| from sentence_transformers import SentenceTransformer | |
| class SentenceEncoder: | |
| def __init__(self, model_name='l3cube-pune/indic-sentence-similarity-sbert'): | |
| try: | |
| self.model = SentenceTransformer(model_name) | |
| print(f"✅ Model '{model_name}' loaded successfully.") | |
| except Exception as e: | |
| print(f"❌ Error loading model: {e}") | |
| self.model = None | |
| def encode(self, texts, batch_size=32, show_progress_bar=False): | |
| if self.model is None: | |
| return None | |
| return self.model.encode(texts, batch_size=batch_size, show_progress_bar=show_progress_bar, convert_to_tensor=True) |