---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: cls-comment-phobert-base-v2-v2.4.1
  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-v2.4.1

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: 0.3274
- Accuracy: 0.9268
- F1 Score: 0.8919
- Recall: 0.8944
- Precision: 0.8897

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 1.7049        | 0.96  | 100  | 1.4950          | 0.4614   | 0.1080   | 0.1681 | 0.2101    |
| 1.3066        | 1.91  | 200  | 1.0451          | 0.6598   | 0.2493   | 0.2970 | 0.2150    |
| 0.9457        | 2.87  | 300  | 0.7491          | 0.7972   | 0.5219   | 0.5230 | 0.5238    |
| 0.6975        | 3.83  | 400  | 0.5574          | 0.8497   | 0.5700   | 0.5935 | 0.7143    |
| 0.5187        | 4.78  | 500  | 0.4681          | 0.8665   | 0.6685   | 0.6592 | 0.7077    |
| 0.4183        | 5.74  | 600  | 0.4121          | 0.8821   | 0.7747   | 0.7478 | 0.8761    |
| 0.3323        | 6.7   | 700  | 0.3488          | 0.9040   | 0.8505   | 0.8391 | 0.8647    |
| 0.2705        | 7.66  | 800  | 0.3179          | 0.9124   | 0.8680   | 0.8694 | 0.8683    |
| 0.229         | 8.61  | 900  | 0.3109          | 0.9160   | 0.8739   | 0.8778 | 0.8704    |
| 0.1964        | 9.57  | 1000 | 0.3028          | 0.9175   | 0.8776   | 0.8813 | 0.8741    |
| 0.1771        | 10.53 | 1100 | 0.3032          | 0.9181   | 0.8807   | 0.8877 | 0.8743    |
| 0.1518        | 11.48 | 1200 | 0.3151          | 0.9166   | 0.8762   | 0.8702 | 0.8828    |
| 0.1368        | 12.44 | 1300 | 0.2938          | 0.9214   | 0.8794   | 0.8800 | 0.8789    |
| 0.1116        | 13.4  | 1400 | 0.2971          | 0.9205   | 0.8795   | 0.8815 | 0.8776    |
| 0.1136        | 14.35 | 1500 | 0.3011          | 0.9235   | 0.8858   | 0.8825 | 0.8894    |
| 0.094         | 15.31 | 1600 | 0.2937          | 0.9268   | 0.8891   | 0.8933 | 0.8855    |
| 0.0905        | 16.27 | 1700 | 0.3049          | 0.9265   | 0.8850   | 0.8819 | 0.8886    |
| 0.0838        | 17.22 | 1800 | 0.3061          | 0.9244   | 0.8823   | 0.8869 | 0.8784    |
| 0.0749        | 18.18 | 1900 | 0.3275          | 0.9205   | 0.8771   | 0.8839 | 0.8717    |
| 0.0686        | 19.14 | 2000 | 0.3092          | 0.9295   | 0.8915   | 0.8990 | 0.8846    |
| 0.0669        | 20.1  | 2100 | 0.3168          | 0.9250   | 0.8836   | 0.8849 | 0.8825    |
| 0.0582        | 21.05 | 2200 | 0.3339          | 0.9235   | 0.8763   | 0.8926 | 0.8631    |
| 0.0516        | 22.01 | 2300 | 0.3274          | 0.9268   | 0.8919   | 0.8944 | 0.8897    |
| 0.0543        | 22.97 | 2400 | 0.3230          | 0.9295   | 0.8913   | 0.8882 | 0.8946    |
| 0.0435        | 23.92 | 2500 | 0.3364          | 0.9253   | 0.8806   | 0.8705 | 0.8918    |
| 0.0405        | 24.88 | 2600 | 0.3492          | 0.9241   | 0.8816   | 0.8821 | 0.8819    |
| 0.0398        | 25.84 | 2700 | 0.3558          | 0.9238   | 0.8799   | 0.8796 | 0.8807    |
| 0.0363        | 26.79 | 2800 | 0.3605          | 0.9223   | 0.8742   | 0.8795 | 0.8698    |


### Framework versions

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