ubb-endava-conll-assistant-ner-only-misc.py

# 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


import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = ""

_DESCRIPTION = ""

#_URL = "."
_TRAINING_FILE = "train.txt"
_DEV_FILE = "validation.txt"
_TEST_FILE = "test.txt"


class UBBDemoConfig(datasets.BuilderConfig):
    """BuilderConfig for UBBDemo"""

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


class UBBDemo(datasets.GeneratorBasedBuilder):
    """UBBDemo dataset."""

    BUILDER_CONFIGS = [
        UBBDemoConfig(name="UBBDemo", version=datasets.Version("1.0.0"), description="UBBDemo 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-PER",
                                "I-PER",
                                "B-ORG",
                                "I-ORG",
                                "B-LOC",
                                "I-LOC",
                                "B-MISC",
                                "I-MISC",
                       
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        
        path = "./"
        data_files = {
            "train": os.path.join(path, _TRAINING_FILE),
            "validation": os.path.join(path, _DEV_FILE),
            "test": os.path.join(path, _TEST_FILE),
        }

        downloaded_file = dl_manager.download_and_extract(data_files)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file ["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_file ["validation"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file ["test"]}),
        ]

    def _generate_examples(self, filepath):
        print("I am here" + filepath)
        logger.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:
                    # UBBDemo tokens are space separated
                    splits = line.split(" ")
                    tokens.append(splits[0])
                    ner_tags.append(splits[3].rstrip())
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "ner_tags": ner_tags,
            }