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huggingface_dataset/Dataset_Card/Cohere_miracl-te-corpus-22-12.md

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+---
+annotations_creators:
+- expert-generated
+
+language:
+- te
+
+multilinguality:
+- multilingual
+
+size_categories: []
+source_datasets: []
+tags: []
+
+task_categories:
+- text-retrieval
+
+license:
+- apache-2.0
+
+task_ids:
+- document-retrieval
+---
+
+# MIRACL (te) 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-te-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-te-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-te-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-te-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-te-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-te-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-te-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-te-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-te-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-te-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-te-corpus-22-12", split="train")
+doc_embeddings = torch.tensor(docs['emb'])
+
+# Load queries 
+queries = load_dataset(f"Cohere/miracl-te-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/NicholasSynovic_bert-autotrain-1.md

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+---
+task_categories:
+- text-classification
+
+---
+# AutoTrain Dataset for project: test
+
+## Dataset Description
+
+This dataset has been automatically processed by AutoTrain for project test.
+
+### 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
+[
+  {
+    "feat_textID": "500d5b1751",
+    "text": "Almost time to say Good Bye to my twimulations. I`ll miss my tweeps",
+    "target": 0
+  },
+  {
+    "feat_textID": "05832fb2c4",
+    "text": "did you kno that  is amazing  and i`ve known him since he got twitter and his most tweeted words are `know` `haha` `****`..",
+    "target": 1
+  }
+]
+```
+
+### Dataset Fields
+
+The dataset has the following fields (also called "features"):
+
+```json
+{
+  "feat_textID": "Value(dtype='string', id=None)",
+  "text": "Value(dtype='string', id=None)",
+  "target": "ClassLabel(num_classes=2, names=['negative', 'positive'], 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        | 2229 |
+| valid        | 558 |

huggingface_dataset/Dataset_Card/adorkin_extended_tweet_emojis.md

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+---
+task_categories:
+- text-classification
+language:
+- en
+size_categories:
+- 10K<n<100K
+---
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Homepage:** 
+- **Repository:** 
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+This dataset is comprised of `emoji` and `emotion` subsets of [tweet_eval](https://huggingface.co/datasets/tweet_eval). The motivation
+is that the original `emoji` subset essentially contains only positive/neutral emojis, while `emotion` subset contains a varied array
+of emotions. So, the idea was to replace emotion labels with corresponding emojis (sad, angry) in the `emotion` subset and mix it together
+with the `emoji` subset.
+
+### Supported Tasks and Leaderboards
+
+Similar to tweet eval the expected usage is text classification.
+
+### Languages
+
+Only English is present in the dataset.
+
+## 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
+
+Refer to [tweet_eval](https://huggingface.co/datasets/tweet_eval). No additional data was added.
+
+#### Annotation process
+
+Same as tweet eval.
+
+#### Who are the annotators?
+
+Same as tweet eval.
+
+### Personal and Sensitive Information
+
+Same as tweet eval.
+
+## 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
+
+[More Information Needed]

huggingface_dataset/Dataset_Card/allenai_multixscience_dense_max.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- summarization
+paperswithcode_id: multi-xscience
+pretty_name: Multi-XScience
+---
+
+This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `test` split have been replaced by a __dense__ retriever. The retrieval pipeline used:
+
+- __query__: The `related_work` field of each example
+- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
+- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
+- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==20`
+
+Retrieval results on the `train` set:
+
+| Recall@100 | Rprec | Precision@k | Recall@k |
+| ----------- | ----------- | ----------- | ----------- |
+| 0.5270 | 0.2005 | 0.0573 | 0.3785 |
+
+Retrieval results on the `validation` set:
+
+| Recall@100 | Rprec | Precision@k | Recall@k |
+| ----------- | ----------- | ----------- | ----------- |
+| 0.5310 | 0.2026 | 0.059 | 0.3831 |
+
+Retrieval results on the `test` set:
+
+| Recall@100 | Rprec | Precision@k | Recall@k |
+| ----------- | ----------- | ----------- | ----------- |
+| 0.5229 | 0.2081 | 0.058 | 0.3794 |

huggingface_dataset/Dataset_Card/anukaver_EstQA.md

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+---
+language: et
+---
+
+# Estonian Question Answering dataset
+
+* Dataset for extractive question answering in Estonian. It is based on Wikipedia articles, pre-filtered via PageRank. Annotation was done by one person.
+* Train set includes 776 context-question-answer triplets. There are several possible answers per question, each in a separate triplet. Number of different questions is 512.
+* Test set includes 603 samples. Each sample contains one or more golden answers. Altogether there are 892 golden ansewrs.
+
+### Change log
+Test set v1.1 adds some more golden answers.
+
+### Reference
+If you use this dataset for research, please cite the following paper:
+
+```
+@mastersthesis{mastersthesis,
+  author       = {Anu Käver}, 
+  title        = {Extractive Question Answering for Estonian Language},
+  school       = {Tallinn University of Technology (TalTech)},
+  year         = 2021
+}
+```

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-emotion-default-2feb36-1456053837.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- emotion
+eval_info:
+  task: multi_class_classification
+  model: Emanuel/twitter-emotion-deberta-v3-base
+  metrics: []
+  dataset_name: emotion
+  dataset_config: default
+  dataset_split: test
+  col_mapping:
+    text: text
+    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 Text Classification
+* Model: Emanuel/twitter-emotion-deberta-v3-base
+* Dataset: emotion
+* Config: default
+* Split: test
+
+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-c76b0e96-8395129.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- scientific_papers
+eval_info:
+  task: summarization
+  model: google/bigbird-pegasus-large-arxiv
+  metrics: ['bertscore', 'meteor']
+  dataset_name: scientific_papers
+  dataset_config: pubmed
+  dataset_split: test
+  col_mapping:
+    text: article
+    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: google/bigbird-pegasus-large-arxiv
+* Dataset: scientific_papers
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-squad_v2-96a02c9c-11975602.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: nlpconnect/roberta-base-squad2-nq
+  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: nlpconnect/roberta-base-squad2-nq
+* 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 [@ankur310794](https://huggingface.co/ankur310794) for evaluating this model.

huggingface_dataset/Dataset_Card/emrecan_stsb-mt-turkish.md

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+---
+language_creators:
+- machine-generated
+language:
+- tr
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|other-sts-b
+task_categories:
+- text-classification
+task_ids:
+- semantic-similarity-scoring
+- text-scoring
+---
+
+# STSb Turkish
+
+Semantic textual similarity dataset for the Turkish language. It is a machine translation (Azure) of the [STSb English](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) dataset. This dataset is not reviewed by expert human translators.
+
+Uploaded from [this repository](https://github.com/emrecncelik/sts-benchmark-tr). 

huggingface_dataset/Dataset_Card/fake_news_filipino.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+language:
+- tl
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- fact-checking
+paperswithcode_id: fake-news-filipino-dataset
+pretty_name: Fake News Filipino
+dataset_info:
+  features:
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': '0'
+          '1': '1'
+  - name: article
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3623685
+    num_examples: 3206
+  download_size: 1313458
+  dataset_size: 3623685
+---
+
+# Dataset Card for Fake News Filipino
+
+## 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:** [Fake News Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
+- **Repository:** [Fake News Filipino repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
+- **Paper:** [LREC 2020 paper](http://www.lrec-conf.org/proceedings/lrec2020/index.html)
+- **Leaderboard:**
+- **Point of Contact:** [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
+
+### Dataset Summary
+
+Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular.
+
+## Dataset Structure
+
+### Data Instances
+
+Sample data:
+```
+{
+  "label": "0",
+  "article": "Sa 8-pahinang desisyon, pinaboran ng Sandiganbayan First Division ang petition for Writ of Preliminary Attachment/Garnishment na inihain ng prosekusyon laban sa mambabatas."
+}
+```
+
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+Fake news articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real news articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera.
+
+### Curation Rationale
+
+We remedy the lack of a proper, curated benchmark dataset for fake news detection in Filipino by constructing and producing what we call “Fake News Filipino.” 
+
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+We construct the dataset by scraping our source websites, encoding all characters into UTF-8. Preprocessing was light to keep information intact: we retain capitalization and punctuation, and do not correct any misspelled words.
+
+#### Who are the source language producers?
+
+Jan Christian Blaise Cruz, Julianne Agatha Tan, and Charibeth Cheng
+
+### 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
+
+[Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph), Julianne Agatha Tan, and Charibeth Cheng
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+    @inproceedings{cruz2020localization,
+      title={Localization of Fake News Detection via Multitask Transfer Learning},
+      author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
+      booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
+      pages={2596--2604},
+      year={2020}
+    }
+
+### Contributions
+
+Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset.

huggingface_dataset/Dataset_Card/huggingartists_the-weeknd.md

@@ -0,0 +1,188 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/the-weeknd"
+
+## 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)
+
+## 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:** 1.849373 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/f0813e600d43b8b43c94e8ba1dde880a.640x640x1.png&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/the-weeknd">
+      <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">The Weeknd</div>
+    <a href="https://genius.com/artists/the-weeknd">
+    	<div style="text-align: center; font-size: 14px;">@the-weeknd</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/the-weeknd).
+
+### 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/the-weeknd")
+```
+
+## 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|
+|------:|---------:|---:|
+|TRAIN_1.849373|         -|   -|
+
+'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/the-weeknd")
+
+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
+}
+```

huggingface_dataset/Dataset_Card/huggingartists_v-x-v-prince.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/v-x-v-prince"
+
+## 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:** 0.198634 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/08ad78acc3e91c45a426390e7524d4e9.853x853x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/v-x-v-prince">
+      <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">V $ X V PRiNCE</div>
+    <a href="https://genius.com/artists/v-x-v-prince">
+    	<div style="text-align: center; font-size: 14px;">@v-x-v-prince</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/v-x-v-prince).
+
+### 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/v-x-v-prince")
+```
+
+## 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|
+|------:|---------:|---:|
+|77|         -|   -|
+
+'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/v-x-v-prince")
+
+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/income_cqadupstack-mathematica-top-20-gen-queries.md

@@ -0,0 +1,510 @@
+---
+annotations_creators: []
+language_creators: []
+language:
+- en
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+paperswithcode_id: beir
+pretty_name: BEIR Benchmark
+size_categories:
+  msmarco:
+  - 1M<n<10M
+  trec-covid:
+  - 100k<n<1M
+  nfcorpus:
+  - 1K<n<10K
+  nq:
+  - 1M<n<10M
+  hotpotqa:
+  - 1M<n<10M
+  fiqa:
+  - 10K<n<100K
+  arguana:
+  - 1K<n<10K
+  touche-2020:
+  - 100K<n<1M
+  cqadupstack:
+  - 100K<n<1M
+  quora:
+  - 100K<n<1M
+  dbpedia:
+  - 1M<n<10M
+  scidocs:
+  - 10K<n<100K
+  fever:
+  - 1M<n<10M
+  climate-fever:
+  - 1M<n<10M
+  scifact:
+  - 1K<n<10K
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# NFCorpus: 20 generated queries (BEIR Benchmark)
+
+This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
+
+- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
+- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
+- Questions generated: 20
+- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
+
+
+Below contains the old dataset card for the BEIR benchmark.
+
+
+# Dataset Card for BEIR Benchmark
+
+## 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://github.com/UKPLab/beir
+- **Repository:** https://github.com/UKPLab/beir
+- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
+- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
+- **Point of Contact:** nandan.thakur@uwaterloo.ca
+
+### Dataset Summary
+
+BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
+
+- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
+- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
+- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
+- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
+- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
+- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
+- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
+- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
+- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
+
+All these datasets have been preprocessed and can be used for your experiments.
+
+
+```python
+
+```
+
+### Supported Tasks and Leaderboards
+
+The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
+
+The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
+
+### Languages
+
+All tasks are in English (`en`).
+
+## Dataset Structure
+
+All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
+- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
+- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
+- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
+
+### Data Instances
+
+A high level example of any beir dataset:
+
+```python
+corpus = {
+    "doc1" : {
+        "title": "Albert Einstein", 
+        "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
+                 one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
+                 its influence on the philosophy of science. He is best known to the general public for his mass–energy \
+                 equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
+                 Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
+                 of the photoelectric effect', a pivotal step in the development of quantum theory."
+        },
+    "doc2" : {
+        "title": "", # Keep title an empty string if not present
+        "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
+                 malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
+                 with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
+    },
+}
+
+queries = {
+    "q1" : "Who developed the mass-energy equivalence formula?",
+    "q2" : "Which beer is brewed with a large proportion of wheat?"
+}
+
+qrels = {
+    "q1" : {"doc1": 1},
+    "q2" : {"doc2": 1},
+}
+```
+
+### Data Fields
+
+Examples from all configurations have the following features:
+
+### Corpus
+- `corpus`: a `dict` feature representing the document title and passage text, made up of:
+    - `_id`: a `string` feature representing the unique document id
+        - `title`: a `string` feature, denoting the title of the document.
+        - `text`: a `string` feature, denoting the text of the document.
+
+### Queries
+- `queries`: a `dict` feature representing the query, made up of:
+    - `_id`: a `string` feature representing the unique query id
+    - `text`: a `string` feature, denoting the text of the query.
+
+### Qrels
+- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
+    - `_id`: a `string` feature representing the query id
+        - `_id`: a `string` feature, denoting the document id.
+        - `score`: a `int32` feature, denoting the relevance judgement between query and document.
+
+
+### Data Splits
+
+| Dataset   | Website| BEIR-Name | Type | Queries  | Corpus | Rel D/Q | Down-load | md5 |
+| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
+| MSMARCO    | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``|  6,980   |  8.84M     |    1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
+| TREC-COVID |  [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50|  171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
+| NFCorpus   | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``|  323     |  3.6K     |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
+| BioASQ     | [Homepage](http://bioasq.org) | ``bioasq``|  ``train``<br>``test`` | 500    |  14.91M    |  8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
+| NQ         | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452   |  2.68M  |  1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
+| HotpotQA   | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``|  7,405   |  5.23M  |  2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip)  | ``f412724f78b0d91183a0e86805e16114`` |
+| FiQA-2018  | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``|  648     |  57K    |  2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip)  | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
+| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97   |  2.86M  |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
+| TREC-NEWS  | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news``    | ``test``| 57    |  595K    |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
+| ArguAna    | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406     |  8.67K    |  1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip)  | ``8ad3e3c2a5867cdced806d6503f29b99`` |
+| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49     |  382K    |  19.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
+| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 |  457K  |  1.4 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
+| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000     |  523K    |  1.6 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
+| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400    |  4.63M    |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
+| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000     |  25K    |  4.9 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
+| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``|  6,666     |  5.42M    |  1.2|  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip)  | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
+| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``|  1,535     |  5.42M |  3.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip)  | ``8b66f0a9126c521bae2bde127b4dc99d`` |
+| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``|  300     |  5K    |  1.1 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip)  | ``5f7d1de60b170fc8027bb7898e2efca1`` |
+| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249  |  528K  |  69.9 |  No  |  [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04)  |
+
+
+## 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
+
+[Needs More Information]
+
+### Citation Information
+
+Cite as:
+```
+@inproceedings{
+thakur2021beir,
+title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
+author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
+booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
+year={2021},
+url={https://openreview.net/forum?id=wCu6T5xFjeJ}
+}
+```
+
+### Contributions
+
+Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus 
+
+
+# Dataset Card for BEIR Benchmark
+
+## 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://github.com/UKPLab/beir
+- **Repository:** https://github.com/UKPLab/beir
+- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
+- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
+- **Point of Contact:** nandan.thakur@uwaterloo.ca
+
+### Dataset Summary
+
+BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
+
+- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
+- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
+- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
+- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
+- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
+- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
+- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
+- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
+- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
+
+All these datasets have been preprocessed and can be used for your experiments.
+
+
+```python
+
+```
+
+### Supported Tasks and Leaderboards
+
+The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
+
+The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
+
+### Languages
+
+All tasks are in English (`en`).
+
+## Dataset Structure
+
+All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
+- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
+- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
+- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
+
+### Data Instances
+
+A high level example of any beir dataset:
+
+```python
+corpus = {
+    "doc1" : {
+        "title": "Albert Einstein", 
+        "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
+                 one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
+                 its influence on the philosophy of science. He is best known to the general public for his mass–energy \
+                 equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
+                 Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
+                 of the photoelectric effect', a pivotal step in the development of quantum theory."
+        },
+    "doc2" : {
+        "title": "", # Keep title an empty string if not present
+        "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
+                 malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
+                 with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
+    },
+}
+
+queries = {
+    "q1" : "Who developed the mass-energy equivalence formula?",
+    "q2" : "Which beer is brewed with a large proportion of wheat?"
+}
+
+qrels = {
+    "q1" : {"doc1": 1},
+    "q2" : {"doc2": 1},
+}
+```
+
+### Data Fields
+
+Examples from all configurations have the following features:
+
+### Corpus
+- `corpus`: a `dict` feature representing the document title and passage text, made up of:
+    - `_id`: a `string` feature representing the unique document id
+        - `title`: a `string` feature, denoting the title of the document.
+        - `text`: a `string` feature, denoting the text of the document.
+
+### Queries
+- `queries`: a `dict` feature representing the query, made up of:
+    - `_id`: a `string` feature representing the unique query id
+    - `text`: a `string` feature, denoting the text of the query.
+
+### Qrels
+- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
+    - `_id`: a `string` feature representing the query id
+        - `_id`: a `string` feature, denoting the document id.
+        - `score`: a `int32` feature, denoting the relevance judgement between query and document.
+
+
+### Data Splits
+
+| Dataset   | Website| BEIR-Name | Type | Queries  | Corpus | Rel D/Q | Down-load | md5 |
+| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
+| MSMARCO    | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``|  6,980   |  8.84M     |    1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
+| TREC-COVID |  [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50|  171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
+| NFCorpus   | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``|  323     |  3.6K     |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
+| BioASQ     | [Homepage](http://bioasq.org) | ``bioasq``|  ``train``<br>``test`` | 500    |  14.91M    |  8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
+| NQ         | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452   |  2.68M  |  1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
+| HotpotQA   | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``|  7,405   |  5.23M  |  2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip)  | ``f412724f78b0d91183a0e86805e16114`` |
+| FiQA-2018  | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``|  648     |  57K    |  2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip)  | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
+| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97   |  2.86M  |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
+| TREC-NEWS  | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news``    | ``test``| 57    |  595K    |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
+| ArguAna    | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406     |  8.67K    |  1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip)  | ``8ad3e3c2a5867cdced806d6503f29b99`` |
+| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49     |  382K    |  19.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
+| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 |  457K  |  1.4 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
+| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000     |  523K    |  1.6 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
+| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400    |  4.63M    |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
+| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000     |  25K    |  4.9 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
+| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``|  6,666     |  5.42M    |  1.2|  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip)  | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
+| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``|  1,535     |  5.42M |  3.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip)  | ``8b66f0a9126c521bae2bde127b4dc99d`` |
+| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``|  300     |  5K    |  1.1 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip)  | ``5f7d1de60b170fc8027bb7898e2efca1`` |
+| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249  |  528K  |  69.9 |  No  |  [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04)  |
+
+
+## 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
+
+[Needs More Information]
+
+### Citation Information
+
+Cite as:
+```
+@inproceedings{
+thakur2021beir,
+title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
+author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
+booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
+year={2021},
+url={https://openreview.net/forum?id=wCu6T5xFjeJ}
+}
+```
+
+### Contributions
+
+Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

huggingface_dataset/Dataset_Card/its5Q_yandex-q.md

@@ -0,0 +1,75 @@
+---
+annotations_creators:
+- crowdsourced
+language:
+- ru
+language_creators:
+- crowdsourced
+license:
+- cc0-1.0
+multilinguality:
+- monolingual
+pretty_name: Yandex.Q
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+tags: []
+task_categories:
+- text-generation
+- question-answering
+task_ids:
+- language-modeling
+- open-domain-qa
+---
+
+# Dataset Card for Yandex.Q
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Repository:** https://github.com/its5Q/yandex-q
+
+### Dataset Summary
+
+This is a dataset of questions and answers scraped from [Yandex.Q](https://yandex.ru/q/). There are 836810 answered questions out of the total of 1297670.
+The full dataset that includes all metadata returned by Yandex.Q APIs and contains unanswered questions can be found in `full.jsonl.gz`
+
+### Languages
+
+The dataset is mostly in Russian, but there may be other languages present
+
+## Dataset Structure
+
+### Data Fields
+
+The dataset consists of 3 fields:
+- `question` - question title (`string`)
+- `description` - question description (`string` or `null`)
+- `answer` - answer to the question (`string`)
+
+### Data Splits
+
+All 836810 examples are in the train split, there is no validation split.
+
+## Dataset Creation
+
+The data was scraped through some "hidden" APIs using several scripts, located in [my GitHub repository](https://github.com/its5Q/yandex-q)
+
+## Additional Information
+
+### Dataset Curators
+
+- https://github.com/its5Q

huggingface_dataset/Dataset_Card/lewtun_github-issues.md

@@ -0,0 +1,161 @@
+# Dataset Card for GitHub Issues
+
+## Dataset Description
+
+- **Point of Contact:** [Lewis Tunstall](lewis@huggingface.co)
+
+### Dataset Summary
+
+GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.
+
+### Supported Tasks and Leaderboards
+
+For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).
+
+- `task-category-tag`: The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* [metric name](https://huggingface.co/metrics/metric_name). The ([model name](https://huggingface.co/model_name) or [model class](https://huggingface.co/transformers/model_doc/model_class.html)) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at [leaderboard url]() and ranks models based on [metric name](https://huggingface.co/metrics/metric_name) while also reporting [other metric name](https://huggingface.co/metrics/other_metric_name).
+
+### Languages
+
+Provide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...
+
+When relevant, please provide [BCP-47 codes](https://tools.ietf.org/html/bcp47), which consist of a [primary language subtag](https://tools.ietf.org/html/bcp47#section-2.2.1), with a [script subtag](https://tools.ietf.org/html/bcp47#section-2.2.3) and/or [region subtag](https://tools.ietf.org/html/bcp47#section-2.2.4) if available.
+
+## Dataset Structure
+
+### Data Instances
+
+Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.
+
+```
+{
+  'example_field': ...,
+  ...
+}
+```
+
+Provide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.
+
+### Data Fields
+
+List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
+
+- `example_field`: description of `example_field`
+
+Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [tagging app](https://github.com/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.
+
+### Data Splits
+
+Describe and name the splits in the dataset if there are more than one.
+
+Describe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.
+
+Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length.  For example:
+
+|                            | Tain   | Valid | Test |
+| -----                      | ------ | ----- | ---- |
+| Input Sentences            |        |       |      |
+| Average Sentence Length    |        |       |      |
+
+## Dataset Creation
+
+### Curation Rationale
+
+What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?
+
+### Source Data
+
+This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)
+
+#### Initial Data Collection and Normalization
+
+Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
+
+If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).
+
+If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.
+
+#### Who are the source language producers?
+
+State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.
+
+If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
+
+Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
+
+Describe other people represented or mentioned in the data. Where possible, link to references for the information.
+
+### Annotations
+
+If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.
+
+#### Annotation process
+
+If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
+
+#### Who are the annotators?
+
+If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.
+
+Describe the people or systems who originally created the annotations and their selection criteria if applicable.
+
+If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
+
+Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
+
+### Personal and Sensitive Information
+
+State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).
+
+State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).  
+
+If efforts were made to anonymize the data, describe the anonymization process.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+Please discuss some of the ways you believe the use of this dataset will impact society.
+
+The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.
+
+Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.
+
+### Discussion of Biases
+
+Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.
+
+For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
+
+If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.
+
+### Other Known Limitations
+
+If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.
+
+## Additional Information
+
+### Dataset Curators
+
+List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
+
+### Licensing Information
+
+Provide the license and link to the license webpage if available.
+
+### Citation Information
+
+Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
+```
+@article{article_id,
+  author    = {Author List},
+  title     = {Dataset Paper Title},
+  journal   = {Publication Venue},
+  year      = {2525}
+}
+```
+
+If the dataset has a [DOI](https://www.doi.org/), please provide it here.
+
+### Contributions
+
+Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.

huggingface_dataset/Dataset_Card/malteos_test-ds.md

@@ -0,0 +1,139 @@
+---
+annotations_creators: []
+language_creators: []
+language:
+- en-US
+license: []
+multilinguality:
+- monolingual
+pretty_name: test ds
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- text-retrieval
+task_ids: []
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## 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/opus_elhuyar.md

@@ -0,0 +1,159 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- es
+- eu
+license:
+- unknown
+multilinguality:
+- translation
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- translation
+task_ids: []
+paperswithcode_id: null
+pretty_name: OpusElhuyar
+dataset_info:
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - es
+        - eu
+  config_name: es-eu
+  splits:
+  - name: train
+    num_bytes: 127833939
+    num_examples: 642348
+  download_size: 44468751
+  dataset_size: 127833939
+---
+
+# Dataset Card for [opus_elhuyar]
+
+## 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:**[Opus Elhuyar](http://opus.nlpl.eu/Elhuyar.php)
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Dataset provided by the foundation Elhuyar (http://webcorpusak.elhuyar.eus/sarrera_paraleloa.html) and submitted to OPUS by Joseba Garcia Beaumont
+
+### Supported Tasks and Leaderboards
+
+The underlying task is machine translation from Spanish to Basque
+
+### Languages
+
+Spanish to Basque
+
+## 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
+
+J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
+
+### Contributions
+
+Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset.

huggingface_dataset/Dataset_Card/projecte-aina_UD_Catalan-AnCora.md

@@ -0,0 +1,176 @@
+---
+YAML tags:
+
+annotations_creators:
+- expert-generated
+language:
+- ca
+language_creators:
+- found
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: UD_Catalan-AnCora
+size_categories: []
+source_datasets: []
+tags: []
+task_categories:
+- token-classification
+task_ids:
+- part-of-speech
+
+---
+
+
+# UD_Catalan-AnCora
+
+## 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
+- **Website:** https://github.com/UniversalDependencies/UD_Catalan-AnCora
+- **Point of Contact:** [Daniel Zeman](zeman@ufal.mff.cuni.cz) 
+
+
+### Dataset Summary
+
+This dataset is composed of the annotations from the [AnCora corpus](http://clic.ub.edu/corpus/), projected on the [Universal Dependencies treebank](https://universaldependencies.org/). We use the POS annotations of this corpus as part of the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
+
+
+### Supported Tasks and Leaderboards
+
+POS tagging
+
+### Languages
+
+The dataset is in Catalan (`ca-CA`)
+
+## Dataset Structure
+
+### Data Instances
+
+Three conllu files.
+
+Annotations are encoded in plain text files (UTF-8, normalized to NFC, using only the LF character as line break, including an LF character at the end of file) with three types of lines:
+
+1) Word lines containing the annotation of a word/token in 10 fields separated by single tab characters (see below).
+2) Blank lines marking sentence boundaries.
+3) Comment lines starting with hash (#).
+
+### Data Fields
+Word lines contain the following fields:
+
+1) ID: Word index, integer starting at 1 for each new sentence; may be a range for multiword tokens; may be a decimal number for empty nodes (decimal numbers can be lower than 1 but must be greater than 0).
+2) FORM: Word form or punctuation symbol.
+3) LEMMA: Lemma or stem of word form.
+4) UPOS: Universal part-of-speech tag.
+5) XPOS: Language-specific part-of-speech tag; underscore if not available.
+6) FEATS: List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.
+7) HEAD: Head of the current word, which is either a value of ID or zero (0).
+8) DEPREL: Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.
+9) DEPS: Enhanced dependency graph in the form of a list of head-deprel pairs.
+10) MISC: Any other annotation.
+  
+From: [https://universaldependencies.org](https://universaldependencies.org/guidelines.html)
+
+### Data Splits
+
+- ca_ancora-ud-train.conllu
+- ca_ancora-ud-dev.conllu
+- ca_ancora-ud-test.conllu
+
+## Dataset Creation
+
+### Curation Rationale
+[N/A] 
+
+### Source Data
+
+- [UD_Catalan-AnCora](https://github.com/UniversalDependencies/UD_Catalan-AnCora)
+
+#### Initial Data Collection and Normalization
+
+The original annotation was done in a constituency framework as a part of the [AnCora project](http://clic.ub.edu/corpus/) at the University of Barcelona. It was converted to dependencies by the [Universal Dependencies team](https://universaldependencies.org/) and used in the CoNLL 2009 shared task. The CoNLL 2009 version was later converted to HamleDT and to Universal Dependencies.
+
+For more information on the AnCora project, visit the [AnCora site](http://clic.ub.edu/corpus/).
+
+To learn about the Universal Dependences, visit the webpage [https://universaldependencies.org](https://universaldependencies.org)
+
+#### Who are the source language producers?
+
+For more information on the AnCora corpus and its sources, visit the [AnCora site](http://clic.ub.edu/corpus/).
+
+### Annotations
+
+#### Annotation process
+
+For more information on the first AnCora annotation, visit the [AnCora site](http://clic.ub.edu/corpus/).
+
+#### Who are the annotators?
+
+For more information on the AnCora annotation team, visit the [AnCora site](http://clic.ub.edu/corpus/).
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset contributes to the development of language models in Catalan, a low-resource language.
+
+### Discussion of Biases
+
+[N/A]
+
+### Other Known Limitations
+
+[N/A]
+
+## Additional Information
+
+### Dataset Curators
+
+
+
+### Licensing Information
+
+This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">CC Attribution 4.0 International License</a>.
+
+### Citation Information
+
+The following paper must be cited when using this corpus:
+
+Taulé, M., M.A. Martí, M. Recasens (2008) 'Ancora: Multilevel Annotated Corpora for Catalan and Spanish', Proceedings of 6th International Conference on Language Resources and Evaluation. Marrakesh (Morocco).
+
+To cite the Universal Dependencies project:
+
+Rueter, J. (Creator), Erina, O. (Contributor), Klementeva, J. (Contributor), Ryabov, I. (Contributor), Tyers, F. M. (Contributor), Zeman, D. (Contributor), Nivre, J. (Creator) (15 Nov 2020). Universal Dependencies version 2.7 Erzya JR. Universal Dependencies Consortium. 
+
+
+

huggingface_dataset/Dataset_Card/shahules786_PoetryFoundationData.md

@@ -0,0 +1,28 @@
+---
+dataset_info:
+  features:
+  - name: poem name
+    dtype: string
+  - name: content
+    dtype: string
+  - name: author
+    dtype: string
+  - name: type
+    dtype: string
+  - name: age
+    dtype: 'null'
+  splits:
+  - name: train
+    num_bytes: 23187576
+    num_examples: 13854
+  download_size: 14466446
+  dataset_size: 23187576
+---
+
+This file contains nearly all poems from the [Poetry Foundation Website](https://www.poetryfoundation.org/).
+
+Content
+All poems have a title and author. Most poems are also labeled with the tags as available from the Poetry Foundation Website. The word cloud above shows the most used tags!
+
+Inspiration
+This dataset can be used for a variety of tasks related to poetry writing. 

huggingface_dataset/Dataset_Card/wmt17.md

@@ -0,0 +1,360 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- cs
+- de
+- en
+- fi
+- lv
+- ru
+- tr
+- zh
+license:
+- unknown
+multilinguality:
+- translation
+size_categories:
+- 10M<n<100M
+source_datasets:
+- extended|europarl_bilingual
+- extended|news_commentary
+- extended|setimes
+- extended|un_multi
+task_categories:
+- translation
+task_ids: []
+pretty_name: WMT17
+paperswithcode_id: null
+dataset_info:
+- config_name: cs-en
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - cs
+        - en
+  splits:
+  - name: train
+    num_bytes: 300698431
+    num_examples: 1018291
+  - name: validation
+    num_bytes: 707870
+    num_examples: 2999
+  - name: test
+    num_bytes: 674430
+    num_examples: 3005
+  download_size: 1784240523
+  dataset_size: 302080731
+- config_name: de-en
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - de
+        - en
+  splits:
+  - name: train
+    num_bytes: 1715537443
+    num_examples: 5906184
+  - name: validation
+    num_bytes: 735516
+    num_examples: 2999
+  - name: test
+    num_bytes: 729519
+    num_examples: 3004
+  download_size: 1945382236
+  dataset_size: 1717002478
+- config_name: fi-en
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - fi
+        - en
+  splits:
+  - name: train
+    num_bytes: 743856525
+    num_examples: 2656542
+  - name: validation
+    num_bytes: 1410515
+    num_examples: 6000
+  - name: test
+    num_bytes: 1388828
+    num_examples: 6004
+  download_size: 434531933
+  dataset_size: 746655868
+- config_name: lv-en
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - lv
+        - en
+  splits:
+  - name: train
+    num_bytes: 517419100
+    num_examples: 3567528
+  - name: validation
+    num_bytes: 544604
+    num_examples: 2003
+  - name: test
+    num_bytes: 530474
+    num_examples: 2001
+  download_size: 169634544
+  dataset_size: 518494178
+- config_name: ru-en
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - ru
+        - en
+  splits:
+  - name: train
+    num_bytes: 11000075522
+    num_examples: 24782720
+  - name: validation
+    num_bytes: 1050677
+    num_examples: 2998
+  - name: test
+    num_bytes: 1040195
+    num_examples: 3001
+  download_size: 3582640660
+  dataset_size: 11002166394
+- config_name: tr-en
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - tr
+        - en
+  splits:
+  - name: train
+    num_bytes: 60416617
+    num_examples: 205756
+  - name: validation
+    num_bytes: 732436
+    num_examples: 3000
+  - name: test
+    num_bytes: 752773
+    num_examples: 3007
+  download_size: 62263061
+  dataset_size: 61901826
+- config_name: zh-en
+  features:
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - zh
+        - en
+  splits:
+  - name: train
+    num_bytes: 5529286149
+    num_examples: 25134743
+  - name: validation
+    num_bytes: 589591
+    num_examples: 2002
+  - name: test
+    num_bytes: 540347
+    num_examples: 2001
+  download_size: 2314906945
+  dataset_size: 5530416087
+---
+
+# Dataset Card for "wmt17"
+
+## 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.statmt.org/wmt17/translation-task.html](http://www.statmt.org/wmt17/translation-task.html)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **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 downloaded dataset files:** 1700.58 MB
+- **Size of the generated dataset:** 288.10 MB
+- **Total amount of disk used:** 1988.68 MB
+
+### Dataset Summary
+
+<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
+  <p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p>
+  <ul>
+    <li>Non-English files contain many English sentences.</li>
+    <li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li>
+  </ul>
+  <p>We have contacted the WMT organizers.</p>
+</div>
+
+Translation dataset based on the data from statmt.org.
+
+Versions exist for different years using a combination of data
+sources. The base `wmt` allows you to create a custom dataset by choosing
+your own data/language pair. This can be done as follows:
+
+```python
+from datasets import inspect_dataset, load_dataset_builder
+
+inspect_dataset("wmt17", "path/to/scripts")
+builder = load_dataset_builder(
+    "path/to/scripts/wmt_utils.py",
+    language_pair=("fr", "de"),
+    subsets={
+        datasets.Split.TRAIN: ["commoncrawl_frde"],
+        datasets.Split.VALIDATION: ["euelections_dev2019"],
+    },
+)
+
+# Standard version
+builder.download_and_prepare()
+ds = builder.as_dataset()
+
+# Streamable version
+ds = builder.as_streaming_dataset()
+```
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### cs-en
+
+- **Size of downloaded dataset files:** 1700.58 MB
+- **Size of the generated dataset:** 288.10 MB
+- **Total amount of disk used:** 1988.68 MB
+
+An example of 'train' looks as follows.
+```
+
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### cs-en
+- `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`.
+
+### Data Splits
+
+|name | train |validation|test|
+|-----|------:|---------:|---:|
+|cs-en|1018291|      2999|3005|
+
+## 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{bojar-EtAl:2017:WMT1,
+  author    = {Bojar, Ond
+{r}ej  and  Chatterjee, Rajen  and  Federmann, Christian  and  Graham, Yvette  and  Haddow, Barry  and  Huang, Shujian  and  Huck, Matthias  and  Koehn, Philipp  and  Liu, Qun  and  Logacheva, Varvara  and  Monz, Christof  and  Negri, Matteo  and  Post, Matt  and  Rubino, Raphael  and  Specia, Lucia  and  Turchi, Marco},
+  title     = {Findings of the 2017 Conference on Machine Translation (WMT17)},
+  booktitle = {Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers},
+  month     = {September},
+  year      = {2017},
+  address   = {Copenhagen, Denmark},
+  publisher = {Association for Computational Linguistics},
+  pages     = {169--214},
+  url       = {http://www.aclweb.org/anthology/W17-4717}
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.