cross_ner.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""CrossNER is a cross-domain dataset for named entity recognition"""


import json
import os

import datasets


_CITATION = """\
@article{liu2020crossner,
      title={CrossNER: Evaluating Cross-Domain Named Entity Recognition}, 
      author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung},
      year={2020},
      eprint={2012.04373},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains 
(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for 
different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five 
domains.

For details, see the paper: 
[CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)
"""

_HOMEPAGE = "https://github.com/zliucr/CrossNER"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "conll2003": {
        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/conll2003/train.txt",
        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/conll2003/dev.txt",
        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/conll2003/test.txt",
    },
    "politics": {
        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/politics/train.txt",
        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/politics/dev.txt",
        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/politics/test.txt",
    },
    "science": {
        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/science/train.txt",
        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/science/dev.txt",
        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/science/test.txt",
    },
    "music": {
        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/music/train.txt",
        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/music/dev.txt",
        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/music/test.txt",
    },
    "literature": {
        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/literature/train.txt",
        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/literature/dev.txt",
        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/literature/test.txt",
    },
    "ai": {
        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/ai/train.txt",
        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/ai/dev.txt",
        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/ai/test.txt",
    },
}


_CLASS_LABELS = [
    "O",
    "B-academicjournal", "I-academicjournal",
    "B-album", "I-album",
    "B-algorithm", "I-algorithm",
    "B-astronomicalobject", "I-astronomicalobject",
    "B-award", "I-award",
    "B-band", "I-band",
    "B-book", "I-book",
    "B-chemicalcompound", "I-chemicalcompound",
    "B-chemicalelement", "I-chemicalelement",
    "B-conference", "I-conference",
    "B-country", "I-country",
    "B-discipline", "I-discipline",
    "B-election", "I-election",
    "B-enzyme", "I-enzyme",
    "B-event", "I-event",
    "B-field", "I-field",
    "B-literarygenre", "I-literarygenre",
    "B-location", "I-location",
    "B-magazine", "I-magazine",
    "B-metrics", "I-metrics",
    "B-misc", "I-misc",
    "B-musicalartist", "I-musicalartist",
    "B-musicalinstrument", "I-musicalinstrument",
    "B-musicgenre", "I-musicgenre",
    "B-organisation", "I-organisation",
    "B-person", "I-person",
    "B-poem", "I-poem",
    "B-politicalparty", "I-politicalparty",
    "B-politician", "I-politician",
    "B-product", "I-product",
    "B-programlang", "I-programlang",
    "B-protein", "I-protein",
    "B-researcher", "I-researcher",
    "B-scientist", "I-scientist",
    "B-song", "I-song",
    "B-task", "I-task",
    "B-theory", "I-theory",
    "B-university", "I-university",
    "B-writer", "I-writer",
]


class CrossNER(datasets.GeneratorBasedBuilder):
    """CrossNER is a cross-domain dataset for named entity recognition"""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="conll2003", version=VERSION,
                               description="This part of CrossNER covers data from the news domain"),
        datasets.BuilderConfig(name="politics", version=VERSION,
                               description="This part of CrossNER covers data from the politics domain"),
        datasets.BuilderConfig(name="science", version=VERSION,
                               description="This part of CrossNER covers data from the science domain"),
        datasets.BuilderConfig(name="music", version=VERSION,
                               description="This part of CrossNER covers data from the music domain"),
        datasets.BuilderConfig(name="literature", version=VERSION,
                               description="This part of CrossNER covers data from the literature domain"),
        datasets.BuilderConfig(name="ai", version=VERSION,
                               description="This part of CrossNER covers data from the AI domain"),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=_CLASS_LABELS)),
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = _URLS[self.config.name]
        downloaded_files = dl_manager.download_and_extract(urls)
        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]})
                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            ner_tags = []
            for line in f:
                if line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    splits = line.split("\t")
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
            # last example
            if tokens:
                yield guid, {
                    "id": str(guid),
                    "tokens": tokens,
                    "ner_tags": ner_tags,
                }