Update README.md

README.md

@@ -1,3 +1,43 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+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-PPI-Affinity", do_lower_case=False)
+model = T5PairRegressionModel.from_pretrained("jiaxie/DeepProtT5-PPI-Affinity", torch_dtype=torch.bfloat16).to("cuda")
+```