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Biobert_json

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Biobert_json / Biobert_json.py

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# coding=utf-8
#
# 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
"""Introduction to the Biobert NER Shared Task:  Named Entity Recognition"""
import datasets
import json


logger = datasets.logging.get_logger(__name__)

_DESCRIPTION = """\
Este es un dataset biomédico Biobert para el español con 29 etiquetas"""

_URL="data56/"
_TRAINING_FILE = "train.json"
_DEV_FILE = "valid.json"
_TEST_FILE = "test.json"


class Biobert_json_Config(datasets.BuilderConfig):
    

    def __init__(self, **kwargs):
        super(Biobert_json_Config, self).__init__(**kwargs)


class Conll2003(datasets.GeneratorBasedBuilder):
    """Conll2003 dataset."""

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

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                   # "id": datasets.Value("string"),
                    "sentencia": datasets.Sequence(datasets.Value("string")),
                    "tag": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "B_CANCER_CONCEPT",
                                "B_CHEMOTHERAPY",
                                "B_DATE",
                                "B_DRUG",
                                "B_FAMILY",
                                "B_FREQ",
                                "B_IMPLICIT_DATE",
                                "B_INTERVAL",
                                "B_METRIC",
                                "B_OCURRENCE_EVENT",
                                "B_QUANTITY",
                                "B_RADIOTHERAPY",
                                "B_SMOKER_STATUS",
                                "B_STAGE", 
                                "B_SURGERY",
                                "B_TNM",
                                "I_CANCER_CONCEPT",
                                "I_DATE",
                                "I_DRUG",
                                "I_FAMILY",
                                "I_FREQ",
                                "I_IMPLICIT_DATE",
                                "I_INTERVAL",
                                "I_METRIC",
                                "I_OCURRENCE_EVENT",
                                "I_SMOKER_STATUS",
                                "I_STAGE", 
                                "I_SURGERY",
                                "I_TNM",
                                "O", 
                                
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "val": 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["val"]}),
            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:
            guid = 0
            for line in f:  
                record = json.loads(line)   
                yield guid, record
                guid += 1
            
           

	# coding=utf-8
	#
	# 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
	"""Introduction to the Biobert NER Shared Task: Named Entity Recognition"""
	import datasets
	import json


	logger = datasets.logging.get_logger(__name__)

	_DESCRIPTION = """\
	Este es un dataset biomédico Biobert para el español con 29 etiquetas"""

	_URL="data56/"
	_TRAINING_FILE = "train.json"
	_DEV_FILE = "valid.json"
	_TEST_FILE = "test.json"


	class Biobert_json_Config(datasets.BuilderConfig):


	def __init__(self, **kwargs):
	super(Biobert_json_Config, self).__init__(**kwargs)


	class Conll2003(datasets.GeneratorBasedBuilder):
	"""Conll2003 dataset."""

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

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	# "id": datasets.Value("string"),
	"sentencia": datasets.Sequence(datasets.Value("string")),
	"tag": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"B_CANCER_CONCEPT",
	"B_CHEMOTHERAPY",
	"B_DATE",
	"B_DRUG",
	"B_FAMILY",
	"B_FREQ",
	"B_IMPLICIT_DATE",
	"B_INTERVAL",
	"B_METRIC",
	"B_OCURRENCE_EVENT",
	"B_QUANTITY",
	"B_RADIOTHERAPY",
	"B_SMOKER_STATUS",
	"B_STAGE",
	"B_SURGERY",
	"B_TNM",
	"I_CANCER_CONCEPT",
	"I_DATE",
	"I_DRUG",
	"I_FAMILY",
	"I_FREQ",
	"I_IMPLICIT_DATE",
	"I_INTERVAL",
	"I_METRIC",
	"I_OCURRENCE_EVENT",
	"I_SMOKER_STATUS",
	"I_STAGE",
	"I_SURGERY",
	"I_TNM",
	"O",

	]
	)
	),
	}
	),
	supervised_keys=None,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{_TRAINING_FILE}",
	"val": 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["val"]}),
	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:
	guid = 0
	for line in f:
	record = json.loads(line)
	yield guid, record
	guid += 1