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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: bert2gpt2_med_v3 |
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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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# bert2gpt2_med_v3 |
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This model is a fine-tuned version of [Chemsseddine/bert2gpt2_med_v2](https://huggingface.co/Chemsseddine/bert2gpt2_med_v2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5474 |
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- Rouge1: 31.8871 |
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- Rouge2: 14.4411 |
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- Rougel: 31.6716 |
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- Rougelsum: 31.579 |
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- Gen Len: 22.8412 |
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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: 1 |
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- eval_batch_size: 1 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 2.5621 | 1.0 | 900 | 1.9724 | 30.3731 | 13.8412 | 29.9606 | 29.9716 | 22.6353 | |
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| 1.3692 | 2.0 | 1800 | 1.9634 | 29.6409 | 13.7674 | 29.5202 | 29.5207 | 22.5059 | |
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| 0.8308 | 3.0 | 2700 | 2.1431 | 30.9317 | 14.5594 | 30.8021 | 30.7287 | 22.6118 | |
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| 0.4689 | 4.0 | 3600 | 2.2970 | 30.1132 | 14.6407 | 29.9657 | 30.0182 | 23.3235 | |
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| 0.2875 | 5.0 | 4500 | 2.3787 | 30.9378 | 14.7108 | 30.861 | 30.9097 | 22.7529 | |
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| 0.1564 | 6.0 | 5400 | 2.4137 | 30.5338 | 13.9702 | 30.1252 | 30.1975 | 23.1588 | |
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| 0.1007 | 7.0 | 6300 | 2.4822 | 30.872 | 14.9353 | 30.835 | 30.7694 | 23.0529 | |
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| 0.0783 | 8.0 | 7200 | 2.4974 | 29.9825 | 14.1702 | 29.7507 | 29.7271 | 23.1882 | |
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| 0.0504 | 9.0 | 8100 | 2.5175 | 31.96 | 15.0705 | 31.9669 | 31.9839 | 23.0588 | |
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| 0.0339 | 10.0 | 9000 | 2.5474 | 31.8871 | 14.4411 | 31.6716 | 31.579 | 22.8412 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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