|
--- |
|
base_model: facebook/bart-base |
|
library_name: peft |
|
license: apache-2.0 |
|
metrics: |
|
- rouge |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: bart-base-summarization-medical_on_cnn-48 |
|
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. --> |
|
|
|
# bart-base-summarization-medical_on_cnn-48 |
|
|
|
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 3.3893 |
|
- Rouge1: 0.2525 |
|
- Rouge2: 0.0944 |
|
- Rougel: 0.2 |
|
- Rougelsum: 0.2242 |
|
- Gen Len: 18.451 |
|
|
|
## 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: 48 |
|
- 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: 6 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
|
| 2.6901 | 1.0 | 1250 | 3.3869 | 0.2516 | 0.0884 | 0.1964 | 0.2218 | 19.066 | |
|
| 2.6035 | 2.0 | 2500 | 3.3751 | 0.2516 | 0.0926 | 0.1975 | 0.2231 | 18.716 | |
|
| 2.564 | 3.0 | 3750 | 3.3818 | 0.2503 | 0.0926 | 0.1974 | 0.2221 | 18.501 | |
|
| 2.5265 | 4.0 | 5000 | 3.3882 | 0.2505 | 0.0927 | 0.1979 | 0.2219 | 18.482 | |
|
| 2.5207 | 5.0 | 6250 | 3.3881 | 0.2532 | 0.0946 | 0.2005 | 0.2247 | 18.394 | |
|
| 2.5356 | 6.0 | 7500 | 3.3893 | 0.2525 | 0.0944 | 0.2 | 0.2242 | 18.451 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.12.0 |
|
- Transformers 4.42.4 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |