Delete configuration_jnu_tsb.py

configuration_jnu_tsb.py

@@ -1,82 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Dict, List, Optional
-
-from transformers import PretrainedConfig
-
-
-class JNUTSBConfig(PretrainedConfig):
-    """Configuration for JNU-TSB.
-
-    JNU-TSB stores a lightweight routing wrapper. It does not store Chronos-2
-    or Polyglot-Ko weights; those base models are loaded by the runtime when
-    they are needed.
-    """
-
-    model_type = "jnu_tsb"
-
-    def __init__(
-        self,
-        repo_id: str = "HONGRIZON/JNU-TSB",
-        display_name: str = "JNU-TSB",
-        full_name: str = "Jeju National University Time-Series Bridge",
-        nickname: str = "Time-Series Bridge / Time-Series Seungbin",
-        chronos_model_id: str = "amazon/chronos-2",
-        llm_model_id: str = "EleutherAI/polyglot-ko-1.3b",
-        route_name: str = "jnu-tsb",
-        quantile_levels: Optional[List[float]] = None,
-        event_categories: Optional[List[str]] = None,
-        covariate_columns: Optional[List[str]] = None,
-        use_llm_extractor: bool = True,
-        **kwargs: Any,
-    ) -> None:
-        super().__init__(**kwargs)
-        self.repo_id = repo_id
-        self.display_name = display_name
-        self.full_name = full_name
-        self.nickname = nickname
-        self.chronos_model_id = chronos_model_id
-        self.llm_model_id = llm_model_id
-        self.route_name = route_name
-        self.quantile_levels = quantile_levels or [0.1, 0.5, 0.9]
-        self.event_categories = event_categories or [
-            "earnings",
-            "product",
-            "macro",
-            "regulation",
-            "supply_chain",
-            "competition",
-            "other",
-        ]
-        self.covariate_columns = covariate_columns or [
-            "cov_earnings_count",
-            "cov_product_count",
-            "cov_macro_count",
-            "cov_regulation_count",
-            "cov_supply_chain_count",
-            "cov_competition_count",
-            "cov_other_count",
-            "cov_sentiment_pos_count",
-            "cov_sentiment_neg_count",
-            "cov_sentiment_neu_count",
-            "cov_news_count",
-            "cov_sentiment_mean",
-            "cov_confidence_mean",
-            "cov_event_score",
-        ]
-        self.use_llm_extractor = bool(use_llm_extractor)
-
-    def to_runtime_dict(self) -> Dict[str, Any]:
-        return {
-            "repo_id": self.repo_id,
-            "display_name": self.display_name,
-            "full_name": self.full_name,
-            "nickname": self.nickname,
-            "chronos_model_id": self.chronos_model_id,
-            "llm_model_id": self.llm_model_id,
-            "route_name": self.route_name,
-            "quantile_levels": self.quantile_levels,
-            "event_categories": self.event_categories,
-            "covariate_columns": self.covariate_columns,
-            "use_llm_extractor": self.use_llm_extractor,
-        }