(self)
| 42 | return self._data |
| 43 | |
| 44 | def label_embedding(self): |
| 45 | text_model = TextModel(self.args.text_encoder) |
| 46 | text_features = [] |
| 47 | raw_texts = self.data.label_name |
| 48 | for text in tqdm.tqdm(raw_texts, desc="Processing label texts"): |
| 49 | text_features.append(text_model(text).unsqueeze(dim=0).cpu()) |
| 50 | self.data.label_emb = torch.cat(text_features, dim=0) |
| 51 | |
| 52 | def feature_embedding(self): |
| 53 | emb_file = f"saved_embs/{self.data.x.shape[0]}.pt" |