Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"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"}}, "supervised_keys": null, "builder_name": "doc_red", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 3435087, "num_examples": 1000, "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": 3435087, "num_examples": 1000, "dataset_name": "doc_red"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7": {"num_bytes": 4299810, "checksum": "85691c5ca1df0048bffab1c1cf53d7d35b5de40f3de0a2c563c03da28746d5cb"}, "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": 21006099, "dataset_size": 20127207, "size_in_bytes": 41133306}}

docred.py

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+"""DocRED: A Large-Scale Document-Level Relation Extraction Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+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}
+}
+"""
+
+_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:
+    - 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": "https://drive.google.com/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7",
+    "train_distant": "https://drive.google.com/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7",
+    "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",
+}
+
+
+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 = {}
+        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")
+
+        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."""
+        relation_name_map = json.load(open(rel_info))
+        data = json.load(open(filepath))
+
+        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

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad5f87bbf1d936f40558f6ec5949e2e3a58c5c902c585082cb17519cc0005006
+size 1888