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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_v2 |
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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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<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/> |
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# bert2gpt2_med_v2 |
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This model is a fine-tuned version of [Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum](https://huggingface.co/Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0684 |
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- Rouge1: 34.1248 |
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- Rouge2: 17.7006 |
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- Rougel: 33.4661 |
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- Rougelsum: 33.4419 |
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- Gen Len: 22.6429 |
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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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| 2.9107 | 1.0 | 1000 | 2.0877 | 30.4547 | 14.4024 | 30.3642 | 30.3788 | 21.9714 | |
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| 1.8782 | 2.0 | 2000 | 1.8151 | 32.6607 | 16.8089 | 32.3844 | 32.4762 | 21.7714 | |
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| 1.291 | 3.0 | 3000 | 1.7523 | 33.6391 | 16.7866 | 32.4256 | 32.3306 | 22.7429 | |
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| 0.819 | 4.0 | 4000 | 1.7650 | 35.0633 | 19.1222 | 34.4902 | 34.6796 | 22.4714 | |
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| 0.4857 | 5.0 | 5000 | 1.8129 | 33.8763 | 16.9303 | 32.8845 | 32.9225 | 22.3857 | |
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| 0.3232 | 6.0 | 6000 | 1.9339 | 33.9272 | 17.1784 | 32.9301 | 33.0253 | 22.4286 | |
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| 0.2022 | 7.0 | 7000 | 1.9634 | 33.9869 | 16.4238 | 33.7336 | 33.65 | 22.6429 | |
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| 0.1452 | 8.0 | 8000 | 2.0090 | 33.8892 | 18.2723 | 33.7514 | 33.6531 | 22.5714 | |
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| 0.0845 | 9.0 | 9000 | 2.0337 | 33.9649 | 17.1339 | 33.5061 | 33.4157 | 22.7857 | |
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| 0.0531 | 10.0 | 10000 | 2.0684 | 34.1248 | 17.7006 | 33.4661 | 33.4419 | 22.6429 | |
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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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