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+inter/few-nerd-validation.parquet filter=lfs diff=lfs merge=lfs -text
+inter/few-nerd-test.parquet filter=lfs diff=lfs merge=lfs -text
+intra/few-nerd-train.parquet filter=lfs diff=lfs merge=lfs -text
+intra/few-nerd-validation.parquet filter=lfs diff=lfs merge=lfs -text
+intra/few-nerd-test.parquet filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,209 +0,0 @@
----
-annotations_creators:
-- expert-generated
-language_creators:
-- found
-language:
-- en
-license:
-- cc-by-sa-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 100K<n<1M
-source_datasets:
-- extended|wikipedia
-task_categories:
-- other
-task_ids:
-- named-entity-recognition
-paperswithcode_id: few-nerd
-pretty_name: Few-NERD
-tags:
-- structure-prediction
----
-
-# Dataset Card for "Few-NERD"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
-- **Repository:** [https://github.com/thunlp/Few-NERD](https://github.com/thunlp/Few-NERD)
-- **Paper:** [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/)
-- **Point of Contact:** See [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/) 
-
-### Dataset Summary
-
-This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/. 
-
-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)).
-
-NER tags use the `IO` tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered.
-
-For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/.
-
-### Supported Tasks and Leaderboards
-
-- **Tasks:** Named Entity Recognition, Few-shot NER
-- **Leaderboards:**
-  - https://ningding97.github.io/fewnerd/
-  - named-entity-recognition:https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup
-  - other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra
-  - other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter
-
-
-### Languages
-
-English
-
-## Dataset Structure
-
-### Data Instances
-
-- **Size of downloaded dataset files:** 
-  - `super`: 14.6 MB
-  - `intra`: 11.4 MB
-  - `inter`: 11.5 MB
-
-- **Size of the generated dataset:**
-  - `super`: 116.9 MB
-  - `intra`: 106.2 MB
-  - `inter`: 106.2 MB
-
-- **Total amount of disk used:** 366.8 MB
-
-
-An example of 'train' looks as follows.
-
-```json
-{
-    'id': '1', 
-    'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'], 
-    'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0], 
-    'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0]
-}
-```
-
-
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-- `id`: a `string` feature.
-- `tokens`: a `list` of `string` features.
-- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `art` (1), `building` (2), `event` (3), `location` (4), `organization` (5), `other`(6), `person` (7), `product` (8)
-- `fine_ner_tags`: a `list` of fine-grained  classification labels, with possible values including `O` (0), `art-broadcastprogram` (1), `art-film` (2), ...
-
-### Data Splits
-
-| Task      | Train  |  Dev  | Test |
-| -----     | ------ | ----- | ---- |
-| SUP       | 131767 | 18824 | 37648 |
-| INTRA     | 99519  | 19358 | 44059 |
-| INTER     | 130112 | 18817 | 14007 |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
-
-### Citation Information
-
-```
-@inproceedings{ding-etal-2021-nerd,
-    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, Haitao  and
-      Liu, Zhiyuan",
-    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
-    month = aug,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.acl-long.248",
-    doi = "10.18653/v1/2021.acl-long.248",
-    pages = "3198--3213",
-}
-```
-
-
-### Contributions

few-nerd.py

@@ -1,317 +0,0 @@
-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).
-"""
-
-_LICENSE = "CC BY-SA 4.0"
-
-# 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."""
-        url_to_download = dl_manager.download_and_extract(_URLs[self.config.name])
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(
-                        url_to_download,
-                        self.config.name,
-                        "train.txt",
-                    )
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(
-                        url_to_download, self.config.name, "dev.txt"
-                    )
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(
-                        url_to_download, 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, encoding="utf-8"))
-        id = 0
-
-        with open(filepath, "r", encoding="utf-8") 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

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