added dataset files and script

.gitattributes

@@ -14,3 +14,11 @@
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+validation.txt filter=lfs diff=lfs merge=lfs -text
+test.txt filter=lfs diff=lfs merge=lfs -text
+train.txt filter=lfs diff=lfs merge=lfs -text
+train.json filter=lfs diff=lfs merge=lfs -text
+validation.json filter=lfs diff=lfs merge=lfs -text
+id2coarse_tags.json filter=lfs diff=lfs merge=lfs -text
+id2fine_tags.json filter=lfs diff=lfs merge=lfs -text
+test.json filter=lfs diff=lfs merge=lfs -text

id2coarse_tags.json

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+oid sha256:3f8ea0be9d72f6d12a799687307099f1b742055095d8f7424ec46c4781769dfc
+size 136

id2fine_tags.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:81dab34fb7c5ce53d9d16ae4fc3a76e472596bb018c7d860a7bbc051f3276e3f
+size 1777

nerd.py

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+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+# FEW-NERD: A Few-shot Named Entity Recognition Dataset
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{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},
+  journal={arXiv preprint arXiv:2105.07464},
+  year={2021}
+}"""
+
+_DESCRIPTION = """\
+Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark
+data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and reorganize
+them into the few-shot setting for empirical study. These strategies conventionally aim to recognize coarse-grained entity types with few
+examples, while in practice, most unseen entity types are fine-grained. In this paper, we present FEW-NERD, a large-scale human-annotated
+few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types. FEW-NERD consists of 188,238 sentences from
+Wikipedia, 4,601,160 words are included and each is annotated as context or a part of a two-level entity type. To the best of our knowledge,
+this is the first few-shot NER dataset and the largest human-crafted NER dataset. We construct benchmark tasks with different emphases to
+comprehensively assess the generalization capability of models. Extensive empirical results and analysis show that FEW-NERD is challenging
+and the problem requires further research. We make Few-NERD public at https://nigding97.github.io/fewnerd/
+"""
+
+
+class NERDConfig(datasets.BuilderConfig):
+    """BuilderConfig for NERD"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for NERD.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(NERDConfig, self).__init__(**kwargs)
+
+
+class NERD(datasets.GeneratorBasedBuilder):
+    """Conll2012 dataset."""
+
+    BUILDER_CONFIGS = [
+        NERDConfig(name="nerd", version=datasets.Version("1.0.0"), description="NERD dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=['O', 'art', 'building', 'event', 'location', 'organization', 'other', 'person', 'product']
+                        )
+                    ),
+                    "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://catalog.ldc.upenn.edu/LDC2013T19",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            # "train": f"{_URL}{_TRAINING_FILE}",
+            # "validation": f"{_URL}{_DEV_FILE}",
+            # "test": f"{_URL}{_TEST_FILE}",
+            'train': 'train.txt',
+            'validation': 'validation.txt',
+            'test': 'test.txt',
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        
+        with open(filepath, encoding="utf-8") as f:
+            lines = f.readlines()
+
+        guid = 0
+        tokens = []
+        ner_tags = []
+        ner_tags_fine = []
+
+        for line in lines:
+            if line.startswith("-DOCSTART-") or line.strip() == "" or line == "\n":
+                if tokens:
+                    yield guid, {
+                        "id": str(guid),
+                        "tokens": tokens,
+                        "ner_tags": ner_tags,
+                        "ner_tags_fine": ner_tags_fine,
+                    }
+                    guid += 1
+                    tokens = []
+                    ner_tags = []
+                    ner_tags_fine = []
+            else:
+                # nerd tokens are tab- separated
+                splits = line.split('\t')
+                tokens.append(splits[0])
+                ner_tags.append(splits[1])
+                ner_tags_fine.append(splits[2].rstrip())
+        # last example
+        yield guid, {
+            "id": str(guid),
+            "tokens": tokens,
+            "ner_tags": ner_tags,
+            "ner_tags_fine": ner_tags_fine,
+        }

prepare_data.py

@@ -0,0 +1,36 @@
+import glob
+import json
+import pickle
+
+
+def main():
+	id2label = {int(i): tag for i, tag in json.load(open('./id2coarse_tags.json', 'r')).items()}
+	id2label_fine = {int(i): tag for i, tag in json.load(open('./id2fine_tags.json', 'r')).items()}
+	
+	files = glob.glob('./*json')
+	
+	for file in files:
+		if 'tags.json' in file:
+			continue
+		
+		with open(file, 'r') as f:
+			sentences = f.readlines()
+			
+		with open(file.replace('.json', '.txt'), 'w') as f:
+			for sentence in sentences:
+				sentence = json.loads(sentence)
+				tokens = sentence['tokens']
+				ner_tags_ids = sentence['coarse_tags']
+				ner_tags_fine_ids = sentence['fine_tags']
+				
+				ner_tags = [id2label[t] for t in ner_tags_ids]
+				ner_tags_fine = [id2label_fine[t] for t in ner_tags_fine_ids]
+				
+				for token, tag, tag_fine in zip(tokens, ner_tags, ner_tags_fine):
+					f.write(f'{token}\t{tag}\t{tag_fine}\n')
+				
+				f.write('\n')
+
+
+if __name__ == '__main__':
+	main()

test.json

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+size 15804621

test.txt

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+size 13203253

train.json

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+oid sha256:9b740b0c841c1d67289fe0c61f83c52a73d190fd6e60bfbfab78f7c6910d8685
+size 55425936

train.txt

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+oid sha256:1b15f7964a22b17b454ed07dd4b98205a3c60c1963e206a31c9f0d5aee856819
+size 46308665

validation.json

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+oid sha256:805c189cbe7b3918b20727c49da42aa6dbadcf2fe6ba469ad4da0e2605ecde3e
+size 7934187

validation.txt

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