| """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 |
|
|
