---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: cls-comment-phobert-base-v2-v2.2.2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# cls-comment-phobert-base-v2-v2.2.2

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: 0.6359
- Accuracy: 0.9235
- F1 Score: 0.8747
- Recall: 0.8709
- Precision: 0.8816

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 1.7287        | 1.05  | 100  | 1.5513          | 0.5041   | 0.1117   | 0.1667 | 0.0840    |
| 1.4006        | 2.11  | 200  | 1.1607          | 0.6608   | 0.3075   | 0.3196 | 0.4739    |
| 1.0635        | 3.16  | 300  | 0.8875          | 0.8212   | 0.5457   | 0.5578 | 0.5393    |
| 0.8514        | 4.21  | 400  | 0.7688          | 0.8522   | 0.5716   | 0.5872 | 0.5581    |
| 0.761         | 5.26  | 500  | 0.7055          | 0.8746   | 0.6412   | 0.6401 | 0.7368    |
| 0.6727        | 6.32  | 600  | 0.6545          | 0.9023   | 0.7811   | 0.7644 | 0.8581    |
| 0.6059        | 7.37  | 700  | 0.6360          | 0.9109   | 0.8464   | 0.8196 | 0.8859    |
| 0.5726        | 8.42  | 800  | 0.6340          | 0.9119   | 0.8564   | 0.8416 | 0.8743    |
| 0.5411        | 9.47  | 900  | 0.6197          | 0.9159   | 0.8692   | 0.8554 | 0.8868    |
| 0.5237        | 10.53 | 1000 | 0.6127          | 0.9192   | 0.8718   | 0.8474 | 0.9042    |
| 0.5055        | 11.58 | 1100 | 0.6201          | 0.9215   | 0.8703   | 0.8603 | 0.8839    |
| 0.5005        | 12.63 | 1200 | 0.6259          | 0.9231   | 0.8790   | 0.8680 | 0.8944    |
| 0.4846        | 13.68 | 1300 | 0.6159          | 0.9225   | 0.8726   | 0.8703 | 0.8759    |
| 0.4798        | 14.74 | 1400 | 0.6205          | 0.9244   | 0.8779   | 0.8636 | 0.8969    |
| 0.4744        | 15.79 | 1500 | 0.6254          | 0.9248   | 0.8742   | 0.8620 | 0.8909    |
| 0.4637        | 16.84 | 1600 | 0.6342          | 0.9228   | 0.8717   | 0.8653 | 0.8819    |
| 0.4584        | 17.89 | 1700 | 0.6359          | 0.9235   | 0.8747   | 0.8709 | 0.8816    |


### Framework versions

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