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huggingface_dataset/Dataset_Card/Atsushi_fungi_trait_circus_database.md

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+---
+annotations_creators:
+- other
+language:
+- en
+- ja
+multilinguality:
+- multilingual
+license:
+- cc-by-4.0
+source_datasets:
+- original
+size_categories:
+- 100K<n<1M
+---
+fungi_trait_circus_database  
+大菌輪「Trait Circus」データセット（統制形質）  
+最終更新日：2022/12/26  
+====
+### Languages
+Japanese and English 
+  
+Please do not use this dataset for academic purposes for the time being. (casual use only)  
+当面の間仮公開とします。学術目的での使用はご遠慮ください。  
+  
+# 概要
+  
+Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、菌類の記載文を自然言語処理の手法を利用して半自動的に処理し、菌類の形態、生態などに関する様々な「形質 (traits)」データを抽出して、集計や解析の便宜を図るために、あらかじめ設定された「統制語 (controlled term)」の形でまとめています。  
+抽出手法については「ニッチェ・ライフ」誌の[こちらの記事](https://media.niche-life.com/series/008/Niche008_06.pdf)（査読なし）で報告しています。  
+自動抽出という性質上、ある程度の誤りが含まれる可能性があることをご承知おきください。  
+  
+統制語は「要素 (element）」「属性（attribute）」「値（value）」の3つ組からなります。  
+例えば「傘_色_黒」はそれぞれ「傘」「色」「黒」の要素/属性/値を持っています。一部の統制語では要素と属性が同一となっています（「生息環境」など）  
+参考までに、データ数上位3件は「要素」で「子実体」「傘」「胞子」、「属性」で「色」「形状」「表面性状」、「値」で「褐」「平滑」「黄」です。      
+  
+また、菌類分類学の学習および同定支援の目的で、そのデータを基にしたインタラクティブな可視化Webアプリ「[Trait Circus](https://tinyurl.com/nrhcfksu)」を提供しています。
+本データセットは、そのWebアプリの生データに相当し、容量の都合等でWebアプリに反映されていない情報も含まれています。 
+
+## 関連データセット 
+「論文3行まとめ」  
+[Atsushi/fungi_indexed_mycological_papers_japanese](https://huggingface.co/datasets/Atsushi/fungi_indexed_mycological_papers_japanese)   
+「識別形質まとめ」  
+[Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese)     
+  
+## 各カラムの説明
+  
+* source … 各情報の出典のURLです。多くは学術文献またはMycoBankの記載文データベースを参照しています。
+* hit_term … 抽出された形質の出典中における表現です。
+* current_name … その形質を有する菌の現行学名です。MycoBankを参照していますが、最新の情報ではない可能性があります。
+* element_j … 「要素」の日本語表記です。
+* attribute_j … 「属性」の日本語表記です。
+* value_j … 「値」の日本語表記です。
+* element … 「要素」の英語表記です。
+* attribute … 「属性」の英語表記です。
+* value … 「値」の英語表記です。

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

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+---
+annotations_creators:
+- expert-generated
+
+language:
+- en
+
+multilinguality:
+- multilingual
+
+size_categories: []
+source_datasets: []
+tags: []
+
+task_categories:
+- text-retrieval
+
+license:
+- apache-2.0
+
+task_ids:
+- document-retrieval
+---
+
+# MIRACL (en) 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-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-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-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-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-en-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-en-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-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-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-en-corpus-22-12", split="train")
+doc_embeddings = torch.tensor(docs['emb'])
+
+# Load queries 
+queries = load_dataset(f"Cohere/miracl-en-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_Kunming_Dialect_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/Kunming_Dialect_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/39LRbrB
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+2,284 native speakers of Kunming dialect participated in the recording, with authentic accent and from multiple age groups. The recorded script covers a wide range of topics such as generic, interactive, on-board, and home. Local people in Kunming participated in quality check and proofreading, and the text was transferred accurately. It matches with mainstream Android and Apple system phones.
+                           
+For more details, please refer to the link: https://bit.ly/39LRbrB
+
+### 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
+
+Kunming Dialect
+## 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/Graverman_Instruct-to-Code.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- code
+- instuction
+size_categories:
+- 100K<n<1M
+dataset_info:
+  features:
+  - name: instruction
+    dtype: string
+  - name: answer
+    dtype: string
+  - name: original ds
+    dtype: string
+  - name: id
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 563327497
+    num_examples: 700000
+  download_size: 246890997
+  dataset_size: 563327497
+---
+Dataset with different instructions and the code that should be generated after those instructions.
+Made for main dataset of Open Assistant.
+
+If you want to contribute, message me on discord (Graverman#0804), here are some types of intructions left to be done:
+
+- Write a python funtion based on these instructions
+
+- What would be a description above on jupyter notebook for this code ✅
+
+- Given description that is above in a jupyter notebook, what could be the code ✅
+
+- Given the docstring, create instructions for the code 
+
+- Given code, create some instructions for that code
+
+- Rewrite the following code ✅
+
+- Explain this snippet of code ✅
+
+- Solve the following problem in python ✅
+
+id is an index in the original dataset that the code was taken from
+
+The dataset will have ~700k examples (in progress)

huggingface_dataset/Dataset_Card/Lloviant_autotrain-data-ex-and-pt.md

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+---
+task_categories:
+- image-classification
+
+---
+# AutoTrain Dataset for project: ex-and-pt
+
+## Dataset Description
+
+This dataset has been automatically processed by AutoTrain for project ex-and-pt.
+
+### Languages
+
+The BCP-47 code for the dataset's language is unk.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from this dataset looks as follows:
+
+```json
+[
+  {
+    "image": "<3840x2160 RGB PIL image>",
+    "target": 2
+  },
+  {
+    "image": "<3840x2160 RGBA PIL image>",
+    "target": 5
+  }
+]
+```
+
+### Dataset Fields
+
+The dataset has the following fields (also called "features"):
+
+```json
+{
+  "image": "Image(decode=True, id=None)",
+  "target": "ClassLabel(names=['EX and PT', 'EX and PT Logo', 'EX and PT Mutant', 'EX and PT Mutants', 'EX and PT TCG', 'Vagitron'], id=None)"
+}
+```
+
+### Dataset Splits
+
+This dataset is split into a train and validation split. The split sizes are as follow:
+
+| Split name   | Num samples         |
+| ------------ | ------------------- |
+| train        | 15 |
+| valid        | 7 |

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-adversarial_qa-adversarialQA-3783aa-1711959846.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- adversarial_qa
+eval_info:
+  task: extractive_question_answering
+  model: mrp/bert-finetuned-squad
+  metrics: []
+  dataset_name: adversarial_qa
+  dataset_config: adversarialQA
+  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: mrp/bert-finetuned-squad
+* Dataset: adversarial_qa
+* Config: adversarialQA
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__feed-top_vi-71f14a-2175469964.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/feed
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-6.7b
+  metrics: []
+  dataset_name: futin/feed
+  dataset_config: top_vi
+  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-6.7b
+* Dataset: futin/feed
+* Config: top_vi
+* 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/djghosh_wds_imagenet-a_test.md

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+# ImageNet-A (Test set only)
+
+Original paper: [Natural Adversarial Examples](https://arxiv.org/abs/1907.07174)
+
+Homepage: https://github.com/hendrycks/natural-adv-examples
+
+Bibtex:
+```
+@article{hendrycks2021nae,
+  title={Natural Adversarial Examples},
+  author={Dan Hendrycks and Kevin Zhao and Steven Basart and Jacob Steinhardt and Dawn Song},
+  journal={CVPR},
+  year={2021}
+}
+```

huggingface_dataset/Dataset_Card/edbeeching_github-issues.md

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+annotations_creators:
+- other
+language_creators:
+- crowdsourced
+languages:
+- en-US
+licenses:
+- other-my-license
+multilinguality:
+- monolingual
+pretty_name: HuggingFace Github Issues
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- text-classification
+- text-retrieval
+task_ids:
+- multi-class-classification
+- multi-label-classification
+- document-retrieval

huggingface_dataset/Dataset_Card/enwik8.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: enwik8
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- fill-mask
+- text-generation
+task_ids:
+- language-modeling
+- masked-language-modeling
+dataset_info:
+- config_name: enwik8
+  features:
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 104299244
+    num_examples: 1128024
+  download_size: 36445475
+  dataset_size: 102383126
+- config_name: enwik8-raw
+  features:
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 100000008
+    num_examples: 1
+  download_size: 36445475
+  dataset_size: 100000008
+---
+
+# Dataset Card for enwik8
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- **Homepage:** http://mattmahoney.net/dc/textdata.html
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** https://paperswithcode.com/sota/language-modelling-on-enwiki8
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+The enwik8 dataset is the first 100,000,000 (100M) bytes of the English Wikipedia XML dump on Mar. 3, 2006 and is typically used to measure a model's ability to compress data.
+
+### Supported Tasks and Leaderboards
+
+A leaderboard for byte-level causal language modelling can be found on [paperswithcode](https://paperswithcode.com/sota/language-modelling-on-enwiki8)
+
+### Languages
+
+en
+
+## Dataset Structure
+
+### Data Instances
+
+- **Size of downloaded dataset files:** 34.76 MB
+- **Size of generated dataset files:** 97.64 MB
+- **Total size:** 132.40 MB
+
+```
+{
+   "text": "In [[Denmark]], the [[Freetown Christiania]] was created in downtown [[Copenhagen]]....",
+}
+```
+
+### Data Fields
+
+The data fields are the same among all sets.
+
+#### enwik8
+
+- `text`: a `string` feature.
+
+#### enwik8-raw
+
+- `text`: a `string` feature.
+
+### Data Splits
+
+| dataset     | train   |
+| ---         | ---     |
+| enwik8      | 1128024	|
+| enwik8- raw | 1       |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data is just English Wikipedia XML dump on Mar. 3, 2006 split by line for enwik8 and not split by line for enwik8-raw.
+
+#### 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
+
+[Needs More Information]
+
+### Citation Information
+
+Dataset is not part of a publication, and can therefore not be cited.
+
+### Contributions
+
+Thanks to [@HallerPatrick](https://github.com/HallerPatrick) for adding this dataset and [@mtanghu](https://github.com/mtanghu) for updating it.

huggingface_dataset/Dataset_Card/irds_gov2.md

@@ -0,0 +1,35 @@
+---
+pretty_name: '`gov2`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `gov2`
+
+The `gov2` 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).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=25,205,179
+
+
+This dataset is used by: [`gov2_trec-tb-2004`](https://huggingface.co/datasets/irds/gov2_trec-tb-2004), [`gov2_trec-tb-2005`](https://huggingface.co/datasets/irds/gov2_trec-tb-2005), [`gov2_trec-tb-2005_efficiency`](https://huggingface.co/datasets/irds/gov2_trec-tb-2005_efficiency), [`gov2_trec-tb-2005_named-page`](https://huggingface.co/datasets/irds/gov2_trec-tb-2005_named-page), [`gov2_trec-tb-2006`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006), [`gov2_trec-tb-2006_efficiency`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency), [`gov2_trec-tb-2006_efficiency_10k`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_10k), [`gov2_trec-tb-2006_efficiency_stream1`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_stream1), [`gov2_trec-tb-2006_efficiency_stream2`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_stream2), [`gov2_trec-tb-2006_efficiency_stream3`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_stream3), [`gov2_trec-tb-2006_efficiency_stream4`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_efficiency_stream4), [`gov2_trec-tb-2006_named-page`](https://huggingface.co/datasets/irds/gov2_trec-tb-2006_named-page)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/gov2', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'url': ..., 'http_headers': ..., 'body': ..., 'body_content_type': ...}
+
+```
+
+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_gov_trec-web-2002.md

@@ -0,0 +1,49 @@
+---
+pretty_name: '`gov/trec-web-2002`'
+viewer: false
+source_datasets: ['irds/gov']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `gov/trec-web-2002`
+
+The `gov/trec-web-2002` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/gov#gov/trec-web-2002).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=50
+ - `qrels`: (relevance assessments); count=56,650
+
+ - For `docs`, use [`irds/gov`](https://huggingface.co/datasets/irds/gov)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/gov_trec-web-2002', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
+
+qrels = load_dataset('irds/gov_trec-web-2002', '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{Craswell2002TrecWeb,
+  title={Overview of the TREC-2002 Web Track},
+  author={Nick Craswell and David Hawking},
+  booktitle={TREC},
+  year={2002}
+}
+```

huggingface_dataset/Dataset_Card/irds_hc4_zh.md

@@ -0,0 +1,48 @@
+---
+pretty_name: '`hc4/zh`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `hc4/zh`
+
+The `hc4/zh` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/hc4#hc4/zh).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=646,305
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/hc4_zh', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'title': ..., 'text': ..., 'url': ..., 'time': ..., 'cc_file': ...}
+
+```
+
+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{Lawrie2022HC4,
+  author = {Dawn Lawrie and James Mayfield and Douglas W. Oard and Eugene Yang},
+  title = {HC4: A New Suite of Test Collections for Ad Hoc CLIR},
+  booktitle = {{Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022)},
+  year = {2022},
+  month = apr,
+  publisher = {Springer},
+  series = {Lecture Notes in Computer Science},
+  site = {Stavanger, Norway},
+  url = {https://arxiv.org/abs/2201.09992}
+}
+```

huggingface_dataset/Dataset_Card/jnieus01_narrative-arc.md

@@ -0,0 +1,159 @@
+---
+license: mit
+dataset_info:
+  features:
+  - name: distilbert-base-cased
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 32
+    num_examples: 2
+  download_size: 631
+  dataset_size: 32
+---
+---
+language_creators:
+- other
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: narrative-arc
+size_categories: []
+source_datasets: []
+tags: []
+task_categories:
+- text-classification
+task_ids: []
+---
+
+# Dataset Card for [narrative-arc]
+
+## 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:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Dataset of stories used for Narrative Arc post-processing. An instance of a story in this dataset will include the original text and its metadata, the transformer model used to make the embeddings, the model's checkpoint, the window indices of the stored embeddings, and the embeddings.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+An example story will look like the following:
+
+    {
+      "book name": "",
+        "book meta data": "",
+        "full text": "",
+        "model": {
+            "distilbert-base-cased": {
+                     "window indices": (first_index, last_index),
+                     "embeddings": [[]] },
+    
+            "distilbert-base-uncased": {
+                      "window indices": (first_index, last_index),
+                      "embeddings": [[]] 
+            }
+            },
+        }
+      ...
+    }
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+The processed text needs to be stored somewhere that is both accessible and can accomodate the large amount of data generated. 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data were sourced from the Project Gutenberg[https://www.gutenberg.org/] library.
+
+#### Who are the source language producers?
+
+Each instance in the dataset represents a text written by a human author. At present, data selected for processing are English-language short stories.
+
+### Personal and Sensitive Information
+
+Not applicable.
+
+## 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

huggingface_dataset/Dataset_Card/kimcando_KOR-RE-natures-and-environments.md

@@ -0,0 +1,21 @@
+---
+license: apache-2.0
+---
+
+# Dataset Card for [KOR-RE-natures-and-environments]
+
+You can find relation map, guidelines(written in Korean), short technical papers in this [github repo](https://github.com/boostcampaitech3/level2-data-annotation_nlp-level2-nlp-03). This work is done by as part of project for Boostcamp AI Tech supported by Naver Connect Foundation.
+
+### Dataset Description
+* Language: Korean
+* Task: Relation Extraction
+* Topics: Natures and Environments
+* Sources: Korean wiki
+
+
+### Main Data Fields
+* Sentences: sentences
+* Subject_entity: infos for subject entity in the sentence including words, start index, end index, type of entity
+* object_entity: infos for object entity in the sentence including words, start index, end index, type of entity
+* label : class ground truth label
+* file : name of the file

huggingface_dataset/Dataset_Card/lopezjm96_spanish_voices.md

@@ -0,0 +1,7 @@
+**DISCLAIMER:** None of the data here is of my property, but this is rather a extraction and compilation of data from different sources into one common place.
+
+Currently the sources are [spanish CSS10](https://www.kaggle.com/datasets/bryanpark/spanish-single-speaker-speech-dataset) and [this Kaggle Dataset](https://www.kaggle.com/datasets/carlfm01/120h-spanish-speech).
+
+The code used to create combine.zip can be found [here](https://github.com/lopezjuanma96/spanish_voices), it requires you to download the full datasets because the Kaggle API was not working properly, at least for me at the time of creating this: it only allowed me access to the first ~30 files of a dataset when trying to download specifically, the other option was downloading the whole dataset.
+
+The main reason I created this is for my project of adapting [this VITS fine-tuning](https://github.com/Plachtaa/VITS-fast-fine-tuning) script to [spanish](https://github.com/lopezjuanma96/VITS-fast-fine-tuning), therefore the format given to the transcript file and the distribution and amount of audio data, but it can probably be adapted to other formats easily.

huggingface_dataset/Dataset_Card/muchocine.md

@@ -0,0 +1,173 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- es
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+pretty_name: Muchocine
+dataset_info:
+  features:
+  - name: review_body
+    dtype: string
+  - name: review_summary
+    dtype: string
+  - name: star_rating
+    dtype:
+      class_label:
+        names:
+          '0': '1'
+          '1': '2'
+          '2': '3'
+          '3': '4'
+          '4': '5'
+  splits:
+  - name: train
+    num_bytes: 11871095
+    num_examples: 3872
+  download_size: 55556703
+  dataset_size: 11871095
+---
+
+# Dataset Card for Muchocine
+
+## 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:** http://www.lsi.us.es/~fermin/index.php/Datasets
+
+### Dataset Summary
+
+The Muchocine reviews dataset contains 3,872 longform movie reviews in Spanish language,
+each with a shorter summary review, and a rating on a 1-5 scale.
+
+### Supported Tasks and Leaderboards
+
+- `text-classification`: This dataset can be used for Text Classification, more precisely Sentiment Classification where the task is to predict the `star_rating` for a `reveiw_body` or a `review summaray`.
+
+### Languages
+
+Spanish.
+
+## Dataset Structure
+
+### Data Instances
+
+An example from the train split:
+
+```
+{
+  'review_body': 'Zoom nos cuenta la historia de Jack Shepard, anteriormente conocido como el Capitán Zoom, Superhéroe que perdió sus poderes y que actualmente vive en el olvido. La llegada de una amenaza para la Tierra hará que la agencia del gobierno que se ocupa de estos temas acuda a él para que entrene a un grupo de jóvenes con poderes para combatir esta amenaza.Zoom es una comedia familiar, con todo lo que eso implica, es decir, guión flojo y previsible, bromas no salidas de tono, historia amorosa de por medio y un desenlace tópico. La gracia está en que los protagonistas son jóvenes con superpoderes, una producción cargada de efectos especiales y unos cuantos guiños frikis. La película además se pasa volando ya que dura poco mas de ochenta minutos y cabe destacar su prologo en forma de dibujos de comics explicando la historia de la cual partimos en la película.Tim Allen protagoniza la cinta al lado de un envejecido Chevy Chase, que hace de doctor encargado del proyecto, un papel bastante gracioso y ridículo, pero sin duda el mejor papel es el de Courteney Cox, en la piel de una científica amante de los comics y de lo más friki. Del grupito de los cuatro niños sin duda la mas graciosa es la niña pequeña con súper fuerza y la que provocara la mayor parte de los gags debido a su poder.Una comedia entretenida y poca cosa más para ver una tarde de domingo. ',
+  'review_summary': 'Una comedia entretenida y poca cosa más para ver una tarde de domingo ', 'star_rating': 2
+}
+```
+
+### Data Fields
+
+- `review_body` - longform review
+- `review_summary` - shorter-form review
+- `star_rating` - an integer star rating (1-5)
+
+The original source also includes part-of-speech tagging for body and summary fields.
+
+### Data Splits
+
+One split (train) with 3,872 reviews.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Data was collected from www.muchocine.net and uploaded by Dr. Fermín L. Cruz Mata
+of La Universidad de Sevilla.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+The text reviews and star ratings came directly from users, so no additional annotation was 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
+
+Dr. Fermín L. Cruz Mata.
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+See http://www.lsi.us.es/~fermin/index.php/Datasets
+
+### Contributions
+
+Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset.

huggingface_dataset/Dataset_Card/muibk_wmt21_metrics_task.md

@@ -0,0 +1,182 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+- machine-generated
+- expert-generated
+language:
+- bn-hi
+- cs-en
+- de-en
+- de-fr
+- en-cs
+- en-de
+- en-ha
+- en-is
+- en-ja
+- en-ru
+- en-zh
+- fr-de
+- ha-en
+- hi-bn
+- is-en
+- ja-en
+- ru-en
+- xh-zh
+- zh-en
+- zu-xh
+license:
+- unknown
+multilinguality:
+- translation
+paperswithcode_id: null
+pretty_name: WMT21 Metrics Shared Task
+size_categories:
+- 100K<n<1M
+source_datasets: []
+task_categories:
+- translation
+task_ids: []
+---
+
+# Dataset Card for WMT21 Metrics Task
+
+## 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:** [WMT21 Metrics Shared Task](https://www.statmt.org/wmt21/metrics-task.html)
+- **Repository:** [MT Metrics Eval Github Repository](https://github.com/google-research/mt-metrics-eval)
+- **Paper:** [Paper](https://aclanthology.org/2021.wmt-1.73/)
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset comprises twenty language pairs:
+- Bengali-Hindi (`bn-hi`)
+- Czech-English (`cs-en`)
+- German-English (`de-en`)
+- German-French (`de-fr`)
+- English-Czech (`en-cs`)
+- English-German (`en-de`)
+- English-Hausa (`en-ha`)
+- English-Icelandic (`en-is`)
+- English-Japanese (`en-ja`)
+- English-Russian (`en-ru`)
+- English-Chinese (`en-zh`)
+- French-German (`fr-de`)
+- Hausa-English (`ha-en`)
+- Hindi-Bengali (`hi-bn`)
+- Icelandic-English (`is-en`)
+- Japenese-English (`ja-en`)
+- Russian-English (`ru-en`)
+- Xhosa-Zulu (`xh-zu`)
+- Chinese-English (`zh-en`)
+- Zulu-Xhosa (`zu-xh`)
+
+## 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

huggingface_dataset/Dataset_Card/nlpso_m2m3_fine_tuning_ref_ptrn_cmbert_io.md

@@ -0,0 +1,51 @@
+---
+language:
+- fr
+multilinguality:
+- monolingual
+task_categories:
+- token-classification
+---
+
+# m2m3_fine_tuning_ref_ptrn_cmbert_io
+
+## Introduction
+
+This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1]. 
+It contains Paris trade directories entries from the 19th century.
+
+## Dataset parameters
+
+* Approachrd : M2 and M3
+* Dataset type : ground-truth
+* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
+* Tagging format : IO
+* Counts : 
+    * Train : 6084
+    * Dev : 676
+    * Test : 1685
+* Associated fine-tuned models :
+    * M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_io)
+    * M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io)
+    
+## Entity types
+
+Abbreviation|Entity group (level)|Description
+-|-|-
+O |1 & 2|Outside of a named entity
+PER |1|Person or company name
+ACT |1 & 2|Person or company professional activity
+TITREH |2|Military or civil distinction
+DESC |1|Entry full description
+TITREP |2|Professionnal reward
+SPAT |1|Address
+LOC |2|Street name
+CARDINAL |2|Street number
+FT |2|Geographical feature
+
+## How to use this dataset
+
+```python
+from datasets import load_dataset
+
+train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ref_ptrn_cmbert_io")

huggingface_dataset/Dataset_Card/projecte-aina_Parafraseja.md

@@ -0,0 +1,155 @@
+---
+annotations_creators:
+- CLiC-UB
+language_creators:
+- found
+language:
+- ca
+license:
+- cc-by-nc-nd-4.0
+multilinguality:
+- monolingual
+pretty_name: Parafraseja
+size_categories:
+- ?
+task_categories:
+- text-classification
+task_ids:
+- multi-input-text-classification
+---
+
+# Dataset Card for Parafraseja
+
+## 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)
+  - [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
+
+- **Point of Contact:** [blanca.calvo@bsc.es](blanca.calvo@bsc.es)
+
+### Dataset Summary
+
+Parafraseja is a dataset of 21,984 pairs of sentences with a label that indicates if they are paraphrases or not. The original sentences were collected from [TE-ca](https://huggingface.co/datasets/projecte-aina/teca) and [STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca). For each sentence, an annotator wrote a sentence that was a paraphrase and another that was not. The guidelines of this annotation are available. 
+
+### Supported Tasks and Leaderboards
+
+This dataset is mainly intended to train models for paraphrase detection.
+
+### Languages
+
+The dataset is in Catalan (`ca-CA`).
+
+## Dataset Structure
+
+The dataset consists of pairs of sentences labelled with "Parafrasis" or "No Parafrasis" in a jsonl format.
+
+### Data Instances
+
+<pre>
+  {
+    "id": "te1_14977_1", 
+    "source": "teca", 
+    "original": "La 2a part consta de 23 cap\u00edtols, cadascun dels quals descriu un ocell diferent.", 
+    "new": "La segona part consisteix en vint-i-tres cap\u00edtols, cada un dels quals descriu un ocell diferent.", 
+    "label": "Parafrasis"
+   }
+</pre>
+
+### Data Fields
+- original: original sentence
+- new: new sentence, which could be a paraphrase or a non-paraphrase
+- label: relation between original and new
+
+### Data Splits
+
+* dev.json: 2,000 examples
+* test.json: 4,000 examples
+* train.json: 15,984 examples
+
+## Dataset Creation
+
+### Curation Rationale
+
+We created this corpus to contribute to the development of language models in Catalan, a low-resource language. 
+
+### Source Data
+
+The original sentences of this dataset came from the [STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) and the [TE-ca](https://huggingface.co/datasets/projecte-aina/teca).
+
+#### Initial Data Collection and Normalization
+
+11,543 of the original sentences came from TE-ca, and 10,441 came from STS-ca.
+
+#### Who are the source language producers?
+
+TE-ca and STS-ca come from the [Catalan Textual Corpus](https://zenodo.org/record/4519349#.Y1Zs__uxXJF), which consists of several corpora gathered from web crawling and public corpora, and [Vilaweb](https://www.vilaweb.cat), a Catalan newswire.
+
+### Annotations
+
+The dataset is annotated with the label "Parafrasis" or "No Parafrasis" for each pair of sentences.
+
+#### Annotation process
+
+The annotation process was done by a single annotator and reviewed by another. 
+
+#### Who are the annotators?
+
+The annotators were Catalan native speakers, with a background on linguistics.
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
+
+### Discussion of Biases
+
+We are aware that this data might contain biases. We have not applied any steps to reduce their impact.
+
+### Other Known Limitations
+
+[N/A]
+
+## Additional Information
+
+### Dataset Curators
+
+Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
+
+
+This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+
+### Licensing Information
+
+[Creative Commons Attribution Non-commercial No-Derivatives 4.0 International](https://creativecommons.org/licenses/by-nc-nd/4.0/).
+
+
+
+### Contributions
+
+[N/A]