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- Host data files (1a34d11295c4d14ace1e7a62af2e5d89cde7c5b7)
- Update loading script (5377fb0d891e8b91e69fa60fd0528794781db4e2)
- Update citation metadata (dd3a93f21e6d3345983d80f7c012514e3c72cb00)
- Delete legacy dataset_infos.json (aa30d279879fe435853aac86d805257a1dec0e33)

README.md CHANGED
@@ -239,13 +239,27 @@ The data fields are the same among all splits.
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  ### Citation Information
240
 
241
  ```
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- @inproceedings{yao2019DocRED,
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- title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},
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- 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},
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- booktitle={Proceedings of ACL 2019},
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- year={2019}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
247
  }
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-
249
  ```
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251
 
 
239
  ### Citation Information
240
 
241
  ```
242
+ @inproceedings{yao-etal-2019-docred,
243
+ title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset",
244
+ author = "Yao, Yuan and
245
+ Ye, Deming and
246
+ Li, Peng and
247
+ Han, Xu and
248
+ Lin, Yankai and
249
+ Liu, Zhenghao and
250
+ Liu, Zhiyuan and
251
+ Huang, Lixin and
252
+ Zhou, Jie and
253
+ Sun, Maosong",
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+ booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
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+ month = jul,
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+ year = "2019",
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+ address = "Florence, Italy",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/P19-1074",
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+ doi = "10.18653/v1/P19-1074",
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+ pages = "764--777",
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  }
 
263
  ```
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265
 
data/dev.json.gz ADDED
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data/rel_info.json.gz ADDED
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data/test.json.gz ADDED
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data/train_annotated.json.gz ADDED
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dataset_infos.json DELETED
@@ -1 +0,0 @@
1
- {"default": {"description": "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:\n - DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.\n - DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.\n - 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.\n", "citation": "@inproceedings{yao2019DocRED,\n title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},\n 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},\n booktitle={Proceedings of ACL 2019},\n year={2019}\n}\n", "homepage": "https://github.com/thunlp/DocRED", "license": "", "features": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "sents": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "vertexSet": [[{"name": {"dtype": "string", "id": null, "_type": "Value"}, "sent_id": {"dtype": "int32", "id": null, "_type": "Value"}, "pos": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "type": {"dtype": "string", "id": null, "_type": "Value"}}]], "labels": {"feature": {"head": {"dtype": "int32", "id": null, "_type": "Value"}, "tail": {"dtype": "int32", "id": null, "_type": "Value"}, "relation_id": {"dtype": "string", "id": null, "_type": "Value"}, "relation_text": {"dtype": "string", "id": null, "_type": "Value"}, "evidence": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "doc_red", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 3425030, "num_examples": 998, "dataset_name": "doc_red"}, "test": {"name": "test", "num_bytes": 2843877, "num_examples": 1000, "dataset_name": "doc_red"}, "train_annotated": {"name": "train_annotated", "num_bytes": 10413156, "num_examples": 3053, "dataset_name": "doc_red"}, "train_distant": {"name": "train_distant", "num_bytes": 346001876, "num_examples": 101873, "dataset_name": "doc_red"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1AHUm1-_V9GCtGuDcc8XrMUCJE8B-HHoL": {"num_bytes": 4287303, "checksum": "4554f7487a6fda3bab4d4e59432e065b7485dfb885bd7f05fd60fc7e93ee7e3e"}, "https://drive.google.com/uc?export=download&id=1Qr4Jct2IJ9BVI86_mCk_Pz0J32ww9dYw": {"num_bytes": 437046821, "checksum": "db6d3cdaab8d36926318bb9339f6fd82d19dbacd186c74d7c20c734355a58b36"}, "https://drive.google.com/uc?export=download&id=1NN33RzyETbanw4Dg2sRrhckhWpzuBQS9": {"num_bytes": 13029595, "checksum": "7e706348a02cf91f38bd8c379f934ab61aedadc901fca10d962c1d82ab78e95b"}, "https://drive.google.com/uc?export=download&id=1lAVDcD94Sigx7gR3jTfStI66o86cflum": {"num_bytes": 3674242, "checksum": "09386b5cb58249d8e087863c379ebd64557169c52ee502193d2f4f215e704ae8"}, "https://drive.google.com/uc?id=1y9A0zKrvETc1ddUFuFhBg3Xfr7FEL4dW&export=download": {"num_bytes": 2452, "checksum": "5ecf4e5e55c179fc83a3a3d19baa01efffecb26ba5edc0b4ac5a54ddf61fe3de"}}, "download_size": 458040413, "post_processing_size": null, "dataset_size": 362683939, "size_in_bytes": 820724352}}
 
 
docred.py CHANGED
@@ -2,18 +2,31 @@
2
 
3
 
4
  import json
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- import os
6
 
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  import datasets
8
 
9
 
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  _CITATION = """\
11
- @inproceedings{yao2019DocRED,
12
- title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},
13
- author={Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu, \
14
- Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong},
15
- booktitle={Proceedings of ACL 2019},
16
- year={2019}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17
  }
18
  """
19
 
@@ -28,11 +41,11 @@ from Wikipedia and Wikidata with three features:
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  """
29
 
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  _URLS = {
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- "dev": "https://drive.google.com/uc?export=download&id=1AHUm1-_V9GCtGuDcc8XrMUCJE8B-HHoL",
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- "train_distant": "https://drive.google.com/uc?export=download&id=1Qr4Jct2IJ9BVI86_mCk_Pz0J32ww9dYw",
33
- "train_annotated": "https://drive.google.com/uc?export=download&id=1NN33RzyETbanw4Dg2sRrhckhWpzuBQS9",
34
- "test": "https://drive.google.com/uc?export=download&id=1lAVDcD94Sigx7gR3jTfStI66o86cflum",
35
- "rel_info": "https://drive.google.com/uc?id=1y9A0zKrvETc1ddUFuFhBg3Xfr7FEL4dW&export=download",
36
  }
37
 
38
 
@@ -73,13 +86,7 @@ class DocRed(datasets.GeneratorBasedBuilder):
73
  )
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75
  def _split_generators(self, dl_manager):
76
- downloads = {}
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- for key in _URLS.keys():
78
- downloads[key] = dl_manager.download_and_extract(_URLS[key])
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- # Fix for dummy data
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- if os.path.isdir(downloads[key]):
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- downloads[key] = os.path.join(downloads[key], key + ".json")
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-
83
  return [
84
  datasets.SplitGenerator(
85
  name=datasets.Split.VALIDATION,
 
2
 
3
 
4
  import json
 
5
 
6
  import datasets
7
 
8
 
9
  _CITATION = """\
10
+ @inproceedings{yao-etal-2019-docred,
11
+ title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset",
12
+ author = "Yao, Yuan and
13
+ Ye, Deming and
14
+ Li, Peng and
15
+ Han, Xu and
16
+ Lin, Yankai and
17
+ Liu, Zhenghao and
18
+ Liu, Zhiyuan and
19
+ Huang, Lixin and
20
+ Zhou, Jie and
21
+ Sun, Maosong",
22
+ booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
23
+ month = jul,
24
+ year = "2019",
25
+ address = "Florence, Italy",
26
+ publisher = "Association for Computational Linguistics",
27
+ url = "https://aclanthology.org/P19-1074",
28
+ doi = "10.18653/v1/P19-1074",
29
+ pages = "764--777",
30
  }
31
  """
32
 
 
41
  """
42
 
43
  _URLS = {
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+ "dev": "data/dev.json.gz",
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+ "train_distant": "data/train_distant.json.gz",
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+ "train_annotated": "data/train_annotated.json.gz",
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+ "test": "data/test.json.gz",
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+ "rel_info": "data/rel_info.json.gz",
49
  }
50
 
51
 
 
86
  )
87
 
88
  def _split_generators(self, dl_manager):
89
+ downloads = dl_manager.download_and_extract(_URLS)
 
 
 
 
 
 
90
  return [
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  datasets.SplitGenerator(
92
  name=datasets.Split.VALIDATION,