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config.json

@@ -18,7 +18,7 @@
   "output_encoder_name": "tohoku-nlp/bert-base-japanese-v3",
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
-  "simcse_temperature": 0.1,
+  "simcse_temperature": 0.05,
   "torch_dtype": "float32",
   "transformers_version": "4.51.3",
   "type_vocab_size": 2,

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cbf77382fb9e1f55b8b60d597e595c1ad004821c68e4cc501596ae40047f8a47
+oid sha256:7890d07d6bf31891da149b03c609f32378f43d58fa5b8554d450870940d92656
 size 894432952

modeling_simcse.py

@@ -14,7 +14,7 @@ class SimCSEInferenceModel(PreTrainedModel):
         self.dense_input  = nn.Linear(hidden, hidden)
         self.dense_output = nn.Linear(hidden, hidden)
         self.activation   = nn.Tanh()
-        self.temperature  = getattr(config, "simcse_temperature", 0.1)
+        self.temperature  = getattr(config, "simcse_temperature", 0.05)
     @torch.no_grad()
     def encode_input(self, tok: BatchEncoding):
         h = self.encoder_input(**tok).last_hidden_state[:, 0]