Upload batch 344 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en_3-fcaae9-2012466610.md)

huggingface_dataset/Dataset_Card/BigScienceBiasEval_bias-shades.md

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+---
+license: cc-by-sa-4.0
+---
+
+Possibly a placeholder dataset for the original here: https://huggingface.co/datasets/bigscience-catalogue-data/bias-shades
+
+# Data Statement for SHADES
+
+> **How to use this document:**
+> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.
+
+> For full details, the best source is the original Data Statements paper, here: https://www.aclweb.org/anthology/Q18-1041/ .
+
+> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as "DATASTATEMENT.md". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.
+
+> Only blockquoted content should be deleted; the final about statement should be left intact.
+
+Data set name: Bias-Shades
+
+Citation (if available): TODO.
+
+Data set developer(s): This dataset was compiled by dozens of research scientists through the BigScience open science collaboration. Collaborators, representing numerous cultures and languages, joined the project of their own volition.
+
+Data statement author(s): Shayne Longpre, Aurélie Névéol, Shanya Sharma[Add name here if you add/edit the data statement :)].
+
+Others who contributed to this document: N/A
+
+License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).
+
+## A. CURATION RATIONALE 
+
+> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.
+
+This dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted. An initial set of sentences was inferred from stereotypes expressed in the crowS-pairs data set(Nangia et al.). Native speakers first crafted templates for sentences expressing a stereotype. These templates are marked for gender and plurality of the target nouns, so the template can be reused by substituting different targets. Next, the template-target noun pair combinations were annotated for the veracity/reliability of the expressed stereotype. The resulting sentences express common and less common stereotypes in a variety of cultures and languages.
+
+
+## B. LANGUAGE VARIETY/VARIETIES
+
+> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., "English as spoken in Palo Alto, California", or "Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin").
+
+* BCP-47 language tags: en-US, fr-FR, hi-IN, es-DO, ar-LY, ru-RU, de-DE, nl-NL, ta-IN.
+* Language variety description: English spoken by native speakers of the United States, native French people from metropolitan France, native Hindi and Tamil speakers from India, Spanish speakers from the Dominican Republic, Arabic speakers from Libya, Russian speakers from Russia, German speakers from Germany, and Dutch speakers from the Netherlands.
+
+## C. CONTRIBUTOR DEMOGRAPHIC
+> ## C. SPEAKER DEMOGRAPHIC
+
+> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include: 
+
+Participants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Listed below.
+
+Speakers:
+* [ADD YOURSELF!]
+* Shayne Longpre: English-speaking, male, 28 years old, culturally Canadian.
+* Aurélie Névéol: French (native), English and Spanish speaking, female, 44 years old, culturally French (also familiar with American culture)
+* Shanya Sharma: Hindi(native), English speaking, female, 24 years old, culturally Indian
+* Margaret Mitchell: English, female, mid-30s, U.S.A.
+* Maraim Masoud: Arabic, English Speaking female.
+
+ 
+## D. ANNOTATOR DEMOGRAPHIC
+
+> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:
+
+Participants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Speaker and annotator contributors listed in section C.
+
+
+## E. SPEECH SITUATION
+
+N/A
+
+## F. TEXT CHARACTERISTICS
+
+> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.
+
+Collected data is a collection of offensive stereotyped statements in numerous languages and cultures. They might be upsetting and/or offensive.
+
+Along with these stereotyped statements are annotation judgements of how prevalent/real the expressed stereotypes are in the real world. Some statements were created from templates with substituted target nouns, and therefore may express an uncommon or unlikely stereotype.
+
+## G. RECORDING QUALITY
+
+N/A
+
+## H. OTHER
+
+> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset. 
+
+## I. PROVENANCE APPENDIX
+
+This initiative is part of the BigScience Workshop: https://bigscience.huggingface.co/.
+
+
+## About this document
+
+A data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.
+
+Data Statements are from the University of Washington. Contact: [datastatements@uw.edu](mailto:datastatements@uw.edu). This document template is licensed as [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
+
+This version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the [2020 LREC workshop on Data Statements](https://sites.google.com/uw.edu/data-statements-for-nlp/), by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski.

huggingface_dataset/Dataset_Card/Biomedical-TeMU_CodiEsp_corpus.md

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+---
+license: cc-by-4.0
+---
+
+## Introduction
+These are the train, development, test and background sets of the CodiEsp corpus. Train and development have gold standard annotations. The unannotated background and test sets are distributed together. All documents are released in the context of the CodiEsp track for CLEF ehealth 2020 (http://temu.bsc.es/codiesp/).
+
+The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish version of ICD10-CM and ICD10-PCS). The CodiEsp corpus has been randomly sampled into three subsets: the train, the development, and the test set. The train set contains 500 clinical cases, and the development and test set 250 clinical cases each. The test set contains 250 clinical cases and it is released together with the background set (with 2751 clinical cases). CodiEsp participants must submit predictions for the test and background set, but they will only be evaluated on the test set.
+
+
+## Structure
+Three folders: train, dev and test. Each one of them contains the files for the train, development and test corpora, respectively.
++ train and dev folders have:
+	+ 3 tab-separated files with the annotation information relevant for each of the 3 sub-tracks of CodiEsp.
+	+ A subfolder named text_files with the plain text files of the clinical cases.
+	+ A subfolder named text_files_en with the plain text files machine-translated to English. Due to the translation process, the text files are sentence-splitted.
++ The test folder has only text_files and text_files_en subfolders with the plain text files.
+
+
+## Corpus format description
+The CodiEsp corpus is distributed in plain text in UTF8 encoding, where each clinical case is stored as a single file whose name is the clinical case identifier. Annotations are released in a tab-separated file. Since the CodiEsp track has 3 sub-tracks, every set of documents (train and test) has 3 tab-separated files associated with it. 
+
+For the sub-tracks CodiEsp-D and CodiEsp-P, the file has the following fields:
+articleID	ICD10-code
+
+Tab-separated files for the sub-track CodiEsp-X contain extra fields that provide the text-reference and its position:
+articleID	label	ICD10-code	text-reference	reference-position
+
+
+## Corpus summary statistics
+The final collection of 1000 clinical cases that make up the corpus had a total of 16504 sentences, with an average of 16.5 sentences per clinical case. It contains a total of 396,988 words, with an average of 396.2 words per clinical case.
+
+For more information, visit the track webpage: http://temu.bsc.es/codiesp/

huggingface_dataset/Dataset_Card/Cohere_miracl-fr-queries-22-12.md

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+---
+annotations_creators:
+- expert-generated
+
+language:
+- fr
+
+multilinguality:
+- multilingual
+
+size_categories: []
+source_datasets: []
+tags: []
+
+task_categories:
+- text-retrieval
+
+license:
+- apache-2.0
+
+task_ids:
+- document-retrieval
+---
+
+# MIRACL (fr) embedded with cohere.ai `multilingual-22-12` encoder
+
+We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
+
+The query embeddings can be found in [Cohere/miracl-fr-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-fr-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-fr-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-fr-corpus-22-12).
+
+For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
+
+
+Dataset info:
+> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
+>
+> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
+
+## Embeddings
+We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages.  If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
+
+
+## Loading the dataset
+
+In [miracl-fr-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-fr-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
+
+You can either load the dataset like this:
+```python
+from datasets import load_dataset
+docs = load_dataset(f"Cohere/miracl-fr-corpus-22-12", split="train")
+```
+
+Or you can also stream it without downloading it before:
+```python
+from datasets import load_dataset
+docs = load_dataset(f"Cohere/miracl-fr-corpus-22-12", split="train", streaming=True)
+
+for doc in docs:
+	docid = doc['docid']
+	title = doc['title']
+	text = doc['text']
+	emb = doc['emb']
+```
+
+## Search
+
+Have a look at [miracl-fr-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-fr-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
+
+To search in the documents, you must use **dot-product**. 
+
+
+And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
+
+A full search example:
+```python
+# Attention! For large datasets, this requires a lot of memory to store
+# all document embeddings and to compute the dot product scores.
+# Only use this for smaller datasets. For large datasets, use a vector DB
+
+from datasets import load_dataset
+import torch
+
+#Load documents + embeddings
+docs = load_dataset(f"Cohere/miracl-fr-corpus-22-12", split="train")
+doc_embeddings = torch.tensor(docs['emb'])
+
+# Load queries 
+queries = load_dataset(f"Cohere/miracl-fr-queries-22-12", split="dev")
+
+# Select the first query as example
+qid = 0
+query = queries[qid]
+query_embedding = torch.tensor(queries['emb'])
+
+# Compute dot score between query embedding and document embeddings
+dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
+top_k = torch.topk(dot_scores, k=3)
+
+# Print results
+print("Query:", query['query'])
+for doc_id in top_k.indices[0].tolist():
+    print(docs[doc_id]['title'])
+    print(docs[doc_id]['text'])
+```
+
+You can get embeddings for new queries using our API:
+```python
+#Run: pip install cohere
+import cohere
+co = cohere.Client(f"{api_key}")  # You should add your cohere API Key here :))
+texts = ['my search query']
+response = co.embed(texts=texts, model='multilingual-22-12')
+query_embedding = response.embeddings[0] # Get the embedding for the first text
+```
+
+## Performance
+
+In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
+
+
+We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
+
+Note: MIRACL only annotated a small fraction  of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.  
+
+
+| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
+|---|---|---|---|---|
+| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
+| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
+| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
+| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
+| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
+| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
+| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
+| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
+| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
+| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
+| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
+
+Further languages (not supported by Elasticsearch):
+| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
+|---|---|---|
+| miracl-fa | 44.8 | 53.6 |
+| miracl-ja | 49.0 | 61.0 |
+| miracl-ko | 50.9 | 64.8 |
+| miracl-sw | 61.4 | 74.5 |
+| miracl-te | 67.8 | 72.3 |
+| miracl-th | 60.2 | 71.9 |
+| miracl-yo | 56.4 | 62.2 |
+| miracl-zh | 43.8 | 56.5 |
+| **Avg** | 54.3 | 64.6 |
+

huggingface_dataset/Dataset_Card/Datatang_Mandarin_Heavy_Accent_Speech_Data_by_Mobile_Phone.md

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+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for Datatang/Mandarin_Heavy_Accent_Speech_Data_by_Mobile_Phone
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://bit.ly/3zO87s9
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+It collects 2,568 local Chinese from Henan, Shanxi, Sichuan, Hunan and Fujian. It is mandarin speech data with heavy accent. The recorded content is a sentence that the speaker freely answers according to the guiding questions.
+                                                                                                                                  
+For more details, please refer to the link: https://bit.ly/3zO87s9
+
+### Supported Tasks and Leaderboards
+
+automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
+
+### Languages
+
+Accented Mandarin
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing  
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions

huggingface_dataset/Dataset_Card/Datatang_Natural_Scenes_OCR_Data_of_12_Languages.md

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+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for Datatang/Natural_Scenes_OCR_Data_of_12_Languages
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://bit.ly/3y30wn8
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+105,941 Images Natural Scenes OCR Data of 12 Languages. The data covers 12 languages (6 Asian languages, 6 European languages), multiple natural scenes, multiple photographic angles. For annotation, line-level quadrilateral bounding box annotation and transcription for the texts were annotated in the data. The data can be used for tasks such as OCR of multi-language.
+                                                                                                                                  
+For more details, please refer to the link: https://bit.ly/3y30wn8
+
+### Supported Tasks and Leaderboards
+
+image-to-text, computer-vision: The dataset can be used to train a model for image-to-text.
+
+### Languages
+
+Japanese, Korean, Indonesian, Malay, Vietnamese, Thai, French, German, Italian, Portuguese, Russian and Spanish
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing  
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions

huggingface_dataset/Dataset_Card/GEM_xsum.md

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+---
+annotations_creators:
+- none
+language_creators:
+- unknown
+language:
+- en
+license:
+- cc-by-sa-4.0
+multilinguality:
+- unknown
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- summarization
+task_ids: []
+pretty_name: xsum
+---
+
+# Dataset Card for GEM/xsum
+
+## Dataset Description
+
+- **Homepage:** n/a
+- **Repository:** https://github.com/EdinburghNLP/XSum
+- **Paper:** https://www.aclweb.org/anthology/D18-1206
+- **Leaderboard:** N/A
+- **Point of Contact:** Shashi Narayan
+
+### Link to Main Data Card
+
+You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xsum).
+
+### Dataset Summary 
+
+XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it.
+
+You can load the dataset via:
+```
+import datasets
+data = datasets.load_dataset('GEM/xsum')
+```
+The data loader can be found [here](https://huggingface.co/datasets/GEM/xsum).
+
+#### website
+n/a
+
+#### paper
+[ACL Anthology](https://www.aclweb.org/anthology/D18-1206)
+
+#### authors
+Shashi Narayan, Shay B. Cohen, Mirella Lapata (all affiliated with University of Edinburgh at the time of dataset creation)
+
+## Dataset Overview
+
+### Where to find the Data and its Documentation
+
+#### Download
+
+<!-- info: What is the link to where the original dataset is hosted? -->
+<!-- scope: telescope -->
+[Github](https://github.com/EdinburghNLP/XSum)
+
+#### Paper
+
+<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
+<!-- scope: telescope -->
+[ACL Anthology](https://www.aclweb.org/anthology/D18-1206)
+
+#### BibTex
+
+<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
+<!-- scope: microscope -->
+```
+@InProceedings{xsum-emnlp,
+  author =      "Shashi Narayan and Shay B. Cohen and Mirella Lapata",
+  title =       "Don't Give Me the Details, Just the Summary! {T}opic-Aware Convolutional Neural Networks for Extreme Summarization",
+  booktitle =   "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ",
+  year =        "2018",
+  address =     "Brussels, Belgium",
+}
+```
+
+#### Contact Name
+
+<!-- quick -->
+<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+Shashi Narayan
+
+#### Contact Email
+
+<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+shashinarayan@google.com
+
+#### Has a Leaderboard?
+
+<!-- info: Does the dataset have an active leaderboard? -->
+<!-- scope: telescope -->
+no
+
+
+### Languages and Intended Use
+
+#### Multilingual?
+
+<!-- quick -->
+<!-- info: Is the dataset multilingual? -->
+<!-- scope: telescope -->
+no
+
+#### Covered Dialects
+
+<!-- info: What dialects are covered? Are there multiple dialects per language? -->
+<!-- scope: periscope -->
+Since the source of the dataset are BBC articles, the language is in British English of the variation written by journalists.
+
+#### Covered Languages
+
+<!-- quick -->
+<!-- info: What languages/dialects are covered in the dataset? -->
+<!-- scope: telescope -->
+`English`
+
+#### Whose Language?
+
+<!-- info: Whose language is in the dataset? -->
+<!-- scope: periscope -->
+Professional journalists
+
+#### License
+
+<!-- quick -->
+<!-- info: What is the license of the dataset? -->
+<!-- scope: telescope -->
+cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
+
+#### Intended Use
+
+<!-- info: What is the intended use of the dataset? -->
+<!-- scope: microscope -->
+The dataset is for the task of abstractive summarization in its extreme form, its about summarizing a document in a single sentence. The idea is to create a short, one-sentence news summary answering the question "What is the article about?".
+
+
+
+#### Primary Task
+
+<!-- info: What primary task does the dataset support? -->
+<!-- scope: telescope -->
+Summarization
+
+#### Communicative Goal
+
+<!-- quick -->
+<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
+<!-- scope: periscope -->
+Given a news article, produce a single sentence summary of the content of the article. 
+
+
+### Credit
+
+#### Curation Organization Type(s)
+
+<!-- info: In what kind of organization did the dataset curation happen? -->
+<!-- scope: telescope -->
+`academic`
+
+#### Curation Organization(s)
+
+<!-- info: Name the organization(s). -->
+<!-- scope: periscope -->
+University of Edinburgh
+
+#### Dataset Creators
+
+<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
+<!-- scope: microscope -->
+Shashi Narayan, Shay B. Cohen, Mirella Lapata (all affiliated with University of Edinburgh at the time of dataset creation)
+
+#### Funding
+
+<!-- info: Who funded the data creation? -->
+<!-- scope: microscope -->
+European Research Council (Lapata; award number 681760), the European Union under the Horizon 2020 SUMMA project (Narayan, Cohen; grant agreement 688139), and Huawei Technologies (Cohen).
+
+#### Who added the Dataset to GEM?
+
+<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
+<!-- scope: microscope -->
+The original data card was written by Laura Perez-Beltrachini and the data loader by Yacine Jernite. Sebastian Gehrmann migrated the data card to the new format and extended it. The v2 data loader was migrated by  Abinaya Mahendiran
+
+
+### Dataset Structure
+
+#### Data Fields
+
+<!-- info: List and describe the fields present in the dataset. -->
+<!-- scope: telescope -->
+- `Document`: Input news article.
+- `Summary`: One sentence summary of the article.
+- `Id`: BBC ID of the article.
+
+#### Reason for Structure
+
+<!-- info: How was the dataset structure determined? -->
+<!-- scope: microscope -->
+The Document/Summary format is standard for summarization datasets.
+
+#### How were labels chosen?
+
+<!-- info: How were the labels chosen? -->
+<!-- scope: microscope -->
+The labels are the first sentence of the source article. 
+
+#### Example Instance
+
+<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
+<!-- scope: periscope -->
+```
+{
+  'document': 'The researchers have sequenced the genome of a strain of bacterium that causes the virulent infection.\nA survey in 2007 showed that bleeding canker had spread rapidly, with almost half of the two million horse chestnuts displaying symptoms of the disease.\nThe findings have been published in the journal PLoS One.\nA visible symptom of the disease is a lesion on the bark, which oozes a resin on to the trunk or sometimes the branches.\nThe bark underneath the canker is killed, and if cankers manage to go all the way around the trunk then the horse chestnut (Aesculus hippocastanum) will die because it cuts off the food supply. [...]',
+  'target': "A team of UK scientists hopes to shed light on the mysteries of bleeding canker, a disease that is threatening the nation's horse chestnut trees.",
+}
+```
+
+#### Data Splits
+
+<!-- info: Describe and name the splits in the dataset if there are more than one. -->
+<!-- scope: periscope -->
+| Section   | Number of Documents          |
+| ------------- |:-------------:|
+| Training     | 204,045 |
+| Validation     | 11,332      |
+| Testing | 11,334    |
+| Total | 226k |
+
+| Section       |  number of words| number of sentences |
+| ------------- |:-------------:| :-------------:|
+| Documents      | 431.07     | 19.77 |
+| Summary    | 23.26      | 1.00 |
+
+
+#### Splitting Criteria
+
+<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
+<!-- scope: microscope -->
+The identifiers in the URLs were used to randomly split the dataset into training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) sets.
+
+
+
+## Dataset Curation
+
+### Original Curation
+
+#### Original Curation Rationale
+
+<!-- info: Original curation rationale -->
+<!-- scope: telescope -->
+Comparable datasets are often very extractive which is not a strategy that works for one-sentence summaries. The dataset curators thus created this dataset as a way to evaluate truly abstractive models
+
+#### Communicative Goal
+
+<!-- info: What was the communicative goal? -->
+<!-- scope: periscope -->
+Same as the communicative goal in GEM: A model should summarize a news article in a single sentence
+
+#### Sourced from Different Sources
+
+<!-- info: Is the dataset aggregated from different data sources? -->
+<!-- scope: telescope -->
+no
+
+
+### Language Data
+
+#### How was Language Data Obtained?
+
+<!-- info: How was the language data obtained? -->
+<!-- scope: telescope -->
+`Found`
+
+#### Where was it found?
+
+<!-- info: If found, where from? -->
+<!-- scope: telescope -->
+`Single website`
+
+#### Language Producers
+
+<!-- info: What further information do we have on the language producers? -->
+<!-- scope: microscope -->
+The data was collected from articles between 2010 and 2017. No other information 
+
+#### Topics Covered
+
+<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
+<!-- scope: periscope -->
+The collected articles included the following topics: News, Politics, Sports, Weather, Business, Technology, Science, Health, Family, Education, Entertainment and Arts 
+
+The dataset curators also used LDA to gain insight into this question and found that the following were the top keywords associated with each topic:
+
+- **T1**:  charge, court, murder, police, arrest, guilty, sentence, boy, bail, space, crown, trial
+- **T2**:   church, abuse, bishop, child, catholic, gay, pope, school, christian, priest, cardinal
+- **T3**:  council, people, government, local, housing, home, house, property, city, plan, authority
+- **T4**:   clinton, party, trump, climate, poll, vote, plaid, election, debate, change, candidate, campaign
+- **T5**:   country, growth, report, business, export, fall, bank, security, economy, rise, global, inflation
+- **T6**:  hospital, patient, trust, nhs, people, care, health, service, staff, report, review, system, child
+
+#### Data Validation
+
+<!-- info: Was the text validated by a different worker or a data curator? -->
+<!-- scope: telescope -->
+not validated
+
+#### Data Preprocessing
+
+<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
+<!-- scope: microscope -->
+The text was extracted from the HTML of the webpage. No further processing was done.
+
+#### Was Data Filtered?
+
+<!-- info: Were text instances selected or filtered? -->
+<!-- scope: telescope -->
+not filtered
+
+
+### Structured Annotations
+
+#### Additional Annotations?
+
+<!-- quick -->
+<!-- info: Does the dataset have additional annotations for each instance? -->
+<!-- scope: telescope -->
+none
+
+#### Annotation Service?
+
+<!-- info: Was an annotation service used? -->
+<!-- scope: telescope -->
+no
+
+
+### Consent
+
+#### Any Consent Policy?
+
+<!-- info: Was there a consent policy involved when gathering the data? -->
+<!-- scope: telescope -->
+no
+
+#### Justification for Using the Data
+
+<!-- info: If not, what is the justification for reusing the data? -->
+<!-- scope: microscope -->
+The copyright license of the data allows reusing it for this purpose. 
+
+
+### Private Identifying Information (PII)
+
+#### Contains PII?
+
+<!-- quick -->
+<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
+<!-- scope: telescope -->
+yes/very likely
+
+#### Categories of PII
+
+<!-- info: What categories of PII are present or suspected in the data? -->
+<!-- scope: periscope -->
+`generic PII`
+
+#### Any PII Identification?
+
+<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
+<!-- scope: periscope -->
+no identification
+
+
+### Maintenance
+
+#### Any Maintenance Plan?
+
+<!-- info: Does the original dataset have a maintenance plan? -->
+<!-- scope: telescope -->
+no
+
+
+
+## Broader Social Context
+
+### Previous Work on the Social Impact of the Dataset
+
+#### Usage of Models based on the Data
+
+<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
+<!-- scope: telescope -->
+no
+
+
+### Impact on Under-Served Communities
+
+#### Addresses needs of underserved Communities?
+
+<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
+<!-- scope: telescope -->
+no
+
+
+### Discussion of Biases
+
+#### Any Documented Social Biases?
+
+<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
+<!-- scope: telescope -->
+unsure
+
+#### Are the Language Producers Representative of the Language?
+
+<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
+<!-- scope: periscope -->
+The language and content of the data is focused on news and language in the UK and as such not representative of the speakers world-wide. Existing selection biases of the BBC exist in this dataset.
+
+

huggingface_dataset/Dataset_Card/Meena_imdb_ratings_table.md

@@ -0,0 +1 @@
+This dataset contains IMDB Ratings of various movies of different languages. This dataset also contains the number of votes each movies received

huggingface_dataset/Dataset_Card/MtCelesteMa_fstdt-quotes.md

@@ -0,0 +1,137 @@
+---
+license: cc-by-4.0
+annotations_creators:
+- found
+language:
+- en
+language_creators:
+- found
+multilinguality:
+- monolingual
+pretty_name: FSTDT Quotes
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- hate-speech-detection
+---
+
+# Dataset Card for FSTDT Quotes
+
+## Dataset Description
+
+- **Homepage:** 
+- **Repository:** 
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+FSTDT Quotes is a snapshot of the [Fundies Say the Darndest Things](https://fstdt.com/) website taken on 2023/02/03 14:16. It is intended for hate and fringe speech detection and classification.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+FSTDT Quotes is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+An example instance looks like this:
+```
+{
+  "id": "G",
+  "submitter": "anonymous",
+  "timestamp": "2005-05-21 00:00:00+00:00",
+  "name": "Jack777 ",
+  "src_url": "http://www.theologyweb.com/forum/showpost.php?p=1034624&postcount=10",
+  "tags": ["#fundie"],
+  "quote": "As long as evolutionists deny their theory is a theory and point out ID or whatever is bunk, people like me will pester them til they drop."
+}
+```
+
+### Data Fields
+
+- `id`: A `string` feature, the ID of the post on FSTDT.
+- `submitter`: A `string` feature, the submitter of the post.
+- `timestamp`: A `string` feature, the time of submission.
+- `name`: A `string` feature, the (user)name of the person who is being quoted.
+- `src_url`: A `string` feature, the source URL of the quote.
+- `tags`: A sequence of `string` features, the tags the post has been tagged with.
+- `quote`: A `string` feature, the quote itself.
+
+### Data Splits
+
+- `train`: 56,448 instances
+- `validation`: 7,111 instances
+- `test`: 7,131 instances
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+The quotes are collected from all over the internet.
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+The data is annotated by users on FSTDT.
+
+### Personal and Sensitive Information
+
+The dataset contains the usernames of submitters as well as those quoted. However, this information is publicly available.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+This dataset contains large amounts of hate speech as well as pseudoscience and quackery.
+
+### Other Known Limitations
+
+Some quotes in the dataset are quoted from news articles depicting acts of hate, which could potentially cause misclassifications on models trained on this dataset.
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en_3-8ea950-2087767172.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/guess
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloomz-1b7
+  metrics: []
+  dataset_name: futin/guess
+  dataset_config: en_3
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: bigscience/bloomz-1b7
+* Dataset: futin/guess
+* Config: en_3
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en_3-fcaae9-2012466610.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/guess
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-66b
+  metrics: []
+  dataset_name: futin/guess
+  dataset_config: en_3
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: facebook/opt-66b
+* Dataset: futin/guess
+* Config: en_3
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/beanjar_sp500-business-description-sentence-bert-embeddings.md

@@ -0,0 +1,4 @@
+---
+license: mit
+---
+Embeddings derived from business descriptions of S&P500 companies using sentence-BERT, SentenceTransformer('all-MiniLM-L6-v2') to be exact.  For more info on evaluation of sentence transformers (specifcailly the huge GPT-3 versus smaller models see: https://twitter.com/Nils_Reimers/status/1487014195568775173)

huggingface_dataset/Dataset_Card/bigbio_msh_wsd.md

@@ -0,0 +1,54 @@
+
+---
+language: 
+- en
+bigbio_language: 
+- English
+license: other
+multilinguality: monolingual
+bigbio_license_shortname: UMLS_LICENSE
+pretty_name: MSH WSD
+homepage: https://lhncbc.nlm.nih.gov/ii/areas/WSD/collaboration.html
+bigbio_pubmed: True
+bigbio_public: False
+bigbio_tasks: 
+- NAMED_ENTITY_DISAMBIGUATION
+---
+
+
+# Dataset Card for MSH WSD
+
+## Dataset Description
+
+- **Homepage:** https://lhncbc.nlm.nih.gov/ii/areas/WSD/collaboration.html
+- **Pubmed:** True
+- **Public:** False
+- **Tasks:** NED
+
+
+Evaluation of Word Sense Disambiguation methods (WSD) in the biomedical domain is difficult because the available
+resources are either too small or too focused on specific types of entities (e.g. diseases or genes). We have
+developed a method that can be used to automatically develop a WSD test collection using the Unified Medical Language
+System (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. The resulting dataset is called MSH WSD and
+consists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203
+ambiguous words. Each instance containing the ambiguous word was assigned a CUI from the 2009AB version of the UMLS.
+For each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from
+MEDLINE; totaling 37,888 ambiguity cases in 37,090 MEDLINE citations.
+
+
+
+## Citation Information
+
+```
+@article{jimeno2011exploiting,
+  title={Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation},
+  author={Jimeno-Yepes, Antonio J and McInnes, Bridget T and Aronson, Alan R},
+  journal={BMC bioinformatics},
+  volume={12},
+  number={1},
+  pages={1--14},
+  year={2011},
+  publisher={BioMed Central}
+}
+
+```

huggingface_dataset/Dataset_Card/billray110_corpus-of-diverse-styles.md

@@ -0,0 +1,57 @@
+---
+annotations_creators: []
+language_creators:
+- found
+language: []
+license: []
+multilinguality:
+- monolingual
+pretty_name: Corpus of Diverse Styles
+size_categories:
+- 10M<n<100M
+source_datasets: []
+task_categories:
+- text-classification
+task_ids: []
+---
+
+# Dataset Card for Corpus of Diverse Styles
+
+
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+
+## Disclaimer
+I am not the original author of the paper that presents the Corpus of Diverse Styles. I uploaded the dataset to HuggingFace as a convenience. 
+
+## Dataset Description
+
+- **Homepage:** http://style.cs.umass.edu/
+- **Repository:** https://github.com/martiansideofthemoon/style-transfer-paraphrase
+- **Paper:** https://arxiv.org/abs/2010.05700
+
+### Dataset Summary
+
+A new benchmark dataset that contains 15M
+sentences from 11 diverse styles.
+
+To create CDS, we obtain data from existing academic
+research datasets and public APIs or online collections
+like Project Gutenberg. We choose
+styles that are easy for human readers to identify at
+a sentence level (e.g., Tweets or Biblical text). While
+prior benchmarks involve a transfer between two
+styles, CDS has 110 potential transfer directions.
+
+### Citation Information
+```
+@inproceedings{style20,
+author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
+Booktitle = {Empirical Methods in Natural Language Processing},
+Year = "2020",
+Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
+}
+```

huggingface_dataset/Dataset_Card/codeparrot_codeparrot-clean-train.md

@@ -0,0 +1,13 @@
+# CodeParrot 🦜 Dataset Cleaned (train)
+
+Train split of [CodeParrot 🦜 Dataset Cleaned](https://huggingface.co/datasets/lvwerra/codeparrot-clean).
+
+## Dataset structure
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
+        num_rows: 5300000
+    })
+})
+```

huggingface_dataset/Dataset_Card/irds_gov2_trec-tb-2005.md

@@ -0,0 +1,49 @@
+---
+pretty_name: '`gov2/trec-tb-2005`'
+viewer: false
+source_datasets: ['irds/gov2']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `gov2/trec-tb-2005`
+
+The `gov2/trec-tb-2005` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2005).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=50
+ - `qrels`: (relevance assessments); count=45,291
+
+ - For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/gov2_trec-tb-2005', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
+
+qrels = load_dataset('irds/gov2_trec-tb-2005', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@inproceedings{Clarke2005TrecTerabyte,
+  title={The TREC 2005 Terabyte Track},
+  author={Charles L. A. Clark and Falk Scholer and Ian Soboroff},
+  booktitle={TREC},
+  year={2005}
+}
+```

huggingface_dataset/Dataset_Card/ithieund_viWikiHow-Abs-Sum.md

@@ -0,0 +1,27 @@
+# viWikiHow-Abs-Sum
+A dataset for Vietnamese Abstractive Summarization task.
+It includes all Vietnamese posts from WikiHow which was released in WikiLingua dataset.
+
+# Introduction
+This dataset was extracted from Train/Test split of WikiLingua dataset. As the target language is Vietnamese, we remove all other files, just keep train.\*.vi, test.\*.vi, and val.\*.vi for Vietnamese Abstractive Summarization task. The raw files then are stored in the *raw* director and after that, we run the python script to generate ready-to-use data files in TSV and JSONLINE formats which are stored in *processed* directory to be easily used for future training scripts.
+
+# Directory structure
+- raw: contains raw text files from WikiLingua
+  - test.src.vi
+  - test.tgt.vi
+  - train.src.vi
+  - train.tgt.vi
+  - val.src.vi
+  - val.tgt.vi
+- processed: contains generated TSV and JSONLINE files
+  - test.tsv
+  - train.tsv
+  - valid.tsv
+  - test.jsonl
+  - train.jsonl
+  - valid.jsonl
+  - [and other variants]
+
+# Credits
+- Special thanks to WikiLingua authors: https://github.com/esdurmus/Wikilingua
+- Article provided by <a href="https://www.wikihow.com/Main-Page" target="_blank">wikiHow</a>, a wiki that is building the world's largest and highest quality how-to manual. Please edit this article and find author credits at the original wikiHow article on How to Tie a Tie. Content on wikiHow can be shared under a <a href="http://creativecommons.org/licenses/by-nc-sa/3.0/" target="_blank">Creative Commons License</a>.

huggingface_dataset/Dataset_Card/kd_conv.md

@@ -0,0 +1,430 @@
+---
+annotations_creators:
+- crowdsourced
+- machine-generated
+language_creators:
+- crowdsourced
+language:
+- zh
+license:
+- apache-2.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-generation
+- fill-mask
+task_ids:
+- dialogue-modeling
+paperswithcode_id: kdconv
+pretty_name: Knowledge-driven Conversation
+dataset_info:
+- config_name: travel_dialogues
+  features:
+  - name: messages
+    sequence:
+    - name: message
+      dtype: string
+    - name: attrs
+      sequence:
+      - name: attrname
+        dtype: string
+      - name: attrvalue
+        dtype: string
+      - name: name
+        dtype: string
+  - name: name
+    dtype: string
+  - name: domain
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3241550
+    num_examples: 1200
+  - name: test
+    num_bytes: 793883
+    num_examples: 150
+  - name: validation
+    num_bytes: 617177
+    num_examples: 150
+  download_size: 11037768
+  dataset_size: 4652610
+- config_name: travel_knowledge_base
+  features:
+  - name: head_entity
+    dtype: string
+  - name: kb_triplets
+    sequence:
+      sequence: string
+  - name: domain
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1517024
+    num_examples: 1154
+  download_size: 11037768
+  dataset_size: 1517024
+- config_name: music_dialogues
+  features:
+  - name: messages
+    sequence:
+    - name: message
+      dtype: string
+    - name: attrs
+      sequence:
+      - name: attrname
+        dtype: string
+      - name: attrvalue
+        dtype: string
+      - name: name
+        dtype: string
+  - name: name
+    dtype: string
+  - name: domain
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3006192
+    num_examples: 1200
+  - name: test
+    num_bytes: 801012
+    num_examples: 150
+  - name: validation
+    num_bytes: 633905
+    num_examples: 150
+  download_size: 11037768
+  dataset_size: 4441109
+- config_name: music_knowledge_base
+  features:
+  - name: head_entity
+    dtype: string
+  - name: kb_triplets
+    sequence:
+      sequence: string
+  - name: domain
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 5980643
+    num_examples: 4441
+  download_size: 11037768
+  dataset_size: 5980643
+- config_name: film_dialogues
+  features:
+  - name: messages
+    sequence:
+    - name: message
+      dtype: string
+    - name: attrs
+      sequence:
+      - name: attrname
+        dtype: string
+      - name: attrvalue
+        dtype: string
+      - name: name
+        dtype: string
+  - name: name
+    dtype: string
+  - name: domain
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 4867659
+    num_examples: 1200
+  - name: test
+    num_bytes: 956995
+    num_examples: 150
+  - name: validation
+    num_bytes: 884232
+    num_examples: 150
+  download_size: 11037768
+  dataset_size: 6708886
+- config_name: film_knowledge_base
+  features:
+  - name: head_entity
+    dtype: string
+  - name: kb_triplets
+    sequence:
+      sequence: string
+  - name: domain
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 10500882
+    num_examples: 8090
+  download_size: 11037768
+  dataset_size: 10500882
+- config_name: all_dialogues
+  features:
+  - name: messages
+    sequence:
+    - name: message
+      dtype: string
+    - name: attrs
+      sequence:
+      - name: attrname
+        dtype: string
+      - name: attrvalue
+        dtype: string
+      - name: name
+        dtype: string
+  - name: name
+    dtype: string
+  - name: domain
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 11115313
+    num_examples: 3600
+  - name: test
+    num_bytes: 2551802
+    num_examples: 450
+  - name: validation
+    num_bytes: 2135226
+    num_examples: 450
+  download_size: 11037768
+  dataset_size: 15802341
+- config_name: all_knowledge_base
+  features:
+  - name: head_entity
+    dtype: string
+  - name: kb_triplets
+    sequence:
+      sequence: string
+  - name: domain
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 17998529
+    num_examples: 13685
+  download_size: 11037768
+  dataset_size: 17998529
+---
+
+# Dataset Card for KdConv
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** [Github](https://github.com/thu-coai/KdConv)
+- **Paper:** [{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://www.aclweb.org/anthology/2020.acl-main.635.pdf)
+
+### Dataset Summary
+
+KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn 
+conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), 
+and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related 
+topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer 
+learning and domain adaptation.
+
+### Supported Tasks and Leaderboards
+
+This dataset can be leveraged for dialogue modelling tasks involving multi-turn and Knowledge base setup.
+
+### Languages
+
+This dataset has only Chinese Language.
+
+## Dataset Structure
+
+### Data Instances
+
+Each data instance is a multi-turn conversation between 2 people with annotated knowledge base data used while talking
+, e.g.:
+```
+{
+  "messages": [
+    {
+      "message": "对《我喜欢上你时的内心活动》这首歌有了解吗？"
+    },
+    {
+      "attrs": [
+        {
+          "attrname": "Information",
+          "attrvalue": "《我喜欢上你时的内心活动》是由韩寒填词，陈光荣作曲，陈绮贞演唱的歌曲，作为电影《喜欢你》的主题曲于2017年4月10日首发。2018年，该曲先后提名第37届香港电影金像奖最佳原创电影歌曲奖、第7届阿比鹿音乐奖流行单曲奖。",
+          "name": "我喜欢上你时的内心活动"
+        }
+      ],
+      "message": "有些了解，是电影《喜欢你》的主题曲。"
+    },
+    ...
+    {
+      "attrs": [
+        {
+          "attrname": "代表作品",
+          "attrvalue": "旅行的意义",
+          "name": "陈绮贞"
+        },
+        {
+          "attrname": "代表作品",
+          "attrvalue": "时间的歌",
+          "name": "陈绮贞"
+        }
+      ],
+      "message": "我还知道《旅行的意义》与《时间的歌》，都算是她的代表作。"
+    },
+    {
+      "message": "好，有时间我找出来听听。"
+    }
+  ],
+  "name": "我喜欢上你时的内心活动"
+}
+```
+
+The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
+, relationship, tail entity), e.g.:
+```
+"忽然之间": [
+  [
+    "忽然之间",
+    "Information",
+    "《忽然之间》是歌手 莫文蔚演唱的歌曲，由 周耀辉， 李卓雄填词， 林健华谱曲，收录在莫文蔚1999年发行专辑《 就是莫文蔚》里。"
+  ],
+  [
+    "忽然之间",
+    "谱曲",
+    "林健华"
+  ]
+  ...
+]
+``` 
+
+### Data Fields
+
+Conversation data fields:
+- `name`: the starting topic (entity) of the conversation
+- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
+- `messages`:  list of all the turns in the dialogue. For each turn:
+    - `message`: the utterance
+    - `attrs`: list of knowledge graph triplets referred by the utterance. For each triplet:
+        - `name`: the head entity
+        - `attrname`: the relation
+        - `attrvalue`: the tail entity
+
+Knowledge Base data fields:
+- `head_entity`: the head entity
+- `kb_triplets`: list of corresponding triplets
+- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
+
+### Data Splits
+
+The conversation dataset is split into a `train`, `validation`, and `test` split with the following sizes:
+
+|        | train | validation | test |
+|--------|------:|-----------:|-----:|
+| travel |  1200 |       1200 | 1200 |
+| film   |  1200 |        150 |  150 |
+| music  |  1200 |        150 |  150 |
+| all    |  3600 |        450 |  450 |
+
+The Knowledge base dataset is having only train split with following sizes:
+
+|        | train |
+|--------|------:|
+| travel |  1154 | 
+| film   |  8090 | 
+| music  |  4441 | 
+| all    | 13685 | 
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Apache License 2.0
+
+### Citation Information
+```
+@inproceedings{zhou-etal-2020-kdconv,
+    title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
+    author = "Zhou, Hao  and
+      Zheng, Chujie  and
+      Huang, Kaili  and
+      Huang, Minlie  and
+      Zhu, Xiaoyan",
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.acl-main.635",
+    doi = "10.18653/v1/2020.acl-main.635",
+    pages = "7098--7108",
+}
+```
+### Contributions
+
+Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.

huggingface_dataset/Dataset_Card/qgallouedec_prj_gia_dataset_metaworld_box_close_v2_1111.md

@@ -0,0 +1,36 @@
+---
+library_name: gia
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- gia
+- multi-task
+- multi-modal
+- imitation-learning
+- offline-reinforcement-learning
+---
+
+An imitation learning environment for the box-close-v2 environment, sample for the policy box-close-v2 
+
+This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia 
+
+
+
+
+## Load dataset
+
+First, clone it with
+
+```sh
+git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_box_close_v2_1111
+```
+
+Then, load it with
+
+```python
+import numpy as np
+dataset = np.load("prj_gia_dataset_metaworld_box_close_v2_1111/dataset.npy", allow_pickle=True).item()
+print(dataset.keys())  # dict_keys(['observations', 'actions', 'dones', 'rewards'])
+```
+
+    

huggingface_dataset/Dataset_Card/relbert_conceptnet_relational_similarity.md

@@ -0,0 +1,60 @@
+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+pretty_name: ConceptNet with High Confidence
+---
+
+# Dataset Card for "relbert/conceptnet_relation_similarity"
+## Dataset Description
+- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
+- **Paper:** [https://home.ttic.edu/~kgimpel/commonsense.html](https://home.ttic.edu/~kgimpel/commonsense.html)
+- **Dataset:** Relational similarity dataset based on the high-confidence subset of ConceptNet
+
+### Dataset Summary
+The selected subset of ConceptNet used in [this work](https://home.ttic.edu/~kgimpel/commonsense.html), which compiled 
+to fine-tune [RelBERT](https://github.com/asahi417/relbert) model.
+We removed `NotCapableOf` and `NotDesires` to keep the positive relation only.
+We consider the original test set as test set, dev1 as the training set, and dev2 as the validation set.
+
+
+## Dataset Structure
+### Data Instances
+An example of `train` looks as follows.
+```shell
+{
+    "relation_type": "AtLocation",
+    "positives": [["fish", "water"], ["cloud", "sky"], ["child", "school"], ... ],
+    "negatives": [["pen", "write"], ["sex", "fun"], ["soccer", "sport"], ["fish", "school"], ... ]
+}
+```
+
+### Data Splits
+
+|   train |validation|      test|
+|--------:|---------:|---------:|
+|       15| 17       |        15|
+
+
+### Citation Information
+```
+@InProceedings{P16-1137,
+  author = 	"Li, Xiang
+		and Taheri, Aynaz
+		and Tu, Lifu
+		and Gimpel, Kevin",
+  title = 	"Commonsense Knowledge Base Completion",
+  booktitle = 	"Proceedings of the 54th Annual Meeting of the Association for      Computational Linguistics (Volume 1: Long Papers)    ",
+  year = 	"2016",
+  publisher = 	"Association for Computational Linguistics",
+  pages = 	"1445--1455",
+  location = 	"Berlin, Germany",
+  doi = 	"10.18653/v1/P16-1137",
+  url = 	"http://aclweb.org/anthology/P16-1137"
+}
+```

huggingface_dataset/Dataset_Card/student_birds_400.md

@@ -0,0 +1,16 @@
+鸟类400.物种图像分类
+58388训练集，2000测试测试集，2000验证图像224X224X3 jpg格式
+
+400种鸟类的数据集。58388张训练图像、2000张测试图像（每种5张图像）和2000张验证图像（每种5张图像）。这是一个非常高质量的数据集，每张图像中只有一只鸟，鸟通常占据图像中至少50%的像素。因此，即使是一个中等复杂的模型也能在90%的范围内实现训练和测试精度。
+
+所有图像均为jpg格式的224 X 224 X 3彩色图像。数据集包括列车集、测试集和验证集。每套包含400个子目录，每种鸟类一个。如果使用Keras ImageDataGenerator，则数据结构非常方便。flowfromdirectory创建列车、测试和有效数据生成器。数据集还包括一个鸟类物种档案。csv。此cvs文件包含三列。“文件路径”列包含图像文件的文件路径。“标签”列包含与图像文件关联的类名。鸟类种类。如果使用df=pandas读入csv文件。birdscsv（Bird Species.csv）将创建一个pandas数据帧，然后可以将其拆分为traindf、testdf和validdf数据帧，以创建您自己的数据划分为train、test和validdf数据集。
+
+注：数据集中的测试和验证图像是手工选择的“最佳”图像，因此使用这些数据集与创建自己的测试和验证集相比，您的模型可能会获得最高的准确度分数。然而，就看不见的图像上的模型性能而言，后一种情况更为准确。
+
+这些图片是通过网络搜索按物种名称收集的。下载一个物种的图像文件后，使用我开发的python duplicate image detector程序检查其重复图像。删除所有检测到的重复项，以防止它们在训练集、测试集和验证集之间成为共同的图像。
+
+之后，对图像进行裁剪，使鸟占据图像中至少50%的像素。然后，这些图像以jpg格式调整为224x224 X3。裁剪确保了当CNN对其进行处理时，图像中有足够的信息来创建高度准确的分类器。即使是一个中等稳健的模型，也应在高90%的范围内实现训练、验证和测试精度。由于数据集很大，我建议您尝试使用150 X 150 X3的模型和图像大小进行训练，以减少训练时间。所有文件也从每个物种的一个开始按顺序编号。所以测试图像被命名为1。jpg至5。jpg。对于验证图像也是如此。训练图像也用“零”填充顺序编号。例如001。jpg，002。jpg…010。jpg，011。jpg…。。099.jpg，100jpg，102。当与python文件函数和目录中的Keras流一起使用时，zero的填充保留了文件顺序。
+
+训练集是不平衡的，每个物种有不同数量的文件。然而，每个物种至少有120个训练图像文件。这种不平衡并没有影响我的内核分类器，因为它在测试集上达到了98%以上的准确率。
+
+数据集中一个显著的不平衡是雄性物种图像与雌性物种图像的比例。大约85%的图片是男性的，15%是女性的。典型的雄性动物的肤色要多样化得多，而一个物种的雌性动物通常是平淡无奇的。因此，男性和女性的形象可能看起来完全不同。几乎所有的测试和验证图像都来自该物种的雄性。因此，分类器可能无法在雌性物种图像上表现良好。