|
--- |
|
license: mit |
|
base_model: duancleypaul/bart-cnn-samsum-finetuned |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: bart-cnn-samsum-peft |
|
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-cnn-samsum-peft |
|
|
|
This model is a fine-tuned version of [duancleypaul/bart-cnn-samsum-finetuned](https://huggingface.co/duancleypaul/bart-cnn-samsum-finetuned) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1351 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 0.1088 | 1.0 | 148 | 0.1342 | |
|
| 0.0754 | 2.0 | 296 | 0.1341 | |
|
| 0.0947 | 3.0 | 444 | 0.1340 | |
|
| 0.0982 | 4.0 | 592 | 0.1344 | |
|
| 0.0704 | 5.0 | 740 | 0.1346 | |
|
| 0.1018 | 6.0 | 888 | 0.1345 | |
|
| 0.0904 | 7.0 | 1036 | 0.1341 | |
|
| 0.091 | 8.0 | 1184 | 0.1346 | |
|
| 0.0957 | 9.0 | 1332 | 0.1346 | |
|
| 0.0785 | 10.0 | 1480 | 0.1345 | |
|
| 0.104 | 11.0 | 1628 | 0.1348 | |
|
| 0.1111 | 12.0 | 1776 | 0.1349 | |
|
| 0.0839 | 13.0 | 1924 | 0.1350 | |
|
| 0.0828 | 14.0 | 2072 | 0.1351 | |
|
| 0.0925 | 15.0 | 2220 | 0.1351 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
