(self, batch, batch_idx)
| 62 | self.log('train_f1_epoch', self.train_f1) |
| 63 | |
| 64 | def validation_step(self, batch, batch_idx): |
| 65 | x, x_mask, y = batch |
| 66 | |
| 67 | outputs = self(x, attention_mask=x_mask, labels=y) |
| 68 | logits = outputs.logits |
| 69 | loss = outputs.loss |
| 70 | |
| 71 | preds = torch.argmax(logits, axis=1) |
| 72 | |
| 73 | if not self.use_question_stance_approach: |
| 74 | self.val_acc(preds, y) |
| 75 | self.log('val_acc_step', self.val_acc) |
| 76 | |
| 77 | self.val_f1(preds, y) |
| 78 | self.log("val_loss", loss) |
| 79 | |
| 80 | return {'val_loss':loss, "src": x, "pred": preds, "target": y} |
| 81 | |
| 82 | def validation_epoch_end(self, outs): |
| 83 | if self.use_question_stance_approach: |
nothing calls this directly
no outgoing calls
no test coverage detected