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jiaxie/
DeepProtT5-TAP

Name: DeepProtT5-TAP
Availability: InStock

safetensors

apache-2.0

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First we define a class T5PairRegressionModel:

python

from transformers import (
    T5Config,
    T5EncoderModel,
    T5Tokenizer,
    PreTrainedModel,
    TrainingArguments,
    Trainer,
    DataCollatorWithPadding,
)
class T5PairRegressionModel(PreTrainedModel):
    config_class = T5Config

    def __init__(self, config, d_model=None):
        super().__init__(config)
        self.encoder = T5EncoderModel.from_pretrained("Rostlab/prot_t5_xl_uniref50")
        hidden_dim = d_model if d_model is not None else config.d_model
        self.regression_head = nn.Linear(hidden_dim, 1)

    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
        encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
        hidden_states = encoder_outputs.last_hidden_state

        mask = attention_mask.unsqueeze(-1)
        pooled_output = (hidden_states * mask).sum(dim=1) / mask.sum(dim=1)
        logits = self.regression_head(pooled_output).squeeze(-1)  # [batch_size]

        loss = None
        if labels is not None:
            labels = labels.to(torch.bfloat16)
            loss = nn.MSELoss()(logits, labels)

        return {"loss": loss, "logits": logits}

Then we load our pretrained model

python

tokenizer = T5Tokenizer.from_pretrained("jiaxie/DeepProtT5-TAP", do_lower_case=False)
model = T5PairRegressionModel.from_pretrained("jiaxie/DeepProtT5-TAP", torch_dtype=torch.bfloat16).to("cuda")