---
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