Source code for forte.data.extractors.subword_extractor

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"""
This file implements SubwordExtractor, which is used to extract feature
from the subwords of an entry.
"""
import logging
from typing import Union, Dict, Optional

from forte.common import ProcessorConfigError
from forte.common.configuration import Config
from forte.data.data_pack import DataPack
from forte.data.converter.feature import Feature
from forte.data.base_extractor import BaseExtractor
from forte.data.ontology import Annotation
from forte.utils import create_import_error_msg

logger = logging.getLogger(__name__)

__all__ = ["SubwordExtractor"]


[docs]class SubwordExtractor(BaseExtractor): r"""SubwordExtractor extracts feature from the subword of entry. Most of the time, a user will not need to call this class explicitly, they will be called by the framework. Args: config: An instance of `Dict` :class:`~forte.common.configuration.Config` """ def initialize(self, config: Union[Dict, Config]): # pylint: disable=attribute-defined-outside-init super().initialize(config=config) if self.config is None: raise ProcessorConfigError( "Configuration for the extractor cannot be None." ) try: from texar.torch.data.tokenizers.bert_tokenizer import ( # pylint:disable=import-outside-toplevel BERTTokenizer, ) except ImportError as e: raise ImportError( create_import_error_msg( "texar-pytorch", "extractor", "SubwordExtractor" ) ) from e self.tokenizer = BERTTokenizer( pretrained_model_name=self.config.pretrained_model_name, cache_dir=None, hparams=None, ) predefined_dict = [key for key, _ in self.tokenizer.vocab.items()] self.predefined_vocab(predefined_dict) if not self.vocab: raise AttributeError("Vocabulary is required in SubwordExtractor.") self.vocab.mark_special_element(self.tokenizer.vocab["[PAD]"], "PAD") self.vocab.mark_special_element(self.tokenizer.vocab["[UNK]"], "UNK")
[docs] @classmethod def default_configs(cls): r"""Returns a dictionary of default hyper-parameters. Here: - "`pretrained_model_name`": The name of the pretrained bert model. Must be the same as used in subword tokenizer. - "`subword_class`": the fully qualified name of the class of the subword, default is `ft.onto.base_ontology.Subword`. """ config = super().default_configs() config.update( { "pretrained_model_name": None, "subword_class": "ft.onto.base_ontology.Subword", } ) return config
[docs] def extract( self, pack: DataPack, context: Optional[Annotation] = None ) -> Feature: r"""Extract the subword feature of one instance. Args: pack: The datapack that contains the current instance. context: The context is an Annotation entry where features will be extracted within its range. If None, then the whole data pack will be used as the context. Default is None. Returns: Feature: a feature that contains the extracted data. """ if self.config is None: raise ProcessorConfigError( "Configuration for the extractor not found." ) data = [] subword: Annotation for subword in pack.get(self.config.subword_class, context): text = subword.text # type: ignore if not subword.is_first_segment: # type: ignore text = "##" + text data.append(self.element2repr(text)) data = ( [self.element2repr("[CLS]")] + data + [self.element2repr("[SEP]")] ) meta_data = { "need_pad": self.vocab.use_pad, # type: ignore "pad_value": self.get_pad_value(), "dim": 1, "dtype": int, } return Feature(data=data, metadata=meta_data, vocab=self.vocab)