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---
base_model: luqh/ClinicalT5-base
tags:
- generated_from_trainer
datasets:
- sem_eval_2024_task_2
metrics:
- rouge
model-index:
- name: ClinicalT5-base-finetuned-biomedical
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: sem_eval_2024_task_2
type: sem_eval_2024_task_2
config: sem_eval_2024_task_2_source
split: validation
args: sem_eval_2024_task_2_source
metrics:
- name: Rouge1
type: rouge
value: 51.0
---
<!-- 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. -->
# ClinicalT5-base-finetuned-biomedical
This model is a fine-tuned version of [luqh/ClinicalT5-base](https://huggingface.co/luqh/ClinicalT5-base) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2017
- Rouge1: 51.0
- Rouge2: 0.0
- Rougel: 51.0
- Rougelsum: 51.0
- Gen Len: 3.71
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 425 | 0.2227 | 49.5 | 0.0 | 49.5 | 49.5 | 3.015 |
| 1.7568 | 2.0 | 850 | 0.2053 | 49.0 | 0.0 | 49.0 | 49.0 | 3.09 |
| 0.227 | 3.0 | 1275 | 0.2012 | 51.0 | 0.0 | 51.0 | 51.0 | 3.24 |
| 0.2186 | 4.0 | 1700 | 0.2011 | 52.0 | 0.0 | 52.0 | 52.0 | 3.29 |
| 0.2173 | 5.0 | 2125 | 0.2017 | 51.0 | 0.0 | 51.0 | 51.0 | 3.71 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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