add redocred

data/dev_revised.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:655cf867f0dac11c81994f9bcd0fa78c0ec02ca55267191e8bb87dc10b2970f3
+size 632045

data/rel_info.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ef27efff537ba89ae66f6f4e60e4908d4df3860a4c2819ea94fa5ed696bdc70
+size 1037

data/test_revised.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d6ad71e4f76973314151fe28408fcbb8053593ac165fdf568de523316c1bbdbc
+size 620220

data/train_revised.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4aa312f3a8074373f24a59e230bffdf39e8fb7bbb39ba0f4e7a19fd4d1d3f4f
+size 3659860

redocred.py

@@ -0,0 +1,130 @@
+"""DocRED: A Large-Scale Document-Level Relation Extraction Dataset"""
+
+import json
+
+import datasets
+
+_CITATION = """\
+@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",
+}
+"""
+
+_DESCRIPTION = """\
+This repository is copied from https://huggingface.co/datasets/thunlp/docred and changed the files to change Re-DocRED.\
+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.
+"""
+
+_URLS = {
+    "dev": "data/dev_revised.json.gz",
+    "train": "data/train_revised.json.gz",
+    "test": "data/test.json.gz",
+    "rel_info": "data/rel_info.json.gz",
+}
+
+
+class DocRed(datasets.GeneratorBasedBuilder):
+    """DocRED: A Large-Scale Document-Level Relation Extraction Dataset"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "title": datasets.Value("string"),
+                    "sents": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
+                    "vertexSet": [
+                        [
+                            {
+                                "name": datasets.Value("string"),
+                                "sent_id": datasets.Value("int32"),
+                                "pos": datasets.features.Sequence(datasets.Value("int32")),
+                                "type": datasets.Value("string"),
+                            }
+                        ]
+                    ],
+                    "labels": datasets.features.Sequence(
+                        {
+                            "head": datasets.Value("int32"),
+                            "tail": datasets.Value("int32"),
+                            "relation_id": datasets.Value("string"),
+                            "relation_text": datasets.Value("string"),
+                            "evidence": datasets.features.Sequence(datasets.Value("int32")),
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/thunlp/DocRED",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloads = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": downloads["dev"], "rel_info": downloads["rel_info"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"], "rel_info": downloads["rel_info"]}
+            ),
+            datasets.SplitGenerator(
+                name="train_annotated",
+                gen_kwargs={"filepath": downloads["train_annotated"], "rel_info": downloads["rel_info"]},
+            ),
+            datasets.SplitGenerator(
+                name="train_distant",
+                gen_kwargs={"filepath": downloads["train_distant"], "rel_info": downloads["rel_info"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, rel_info):
+        """Generate DocRED examples."""
+
+        with open(rel_info, encoding="utf-8") as f:
+            relation_name_map = json.load(f)
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+
+        for idx, example in enumerate(data):
+
+            # Test set has no labels - Results need to be uploaded to Codalab
+            if "labels" not in example.keys():
+                example["labels"] = []
+
+            for label in example["labels"]:
+                # Rename and include full relation names
+                label["relation_text"] = relation_name_map[label["r"]]
+                label["relation_id"] = label["r"]
+                label["head"] = label["h"]
+                label["tail"] = label["t"]
+                del label["r"]
+                del label["h"]
+                del label["t"]
+
+            yield idx, example