(model, text)
| 12 | |
| 13 | |
| 14 | def embed_text(model, text): |
| 15 | return np.array(model.encode(text)).astype(np.float32).tobytes() |
| 16 | |
| 17 | warnings.filterwarnings("ignore", category=FutureWarning, message=r".*clean_up_tokenization_spaces.*") |
| 18 | model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') |