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--- |
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license: apache-2.0 |
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tags: |
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- summarization |
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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: t5-small-medical_transcription |
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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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# t5-small-medical_transcription |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7744 |
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- Rouge1: 0.3722 |
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- Rouge2: 0.2709 |
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- Rougel: 0.3628 |
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- Rougelsum: 0.3619 |
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- Gen Len: 17.0877 |
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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: 5.6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 8 |
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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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| 2.4475 | 1.0 | 27 | 2.0647 | 0.3288 | 0.2503 | 0.3252 | 0.3212 | 17.2281 | |
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| 2.0319 | 2.0 | 54 | 1.9089 | 0.3456 | 0.2527 | 0.3407 | 0.3377 | 16.3684 | |
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| 1.8772 | 3.0 | 81 | 1.8572 | 0.3528 | 0.2615 | 0.3478 | 0.3457 | 16.1579 | |
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| 1.796 | 4.0 | 108 | 1.8211 | 0.3274 | 0.2479 | 0.324 | 0.3216 | 15.7719 | |
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| 1.7107 | 5.0 | 135 | 1.7972 | 0.3689 | 0.277 | 0.3614 | 0.3586 | 16.3509 | |
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| 1.711 | 6.0 | 162 | 1.7852 | 0.359 | 0.2599 | 0.3505 | 0.348 | 16.2105 | |
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| 1.7581 | 7.0 | 189 | 1.7773 | 0.3659 | 0.2646 | 0.3566 | 0.3559 | 16.9298 | |
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| 1.704 | 8.0 | 216 | 1.7744 | 0.3722 | 0.2709 | 0.3628 | 0.3619 | 17.0877 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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