Host data files (#6)

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

README.md

@@ -239,13 +239,27 @@ The data fields are the same among all splits.
 ### Citation Information
 
 ```
-@inproceedings{yao2019DocRED,
-  title={{DocRED}: 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 ACL 2019},
-  year={2019}
+@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",
 }
-
 ```
 
 

data/dev.json.gz

@@ -0,0 +1,3 @@
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+oid sha256:6ae4d7f5b0b9d2cbe74b9634ed43b35b7cb5b7c0dc3a16226dbe343139a4ae05
+size 928690

data/rel_info.json.gz

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+size 1037

data/test.json.gz

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+oid sha256:eeb68c4c9c5925c20f6b7cbf014074bde4d3c5b976c4dfb4368936f013b3bead
+size 835688

data/train_annotated.json.gz

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+size 2810860

data/train_distant.json.gz

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+oid sha256:c420c0429310583cfc9459f7daa26b1f4c11ff5c7a1481aa64ab9db2b296b905
+size 90991384

dataset_infos.json

@@ -1 +0,0 @@
-{"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

@@ -2,18 +2,31 @@
 
 
 import json
-import os
 
 import datasets
 
 
 _CITATION = """\
-@inproceedings{yao2019DocRED,
-  title={{DocRED}: 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 ACL 2019},
-  year={2019}
+@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",
 }
 """
 
@@ -28,11 +41,11 @@ from Wikipedia and Wikidata with three features:
 """
 
 _URLS = {
-    "dev": "https://drive.google.com/uc?export=download&id=1AHUm1-_V9GCtGuDcc8XrMUCJE8B-HHoL",
-    "train_distant": "https://drive.google.com/uc?export=download&id=1Qr4Jct2IJ9BVI86_mCk_Pz0J32ww9dYw",
-    "train_annotated": "https://drive.google.com/uc?export=download&id=1NN33RzyETbanw4Dg2sRrhckhWpzuBQS9",
-    "test": "https://drive.google.com/uc?export=download&id=1lAVDcD94Sigx7gR3jTfStI66o86cflum",
-    "rel_info": "https://drive.google.com/uc?id=1y9A0zKrvETc1ddUFuFhBg3Xfr7FEL4dW&export=download",
+    "dev": "data/dev.json.gz",
+    "train_distant": "data/train_distant.json.gz",
+    "train_annotated": "data/train_annotated.json.gz",
+    "test": "data/test.json.gz",
+    "rel_info": "data/rel_info.json.gz",
 }
 
 
@@ -73,13 +86,7 @@ class DocRed(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        downloads = {}
-        for key in _URLS.keys():
-            downloads[key] = dl_manager.download_and_extract(_URLS[key])
-            #  Fix for dummy data
-            if os.path.isdir(downloads[key]):
-                downloads[key] = os.path.join(downloads[key], key + ".json")
-
+        downloads = dl_manager.download_and_extract(_URLS)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,