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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-51 |
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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-51 |
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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: 2.1339 |
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- Rouge1: 0.4189 |
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- Rouge2: 0.2237 |
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- Rougel: 0.356 |
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- Rougelsum: 0.3558 |
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- Gen Len: 18.191 |
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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: 51 |
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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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| 2.7175 | 1.0 | 1250 | 2.2013 | 0.4107 | 0.219 | 0.35 | 0.3496 | 17.772 | |
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| 2.5872 | 2.0 | 2500 | 2.1674 | 0.4158 | 0.2228 | 0.3536 | 0.3531 | 18.005 | |
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| 2.5979 | 3.0 | 3750 | 2.1502 | 0.415 | 0.2215 | 0.351 | 0.3509 | 18.111 | |
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| 2.5579 | 4.0 | 5000 | 2.1428 | 0.4178 | 0.226 | 0.3548 | 0.3549 | 18.143 | |
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| 2.5332 | 5.0 | 6250 | 2.1350 | 0.4172 | 0.2243 | 0.3546 | 0.3542 | 18.149 | |
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| 2.5331 | 6.0 | 7500 | 2.1339 | 0.4189 | 0.2237 | 0.356 | 0.3558 | 18.191 | |
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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 |
