File size: 1,335 Bytes
cff9a03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig
class LexicalConfig(PretrainedConfig):
model_type = "lexical_embedding"
def __init__(
self,
vocab_size=30522,
embed_dim=2048,
padding_idx=0,
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.embed_dim = embed_dim
self.padding_idx = padding_idx
class LexicalHFModel(PreTrainedModel):
config_class = LexicalConfig
def __init__(self, config):
super().__init__(config)
self.config = config
self.embedding = nn.Embedding(
config.vocab_size,
config.embed_dim,
padding_idx=config.padding_idx
)
def forward(self, input_ids, attention_mask=None, **kwargs):
embeds = self.embedding(input_ids)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
mask_expanded = attention_mask.unsqueeze(-1).expand(embeds.size()).float()
sum_embeddings = torch.sum(embeds * mask_expanded, 1)
sum_mask = torch.clamp(mask_expanded.sum(1), min=1e-9)
mean_pooled = sum_embeddings / sum_mask
return torch.nn.functional.normalize(mean_pooled, p=2, dim=1) |