
+
dtype: string
|
| 90 |
+
- name: url
|
| 91 |
+
dtype: string
|
| 92 |
+
- name: status
|
| 93 |
+
dtype: string
|
| 94 |
+
- name: value
|
| 95 |
+
dtype: string
|
| 96 |
+
splits:
|
| 97 |
+
- name: train
|
| 98 |
+
num_bytes: 10075609
|
| 99 |
+
num_examples: 17889
|
| 100 |
+
- name: validation
|
| 101 |
+
num_bytes: 544739
|
| 102 |
+
num_examples: 988
|
| 103 |
+
- name: test
|
| 104 |
+
num_bytes: 567292
|
| 105 |
+
num_examples: 1017
|
| 106 |
+
download_size: 2739055
|
| 107 |
+
dataset_size: 11187640
|
| 108 |
+
- config_name: alt-jp
|
| 109 |
+
features:
|
| 110 |
+
- name: SNT.URLID
|
| 111 |
+
dtype: string
|
| 112 |
+
- name: SNT.URLID.SNTID
|
| 113 |
+
dtype: string
|
| 114 |
+
- name: url
|
| 115 |
+
dtype: string
|
| 116 |
+
- name: status
|
| 117 |
+
dtype: string
|
| 118 |
+
- name: value
|
| 119 |
+
dtype: string
|
| 120 |
+
- name: word_alignment
|
| 121 |
+
dtype: string
|
| 122 |
+
- name: jp_tokenized
|
| 123 |
+
dtype: string
|
| 124 |
+
- name: en_tokenized
|
| 125 |
+
dtype: string
|
| 126 |
+
splits:
|
| 127 |
+
- name: train
|
| 128 |
+
num_bytes: 21891867
|
| 129 |
+
num_examples: 17202
|
| 130 |
+
- name: validation
|
| 131 |
+
num_bytes: 1181587
|
| 132 |
+
num_examples: 953
|
| 133 |
+
- name: test
|
| 134 |
+
num_bytes: 1175624
|
| 135 |
+
num_examples: 931
|
| 136 |
+
download_size: 12007999
|
| 137 |
+
dataset_size: 24249078
|
| 138 |
+
- config_name: alt-my
|
| 139 |
+
features:
|
| 140 |
+
- name: SNT.URLID
|
| 141 |
+
dtype: string
|
| 142 |
+
- name: SNT.URLID.SNTID
|
| 143 |
+
dtype: string
|
| 144 |
+
- name: url
|
| 145 |
+
dtype: string
|
| 146 |
+
- name: value
|
| 147 |
+
dtype: string
|
| 148 |
+
splits:
|
| 149 |
+
- name: train
|
| 150 |
+
num_bytes: 20433275
|
| 151 |
+
num_examples: 18088
|
| 152 |
+
- name: validation
|
| 153 |
+
num_bytes: 1111410
|
| 154 |
+
num_examples: 1000
|
| 155 |
+
- name: test
|
| 156 |
+
num_bytes: 1135209
|
| 157 |
+
num_examples: 1018
|
| 158 |
+
download_size: 3028302
|
| 159 |
+
dataset_size: 22679894
|
| 160 |
+
- config_name: alt-km
|
| 161 |
+
features:
|
| 162 |
+
- name: SNT.URLID
|
| 163 |
+
dtype: string
|
| 164 |
+
- name: SNT.URLID.SNTID
|
| 165 |
+
dtype: string
|
| 166 |
+
- name: url
|
| 167 |
+
dtype: string
|
| 168 |
+
- name: km_pos_tag
|
| 169 |
+
dtype: string
|
| 170 |
+
- name: km_tokenized
|
| 171 |
+
dtype: string
|
| 172 |
+
splits:
|
| 173 |
+
- name: train
|
| 174 |
+
num_bytes: 12015411
|
| 175 |
+
num_examples: 18088
|
| 176 |
+
- name: validation
|
| 177 |
+
num_bytes: 655232
|
| 178 |
+
num_examples: 1000
|
| 179 |
+
- name: test
|
| 180 |
+
num_bytes: 673753
|
| 181 |
+
num_examples: 1018
|
| 182 |
+
download_size: 2410832
|
| 183 |
+
dataset_size: 13344396
|
| 184 |
+
- config_name: alt-my-transliteration
|
| 185 |
+
features:
|
| 186 |
+
- name: en
|
| 187 |
+
dtype: string
|
| 188 |
+
- name: my
|
| 189 |
+
sequence: string
|
| 190 |
+
splits:
|
| 191 |
+
- name: train
|
| 192 |
+
num_bytes: 4249424
|
| 193 |
+
num_examples: 84022
|
| 194 |
+
download_size: 1232127
|
| 195 |
+
dataset_size: 4249424
|
| 196 |
+
- config_name: alt-my-west-transliteration
|
| 197 |
+
features:
|
| 198 |
+
- name: en
|
| 199 |
+
dtype: string
|
| 200 |
+
- name: my
|
| 201 |
+
sequence: string
|
| 202 |
+
splits:
|
| 203 |
+
- name: train
|
| 204 |
+
num_bytes: 7412043
|
| 205 |
+
num_examples: 107121
|
| 206 |
+
download_size: 2830071
|
| 207 |
+
dataset_size: 7412043
|
| 208 |
+
---
|
| 209 |
+
|
| 210 |
+
# Dataset Card for Asian Language Treebank (ALT)
|
| 211 |
+
|
| 212 |
+
## Table of Contents
|
| 213 |
+
- [Dataset Description](#dataset-description)
|
| 214 |
+
- [Dataset Summary](#dataset-summary)
|
| 215 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 216 |
+
- [Languages](#languages)
|
| 217 |
+
- [Dataset Structure](#dataset-structure)
|
| 218 |
+
- [Data Instances](#data-instances)
|
| 219 |
+
- [Data Fields](#data-fields)
|
| 220 |
+
- [Data Splits](#data-splits)
|
| 221 |
+
- [Dataset Creation](#dataset-creation)
|
| 222 |
+
- [Curation Rationale](#curation-rationale)
|
| 223 |
+
- [Source Data](#source-data)
|
| 224 |
+
- [Annotations](#annotations)
|
| 225 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 226 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 227 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 228 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 229 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 230 |
+
- [Additional Information](#additional-information)
|
| 231 |
+
- [Dataset Curators](#dataset-curators)
|
| 232 |
+
- [Licensing Information](#licensing-information)
|
| 233 |
+
- [Citation Information](#citation-information)
|
| 234 |
+
- [Contributions](#contributions)
|
| 235 |
+
|
| 236 |
+
## Dataset Description
|
| 237 |
+
|
| 238 |
+
- **Homepage:** https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/
|
| 239 |
+
- **Leaderboard:**
|
| 240 |
+
- **Paper:** [Introduction of the Asian Language Treebank](https://ieeexplore.ieee.org/abstract/document/7918974)
|
| 241 |
+
- **Point of Contact:** [ALT info](alt-info@khn.nict.go.jp)
|
| 242 |
+
|
| 243 |
+
### Dataset Summary
|
| 244 |
+
The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under [ASEAN IVO](https://www.nict.go.jp/en/asean_ivo/index.html) as described in this Web page.
|
| 245 |
+
|
| 246 |
+
The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages.
|
| 247 |
+
|
| 248 |
+
### Supported Tasks and Leaderboards
|
| 249 |
+
|
| 250 |
+
Machine Translation, Dependency Parsing
|
| 251 |
+
|
| 252 |
+
|
| 253 |
+
### Languages
|
| 254 |
+
|
| 255 |
+
It supports 13 language:
|
| 256 |
+
* Bengali
|
| 257 |
+
* English
|
| 258 |
+
* Filipino
|
| 259 |
+
* Hindi
|
| 260 |
+
* Bahasa Indonesia
|
| 261 |
+
* Japanese
|
| 262 |
+
* Khmer
|
| 263 |
+
* Lao
|
| 264 |
+
* Malay
|
| 265 |
+
* Myanmar (Burmese)
|
| 266 |
+
* Thai
|
| 267 |
+
* Vietnamese
|
| 268 |
+
* Chinese (Simplified Chinese).
|
| 269 |
+
|
| 270 |
+
## Dataset Structure
|
| 271 |
+
|
| 272 |
+
### Data Instances
|
| 273 |
+
|
| 274 |
+
#### ALT Parallel Corpus
|
| 275 |
+
```
|
| 276 |
+
{
|
| 277 |
+
"SNT.URLID": "80188",
|
| 278 |
+
"SNT.URLID.SNTID": "1",
|
| 279 |
+
"url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal",
|
| 280 |
+
"bg": "[translated sentence]",
|
| 281 |
+
"en": "[translated sentence]",
|
| 282 |
+
"en_tok": "[translated sentence]",
|
| 283 |
+
"fil": "[translated sentence]",
|
| 284 |
+
"hi": "[translated sentence]",
|
| 285 |
+
"id": "[translated sentence]",
|
| 286 |
+
"ja": "[translated sentence]",
|
| 287 |
+
"khm": "[translated sentence]",
|
| 288 |
+
"lo": "[translated sentence]",
|
| 289 |
+
"ms": "[translated sentence]",
|
| 290 |
+
"my": "[translated sentence]",
|
| 291 |
+
"th": "[translated sentence]",
|
| 292 |
+
"vi": "[translated sentence]",
|
| 293 |
+
"zh": "[translated sentence]"
|
| 294 |
+
}
|
| 295 |
+
```
|
| 296 |
+
|
| 297 |
+
#### ALT Treebank
|
| 298 |
+
```
|
| 299 |
+
{
|
| 300 |
+
"SNT.URLID": "80188",
|
| 301 |
+
"SNT.URLID.SNTID": "1",
|
| 302 |
+
"url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal",
|
| 303 |
+
"status": "draft/reviewed",
|
| 304 |
+
"value": "(S (S (BASENP (NNP Italy)) (VP (VBP have) (VP (VP (VP (VBN defeated) (BASENP (NNP Portugal))) (ADVP (RB 31-5))) (PP (IN in) (NP (BASENP (NNP Pool) (NNP C)) (PP (IN of) (NP (BASENP (DT the) (NN 2007) (NNP Rugby) (NNP World) (NNP Cup)) (PP (IN at) (NP (BASENP (NNP Parc) (FW des) (NNP Princes)) (COMMA ,) (BASENP (NNP Paris) (COMMA ,) (NNP France))))))))))) (PERIOD .))"
|
| 305 |
+
}
|
| 306 |
+
```
|
| 307 |
+
|
| 308 |
+
#### ALT Myanmar transliteration
|
| 309 |
+
```
|
| 310 |
+
{
|
| 311 |
+
"en": "CASINO",
|
| 312 |
+
"my": [
|
| 313 |
+
"ကက်စီနို",
|
| 314 |
+
"ကစီနို",
|
| 315 |
+
"ကာစီနို",
|
| 316 |
+
"ကာဆီနို"
|
| 317 |
+
]
|
| 318 |
+
}
|
| 319 |
+
```
|
| 320 |
+
|
| 321 |
+
### Data Fields
|
| 322 |
+
|
| 323 |
+
|
| 324 |
+
#### ALT Parallel Corpus
|
| 325 |
+
- SNT.URLID: URL link to the source article listed in [URL.txt](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ALT-Parallel-Corpus-20191206/URL.txt)
|
| 326 |
+
- SNT.URLID.SNTID: index number from 1 to 20000. It is a seletected sentence from `SNT.URLID`
|
| 327 |
+
|
| 328 |
+
and bg, en, fil, hi, id, ja, khm, lo, ms, my, th, vi, zh correspond to the target language
|
| 329 |
+
|
| 330 |
+
#### ALT Treebank
|
| 331 |
+
- status: it indicates how a sentence is annotated; `draft` sentences are annotated by one annotater and `reviewed` sentences are annotated by two annotater
|
| 332 |
+
|
| 333 |
+
The annotatation is different from language to language, please see [their guildlines](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/) for more detail.
|
| 334 |
+
|
| 335 |
+
### Data Splits
|
| 336 |
+
|
| 337 |
+
| | train | valid | test |
|
| 338 |
+
|-----------|-------|-------|-------|
|
| 339 |
+
| # articles | 1698 | 98 | 97 |
|
| 340 |
+
| # sentences | 18088 | 1000 | 1018 |
|
| 341 |
+
|
| 342 |
+
|
| 343 |
+
## Dataset Creation
|
| 344 |
+
|
| 345 |
+
### Curation Rationale
|
| 346 |
+
|
| 347 |
+
The ALT project was initiated by the [National Institute of Information and Communications Technology, Japan](https://www.nict.go.jp/en/) (NICT) in 2014. NICT started to build Japanese and English ALT and worked with the University of Computer Studies, Yangon, Myanmar (UCSY) to build Myanmar ALT in 2014. Then, the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT), the Institute for Infocomm Research, Singapore (I2R), the Institute of Information Technology, Vietnam (IOIT), and the National Institute of Posts, Telecoms and ICT, Cambodia (NIPTICT) joined to make ALT for Indonesian, Malay, Vietnamese, and Khmer in 2015.
|
| 348 |
+
|
| 349 |
+
|
| 350 |
+
### Source Data
|
| 351 |
+
|
| 352 |
+
#### Initial Data Collection and Normalization
|
| 353 |
+
|
| 354 |
+
[More Information Needed]
|
| 355 |
+
|
| 356 |
+
#### Who are the source language producers?
|
| 357 |
+
|
| 358 |
+
The dataset is sampled from the English Wikinews in 2014. These will be annotated with word segmentation, POS tags, and syntax information, in addition to the word alignment information by linguistic experts from
|
| 359 |
+
* National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English
|
| 360 |
+
* University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar
|
| 361 |
+
* the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian
|
| 362 |
+
* the Institute for Infocomm Research, Singapore (I2R) for Malay
|
| 363 |
+
* the Institute of Information Technology, Vietnam (IOIT) for Vietnamese
|
| 364 |
+
* the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer
|
| 365 |
+
|
| 366 |
+
### Annotations
|
| 367 |
+
|
| 368 |
+
#### Annotation process
|
| 369 |
+
|
| 370 |
+
[More Information Needed]
|
| 371 |
+
|
| 372 |
+
#### Who are the annotators?
|
| 373 |
+
|
| 374 |
+
[More Information Needed]
|
| 375 |
+
|
| 376 |
+
### Personal and Sensitive Information
|
| 377 |
+
|
| 378 |
+
[More Information Needed]
|
| 379 |
+
|
| 380 |
+
## Considerations for Using the Data
|
| 381 |
+
|
| 382 |
+
### Social Impact of Dataset
|
| 383 |
+
|
| 384 |
+
[More Information Needed]
|
| 385 |
+
|
| 386 |
+
### Discussion of Biases
|
| 387 |
+
|
| 388 |
+
[More Information Needed]
|
| 389 |
+
|
| 390 |
+
### Other Known Limitations
|
| 391 |
+
|
| 392 |
+
[More Information Needed]
|
| 393 |
+
|
| 394 |
+
|
| 395 |
+
## Additional Information
|
| 396 |
+
|
| 397 |
+
### Dataset Curators
|
| 398 |
+
|
| 399 |
+
* National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English
|
| 400 |
+
* University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar
|
| 401 |
+
* the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian
|
| 402 |
+
* the Institute for Infocomm Research, Singapore (I2R) for Malay
|
| 403 |
+
* the Institute of Information Technology, Vietnam (IOIT) for Vietnamese
|
| 404 |
+
* the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer
|
| 405 |
+
|
| 406 |
+
### Licensing Information
|
| 407 |
+
|
| 408 |
+
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
|
| 409 |
+
|
| 410 |
+
### Citation Information
|
| 411 |
+
|
| 412 |
+
Please cite the following if you make use of the dataset:
|
| 413 |
+
|
| 414 |
+
Hammam Riza, Michael Purwoadi, Gunarso, Teduh Uliniansyah, Aw Ai Ti, Sharifah Mahani Aljunied, Luong Chi Mai, Vu Tat Thang, Nguyen Phuong Thai, Vichet Chea, Rapid Sun, Sethserey Sam, Sopheap Seng, Khin Mar Soe, Khin Thandar Nwet, Masao Utiyama, Chenchen Ding. (2016) "Introduction of the Asian Language Treebank" Oriental COCOSDA.
|
| 415 |
+
|
| 416 |
+
BibTeX:
|
| 417 |
+
```
|
| 418 |
+
@inproceedings{riza2016introduction,
|
| 419 |
+
title={Introduction of the asian language treebank},
|
| 420 |
+
author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others},
|
| 421 |
+
booktitle={2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA)},
|
| 422 |
+
pages={1--6},
|
| 423 |
+
year={2016},
|
| 424 |
+
organization={IEEE}
|
| 425 |
+
}
|
| 426 |
+
```
|
| 427 |
+
|
| 428 |
+
### Contributions
|
| 429 |
+
|
| 430 |
+
Thanks to [@chameleonTK](https://github.com/chameleonTK) for adding this dataset.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-vi-f50546-2087567167.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- futin/guess
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: bigscience/bloomz-1b7
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: futin/guess
|
| 13 |
+
dataset_config: vi
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: text
|
| 17 |
+
classes: classes
|
| 18 |
+
target: target
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: bigscience/bloomz-1b7
|
| 26 |
+
* Dataset: futin/guess
|
| 27 |
+
* Config: vi
|
| 28 |
+
* Split: test
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__41-inverse-scaling__41-e36c9c-1692459560.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- inverse-scaling/41
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: inverse-scaling/opt-13b_eval
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: inverse-scaling/41
|
| 13 |
+
dataset_config: inverse-scaling--41
|
| 14 |
+
dataset_split: train
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: prompt
|
| 17 |
+
classes: classes
|
| 18 |
+
target: answer_index
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: inverse-scaling/opt-13b_eval
|
| 26 |
+
* Dataset: inverse-scaling/41
|
| 27 |
+
* Config: inverse-scaling--41
|
| 28 |
+
* Split: train
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__examplehsd-raw-ff3db7-1730160389.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- phpthinh/examplehsd
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: bigscience/bloom-7b1
|
| 11 |
+
metrics: ['f1']
|
| 12 |
+
dataset_name: phpthinh/examplehsd
|
| 13 |
+
dataset_config: raw
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: text
|
| 17 |
+
classes: classes
|
| 18 |
+
target: target
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: bigscience/bloom-7b1
|
| 26 |
+
* Dataset: phpthinh/examplehsd
|
| 27 |
+
* Config: raw
|
| 28 |
+
* Split: test
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.
|
huggingface_dataset/Dataset_Card/georeactor_reddit_one_ups_2014.md
ADDED
|
@@ -0,0 +1,78 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
task_categories:
|
| 3 |
+
- text-classification
|
| 4 |
+
tags:
|
| 5 |
+
- reddit
|
| 6 |
+
language: en
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for reddit_one_ups_2014
|
| 10 |
+
|
| 11 |
+
## Dataset Description
|
| 12 |
+
|
| 13 |
+
- **Homepage:** https://github.com/Georeactor/reddit-one-ups
|
| 14 |
+
|
| 15 |
+
### Dataset Summary
|
| 16 |
+
|
| 17 |
+
Reddit 'one-ups' or 'clapbacks' - replies which scored higher than the original comments. This task makes one-ups easier by focusing on a set of common, often meme-like replies (e.g. 'yes', 'nope', '(͡°͜ʖ͡°)').
|
| 18 |
+
|
| 19 |
+
For commentary on predictions with a previous version of the dataset, see https://blog.goodaudience.com/can-deepclapback-learn-when-to-lol-e4a2092a8f2c
|
| 20 |
+
|
| 21 |
+
For unique / non-meme seq2seq version of this dataset, see https://huggingface.co/datasets/georeactor/reddit_one_ups_seq2seq_2014
|
| 22 |
+
|
| 23 |
+
Replies were selected from PushShift's archive of posts from 2014.
|
| 24 |
+
|
| 25 |
+
### Supported Tasks
|
| 26 |
+
|
| 27 |
+
Text classification task: finding the common reply (out of ~37) to match the parent comment text.
|
| 28 |
+
|
| 29 |
+
Text prediction task: estimating the vote score, or parent:reply ratio, of a meme response, as a measure of relevancy/cleverness of reply.
|
| 30 |
+
|
| 31 |
+
### Languages
|
| 32 |
+
|
| 33 |
+
Primarily English - includes some emoticons such as ┬─┬ノ(ಠ_ಠノ)
|
| 34 |
+
|
| 35 |
+
## Dataset Structure
|
| 36 |
+
|
| 37 |
+
### Data Instances
|
| 38 |
+
|
| 39 |
+
29,375 rows
|
| 40 |
+
|
| 41 |
+
### Data Fields
|
| 42 |
+
|
| 43 |
+
- id: the Reddit alphanumeric ID for the reply
|
| 44 |
+
- body: the content of the original reply
|
| 45 |
+
- score: the net vote score of the original reply
|
| 46 |
+
- parent_id: the Reddit alphanumeric ID for the parent
|
| 47 |
+
- author: the Reddit username of the reply
|
| 48 |
+
- subreddit: the Reddit community where the discussion occurred
|
| 49 |
+
- parent_score: the net vote score of the parent comment
|
| 50 |
+
- cleantext: the simplified reply (one of 37 classes)
|
| 51 |
+
- tstamp: the timestamp of the reply
|
| 52 |
+
- parent_body: the content of the original parent
|
| 53 |
+
|
| 54 |
+
## Dataset Creation
|
| 55 |
+
|
| 56 |
+
### Source Data
|
| 57 |
+
|
| 58 |
+
Reddit comments collected through PushShift.io archives for 2014.
|
| 59 |
+
|
| 60 |
+
#### Initial Data Collection and Normalization
|
| 61 |
+
|
| 62 |
+
- Removed deleted or empty comments.
|
| 63 |
+
- Selected only replies which scored 1.5x higher than a parent comment, where both have a positive score.
|
| 64 |
+
- Found the top/repeating phrases common to these one-ups/clapback comments.
|
| 65 |
+
- Selected only replies which had one of these top/repeating phrases.
|
| 66 |
+
- Made rows in PostgreSQL and output as CSV.
|
| 67 |
+
|
| 68 |
+
## Considerations for Using the Data
|
| 69 |
+
|
| 70 |
+
Comments and responses in the Reddit archives and output datasets all include NSFW and otherwise toxic language and links!
|
| 71 |
+
|
| 72 |
+
- You can use the subreddit and score columns to filter content.
|
| 73 |
+
- Imbalanced dataset: replies 'yes' and 'no' are more common than others.
|
| 74 |
+
- Overlap of labels: replies such as 'yes', 'yep', and 'yup' serve similar purposes; in other cases 'no' vs. 'nope' may be interesting.
|
| 75 |
+
- Timestamps: the given timestamp may help identify trends in meme replies
|
| 76 |
+
- Usernames: a username was included to identify the 'username checks out' meme, but this was not common enough in 2014, and the included username is from the reply.
|
| 77 |
+
|
| 78 |
+
Reddit comments are properties of Reddit and comment owners using their Terms of Service.
|
huggingface_dataset/Dataset_Card/gigaword.md
ADDED
|
@@ -0,0 +1,220 @@
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
|
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|
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|
|
|
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|
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|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- found
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- mit
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 100K<n<1M
|
| 14 |
+
source_datasets:
|
| 15 |
+
- extended|gigaword_2003
|
| 16 |
+
task_categories:
|
| 17 |
+
- summarization
|
| 18 |
+
task_ids: []
|
| 19 |
+
paperswithcode_id: null
|
| 20 |
+
pretty_name: Gigaword
|
| 21 |
+
train-eval-index:
|
| 22 |
+
- config: default
|
| 23 |
+
task: summarization
|
| 24 |
+
task_id: summarization
|
| 25 |
+
splits:
|
| 26 |
+
train_split: train
|
| 27 |
+
eval_split: test
|
| 28 |
+
col_mapping:
|
| 29 |
+
document: text
|
| 30 |
+
summary: target
|
| 31 |
+
metrics:
|
| 32 |
+
- type: rouge
|
| 33 |
+
name: Rouge
|
| 34 |
+
tags:
|
| 35 |
+
- headline-generation
|
| 36 |
+
dataset_info:
|
| 37 |
+
features:
|
| 38 |
+
- name: document
|
| 39 |
+
dtype: string
|
| 40 |
+
- name: summary
|
| 41 |
+
dtype: string
|
| 42 |
+
splits:
|
| 43 |
+
- name: train
|
| 44 |
+
num_bytes: 915249388
|
| 45 |
+
num_examples: 3803957
|
| 46 |
+
- name: validation
|
| 47 |
+
num_bytes: 45767096
|
| 48 |
+
num_examples: 189651
|
| 49 |
+
- name: test
|
| 50 |
+
num_bytes: 450782
|
| 51 |
+
num_examples: 1951
|
| 52 |
+
download_size: 578402958
|
| 53 |
+
dataset_size: 961467266
|
| 54 |
+
---
|
| 55 |
+
|
| 56 |
+
# Dataset Card for Gigaword
|
| 57 |
+
|
| 58 |
+
## Table of Contents
|
| 59 |
+
- [Dataset Description](#dataset-description)
|
| 60 |
+
- [Dataset Summary](#dataset-summary)
|
| 61 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 62 |
+
- [Languages](#languages)
|
| 63 |
+
- [Dataset Structure](#dataset-structure)
|
| 64 |
+
- [Data Instances](#data-instances)
|
| 65 |
+
- [Data Fields](#data-fields)
|
| 66 |
+
- [Data Splits](#data-splits)
|
| 67 |
+
- [Dataset Creation](#dataset-creation)
|
| 68 |
+
- [Curation Rationale](#curation-rationale)
|
| 69 |
+
- [Source Data](#source-data)
|
| 70 |
+
- [Annotations](#annotations)
|
| 71 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 72 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 73 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 74 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 75 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 76 |
+
- [Additional Information](#additional-information)
|
| 77 |
+
- [Dataset Curators](#dataset-curators)
|
| 78 |
+
- [Licensing Information](#licensing-information)
|
| 79 |
+
- [Citation Information](#citation-information)
|
| 80 |
+
- [Contributions](#contributions)
|
| 81 |
+
|
| 82 |
+
## Dataset Description
|
| 83 |
+
|
| 84 |
+
- **Repository:** [Gigaword repository](https://github.com/harvardnlp/sent-summary)
|
| 85 |
+
- **Leaderboard:** [Gigaword leaderboard](https://paperswithcode.com/sota/text-summarization-on-gigaword)
|
| 86 |
+
- **Paper:** [A Neural Attention Model for Abstractive Sentence Summarization](https://arxiv.org/abs/1509.00685)
|
| 87 |
+
- **Point of Contact:** [Alexander Rush](mailto:arush@cornell.edu)
|
| 88 |
+
- **Size of downloaded dataset files:** 551.61 MB
|
| 89 |
+
- **Size of the generated dataset:** 918.35 MB
|
| 90 |
+
- **Total amount of disk used:** 1469.96 MB
|
| 91 |
+
|
| 92 |
+
### Dataset Summary
|
| 93 |
+
|
| 94 |
+
Headline-generation on a corpus of article pairs from Gigaword consisting of
|
| 95 |
+
around 4 million articles. Use the 'org_data' provided by
|
| 96 |
+
https://github.com/microsoft/unilm/ which is identical to
|
| 97 |
+
https://github.com/harvardnlp/sent-summary but with better format.
|
| 98 |
+
|
| 99 |
+
### Supported Tasks and Leaderboards
|
| 100 |
+
|
| 101 |
+
- `summarization`: This dataset can be used for Summarization, where given a dicument, the goal is to predict its summery. The model performance is evaluated using the [ROUGE](https://huggingface.co/metrics/rouge) metric. The leaderboard for this task is available [here](https://paperswithcode.com/sota/text-summarization-on-gigaword).
|
| 102 |
+
|
| 103 |
+
### Languages
|
| 104 |
+
|
| 105 |
+
English.
|
| 106 |
+
|
| 107 |
+
## Dataset Structure
|
| 108 |
+
|
| 109 |
+
### Data Instances
|
| 110 |
+
|
| 111 |
+
An example of 'train' looks as follows.
|
| 112 |
+
```
|
| 113 |
+
{
|
| 114 |
+
'document': "australia 's current account deficit shrunk by a record #.## billion dollars -lrb- #.## billion us -rrb- in the june quarter due to soaring commodity prices , figures released monday showed .",
|
| 115 |
+
'summary': 'australian current account deficit narrows sharply'
|
| 116 |
+
}
|
| 117 |
+
```
|
| 118 |
+
|
| 119 |
+
### Data Fields
|
| 120 |
+
|
| 121 |
+
The data fields are the same among all splits.
|
| 122 |
+
|
| 123 |
+
- `document`: a `string` feature.
|
| 124 |
+
- `summary`: a `string` feature.
|
| 125 |
+
|
| 126 |
+
### Data Splits
|
| 127 |
+
|
| 128 |
+
| name | train |validation|test|
|
| 129 |
+
|-------|------:|---------:|---:|
|
| 130 |
+
|default|3803957| 189651|1951|
|
| 131 |
+
|
| 132 |
+
## Dataset Creation
|
| 133 |
+
|
| 134 |
+
### Curation Rationale
|
| 135 |
+
|
| 136 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 137 |
+
|
| 138 |
+
### Source Data
|
| 139 |
+
|
| 140 |
+
#### Initial Data Collection and Normalization
|
| 141 |
+
|
| 142 |
+
From the paper:
|
| 143 |
+
> For our training set, we pair the headline of each article with its first sentence to create an inputsummary pair. While the model could in theory be trained on any pair, Gigaword contains many spurious headline-article pairs. We therefore prune training based on the following heuristic filters: (1) Are there no non-stop-words in common? (2) Does the title contain a byline or other extraneous editing marks? (3) Does the title have a question mark or colon? After applying these filters, the training set consists of roughly J = 4 million title-article pairs. We apply a minimal preprocessing step using PTB tokenization, lower-casing, replacing all digit characters with #, and replacing of word types seen less than 5 times with UNK. We also remove all articles from the time-period of the DUC evaluation. release.
|
| 144 |
+
The complete input training vocabulary consists of 119 million word tokens and 110K unique word types with an average sentence size of 31.3 words. The headline vocabulary consists of 31 million tokens and 69K word types with the average title of length 8.3 words (note that this is significantly shorter than the DUC summaries). On average there are 4.6 overlapping word types between the headline and the input; although only 2.6 in the
|
| 145 |
+
first 75-characters of the input.
|
| 146 |
+
|
| 147 |
+
#### Who are the source language producers?
|
| 148 |
+
|
| 149 |
+
From the paper:
|
| 150 |
+
> For training data for both tasks, we utilize the annotated Gigaword data set (Graff et al., 2003; Napoles et al., 2012), which consists of standard Gigaword, preprocessed with Stanford CoreNLP tools (Manning et al., 2014).
|
| 151 |
+
|
| 152 |
+
### Annotations
|
| 153 |
+
|
| 154 |
+
#### Annotation process
|
| 155 |
+
|
| 156 |
+
Annotations are inherited from the annotatated Gigaword data set.
|
| 157 |
+
|
| 158 |
+
Additional information from the paper:
|
| 159 |
+
> Our model only uses annotations for tokenization and sentence separation, although several of the baselines use parsing and tagging as well.
|
| 160 |
+
|
| 161 |
+
#### Who are the annotators?
|
| 162 |
+
|
| 163 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 164 |
+
|
| 165 |
+
### Personal and Sensitive Information
|
| 166 |
+
|
| 167 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 168 |
+
|
| 169 |
+
## Considerations for Using the Data
|
| 170 |
+
|
| 171 |
+
### Social Impact of Dataset
|
| 172 |
+
|
| 173 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 174 |
+
|
| 175 |
+
### Discussion of Biases
|
| 176 |
+
|
| 177 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 178 |
+
|
| 179 |
+
### Other Known Limitations
|
| 180 |
+
|
| 181 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 182 |
+
|
| 183 |
+
## Additional Information
|
| 184 |
+
|
| 185 |
+
### Dataset Curators
|
| 186 |
+
|
| 187 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 188 |
+
|
| 189 |
+
### Licensing Information
|
| 190 |
+
|
| 191 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 192 |
+
|
| 193 |
+
### Citation Information
|
| 194 |
+
|
| 195 |
+
```bibtex
|
| 196 |
+
@article{graff2003english,
|
| 197 |
+
title={English gigaword},
|
| 198 |
+
author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
|
| 199 |
+
journal={Linguistic Data Consortium, Philadelphia},
|
| 200 |
+
volume={4},
|
| 201 |
+
number={1},
|
| 202 |
+
pages={34},
|
| 203 |
+
year={2003}
|
| 204 |
+
}
|
| 205 |
+
|
| 206 |
+
@article{Rush_2015,
|
| 207 |
+
title={A Neural Attention Model for Abstractive Sentence Summarization},
|
| 208 |
+
url={http://dx.doi.org/10.18653/v1/D15-1044},
|
| 209 |
+
DOI={10.18653/v1/d15-1044},
|
| 210 |
+
journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
|
| 211 |
+
publisher={Association for Computational Linguistics},
|
| 212 |
+
author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},
|
| 213 |
+
year={2015}
|
| 214 |
+
}
|
| 215 |
+
```
|
| 216 |
+
|
| 217 |
+
|
| 218 |
+
### Contributions
|
| 219 |
+
|
| 220 |
+
Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
|
huggingface_dataset/Dataset_Card/hope_edi.md
ADDED
|
@@ -0,0 +1,267 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
- ml
|
| 9 |
+
- ta
|
| 10 |
+
license:
|
| 11 |
+
- cc-by-4.0
|
| 12 |
+
multilinguality:
|
| 13 |
+
- monolingual
|
| 14 |
+
- multilingual
|
| 15 |
+
size_categories:
|
| 16 |
+
- 10K<n<100K
|
| 17 |
+
- 1K<n<10K
|
| 18 |
+
source_datasets:
|
| 19 |
+
- original
|
| 20 |
+
task_categories:
|
| 21 |
+
- text-classification
|
| 22 |
+
task_ids: []
|
| 23 |
+
paperswithcode_id: hopeedi
|
| 24 |
+
pretty_name: 'HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality,
|
| 25 |
+
Diversity, and Inclusion'
|
| 26 |
+
configs:
|
| 27 |
+
- english
|
| 28 |
+
- malayalam
|
| 29 |
+
- tamil
|
| 30 |
+
tags:
|
| 31 |
+
- hope-speech-classification
|
| 32 |
+
dataset_info:
|
| 33 |
+
- config_name: english
|
| 34 |
+
features:
|
| 35 |
+
- name: text
|
| 36 |
+
dtype: string
|
| 37 |
+
- name: label
|
| 38 |
+
dtype:
|
| 39 |
+
class_label:
|
| 40 |
+
names:
|
| 41 |
+
'0': Hope_speech
|
| 42 |
+
'1': Non_hope_speech
|
| 43 |
+
'2': not-English
|
| 44 |
+
splits:
|
| 45 |
+
- name: train
|
| 46 |
+
num_bytes: 2306656
|
| 47 |
+
num_examples: 22762
|
| 48 |
+
- name: validation
|
| 49 |
+
num_bytes: 288663
|
| 50 |
+
num_examples: 2843
|
| 51 |
+
download_size: 2739901
|
| 52 |
+
dataset_size: 2595319
|
| 53 |
+
- config_name: tamil
|
| 54 |
+
features:
|
| 55 |
+
- name: text
|
| 56 |
+
dtype: string
|
| 57 |
+
- name: label
|
| 58 |
+
dtype:
|
| 59 |
+
class_label:
|
| 60 |
+
names:
|
| 61 |
+
'0': Hope_speech
|
| 62 |
+
'1': Non_hope_speech
|
| 63 |
+
'2': not-Tamil
|
| 64 |
+
splits:
|
| 65 |
+
- name: train
|
| 66 |
+
num_bytes: 1531013
|
| 67 |
+
num_examples: 16160
|
| 68 |
+
- name: validation
|
| 69 |
+
num_bytes: 197378
|
| 70 |
+
num_examples: 2018
|
| 71 |
+
download_size: 1795767
|
| 72 |
+
dataset_size: 1728391
|
| 73 |
+
- config_name: malayalam
|
| 74 |
+
features:
|
| 75 |
+
- name: text
|
| 76 |
+
dtype: string
|
| 77 |
+
- name: label
|
| 78 |
+
dtype:
|
| 79 |
+
class_label:
|
| 80 |
+
names:
|
| 81 |
+
'0': Hope_speech
|
| 82 |
+
'1': Non_hope_speech
|
| 83 |
+
'2': not-malayalam
|
| 84 |
+
splits:
|
| 85 |
+
- name: train
|
| 86 |
+
num_bytes: 1492031
|
| 87 |
+
num_examples: 8564
|
| 88 |
+
- name: validation
|
| 89 |
+
num_bytes: 180713
|
| 90 |
+
num_examples: 1070
|
| 91 |
+
download_size: 1721534
|
| 92 |
+
dataset_size: 1672744
|
| 93 |
+
---
|
| 94 |
+
|
| 95 |
+
# Dataset Card for [Dataset Name]
|
| 96 |
+
|
| 97 |
+
## Table of Contents
|
| 98 |
+
- [Dataset Description](#dataset-description)
|
| 99 |
+
- [Dataset Summary](#dataset-summary)
|
| 100 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 101 |
+
- [Languages](#languages)
|
| 102 |
+
- [Dataset Structure](#dataset-structure)
|
| 103 |
+
- [Data Instances](#data-instances)
|
| 104 |
+
- [Data Fields](#data-fields)
|
| 105 |
+
- [Data Splits](#data-splits)
|
| 106 |
+
- [Dataset Creation](#dataset-creation)
|
| 107 |
+
- [Curation Rationale](#curation-rationale)
|
| 108 |
+
- [Source Data](#source-data)
|
| 109 |
+
- [Annotations](#annotations)
|
| 110 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 111 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 112 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 113 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 114 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 115 |
+
- [Additional Information](#additional-information)
|
| 116 |
+
- [Dataset Curators](#dataset-curators)
|
| 117 |
+
- [Licensing Information](#licensing-information)
|
| 118 |
+
- [Citation Information](#citation-information)
|
| 119 |
+
- [Contributions](#contributions)
|
| 120 |
+
|
| 121 |
+
## Dataset Description
|
| 122 |
+
|
| 123 |
+
- **Homepage:** [Hope Speech Detection for Equality, Diversity, and Inclusion-EACL 2021](https://competitions.codalab.org/competitions/27653#learn_the_details)
|
| 124 |
+
- **Repository:** [HopeEDI data repository](https://competitions.codalab.org/competitions/27653#participate-get_data)
|
| 125 |
+
- **Paper:** [HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion](https://www.aclweb.org/anthology/2020.peoples-1.5/)
|
| 126 |
+
- **Leaderboard:** [Rank list](https://competitions.codalab.org/competitions/27653#results)
|
| 127 |
+
- **Point of Contact:** [Bharathi Raja Chakravarthi](mailto:bharathiraja.akr@gmail.com)
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
### Dataset Summary
|
| 131 |
+
|
| 132 |
+
A Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting.
|
| 133 |
+
|
| 134 |
+
### Supported Tasks and Leaderboards
|
| 135 |
+
|
| 136 |
+
To identify hope speech in the comments/posts in social media.
|
| 137 |
+
|
| 138 |
+
### Languages
|
| 139 |
+
|
| 140 |
+
English, Tamil and Malayalam
|
| 141 |
+
|
| 142 |
+
## Dataset Structure
|
| 143 |
+
|
| 144 |
+
### Data Instances
|
| 145 |
+
|
| 146 |
+
An example from the English dataset looks as follows:
|
| 147 |
+
|
| 148 |
+
| text | label |
|
| 149 |
+
| :------ | :----- |
|
| 150 |
+
| all lives matter .without that we never have peace so to me forever all lives matter. | Hope_speech |
|
| 151 |
+
| I think it's cool that you give people a voice to speak out with here on this channel. | Hope_speech |
|
| 152 |
+
|
| 153 |
+
|
| 154 |
+
An example from the Tamil dataset looks as follows:
|
| 155 |
+
|
| 156 |
+
| text | label |
|
| 157 |
+
| :------ | :----- |
|
| 158 |
+
| Idha solla ivalo naala | Non_hope_speech |
|
| 159 |
+
| இன்று தேசிய பெண் குழந்தைகள் தினம்.. பெண் குழந்தைகளை போற்றுவோம்..அவர்களை பாதுகாப்போம்... | Hope_speech |
|
| 160 |
+
|
| 161 |
+
|
| 162 |
+
An example from the Malayalam dataset looks as follows:
|
| 163 |
+
|
| 164 |
+
| text | label |
|
| 165 |
+
| :------ | :----- |
|
| 166 |
+
| ഇത്രെ���ും കഷ്ടപ്പെട്ട് വളർത്തിയ ആ അമ്മയുടെ മുഖം കണ്ടപ്പോൾ കണ്ണ് നിറഞ്ഞു പോയി | Hope_speech |
|
| 167 |
+
| snehikunavar aanayalum pennayalum onnichu jeevikatte..aareyum compel cheythitallalooo..parasparamulla ishtathodeyalle...avarum jeevikatte..🥰🥰 | Hope_speech |
|
| 168 |
+
|
| 169 |
+
### Data Fields
|
| 170 |
+
|
| 171 |
+
English
|
| 172 |
+
- `text`: English comment.
|
| 173 |
+
- `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-English"
|
| 174 |
+
|
| 175 |
+
Tamil
|
| 176 |
+
- `text`: Tamil-English code mixed comment.
|
| 177 |
+
- `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-Tamil"
|
| 178 |
+
|
| 179 |
+
Malayalam
|
| 180 |
+
- `text`: Malayalam-English code mixed comment.
|
| 181 |
+
- `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-malayalam"
|
| 182 |
+
|
| 183 |
+
|
| 184 |
+
### Data Splits
|
| 185 |
+
|
| 186 |
+
| | train | validation |
|
| 187 |
+
| ----- |------:|-----------:|
|
| 188 |
+
| English | 22762 | 2843 |
|
| 189 |
+
| Tamil | 16160 | 2018 |
|
| 190 |
+
| Malayalam | 8564 | 1070 |
|
| 191 |
+
|
| 192 |
+
## Dataset Creation
|
| 193 |
+
|
| 194 |
+
### Curation Rationale
|
| 195 |
+
Hope is considered significant for the well-being, recuperation and restoration of human life by health professionals.
|
| 196 |
+
Hate speech or offensive language detection dataset is not available for code-mixed Tamil and code-mixed Malayalam, and it does not take into account LGBTIQ, women in STEM and other minorities. Thus, we cannot use existing hate speech or offensive language detection datasets to detect hope or non-hope for EDI of minorities.
|
| 197 |
+
|
| 198 |
+
### Source Data
|
| 199 |
+
|
| 200 |
+
#### Initial Data Collection and Normalization
|
| 201 |
+
|
| 202 |
+
For English, we collected data on recent topics of EDI, including women in STEM, LGBTIQ issues, COVID-19, Black Lives Matters, United Kingdom (UK) versus China, United States of America (USA) versus China and Australia versus China from YouTube video comments. The data was collected from videos of people from English-speaking countries, such as Australia, Canada, the Republic of Ireland, United Kingdom, the United States of America and New Zealand.
|
| 203 |
+
|
| 204 |
+
For Tamil and Malayalam, we collected data from India on the recent topics regarding LGBTIQ issues, COVID-19, women in STEM, the Indo-China war and Dravidian affairs.
|
| 205 |
+
|
| 206 |
+
#### Who are the source language producers?
|
| 207 |
+
|
| 208 |
+
Youtube users
|
| 209 |
+
|
| 210 |
+
### Annotations
|
| 211 |
+
|
| 212 |
+
#### Annotation process
|
| 213 |
+
|
| 214 |
+
We created Google forms to collect annotations from annotators. Each form contained a maximum of 100 comments, and each page contained a maximum of 10 comments to maintain the quality of annotation. We collected information on the gender, educational background and the medium of schooling of the annotator to know the diversity of the annotator and avoid bias. We educated annotators by providing them with YouTube videos on EDI. A minimum of three annotators annotated each form.
|
| 215 |
+
|
| 216 |
+
#### Who are the annotators?
|
| 217 |
+
|
| 218 |
+
For English language comments, annotators were from Australia, the Republic of Ireland, the United Kingdom and the United States of America. For Tamil, we were able to get annotations from both people from the state of Tamil Nadu of India and from Sri Lanka. Most of the annotators were graduate or post-graduate students.
|
| 219 |
+
|
| 220 |
+
### Personal and Sensitive Information
|
| 221 |
+
|
| 222 |
+
Social media data is highly sensitive, and even more so when it is related to the minority population, such as the LGBTIQ community or women. We have taken full consideration to minimise the risk associated with individual identity in the data by removing personal information from dataset, such as names but not celebrity names. However, to study EDI, we needed to keep information relating to the following characteristics; racial, gender, sexual orientation, ethnic origin and philosophical beliefs. Annotators were only shown anonymised posts and agreed to make no attempts to contact the comment creator. The dataset will only be made available for research purpose to the researcher who agree to follow ethical
|
| 223 |
+
guidelines
|
| 224 |
+
|
| 225 |
+
## Considerations for Using the Data
|
| 226 |
+
|
| 227 |
+
### Social Impact of Dataset
|
| 228 |
+
|
| 229 |
+
[More Information Needed]
|
| 230 |
+
|
| 231 |
+
### Discussion of Biases
|
| 232 |
+
|
| 233 |
+
[More Information Needed]
|
| 234 |
+
|
| 235 |
+
### Other Known Limitations
|
| 236 |
+
|
| 237 |
+
[More Information Needed]
|
| 238 |
+
|
| 239 |
+
## Additional Information
|
| 240 |
+
|
| 241 |
+
### Dataset Curators
|
| 242 |
+
|
| 243 |
+
[More Information Needed]
|
| 244 |
+
|
| 245 |
+
### Licensing Information
|
| 246 |
+
|
| 247 |
+
This work is licensed under a [Creative Commons Attribution 4.0 International Licence](http://creativecommons.org/licenses/by/4.0/.)
|
| 248 |
+
|
| 249 |
+
### Citation Information
|
| 250 |
+
|
| 251 |
+
```
|
| 252 |
+
@inproceedings{chakravarthi-2020-hopeedi,
|
| 253 |
+
title = "{H}ope{EDI}: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion",
|
| 254 |
+
author = "Chakravarthi, Bharathi Raja",
|
| 255 |
+
booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
|
| 256 |
+
month = dec,
|
| 257 |
+
year = "2020",
|
| 258 |
+
address = "Barcelona, Spain (Online)",
|
| 259 |
+
publisher = "Association for Computational Linguistics",
|
| 260 |
+
url = "https://www.aclweb.org/anthology/2020.peoples-1.5",
|
| 261 |
+
pages = "41--53",
|
| 262 |
+
abstract = "Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this form of censorship to obstruct the democratic right of freedom of speech. Therefore, it is imperative that research should take a positive reinforcement approach towards online content that is encouraging, positive and supportive contents. Until now, most studies have focused on solving this problem of negativity in the English language, though the problem is much more than just harmful content. Furthermore, it is multilingual as well. Thus, we have constructed a Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting. We determined that the inter-annotator agreement of our dataset using Krippendorff{'}s alpha. Further, we created several baselines to benchmark the resulting dataset and the results have been expressed using precision, recall and F1-score. The dataset is publicly available for the research community. We hope that this resource will spur further research on encouraging inclusive and responsive speech that reinforces positiveness.",
|
| 263 |
+
}
|
| 264 |
+
```
|
| 265 |
+
### Contributions
|
| 266 |
+
|
| 267 |
+
Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset.
|
huggingface_dataset/Dataset_Card/huggingnft_azuki.md
ADDED
|
@@ -0,0 +1,175 @@
|
|
|
|
|
|
|
|
|
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|
|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- huggingnft
|
| 4 |
+
- nft
|
| 5 |
+
- huggan
|
| 6 |
+
- gan
|
| 7 |
+
- image
|
| 8 |
+
- images
|
| 9 |
+
task:
|
| 10 |
+
- unconditional-image-generation
|
| 11 |
+
datasets:
|
| 12 |
+
- huggingnft/azuki
|
| 13 |
+
license: mit
|
| 14 |
+
---
|
| 15 |
+
|
| 16 |
+
# Dataset Card
|
| 17 |
+
|
| 18 |
+
## Disclaimer
|
| 19 |
+
|
| 20 |
+
All rights belong to their owners.
|
| 21 |
+
Models and datasets can be removed from the site at the request of the copyright holder.
|
| 22 |
+
|
| 23 |
+
## Table of Contents
|
| 24 |
+
- [Dataset Description](#dataset-description)
|
| 25 |
+
- [Dataset Summary](#dataset-summary)
|
| 26 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 27 |
+
- [Languages](#languages)
|
| 28 |
+
- [How to use](#how-to-use)
|
| 29 |
+
- [Dataset Structure](#dataset-structure)
|
| 30 |
+
- [Data Fields](#data-fields)
|
| 31 |
+
- [Data Splits](#data-splits)
|
| 32 |
+
- [Dataset Creation](#dataset-creation)
|
| 33 |
+
- [Curation Rationale](#curation-rationale)
|
| 34 |
+
- [Source Data](#source-data)
|
| 35 |
+
- [Annotations](#annotations)
|
| 36 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 37 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 38 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 39 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 40 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 41 |
+
- [Additional Information](#additional-information)
|
| 42 |
+
- [Dataset Curators](#dataset-curators)
|
| 43 |
+
- [Licensing Information](#licensing-information)
|
| 44 |
+
- [Citation Information](#citation-information)
|
| 45 |
+
- [About](#about)
|
| 46 |
+
|
| 47 |
+
## Dataset Description
|
| 48 |
+
|
| 49 |
+
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
|
| 50 |
+
- **Repository:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
|
| 51 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 52 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
### Dataset Summary
|
| 56 |
+
|
| 57 |
+
NFT images dataset for unconditional generation.
|
| 58 |
+
|
| 59 |
+
NFT collection available [here](https://opensea.io/collection/azuki).
|
| 60 |
+
|
| 61 |
+
Model is available [here](https://huggingface.co/huggingnft/azuki).
|
| 62 |
+
|
| 63 |
+
Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
|
| 64 |
+
|
| 65 |
+
### Supported Tasks and Leaderboards
|
| 66 |
+
|
| 67 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
## How to use
|
| 71 |
+
|
| 72 |
+
How to load this dataset directly with the datasets library:
|
| 73 |
+
|
| 74 |
+
```python
|
| 75 |
+
from datasets import load_dataset
|
| 76 |
+
|
| 77 |
+
dataset = load_dataset("huggingnft/azuki")
|
| 78 |
+
```
|
| 79 |
+
|
| 80 |
+
## Dataset Structure
|
| 81 |
+
|
| 82 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
### Data Fields
|
| 86 |
+
|
| 87 |
+
The data fields are the same among all splits.
|
| 88 |
+
|
| 89 |
+
- `image`: an `image` feature.
|
| 90 |
+
- `id`: an `int` feature.
|
| 91 |
+
- `token_metadata`: a `str` feature.
|
| 92 |
+
- `image_original_url`: a `str` feature.
|
| 93 |
+
|
| 94 |
+
### Data Splits
|
| 95 |
+
|
| 96 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
## Dataset Creation
|
| 100 |
+
|
| 101 |
+
### Curation Rationale
|
| 102 |
+
|
| 103 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 104 |
+
|
| 105 |
+
### Source Data
|
| 106 |
+
|
| 107 |
+
#### Initial Data Collection and Normalization
|
| 108 |
+
|
| 109 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 110 |
+
|
| 111 |
+
#### Who are the source language producers?
|
| 112 |
+
|
| 113 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 114 |
+
|
| 115 |
+
### Annotations
|
| 116 |
+
|
| 117 |
+
#### Annotation process
|
| 118 |
+
|
| 119 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 120 |
+
|
| 121 |
+
#### Who are the annotators?
|
| 122 |
+
|
| 123 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 124 |
+
|
| 125 |
+
### Personal and Sensitive Information
|
| 126 |
+
|
| 127 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 128 |
+
|
| 129 |
+
## Considerations for Using the Data
|
| 130 |
+
|
| 131 |
+
### Social Impact of Dataset
|
| 132 |
+
|
| 133 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 134 |
+
|
| 135 |
+
### Discussion of Biases
|
| 136 |
+
|
| 137 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 138 |
+
|
| 139 |
+
### Other Known Limitations
|
| 140 |
+
|
| 141 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 142 |
+
|
| 143 |
+
## Additional Information
|
| 144 |
+
|
| 145 |
+
### Dataset Curators
|
| 146 |
+
|
| 147 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 148 |
+
|
| 149 |
+
### Licensing Information
|
| 150 |
+
|
| 151 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 152 |
+
|
| 153 |
+
### Citation Information
|
| 154 |
+
|
| 155 |
+
```
|
| 156 |
+
@InProceedings{huggingnft,
|
| 157 |
+
author={Aleksey Korshuk}
|
| 158 |
+
year=2022
|
| 159 |
+
}
|
| 160 |
+
```
|
| 161 |
+
|
| 162 |
+
|
| 163 |
+
## About
|
| 164 |
+
|
| 165 |
+
*Built by Aleksey Korshuk*
|
| 166 |
+
|
| 167 |
+
[](https://github.com/AlekseyKorshuk)
|
| 168 |
+
|
| 169 |
+
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
|
| 170 |
+
|
| 171 |
+
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
|
| 172 |
+
|
| 173 |
+
For more details, visit the project repository.
|
| 174 |
+
|
| 175 |
+
[](https://github.com/AlekseyKorshuk/huggingnft)
|
huggingface_dataset/Dataset_Card/indic_glue.md
ADDED
|
@@ -0,0 +1,1796 @@
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- other
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- as
|
| 8 |
+
- bn
|
| 9 |
+
- en
|
| 10 |
+
- gu
|
| 11 |
+
- hi
|
| 12 |
+
- kn
|
| 13 |
+
- ml
|
| 14 |
+
- mr
|
| 15 |
+
- or
|
| 16 |
+
- pa
|
| 17 |
+
- ta
|
| 18 |
+
- te
|
| 19 |
+
license:
|
| 20 |
+
- other
|
| 21 |
+
multilinguality:
|
| 22 |
+
- multilingual
|
| 23 |
+
size_categories:
|
| 24 |
+
- 100K<n<1M
|
| 25 |
+
source_datasets:
|
| 26 |
+
- extended|other
|
| 27 |
+
task_categories:
|
| 28 |
+
- text-classification
|
| 29 |
+
- token-classification
|
| 30 |
+
- multiple-choice
|
| 31 |
+
task_ids:
|
| 32 |
+
- topic-classification
|
| 33 |
+
- natural-language-inference
|
| 34 |
+
- sentiment-analysis
|
| 35 |
+
- semantic-similarity-scoring
|
| 36 |
+
- named-entity-recognition
|
| 37 |
+
- multiple-choice-qa
|
| 38 |
+
pretty_name: IndicGLUE
|
| 39 |
+
tags:
|
| 40 |
+
- discourse-mode-classification
|
| 41 |
+
- paraphrase-identification
|
| 42 |
+
- cross-lingual-similarity
|
| 43 |
+
- headline-classification
|
| 44 |
+
dataset_info:
|
| 45 |
+
- config_name: wnli.en
|
| 46 |
+
features:
|
| 47 |
+
- name: hypothesis
|
| 48 |
+
dtype: string
|
| 49 |
+
- name: premise
|
| 50 |
+
dtype: string
|
| 51 |
+
- name: label
|
| 52 |
+
dtype:
|
| 53 |
+
class_label:
|
| 54 |
+
names:
|
| 55 |
+
'0': not_entailment
|
| 56 |
+
'1': entailment
|
| 57 |
+
'2': None
|
| 58 |
+
splits:
|
| 59 |
+
- name: train
|
| 60 |
+
num_bytes: 104577
|
| 61 |
+
num_examples: 635
|
| 62 |
+
- name: validation
|
| 63 |
+
num_bytes: 11886
|
| 64 |
+
num_examples: 71
|
| 65 |
+
- name: test
|
| 66 |
+
num_bytes: 37305
|
| 67 |
+
num_examples: 146
|
| 68 |
+
download_size: 591249
|
| 69 |
+
dataset_size: 153768
|
| 70 |
+
- config_name: wnli.hi
|
| 71 |
+
features:
|
| 72 |
+
- name: hypothesis
|
| 73 |
+
dtype: string
|
| 74 |
+
- name: premise
|
| 75 |
+
dtype: string
|
| 76 |
+
- name: label
|
| 77 |
+
dtype:
|
| 78 |
+
class_label:
|
| 79 |
+
names:
|
| 80 |
+
'0': not_entailment
|
| 81 |
+
'1': entailment
|
| 82 |
+
'2': None
|
| 83 |
+
splits:
|
| 84 |
+
- name: train
|
| 85 |
+
num_bytes: 253342
|
| 86 |
+
num_examples: 635
|
| 87 |
+
- name: validation
|
| 88 |
+
num_bytes: 28684
|
| 89 |
+
num_examples: 71
|
| 90 |
+
- name: test
|
| 91 |
+
num_bytes: 90831
|
| 92 |
+
num_examples: 146
|
| 93 |
+
download_size: 591249
|
| 94 |
+
dataset_size: 372857
|
| 95 |
+
- config_name: wnli.gu
|
| 96 |
+
features:
|
| 97 |
+
- name: hypothesis
|
| 98 |
+
dtype: string
|
| 99 |
+
- name: premise
|
| 100 |
+
dtype: string
|
| 101 |
+
- name: label
|
| 102 |
+
dtype:
|
| 103 |
+
class_label:
|
| 104 |
+
names:
|
| 105 |
+
'0': not_entailment
|
| 106 |
+
'1': entailment
|
| 107 |
+
'2': None
|
| 108 |
+
splits:
|
| 109 |
+
- name: train
|
| 110 |
+
num_bytes: 251562
|
| 111 |
+
num_examples: 635
|
| 112 |
+
- name: validation
|
| 113 |
+
num_bytes: 28183
|
| 114 |
+
num_examples: 71
|
| 115 |
+
- name: test
|
| 116 |
+
num_bytes: 94586
|
| 117 |
+
num_examples: 146
|
| 118 |
+
download_size: 591249
|
| 119 |
+
dataset_size: 374331
|
| 120 |
+
- config_name: wnli.mr
|
| 121 |
+
features:
|
| 122 |
+
- name: hypothesis
|
| 123 |
+
dtype: string
|
| 124 |
+
- name: premise
|
| 125 |
+
dtype: string
|
| 126 |
+
- name: label
|
| 127 |
+
dtype:
|
| 128 |
+
class_label:
|
| 129 |
+
names:
|
| 130 |
+
'0': not_entailment
|
| 131 |
+
'1': entailment
|
| 132 |
+
'2': None
|
| 133 |
+
splits:
|
| 134 |
+
- name: train
|
| 135 |
+
num_bytes: 256657
|
| 136 |
+
num_examples: 635
|
| 137 |
+
- name: validation
|
| 138 |
+
num_bytes: 29226
|
| 139 |
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num_examples: 71
|
| 140 |
+
- name: test
|
| 141 |
+
num_bytes: 97136
|
| 142 |
+
num_examples: 146
|
| 143 |
+
download_size: 591249
|
| 144 |
+
dataset_size: 383019
|
| 145 |
+
- config_name: copa.en
|
| 146 |
+
features:
|
| 147 |
+
- name: premise
|
| 148 |
+
dtype: string
|
| 149 |
+
- name: choice1
|
| 150 |
+
dtype: string
|
| 151 |
+
- name: choice2
|
| 152 |
+
dtype: string
|
| 153 |
+
- name: question
|
| 154 |
+
dtype: string
|
| 155 |
+
- name: label
|
| 156 |
+
dtype: int32
|
| 157 |
+
splits:
|
| 158 |
+
- name: train
|
| 159 |
+
num_bytes: 46049
|
| 160 |
+
num_examples: 400
|
| 161 |
+
- name: validation
|
| 162 |
+
num_bytes: 11695
|
| 163 |
+
num_examples: 100
|
| 164 |
+
- name: test
|
| 165 |
+
num_bytes: 55862
|
| 166 |
+
num_examples: 500
|
| 167 |
+
download_size: 757679
|
| 168 |
+
dataset_size: 113606
|
| 169 |
+
- config_name: copa.hi
|
| 170 |
+
features:
|
| 171 |
+
- name: premise
|
| 172 |
+
dtype: string
|
| 173 |
+
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| 1486 |
+
download_size: 5980272
|
| 1487 |
+
dataset_size: 4847598
|
| 1488 |
+
---
|
| 1489 |
+
|
| 1490 |
+
# Dataset Card for "indic_glue"
|
| 1491 |
+
|
| 1492 |
+
## Table of Contents
|
| 1493 |
+
- [Dataset Description](#dataset-description)
|
| 1494 |
+
- [Dataset Summary](#dataset-summary)
|
| 1495 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 1496 |
+
- [Languages](#languages)
|
| 1497 |
+
- [Dataset Structure](#dataset-structure)
|
| 1498 |
+
- [Data Instances](#data-instances)
|
| 1499 |
+
- [Data Fields](#data-fields)
|
| 1500 |
+
- [Data Splits](#data-splits)
|
| 1501 |
+
- [Dataset Creation](#dataset-creation)
|
| 1502 |
+
- [Curation Rationale](#curation-rationale)
|
| 1503 |
+
- [Source Data](#source-data)
|
| 1504 |
+
- [Annotations](#annotations)
|
| 1505 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 1506 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 1507 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 1508 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 1509 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 1510 |
+
- [Additional Information](#additional-information)
|
| 1511 |
+
- [Dataset Curators](#dataset-curators)
|
| 1512 |
+
- [Licensing Information](#licensing-information)
|
| 1513 |
+
- [Citation Information](#citation-information)
|
| 1514 |
+
- [Contributions](#contributions)
|
| 1515 |
+
|
| 1516 |
+
## Dataset Description
|
| 1517 |
+
|
| 1518 |
+
- **Homepage:** https://ai4bharat.iitm.ac.in/indic-glue
|
| 1519 |
+
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1520 |
+
- **Paper:** [IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages](https://aclanthology.org/2020.findings-emnlp.445/)
|
| 1521 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1522 |
+
- **Size of downloaded dataset files:** 3351.18 MB
|
| 1523 |
+
- **Size of the generated dataset:** 1573.33 MB
|
| 1524 |
+
- **Total amount of disk used:** 4924.51 MB
|
| 1525 |
+
|
| 1526 |
+
### Dataset Summary
|
| 1527 |
+
|
| 1528 |
+
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
|
| 1529 |
+
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
|
| 1530 |
+
|
| 1531 |
+
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task
|
| 1532 |
+
in which a system must read a sentence with a pronoun and select the referent of that pronoun from
|
| 1533 |
+
a list of choices. The examples are manually constructed to foil simple statistical methods: Each
|
| 1534 |
+
one is contingent on contextual information provided by a single word or phrase in the sentence.
|
| 1535 |
+
To convert the problem into sentence pair classification, we construct sentence pairs by replacing
|
| 1536 |
+
the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the
|
| 1537 |
+
pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of
|
| 1538 |
+
new examples derived from fiction books that was shared privately by the authors of the original
|
| 1539 |
+
corpus. While the included training set is balanced between two classes, the test set is imbalanced
|
| 1540 |
+
between them (65% not entailment). Also, due to a data quirk, the development set is adversarial:
|
| 1541 |
+
hypotheses are sometimes shared between training and development examples, so if a model memorizes the
|
| 1542 |
+
training examples, they will predict the wrong label on corresponding development set
|
| 1543 |
+
example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence
|
| 1544 |
+
between a model's score on this task and its score on the unconverted original task. We
|
| 1545 |
+
call converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3
|
| 1546 |
+
Indian languages by AI4Bharat.
|
| 1547 |
+
|
| 1548 |
+
### Supported Tasks and Leaderboards
|
| 1549 |
+
|
| 1550 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1551 |
+
|
| 1552 |
+
### Languages
|
| 1553 |
+
|
| 1554 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1555 |
+
|
| 1556 |
+
## Dataset Structure
|
| 1557 |
+
|
| 1558 |
+
### Data Instances
|
| 1559 |
+
|
| 1560 |
+
#### actsa-sc.te
|
| 1561 |
+
|
| 1562 |
+
- **Size of downloaded dataset files:** 0.36 MB
|
| 1563 |
+
- **Size of the generated dataset:** 1.63 MB
|
| 1564 |
+
- **Total amount of disk used:** 1.99 MB
|
| 1565 |
+
|
| 1566 |
+
An example of 'validation' looks as follows.
|
| 1567 |
+
```
|
| 1568 |
+
This example was too long and was cropped:
|
| 1569 |
+
|
| 1570 |
+
{
|
| 1571 |
+
"label": 0,
|
| 1572 |
+
"text": "\"ప్రయాణాల్లో ఉన్నవారికోసం బస్ స్టేషన్లు, రైల్వే స్టేషన్లలో పల్స్పోలియో బూతులను ఏర్పాటు చేసి చిన్నారులకు పోలియో చుక్కలు వేసేలా ఏర..."
|
| 1573 |
+
}
|
| 1574 |
+
```
|
| 1575 |
+
|
| 1576 |
+
#### bbca.hi
|
| 1577 |
+
|
| 1578 |
+
- **Size of downloaded dataset files:** 5.50 MB
|
| 1579 |
+
- **Size of the generated dataset:** 26.35 MB
|
| 1580 |
+
- **Total amount of disk used:** 31.85 MB
|
| 1581 |
+
|
| 1582 |
+
An example of 'train' looks as follows.
|
| 1583 |
+
```
|
| 1584 |
+
This example was too long and was cropped:
|
| 1585 |
+
|
| 1586 |
+
{
|
| 1587 |
+
"label": "pakistan",
|
| 1588 |
+
"text": "\"नेटिजन यानि इंटरनेट पर सक्रिय नागरिक अब ट्विटर पर सरकार द्वारा लगाए प्रतिबंधों के समर्थन या विरोध में अपने विचार व्यक्त करते ��ै..."
|
| 1589 |
+
}
|
| 1590 |
+
```
|
| 1591 |
+
|
| 1592 |
+
#### copa.en
|
| 1593 |
+
|
| 1594 |
+
- **Size of downloaded dataset files:** 0.72 MB
|
| 1595 |
+
- **Size of the generated dataset:** 0.11 MB
|
| 1596 |
+
- **Total amount of disk used:** 0.83 MB
|
| 1597 |
+
|
| 1598 |
+
An example of 'validation' looks as follows.
|
| 1599 |
+
```
|
| 1600 |
+
{
|
| 1601 |
+
"choice1": "I swept the floor in the unoccupied room.",
|
| 1602 |
+
"choice2": "I shut off the light in the unoccupied room.",
|
| 1603 |
+
"label": 1,
|
| 1604 |
+
"premise": "I wanted to conserve energy.",
|
| 1605 |
+
"question": "effect"
|
| 1606 |
+
}
|
| 1607 |
+
```
|
| 1608 |
+
|
| 1609 |
+
#### copa.gu
|
| 1610 |
+
|
| 1611 |
+
- **Size of downloaded dataset files:** 0.72 MB
|
| 1612 |
+
- **Size of the generated dataset:** 0.22 MB
|
| 1613 |
+
- **Total amount of disk used:** 0.94 MB
|
| 1614 |
+
|
| 1615 |
+
An example of 'train' looks as follows.
|
| 1616 |
+
```
|
| 1617 |
+
This example was too long and was cropped:
|
| 1618 |
+
|
| 1619 |
+
{
|
| 1620 |
+
"choice1": "\"સ્ત્રી જાણતી હતી કે તેનો મિત્ર મુશ્કેલ સમયમાંથી પસાર થઈ રહ્યો છે.\"...",
|
| 1621 |
+
"choice2": "\"મહિલાને લાગ્યું કે તેના મિત્રએ તેની દયાળુ લાભ લીધો છે.\"...",
|
| 1622 |
+
"label": 0,
|
| 1623 |
+
"premise": "મહિલાએ તેના મિત્રની મુશ્કેલ વર્તન સહન કરી.",
|
| 1624 |
+
"question": "cause"
|
| 1625 |
+
}
|
| 1626 |
+
```
|
| 1627 |
+
|
| 1628 |
+
#### copa.hi
|
| 1629 |
+
|
| 1630 |
+
- **Size of downloaded dataset files:** 0.72 MB
|
| 1631 |
+
- **Size of the generated dataset:** 0.22 MB
|
| 1632 |
+
- **Total amount of disk used:** 0.94 MB
|
| 1633 |
+
|
| 1634 |
+
An example of 'validation' looks as follows.
|
| 1635 |
+
```
|
| 1636 |
+
{
|
| 1637 |
+
"choice1": "मैंने उसका प्रस्ताव ठुकरा दिया।",
|
| 1638 |
+
"choice2": "उन्होंने मुझे उत्पाद खरीदने के लिए राजी किया।",
|
| 1639 |
+
"label": 0,
|
| 1640 |
+
"premise": "मैंने सेल्समैन की पिच पर शक किया।",
|
| 1641 |
+
"question": "effect"
|
| 1642 |
+
}
|
| 1643 |
+
```
|
| 1644 |
+
|
| 1645 |
+
### Data Fields
|
| 1646 |
+
|
| 1647 |
+
The data fields are the same among all splits.
|
| 1648 |
+
|
| 1649 |
+
#### actsa-sc.te
|
| 1650 |
+
- `text`: a `string` feature.
|
| 1651 |
+
- `label`: a classification label, with possible values including `positive` (0), `negative` (1).
|
| 1652 |
+
|
| 1653 |
+
#### bbca.hi
|
| 1654 |
+
- `label`: a `string` feature.
|
| 1655 |
+
- `text`: a `string` feature.
|
| 1656 |
+
|
| 1657 |
+
#### copa.en
|
| 1658 |
+
- `premise`: a `string` feature.
|
| 1659 |
+
- `choice1`: a `string` feature.
|
| 1660 |
+
- `choice2`: a `string` feature.
|
| 1661 |
+
- `question`: a `string` feature.
|
| 1662 |
+
- `label`: a `int32` feature.
|
| 1663 |
+
|
| 1664 |
+
#### copa.gu
|
| 1665 |
+
- `premise`: a `string` feature.
|
| 1666 |
+
- `choice1`: a `string` feature.
|
| 1667 |
+
- `choice2`: a `string` feature.
|
| 1668 |
+
- `question`: a `string` feature.
|
| 1669 |
+
- `label`: a `int32` feature.
|
| 1670 |
+
|
| 1671 |
+
#### copa.hi
|
| 1672 |
+
- `premise`: a `string` feature.
|
| 1673 |
+
- `choice1`: a `string` feature.
|
| 1674 |
+
- `choice2`: a `string` feature.
|
| 1675 |
+
- `question`: a `string` feature.
|
| 1676 |
+
- `label`: a `int32` feature.
|
| 1677 |
+
|
| 1678 |
+
### Data Splits
|
| 1679 |
+
|
| 1680 |
+
#### actsa-sc.te
|
| 1681 |
+
|
| 1682 |
+
| |train|validation|test|
|
| 1683 |
+
|-----------|----:|---------:|---:|
|
| 1684 |
+
|actsa-sc.te| 4328| 541| 541|
|
| 1685 |
+
|
| 1686 |
+
#### bbca.hi
|
| 1687 |
+
|
| 1688 |
+
| |train|test|
|
| 1689 |
+
|-------|----:|---:|
|
| 1690 |
+
|bbca.hi| 3467| 866|
|
| 1691 |
+
|
| 1692 |
+
#### copa.en
|
| 1693 |
+
|
| 1694 |
+
| |train|validation|test|
|
| 1695 |
+
|-------|----:|---------:|---:|
|
| 1696 |
+
|copa.en| 400| 100| 500|
|
| 1697 |
+
|
| 1698 |
+
#### copa.gu
|
| 1699 |
+
|
| 1700 |
+
| |train|validation|test|
|
| 1701 |
+
|-------|----:|---------:|---:|
|
| 1702 |
+
|copa.gu| 362| 88| 448|
|
| 1703 |
+
|
| 1704 |
+
#### copa.hi
|
| 1705 |
+
|
| 1706 |
+
| |train|validation|test|
|
| 1707 |
+
|-------|----:|---------:|---:|
|
| 1708 |
+
|copa.hi| 362| 88| 449|
|
| 1709 |
+
|
| 1710 |
+
## Dataset Creation
|
| 1711 |
+
|
| 1712 |
+
### Curation Rationale
|
| 1713 |
+
|
| 1714 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1715 |
+
|
| 1716 |
+
### Source Data
|
| 1717 |
+
|
| 1718 |
+
#### Initial Data Collection and Normalization
|
| 1719 |
+
|
| 1720 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1721 |
+
|
| 1722 |
+
#### Who are the source language producers?
|
| 1723 |
+
|
| 1724 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1725 |
+
|
| 1726 |
+
### Annotations
|
| 1727 |
+
|
| 1728 |
+
#### Annotation process
|
| 1729 |
+
|
| 1730 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1731 |
+
|
| 1732 |
+
#### Who are the annotators?
|
| 1733 |
+
|
| 1734 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1735 |
+
|
| 1736 |
+
### Personal and Sensitive Information
|
| 1737 |
+
|
| 1738 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1739 |
+
|
| 1740 |
+
## Considerations for Using the Data
|
| 1741 |
+
|
| 1742 |
+
### Social Impact of Dataset
|
| 1743 |
+
|
| 1744 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1745 |
+
|
| 1746 |
+
### Discussion of Biases
|
| 1747 |
+
|
| 1748 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1749 |
+
|
| 1750 |
+
### Other Known Limitations
|
| 1751 |
+
|
| 1752 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1753 |
+
|
| 1754 |
+
## Additional Information
|
| 1755 |
+
|
| 1756 |
+
### Dataset Curators
|
| 1757 |
+
|
| 1758 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1759 |
+
|
| 1760 |
+
### Licensing Information
|
| 1761 |
+
|
| 1762 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 1763 |
+
|
| 1764 |
+
### Citation Information
|
| 1765 |
+
|
| 1766 |
+
```
|
| 1767 |
+
@inproceedings{kakwani-etal-2020-indicnlpsuite,
|
| 1768 |
+
title = "{I}ndic{NLPS}uite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for {I}ndian Languages",
|
| 1769 |
+
author = "Kakwani, Divyanshu and
|
| 1770 |
+
Kunchukuttan, Anoop and
|
| 1771 |
+
Golla, Satish and
|
| 1772 |
+
N.C., Gokul and
|
| 1773 |
+
Bhattacharyya, Avik and
|
| 1774 |
+
Khapra, Mitesh M. and
|
| 1775 |
+
Kumar, Pratyush",
|
| 1776 |
+
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
|
| 1777 |
+
month = nov,
|
| 1778 |
+
year = "2020",
|
| 1779 |
+
address = "Online",
|
| 1780 |
+
publisher = "Association for Computational Linguistics",
|
| 1781 |
+
url = "https://aclanthology.org/2020.findings-emnlp.445",
|
| 1782 |
+
doi = "10.18653/v1/2020.findings-emnlp.445",
|
| 1783 |
+
pages = "4948--4961",
|
| 1784 |
+
}
|
| 1785 |
+
|
| 1786 |
+
@inproceedings{Levesque2011TheWS,
|
| 1787 |
+
title={The Winograd Schema Challenge},
|
| 1788 |
+
author={H. Levesque and E. Davis and L. Morgenstern},
|
| 1789 |
+
booktitle={KR},
|
| 1790 |
+
year={2011}
|
| 1791 |
+
}
|
| 1792 |
+
```
|
| 1793 |
+
|
| 1794 |
+
### Contributions
|
| 1795 |
+
|
| 1796 |
+
Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset.
|
huggingface_dataset/Dataset_Card/johnowhitaker_vqgan1024_encs_sf.md
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Images from CC12M encoded with VQGAN f16 1024
|
| 2 |
+
|
| 3 |
+
Script to continue prep is included in the repo if you want more than the ~1.5M images I did here.
|
| 4 |
+
|
| 5 |
+
VQGAN model:
|
| 6 |
+
```
|
| 7 |
+
!curl -L 'https://heibox.uni-heidelberg.de/d/8088892a516d4e3baf92/files/?p=%2Fckpts%2Flast.ckpt&dl=1' > vqgan_im1024.ckpt
|
| 8 |
+
!curl -L 'https://heibox.uni-heidelberg.de/d/8088892a516d4e3baf92/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1' > vqgan_im1024.yaml
|
| 9 |
+
```
|
| 10 |
+
|
| 11 |
+
Try it out: TODO
|
huggingface_dataset/Dataset_Card/jordanparker6_publaynet.md
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
title: PubLayNet
|
| 3 |
+
license: other
|
| 4 |
+
annotations_creators: []
|
| 5 |
+
language:
|
| 6 |
+
- en
|
| 7 |
+
size_categories:
|
| 8 |
+
- 100B<n<1T
|
| 9 |
+
source_datasets: []
|
| 10 |
+
task_categories:
|
| 11 |
+
- image-to-text
|
| 12 |
+
task_ids: []
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+
# PubLayNet
|
| 16 |
+
|
| 17 |
+
PubLayNet is a large dataset of document images, of which the layout is annotated with both bounding boxes and polygonal segmentations. The source of the documents is [PubMed Central Open Access Subset (commercial use collection)](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). The annotations are automatically generated by matching the PDF format and the XML format of the articles in the PubMed Central Open Access Subset. More details are available in our paper ["PubLayNet: largest dataset ever for document layout analysis."](https://arxiv.org/abs/1908.07836).
|
| 18 |
+
|
| 19 |
+
The public dataset is in tar.gz format which doesn't fit nicely with huggingface streaming. Modifications have been made to optimise the delivery of the dataset for the hugginface datset api. The original files can be found [here](https://developer.ibm.com/exchanges/data/all/publaynet/).
|
| 20 |
+
|
| 21 |
+
Licence: [Community Data License Agreement – Permissive – Version 1.0 License](https://cdla.dev/permissive-1-0/)
|
| 22 |
+
|
| 23 |
+
Author: IBM
|
| 24 |
+
|
| 25 |
+
GitHub: https://github.com/ibm-aur-nlp/PubLayNet
|
| 26 |
+
|
| 27 |
+
@article{ zhong2019publaynet,
|
| 28 |
+
title = { PubLayNet: largest dataset ever for document layout analysis },
|
| 29 |
+
author = { Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno },
|
| 30 |
+
journal = { arXiv preprint arXiv:1908.07836},
|
| 31 |
+
year. = { 2019 }
|
| 32 |
+
}
|
huggingface_dataset/Dataset_Card/julien-c_reactiongif.md
ADDED
|
@@ -0,0 +1,57 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- crowdsourced
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 10K<n<100K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- text-classification
|
| 18 |
+
task_ids:
|
| 19 |
+
- sentiment-classification
|
| 20 |
+
paperswithcode_id: reactiongif
|
| 21 |
+
---
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
## ReactionGIF
|
| 25 |
+
|
| 26 |
+
> From https://github.com/bshmueli/ReactionGIF
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
___
|
| 32 |
+
|
| 33 |
+
## Excerpt from original repo readme
|
| 34 |
+
|
| 35 |
+
ReactionGIF is a unique, first-of-its-kind dataset of 30K sarcastic tweets and their GIF reactions.
|
| 36 |
+
|
| 37 |
+
To find out more about ReactionGIF,
|
| 38 |
+
check out our ACL 2021 paper:
|
| 39 |
+
|
| 40 |
+
* Shmueli, Ray and Ku, [Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter](https://arxiv.org/abs/2105.09967)
|
| 41 |
+
|
| 42 |
+
|
| 43 |
+
## Citation
|
| 44 |
+
|
| 45 |
+
If you use our dataset, kindly cite the paper using the following BibTex entry:
|
| 46 |
+
|
| 47 |
+
```bibtex
|
| 48 |
+
@misc{shmueli2021happy,
|
| 49 |
+
title={Happy Dance, Slow Clap: Using Reaction {GIFs} to Predict Induced Affect on {Twitter}},
|
| 50 |
+
author={Boaz Shmueli and Soumya Ray and Lun-Wei Ku},
|
| 51 |
+
year={2021},
|
| 52 |
+
eprint={2105.09967},
|
| 53 |
+
archivePrefix={arXiv},
|
| 54 |
+
primaryClass={cs.CL}
|
| 55 |
+
}
|
| 56 |
+
```
|
| 57 |
+
|
huggingface_dataset/Dataset_Card/kor_sae.md
ADDED
|
@@ -0,0 +1,181 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- expert-generated
|
| 6 |
+
language:
|
| 7 |
+
- ko
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-sa-4.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 10K<n<100K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- text-classification
|
| 18 |
+
task_ids:
|
| 19 |
+
- intent-classification
|
| 20 |
+
pretty_name: Structured Argument Extraction for Korean
|
| 21 |
+
dataset_info:
|
| 22 |
+
features:
|
| 23 |
+
- name: intent_pair1
|
| 24 |
+
dtype: string
|
| 25 |
+
- name: intent_pair2
|
| 26 |
+
dtype: string
|
| 27 |
+
- name: label
|
| 28 |
+
dtype:
|
| 29 |
+
class_label:
|
| 30 |
+
names:
|
| 31 |
+
'0': yes/no
|
| 32 |
+
'1': alternative
|
| 33 |
+
'2': wh- questions
|
| 34 |
+
'3': prohibitions
|
| 35 |
+
'4': requirements
|
| 36 |
+
'5': strong requirements
|
| 37 |
+
splits:
|
| 38 |
+
- name: train
|
| 39 |
+
num_bytes: 2885167
|
| 40 |
+
num_examples: 30837
|
| 41 |
+
download_size: 2545926
|
| 42 |
+
dataset_size: 2885167
|
| 43 |
+
---
|
| 44 |
+
|
| 45 |
+
# Dataset Card for Structured Argument Extraction for Korean
|
| 46 |
+
|
| 47 |
+
## Table of Contents
|
| 48 |
+
- [Dataset Description](#dataset-description)
|
| 49 |
+
- [Dataset Summary](#dataset-summary)
|
| 50 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 51 |
+
- [Languages](#languages)
|
| 52 |
+
- [Dataset Structure](#dataset-structure)
|
| 53 |
+
- [Data Instances](#data-instances)
|
| 54 |
+
- [Data Fields](#data-fields)
|
| 55 |
+
- [Data Splits](#data-splits)
|
| 56 |
+
- [Dataset Creation](#dataset-creation)
|
| 57 |
+
- [Curation Rationale](#curation-rationale)
|
| 58 |
+
- [Source Data](#source-data)
|
| 59 |
+
- [Annotations](#annotations)
|
| 60 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 61 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 62 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 63 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 64 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 65 |
+
- [Additional Information](#additional-information)
|
| 66 |
+
- [Dataset Curators](#dataset-curators)
|
| 67 |
+
- [Licensing Information](#licensing-information)
|
| 68 |
+
- [Citation Information](#citation-information)
|
| 69 |
+
- [Contributions](#contributions)
|
| 70 |
+
|
| 71 |
+
## Dataset Description
|
| 72 |
+
|
| 73 |
+
- **Homepage:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
|
| 74 |
+
- **Repository:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
|
| 75 |
+
- **Paper:** [Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives](https://arxiv.org/abs/1912.00342)
|
| 76 |
+
- **Point of Contact:** [Won Ik Cho](wicho@hi.snu.ac.kr)
|
| 77 |
+
|
| 78 |
+
### Dataset Summary
|
| 79 |
+
|
| 80 |
+
The Structured Argument Extraction for Korean dataset is a set of question-argument and command-argument pairs with their respective question type label and negativeness label. Often times, agents like Alexa or Siri, encounter conversations without a clear objective from the user. The goal of this dataset is to extract the intent argument of a given utterance pair without a clear directive. This may yield a more robust agent capable of parsing more non-canonical forms of speech.
|
| 81 |
+
|
| 82 |
+
### Supported Tasks and Leaderboards
|
| 83 |
+
|
| 84 |
+
* `intent_classification`: The dataset can be trained with a Transformer like [BERT](https://huggingface.co/bert-base-uncased) to classify the intent argument or a question/command pair in Korean, and it's performance can be measured by it's BERTScore.
|
| 85 |
+
|
| 86 |
+
### Languages
|
| 87 |
+
|
| 88 |
+
The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.
|
| 89 |
+
|
| 90 |
+
## Dataset Structure
|
| 91 |
+
|
| 92 |
+
### Data Instances
|
| 93 |
+
|
| 94 |
+
An example data instance contains a question or command pair and its label:
|
| 95 |
+
|
| 96 |
+
```
|
| 97 |
+
{
|
| 98 |
+
"intent_pair1": "내일 오후 다섯시 조별과제 일정 추가해줘"
|
| 99 |
+
"intent_pair2": "내일 오후 다섯시 조별과제 일정 추가하기"
|
| 100 |
+
"label": 4
|
| 101 |
+
}
|
| 102 |
+
```
|
| 103 |
+
|
| 104 |
+
### Data Fields
|
| 105 |
+
|
| 106 |
+
* `intent_pair1`: a question/command pair
|
| 107 |
+
* `intent_pair2`: a corresponding question/command pair
|
| 108 |
+
* `label`: determines the intent argument of the pair and can be one of `yes/no` (0), `alternative` (1), `wh- questions` (2), `prohibitions` (3), `requirements` (4) and `strong requirements` (5)
|
| 109 |
+
|
| 110 |
+
### Data Splits
|
| 111 |
+
|
| 112 |
+
The corpus contains 30,837 examples.
|
| 113 |
+
|
| 114 |
+
## Dataset Creation
|
| 115 |
+
|
| 116 |
+
### Curation Rationale
|
| 117 |
+
|
| 118 |
+
The Structured Argument Extraction for Korean dataset was curated to help train models extract intent arguments from utterances without a clear objective or when the user uses non-canonical forms of speech. This is especially helpful in Korean because in English, the `Who, what, where, when and why` usually comes in the beginning, but this isn't necessarily the case in the Korean language. So for low-resource languages, this lack of data can be a bottleneck for comprehension performance.
|
| 119 |
+
|
| 120 |
+
### Source Data
|
| 121 |
+
|
| 122 |
+
#### Initial Data Collection and Normalization
|
| 123 |
+
|
| 124 |
+
The corpus was taken from the one constructed by [Cho et al.](https://arxiv.org/abs/1811.04231), a Korean single utterance corpus for identifying directives/non-directives that contains a wide variety of non-canonical directives.
|
| 125 |
+
|
| 126 |
+
#### Who are the source language producers?
|
| 127 |
+
|
| 128 |
+
Korean speakers are the source language producers.
|
| 129 |
+
|
| 130 |
+
### Annotations
|
| 131 |
+
|
| 132 |
+
#### Annotation process
|
| 133 |
+
|
| 134 |
+
Utterances were categorized as question or command arguments and then further classified according to their intent argument.
|
| 135 |
+
|
| 136 |
+
#### Who are the annotators?
|
| 137 |
+
|
| 138 |
+
The annotation was done by three Korean natives with a background in computational linguistics.
|
| 139 |
+
|
| 140 |
+
### Personal and Sensitive Information
|
| 141 |
+
|
| 142 |
+
[More Information Needed]
|
| 143 |
+
|
| 144 |
+
## Considerations for Using the Data
|
| 145 |
+
|
| 146 |
+
### Social Impact of Dataset
|
| 147 |
+
|
| 148 |
+
[More Information Needed]
|
| 149 |
+
|
| 150 |
+
### Discussion of Biases
|
| 151 |
+
|
| 152 |
+
[More Information Needed]
|
| 153 |
+
|
| 154 |
+
### Other Known Limitations
|
| 155 |
+
|
| 156 |
+
[More Information Needed]
|
| 157 |
+
|
| 158 |
+
## Additional Information
|
| 159 |
+
|
| 160 |
+
### Dataset Curators
|
| 161 |
+
|
| 162 |
+
The dataset is curated by Won Ik Cho, Young Ki Moon, Sangwhan Moon, Seok Min Kim and Nam Soo Kim.
|
| 163 |
+
|
| 164 |
+
### Licensing Information
|
| 165 |
+
|
| 166 |
+
The dataset is licensed under the CC BY-SA-4.0.
|
| 167 |
+
|
| 168 |
+
### Citation Information
|
| 169 |
+
|
| 170 |
+
```
|
| 171 |
+
@article{cho2019machines,
|
| 172 |
+
title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
|
| 173 |
+
author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
|
| 174 |
+
journal={arXiv preprint arXiv:1912.00342},
|
| 175 |
+
year={2019}
|
| 176 |
+
}
|
| 177 |
+
```
|
| 178 |
+
|
| 179 |
+
### Contributions
|
| 180 |
+
|
| 181 |
+
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.
|
huggingface_dataset/Dataset_Card/multi_woz_v22.md
ADDED
|
@@ -0,0 +1,453 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- machine-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
- machine-generated
|
| 7 |
+
language:
|
| 8 |
+
- en
|
| 9 |
+
license:
|
| 10 |
+
- apache-2.0
|
| 11 |
+
multilinguality:
|
| 12 |
+
- monolingual
|
| 13 |
+
size_categories:
|
| 14 |
+
- 10K<n<100K
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
task_categories:
|
| 18 |
+
- text-generation
|
| 19 |
+
- fill-mask
|
| 20 |
+
- token-classification
|
| 21 |
+
- text-classification
|
| 22 |
+
task_ids:
|
| 23 |
+
- dialogue-modeling
|
| 24 |
+
- multi-class-classification
|
| 25 |
+
- parsing
|
| 26 |
+
paperswithcode_id: multiwoz
|
| 27 |
+
pretty_name: Multi-domain Wizard-of-Oz
|
| 28 |
+
dataset_info:
|
| 29 |
+
- config_name: v2.2
|
| 30 |
+
features:
|
| 31 |
+
- name: dialogue_id
|
| 32 |
+
dtype: string
|
| 33 |
+
- name: services
|
| 34 |
+
sequence: string
|
| 35 |
+
- name: turns
|
| 36 |
+
sequence:
|
| 37 |
+
- name: turn_id
|
| 38 |
+
dtype: string
|
| 39 |
+
- name: speaker
|
| 40 |
+
dtype:
|
| 41 |
+
class_label:
|
| 42 |
+
names:
|
| 43 |
+
'0': USER
|
| 44 |
+
'1': SYSTEM
|
| 45 |
+
- name: utterance
|
| 46 |
+
dtype: string
|
| 47 |
+
- name: frames
|
| 48 |
+
sequence:
|
| 49 |
+
- name: service
|
| 50 |
+
dtype: string
|
| 51 |
+
- name: state
|
| 52 |
+
struct:
|
| 53 |
+
- name: active_intent
|
| 54 |
+
dtype: string
|
| 55 |
+
- name: requested_slots
|
| 56 |
+
sequence: string
|
| 57 |
+
- name: slots_values
|
| 58 |
+
sequence:
|
| 59 |
+
- name: slots_values_name
|
| 60 |
+
dtype: string
|
| 61 |
+
- name: slots_values_list
|
| 62 |
+
sequence: string
|
| 63 |
+
- name: slots
|
| 64 |
+
sequence:
|
| 65 |
+
- name: slot
|
| 66 |
+
dtype: string
|
| 67 |
+
- name: value
|
| 68 |
+
dtype: string
|
| 69 |
+
- name: start
|
| 70 |
+
dtype: int32
|
| 71 |
+
- name: exclusive_end
|
| 72 |
+
dtype: int32
|
| 73 |
+
- name: copy_from
|
| 74 |
+
dtype: string
|
| 75 |
+
- name: copy_from_value
|
| 76 |
+
sequence: string
|
| 77 |
+
- name: dialogue_acts
|
| 78 |
+
struct:
|
| 79 |
+
- name: dialog_act
|
| 80 |
+
sequence:
|
| 81 |
+
- name: act_type
|
| 82 |
+
dtype: string
|
| 83 |
+
- name: act_slots
|
| 84 |
+
sequence:
|
| 85 |
+
- name: slot_name
|
| 86 |
+
dtype: string
|
| 87 |
+
- name: slot_value
|
| 88 |
+
dtype: string
|
| 89 |
+
- name: span_info
|
| 90 |
+
sequence:
|
| 91 |
+
- name: act_type
|
| 92 |
+
dtype: string
|
| 93 |
+
- name: act_slot_name
|
| 94 |
+
dtype: string
|
| 95 |
+
- name: act_slot_value
|
| 96 |
+
dtype: string
|
| 97 |
+
- name: span_start
|
| 98 |
+
dtype: int32
|
| 99 |
+
- name: span_end
|
| 100 |
+
dtype: int32
|
| 101 |
+
splits:
|
| 102 |
+
- name: train
|
| 103 |
+
num_bytes: 68222649
|
| 104 |
+
num_examples: 8437
|
| 105 |
+
- name: validation
|
| 106 |
+
num_bytes: 8990945
|
| 107 |
+
num_examples: 1000
|
| 108 |
+
- name: test
|
| 109 |
+
num_bytes: 9027095
|
| 110 |
+
num_examples: 1000
|
| 111 |
+
download_size: 276592909
|
| 112 |
+
dataset_size: 86240689
|
| 113 |
+
- config_name: v2.2_active_only
|
| 114 |
+
features:
|
| 115 |
+
- name: dialogue_id
|
| 116 |
+
dtype: string
|
| 117 |
+
- name: services
|
| 118 |
+
sequence: string
|
| 119 |
+
- name: turns
|
| 120 |
+
sequence:
|
| 121 |
+
- name: turn_id
|
| 122 |
+
dtype: string
|
| 123 |
+
- name: speaker
|
| 124 |
+
dtype:
|
| 125 |
+
class_label:
|
| 126 |
+
names:
|
| 127 |
+
'0': USER
|
| 128 |
+
'1': SYSTEM
|
| 129 |
+
- name: utterance
|
| 130 |
+
dtype: string
|
| 131 |
+
- name: frames
|
| 132 |
+
sequence:
|
| 133 |
+
- name: service
|
| 134 |
+
dtype: string
|
| 135 |
+
- name: state
|
| 136 |
+
struct:
|
| 137 |
+
- name: active_intent
|
| 138 |
+
dtype: string
|
| 139 |
+
- name: requested_slots
|
| 140 |
+
sequence: string
|
| 141 |
+
- name: slots_values
|
| 142 |
+
sequence:
|
| 143 |
+
- name: slots_values_name
|
| 144 |
+
dtype: string
|
| 145 |
+
- name: slots_values_list
|
| 146 |
+
sequence: string
|
| 147 |
+
- name: slots
|
| 148 |
+
sequence:
|
| 149 |
+
- name: slot
|
| 150 |
+
dtype: string
|
| 151 |
+
- name: value
|
| 152 |
+
dtype: string
|
| 153 |
+
- name: start
|
| 154 |
+
dtype: int32
|
| 155 |
+
- name: exclusive_end
|
| 156 |
+
dtype: int32
|
| 157 |
+
- name: copy_from
|
| 158 |
+
dtype: string
|
| 159 |
+
- name: copy_from_value
|
| 160 |
+
sequence: string
|
| 161 |
+
- name: dialogue_acts
|
| 162 |
+
struct:
|
| 163 |
+
- name: dialog_act
|
| 164 |
+
sequence:
|
| 165 |
+
- name: act_type
|
| 166 |
+
dtype: string
|
| 167 |
+
- name: act_slots
|
| 168 |
+
sequence:
|
| 169 |
+
- name: slot_name
|
| 170 |
+
dtype: string
|
| 171 |
+
- name: slot_value
|
| 172 |
+
dtype: string
|
| 173 |
+
- name: span_info
|
| 174 |
+
sequence:
|
| 175 |
+
- name: act_type
|
| 176 |
+
dtype: string
|
| 177 |
+
- name: act_slot_name
|
| 178 |
+
dtype: string
|
| 179 |
+
- name: act_slot_value
|
| 180 |
+
dtype: string
|
| 181 |
+
- name: span_start
|
| 182 |
+
dtype: int32
|
| 183 |
+
- name: span_end
|
| 184 |
+
dtype: int32
|
| 185 |
+
splits:
|
| 186 |
+
- name: train
|
| 187 |
+
num_bytes: 40937577
|
| 188 |
+
num_examples: 8437
|
| 189 |
+
- name: validation
|
| 190 |
+
num_bytes: 5377939
|
| 191 |
+
num_examples: 1000
|
| 192 |
+
- name: test
|
| 193 |
+
num_bytes: 5410819
|
| 194 |
+
num_examples: 1000
|
| 195 |
+
download_size: 276592909
|
| 196 |
+
dataset_size: 51726335
|
| 197 |
+
---
|
| 198 |
+
|
| 199 |
+
# Dataset Card for MultiWOZ
|
| 200 |
+
|
| 201 |
+
## Table of Contents
|
| 202 |
+
- [Dataset Description](#dataset-description)
|
| 203 |
+
- [Dataset Summary](#dataset-summary)
|
| 204 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 205 |
+
- [Languages](#languages)
|
| 206 |
+
- [Dataset Structure](#dataset-structure)
|
| 207 |
+
- [Data Instances](#data-instances)
|
| 208 |
+
- [Data Fields](#data-fields)
|
| 209 |
+
- [Data Splits](#data-splits)
|
| 210 |
+
- [Dataset Creation](#dataset-creation)
|
| 211 |
+
- [Curation Rationale](#curation-rationale)
|
| 212 |
+
- [Source Data](#source-data)
|
| 213 |
+
- [Annotations](#annotations)
|
| 214 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 215 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 216 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 217 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 218 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 219 |
+
- [Additional Information](#additional-information)
|
| 220 |
+
- [Dataset Curators](#dataset-curators)
|
| 221 |
+
- [Licensing Information](#licensing-information)
|
| 222 |
+
- [Citation Information](#citation-information)
|
| 223 |
+
- [Contributions](#contributions)
|
| 224 |
+
|
| 225 |
+
## Dataset Description
|
| 226 |
+
|
| 227 |
+
- **Repository:** [MultiWOZ 2.2 github repository](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2)
|
| 228 |
+
- **Paper:** [MultiWOZ v2](https://arxiv.org/abs/1810.00278), and [MultiWOZ v2.2](https://www.aclweb.org/anthology/2020.nlp4convai-1.13.pdf)
|
| 229 |
+
- **Point of Contact:** [Paweł Budzianowski](pfb30@cam.ac.uk)
|
| 230 |
+
|
| 231 |
+
### Dataset Summary
|
| 232 |
+
|
| 233 |
+
Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
|
| 234 |
+
MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an
|
| 235 |
+
improved version of the dataset. MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fixes dialogue state annotation errors
|
| 236 |
+
across 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values
|
| 237 |
+
(e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots.
|
| 238 |
+
|
| 239 |
+
### Supported Tasks and Leaderboards
|
| 240 |
+
|
| 241 |
+
This dataset supports a range of task.
|
| 242 |
+
- **Generative dialogue modeling** or `dialogue-modeling`: the text of the dialogues can be used to train a sequence model on the utterances. Performance on this task is typically evaluated with delexicalized-[BLEU](https://huggingface.co/metrics/bleu), inform rate and request success.
|
| 243 |
+
- **Intent state tracking**, a `multi-class-classification` task: predict the belief state of the user side of the conversation, performance is measured by [F1](https://huggingface.co/metrics/f1).
|
| 244 |
+
- **Dialog act prediction**, a `parsing` task: parse an utterance into the corresponding dialog acts for the system to use. [F1](https://huggingface.co/metrics/f1) is typically reported.
|
| 245 |
+
|
| 246 |
+
### Languages
|
| 247 |
+
|
| 248 |
+
The text in the dataset is in English (`en`).
|
| 249 |
+
|
| 250 |
+
## Dataset Structure
|
| 251 |
+
|
| 252 |
+
### Data Instances
|
| 253 |
+
|
| 254 |
+
A data instance is a full multi-turn dialogue between a `USER` and a `SYSTEM`. Each turn has a single utterance, e.g.:
|
| 255 |
+
```
|
| 256 |
+
['What fun places can I visit in the East?',
|
| 257 |
+
'We have five spots which include boating, museums and entertainment. Any preferences that you have?']
|
| 258 |
+
```
|
| 259 |
+
The utterances of the `USER` are also annotated with frames denoting their intent and believe state:
|
| 260 |
+
```
|
| 261 |
+
[{'service': ['attraction'],
|
| 262 |
+
'slots': [{'copy_from': [],
|
| 263 |
+
'copy_from_value': [],
|
| 264 |
+
'exclusive_end': [],
|
| 265 |
+
'slot': [],
|
| 266 |
+
'start': [],
|
| 267 |
+
'value': []}],
|
| 268 |
+
'state': [{'active_intent': 'find_attraction',
|
| 269 |
+
'requested_slots': [],
|
| 270 |
+
'slots_values': {'slots_values_list': [['east']],
|
| 271 |
+
'slots_values_name': ['attraction-area']}}]},
|
| 272 |
+
{'service': [], 'slots': [], 'state': []}]
|
| 273 |
+
```
|
| 274 |
+
Finally, each of the utterances is annotated with dialog acts which provide a structured representation of what the `USER` or `SYSTEM` is inquiring or giving information about.
|
| 275 |
+
```
|
| 276 |
+
[{'dialog_act': {'act_slots': [{'slot_name': ['east'],
|
| 277 |
+
'slot_value': ['area']}],
|
| 278 |
+
'act_type': ['Attraction-Inform']},
|
| 279 |
+
'span_info': {'act_slot_name': ['area'],
|
| 280 |
+
'act_slot_value': ['east'],
|
| 281 |
+
'act_type': ['Attraction-Inform'],
|
| 282 |
+
'span_end': [39],
|
| 283 |
+
'span_start': [35]}},
|
| 284 |
+
{'dialog_act': {'act_slots': [{'slot_name': ['none'], 'slot_value': ['none']},
|
| 285 |
+
{'slot_name': ['boating', 'museums', 'entertainment', 'five'],
|
| 286 |
+
'slot_value': ['type', 'type', 'type', 'choice']}],
|
| 287 |
+
'act_type': ['Attraction-Select', 'Attraction-Inform']},
|
| 288 |
+
'span_info': {'act_slot_name': ['type', 'type', 'type', 'choice'],
|
| 289 |
+
'act_slot_value': ['boating', 'museums', 'entertainment', 'five'],
|
| 290 |
+
'act_type': ['Attraction-Inform',
|
| 291 |
+
'Attraction-Inform',
|
| 292 |
+
'Attraction-Inform',
|
| 293 |
+
'Attraction-Inform'],
|
| 294 |
+
'span_end': [40, 49, 67, 12],
|
| 295 |
+
'span_start': [33, 42, 54, 8]}}]
|
| 296 |
+
```
|
| 297 |
+
|
| 298 |
+
### Data Fields
|
| 299 |
+
|
| 300 |
+
Each dialogue instance has the following fields:
|
| 301 |
+
- `dialogue_id`: a unique ID identifying the dialog. The MUL and PMUL names refer to strictly multi domain dialogues (at least 2 main domains are involved) while the SNG, SSNG and WOZ names refer to single domain dialogues with potentially sub-domains like booking.
|
| 302 |
+
- `services`: a list of services mentioned in the dialog, such as `train` or `hospitals`.
|
| 303 |
+
- `turns`: the sequence of utterances with their annotations, including:
|
| 304 |
+
- `turn_id`: a turn identifier, unique per dialog.
|
| 305 |
+
- `speaker`: either the `USER` or `SYSTEM`.
|
| 306 |
+
- `utterance`: the text of the utterance.
|
| 307 |
+
- `dialogue_acts`: The structured parse of the utterance into dialog acts in the system's grammar
|
| 308 |
+
- `act_type`: Such as e.g. `Attraction-Inform` to seek or provide information about an `attraction`
|
| 309 |
+
- `act_slots`: provide more details about the action
|
| 310 |
+
- `span_info`: maps these `act_slots` to the `utterance` text.
|
| 311 |
+
- `frames`: only for `USER` utterances, track the user's belief state, i.e. a structured representation of what they are trying to achieve in the fialog. This decomposes into:
|
| 312 |
+
- `service`: the service they are interested in
|
| 313 |
+
- `state`: their belief state including their `active_intent` and further information expressed in `requested_slots`
|
| 314 |
+
- `slots`: a mapping of the `requested_slots` to where they are mentioned in the text. It takes one of two forms, detailed next:
|
| 315 |
+
The first type are span annotations that identify the location where slot values have been mentioned in the utterances for non-categorical slots. These span annotations are represented as follows:
|
| 316 |
+
```
|
| 317 |
+
{
|
| 318 |
+
"slots": [
|
| 319 |
+
{
|
| 320 |
+
"slot": String of slot name.
|
| 321 |
+
"start": Int denoting the index of the starting character in the utterance corresponding to the slot value.
|
| 322 |
+
"exclusive_end": Int denoting the index of the character just after the last character corresponding to the slot value in the utterance. In python, utterance[start:exclusive_end] gives the slot value.
|
| 323 |
+
"value": String of value. It equals to utterance[start:exclusive_end], where utterance is the current utterance in string.
|
| 324 |
+
}
|
| 325 |
+
]
|
| 326 |
+
}
|
| 327 |
+
```
|
| 328 |
+
There are also some non-categorical slots whose values are carried over from another slot in the dialogue state. Their values don"t explicitly appear in the utterances. For example, a user utterance can be "I also need a taxi from the restaurant to the hotel.", in which the state values of "taxi-departure" and "taxi-destination" are respectively carried over from that of "restaurant-name" and "hotel-name". For these slots, instead of annotating them as spans, a "copy from" annotation identifies the slot it copies the value from. This annotation is formatted as follows,
|
| 329 |
+
```
|
| 330 |
+
{
|
| 331 |
+
"slots": [
|
| 332 |
+
{
|
| 333 |
+
"slot": Slot name string.
|
| 334 |
+
"copy_from": The slot to copy from.
|
| 335 |
+
"value": A list of slot values being . It corresponds to the state values of the "copy_from" slot.
|
| 336 |
+
}
|
| 337 |
+
]
|
| 338 |
+
}
|
| 339 |
+
```
|
| 340 |
+
|
| 341 |
+
### Data Splits
|
| 342 |
+
|
| 343 |
+
The dataset is split into a `train`, `validation`, and `test` split with the following sizes:
|
| 344 |
+
|
| 345 |
+
| | train | validation | test |
|
| 346 |
+
|---------------------|------:|-----------:|-----:|
|
| 347 |
+
| Number of dialogues | 8438 | 1000 | 1000 |
|
| 348 |
+
| Number of turns | 42190 | 5000 | 5000 |
|
| 349 |
+
|
| 350 |
+
## Dataset Creation
|
| 351 |
+
|
| 352 |
+
### Curation Rationale
|
| 353 |
+
|
| 354 |
+
[More Information Needed]
|
| 355 |
+
|
| 356 |
+
### Source Data
|
| 357 |
+
|
| 358 |
+
#### Initial Data Collection and Normalization
|
| 359 |
+
|
| 360 |
+
[More Information Needed]
|
| 361 |
+
|
| 362 |
+
#### Who are the source language producers?
|
| 363 |
+
|
| 364 |
+
[More Information Needed]
|
| 365 |
+
|
| 366 |
+
### Annotations
|
| 367 |
+
|
| 368 |
+
#### Annotation process
|
| 369 |
+
|
| 370 |
+
[More Information Needed]
|
| 371 |
+
|
| 372 |
+
#### Who are the annotators?
|
| 373 |
+
|
| 374 |
+
[More Information Needed]
|
| 375 |
+
|
| 376 |
+
### Personal and Sensitive Information
|
| 377 |
+
|
| 378 |
+
[More Information Needed]
|
| 379 |
+
|
| 380 |
+
## Considerations for Using the Data
|
| 381 |
+
|
| 382 |
+
### Social Impact of Dataset
|
| 383 |
+
|
| 384 |
+
[More Information Needed]
|
| 385 |
+
|
| 386 |
+
### Discussion of Biases
|
| 387 |
+
|
| 388 |
+
[More Information Needed]
|
| 389 |
+
|
| 390 |
+
### Other Known Limitations
|
| 391 |
+
|
| 392 |
+
[More Information Needed]
|
| 393 |
+
|
| 394 |
+
## Additional Information
|
| 395 |
+
|
| 396 |
+
### Dataset Curators
|
| 397 |
+
|
| 398 |
+
The initial dataset (Versions 1.0 and 2.0) was created by a team of researchers from the [Cambridge Dialogue Systems Group](https://mi.eng.cam.ac.uk/research/dialogue/corpora/). Version 2.1 was developed on top of v2.0 by a team from Amazon, and v2.2 was developed by a team of Google researchers.
|
| 399 |
+
|
| 400 |
+
### Licensing Information
|
| 401 |
+
|
| 402 |
+
The dataset is released under the Apache License 2.0.
|
| 403 |
+
|
| 404 |
+
### Citation Information
|
| 405 |
+
|
| 406 |
+
You can cite the following for the various versions of MultiWOZ:
|
| 407 |
+
|
| 408 |
+
Version 1.0
|
| 409 |
+
```
|
| 410 |
+
@inproceedings{ramadan2018large,
|
| 411 |
+
title={Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing},
|
| 412 |
+
author={Ramadan, Osman and Budzianowski, Pawe{\l} and Gasic, Milica},
|
| 413 |
+
booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics},
|
| 414 |
+
volume={2},
|
| 415 |
+
pages={432--437},
|
| 416 |
+
year={2018}
|
| 417 |
+
}
|
| 418 |
+
```
|
| 419 |
+
|
| 420 |
+
Version 2.0
|
| 421 |
+
```
|
| 422 |
+
@inproceedings{budzianowski2018large,
|
| 423 |
+
Author = {Budzianowski, Pawe{\l} and Wen, Tsung-Hsien and Tseng, Bo-Hsiang and Casanueva, I{\~n}igo and Ultes Stefan and Ramadan Osman and Ga{\v{s}}i\'c, Milica},
|
| 424 |
+
title={MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling},
|
| 425 |
+
booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
|
| 426 |
+
year={2018}
|
| 427 |
+
}
|
| 428 |
+
```
|
| 429 |
+
|
| 430 |
+
Version 2.1
|
| 431 |
+
```
|
| 432 |
+
@article{eric2019multiwoz,
|
| 433 |
+
title={MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines},
|
| 434 |
+
author={Eric, Mihail and Goel, Rahul and Paul, Shachi and Sethi, Abhishek and Agarwal, Sanchit and Gao, Shuyag and Hakkani-Tur, Dilek},
|
| 435 |
+
journal={arXiv preprint arXiv:1907.01669},
|
| 436 |
+
year={2019}
|
| 437 |
+
}
|
| 438 |
+
```
|
| 439 |
+
|
| 440 |
+
Version 2.2
|
| 441 |
+
```
|
| 442 |
+
@inproceedings{zang2020multiwoz,
|
| 443 |
+
title={MultiWOZ 2.2: A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines},
|
| 444 |
+
author={Zang, Xiaoxue and Rastogi, Abhinav and Sunkara, Srinivas and Gupta, Raghav and Zhang, Jianguo and Chen, Jindong},
|
| 445 |
+
booktitle={Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, ACL 2020},
|
| 446 |
+
pages={109--117},
|
| 447 |
+
year={2020}
|
| 448 |
+
}
|
| 449 |
+
```
|
| 450 |
+
|
| 451 |
+
### Contributions
|
| 452 |
+
|
| 453 |
+
Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
|
huggingface_dataset/Dataset_Card/rocca_top-reddit-posts.md
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
The `post-data-by-subreddit.tar` file contains 5000 gzipped json files - one for each of the top 5000 subreddits (as roughly measured by subscriber count and comment activity). Each of those json files (e.g. `askreddit.json`) contains an array of the data for the top 1000 posts of all time.
|
| 6 |
+
|
| 7 |
+
Notes:
|
| 8 |
+
* I stopped crawling a subreddit's top-posts list if I reached a batch that had a post with a score less than 5, so some subreddits won't have the full 1000 posts.
|
| 9 |
+
* No posts comments are included. Only the posts themselves.
|
| 10 |
+
* See the example file `askreddit.json` in this repo if you want to see what you're getting before downloading all the data.
|
| 11 |
+
* The list of subreddits included are listed in `top-5k-subreddits.json`.
|
| 12 |
+
* NSFW subreddits have been included in the crawl, so you might have to filter them out depending on your use case.
|
| 13 |
+
* The Deno scraping/crawling script is included as `crawl.js`, and can be started with `deno run --allow-net --allow-read=. --allow-write=. crawl.js` once you've [installed Deno](https://deno.land/manual/getting_started/installation) and have downloaded `top-5k-subreddits.json` into the same folder as `crawl.js`.
|
huggingface_dataset/Dataset_Card/ruanchaves_stan_small.md
ADDED
|
@@ -0,0 +1,106 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- machine-generated
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- unknown
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- structure-prediction
|
| 18 |
+
- conditional-text-generation
|
| 19 |
+
task_ids: []
|
| 20 |
+
pretty_name: STAN Small
|
| 21 |
+
tags:
|
| 22 |
+
- word-segmentation
|
| 23 |
+
---
|
| 24 |
+
|
| 25 |
+
# Dataset Card for STAN Small
|
| 26 |
+
|
| 27 |
+
## Table of Contents
|
| 28 |
+
- [Table of Contents](#table-of-contents)
|
| 29 |
+
- [Dataset Description](#dataset-description)
|
| 30 |
+
- [Dataset Summary](#dataset-summary)
|
| 31 |
+
- [Languages](#languages)
|
| 32 |
+
- [Dataset Structure](#dataset-structure)
|
| 33 |
+
- [Data Instances](#data-instances)
|
| 34 |
+
- [Data Fields](#data-fields)
|
| 35 |
+
- [Dataset Creation](#dataset-creation)
|
| 36 |
+
- [Additional Information](#additional-information)
|
| 37 |
+
- [Citation Information](#citation-information)
|
| 38 |
+
- [Contributions](#contributions)
|
| 39 |
+
|
| 40 |
+
## Dataset Description
|
| 41 |
+
|
| 42 |
+
- **Repository:** [mounicam/hashtag_master](https://github.com/mounicam/hashtag_master)
|
| 43 |
+
- **Paper:** [Multi-task Pairwise Neural Ranking for Hashtag Segmentation](https://aclanthology.org/P19-1242/)
|
| 44 |
+
|
| 45 |
+
### Dataset Summary
|
| 46 |
+
|
| 47 |
+
Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al..
|
| 48 |
+
|
| 49 |
+
### Languages
|
| 50 |
+
|
| 51 |
+
English
|
| 52 |
+
|
| 53 |
+
## Dataset Structure
|
| 54 |
+
|
| 55 |
+
### Data Instances
|
| 56 |
+
|
| 57 |
+
```
|
| 58 |
+
{
|
| 59 |
+
"index": 300,
|
| 60 |
+
"hashtag": "microsoftfail",
|
| 61 |
+
"segmentation": "microsoft fail",
|
| 62 |
+
"alternatives": {
|
| 63 |
+
"segmentation": [
|
| 64 |
+
"Microsoft fail"
|
| 65 |
+
]
|
| 66 |
+
}
|
| 67 |
+
}
|
| 68 |
+
```
|
| 69 |
+
|
| 70 |
+
### Data Fields
|
| 71 |
+
|
| 72 |
+
- `index`: a numerical index.
|
| 73 |
+
- `hashtag`: the original hashtag.
|
| 74 |
+
- `segmentation`: the gold segmentation for the hashtag.
|
| 75 |
+
- `alternatives`: other segmentations that are also accepted as a gold segmentation.
|
| 76 |
+
|
| 77 |
+
Although `segmentation` has exactly the same characters as `hashtag` except for the spaces, the segmentations inside `alternatives` may have characters corrected to uppercase.
|
| 78 |
+
|
| 79 |
+
## Dataset Creation
|
| 80 |
+
|
| 81 |
+
- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
|
| 82 |
+
|
| 83 |
+
- The only difference between `hashtag` and `segmentation` or between `identifier` and `segmentation` are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.
|
| 84 |
+
|
| 85 |
+
- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ).
|
| 86 |
+
|
| 87 |
+
- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
|
| 88 |
+
|
| 89 |
+
## Additional Information
|
| 90 |
+
|
| 91 |
+
### Citation Information
|
| 92 |
+
|
| 93 |
+
```
|
| 94 |
+
@misc{bansal2015deep,
|
| 95 |
+
title={Towards Deep Semantic Analysis Of Hashtags},
|
| 96 |
+
author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
|
| 97 |
+
year={2015},
|
| 98 |
+
eprint={1501.03210},
|
| 99 |
+
archivePrefix={arXiv},
|
| 100 |
+
primaryClass={cs.IR}
|
| 101 |
+
}
|
| 102 |
+
```
|
| 103 |
+
|
| 104 |
+
### Contributions
|
| 105 |
+
|
| 106 |
+
This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github.com/ruanchaves/hashformers) library.
|
huggingface_dataset/Dataset_Card/saibo_bookcorpus_compact_1024_test.md
ADDED
|
@@ -0,0 +1,30 @@
|
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|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
dataset_info:
|
| 3 |
+
features:
|
| 4 |
+
- name: text
|
| 5 |
+
dtype: string
|
| 6 |
+
- name: concept_with_offset
|
| 7 |
+
dtype: string
|
| 8 |
+
splits:
|
| 9 |
+
- name: train
|
| 10 |
+
num_bytes: 75334225
|
| 11 |
+
num_examples: 6160
|
| 12 |
+
download_size: 38920916
|
| 13 |
+
dataset_size: 75334225
|
| 14 |
+
---
|
| 15 |
+
# Dataset Card for "bookcorpus_compact_1024_test"
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
6160 samples randomly sampled from the shard9 of Bookcorpus_compact_1024
|
| 19 |
+
```python
|
| 20 |
+
from datasets import load_dataset
|
| 21 |
+
from datasets import Dataset
|
| 22 |
+
corpus_name="xxx"
|
| 23 |
+
|
| 24 |
+
ds = load_dataset(corpus_name, split="train")
|
| 25 |
+
shuffled_ds = ds.shuffle(seed=42)
|
| 26 |
+
test_ds = Dataset.from_dict{shuffled_ds[:6160]} # len(ds)//10
|
| 27 |
+
```
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
huggingface_dataset/Dataset_Card/wikihow.md
ADDED
|
@@ -0,0 +1,53 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
|
|
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|
|
|
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|
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|
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|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
paperswithcode_id: wikihow
|
| 3 |
+
pretty_name: WikiHow
|
| 4 |
+
dataset_info:
|
| 5 |
+
- config_name: all
|
| 6 |
+
features:
|
| 7 |
+
- name: text
|
| 8 |
+
dtype: string
|
| 9 |
+
- name: headline
|
| 10 |
+
dtype: string
|
| 11 |
+
- name: title
|
| 12 |
+
dtype: string
|
| 13 |
+
splits:
|
| 14 |
+
- name: train
|
| 15 |
+
num_bytes: 513238309
|
| 16 |
+
num_examples: 157252
|
| 17 |
+
- name: validation
|
| 18 |
+
num_bytes: 18246897
|
| 19 |
+
num_examples: 5599
|
| 20 |
+
- name: test
|
| 21 |
+
num_bytes: 18276023
|
| 22 |
+
num_examples: 5577
|
| 23 |
+
download_size: 5460385
|
| 24 |
+
dataset_size: 549761229
|
| 25 |
+
- config_name: sep
|
| 26 |
+
features:
|
| 27 |
+
- name: text
|
| 28 |
+
dtype: string
|
| 29 |
+
- name: headline
|
| 30 |
+
dtype: string
|
| 31 |
+
- name: title
|
| 32 |
+
dtype: string
|
| 33 |
+
- name: overview
|
| 34 |
+
dtype: string
|
| 35 |
+
- name: sectionLabel
|
| 36 |
+
dtype: string
|
| 37 |
+
splits:
|
| 38 |
+
- name: train
|
| 39 |
+
num_bytes: 990499776
|
| 40 |
+
num_examples: 1060732
|
| 41 |
+
- name: validation
|
| 42 |
+
num_bytes: 35173966
|
| 43 |
+
num_examples: 37932
|
| 44 |
+
- name: test
|
| 45 |
+
num_bytes: 35271826
|
| 46 |
+
num_examples: 37800
|
| 47 |
+
download_size: 5460385
|
| 48 |
+
dataset_size: 1060945568
|
| 49 |
+
---
|
| 50 |
+
|
| 51 |
+
### Contributions
|
| 52 |
+
|
| 53 |
+
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
|