Source code for orkgnlp.annotation.rfclf.classifier

# -*- coding: utf-8 -*-
""" ResearchFieldClassifier service. """
from typing import Any

from orkgnlp.annotation.rfclf.decoder import ResearchFieldClassifierDecoder
from orkgnlp.annotation.rfclf.encoder import ResearchFieldClassifierEncoder
from orkgnlp.common.config import orkgnlp_context
from orkgnlp.common.service.base import (
    ORKGNLPBaseDecoder,
    ORKGNLPBaseEncoder,
    ORKGNLPBaseRunner,
    ORKGNLPBaseService,
)
from orkgnlp.common.service.runners import ORKGNLPTorchRunner
from orkgnlp.common.util import io
from orkgnlp.common.util.exceptions import ORKGNLPValidationError


[docs] class ResearchFieldClassifier(ORKGNLPBaseService): """ The ResearchFieldClassifier requires a torch-script model trained on abstracts and a label dictionary file containing research fields and corresponding indices. The required files are downloaded while initiation, if it has not happened before You can pass the parameter ``force_download=True`` to remove and re-download the previous downloaded service files. """ SERVICE_NAME = "research-fields-classification" def __init__(self, *args, **kwargs): super().__init__(self.SERVICE_NAME, *args, **kwargs) requirements = self._config.requirements encoder: ORKGNLPBaseEncoder = ResearchFieldClassifierEncoder() runner: ORKGNLPBaseRunner = ORKGNLPTorchRunner(io.load_torch_jit(requirements["model"])) decoder: ORKGNLPBaseDecoder = ResearchFieldClassifierDecoder( io.read_json(requirements["label_dict"]) ) self._register_pipeline("main", encoder, runner, decoder)
[docs] def __call__(self, raw_input: str, top_n: int = 5) -> Any: """ Classifies a paper into a research field by processing the given abstract. :param raw_input: Combined paper's title and abstract :param top_n: The top n research fields to be retrieved :return: A list of n most likely research fields for the paper :raise orkgnlp.common.util.exceptions.ORKGNLPValidationError: If no abstract is given """ if not raw_input: raise ORKGNLPValidationError("Abstract must be provided") return self._run(raw_input=raw_input, top_n=top_n)
orkgnlp_context.get("SERVICE_MAP")[ResearchFieldClassifier.SERVICE_NAME] = ResearchFieldClassifier