Upload dataloading script and README.md

README.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.

cross_re.py

@@ -0,0 +1,184 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""CrossRE is a cross-domain dataset for relation extraction"""
+
+
+import json
+import datasets
+
+
+_CITATION = """\
+@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"
+}
+"""
+
+_DESCRIPTION = """\
+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
+"""
+
+_HOMEPAGE = "https://github.com/mainlp/CrossRE"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "news": {
+        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/news-train.json",
+        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/news-dev.json",
+        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/news-test.json",
+    },
+    "politics": {
+        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/politics-train.json",
+        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/politics-dev.json",
+        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/politics-test.json",
+    },
+    "science": {
+        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/science-train.json",
+        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/science-dev.json",
+        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/science-test.json",
+    },
+    "music": {
+        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/music-train.json",
+        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/music-dev.json",
+        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/music-test.json",
+    },
+    "literature": {
+        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/literature-train.json",
+        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/literature-dev.json",
+        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/literature-test.json",
+    },
+    "ai": {
+        "train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/ai-train.json",
+        "validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/ai-dev.json",
+        "test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/ai-test.json",
+    },
+}
+
+
+class CrossRE(datasets.GeneratorBasedBuilder):
+    """CrossRE is a cross-domain dataset for relation extraction"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="news", version=VERSION,
+                               description="This part of CrossRE covers data from the news domain"),
+        datasets.BuilderConfig(name="politics", version=VERSION,
+                               description="This part of CrossRE covers data from the politics domain"),
+        datasets.BuilderConfig(name="science", version=VERSION,
+                               description="This part of CrossRE covers data from the science domain"),
+        datasets.BuilderConfig(name="music", version=VERSION,
+                               description="This part of CrossRE covers data from the music domain"),
+        datasets.BuilderConfig(name="literature", version=VERSION,
+                               description="This part of CrossRE covers data from the literature domain"),
+        datasets.BuilderConfig(name="ai", version=VERSION,
+                               description="This part of CrossRE covers data from the AI domain"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "doc_key": datasets.Value("string"),
+                "sentence": datasets.Sequence(datasets.Value("string")),
+                "ner": [{
+                    "id-start": datasets.Value("int32"),
+                    "id-end": datasets.Value("int32"),
+                    "entity-type": datasets.Value("string"),
+                }],
+                "relations": [{
+                    "id_1-start": datasets.Value("int32"),
+                    "id_1-end": datasets.Value("int32"),
+                    "id_2-start": datasets.Value("int32"),
+                    "id_2-end": datasets.Value("int32"),
+                    "relation-type": datasets.Value("string"),
+                    "Exp": datasets.Value("string"),  # Explanation of the relation type assigned
+                    "Un": datasets.Value("bool"),  # Uncertainty of the annotator
+                    "SA": datasets.Value("bool"),  # Syntax Ambiguity which poses a challenge for the annotator
+                }]
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        downloaded_files = dl_manager.download_and_extract(urls)
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]})
+                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for row in f:
+                doc = json.loads(row)
+                doc_key = doc["doc_key"]
+                ner = []
+                for entity in doc["ner"]:
+                    ner.append({
+                        "id-start": entity[0],
+                        "id-end": entity[1],
+                        "entity-type": entity[2],
+                    })
+                relations = []
+                for relation in doc["relations"]:
+                    relations.append({
+                        "id_1-start": relation[0],
+                        "id_1-end": relation[1],
+                        "id_2-start": relation[2],
+                        "id_2-end": relation[3],
+                        "relation-type": relation[4],
+                        "Exp": relation[5],
+                        "Un": relation[6],
+                        "SA": relation[7],
+                    })
+                yield doc_key, {
+                    "doc_key": doc_key,
+                    "sentence": doc["sentence"],
+                    "ner": ner,
+                    "relations": relations
+                }