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"""Contains various kinds of embeddings like Glove, BERT, etc."""
from torch.nn import Module, Embedding, Flatten
from src.utils.mapper import configmapper
@configmapper.map("embeddings", "glove")
class GloveEmbedding(Module):
"""Implement Glove based Word Embedding."""
def __init__(self, embedding_matrix, padding_idx, static=True):
"""Construct GloveEmbedding.
Args:
embedding_matrix (torch.Tensor): The matrix contrainining the embedding weights
padding_idx (int): The padding index in the tokenizer.
static (bool): Whether or not to freeze embeddings.
"""
super(GloveEmbedding, self).__init__()
self.embedding = Embedding.from_pretrained(embedding_matrix)
self.embedding.padding_idx = padding_idx
if static:
self.embedding.weight.required_grad = False
self.flatten = Flatten(start_dim=1)
def forward(self, x_input):
"""Pass the input through the embedding.
Args:
x_input (torch.Tensor): The numericalized tokenized input
Returns:
x_output (torch.Tensor): The output from the embedding
"""
x_output = self.embedding(x_input)
x_output = self.flatten(x_output)
return x_output
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