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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 sha256:3d795c5e4c76372ced56ecab16523123a30ab8f45320ca728ca49240decba958
+oid sha256:fb5f39d2f751a721eba5941333717c308820339d438cef850854eb62061c6584
 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]