---
library_name: transformers
license: apache-2.0
base_model: Falconsai/medical_summarization
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: medical_summarization-finetuned-Medical-summary
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# medical_summarization-finetuned-Medical-summary

This model is a fine-tuned version of [Falconsai/medical_summarization](https://huggingface.co/Falconsai/medical_summarization) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2124
- Rouge1: 22.5658
- Rouge2: 14.2244
- Rougel: 20.2774
- Rougelsum: 21.7581
- Gen Len: 19.0

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3725        | 1.0   | 579  | 1.2124          | 22.5658 | 14.2244 | 20.2774 | 21.7581   | 19.0    |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3