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--- |
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base_model: facebook/bart-base |
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library_name: peft |
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license: apache-2.0 |
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metrics: |
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- rouge |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-base-summarization-medical-44 |
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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-base-summarization-medical-44 |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1287 |
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- Rouge1: 0.4223 |
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- Rouge2: 0.2251 |
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- Rougel: 0.3572 |
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- Rougelsum: 0.357 |
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- Gen Len: 18.196 |
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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: 3e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 1 |
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- seed: 44 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 6 |
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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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| 2.7031 | 1.0 | 1250 | 2.1999 | 0.413 | 0.2201 | 0.3533 | 0.3528 | 17.704 | |
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| 2.6144 | 2.0 | 2500 | 2.1644 | 0.4143 | 0.2198 | 0.3521 | 0.3518 | 17.965 | |
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| 2.5745 | 3.0 | 3750 | 2.1561 | 0.4142 | 0.2169 | 0.3486 | 0.3483 | 18.171 | |
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| 2.5622 | 4.0 | 5000 | 2.1389 | 0.419 | 0.2222 | 0.3523 | 0.3524 | 18.221 | |
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| 2.5308 | 5.0 | 6250 | 2.1308 | 0.422 | 0.2255 | 0.3569 | 0.3569 | 18.183 | |
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| 2.5394 | 6.0 | 7500 | 2.1287 | 0.4223 | 0.2251 | 0.3572 | 0.357 | 18.196 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
