Source code for

# Copyright 2021 The Forte Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# distributed under the License is distributed on an "AS IS" BASIS,
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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.configuration import Config
from import DataPack
from import Feature
from import BaseExtractor
from 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) try: from 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. """ 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)