| import torch | |
| import numpy as np | |
| def load_glove_embeddings(embeddings_file): | |
| """Load embeddings from a file.""" | |
| embeddings = {} | |
| with open(embeddings_file, "r", encoding="utf8") as fp: | |
| for index, line in enumerate(fp): | |
| values = line.split() | |
| word = values[0] | |
| embedding = np.asarray(values[1:], dtype='float32') | |
| embeddings[word] = embedding | |
| return embeddings | |
| def make_embeddings_matrix(embeddings, word_index, embedding_dim): | |
| """Create embeddings matrix to use in Embedding layer.""" | |
| embedding_matrix = np.zeros((len(word_index), embedding_dim)) | |
| for word, i in word_index.items(): | |
| embedding_vector = embeddings.get(word) | |
| if embedding_vector is not None: | |
| embedding_matrix[i] = embedding_vector | |
| return embedding_matrix | |
| def get_embeddings(embedding_file_path, tokenizer, embedding_dim): | |
| glove_embeddings = load_glove_embeddings(embeddings_file=embedding_file_path) | |
| embedding_matrix = make_embeddings_matrix(embeddings=glove_embeddings, word_index=tokenizer.token_to_index, embedding_dim=embedding_dim) | |
| return embedding_matrix |