Source code for kashgari.embeddings.transformer_embedding

# encoding: utf-8

# author: BrikerMan
# contact:
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# file:
# time: 11:41 上午

import codecs
import json
from typing import Dict, List, Any, Optional

from bert4keras.models import build_transformer_model

from kashgari.embeddings.abc_embedding import ABCEmbedding
from kashgari.logger import logger

[docs]class TransformerEmbedding(ABCEmbedding): """ TransformerEmbedding is based on bert4keras. The embeddings itself are wrapped into our simple embedding interface so that they can be used like any other embedding. """
[docs] def to_dict(self) -> Dict[str, Any]: info_dic = super(TransformerEmbedding, self).to_dict() info_dic['config']['vocab_path'] = self.vocab_path info_dic['config']['config_path'] = self.config_path info_dic['config']['checkpoint_path'] = self.checkpoint_path info_dic['config']['model_type'] = self.model_type return info_dic
[docs] def __init__(self, vocab_path: str, config_path: str, checkpoint_path: str, model_type: str = 'bert', **kwargs: Any): """ Args: vocab_path: vocab file path, example `vocab.txt` config_path: model config path, example `config.json` checkpoint_path: model weight path, example `model.ckpt-100000` model_type: transfer model type, {bert, albert, nezha, gpt2_ml, t5} kwargs: additional params """ self.vocab_path = vocab_path self.config_path = config_path self.checkpoint_path = checkpoint_path self.model_type = model_type self.vocab_list: List[str] = [] kwargs['segment'] = True super(TransformerEmbedding, self).__init__(**kwargs)
[docs] def load_embed_vocab(self) -> Optional[Dict[str, int]]: token2idx: Dict[str, int] = {} with, 'r', 'utf8') as reader: for line in reader: token = line.strip() self.vocab_list.append(token) token2idx[token] = len(token2idx) top_words = [k for k, v in list(token2idx.items())[:50]] logger.debug('------------------------------------------------') logger.debug("Loaded transformer model's vocab") logger.debug(f'config_path : {self.config_path}') logger.debug(f'vocab_path : {self.vocab_path}') logger.debug(f'checkpoint_path : {self.checkpoint_path}') logger.debug(f'Top 50 words : {top_words}') logger.debug('------------------------------------------------') return token2idx
[docs] def build_embedding_model(self, *, vocab_size: int = None, force: bool = False, **kwargs: Dict) -> None: if self.embed_model is None: config_path = self.config_path with open(config_path, 'r') as f: config = json.loads( if 'max_position' in config: self.max_position = config['max_position'] else: self.max_position = config.get('max_position_embeddings') bert_model = build_transformer_model(config_path=self.config_path, checkpoint_path=self.checkpoint_path, model=self.model_type, application='encoder', return_keras_model=True) for layer in bert_model.layers: layer.trainable = False self.embed_model = bert_model self.embedding_size = bert_model.output.shape[-1]
if __name__ == "__main__": pass