(self, texts, batch=1000)
| 88 | return neighbors |
| 89 | |
| 90 | def get_text_embedding(self, texts, batch=1000): |
| 91 | embeddings = [] |
| 92 | for i in range(0, len(texts), batch): |
| 93 | text_batch = texts[i : (i + batch)] |
| 94 | emb_batch = self.use(text_batch) |
| 95 | embeddings.append(emb_batch) |
| 96 | embeddings = np.vstack(embeddings) |
| 97 | return embeddings |
| 98 | |
| 99 | |
| 100 | def load_recommender(path, start_page=1): |