Upload batch 321 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__feed-sen_vi_-0f1239-2245871651.md)

huggingface_dataset/Dataset_Card/3ee_regularization-tiger.md

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+---
+license: mit
+tags:
+- stable-diffusion
+- regularization-images
+- text-to-image
+- image-to-image
+- dreambooth
+- class-instance
+- preservation-loss-training 
+---
+
+# Tiger Regularization Images
+
+A collection of regularization & class instance datasets of tigers for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training.

huggingface_dataset/Dataset_Card/DFKI-SLT_cross_re.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+language_creators:
+- found
+license: []
+multilinguality:
+- monolingual
+pretty_name: CrossRE is a cross-domain dataset for relation extraction
+size_categories:
+- 10K<n<100K
+source_datasets: 
+- extended|cross_ner
+tags:
+- cross domain
+- ai
+- news
+- music
+- literature
+- politics
+- science
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+dataset_info:
+- config_name: ai
+  features:
+  - name: doc_key
+    dtype: string
+  - name: sentence
+    sequence: string
+  - name: ner
+    sequence:
+    - name: id-start
+      dtype: int32
+    - name: id-end
+      dtype: int32
+    - name: entity-type
+      dtype: string
+  - name: relations
+    sequence:
+    - name: id_1-start
+      dtype: int32
+    - name: id_1-end
+      dtype: int32
+    - name: id_2-start
+      dtype: int32
+    - name: id_2-end
+      dtype: int32
+    - name: relation-type
+      dtype: string
+    - name: Exp
+      dtype: string
+    - name: Un
+      dtype: bool
+    - name: SA
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 62411
+    num_examples: 100
+  - name: validation
+    num_bytes: 183717
+    num_examples: 350
+  - name: test
+    num_bytes: 217353
+    num_examples: 431
+  download_size: 508107
+  dataset_size: 463481
+- config_name: literature
+  features:
+  - name: doc_key
+    dtype: string
+  - name: sentence
+    sequence: string
+  - name: ner
+    sequence:
+    - name: id-start
+      dtype: int32
+    - name: id-end
+      dtype: int32
+    - name: entity-type
+      dtype: string
+  - name: relations
+    sequence:
+    - name: id_1-start
+      dtype: int32
+    - name: id_1-end
+      dtype: int32
+    - name: id_2-start
+      dtype: int32
+    - name: id_2-end
+      dtype: int32
+    - name: relation-type
+      dtype: string
+    - name: Exp
+      dtype: string
+    - name: Un
+      dtype: bool
+    - name: SA
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 62699
+    num_examples: 100
+  - name: validation
+    num_bytes: 246214
+    num_examples: 400
+  - name: test
+    num_bytes: 264450
+    num_examples: 416
+  download_size: 635130
+  dataset_size: 573363
+- config_name: music
+  features:
+  - name: doc_key
+    dtype: string
+  - name: sentence
+    sequence: string
+  - name: ner
+    sequence:
+    - name: id-start
+      dtype: int32
+    - name: id-end
+      dtype: int32
+    - name: entity-type
+      dtype: string
+  - name: relations
+    sequence:
+    - name: id_1-start
+      dtype: int32
+    - name: id_1-end
+      dtype: int32
+    - name: id_2-start
+      dtype: int32
+    - name: id_2-end
+      dtype: int32
+    - name: relation-type
+      dtype: string
+    - name: Exp
+      dtype: string
+    - name: Un
+      dtype: bool
+    - name: SA
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 69846
+    num_examples: 100
+  - name: validation
+    num_bytes: 261497
+    num_examples: 350
+  - name: test
+    num_bytes: 312165
+    num_examples: 399
+  download_size: 726956
+  dataset_size: 643508
+- config_name: news
+  features:
+  - name: doc_key
+    dtype: string
+  - name: sentence
+    sequence: string
+  - name: ner
+    sequence:
+    - name: id-start
+      dtype: int32
+    - name: id-end
+      dtype: int32
+    - name: entity-type
+      dtype: string
+  - name: relations
+    sequence:
+    - name: id_1-start
+      dtype: int32
+    - name: id_1-end
+      dtype: int32
+    - name: id_2-start
+      dtype: int32
+    - name: id_2-end
+      dtype: int32
+    - name: relation-type
+      dtype: string
+    - name: Exp
+      dtype: string
+    - name: Un
+      dtype: bool
+    - name: SA
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 49102
+    num_examples: 164
+  - name: validation
+    num_bytes: 77952
+    num_examples: 350
+  - name: test
+    num_bytes: 96301
+    num_examples: 400
+  download_size: 239763
+  dataset_size: 223355
+- config_name: politics
+  features:
+  - name: doc_key
+    dtype: string
+  - name: sentence
+    sequence: string
+  - name: ner
+    sequence:
+    - name: id-start
+      dtype: int32
+    - name: id-end
+      dtype: int32
+    - name: entity-type
+      dtype: string
+  - name: relations
+    sequence:
+    - name: id_1-start
+      dtype: int32
+    - name: id_1-end
+      dtype: int32
+    - name: id_2-start
+      dtype: int32
+    - name: id_2-end
+      dtype: int32
+    - name: relation-type
+      dtype: string
+    - name: Exp
+      dtype: string
+    - name: Un
+      dtype: bool
+    - name: SA
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 76004
+    num_examples: 101
+  - name: validation
+    num_bytes: 277633
+    num_examples: 350
+  - name: test
+    num_bytes: 295294
+    num_examples: 400
+  download_size: 726427
+  dataset_size: 648931
+- config_name: science
+  features:
+  - name: doc_key
+    dtype: string
+  - name: sentence
+    sequence: string
+  - name: ner
+    sequence:
+    - name: id-start
+      dtype: int32
+    - name: id-end
+      dtype: int32
+    - name: entity-type
+      dtype: string
+  - name: relations
+    sequence:
+    - name: id_1-start
+      dtype: int32
+    - name: id_1-end
+      dtype: int32
+    - name: id_2-start
+      dtype: int32
+    - name: id_2-end
+      dtype: int32
+    - name: relation-type
+      dtype: string
+    - name: Exp
+      dtype: string
+    - name: Un
+      dtype: bool
+    - name: SA
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 63876
+    num_examples: 103
+  - name: validation
+    num_bytes: 224402
+    num_examples: 351
+  - name: test
+    num_bytes: 249075
+    num_examples: 400
+  download_size: 594058
+  dataset_size: 537353
+---
+# Dataset Card for CrossRE
+## 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
+- **Repository:** [CrossRE](https://github.com/mainlp/CrossRE)
+- **Paper:** [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://arxiv.org/abs/2210.09345)
+
+### Dataset Summary
+CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes 
+multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and 
+artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
+dataset for NER which contains domain-specific entity types.
+The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL, 
+GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and 
+RELATED-TO.
+
+For details, see the paper: https://arxiv.org/abs/2210.09345
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+The language data in CrossRE is in English (BCP-47 en)
+
+## Dataset Structure
+
+### Data Instances
+
+#### news
+- **Size of downloaded dataset files:** 0.24 MB
+- **Size of the generated dataset:** 0.22 MB
+
+An example of 'train' looks as follows:
+```python
+{
+  "doc_key": "news-train-1", 
+  "sentence": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."], 
+  "ner": [
+    {"id-start": 0, "id-end": 0, "entity-type": "organisation"}, 
+    {"id-start": 2, "id-end": 3, "entity-type": "misc"}, 
+    {"id-start": 6, "id-end": 7, "entity-type": "misc"}
+  ], 
+  "relations": [
+    {"id_1-start": 0, "id_1-end": 0, "id_2-start": 2, "id_2-end": 3, "relation-type": "opposite", "Exp": "rejects", "Un": False, "SA": False}, 
+    {"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "opposite", "Exp": "calls_for_boycot_of", "Un": False, "SA": False}, 
+    {"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "topic", "Exp": "", "Un": False, "SA": False}
+  ]
+}
+```
+
+#### politics
+- **Size of downloaded dataset files:** 0.73 MB
+- **Size of the generated dataset:** 0.65 MB
+
+An example of 'train' looks as follows:
+```python
+{
+  "doc_key": "politics-train-1", 
+  "sentence": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."], 
+  "ner": [
+    {"id-start": 8, "id-end": 9, "entity-type": "politicalparty"}, 
+    {"id-start": 15, "id-end": 20, "entity-type": "politicalparty"}, 
+    {"id-start": 22, "id-end": 26, "entity-type": "politicalparty"}
+  ], 
+  "relations": [
+    {"id_1-start": 8, "id_1-end": 9, "id_2-start": 15, "id_2-end": 20, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}, 
+    {"id_1-start": 8, "id_1-end": 9, "id_2-start": 22, "id_2-end": 26, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}
+  ]
+}
+```
+
+#### science
+- **Size of downloaded dataset files:** 0.59 MB
+- **Size of the generated dataset:** 0.54 MB
+
+An example of 'train' looks as follows:
+```python
+{
+  "doc_key": "science-train-1", 
+  "sentence": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."], 
+  "ner": [
+    {"id-start": 4, "id-end": 5, "entity-type": "chemicalcompound"}, 
+    {"id-start": 7, "id-end": 8, "entity-type": "chemicalcompound"}, 
+    {"id-start": 11, "id-end": 11, "entity-type": "chemicalcompound"}
+  ], 
+  "relations": []
+}
+```
+
+#### music
+- **Size of downloaded dataset files:** 0.73 MB
+- **Size of the generated dataset:** 0.64 MB
+
+An example of 'train' looks as follows:
+```python
+{
+  "doc_key": "music-train-1", 
+  "sentence": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."], 
+  "ner": [
+    {"id-start": 4, "id-end": 6, "entity-type": "location"}, 
+    {"id-start": 13, "id-end": 17, "entity-type": "event"}
+  ], 
+  "relations": [
+    {"id_1-start": 13, "id_1-end": 17, "id_2-start": 4, "id_2-end": 6, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}
+  ]
+}
+```
+
+#### literature
+- **Size of downloaded dataset files:** 0.64 MB
+- **Size of the generated dataset:** 0.57 MB
+
+An example of 'train' looks as follows:
+```python
+{
+  "doc_key": "literature-train-1", 
+  "sentence": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."], 
+  "ner": [
+    {"id-start": 7, "id-end": 9, "entity-type": "person"}, 
+    {"id-start": 12, "id-end": 13, "entity-type": "country"}, 
+    {"id-start": 17, "id-end": 18, "entity-type": "writer"}, 
+    {"id-start": 20, "id-end": 20, "entity-type": "writer"}, 
+    {"id-start": 26, "id-end": 27, "entity-type": "writer"}, 
+    {"id-start": 29, "id-end": 29, "entity-type": "writer"}, 
+    {"id-start": 33, "id-end": 33, "entity-type": "country"}, 
+    {"id-start": 35, "id-end": 35, "entity-type": "country"}, 
+    {"id-start": 38, "id-end": 38, "entity-type": "misc"}, 
+    {"id-start": 45, "id-end": 46, "entity-type": "person"}, 
+    {"id-start": 49, "id-end": 50, "entity-type": "misc"}, 
+    {"id-start": 55, "id-end": 55, "entity-type": "person"}
+  ], 
+  "relations": [
+    {"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "role", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 17, "id_1-end": 18, "id_2-start": 26, "id_2-end": 27, "relation-type": "social", "Exp": "family", "Un": False, "SA": False}, 
+    {"id_1-start": 20, "id_1-end": 20, "id_2-start": 17, "id_2-end": 18, "relation-type": "named", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 26, "id_1-end": 27, "id_2-start": 33, "id_2-end": 33, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 26, "id_1-end": 27, "id_2-start": 35, "id_2-end": 35, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 26, "id_1-end": 27, "id_2-start": 38, "id_2-end": 38, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 26, "id_1-end": 27, "id_2-start": 45, "id_2-end": 46, "relation-type": "social", "Exp": "greeted_by", "Un": False, "SA": False}, 
+    {"id_1-start": 29, "id_1-end": 29, "id_2-start": 26, "id_2-end": 27, "relation-type": "named", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 45, "id_1-end": 46, "id_2-start": 55, "id_2-end": 55, "relation-type": "social", "Exp": "marriage", "Un": False, "SA": False}, 
+    {"id_1-start": 49, "id_1-end": 50, "id_2-start": 45, "id_2-end": 46, "relation-type": "named", "Exp": "", "Un": False, "SA": False}
+  ]
+}
+```
+
+#### ai
+- **Size of downloaded dataset files:** 0.51 MB
+- **Size of the generated dataset:** 0.46 MB
+
+An example of 'train' looks as follows:
+```python
+{
+  "doc_key": "ai-train-1", 
+  "sentence": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."], 
+  "ner": [
+    {"id-start": 3, "id-end": 5, "entity-type": "product"}, 
+    {"id-start": 10, "id-end": 11, "entity-type": "field"}, 
+    {"id-start": 13, "id-end": 14, "entity-type": "task"}, 
+    {"id-start": 16, "id-end": 17, "entity-type": "task"}, 
+    {"id-start": 21, "id-end": 23, "entity-type": "task"}, 
+    {"id-start": 26, "id-end": 27, "entity-type": "field"}, 
+    {"id-start": 28, "id-end": 29, "entity-type": "researcher"}, 
+    {"id-start": 31, "id-end": 32, "entity-type": "researcher"}, 
+    {"id-start": 34, "id-end": 35, "entity-type": "researcher"}, 
+    {"id-start": 37, "id-end": 38, "entity-type": "researcher"}, 
+    {"id-start": 40, "id-end": 41, "entity-type": "researcher"}, 
+    {"id-start": 43, "id-end": 44, "entity-type": "researcher"}, 
+    {"id-start": 46, "id-end": 47, "entity-type": "researcher"}, 
+    {"id-start": 49, "id-end": 50, "entity-type": "researcher"}
+  ], 
+  "relations": [
+    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
+    {"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "type-of", "Exp": "", "Un": False, "SA": False}
+  ]
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+- `doc_key`: the instance id of this sentence, a `string` feature.
+- `sentence`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features.
+- `ner`: the list of named entities in this sentence, a `list` of `dict` features.
+  - `id-start`: the start index of the entity, a `int` feature.
+  - `id-end`: the end index of the entity, a `int` feature.
+  - `entity-type`: the type of the entity, a `string` feature.
+- `relations`: the list of relations in this sentence, a `list` of `dict` features.
+  - `id_1-start`: the start index of the first entity, a `int` feature.
+  - `id_1-end`: the end index of the first entity, a `int` feature.
+  - `id_2-start`: the start index of the second entity, a `int` feature.
+  - `id_2-end`: the end index of the second entity, a `int` feature.
+  - `relation-type`: the type of the relation, a `string` feature.
+  - `Exp`: the explanation of the relation type assigned, a `string` feature.
+  - `Un`: uncertainty of the annotator, a `bool` feature.
+  - `SA`: existence of syntax ambiguity which poses a challenge for the annotator, a `bool` feature.
+
+### Data Splits
+#### Sentences
+|              | Train   | Dev     | Test    | Total   |
+|--------------|---------|---------|---------|---------|
+| news         | 164     | 350     | 400     | 914     |
+| politics     | 101     | 350     | 400     | 851     |
+| science      | 103     | 351     | 400     | 854     |
+| music        | 100     | 350     | 399     | 849     |
+| literature   | 100     | 400     | 416     | 916     |
+| ai           | 100     | 350     | 431     | 881     |
+| ------------ | ------- | ------- | ------- | ------- |
+| total        | 668     | 2,151   | 2,46    | 5,265   |
+
+#### Relations
+|              | Train   | Dev     | Test    | Total   |
+|--------------|---------|---------|---------|---------|
+| news         | 175     | 300     | 396     | 871     |
+| politics     | 502     | 1,616   | 1,831   | 3,949   |
+| science      | 355     | 1,340   | 1,393   | 3,088   |
+| music        | 496     | 1,861   | 2,333   | 4,690   |
+| literature   | 397     | 1,539   | 1,591   | 3,527   |
+| ai           | 350     | 1,006   | 1,127   | 2,483   |
+| ------------ | ------- | ------- | ------- | ------- |
+| total        | 2,275   | 7,662   | 8,671   | 18,608  |
+
+## 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{bassignana-plank-2022-crossre,
+    title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction",
+    author = "Bassignana, Elisa and Plank, Barbara",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
+    year = "2022",
+    publisher = "Association for Computational Linguistics"
+}
+```
+
+### Contributions
+
+Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.

huggingface_dataset/Dataset_Card/MicPie_unpredictable_dividend-com.md

@@ -0,0 +1,250 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license:
+- apache-2.0
+multilinguality:
+- monolingual
+pretty_name: UnpredicTable-dividend-com
+size_categories:
+- 100K<n<1M
+source_datasets: []
+task_categories:
+- multiple-choice
+- question-answering
+- zero-shot-classification
+- text2text-generation
+- table-question-answering
+- text-generation
+- text-classification
+- tabular-classification
+task_ids:
+- multiple-choice-qa
+- extractive-qa
+- open-domain-qa
+- closed-domain-qa
+- closed-book-qa
+- open-book-qa
+- language-modeling
+- multi-class-classification
+- natural-language-inference
+- topic-classification
+- multi-label-classification
+- tabular-multi-class-classification
+- tabular-multi-label-classification
+---
+
+
+# Dataset Card for "UnpredicTable-dividend-com" - Dataset of Few-shot Tasks from Tables
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://ethanperez.net/unpredictable
+- **Repository:** https://github.com/JunShern/few-shot-adaptation
+- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
+- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
+
+### Dataset Summary
+
+The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
+
+There are several dataset versions available:
+
+* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
+
+* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
+
+* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
+
+* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
+    * [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
+    * [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
+    * [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
+
+* UnpredicTable data subsets based on the website of origin:
+    * [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com) 
+    * [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
+    * [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com) 
+    * [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
+    * [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com) 
+    * [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
+    * [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
+    * [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
+    * [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
+    * [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
+    * [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
+    * [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
+    * [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
+    * [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
+    * [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
+    * [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
+    * [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
+    * [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
+    * [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
+    * [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
+
+
+* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
+    * [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
+    * [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
+    * [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
+    * [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
+    * [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
+    * [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
+    * [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
+    * [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
+    * [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
+    * [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
+    * [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
+    * [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
+    * [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
+    * [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
+    * [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
+    * [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
+    * [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
+    * [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
+    * [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
+    * [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
+    * [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
+    * [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
+    * [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
+    * [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
+    * [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
+    * [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
+    * [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
+    * [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
+    * [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
+    * [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
+    * [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
+
+### Supported Tasks and Leaderboards
+
+Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
+
+The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
+
+There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'. 
+
+### Data Fields
+
+'task': task identifier
+
+'input': column elements of a specific row in the table. 
+
+'options': for multiple choice classification, it provides the options to choose from.
+
+'output': target column element of the same row as input.
+
+'pageTitle': the title of the page containing the table. 
+
+'outputColName': output column name
+
+'url': url to the website containing the table
+
+'wdcFile': WDC Web Table Corpus file
+
+### Data Splits
+
+The UnpredicTable datasets do not come with additional data splits.
+
+## Dataset Creation
+
+### Curation Rationale
+
+Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
+
+#### Who are the source language producers?
+
+The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
+
+### Annotations
+
+#### Annotation process
+
+Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
+[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
+
+#### Who are the annotators?
+
+Annotations were carried out by a lab assistant.
+
+### Personal and Sensitive Information
+
+The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (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, etc.) might be contained in our dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
+
+### Discussion of Biases
+
+Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
+
+### Other Known Limitations
+
+No additional known limitations.
+
+## Additional Information
+
+### Dataset Curators
+Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
+
+### Licensing Information
+Apache 2.0
+
+### Citation Information
+
+```
+@misc{chan2022few,
+  author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
+  title = {Few-shot Adaptation Works with UnpredicTable Data},
+  publisher={arXiv},
+  year = {2022},
+  url = {https://arxiv.org/abs/2208.01009}
+}
+```

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__feed-sen_vi_-0f1239-2245871651.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/feed
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-1b1
+  metrics: []
+  dataset_name: futin/feed
+  dataset_config: sen_vi_
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: bigscience/bloom-1b1
+* Dataset: futin/feed
+* Config: sen_vi_
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-emotion-41e4622b-10765447.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- emotion
+eval_info:
+  task: multi_class_classification
+  model: aatmasidha/distilbert-base-uncased-finetuned-emotion
+  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: aatmasidha/distilbert-base-uncased-finetuned-emotion
+* 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 [@aatmasidha](https://huggingface.co/aatmasidha) for evaluating this model.

huggingface_dataset/Dataset_Card/breadlicker45_autotrain-data-yahoo-answer-small.md

@@ -0,0 +1,53 @@
+---
+task_categories:
+- summarization
+
+---
+# AutoTrain Dataset for project: yahoo-answer-small
+
+## Dataset Description
+
+This dataset has been automatically processed by AutoTrain for project yahoo-answer-small.
+
+### 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
+[
+  {
+    "text": "how do you get a girl to like you? and how can you make her your girlfriend?",
+    "target": "Be yourself.  It's the oldest and best advice.  She may still not like you, but that's the risk, and you keep your manliness and dignity.  Never, never forget this."
+  },
+  {
+    "text": "how long is a bacterium's life?",
+    "target": "It depends on the bacterium. For E. coli (common lab bacteria) 20-30 minutes is an average doubling time, but different strains vary.\\n\\nI heard something somewhere about a weird form of bacteria that lives miles underground in granite formations and only divides once every ten thousand years, or something crazy like that. I can't give you a source, it's just a freaky thing off the top of my head that I haven't gone to the trouble to confirm. My contention is that if reincarnation is true, that would be the *worst* thing to come back as. So mind your karma.\\n\\nPart of the reason it would be the worst is that bacteria reproduce by one cell dividing into two, so as long as there are any of that strain still alive, it hasn't really died.\\n\\nThey can die though. In a liquid culture you can tell because of a lot of turbidity (cloudiness) some of which is from cells and some from debris from dead cells. Or, you could have agar plates that get really nasty and dried up, and most of those bacteria are probably dead. Or you could tell because they look really crappy under a microscope. If you try to streak it and grow it on a plate and it doesn't grow, it's probably dead."
+  }
+]
+```
+
+### Dataset Fields
+
+The dataset has the following fields (also called "features"):
+
+```json
+{
+  "text": "Value(dtype='string', id=None)",
+  "target": "Value(dtype='string', 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        | 2399 |
+| valid        | 600 |

huggingface_dataset/Dataset_Card/brucethemoose_Korra_Raw_Screenshots.md

@@ -0,0 +1,19 @@
+For the V5 dataset, screenshots are grabbed from the raw B1, B3 and B4 LoK Blu-Rays with a custom VapourSynth script. The short version of procedure is:
+
+- Detect scene changes, and select 2-3 frames from each scene depending on how long it is, at the beginning, middle, and end of the scene.
+- Convert the frames to 16-bit RGB.
+- Very mildly deblock with Deblock QED, to get rid of the blocking artifacts: https://github.com/HomeOfVapourSynthEvolution/havsfunc
+- Very mildly temporally denoise with BM3D, using several adjacent frames, to get rid of other minor artifacts like noise, banding, and to soften whatever blocking is left: https://github.com/HomeOfVapourSynthEvolution/VapourSynth-BM3D
+- Write the frames as 16-bit RGB PNGs with imagemagick.
+- Optimize the filesize of the resulting frames with Efficient Compression Utility.
+
+This results in 1865 high quality 1920x1080 frames. Book 2 is skipped because the B2 blu-ray is interlaced, which (even with a deinterlacing algorithm) may cause training issues.
+
+The V6 dataset is simply a culled version of the V5 dataset, with frames I deemed redundant or problematic removed. The end result is 753 very high quality frames. 
+
+Previews coming soon.
+
+
+---
+license: wtfpl
+---

huggingface_dataset/Dataset_Card/cjvt_ssj500k.md

@@ -0,0 +1,175 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+- expert-generated
+language:
+- sl
+license:
+- cc-by-nc-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+- 10K<n<100K
+source_datasets: []
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+- part-of-speech
+- lemmatization
+- parsing
+pretty_name: ssj500k
+tags:
+- semantic-role-labeling
+- multiword-expression-detection
+---
+
+# Dataset Card for ssj500k
+
+**Important**: there exists another HF implementation of the dataset ([classla/ssj500k](https://huggingface.co/datasets/classla/ssj500k)), but it seems to be more narrowly focused. **This implementation is designed for more general use** - the CLASSLA version seems to expose only the specific training/validation/test annotations used in the CLASSLA library, for only a subset of the data. 
+
+### Dataset Summary
+
+The ssj500k training corpus contains about 500 000 tokens manually annotated on the levels of tokenization, sentence segmentation, morphosyntactic tagging, and lemmatization. It is also partially annotated for the following tasks:
+- named entity recognition (config `named_entity_recognition`)  
+- dependency parsing(*), Universal Dependencies style (config `dependency_parsing_ud`)  
+- dependency parsing, JOS/MULTEXT-East style (config `dependency_parsing_jos`)  
+- semantic role labeling (config `semantic_role_labeling`)  
+- multi-word expressions (config `multiword_expressions`)  
+
+If you want to load all the data along with their partial annotations, please use the config `all_data`.  
+
+\* _The UD dependency parsing labels are included here for completeness, but using the dataset [universal_dependencies](https://huggingface.co/datasets/universal_dependencies) should be preferred for dependency parsing applications to ensure you are using the most up-to-date data._  
+
+### Supported Tasks and Leaderboards
+
+Sentence tokenization, sentence segmentation, morphosyntactic tagging, lemmatization, named entity recognition, dependency parsing, semantic role labeling, multi-word expression detection.
+
+### Languages
+
+Slovenian.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample instance from the dataset (using the config `all_data`):
+```
+{
+  'id_doc': 'ssj1', 
+  'idx_par': 0, 
+  'idx_sent': 0, 
+  'id_words': ['ssj1.1.1.t1', 'ssj1.1.1.t2', 'ssj1.1.1.t3', 'ssj1.1.1.t4', 'ssj1.1.1.t5', 'ssj1.1.1.t6', 'ssj1.1.1.t7', 'ssj1.1.1.t8', 'ssj1.1.1.t9', 'ssj1.1.1.t10', 'ssj1.1.1.t11', 'ssj1.1.1.t12', 'ssj1.1.1.t13', 'ssj1.1.1.t14', 'ssj1.1.1.t15', 'ssj1.1.1.t16', 'ssj1.1.1.t17', 'ssj1.1.1.t18', 'ssj1.1.1.t19', 'ssj1.1.1.t20', 'ssj1.1.1.t21', 'ssj1.1.1.t22', 'ssj1.1.1.t23', 'ssj1.1.1.t24'], 
+  'words': ['"', 'Tistega', 'večera', 'sem', 'preveč', 'popil', ',', 'zgodilo', 'se', 'je', 'mesec', 'dni', 'po', 'tem', ',', 'ko', 'sem', 'izvedel', ',', 'da', 'me', 'žena', 'vara', '.'], 
+  'lemmas': ['"', 'tisti', 'večer', 'biti', 'preveč', 'popiti', ',', 'zgoditi', 'se', 'biti', 'mesec', 'dan', 'po', 'ta', ',', 'ko', 'biti', 'izvedeti', ',', 'da', 'jaz', 'žena', 'varati', '.'], 
+  'msds': ['UPosTag=PUNCT', 'UPosTag=DET|Case=Gen|Gender=Masc|Number=Sing|PronType=Dem', 'UPosTag=NOUN|Case=Gen|Gender=Masc|Number=Sing', 'UPosTag=AUX|Mood=Ind|Number=Sing|Person=1|Polarity=Pos|Tense=Pres|VerbForm=Fin', 'UPosTag=DET|PronType=Ind', 'UPosTag=VERB|Aspect=Perf|Gender=Masc|Number=Sing|VerbForm=Part', 'UPosTag=PUNCT', 'UPosTag=VERB|Aspect=Perf|Gender=Neut|Number=Sing|VerbForm=Part', 'UPosTag=PRON|PronType=Prs|Reflex=Yes|Variant=Short', 'UPosTag=AUX|Mood=Ind|Number=Sing|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin', 'UPosTag=NOUN|Animacy=Inan|Case=Acc|Gender=Masc|Number=Sing', 'UPosTag=NOUN|Case=Gen|Gender=Masc|Number=Plur', 'UPosTag=ADP|Case=Loc', 'UPosTag=DET|Case=Loc|Gender=Neut|Number=Sing|PronType=Dem', 'UPosTag=PUNCT', 'UPosTag=SCONJ', 'UPosTag=AUX|Mood=Ind|Number=Sing|Person=1|Polarity=Pos|Tense=Pres|VerbForm=Fin', 'UPosTag=VERB|Aspect=Perf|Gender=Masc|Number=Sing|VerbForm=Part', 'UPosTag=PUNCT', 'UPosTag=SCONJ', 'UPosTag=PRON|Case=Acc|Number=Sing|Person=1|PronType=Prs|Variant=Short', 'UPosTag=NOUN|Case=Nom|Gender=Fem|Number=Sing', 'UPosTag=VERB|Aspect=Imp|Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin', 'UPosTag=PUNCT'], 
+  'has_ne_ann': True, 
+  'has_ud_dep_ann': True, 
+  'has_jos_dep_ann': True, 
+  'has_srl_ann': True, 
+  'has_mwe_ann': True, 
+  'ne_tags': ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'], 
+  'ud_dep_head': [5, 2, 5, 5, 5, -1, 7, 5, 7, 7, 7, 10, 13, 10, 17, 17, 17, 13, 22, 22, 22, 22, 17, 5], 
+  'ud_dep_rel': ['punct', 'det', 'obl', 'aux', 'advmod', 'root', 'punct', 'parataxis', 'expl', 'aux', 'obl', 'nmod', 'case', 'nmod', 'punct', 'mark', 'aux', 'acl', 'punct', 'mark', 'obj', 'nsubj', 'ccomp', 'punct'], 
+  'jos_dep_head': [-1, 2, 5, 5, 5, -1, -1, -1, 7, 7, 7, 10, 13, 10, -1, 17, 17, 13, -1, 22, 22, 22, 17, -1], 
+  'jos_dep_rel': ['Root', 'Atr', 'AdvO', 'PPart', 'AdvM', 'Root', 'Root', 'Root', 'PPart', 'PPart', 'AdvO', 'Atr', 'Atr', 'Atr', 'Root', 'Conj', 'PPart', 'Atr', 'Root', 'Conj', 'Obj', 'Sb', 'Obj', 'Root'], 
+  'srl_info': [
+    {'idx_arg': 2, 'idx_head': 5, 'role': 'TIME'},
+    {'idx_arg': 4, 'idx_head': 5, 'role': 'QUANT'},
+    {'idx_arg': 10, 'idx_head': 7, 'role': 'TIME'},
+    {'idx_arg': 20, 'idx_head': 22, 'role': 'PAT'},
+    {'idx_arg': 21, 'idx_head': 22, 'role': 'ACT'},
+    {'idx_arg': 22, 'idx_head': 17, 'role': 'RESLT'}
+  ],
+  'mwe_info': [
+    {'type': 'IRV', 'word_indices': [7, 8]}
+  ]
+}
+```
+
+### Data Fields
+
+The following attributes are present in the most general config (`all_data`). Please see below for attributes present in the specific configs.  
+- `id_doc`: a string containing the identifier of the document;  
+- `idx_par`: an int32 containing the consecutive number of the paragraph, which the current sentence is a part of;  
+- `idx_sent`: an int32 containing the consecutive number of the current sentence inside the current paragraph;  
+- `id_words`: a list of strings containing the identifiers of words - potentially redundant, helpful for connecting the dataset with external datasets like coref149;  
+- `words`: a list of strings containing the words in the current sentence;  
+- `lemmas`: a list of strings containing the lemmas in the current sentence;  
+- `msds`: a list of strings containing the morphosyntactic description of words in the current sentence;  
+- `has_ne_ann`: a bool indicating whether the current example has named entities annotated;  
+- `has_ud_dep_ann`: a bool indicating whether the current example has dependencies (in UD style) annotated;  
+- `has_jos_dep_ann`: a bool indicating whether the current example has dependencies (in JOS style) annotated;  
+- `has_srl_ann`: a bool indicating whether the current example has semantic roles annotated;  
+- `has_mwe_ann`: a bool indicating whether the current example has multi-word expressions annotated;    
+- `ne_tags`: a list of strings containing the named entity tags encoded using IOB2 - if `has_ne_ann=False` all tokens are annotated with `"N/A"`;  
+- `ud_dep_head`: a list of int32 containing the head index for each word (using UD guidelines) - the head index of the root word is `-1`; if `has_ud_dep_ann=False` all tokens are annotated with `-2`;   
+- `ud_dep_rel`: a list of strings containing the relation with the head for each word (using UD guidelines) - if `has_ud_dep_ann=False` all tokens are annotated with `"N/A"`;  
+- `jos_dep_head`: a list of int32 containing the head index for each word (using JOS guidelines) - the head index of the root word is `-1`; if `has_jos_dep_ann=False` all tokens are annotated with `-2`;  
+- `jos_dep_rel`: a list of strings containing the relation with the head for each word (using JOS guidelines) - if `has_jos_dep_ann=False` all tokens are annotated with `"N/A"`;  
+- `srl_info`: a list of dicts, each containing index of the argument word, the head (verb) word, and the semantic role - if `has_srl_ann=False` this list is empty;  
+- `mwe_info`: a list of dicts, each containing word indices and the type of a multi-word expression;  
+
+#### Data fields in 'named_entity_recognition'
+```
+['id_doc', 'idx_par', 'idx_sent', 'id_words', 'words', 'lemmas', 'msds', 'ne_tags']
+```
+
+#### Data fields in 'dependency_parsing_ud'
+```
+['id_doc', 'idx_par', 'idx_sent', 'id_words', 'words', 'lemmas', 'msds', 'ud_dep_head', 'ud_dep_rel']
+```
+
+#### Data fields in 'dependency_parsing_jos'
+```
+['id_doc', 'idx_par', 'idx_sent', 'id_words', 'words', 'lemmas', 'msds', 'jos_dep_head', 'jos_dep_rel']
+```
+
+#### Data fields in 'semantic_role_labeling'
+```
+['id_doc', 'idx_par', 'idx_sent', 'id_words', 'words', 'lemmas', 'msds', 'srl_info']
+```
+
+#### Data fields in 'multiword_expressions'
+```
+['id_doc', 'idx_par', 'idx_sent', 'id_words', 'words', 'lemmas', 'msds', 'mwe_info']
+```
+
+## Additional Information
+
+### Dataset Curators
+
+Simon Krek; et al. (please see http://hdl.handle.net/11356/1434 for the full list)
+
+### Licensing Information
+
+CC BY-NC-SA 4.0.
+
+### Citation Information
+
+The paper describing the dataset:
+```
+@InProceedings{krek2020ssj500k,
+title = {The ssj500k Training Corpus for Slovene Language Processing},
+author={Krek, Simon and Erjavec, Tomaž and Dobrovoljc, Kaja and Gantar, Polona and Arhar Holdt, Spela and Čibej, Jaka and Brank, Janez},
+booktitle={Proceedings of the Conference on Language Technologies and Digital Humanities},
+year={2020},
+pages={24-33}
+}
+```
+
+The resource itself:
+```
+@misc{krek2021clarinssj500k,
+title = {Training corpus ssj500k 2.3},
+author = {Krek, Simon and Dobrovoljc, Kaja and Erjavec, Toma{\v z} and Mo{\v z}e, Sara and Ledinek, Nina and Holz, Nanika and Zupan, Katja and Gantar, Polona and Kuzman, Taja and {\v C}ibej, Jaka and Arhar Holdt, {\v S}pela and Kav{\v c}i{\v c}, Teja and {\v S}krjanec, Iza and Marko, Dafne and Jezer{\v s}ek, Lucija and Zajc, Anja},
+url = {http://hdl.handle.net/11356/1434},
+year = {2021} }
+```
+
+### Contributions
+
+Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.

huggingface_dataset/Dataset_Card/giga_fren.md

@@ -0,0 +1,165 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- en
+- fr
+license:
+- unknown
+multilinguality:
+- multilingual
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- translation
+task_ids: []
+paperswithcode_id: null
+pretty_name: GigaFren
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - en
+        - fr
+  config_name: en-fr
+  splits:
+  - name: train
+    num_bytes: 8690296821
+    num_examples: 22519904
+  download_size: 2701536198
+  dataset_size: 8690296821
+---
+
+# Dataset Card for GigaFren
+
+## 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://opus.nlpl.eu/giga-fren.php
+- **Repository:** None
+- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
+- **Leaderboard:** [More Information Needed]
+- **Point of Contact:** [More Information Needed]
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+Here are some examples of questions and facts:
+
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### 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 [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

huggingface_dataset/Dataset_Card/huggingartists_aaron-watson.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/aaron-watson"
+
+## 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:** 0.266584 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/894021d09a748eef8c6d63ad898b814b.650x430x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/aaron-watson">
+      <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">Aaron Watson</div>
+    <a href="https://genius.com/artists/aaron-watson">
+    	<div style="text-align: center; font-size: 14px;">@aaron-watson</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/aaron-watson).
+
+### 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/aaron-watson")
+```
+
+## 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|
+|------:|---------:|---:|
+|181|         -|   -|
+
+'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/aaron-watson")
+
+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-english-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/irds_codesearchnet_challenge.md

@@ -0,0 +1,49 @@
+---
+pretty_name: '`codesearchnet/challenge`'
+viewer: false
+source_datasets: ['irds/codesearchnet']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `codesearchnet/challenge`
+
+The `codesearchnet/challenge` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/codesearchnet#codesearchnet/challenge).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=99
+ - `qrels`: (relevance assessments); count=4,006
+
+ - For `docs`, use [`irds/codesearchnet`](https://huggingface.co/datasets/irds/codesearchnet)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/codesearchnet_challenge', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/codesearchnet_challenge', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'note': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@article{Husain2019CodeSearchNet,
+  title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
+  author={Hamel Husain and Ho-Hsiang Wu and Tiferet Gazit and Miltiadis Allamanis and Marc Brockschmidt},
+  journal={ArXiv},
+  year={2019}
+}
+```

huggingface_dataset/Dataset_Card/isixhosa_ner_corpus.md

@@ -0,0 +1,199 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- xh
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: IsixhosaNerCorpus
+license_details: Creative Commons Attribution 2.5 South Africa License
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': OUT
+          '1': B-PERS
+          '2': I-PERS
+          '3': B-ORG
+          '4': I-ORG
+          '5': B-LOC
+          '6': I-LOC
+          '7': B-MISC
+          '8': I-MISC
+  config_name: isixhosa_ner_corpus
+  splits:
+  - name: train
+    num_bytes: 2414995
+    num_examples: 6284
+  download_size: 14513302
+  dataset_size: 2414995
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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:**  [IsiXhosa Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/312)
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za)
+
+
+### Dataset Summary
+
+The isiXhosa Ner Corpus is a Xhosa dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Xhosa language. The dataset uses CoNLL shared task annotation standards.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The language supported is Xhosa.
+
+## Dataset Structure
+
+### Data Instances
+
+A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. 
+{'id': '0',
+ 'ner_tags': [7, 8, 5, 6, 0],
+ 'tokens': ['Injongo', 'ye-website', 'yaseMzantsi', 'Afrika', 'kukuvelisa']
+}
+
+### Data Fields
+
+- `id`: id of the sample
+- `tokens`: the tokens of the example text
+- `ner_tags`: the NER tags of each token
+
+The NER tags correspond to this list:
+```
+"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
+```
+The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
+
+### Data Splits
+
+The data was not split.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The data was created to help introduce resources to new language - Xhosa.
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data is based on South African government domain and was crawled from gov.za websites.
+
+[More Information Needed]
+#### Who are the source language producers?
+
+The data was produced by writers of South African government websites - gov.za
+
+[More Information Needed]
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+The data was annotated during the NCHLT text resource development project.
+
+[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
+
+The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
+
+See: [more information](http://www.nwu.ac.za/ctext)
+
+### Licensing Information
+
+The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
+
+### Citation Information
+
+```
+@inproceedings{isixhosa_ner_corpus,
+  author    = {	K. Podile and
+              Roald Eiselen},
+  title     = {NCHLT isiXhosa Named Entity Annotated Corpus},
+  booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th      Language Resource and Evaluation Conference, Portorož, Slovenia.},
+  year      = {2016},
+  url       = {https://repo.sadilar.org/handle/20.500.12185/312},
+}
+```
+
+### Contributions
+
+Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.

huggingface_dataset/Dataset_Card/malteos_test2.md

@@ -0,0 +1,145 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license:
+- apache-2.0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- summarization
+paperswithcode_id: cnn-daily-mail-1
+pretty_name: CNN / Daily Mail
+---
+
+# 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/proxima_SD_1-5_reg_images.md

@@ -0,0 +1,11 @@
+---
+license: creativeml-openrail-m
+---
+1k images for the class "artstyle" that were made with & for the [JoePenna Dreambooth repo](https://github.com/JoePenna/Dreambooth-Stable-Diffusion) with Stable Diffusion 1.5
+
+```
+seed: 10
+ddim_eta: 0.0
+scale: 10.0
+ddim_steps: 50
+```

huggingface_dataset/Dataset_Card/sc2qa_sc2qa_commoncrawl.md

@@ -0,0 +1,5 @@
+For details, please refer to the following links.
+
+Github repo: https://github.com/amazon-research/SC2QA-DRIL
+
+Paper: [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://arxiv.org/pdf/2109.04689.pdf)

huggingface_dataset/Dataset_Card/sem_eval_2020_task_11.md

@@ -0,0 +1,252 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text-classification
+- token-classification
+task_ids: []
+pretty_name: SemEval-2020 Task 11
+tags:
+- propaganda-span-identification
+- propaganda-technique-classification
+dataset_info:
+  features:
+  - name: article_id
+    dtype: string
+  - name: text
+    dtype: string
+  - name: span_identification
+    sequence:
+    - name: start_char_offset
+      dtype: int64
+    - name: end_char_offset
+      dtype: int64
+  - name: technique_classification
+    sequence:
+    - name: start_char_offset
+      dtype: int64
+    - name: end_char_offset
+      dtype: int64
+    - name: technique
+      dtype:
+        class_label:
+          names:
+            '0': Appeal_to_Authority
+            '1': Appeal_to_fear-prejudice
+            '2': Bandwagon,Reductio_ad_hitlerum
+            '3': Black-and-White_Fallacy
+            '4': Causal_Oversimplification
+            '5': Doubt
+            '6': Exaggeration,Minimisation
+            '7': Flag-Waving
+            '8': Loaded_Language
+            '9': Name_Calling,Labeling
+            '10': Repetition
+            '11': Slogans
+            '12': Thought-terminating_Cliches
+            '13': Whataboutism,Straw_Men,Red_Herring
+  splits:
+  - name: train
+    num_bytes: 2358613
+    num_examples: 371
+  - name: test
+    num_bytes: 454100
+    num_examples: 90
+  - name: validation
+    num_bytes: 396410
+    num_examples: 75
+  download_size: 0
+  dataset_size: 3209123
+---
+
+# Dataset Card for SemEval-2020 Task 11
+
+## 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:** [PTC TASKS ON "DETECTION OF PROPAGANDA TECHNIQUES IN NEWS ARTICLES"](https://propaganda.qcri.org/ptc/index.html)
+- **Paper:** [SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://arxiv.org/abs/2009.02696)
+- **Leaderboard:** [PTC Tasks Leaderboard](https://propaganda.qcri.org/ptc/leaderboard.php)
+- **Point of Contact:** [Task organizers contact](semeval-2020-task-11-organizers@googlegroups.com)
+
+### Dataset Summary
+
+Propagandistic news articles use specific techniques to convey their message, such as whataboutism, red Herring, and name calling, among many others. The Propaganda Techniques Corpus (PTC) allows to study automatic algorithms to detect them. We provide a permanent leaderboard to allow researchers both to advertise their progress and to be up-to-speed with the state of the art on the tasks offered (see below for a definition).
+
+### Supported Tasks and Leaderboards
+
+
+More information on scoring methodology can be found in [propaganda tasks evaluation document](https://propaganda.qcri.org/ptc/data/propaganda_tasks_evaluation.pdf)
+
+### Languages
+
+This dataset consists of English news articles
+
+## Dataset Structure
+
+### Data Instances
+
+Each example is structured as follows:
+
+```
+{
+  "span_identification": {
+    "end_char_offset": [720, 6322, ...],
+    "start_char_offset": [683, 6314, ...]
+  },
+  "technique_classification": {
+    "end_char_offset": [720,6322, ...],
+    "start_char_offset": [683,6314, ...],
+    "technique": [7,8, ...]
+  },
+  "text": "Newt Gingrich: The truth about Trump, Putin, and Obama\n\nPresident Trump..."
+}
+
+```
+
+### Data Fields
+
+- `text`: The full text of the news article.
+- `span_identification`: a dictionary feature containing:
+  - `start_char_offset`: The start character offset of the span for the SI task
+  - `end_char_offset`: The end character offset of the span for the SI task
+- `technique_classification`: a dictionary feature containing:
+  - `start_char_offset`: The start character offset of the span for the TC task
+  - `end_char_offset`: The start character offset of the span for the TC task
+  - `technique`: the propaganda technique classification label, with possible values including `Appeal_to_Authority`, `Appeal_to_fear-prejudice`, `Bandwagon,Reductio_ad_hitlerum`, `Black-and-White_Fallacy`, `Causal_Oversimplification`.
+
+### Data Splits
+
+|                            | Train  | Valid | Test |
+| -----                      | ------ | ----- | ---- |
+| Input Sentences            |   371  |   75  |  90  |
+| Total Annotations  SI      |  5468  |  940  |   0  |
+| Total Annotations  TC      |  6128  | 1063  |   0  |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+In order to build the PTC-SemEval20 corpus, we retrieved a sample of news articles from the period
+starting in mid-2017 and ending in early 2019. We selected 13 propaganda and 36 non-propaganda news
+media outlets, as labeled by Media Bias/Fact Check,3
+and we retrieved articles from these sources. We
+deduplicated the articles on the basis of word n-grams matching (Barron-Cede ´ no and Rosso, 2009) and ˜
+we discarded faulty entries (e.g., empty entries from blocking websites).
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+The annotation job consisted of both spotting a propaganda snippet and, at the same time, labeling
+it with a specific propaganda technique. The annotation guidelines are shown in the appendix; they
+are also available online.4 We ran the annotation in two phases: (i) two annotators label an article
+independently and (ii) the same two annotators gather together with a consolidator to discuss dubious
+instances (e.g., spotted only by one annotator, boundary discrepancies, label mismatch, etc.). This protocol
+was designed after a pilot annotation stage, in which a relatively large number of snippets had been spotted
+by one annotator only. The annotation team consisted of six professional annotators from A Data Pro trained to spot and label the propaganda snippets from free text. The job was carried out on an instance of
+the Anafora annotation platform (Chen and Styler, 2013), which we tailored for our propaganda annotation
+task.
+We evaluated the annotation process in terms of γ agreement (Mathet et al., 2015) between each of
+the annotators and the final gold labels. The γ agreement on the annotated articles is on average 0.6;
+see (Da San Martino et al., 2019b) for a more detailed discussion of inter-annotator agreement. The
+training and the development part of the PTC-SemEval20 corpus are the same as the training and the
+testing datasets described in (Da San Martino et al., 2019b). The test part of the PTC-SemEval20 corpus
+consists of 90 additional articles selected from the same sources as for training and development. For
+the test articles, we further extended the annotation process by adding one extra consolidation step: we
+revisited all the articles in that partition and we performed the necessary adjustments to the spans and to
+the labels as necessary, after a thorough discussion and convergence among at least three experts who
+were not involved in the initial annotations.
+
+#### 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
+
+```
+@misc{martino2020semeval2020,
+      title={SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles}, 
+      author={G. Da San Martino and A. Barrón-Cedeño and H. Wachsmuth and R. Petrov and P. Nakov},
+      year={2020},
+      eprint={2009.02696},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+### Contributions
+
+Thanks to [@ZacharySBrown](https://github.com/ZacharySBrown) for adding this dataset.

huggingface_dataset/Dataset_Card/theblackcat102_joke_explaination.md

@@ -0,0 +1,42 @@
+---
+license: mit
+task_categories:
+- text-generation
+- text2text-generation
+language:
+- en
+tags:
+- joke
+- high quality
+size_categories:
+- n<1K
+---
+
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Homepage:** : https://explainthejoke.com/
+
+### Dataset Summary
+
+Corpus for testing whether your LLM can explain the joke well. But this is a rather small dataset, if someone can point to a larger ones would be very nice. 
+
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Fields
+
+* url : link to the explaination
+
+* joke : the original joke
+
+* explaination : the explaination of the joke
+
+### Data Splits
+
+Since its so small, there's no splits just like gsm8k

huggingface_dataset/Dataset_Card/udayl_rocks.md

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+---
+license: mit
+---
+
+Rocks dataset with 7 classes: [Coal, Limestone, Marble, Sandstone, Quartzite, Basalt, Granite]

huggingface_dataset/Dataset_Card/z-uo_squad-it.md

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+---
+language:
+- it
+multilinguality:
+- monolingual
+size_categories:
+- 8k<n<10k
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+---
+
+# Squad-it
+This dataset is an adapted version of that [squad-it](https://github.com/crux82/squad-it) to train on HuggingFace models.
+
+It contains:
+- train samples: 87599
+- test samples : 10570
+
+This dataset is for question answering and his format is the following:
+```
+[
+  {
+    "answers": [
+      {
+        "answer_start": [1],
+        "text": ["Questo è un testo"]
+      },
+    ],
+    "context": "Questo è un testo relativo al contesto.",
+    "id": "1",
+    "question": "Questo è un testo?",
+    "title": "train test"
+  }
+]
+```
+
+It can be used to train many models like T5, Bert, Distilbert...