Source code for nlp_architect.data.cdc_resources.embedding.embed_glove

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import logging
import pickle

import numpy as np

from nlp_architect.common.cdc.mention_data import MentionDataLight

logger = logging.getLogger(__name__)


[docs]class GloveEmbedding(object): def __init__(self, glove_file): logger.info('Loading Glove Online Embedding module, This my take a while...') self.word_to_ix, self.word_embeddings = self.load_glove_for_vocab(glove_file) logger.info('Glove Offline Embedding module lead successfully')
[docs] @staticmethod def load_glove_for_vocab(glove_filename): vocab = [] embd = [] with open(glove_filename) as glove_file: for line in glove_file: row = line.strip().split(' ') word = row[0] vocab.append(word) embd.append(row[1:]) embeddings = np.asarray(embd, dtype=float) word_to_ix = {word: i for i, word in enumerate(vocab)} return word_to_ix, embeddings
[docs]class GloveEmbeddingOffline(object): def __init__(self, embed_resources): logger.info('Loading Glove Offline Embedding module') with open(embed_resources, 'rb') as out: self.word_to_ix, self.word_embeddings = pickle.load(out, encoding='latin1') logger.info('Glove Offline Embedding module lead successfully')
[docs] def get_feature_vector(self, mention: MentionDataLight): embed = None head = mention.mention_head lemma = mention.mention_head_lemma if head in self.word_to_ix: embed = self.word_embeddings[self.word_to_ix[head]] elif lemma in self.word_to_ix: embed = self.word_embeddings[self.word_to_ix[lemma]] return embed
[docs] def get_avrg_feature_vector(self, tokens_str): embed = np.zeros(300, dtype=np.float64) mention_size = 0 for token in tokens_str.split(): if token in self.word_to_ix: token_embed = self.word_embeddings[self.word_to_ix[token]] embed = np.add(embed, token_embed) mention_size += 1 if mention_size == 0: mention_size = 1 return np.true_divide(embed, mention_size)