
# Dataset Card for DocRED

## 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:** [https://github.com/thunlp/DocRED](https://github.com/thunlp/DocRED)
- **Paper:** [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://arxiv.org/abs/1906.06127)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 21.00 MB
- **Size of the generated dataset:** 20.12 MB
- **Total amount of disk used:** 41.14 MB

### Dataset Summary

Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features:
    - DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
    - DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
    - Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.

### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Dataset Structure

### Data Instances

#### default

- **Size of downloaded dataset files:** 21.00 MB
- **Size of the generated dataset:** 20.12 MB
- **Total amount of disk used:** 41.14 MB

An example of 'train_annotated' looks as follows.
```
{
    "labels": {
        "evidence": [[0]],
        "head": [0],
        "relation_id": ["P1"],
        "relation_text": ["is_a"],
        "tail": [0]
    },
    "sents": [["This", "is", "a", "sentence"], ["This", "is", "another", "sentence"]],
    "title": "Title of the document",
    "vertexSet": [[{
        "name": "sentence",
        "pos": [3],
        "sent_id": 0,
        "type": "NN"
    }, {
        "name": "sentence",
        "pos": [3],
        "sent_id": 1,
        "type": "NN"
    }], [{
        "name": "This",
        "pos": [0],
        "sent_id": 0,
        "type": "NN"
    }]]
}
```

### Data Fields

The data fields are the same among all splits.

#### default
- `title`: a `string` feature.
- `sents`: a dictionary feature containing:
  - `feature`: a `string` feature.
- `name`: a `string` feature.
- `sent_id`: a `int32` feature.
- `pos`: a `list` of `int32` features.
- `type`: a `string` feature.
- `labels`: a dictionary feature containing:
  - `head`: a `int32` feature.
  - `tail`: a `int32` feature.
  - `relation_id`: a `string` feature.
  - `relation_text`: a `string` feature.
  - `evidence`: a `list` of `int32` features.

### Data Splits

| name  |train_annotated|train_distant|validation|test|
|-------|--------------:|------------:|---------:|---:|
|default|           3053|       101873|       998|1000|

## 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{yao-etal-2019-docred,
    title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset",
    author = "Yao, Yuan  and
      Ye, Deming  and
      Li, Peng  and
      Han, Xu  and
      Lin, Yankai  and
      Liu, Zhenghao  and
      Liu, Zhiyuan  and
      Huang, Lixin  and
      Zhou, Jie  and
      Sun, Maosong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1074",
    doi = "10.18653/v1/P19-1074",
    pages = "764--777",
}
```


### Contributions

Thanks to [@ghomasHudson](https://github.com/ghomasHudson), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
task_categories:text-retrieval task_ids:entity-linking-retrieval annotations_creators:expert-generated language_creators:crowdsourced multilinguality:monolingual size_categories:100K<n<1M source_datasets:original language:en license:mit arxiv:1906.06127 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-retrieval"], "task_ids": ["entity-linking-retrieval"], "paperswithcode_id": "docred", "pretty_name": "DocRED", "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "sents", "sequence": {"sequence": "string"}}, {"name": "vertexSet", "list": {"list": [{"name": "name", "dtype": "string"}, {"name": "sent_id", "dtype": "int32"}, {"name": "pos", "sequence": "int32"}, {"name": "type", "dtype": "string"}]}}, {"name": "labels", "sequence": [{"name": "head", "dtype": "int32"}, {"name": "tail", "dtype": "int32"}, {"name": "relation_id", "dtype": "string"}, {"name": "relation_text", "dtype": "string"}, {"name": "evidence", "sequence": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 3425030, "num_examples": 998}, {"name": "test", "num_bytes": 2843877, "num_examples": 1000}, {"name": "train_annotated", "num_bytes": 10413156, "num_examples": 3053}, {"name": "train_distant", "num_bytes": 346001876, "num_examples": 101873}], "download_size": 458040413, "dataset_size": 362683939}}
TAGS
#task_categories-text-retrieval #task_ids-entity-linking-retrieval #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-mit #arxiv-1906.06127 #region-us 

Dataset Card for DocRED
=======================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Repository: URL
* Paper: DocRED: A Large-Scale Document-Level Relation Extraction Dataset
* Point of Contact:
* Size of downloaded dataset files: 21.00 MB
* Size of the generated dataset: 20.12 MB
* Total amount of disk used: 41.14 MB


### Dataset Summary


Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features:
- DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
- DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
- Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.


### Supported Tasks and Leaderboards


### Languages


Dataset Structure
-----------------


### Data Instances


#### default


* Size of downloaded dataset files: 21.00 MB
* Size of the generated dataset: 20.12 MB
* Total amount of disk used: 41.14 MB


An example of 'train\_annotated' looks as follows.


### Data Fields


The data fields are the same among all splits.


#### default


* 'title': a 'string' feature.
* 'sents': a dictionary feature containing:
	+ 'feature': a 'string' feature.
* 'name': a 'string' feature.
* 'sent\_id': a 'int32' feature.
* 'pos': a 'list' of 'int32' features.
* 'type': a 'string' feature.
* 'labels': a dictionary feature containing:
	+ 'head': a 'int32' feature.
	+ 'tail': a 'int32' feature.
	+ 'relation\_id': a 'string' feature.
	+ 'relation\_text': a 'string' feature.
	+ 'evidence': a 'list' of 'int32' features.


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


### Contributions


Thanks to @ghomasHudson, @thomwolf, @lhoestq for adding this dataset.



### Dataset Summary


Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features:
- DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
- DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
- Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios. ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### default


* Size of downloaded dataset files: 21.00 MB
* Size of the generated dataset: 20.12 MB
* Total amount of disk used: 41.14 MB


An example of 'train\_annotated' looks as follows. ### Data Fields


The data fields are the same among all splits. #### default


* 'title': a 'string' feature.
* 'sents': a dictionary feature containing:
	+ 'feature': a 'string' feature.
* 'name': a 'string' feature.
* 'sent\_id': a 'int32' feature.
* 'pos': a 'list' of 'int32' features.
* 'type': a 'string' feature.
* 'labels': a dictionary feature containing:
	+ 'head': a 'int32' feature.
	+ 'tail': a 'int32' feature.
	+ 'relation\_id': a 'string' feature.
	+ 'relation\_text': a 'string' feature.
	+ 'evidence': a 'list' of 'int32' features. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @ghomasHudson, @thomwolf, @lhoestq for adding this dataset.
TAGS
#task_categories-text-retrieval #task_ids-entity-linking-retrieval #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-mit #arxiv-1906.06127 #region-us 
 ### Dataset Summary


Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features:
- DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
- DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
- Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios. ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### default


* Size of downloaded dataset files: 21.00 MB
* Size of the generated dataset: 20.12 MB
* Total amount of disk used: 41.14 MB


An example of 'train\_annotated' looks as follows. ### Data Fields


The data fields are the same among all splits. #### default


* 'title': a 'string' feature.
* 'sents': a dictionary feature containing:
	+ 'feature': a 'string' feature.
* 'name': a 'string' feature.
* 'sent\_id': a 'int32' feature.
* 'pos': a 'list' of 'int32' features.
* 'type': a 'string' feature.
* 'labels': a dictionary feature containing:
	+ 'head': a 'int32' feature.
	+ 'tail': a 'int32' feature.
	+ 'relation\_id': a 'string' feature.
	+ 'relation\_text': a 'string' feature.
	+ 'evidence': a 'list' of 'int32' features. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @ghomasHudson, @thomwolf, @lhoestq for adding this dataset.
passage: TAGS
#task_categories-text-retrieval #task_ids-entity-linking-retrieval #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-mit #arxiv-1906.06127 #region-us 
### Dataset Summary


Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features:
- DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
- DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
- Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.### Supported Tasks and Leaderboards### Languages


Dataset Structure
-----------------### Data Instances#### default


* Size of downloaded dataset files: 21.00 MB
* Size of the generated dataset: 20.12 MB
* Total amount of disk used: 41.14 MB


An example of 'train\_annotated' looks as follows.### Data Fields


The data fields are the same among all splits.