Add HF-standard offline package (auto_map + modeling_kbert_mtl.py)

modeling_kbert_mtl.py

@@ -1,43 +1,32 @@
 # modeling_kbert_mtl.py
 import torch
 import torch.nn as nn
-from transformers import PreTrainedModel, AutoModel, AutoConfig
+from transformers import PreTrainedModel, AutoModel, AutoConfig, BertConfig  # ← 추가
 
 def _config_from_base_dict(base_cfg_dict: dict):
     if base_cfg_dict is None:
         raise ValueError("config.base_model_config is required for offline load.")
-    model_type = base_cfg_dict.get("model_type", None)
-    if model_type is None:
-        model_type = "bert"
+    model_type = base_cfg_dict.get("model_type", "bert")
     kwargs = {k: v for k, v in base_cfg_dict.items() if k != "model_type"}
     return AutoConfig.for_model(model_type, **kwargs)
 
 class KbertMTL(PreTrainedModel):
-    """
-    LangQuant KBERT Multi-Task Head (HF-standard, offline-friendly)
+    config_class = BertConfig 
 
-    Outputs (dict):
-      - logits_senti: (B,5)
-      - logits_act:   (B,6)
-      - logits_emo:   (B,7)
-      - pred_reg:     (B,3)  # [certainty, relevance, toxicity]
-      - last_hidden_state: (B, L, H)
-    """
     def __init__(self, config):
         super().__init__(config)
 
         base_cfg_dict = getattr(config, "base_model_config", None)
         base_cfg = _config_from_base_dict(base_cfg_dict)
-
-        self.bert = AutoModel.from_config(base_cfg)
+        self.bert = AutoModel.from_config(base_cfg) 
 
         hidden = self.bert.config.hidden_size
         self.head_senti = nn.Linear(hidden, 5)
         self.head_act   = nn.Linear(hidden, 6)
         self.head_emo   = nn.Linear(hidden, 7)
         self.head_reg   = nn.Linear(hidden, 3)
-
         self.has_token_type = getattr(self.bert.embeddings, "token_type_embeddings", None) is not None
+
         self.post_init()
 
     def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, **kwargs):
@@ -45,7 +34,7 @@ class KbertMTL(PreTrainedModel):
         if self.has_token_type and token_type_ids is not None:
             kw["token_type_ids"] = token_type_ids
         out = self.bert(**kw)
-        h = out.last_hidden_state[:, 0]  # [CLS]
+        h = out.last_hidden_state[:, 0]
         return {
             "logits_senti": self.head_senti(h),
             "logits_act":   self.head_act(h),