---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: cls-comment-phobert-base-v2-v2.2
  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-v2.2

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: 0.3436
- Accuracy: 0.9284
- F1 Score: 0.8830
- Recall: 0.8726
- Precision: 0.8977

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 1.663         | 1.05  | 100  | 1.4870          | 0.5041   | 0.1117   | 0.1667 | 0.0840    |
| 1.294         | 2.11  | 200  | 0.9956          | 0.6975   | 0.3911   | 0.3900 | 0.4902    |
| 0.898         | 3.16  | 300  | 0.6779          | 0.8232   | 0.5499   | 0.5697 | 0.5354    |
| 0.6411        | 4.21  | 400  | 0.5164          | 0.8568   | 0.5740   | 0.5895 | 0.5613    |
| 0.5031        | 5.26  | 500  | 0.4106          | 0.8938   | 0.7181   | 0.7114 | 0.7319    |
| 0.38          | 6.32  | 600  | 0.3474          | 0.9096   | 0.8326   | 0.8145 | 0.8739    |
| 0.2927        | 7.37  | 700  | 0.3110          | 0.9142   | 0.8598   | 0.8455 | 0.8810    |
| 0.2532        | 8.42  | 800  | 0.3046          | 0.9188   | 0.8702   | 0.8551 | 0.8881    |
| 0.2049        | 9.47  | 900  | 0.2851          | 0.9218   | 0.8689   | 0.8539 | 0.8902    |
| 0.1785        | 10.53 | 1000 | 0.2802          | 0.9251   | 0.8769   | 0.8561 | 0.9045    |
| 0.1511        | 11.58 | 1100 | 0.2875          | 0.9231   | 0.8744   | 0.8748 | 0.8770    |
| 0.1392        | 12.63 | 1200 | 0.2811          | 0.9264   | 0.8775   | 0.8597 | 0.9005    |
| 0.1166        | 13.68 | 1300 | 0.2757          | 0.9248   | 0.8751   | 0.8746 | 0.8786    |
| 0.1087        | 14.74 | 1400 | 0.2727          | 0.9258   | 0.8804   | 0.8761 | 0.8858    |
| 0.0918        | 15.79 | 1500 | 0.2862          | 0.9284   | 0.8830   | 0.8712 | 0.8988    |
| 0.0824        | 16.84 | 1600 | 0.2915          | 0.9291   | 0.8833   | 0.8689 | 0.9009    |
| 0.0745        | 17.89 | 1700 | 0.2994          | 0.9291   | 0.8797   | 0.8796 | 0.8847    |
| 0.0743        | 18.95 | 1800 | 0.3092          | 0.9254   | 0.8783   | 0.8686 | 0.8910    |
| 0.0636        | 20.0  | 1900 | 0.3142          | 0.9291   | 0.8811   | 0.8743 | 0.8916    |
| 0.0605        | 21.05 | 2000 | 0.3099          | 0.9291   | 0.8823   | 0.8700 | 0.8974    |
| 0.0501        | 22.11 | 2100 | 0.3163          | 0.9317   | 0.8875   | 0.8777 | 0.9015    |
| 0.0519        | 23.16 | 2200 | 0.3290          | 0.9297   | 0.8837   | 0.8692 | 0.9011    |
| 0.0464        | 24.21 | 2300 | 0.3406          | 0.9274   | 0.8805   | 0.8772 | 0.8872    |
| 0.0432        | 25.26 | 2400 | 0.3305          | 0.9284   | 0.8810   | 0.8775 | 0.8876    |
| 0.0404        | 26.32 | 2500 | 0.3378          | 0.9294   | 0.8826   | 0.8785 | 0.8901    |
| 0.0416        | 27.37 | 2600 | 0.3436          | 0.9284   | 0.8830   | 0.8726 | 0.8977    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2