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import torch
import tqdm
from torch import nn
from transformers import T5PreTrainedModel, T5EncoderModel
class T5EncoderWithProjection(T5PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.t5_encoder = T5EncoderModel(config)
self.projection = nn.Linear(config.d_model, config.d_model, bias=False)
# Initialize weights and apply final processing
self.post_init()
def forward(self, **input_args):
hidden_states = self.t5_encoder(**input_args).last_hidden_state
hidden_states = hidden_states[:, 0, :]
batch_embeddings = self.projection(hidden_states)
return batch_embeddings
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