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---
license: mit
base_model: flax-community/spanish-t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: clinical_document_summarization
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. -->
# clinical_document_summarization
This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4429
- Rouge1: 0.3814
- Rouge2: 0.3162
- Rougel: 0.3727
- Rougelsum: 0.3727
- 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.9449 | 1.0 | 592 | 0.6076 | 0.3739 | 0.303 | 0.3633 | 0.3632 | 18.9996 |
| 0.6902 | 2.0 | 1184 | 0.5278 | 0.3771 | 0.3101 | 0.3686 | 0.3685 | 19.0 |
| 0.601 | 3.0 | 1776 | 0.4962 | 0.3797 | 0.3143 | 0.3721 | 0.3721 | 19.0 |
| 0.568 | 4.0 | 2368 | 0.4721 | 0.3792 | 0.3134 | 0.3701 | 0.3701 | 19.0 |
| 0.5334 | 5.0 | 2960 | 0.4597 | 0.3795 | 0.3143 | 0.3713 | 0.3713 | 19.0 |
| 0.4968 | 6.0 | 3552 | 0.4496 | 0.3816 | 0.3165 | 0.3729 | 0.3729 | 19.0 |
| 0.4873 | 7.0 | 4144 | 0.4449 | 0.3812 | 0.316 | 0.3726 | 0.3726 | 19.0 |
| 0.4794 | 8.0 | 4736 | 0.4429 | 0.3814 | 0.3162 | 0.3727 | 0.3727 | 19.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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