---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: cls-comment-phobert-base-v2-v2.3
  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-v2.3

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: 0.6228
- Accuracy: 0.9272
- F1 Score: 0.8944
- Recall: 0.8843
- Precision: 0.9063

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 1.1884        | 1.04  | 100  | 1.0905          | 0.7277   | 0.4371   | 0.4229 | 0.5017    |
| 1.0502        | 2.08  | 200  | 0.9278          | 0.7956   | 0.5245   | 0.5274 | 0.5244    |
| 0.9031        | 3.12  | 300  | 0.7793          | 0.8446   | 0.5653   | 0.5897 | 0.7067    |
| 0.7772        | 4.17  | 400  | 0.7090          | 0.8697   | 0.6917   | 0.6804 | 0.7269    |
| 0.6741        | 5.21  | 500  | 0.6483          | 0.8959   | 0.8078   | 0.7818 | 0.8798    |
| 0.6156        | 6.25  | 600  | 0.6268          | 0.9079   | 0.8601   | 0.8345 | 0.8933    |
| 0.5745        | 7.29  | 700  | 0.6176          | 0.9161   | 0.8739   | 0.8610 | 0.8881    |
| 0.5466        | 8.33  | 800  | 0.6134          | 0.9181   | 0.8730   | 0.8555 | 0.8951    |
| 0.5254        | 9.38  | 900  | 0.6196          | 0.9184   | 0.8794   | 0.8634 | 0.8980    |
| 0.5059        | 10.42 | 1000 | 0.6175          | 0.9220   | 0.8799   | 0.8702 | 0.8925    |
| 0.4971        | 11.46 | 1100 | 0.6110          | 0.9226   | 0.8771   | 0.8639 | 0.8929    |
| 0.4872        | 12.5  | 1200 | 0.6191          | 0.9230   | 0.8834   | 0.8774 | 0.8914    |
| 0.4756        | 13.54 | 1300 | 0.6240          | 0.9243   | 0.8929   | 0.8931 | 0.8938    |
| 0.4737        | 14.58 | 1400 | 0.6246          | 0.9203   | 0.8794   | 0.8582 | 0.9038    |
| 0.4626        | 15.62 | 1500 | 0.6267          | 0.9249   | 0.8890   | 0.8842 | 0.8955    |
| 0.4641        | 16.67 | 1600 | 0.6228          | 0.9272   | 0.8944   | 0.8843 | 0.9063    |
| 0.4562        | 17.71 | 1700 | 0.6256          | 0.9278   | 0.8923   | 0.8800 | 0.9065    |
| 0.4522        | 18.75 | 1800 | 0.6203          | 0.9285   | 0.8913   | 0.8845 | 0.8993    |
| 0.4476        | 19.79 | 1900 | 0.6258          | 0.9262   | 0.8849   | 0.8692 | 0.9034    |
| 0.4474        | 20.83 | 2000 | 0.6333          | 0.9272   | 0.8932   | 0.8751 | 0.9134    |
| 0.4421        | 21.88 | 2100 | 0.6340          | 0.9292   | 0.8929   | 0.8896 | 0.8975    |


### Framework versions

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