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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/bart-base
metrics:
- rouge
model-index:
- name: bart-base-lora-summarization-medical
results: []
datasets:
- mystic-leung/medical_cord19
language:
- en
pipeline_tag: summarization
---
<!-- 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. -->
# bart-base-lora-summarization-medical
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the 'mystic-leung/medical_cord19' dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4119
- Rouge1: 0.4304
- Rouge2: 0.2352
- Rougel: 0.3663
- Rougelsum: 0.3660
- Gen Len: 18.1767
## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.5079 | 1.0 | 6250 | 2.121959 | 0.4263 | 0.2290 | 0.3597 | 0.3594 | 18.3300 |
| 2.4566 | 2.0 | 12500 | 2.084411 | 0.4267 | 0.2312 | 0.3622 | 0.3618 | 18.2773 |
| 2.4242 | 3.0 | 18750 | 2.061557 | 0.4311 | 0.2358 | 0.3660 | 0.3656 | 18.1307 |
| 2.4058 | 4.0 | 25000 | 2.053182 | 0.4316 | 0.2367 | 0.3660 | 0.3659 | 18.1753 |
| 2.4119 | 5.0 | 31250 | 2.052128 | 0.4304 | 0.2352 | 0.3663 | 0.3660 | 18.1767 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |