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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-paraphrase-pubmed-1.1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bart-paraphrase-pubmed-1.1 |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4236 |
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- Rouge2 Precision: 0.8482 |
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- Rouge2 Recall: 0.673 |
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- Rouge2 Fmeasure: 0.7347 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
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| 0.6534 | 1.0 | 663 | 0.4641 | 0.8448 | 0.6691 | 0.7313 | |
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| 0.5078 | 2.0 | 1326 | 0.4398 | 0.8457 | 0.6719 | 0.7333 | |
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| 0.4367 | 3.0 | 1989 | 0.4274 | 0.847 | 0.6717 | 0.7335 | |
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| 0.3575 | 4.0 | 2652 | 0.4149 | 0.8481 | 0.6733 | 0.735 | |
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| 0.3319 | 5.0 | 3315 | 0.4170 | 0.8481 | 0.6724 | 0.7343 | |
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| 0.3179 | 6.0 | 3978 | 0.4264 | 0.8484 | 0.6733 | 0.735 | |
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| 0.2702 | 7.0 | 4641 | 0.4207 | 0.8489 | 0.6732 | 0.7353 | |
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| 0.2606 | 8.0 | 5304 | 0.4205 | 0.8487 | 0.6725 | 0.7347 | |
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| 0.2496 | 9.0 | 5967 | 0.4247 | 0.8466 | 0.6717 | 0.7334 | |
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| 0.2353 | 10.0 | 6630 | 0.4236 | 0.8482 | 0.673 | 0.7347 | |
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### Framework versions |
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- Transformers 4.12.3 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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