only add fro norm once
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@ -135,7 +135,7 @@ class TransformerLitModel(BaseLitModel):
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_, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
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bs = input_ids.shape[0]
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mask_logits = logits[torch.arange(bs), mask_idx][:, self.entity_id_st:self.entity_id_ed]
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loss += self.loss_fn(mask_logits, label) + self.frobenius_norm_loss()
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loss += self.loss_fn(mask_logits, label)
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labels = batch.pop("labels")
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label = batch.pop("label")
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@ -173,7 +173,10 @@ class TransformerLitModel(BaseLitModel):
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if self.args.bce:
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loss += self.loss_fn(mask_logits, labels)
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else:
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loss += self.loss_fn(mask_logits, label) + self.frobenius_norm_loss()
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loss += self.loss_fn(mask_logits, label)
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if self.smoothing is not None and self.smoothing != 0.0:
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loss += self.frobenius_norm_loss()
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if batch_idx == 0:
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print('\n'.join(self.decode(batch['input_ids'][:4])))
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@ -1,9 +1,9 @@
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nohup python -u main.py --gpus "2," --max_epochs=16 --num_workers=32 \
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nohup python -u main.py --gpus "3," --max_epochs=16 --num_workers=32 \
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--model_name_or_path bert-base-uncased \
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--accumulate_grad_batches 1 \
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--model_class BertKGC \
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--batch_size 16 \
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--checkpoint /root/kg_374/Relphormer/pretrain/output/FB15k-237/epoch\=15-step\=38899-Eval/hits10=0.96.ckpt \
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--checkpoint /root/kg_374/Relphormer_instance_1/pretrain/output/FB15k-237/epoch\=15-step\=38899-Eval/hits10=0.96.ckpt \
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--pretrain 0 \
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--bce 0 \
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--check_val_every_n_epoch 1 \
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