|
--- |
|
base_model: vinai/phobert-base-v2 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- recall |
|
- precision |
|
model-index: |
|
- name: cls-comment-phobert-base-v2-v2.4 |
|
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. --> |
|
|
|
# cls-comment-phobert-base-v2-v2.4 |
|
|
|
This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6177 |
|
- Accuracy: 0.9241 |
|
- F1 Score: 0.8919 |
|
- Recall: 0.8930 |
|
- Precision: 0.8911 |
|
|
|
## 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: 64 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 128 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- training_steps: 4000 |
|
- label_smoothing_factor: 0.1 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:| |
|
| 1.7008 | 0.96 | 100 | 1.5259 | 0.4623 | 0.1091 | 0.1687 | 0.2296 | |
|
| 1.4089 | 1.91 | 200 | 1.1875 | 0.6568 | 0.2499 | 0.2948 | 0.2170 | |
|
| 1.0776 | 2.87 | 300 | 0.9009 | 0.8038 | 0.5304 | 0.5309 | 0.5333 | |
|
| 0.8625 | 3.83 | 400 | 0.7617 | 0.8575 | 0.6321 | 0.6372 | 0.7107 | |
|
| 0.7245 | 4.78 | 500 | 0.6894 | 0.8818 | 0.7282 | 0.7214 | 0.8803 | |
|
| 0.6573 | 5.74 | 600 | 0.6651 | 0.8968 | 0.8406 | 0.8213 | 0.8770 | |
|
| 0.6082 | 6.7 | 700 | 0.6335 | 0.9079 | 0.8630 | 0.8667 | 0.8595 | |
|
| 0.5674 | 7.66 | 800 | 0.6363 | 0.9106 | 0.8692 | 0.8795 | 0.8621 | |
|
| 0.5477 | 8.61 | 900 | 0.6269 | 0.9151 | 0.8776 | 0.8693 | 0.8877 | |
|
| 0.5256 | 9.57 | 1000 | 0.6178 | 0.9205 | 0.8835 | 0.8849 | 0.8826 | |
|
| 0.5148 | 10.53 | 1100 | 0.6214 | 0.9199 | 0.8796 | 0.8839 | 0.8762 | |
|
| 0.4999 | 11.48 | 1200 | 0.6158 | 0.9229 | 0.8856 | 0.8853 | 0.8862 | |
|
| 0.4916 | 12.44 | 1300 | 0.6186 | 0.9232 | 0.8839 | 0.8795 | 0.8888 | |
|
| 0.479 | 13.4 | 1400 | 0.6285 | 0.9202 | 0.8847 | 0.8833 | 0.8864 | |
|
| 0.4812 | 14.35 | 1500 | 0.6177 | 0.9241 | 0.8919 | 0.8930 | 0.8911 | |
|
| 0.4667 | 15.31 | 1600 | 0.6206 | 0.9256 | 0.8848 | 0.8853 | 0.8843 | |
|
| 0.4668 | 16.27 | 1700 | 0.6201 | 0.9265 | 0.8854 | 0.8876 | 0.8837 | |
|
| 0.4635 | 17.22 | 1800 | 0.6252 | 0.9253 | 0.8901 | 0.8877 | 0.8927 | |
|
| 0.4593 | 18.18 | 1900 | 0.6264 | 0.9274 | 0.8891 | 0.8899 | 0.8887 | |
|
| 0.4538 | 19.14 | 2000 | 0.6228 | 0.9265 | 0.8891 | 0.8913 | 0.8870 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
