Create few-nerd.py

few-nerd.py

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+import os
+import json
+import datasets
+from tqdm import tqdm
+
+
+_CITATION = """
+@inproceedings{ding2021few,
+title={Few-NERD: A Few-Shot Named Entity Recognition Dataset},
+author={Ding, Ning and Xu, Guangwei and Chen, Yulin, and Wang, Xiaobin and Han, Xu and Xie, 
+Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan},
+booktitle={ACL-IJCNLP},
+year={2021}
+}
+"""
+
+_DESCRIPTION = """
+Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, 
+which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities 
+and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the 
+other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER).
+"""
+
+# the original data files (zip of .txt) can be downloaded from tsinghua cloud
+_URLs = {
+    "supervised": "https://cloud.tsinghua.edu.cn/f/09265750ae6340429827/?dl=1",
+    "intra": "https://cloud.tsinghua.edu.cn/f/a0d3efdebddd4412b07c/?dl=1",
+    "inter": "https://cloud.tsinghua.edu.cn/f/165693d5e68b43558f9b/?dl=1",
+}
+
+# the label ids, for coarse(NER_TAGS_DICT) and fine(FINE_NER_TAGS_DICT)
+NER_TAGS_DICT = {
+    "O": 0,
+    "art": 1,
+    "building": 2,
+    "event": 3,
+    "location": 4,
+    "organization": 5,
+    "other": 6,
+    "person": 7,
+    "product": 8,
+}
+
+FINE_NER_TAGS_DICT = {
+    "O": 0,
+    "art-broadcastprogram": 1,
+    "art-film": 2,
+    "art-music": 3,
+    "art-other": 4,
+    "art-painting": 5,
+    "art-writtenart": 6,
+    "building-airport": 7,
+    "building-hospital": 8,
+    "building-hotel": 9,
+    "building-library": 10,
+    "building-other": 11,
+    "building-restaurant": 12,
+    "building-sportsfacility": 13,
+    "building-theater": 14,
+    "event-attack/battle/war/militaryconflict": 15,
+    "event-disaster": 16,
+    "event-election": 17,
+    "event-other": 18,
+    "event-protest": 19,
+    "event-sportsevent": 20,
+    "location-GPE": 21,
+    "location-bodiesofwater": 22,
+    "location-island": 23,
+    "location-mountain": 24,
+    "location-other": 25,
+    "location-park": 26,
+    "location-road/railway/highway/transit": 27,
+    "organization-company": 28,
+    "organization-education": 29,
+    "organization-government/governmentagency": 30,
+    "organization-media/newspaper": 31,
+    "organization-other": 32,
+    "organization-politicalparty": 33,
+    "organization-religion": 34,
+    "organization-showorganization": 35,
+    "organization-sportsleague": 36,
+    "organization-sportsteam": 37,
+    "other-astronomything": 38,
+    "other-award": 39,
+    "other-biologything": 40,
+    "other-chemicalthing": 41,
+    "other-currency": 42,
+    "other-disease": 43,
+    "other-educationaldegree": 44,
+    "other-god": 45,
+    "other-language": 46,
+    "other-law": 47,
+    "other-livingthing": 48,
+    "other-medical": 49,
+    "person-actor": 50,
+    "person-artist/author": 51,
+    "person-athlete": 52,
+    "person-director": 53,
+    "person-other": 54,
+    "person-politician": 55,
+    "person-scholar": 56,
+    "person-soldier": 57,
+    "product-airplane": 58,
+    "product-car": 59,
+    "product-food": 60,
+    "product-game": 61,
+    "product-other": 62,
+    "product-ship": 63,
+    "product-software": 64,
+    "product-train": 65,
+    "product-weapon": 66,
+}
+
+
+class FewNERDConfig(datasets.BuilderConfig):
+    """BuilderConfig for FewNERD"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for FewNERD.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FewNERDConfig, self).__init__(**kwargs)
+
+
+class FewNERD(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        FewNERDConfig(name="supervised", description="Fully supervised setting."),
+        FewNERDConfig(
+            name="inter",
+            description="Few-shot setting. Each file contains all 8 coarse "
+            "types but different fine-grained types.",
+        ),
+        FewNERDConfig(
+            name="intra", description="Few-shot setting. Randomly split by coarse type."
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.features.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.features.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "art",
+                                "building",
+                                "event",
+                                "location",
+                                "organization",
+                                "other",
+                                "person",
+                                "product",
+                            ]
+                        )
+                    ),
+                    "fine_ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "art-broadcastprogram",
+                                "art-film",
+                                "art-music",
+                                "art-other",
+                                "art-painting",
+                                "art-writtenart",
+                                "building-airport",
+                                "building-hospital",
+                                "building-hotel",
+                                "building-library",
+                                "building-other",
+                                "building-restaurant",
+                                "building-sportsfacility",
+                                "building-theater",
+                                "event-attack/battle/war/militaryconflict",
+                                "event-disaster",
+                                "event-election",
+                                "event-other",
+                                "event-protest",
+                                "event-sportsevent",
+                                "location-GPE",
+                                "location-bodiesofwater",
+                                "location-island",
+                                "location-mountain",
+                                "location-other",
+                                "location-park",
+                                "location-road/railway/highway/transit",
+                                "organization-company",
+                                "organization-education",
+                                "organization-government/governmentagency",
+                                "organization-media/newspaper",
+                                "organization-other",
+                                "organization-politicalparty",
+                                "organization-religion",
+                                "organization-showorganization",
+                                "organization-sportsleague",
+                                "organization-sportsteam",
+                                "other-astronomything",
+                                "other-award",
+                                "other-biologything",
+                                "other-chemicalthing",
+                                "other-currency",
+                                "other-disease",
+                                "other-educationaldegree",
+                                "other-god",
+                                "other-language",
+                                "other-law",
+                                "other-livingthing",
+                                "other-medical",
+                                "person-actor",
+                                "person-artist/author",
+                                "person-athlete",
+                                "person-director",
+                                "person-other",
+                                "person-politician",
+                                "person-scholar",
+                                "person-soldier",
+                                "product-airplane",
+                                "product-car",
+                                "product-food",
+                                "product-game",
+                                "product-other",
+                                "product-ship",
+                                "product-software",
+                                "product-train",
+                                "product-weapon",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://ningding97.github.io/fewnerd/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        urls_to_download[self.config.name],
+                        self.config.name,
+                        "train.txt",
+                    )
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        urls_to_download[self.config.name], self.config.name, "dev.txt"
+                    )
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        urls_to_download[self.config.name], self.config.name, "test.txt"
+                    )
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath=None):
+        # check file type
+        assert filepath[-4:] == ".txt"
+
+        num_lines = sum(1 for _ in open(filepath))
+        id = 0
+
+        with open(filepath, "r") as f:
+            tokens, ner_tags, fine_ner_tags = [], [], []
+            for line in tqdm(f, total=num_lines):
+                line = line.strip().split()
+
+                if line:
+                    assert len(line) == 2
+                    token, fine_ner_tag = line
+                    ner_tag = fine_ner_tag.split("-")[0]
+
+                    tokens.append(token)
+                    ner_tags.append(NER_TAGS_DICT[ner_tag])
+                    fine_ner_tags.append(FINE_NER_TAGS_DICT[fine_ner_tag])
+
+                elif tokens:
+                    # organize a record to be written into json
+                    record = {
+                        "tokens": tokens,
+                        "id": str(id),
+                        "ner_tags": ner_tags,
+                        "fine_ner_tags": fine_ner_tags,
+                    }
+                    tokens, ner_tags, fine_ner_tags = [], [], []
+                    id += 1
+                    yield record["id"], record
+            
+            # take the last sentence
+            if tokens:
+                record = {
+                    "tokens": tokens,
+                    "id": str(id),
+                    "ner_tags": ner_tags,
+                    "fine_ner_tags": fine_ner_tags,
+                }
+                yield record["id"], record