naacl2022.py

# coding=utf-8
# Copyright 2022 Haotian Teng
#
# 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
"""CrossWeigh: Training Named Entity Tagger from Imperfect Annotations"""

import logging

import datasets


_CITATION = """\
"""

_DESCRIPTION = """\
NACL22 is a dataset labelled for Science Entity Recognition task, which is a subtask of NER task. 
The text is from 2022 conference papers collected from ACL anthology. 
The dataset is collected by Haotian Teng and Xiaoyue Cui. 
Annotation standard can be found here https://github.com/neubig/nlp-from-scratch-assignment-2022/blob/main/annotation_standard.md
"""

_URL = "https://github.com/haotianteng/nacl22/raw/master/"
_TRAINING_FILE = "train.text"
_DEV_FILE = "dev.text"
_TEST_FILE = "test.text"#Test dataset need to be added.


class nacl22Config(datasets.BuilderConfig):
    """BuilderConfig for NACL2022"""

    def __init__(self, **kwargs):
        """BuilderConfig for NACL2022.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(nacl22Config, self).__init__(**kwargs)


class nacl22(datasets.GeneratorBasedBuilder):
    """NACL2022 dataset."""

    BUILDER_CONFIGS = [
        nacl22Config(name="nacl22", version=datasets.Version("1.0.0"), description="nacl22 dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-MethodName",
                                "I-MethodName",
                                "B-HyperparameterName",
                                "I-HyperparameterName",
                                "B-HyperparameterValue",
                                "I-HyperparameterValue",
                                "B-MetricName",
                                "I-MetricName",
                                "B-MetricValue",
                                "I-MetricValue",
                                "B-TaskName",
                                "I-TaskName",
                                "B-DatasetName",
                                "I-DatasetName",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/neubig/nlp-from-scratch-assignment-2022",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev": f"{_URL}{_DEV_FILE}",
            "test": f"{_URL}{_TEST_FILE}",
        }
        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["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        logging.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            ner_tags = []
            for line in f:
                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    # conll2003 tokens are space separated
                    splits = line.split(" ")
                    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,
                }