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c22b544 | 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 45 46 47 48 49 50 51 52 53 54 | import torch
import torch.nn as nn
from transformers import RobertaModel, RobertaTokenizer
from typing import List
class RobertaTextEncoder(nn.Module):
def __init__(self, joint_embed_dim=512, mlp_act='relu'):
super().__init__()
self.roberta = RobertaModel.from_pretrained("roberta-base")
self.tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
self.input_dim = 768 # fixed for roberta-base
self.joint_embed_dim = joint_embed_dim
if mlp_act == 'relu':
act_layer = nn.ReLU()
elif mlp_act == 'gelu':
act_layer = nn.GELU()
else:
raise NotImplementedError(f"Unsupported activation: {mlp_act}")
self.text_projection = nn.Sequential(
nn.Linear(self.input_dim, joint_embed_dim),
act_layer,
nn.Linear(joint_embed_dim, joint_embed_dim)
)
def forward(self, texts: List[str]):
"""
text: dictionary with keys "input_ids" and "attention_mask"
Returns: normalized embedding of shape [batch_size, joint_embed_dim]
"""
tokenized = self.tokenizer(
texts,
padding=True,
return_tensors="pt"
)
text = {
key: value.to(next(self.parameters()).device)
for key, value in tokenized.items()
}
x = self.roberta(
input_ids=text["input_ids"],
attention_mask=text["attention_mask"]
)["pooler_output"]
x = self.text_projection(x)
x = nn.functional.normalize(x, dim=-1)
return x
def load_default_state_dict(self):
pass
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