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import numpy as np
import joblib
import torch
from transformers import AutoModel
import os
class FinancialFilingClassifier:
def __init__(self, model_dir):
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Loading Jina Encoder on {self.device}...")
self.encoder = AutoModel.from_pretrained(
"jinaai/jina-embeddings-v3",
trust_remote_code=True,
torch_dtype=torch.float16 if self.device == 'cuda' else torch.float32
).to(self.device)
print("Loading XGBoost Cascade...")
self.router = joblib.load(os.path.join(model_dir, "router_xgb.pkl"))
self.router_le = joblib.load(os.path.join(model_dir, "router_le.pkl"))
self.specialists = {}
self.model_dir = model_dir
def _get_vector(self, text):
log_len = np.log1p(len(str(text)))
with torch.no_grad():
vec = self.encoder.encode([text], task="classification", max_length=8192)
return np.hstack([vec, [[log_len]]])
def _load_specialist(self, category):
safe_name = category.replace(" ", "_").replace("&", "and").replace("/", "_")
if safe_name not in self.specialists:
try:
clf = joblib.load(os.path.join(self.model_dir, f"specialist_{safe_name}_xgb.pkl"))
le = joblib.load(os.path.join(self.model_dir, f"specialist_{safe_name}_le.pkl"))
self.specialists[safe_name] = (clf, le)
except FileNotFoundError:
return None
return self.specialists[safe_name]
def predict(self, text):
vector = self._get_vector(text)
router_probs = self.router.predict_proba(vector)[0]
top_indices = np.argsort(router_probs)[::-1][:2]
candidates = []
for idx in top_indices:
category = self.router_le.classes_[idx]
router_conf = router_probs[idx]
specialist = self._load_specialist(category)
if specialist:
clf, le = specialist
spec_probs = clf.predict_proba(vector)[0]
best_idx = np.argmax(spec_probs)
label = le.classes_[best_idx]
spec_conf = spec_probs[best_idx]
combined_score = np.sqrt(router_conf * spec_conf)
candidates.append({"category": category, "label": label, "score": float(combined_score)})
else:
candidates.append({"category": category, "label": category, "score": float(router_conf)})
return max(candidates, key=lambda x: x['score'])
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