Upload batch 332 (20 files, last=huggingface_dataset/Dataset_Card/nikitam_ACES.md)

huggingface_dataset/Dataset_Card/DMetaSoul_chinese-semantic-textual-similarity.md

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+---
+license: apache-2.0
+---
+
+为了对 like-BERT 预训练模型进行 fine-tune 调优和评测以得到更好的文本表征模，对业界开源的语义相似（STS）、自然语言推理（NLI）、问题匹配（QMC）以及相关性等数据集进行了搜集整理，具体介绍如下：
+
+| 类型           | 数据集                                                       | 简介                                                         | 规模                                               |
+| -------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------------------------------------------- |
+| **通用领域**   | [OCNLI](https://www.cluebenchmarks.com/introduce.html)       | 原生中文自然语言推理数据集，是第一个非翻译的、使用原生汉语的大型中文自然语言推理数据集。OCNLI为中文语言理解基准测评（CLUE）的一部分。 | **Train**:  50437, **Dev**: 2950                   |
+|                | [CMNLI](https://github.com/pluto-junzeng/CNSD)               | 翻译自英文自然语言推理数据集 XNLI 和 MNLI，曾经是中文语言理解基准测评（CLUE）的一部分，现在被 OCNLI 取代。 | **Train**: 391783, **Dev**: 12241                  |
+|                | [CSNLI](https://github.com/pluto-junzeng/CNSD)               | 翻译自英文自然语言推理数据集 SNLI。                          | **Train**: 545833, **Dev**: 9314, **Test**: 9176   |
+|                | [STS-B-Chinese](https://github.com/pluto-junzeng/CNSD)       | 翻译自英文语义相似数据集 STSbenchmark。                      | **Train**: 5231, **Dev**: 1458, **Test**: 1361     |
+|                | [PAWS-X](https://www.luge.ai/#/luge/dataDetail?id=16)        | 释义（含义）匹配数据集，特点是具有高度重叠词汇，重点考察模型对句法结构的理解能力。 | **Train**: 49401, **Dev**: 2000, **Test**: 2000    |
+|                | [PKU-Paraphrase-Bank](https://github.com/pkucoli/PKU-Paraphrase-Bank/) | 中文语句复述数据集，也即一句话换种方式描述但语义保持一致。   | 共509832个语句对                                   |
+| **问题匹配**   | [LCQMC](https://www.luge.ai/#/luge/dataDetail?id=14)         | 百度知道领域的中文问题匹配大规模数据集，该数据集从百度知道不同领域的用户问题中抽取构建数据。 | **Train**: 238766, **Dev**: 8802, **Test**: 12500  |
+|                | [BQCorpus](https://www.luge.ai/#/luge/dataDetail?id=15)      | 银行金融领域的问题匹配数据，包括了从一年的线上银行系统日志里抽取的问题pair对，是目前最大的银行领域问题匹配数据。 | **Train**: 100000, **Dev**: 10000, **Test**: 10000 |
+|                | [AFQMC](https://www.cluebenchmarks.com/introduce.html)       | 蚂蚁金服真实金融业务场景中的问题匹配数据集（已脱敏），是中文语言理解基准测评（CLUE）的一部分。 | **Train**: 34334, **Dev**: 4316                    |
+|                | [DuQM](https://www.luge.ai/#/luge/dataDetail?id=27)          | 问题匹配评测数据集（label没有公开），属于百度大规模阅读理解数据集（DuReader）的[一部分](https://github.com/baidu/DuReader/tree/master/DuQM)。 | 共50000个语句对                                    |
+| **对话和搜索** | [BUSTM: OPPO-xiaobu](https://www.luge.ai/#/luge/dataDetail?id=28) | 通过对闲聊、智能客服、影音娱乐、信息查询等多领域真实用户交互语料进行用户信息脱敏、相似度筛选处理得到，该对话匹配（Dialogue Short Text Matching）数据集主要特点是文本较短、非常口语化、存在文本高度相似而语义不同的难例。 | **Train**: 167173, **Dev**: 10000                  |
+|                | [QBQTC](https://github.com/CLUEbenchmark/QBQTC)              | QQ浏览器搜索相关性数据集（QBQTC,QQ Browser Query Title Corpus），是QQ浏览器搜索引擎目前针对大搜场景构建的一个融合了相关性、权威性、内容质量、 时效性等维度标注的学习排序（LTR）数据集，广泛应用在搜索引擎业务场景中。（相关性的含义：0，相关程度差；1，有一定相关性；2，非常相关。） | **Train**: 180000, **Dev**: 20000, **Test**: 5000  |
+
+*以上数据集主要收集整理自[CLUE](https://www.cluebenchmarks.com/introduce.html)（中文语言理解基准评测数据集）、[SimCLUE](https://github.com/CLUEbenchmark/SimCLUE) (整合许多开源文本相似数据集)、[百度千言](https://www.luge.ai/#/)的文本相似度等数据集。*
+
+根据以上收集的数据集构建如下**评测 benchmark**：
+
+| Name                   | Size  | Type          |
+| ---------------------- | ----- | ------------- |
+| **Chinese-STS-B-dev**  | 1458  | label=0.0~1.0 |
+| **Chinese-STS-B-test** | 1361  | label=0.0~1.0 |
+| **afqmc-dev**          | 4316  | label=0,1     |
+| **lcqmc-dev**          | 8802  | label=0,1     |
+| **bqcorpus-dev**       | 10000 | label=0,1     |
+| **pawsx_dev**          | 2000  | label=0,1     |
+| **oppo-xiaobu-dev**    | 10000 | label=0,1     |
+
+*TODO：目前收集的数据集不论是数量还是多样性都需要进一步扩充以更真实的反映表征模型的效果*

huggingface_dataset/Dataset_Card/KnutJaegersberg_science_finding_sentence_certainty.md

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+---
+license: mit
+---
+
+
+This data is from the github repo https://github.com/Jiaxin-Pei/Certainty-in-Science-Communication about the paper "Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications" by Jiaxin Pei and David Jurgens.
+I put it here for later use, to train an ML model which estimates claim certainty. 

huggingface_dataset/Dataset_Card/LHF_escorpius.md

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+---
+license: cc-by-nc-nd-4.0
+language:
+- es
+multilinguality:
+- monolingual
+size_categories:
+- 100M<n<1B
+source_datasets:
+- original
+task_categories:
+  - text-generation
+  - fill-mask
+task_ids:
+  - language-modeling
+  - masked-language-modeling
+---
+
+
+# esCorpius: A Massive Spanish Crawling Corpus
+
+## Introduction
+In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, the results in Spanish present important shortcomings, as they are either too small in comparison with other languages, or present a low quality derived from sub-optimal cleaning and deduplication. In this work, we introduce esCorpius, a Spanish crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in Spanish with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius has been released under CC BY-NC-ND 4.0 license.
+
+## Statistics
+| **Corpus**              | OSCAR<br>22.01 | mC4          | CC-100          | ParaCrawl<br>v9 | esCorpius<br>(ours) |
+|-------------------------|----------------|--------------|-----------------|-----------------|-------------------------|
+| **Size (ES)**           | 381.9 GB       | 1,600.0 GB   | 53.3 GB         | 24.0 GB         | 322.5 GB                |
+| **Docs (ES)**           | 51M            | 416M         | -               | -               | 104M                    |
+| **Words (ES)**          | 42,829M        | 433,000M     | 9,374M          | 4,374M          | 50,773M                 |
+| **Lang.<br>identifier** | fastText       | CLD3         | fastText        | CLD2            | CLD2 + fastText         |
+| **Elements**            | Document       | Document     | Document        | Sentence        | Document and paragraph  |
+| **Parsing quality**     | Medium         | Low          | Medium          | High            | High                    |
+| **Cleaning quality**    | Low            | No cleaning  | Low             | High            | High                    |
+| **Deduplication**       | No             | No           | No              | Bicleaner       | dLHF                    |
+| **Language**            | Multilingual   | Multilingual | Multilingual    | Multilingual    | Spanish                 |
+| **License**             | CC-BY-4.0      | ODC-By-v1.0  | Common<br>Crawl | CC0             | CC-BY-NC-ND             |
+
+
+## Citation
+Link to the paper: https://www.isca-speech.org/archive/pdfs/iberspeech_2022/gutierrezfandino22_iberspeech.pdf / https://arxiv.org/abs/2206.15147
+
+Cite this work:
+```
+@inproceedings{gutierrezfandino22_iberspeech,
+  author={Asier Gutiérrez-Fandiño and David Pérez-Fernández and Jordi Armengol-Estapé and David Griol and Zoraida Callejas},
+  title={{esCorpius: A Massive Spanish Crawling Corpus}},
+  year=2022,
+  booktitle={Proc. IberSPEECH 2022},
+  pages={126--130},
+  doi={10.21437/IberSPEECH.2022-26}
+}
+```
+## Disclaimer
+We did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus.

huggingface_dataset/Dataset_Card/LisaBanana_french_QAGenerator.md

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+---
+license: unknown
+---
+This Dataset is ispired by the https://huggingface.co/iarfmoose/t5-base-question-generator designed to train a T5 model for question generation. 
+It contains edited datas from the FQuad, the Piaff and some synthetic data generated from the french wikipedia dataset with the promptify library.
+
+References :
+
+FQuad : https://fquad.illuin.tech/
+
+@ARTICLE{2020arXiv200206071
+       author = {{Martin}, d'Hoffschmidt and {Maxime}, Vidal and
+         {Wacim}, Belblidia and {Tom}, Brendlé},
+        title = "{FQuAD: French Question Answering Dataset}",
+      journal = {arXiv e-prints},
+     keywords = {Computer Science - Computation and Language},
+         year = "2020",
+        month = "Feb",
+          eid = {arXiv:2002.06071},
+        pages = {arXiv:2002.06071},
+archivePrefix = {arXiv},
+       eprint = {2002.06071},
+ primaryClass = {cs.CL}
+}
+Piaf : https://www.data.gouv.fr/en/datasets/piaf-le-dataset-francophone-de-questions-reponses/
+
+French Wikipedia dataset : http://redac.univ-tlse2.fr/corpora/wikipedia_en.html
+Promptify : https://github.com/promptslab/Promptify

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-acronym_identification-default-641c5d-40516105295.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- acronym_identification
+eval_info:
+  task: entity_extraction
+  model: lewtun/autotrain-acronym-identification-7324788
+  metrics: ['angelina-wang/directional_bias_amplification']
+  dataset_name: acronym_identification
+  dataset_config: default
+  dataset_split: validation
+  col_mapping:
+    tokens: tokens
+    tags: labels
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Token Classification
+* Model: lewtun/autotrain-acronym-identification-7324788
+* Dataset: acronym_identification
+* Config: default
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@wandb.init(project=PROJECT](https://huggingface.co/wandb.init(project=PROJECT) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-Blaise-g__SumPubmed-24db4c9a-12575677.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- Blaise-g/SumPubmed
+eval_info:
+  task: summarization
+  model: L-macc/autotrain-Biomedical_sc_summ-1217846148
+  metrics: []
+  dataset_name: Blaise-g/SumPubmed
+  dataset_config: Blaise-g--SumPubmed
+  dataset_split: test
+  col_mapping:
+    text: text
+    target: abstract
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: L-macc/autotrain-Biomedical_sc_summ-1217846148
+* Dataset: Blaise-g/SumPubmed
+* Config: Blaise-g--SumPubmed
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@L-macc](https://huggingface.co/L-macc) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-e1907042-7494835.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- clinc_oos
+eval_info:
+  task: multi_class_classification
+  model: jackmleitch/distilbert-base-uncased-distilled-clinc
+  metrics: []
+  dataset_name: clinc_oos
+  dataset_config: small
+  dataset_split: test
+  col_mapping:
+    text: text
+    target: intent
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Multi-class Text Classification
+* Model: jackmleitch/distilbert-base-uncased-distilled-clinc
+* Dataset: clinc_oos
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-f9a2c1a2-7774983.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- lewtun/dog_food
+eval_info:
+  task: image_multi_class_classification
+  model: abhishek/convnext-tiny-finetuned-dogfood
+  metrics: ['matthews_correlation']
+  dataset_name: lewtun/dog_food
+  dataset_config: lewtun--dog_food
+  dataset_split: test
+  col_mapping:
+    image: image
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Multi-class Image Classification
+* Model: abhishek/convnext-tiny-finetuned-dogfood
+* Dataset: lewtun/dog_food
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-squad_v2-e85023ec-11745563.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: deepset/deberta-v3-base-squad2
+  metrics: []
+  dataset_name: squad_v2
+  dataset_config: squad_v2
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: deepset/deberta-v3-base-squad2
+* Dataset: squad_v2
+* Config: squad_v2
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model.

huggingface_dataset/Dataset_Card/bio-datasets_e3c-llm.md

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+---
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: tokens_offsets
+    sequence:
+      sequence: int32
+  - name: clinical_entity_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-CLINENTITY
+          '2': I-CLINENTITY
+  config_name: e3c-llm
+  splits:
+  - name: fr.layer2
+    num_bytes: 1583560
+    num_examples: 2389
+  - name: fr.layer2.validation
+    num_bytes: 170233
+    num_examples: 293
+  - name: fr.layer1
+    num_bytes: 758368
+    num_examples: 1109
+  download_size: 0
+  dataset_size: 2512161
+---
+# Dataset Card for E3C
+
+## Dataset Description
+
+- **Public:** True
+- **Tasks:** NER
+
+This dataset is an annotated corpus of clinical texts from E3C using Large Language Models (LLM).

huggingface_dataset/Dataset_Card/collectivat_araina-text-corpus.md

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+---
+license: cc0-1.0
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- oc
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+---
+
+# Araina Text Corpus
+
+Text corpus in [Aranese variety of Gascon dialect of Occitan](https://en.wikipedia.org/wiki/Aranese_dialect).
+
+## Corpora
+
+- `_nogues`: Literary texts translated by Antòni Nogués. Sourced from [institutestudisaranesi.cat](http://www.institutestudisaranesi.cat/colleccion-antoni-nogues/#1541013646532-338ed5f5-a3aa)
+- `_suils`: Language educational material by Jordi Suïls Subirà
+- `_conselh`: Administrative proceedings from Conselh Generau d'Aran
+
+## Project Araina
+
+This corpus was prepared as part of [Project Araina](https://www.projecte-araina.org) with support from Culture Department of the Catalan autonomous government.
+
+Aquest corpus s'ha elaborat en el marc del [Projecte Araina](https://www.projecte-araina.org) amb el suport del Departament de Cultura de la Generalitat de Catalunya.
+
+<img src="https://github.com/collectivat/cmusphinx-models/raw/master/img/logo_generalitat.png" width="400"/>
+

huggingface_dataset/Dataset_Card/cwinkler_patents_green_plastics.md

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+---
+dataset_info:
+  features:
+  - name: abstract
+    dtype: string
+  - name: label
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 8088461
+    num_examples: 11196
+  download_size: 4025753
+  dataset_size: 8088461
+language:
+- en
+size_categories:
+- 10K<n<100K
+---
+# Dataset Card for "Patents_Green_Plastics"
+
+    number of rows: 11.196
+    features: [title, label]
+    label: 0, 1
+
+The dataset contains patent abstracts that are labeled as 1 (="Green Plastics") and 0 (="Not Green Plastics").
+
+# Dataset Creation
+
+The [BIGPATENT](https://huggingface.co/datasets/big_patent) dataset is the source for this dataset.
+
+In a first step, abstracts of BIGPATENT were filtered by the terms "plastics" and "polymer". The resulting "Plastics" dataset contained 64.372 samples.
+
+In a second step, the 64.372 samples were filtered by terms which define "green plastics". 
+
+"Green Plastics" are defined by the list of terms: 
+"degrada", "recycl", "bio", "compost", "bact", "waste recovery", "zero waste", "sustainab", "Bio-Based", "Bio-Degradable", "Renewable", "Green Plastics", "Renewable",  "Degradable", "Compostable", "Bio-resorbable", "Bio-soluble", "Cellulose", "Biodegradable","Mycelium", "Recyclability", "Degradability", "Bio-Polymer", "reuse", "reusable",  "reusing", "Degradation", "Multiple Use", "Bioplastic", "Polyhydroxyalkanoates", "PHA", "Polylactide", "PLA", "Polyglycolide", "PGA"
+(some terms might repeat)
+
+The group of "Green Plastics" containing 5.598 rows was labeled as 1. 
+
+An equal amount of samples (=5.598 rows) was randomly chosen from the "Plastics" dataset, defined as "Not Green Plastics" and labeled as 0. 
+
+Both groups ("Green Plastics" and "Not Green Plastics") were merged together.

huggingface_dataset/Dataset_Card/huggingartists_nicki-minaj.md

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+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/nicki-minaj"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [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)
+- [About](#about)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of the generated dataset:** 2.01836 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/8ae5a5e5e030cb67814165bd038af48f.1000x1000x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/nicki-minaj">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">Nicki Minaj</div>
+    <a href="https://genius.com/artists/nicki-minaj">
+    	<div style="text-align: center; font-size: 14px;">@nicki-minaj</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/nicki-minaj).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/nicki-minaj")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|847|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/nicki-minaj")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/irds_c4_en-noclean-tr_trec-misinfo-2021.md

@@ -0,0 +1,33 @@
+---
+pretty_name: '`c4/en-noclean-tr/trec-misinfo-2021`'
+viewer: false
+source_datasets: ['irds/c4_en-noclean-tr']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `c4/en-noclean-tr/trec-misinfo-2021`
+
+The `c4/en-noclean-tr/trec-misinfo-2021` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/c4#c4/en-noclean-tr/trec-misinfo-2021).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=50
+
+ - For `docs`, use [`irds/c4_en-noclean-tr`](https://huggingface.co/datasets/irds/c4_en-noclean-tr)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/c4_en-noclean-tr_trec-misinfo-2021', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ..., 'description': ..., 'narrative': ..., 'disclaimer': ..., 'stance': ..., 'evidence': ...}
+
+```
+
+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.

huggingface_dataset/Dataset_Card/irds_cord19.md

@@ -0,0 +1,46 @@
+---
+pretty_name: '`cord19`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `cord19`
+
+The `cord19` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/cord19#cord19).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=192,509
+
+
+This dataset is used by: [`cord19_trec-covid`](https://huggingface.co/datasets/irds/cord19_trec-covid), [`cord19_trec-covid_round5`](https://huggingface.co/datasets/irds/cord19_trec-covid_round5)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/cord19', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'title': ..., 'doi': ..., 'date': ..., 'abstract': ...}
+
+```
+
+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
+
+```
+@article{Wang2020Cord19,
+  title={CORD-19: The Covid-19 Open Research Dataset},
+  author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
+  journal={ArXiv},
+  year={2020}
+}
+```

huggingface_dataset/Dataset_Card/nikitam_ACES.md

@@ -0,0 +1,160 @@
+---
+language:
+- multilingual
+license:
+- cc-by-nc-sa-4.0 
+multilinguality:
+- multilingual
+source_datasets:
+- FLORES-101, FLORES-200, PAWS-X, XNLI, XTREME, WinoMT, Wino-X, MuCOW, EuroParl ConDisco, ParcorFull
+task_categories:
+- translation
+pretty_name: ACES
+
+---
+
+# Dataset Card for ACES
+
+## 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)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Usage](#usage)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contact](#contact)
+  
+## Dataset Description
+
+- **Repository:** [ACES dataset repository](https://github.com/EdinburghNLP/ACES)
+- **Paper:** [arXiv](https://arxiv.org/abs/2210.15615)
+
+
+### Dataset Summary
+
+ACES consists of 36,476 examples covering 146 language pairs and representing challenges from 68 phenomena for evaluating machine translation metrics. We focus on translation accuracy errors and base the phenomena covered in our challenge set on the Multidimensional Quality Metrics (MQM) ontology. The phenomena range from simple perturbations at the word/character level to more complex errors based on discourse and real-world knowledge. 
+### Supported Tasks and Leaderboards
+
+-Machine translation evaluation of metrics
+
+-Potentially useful for contrastive machine translation evaluation
+
+### Languages
+
+The dataset covers 146 language pairs as follows: 
+
+af-en, af-fa, ar-en, ar-fr, ar-hi, be-en, bg-en, bg-lt, ca-en, ca-es, cs-en, da-en, de-en, de-es, de-fr, de-ja, de-ko, de-ru, de-zh, el-en, en-af, en-ar, en-be, en-bg, en-ca, en-cs, en-da, en-de, en-el, en-es, en-et, en-fa, en-fi, en-fr, en-gl, en-he, en-hi, en-hr, en-hu, en-hy, en-id, en-it, en-ja, en-ko, en-lt, en-lv, en-mr, en-nl, en-no, en-pl, en-pt, en-ro, en-ru, en-sk, en-sl, en-sr, en-sv, en-ta, en-tr, en-uk, en-ur, en-vi, en-zh, es-ca, es-de, es-en, es-fr, es-ja, es-ko, es-zh, et-en, fa-af, fa-en, fi-en, fr-de, fr-en, fr-es, fr-ja, fr-ko, fr-mr, fr-ru, fr-zh, ga-en, gl-en, he-en, he-sv, hi-ar, hi-en, hr-en, hr-lv, hu-en, hy-en, hy-vi, id-en, it-en, ja-de, ja-en, ja-es, ja-fr, ja-ko, ja-zh, ko-de, ko-en, ko-es, ko-fr, ko-ja, ko-zh, lt-bg, lt-en, lv-en, lv-hr, mr-en, nl-en, no-en, pl-en, pl-mr, pl-sk, pt-en, pt-sr, ro-en, ru-de, ru-en, ru-es, ru-fr, sk-en, sk-pl, sl-en, sr-en, sr-pt, sv-en, sv-he, sw-en, ta-en, th-en, tr-en, uk-en, ur-en, vi-en, vi-hy, wo-en, zh-de, zh-en, zh-es, zh-fr, zh-ja, zh-ko
+
+## Dataset Structure
+
+### Data Instances
+
+Each data instance contains the following features: _source_, _good-translation_, _incorrect-translation_, _reference_, _phenomena_, _langpair_
+
+See the [ACES corpus viewer](https://huggingface.co/datasets/nikitam/ACES/viewer/nikitam--ACES/train) to explore more examples.
+
+An example from the ACES challenge set looks like the following:
+```
+{'source': "Proper nutritional practices alone cannot generate elite performances, but they can significantly affect athletes' overall wellness.", 'good-translation': 'Las prácticas nutricionales adecuadas por sí solas no pueden generar rendimiento de élite, pero pueden afectar significativamente el bienestar general de los atletas.', 'incorrect-translation': 'Las prácticas nutricionales adecuadas por sí solas no pueden generar rendimiento de élite, pero pueden afectar significativamente el bienestar general de los jóvenes atletas.', 'reference': 'No es posible que las prácticas nutricionales adecuadas, por sí solas, generen un rendimiento de elite, pero puede influir en gran medida el bienestar general de los atletas .', 'phenomena': 'addition', 'langpair': 'en-es'}
+
+
+```
+
+### Data Fields
+
+- 'source': a string containing the text that needs to be translated
+- 'good-translation': possible translation of the source sentence
+- 'incorrect-translation': translation of the source sentence that contains an error or phenomenon of interest
+- 'reference': the gold standard translation 
+- 'phenomena': the type of error or phenomena being studied in the example
+- 'langpair': the source language and the target language pair of the example
+
+Note that the _good-translation_ may not be free of errors but it is a better translation than the _incorrect-translation_
+
+### Data Splits
+
+The ACES dataset has 1 split: _train_ which contains the challenge set. There are  36476 examples. 
+
+## Dataset Creation
+
+### Curation Rationale
+
+With the advent of neural networks and especially Transformer-based architectures, machine translation outputs have become more and more fluent. Fluency errors are also judged less severely than accuracy errors by human evaluators \citep{freitag-etal-2021-experts} which reflects the fact that accuracy errors can have dangerous consequences in certain contexts, for example in the medical and legal domains. For these reasons, we decided to build a challenge set focused on accuracy errors. 
+
+Another aspect we focus on is including a broad range of language pairs in ACES. Whenever possible we create examples for all language pairs covered in a source dataset when we use automatic approaches. For phenomena where we create examples manually, we also aim to cover at least two language pairs per phenomenon but are of course limited to the languages spoken by the authors.
+
+We aim to offer a collection of challenge sets covering both easy and hard phenomena. While it may be of interest to the community to continuously test on harder examples to check where machine translation evaluation metrics still break, we believe that easy challenge sets are just as important to ensure that metrics do not suddenly become worse at identifying error types that were previously considered ``solved''. Therefore, we take a holistic view when creating ACES and do not filter out individual examples or exclude challenge sets based on baseline metric performance or other factors.
+
+
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Please see Sections 4 and 5 of the paper. 
+
+#### Who are the source language producers?
+
+The dataset contains sentences found in FLORES-101, FLORES-200, PAWS-X, XNLI, XTREME, WinoMT, Wino-X, MuCOW, EuroParl ConDisco, ParcorFull datasets. Please refer to the respective papers for further details. 
+
+### Personal and Sensitive Information
+
+The external datasets may contain sensitive information. Refer to the respective datasets for further details. 
+
+## Considerations for Using the Data
+
+### Usage
+
+ACES has been primarily designed to evaluate machine translation metrics on the accuracy errors. We expect the metric to score _good-translation_ consistently higher than _incorrect-translation_. We report the performance of metric based on Kendall-tau like correlation. It measures the number of times a metric scores the good translation above the incorrect translation (concordant) and equal to or lower than the incorrect translation (discordant).
+
+### Discussion of Biases
+
+Some examples within the challenge set exhibit biases, however, this is necessary in order to expose the limitations of existing metrics.
+
+### Other Known Limitations
+The ACES challenge set exhibits a number of biases. Firstly, there is greater coverage in terms of phenomena and the number of examples for the en-de and en-fr language pairs. This is in part due to the manual effort required to construct examples for some phenomena, in particular, those belonging to the discourse-level and real-world knowledge categories. Further, our choice of language pairs is also limited to the ones available in XLM-R. Secondly, ACES contains more examples for those phenomena for which examples could be generated automatically, compared to those that required manual construction/filtering. Thirdly, some of the automatically generated examples require external libraries which are only available for a few languages (e.g. Multilingual Wordnet). Fourthly, the focus of the challenge set is on accuracy errors. We leave the development of challenge sets for fluency errors to future work.
+
+As a result of using existing datasets as the basis for many of the examples, errors present in these datasets may be propagated through into ACES. Whilst we acknowledge that this is undesirable, in our methods for constructing the incorrect translation we aim to ensure that the quality of the incorrect translation is always worse than the corresponding good translation.
+
+The results and analyses presented in the paper exclude those metrics submitted to the WMT 2022 metrics shared task that provides only system-level outputs. We focus on metrics that provide segment-level outputs as this enables us to provide a broad overview of metric performance on different phenomenon categories and to conduct fine-grained analyses of performance on individual phenomena. For some of the fine-grained analyses, we apply additional constraints based on the language pairs covered by the metrics, or whether the metrics take the source as input, to address specific questions of interest. As a result of applying some of these additional constraints, our investigations tend to focus more on high and medium-resource languages than on low-resource languages. We hope to address this shortcoming in future work.
+
+
+## Additional Information
+
+
+### Licensing Information
+
+The ACES dataset is Creative Commons Attribution Non-Commercial Share Alike 4.0 (cc-by-nc-sa-4.0) 
+
+### Citation Information
+
+@inproceedings{amrhein-aces-2022,
+title = "{ACES}: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics",
+author = {Amrhein, Chantal  and
+  Moghe, Nikita and
+  Guillou, Liane},
+booktitle = "Seventh Conference on Machine Translation (WMT22)",
+month = dec,
+year = "2022",
+address = "Abu Dhabi, United Arab Emirates",
+publisher = "Association for Computational Linguistics",
+eprint = {2210.15615}
+}
+
+### Contact
+[Chantal Amrhein](mailto:amrhein@cl.uzh.ch) and [Nikita Moghe](mailto:nikita.moghe@ed.ac.uk) and [Liane Guillou](mailto:lguillou@ed.ac.uk)
+
+Dataset card based on [Allociné](https://huggingface.co/datasets/allocine) 

huggingface_dataset/Dataset_Card/nlpso_m0_fine_tuning_ocr_cmbert_io.md

@@ -0,0 +1,46 @@
+---
+language:
+- fr
+multilinguality:
+- monolingual
+task_categories:
+- token-classification
+---
+
+# m0_fine_tuning_ocr_cmbert_io
+
+## Introduction
+
+This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **flat NER task** using Flat NER approach [M0]. 
+It contains 19th-century Paris trade directories' entries.
+
+## Dataset parameters
+
+* Approach : M0
+* Dataset type : noisy (Pero OCR)
+* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
+* Tagging format : IO
+* Counts : 
+    * Train : 6084
+    * Dev : 676
+    * Test : 1685
+* Associated fine-tuned model : [nlpso/m0_flat_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ocr_cmbert_io)
+    
+## Entity types
+
+Abbreviation|Description
+-|-
+O |Outside of a named entity
+PER |Person or company name
+ACT |Person or company professional activity
+TITRE |Distinction
+LOC |Street name
+CARDINAL |Street number
+FT |Geographical feature
+
+## How to use this dataset
+
+```python
+from datasets import load_dataset
+
+train_dev_test = load_dataset("nlpso/m0_fine_tuning_ocr_cmbert_io")

huggingface_dataset/Dataset_Card/silver_mmchat.md

@@ -0,0 +1,258 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- zh
+license:
+- other
+multilinguality:
+- monolingual
+paperswithcode_id: mmchat-multi-modal-chat-dataset-on-social
+pretty_name: "MMChat: Multi-Modal Chat Dataset on Social Media"
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- conversational
+task_ids:
+- dialogue-generation
+---
+
+# Dataset Card for MMChat
+
+## Table of Contents
+- [Dataset Card for MMChat](#dataset-card-for-mmchat)
+  - [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)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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://www.zhengyinhe.com/datasets/
+- **Repository:** https://github.com/silverriver/MMChat
+- **Paper:** https://arxiv.org/abs/2108.07154
+
+### Dataset Summary
+
+MMChat is a large-scale dialogue dataset that contains image-grounded dialogues in Chinese. Each dialogue in MMChat is associated with one or more images (maximum 9 images per dialogue). We design various strategies to ensure the quality of the dialogues in MMChat.
+
+MMChat comes with 4 different versions: 
+
+- `mmchat`: The MMChat dataset used in our paper.
+- `mmchat_hf`: Contains human annotation on 100K sessions of dialogues. 
+- `mmchat_raw`: Raw dialogues used to construct MMChat.
+  `mmchat_lccc_filtered`: Raw dialogues filtered using the LCCC dataset.
+
+If you what to use high quality multi-modal dialogues that are closed related to the given images, I suggest you to use the `mmchat_hf` version.
+If you only care about the quality of dialogue texts, I suggest you to use the `mmchat_lccc_filtered` version.
+
+### Supported Tasks and Leaderboards
+
+- dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
+- response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.
+
+### Languages
+
+MMChat is in Chinese
+
+MMChat中的对话是中文的
+
+## Dataset Structure
+
+### Data Instances
+
+Several versions of MMChat are available. For `mmchat`, `mmchat_raw`, `mmchat_lccc_filtered`, the following instance applies:
+
+```json
+{
+  "dialog": ["你只拍出了你十分之一的美", "你的头像竟然换了，奥"],
+  "weibo_content": "分享图片",
+  "imgs": ["https://wx4.sinaimg.cn/mw2048/d716a6e2ly1fmug2w2l9qj21o02yox6p.jpg"]
+}
+```
+
+For `mmchat_hf`, the following instance applies:
+
+```json
+{
+  "dialog": ["白百合", "啊？", "有点像", "还好吧哈哈哈牙像", "有男盆友没呢", "还没", "和你说话呢。没回我"],
+  "weibo_content": "补一张昨天礼仪的照片",
+  "imgs": ["https://ww2.sinaimg.cn/mw2048/005Co9wdjw1eyoz7ib9n5j307w0bu3z5.jpg"],
+  "labels": {
+    "image_qualified": true, 
+    "dialog_qualified": true, 
+    "dialog_image_related": true
+  }
+}
+```
+
+### Data Fields
+
+- `dialog` (list of strings): List of utterances consisting of a dialogue.
+- `weibo_content` (string): Weibo content of the dialogue.
+- `imgs` (list of strings): List of URLs of images.
+- `labels` (dict): Human-annotated labels of the dialogue.
+- `image_qualified` (bool): Whether the image is of high quality.
+- `dialog_qualified` (bool): Whether the dialogue is of high quality.
+- `dialog_image_related` (bool): Whether the dialogue is related to the image.
+
+### Data Splits
+
+For `mmchat`, we provide the following splits:
+
+|train|valid|test|
+|---:|---:|---:|
+|115,842 | 4,000 | 1,000 |
+
+For other versions, we do not provide the offical split.
+More stastics are listed here:
+
+| `mmchat`                   | Count   |
+|--------------------------------------|--------:|
+| Sessions                             | 120.84 K |
+| Sessions with more than 4 utterances |  17.32 K |
+| Utterances                           | 314.13 K |
+| Images                               |  198.82 K |
+| Avg. utterance per session           |  2.599 |
+| Avg. image per session               |  2.791 |
+| Avg. character per utterance         |  8.521 |
+
+| `mmchat_hf`                     | Count   |
+|--------------------------------------|--------:|
+| Sessions                             | 19.90 K |
+| Sessions with more than 4 utterances | 8.91 K |
+| Totally annotated sessions           | 100.01 K |
+| Utterances                           | 81.06 K |
+| Images                               | 52.66K |
+| Avg. utterance per session           | 4.07 |
+| Avg. image per session               | 2.70 |
+| Avg. character per utterance         | 11.93 |
+
+| `mmchat_raw`                     | Count    |
+|--------------------------------------|---------:|
+| Sessions                             | 4.257 M  |
+| Sessions with more than 4 utterances | 2.304 M  |
+| Utterances                           | 18.590 M |
+| Images                               | 4.874 M  |
+| Avg. utterance per session           | 4.367    |
+| Avg. image per session               | 1.670    |
+| Avg. character per utterance         | 14.104   |
+
+| `mmchat_lccc_filtered`                     | Count   |
+|--------------------------------------|--------:|
+| Sessions                             | 492.6 K |
+| Sessions with more than 4 utterances | 208.8 K |
+| Utterances                           | 1.986 M |
+| Images                               | 1.066 M |
+| Avg. utterance per session           | 4.031   |
+| Avg. image per session               | 2.514   |
+| Avg. character per utterance         | 11.336  |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+other-weibo
+
+This dataset is collected from Weibo.
+You can refer to the [detailed policy](https://weibo.com/signup/v5/privacy) required to use this dataset.
+Please restrict the usage of this dataset to non-commerical purposes.
+
+### Citation Information
+
+```
+@inproceedings{zheng2022MMChat,
+  author    = {Zheng, Yinhe and Chen, Guanyi and Liu, Xin and Sun, Jian},
+  title     = {MMChat: Multi-Modal Chat Dataset on Social Media},
+  booktitle = {Proceedings of The 13th Language Resources and Evaluation Conference},
+  year      = {2022},
+  publisher = {European Language Resources Association},
+}
+
+@inproceedings{wang2020chinese,
+  title={A Large-Scale Chinese Short-Text Conversation Dataset},
+  author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
+  booktitle={NLPCC},
+  year={2020},
+  url={https://arxiv.org/abs/2008.03946}
+}
+```
+
+### Contributions
+
+Thanks to [Yinhe Zheng](https://github.com/silverriver) for adding this dataset.

huggingface_dataset/Dataset_Card/solomonk_reddit_mental_health_posts.md

@@ -0,0 +1,24 @@
+# Reddit posts about mental health
+
+## files
+
+- adhd.csv from r/adhd
+- aspergers.csv from r/aspergers
+- depression.csv from r/depression
+- ocd.csv from r/ocd
+- ptsd.csv from r/ptsd
+
+## fields
+
+- author
+- body
+- created_utc
+- id
+- num_comments
+- score
+- subreddit
+- title
+- upvote_ratio
+- url
+
+for more details about theses fields [Praw Submission](https://praw.readthedocs.io/en/latest/code_overview/models/submission.html).

huggingface_dataset/Dataset_Card/udhr.md

@@ -0,0 +1,646 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- aa
+- ab
+- ace
+- acu
+- ada
+- ady
+- af
+- agr
+- aii
+- ajg
+- als
+- alt
+- am
+- amc
+- ame
+- ami
+- amr
+- ar
+- arl
+- arn
+- ast
+- auc
+- ay
+- az
+- ban
+- bax
+- bba
+- bci
+- be
+- bem
+- bfa
+- bg
+- bho
+- bi
+- bik
+- bin
+- blt
+- bm
+- bn
+- bo
+- boa
+- br
+- bs
+- buc
+- bug
+- bum
+- ca
+- cab
+- cak
+- cbi
+- cbr
+- cbs
+- cbt
+- cbu
+- ccp
+- ceb
+- cfm
+- ch
+- chj
+- chk
+- chr
+- cic
+- cjk
+- cjs
+- cjy
+- ckb
+- cnh
+- cni
+- cnr
+- co
+- cof
+- cot
+- cpu
+- crh
+- cri
+- crs
+- cs
+- csa
+- csw
+- ctd
+- cy
+- da
+- dag
+- ddn
+- de
+- dga
+- dip
+- duu
+- dv
+- dyo
+- dyu
+- dz
+- ee
+- el
+- en
+- eo
+- es
+- ese
+- et
+- eu
+- eve
+- evn
+- fa
+- fat
+- fi
+- fj
+- fkv
+- fo
+- fon
+- fr
+- fuf
+- fur
+- fuv
+- fvr
+- fy
+- ga
+- gaa
+- gag
+- gan
+- gd
+- gjn
+- gkp
+- gl
+- gld
+- gn
+- gsw
+- gu
+- guc
+- guu
+- gv
+- gyr
+- ha
+- hak
+- haw
+- he
+- hi
+- hil
+- hlt
+- hmn
+- hms
+- hna
+- hni
+- hnj
+- hns
+- hr
+- hsb
+- hsn
+- ht
+- hu
+- hus
+- huu
+- hy
+- ia
+- ibb
+- id
+- idu
+- ig
+- ii
+- ijs
+- ilo
+- io
+- is
+- it
+- iu
+- ja
+- jiv
+- jv
+- ka
+- kaa
+- kbd
+- kbp
+- kde
+- kdh
+- kea
+- kek
+- kg
+- kha
+- kjh
+- kk
+- kkh
+- kl
+- km
+- kmb
+- kn
+- ko
+- koi
+- koo
+- kqn
+- kqs
+- kr
+- kri
+- krl
+- ktu
+- ku
+- kwi
+- ky
+- la
+- lad
+- lah
+- lb
+- lg
+- lia
+- lij
+- lld
+- ln
+- lns
+- lo
+- lob
+- lot
+- loz
+- lt
+- lua
+- lue
+- lun
+- lus
+- lv
+- mad
+- mag
+- mai
+- mam
+- man
+- maz
+- mcd
+- mcf
+- men
+- mfq
+- mg
+- mh
+- mi
+- mic
+- min
+- miq
+- mk
+- ml
+- mn
+- mnw
+- mor
+- mos
+- mr
+- mt
+- mto
+- mxi
+- mxv
+- my
+- mzi
+- nan
+- nb
+- nba
+- nds
+- ne
+- ng
+- nhn
+- nio
+- niu
+- niv
+- njo
+- nku
+- nl
+- nn
+- not
+- nr
+- nso
+- nv
+- ny
+- nym
+- nyn
+- nzi
+- oaa
+- oc
+- ojb
+- oki
+- om
+- orh
+- os
+- ote
+- pa
+- pam
+- pap
+- pau
+- pbb
+- pcd
+- pcm
+- pis
+- piu
+- pl
+- pon
+- pov
+- ppl
+- prq
+- ps
+- pt
+- qu
+- quc
+- qug
+- quh
+- quy
+- qva
+- qvc
+- qvh
+- qvm
+- qvn
+- qwh
+- qxn
+- qxu
+- rar
+- rgn
+- rm
+- rmn
+- rn
+- ro
+- ru
+- rup
+- rw
+- sa
+- sah
+- sc
+- sco
+- se
+- sey
+- sg
+- shk
+- shn
+- shp
+- si
+- sk
+- skr
+- sl
+- slr
+- sm
+- sn
+- snk
+- snn
+- so
+- sr
+- srr
+- ss
+- st
+- su
+- suk
+- sus
+- sv
+- sw
+- swb
+- ta
+- taj
+- tbz
+- tca
+- tdt
+- te
+- tem
+- tet
+- tg
+- th
+- ti
+- tiv
+- tk
+- tl
+- tly
+- tn
+- to
+- tob
+- toi
+- toj
+- top
+- tpi
+- tr
+- ts
+- tsz
+- tt
+- tw
+- ty
+- tyv
+- tzh
+- tzm
+- tzo
+- udu
+- ug
+- uk
+- umb
+- und
+- ur
+- ura
+- uz
+- vai
+- ve
+- vec
+- vep
+- vi
+- vmw
+- wa
+- war
+- wo
+- wuu
+- wwa
+- xh
+- xsm
+- yad
+- yao
+- yap
+- yi
+- ykg
+- yo
+- yrk
+- yua
+- yue
+- za
+- zam
+- zdj
+- zgh
+- zh
+- zlm
+- zro
+- ztu
+- zu
+language_bcp47:
+- az-Cyrl
+- az-Latn
+- bs-Cyrl
+- bs-Latn
+- ckb-Latn
+- de-1901
+- de-1996
+- el-monoton
+- el-polyton
+- fa-AF
+- fuf-Adlm
+- ha-NE
+- ha-NG
+- jv-Java
+- kg-AO
+- kkh-Lana
+- mn-Cyrl
+- pt-BR
+- pt-PT
+- rm-puter
+- rm-rumgr
+- rm-surmiran
+- rm-sursilv
+- rm-sutsilv
+- rm-vallader
+- sa-Gran
+- sr-Cyrl
+- sr-Latn
+- ta-LK
+- tk-Cyrl
+- tk-Latn
+- tw-akuapem
+- tw-asante
+- ug-Arab
+- ug-Latn
+- uz-Cyrl
+- uz-Latn
+- vi-Hani
+- zh-Hant
+- zlm-Arab
+- zlm-Latn
+license:
+- unknown
+multilinguality:
+- multilingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- translation
+task_ids: []
+paperswithcode_id: null
+pretty_name: The Universal Declaration of Human Rights (UDHR)
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: lang_key
+    dtype: string
+  - name: lang_name
+    dtype: string
+  - name: iso639-3
+    dtype: string
+  - name: iso15924
+    dtype: string
+  - name: bcp47
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 6753383
+    num_examples: 488
+  download_size: 2389690
+  dataset_size: 6753383
+---
+
+# Dataset Card for The Universal Declaration of Human Rights (UDHR)
+
+## 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://www.ohchr.org/en/universal-declaration-of-human-rights, https://unicode.org/udhr/index.html
+- **Repository:** https://github.com/unicode-org/udhr
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The Universal Declaration of Human Rights (UDHR) is a milestone document in the history of human rights. Drafted by
+representatives with different legal and cultural backgrounds from all regions of the world, it set out, for the
+first time, fundamental human rights to be universally protected. The Declaration was adopted by the UN General
+Assembly in Paris on 10 December 1948 during its 183rd plenary meeting.
+
+© 1996 – 2009 The Office of the High Commissioner for Human Rights
+
+This plain text version prepared by the "UDHR in Unicode" project, https://www.unicode.org/udhr.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset includes translations of the document in over 400 languages and dialects. The list of languages can be found
+[here](https://unicode.org/udhr/translations.html).
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance corresponds to a different language and includes information about the language and the full document
+text.
+
+### Data Fields
+
+- `text`: The full document text with each line of text delimited by a newline (`\n`).
+- `lang_key`: The unique identifier of a given translation.
+- `lang_name`: The textual description of language/dialect.
+- `iso639-3`: The [iso639-3](https://iso639-3.sil.org/) language identifier.
+- `iso15924`: The [iso15924](https://unicode.org/iso15924/iso15924-codes.html) language identifier.
+- `bcp47`: The [BCP 47](https://www.rfc-editor.org/info/bcp47) language identifier.
+
+### Data Splits
+
+Only a `train` split included which includes the full document in all languages.
+
+|                    | train |
+|--------------------|------:|
+| Number of examples |   488 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+In addition to its social significance, the document set a world record in 1999 for being the most translated
+document in the world and as such can be useful for settings requiring paired text between many languages.
+
+### 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
+
+In addition to the social and political significance of the United Nations' Universal Declaration of Human Rights,
+the document set a world record in 1999 for being the most translated document in the world and as such can be useful
+for settings requiring paired text between many languages including those that are low resource and significantly
+underrepresented in NLP research.
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+Although the document is translated into a very large number of languages, the text is very short and therefore may
+have limited usefulness for most types of modeling and evaluation.
+
+## Additional Information
+
+### Dataset Curators
+
+The txt/xml data files used here were compiled by The Unicode Consortium, which can be found
+[here](https://unicode.org/udhr/index.html). The original texts can be found on the
+[United Nations website](https://www.ohchr.org/EN/UDHR/Pages/UDHRIndex.aspx).
+
+### Licensing Information
+
+Source text © 1996 – 2022 The Office of the High Commissioner for Human Rights
+
+The [Unicode license](https://www.unicode.org/license.txt) applies to these translations.
+
+
+### Citation Information
+
+United Nations. (1998). The Universal Declaration of Human Rights, 1948-1998. New York: United Nations Dept. of Public Information.
+
+### Contributions
+
+Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. Updated May 2022 [@leondz](https://github.com/leondz).