|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: vinai/bartpho-syllable |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: ViT5-base-normalized |
|
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. --> |
|
|
|
# ViT5-base-normalized |
|
|
|
This model is a fine-tuned version of [vinai/bartpho-syllable](https://huggingface.co/vinai/bartpho-syllable) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0194 |
|
- Rouge1: 89.1883 |
|
- Rouge2: 85.545 |
|
- Rougel: 89.0077 |
|
- Rougelsum: 89.0313 |
|
- Gen Len: 15.0895 |
|
|
|
## 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: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
|
| 0.0911 | 1.0 | 524 | 0.0287 | 86.8798 | 81.8723 | 86.6699 | 86.7029 | 15.1629 | |
|
| 0.0213 | 2.0 | 1048 | 0.0216 | 88.2321 | 84.1301 | 88.0463 | 88.0681 | 15.1238 | |
|
| 0.0147 | 3.0 | 1572 | 0.0202 | 88.8138 | 84.9897 | 88.6248 | 88.6669 | 15.08 | |
|
| 0.0115 | 4.0 | 2096 | 0.0203 | 89.1624 | 85.4043 | 88.9621 | 88.9905 | 15.0676 | |
|
| 0.0094 | 5.0 | 2620 | 0.0194 | 89.1883 | 85.545 | 89.0077 | 89.0313 | 15.0895 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.0 |
|
- Datasets 3.0.0 |
|
- Tokenizers 0.19.1 |
|
|
