feat(phase-2): KR-FinBERT 감성 스코어링 + 일별 집계 뷰

- backend/app/nlp/finbert.py: snunlp/KR-FinBert-SC 어댑터.
  - score = P(pos) - P(neg) ∈ [-1, +1], label = argmax (neg/neu/pos)
  - 768d mean-pooled last hidden state → news.embedding (VECTOR) 저장
  - settings.huggingface_token 인증, lazy singleton, cuda/cpu auto
- backend/app/nlp/score_news.py: news 테이블에서 sentiment_score IS NULL
  행을 배치 스코어 → UPDATE (... embedding=(:e)::vector). 종목 필터 + limit 옵션.
- backend/app/db/migrations/002_sentiment_view.sql: v_sentiment_daily 뷰.
  종목·KST 일별 n_articles, mean_score, pos/neg/neu_ratio, weighted_score
  (naver_finance 1.0 / google_rss 0.7 / dart 0.5).
- backend/app/db/migrate.py: 이미 실행 중인 DB 에 새 SQL 마이그레이션 적용용
  CLI. 모든 SQL 파일은 idempotent.
- refresh_one.py: refresh 끝에 종목당 200건까지 finbert 스코어, finbert
  SourceStatus 를 RefreshReport 에 추가.
- daily_batch.py: 모든 종목 처리 후 score_pending_news(limit=2000) 로 mop-up.

모델 캐시는 docker-compose hf_cache 볼륨(/root/.cache/huggingface).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend/app/nlp/finbert.py

@@ -0,0 +1,150 @@
+"""KR-FinBERT 감성 분석 어댑터.
+
+모델: snunlp/KR-FinBert-SC (3-class: negative / neutral / positive)
+
+- score      : prob(positive) - prob(negative)  ∈ [-1, +1]
+- label      : argmax 결과 ('positive' / 'neutral' / 'negative')
+- embedding  : 마지막 hidden state mean pool (768d) — `news.embedding` (VECTOR) 저장용
+
+디바이스: settings.model_device ('auto' → cuda 가용 시 cuda, 아니면 cpu).
+인증: settings.huggingface_token (gated 모델은 아니지만 HF rate limit 우회 + 일관성).
+캐시: HF_HOME=/root/.cache/huggingface (docker-compose 의 `hf_cache` 볼륨).
+
+lazy singleton — FastAPI 기동 시점에 모델을 로드하지 않고, 첫 score_texts() 호출
+또는 ping() 호출 시점에 로드.
+"""
+from __future__ import annotations
+
+import logging
+import os
+import threading
+from dataclasses import dataclass
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+MODEL_NAME = "snunlp/KR-FinBert-SC"
+# KR-FinBert-SC 의 id2label : {0: 'negative', 1: 'neutral', 2: 'positive'}
+_LABELS = ("negative", "neutral", "positive")
+
+_lock = threading.Lock()
+_state: dict[str, object] = {
+    "loaded": False,
+    "tokenizer": None,
+    "model": None,
+    "device": None,
+}
+
+
+@dataclass
+class FinbertOutput:
+    label: str
+    score: float                # prob_positive - prob_negative ∈ [-1, +1]
+    prob_negative: float
+    prob_neutral: float
+    prob_positive: float
+    embedding: list[float]      # 768d mean-pooled last hidden state
+
+
+def _resolve_device() -> str:
+    """settings.model_device 값에 따라 'cuda'/'cpu' 결정."""
+    import torch  # lazy
+
+    pref = (settings.model_device or "auto").lower()
+    if pref == "cuda":
+        return "cuda" if torch.cuda.is_available() else "cpu"
+    if pref == "cpu":
+        return "cpu"
+    # 'auto'
+    return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def _load() -> None:
+    global _state
+    with _lock:
+        if _state["loaded"]:
+            return
+        import torch
+        from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+        token = settings.huggingface_token or None
+        if token:
+            # transformers/datasets 모두 이 env 를 인식.
+            os.environ.setdefault("HUGGINGFACE_HUB_TOKEN", token)
+            os.environ.setdefault("HF_TOKEN", token)
+
+        device = _resolve_device()
+        logger.info("loading %s on %s", MODEL_NAME, device)
+        tok = AutoTokenizer.from_pretrained(MODEL_NAME, token=token)
+        mdl = AutoModelForSequenceClassification.from_pretrained(
+            MODEL_NAME,
+            token=token,
+            output_hidden_states=True,
+        )
+        mdl.eval()
+        mdl.to(device)
+        _state.update({"loaded": True, "tokenizer": tok, "model": mdl, "device": device})
+        logger.info("KR-FinBERT loaded (device=%s)", device)
+
+
+def score_texts(
+    texts: list[str],
+    *,
+    batch_size: int = 16,
+    max_length: int = 256,
+) -> list[FinbertOutput]:
+    """주어진 텍스트 리스트에 대해 감성 점수 + 라벨 + 768d embedding 반환.
+
+    빈 문자열은 placeholder('_')로 치환해서 라벨은 neutral 에 가깝게 나오게 함.
+    """
+    if not texts:
+        return []
+    _load()
+    import torch
+
+    tok = _state["tokenizer"]
+    mdl = _state["model"]
+    device = _state["device"]
+
+    results: list[FinbertOutput] = []
+    with torch.no_grad():
+        for i in range(0, len(texts), batch_size):
+            chunk = [(t or "").strip() or "_" for t in texts[i : i + batch_size]]
+            enc = tok(
+                chunk,
+                padding=True,
+                truncation=True,
+                max_length=max_length,
+                return_tensors="pt",
+            ).to(device)
+            out = mdl(**enc)
+            probs = torch.softmax(out.logits, dim=-1).cpu()
+            last_hidden = out.hidden_states[-1]                # (B, T, H)
+            mask = enc["attention_mask"].unsqueeze(-1).float()
+            pooled = (last_hidden * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1.0)
+            pooled = pooled.cpu().tolist()
+
+            for row, vec in zip(probs.tolist(), pooled):
+                p_neg, p_neu, p_pos = row[0], row[1], row[2]
+                label_idx = int(max(range(3), key=lambda k: row[k]))
+                results.append(
+                    FinbertOutput(
+                        label=_LABELS[label_idx],
+                        score=float(p_pos - p_neg),
+                        prob_negative=float(p_neg),
+                        prob_neutral=float(p_neu),
+                        prob_positive=float(p_pos),
+                        embedding=[float(x) for x in vec],
+                    )
+                )
+    return results
+
+
+def ping() -> dict[str, object]:
+    """모델 로드 가능 여부 빠르게 확인. 한 번 로드되면 캐시됨."""
+    try:
+        _load()
+        return {"status": "ok", "model": MODEL_NAME, "device": _state["device"]}
+    except Exception as exc:  # noqa: BLE001
+        return {"status": "failed", "model": MODEL_NAME, "error": str(exc)}