---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: cls-comment-phobert-base-v2-v3.2
  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-v3.2

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: 0.6534
- Accuracy: 0.9222
- F1 Score: 0.9110
- Recall: 0.9115
- Precision: 0.9149

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 1.8971        | 0.87  | 100  | 1.7443          | 0.4064   | 0.0826   | 0.1429 | 0.0581    |
| 1.5979        | 1.73  | 200  | 1.3562          | 0.6147   | 0.2496   | 0.2794 | 0.2703    |
| 1.2633        | 2.6   | 300  | 1.0631          | 0.7399   | 0.4779   | 0.4720 | 0.5906    |
| 1.0047        | 3.46  | 400  | 0.8834          | 0.8255   | 0.5990   | 0.6173 | 0.5876    |
| 0.8689        | 4.33  | 500  | 0.8139          | 0.8434   | 0.6169   | 0.6459 | 0.5936    |
| 0.7901        | 5.19  | 600  | 0.7564          | 0.8659   | 0.6766   | 0.6790 | 0.7339    |
| 0.7217        | 6.06  | 700  | 0.7114          | 0.8886   | 0.8211   | 0.8018 | 0.8731    |
| 0.6635        | 6.93  | 800  | 0.6800          | 0.9041   | 0.8787   | 0.8775 | 0.8829    |
| 0.6119        | 7.79  | 900  | 0.6681          | 0.9073   | 0.8870   | 0.8844 | 0.8917    |
| 0.5888        | 8.66  | 1000 | 0.6620          | 0.9130   | 0.8948   | 0.8961 | 0.8975    |
| 0.5721        | 9.52  | 1100 | 0.6746          | 0.9098   | 0.8931   | 0.9058 | 0.8849    |
| 0.5521        | 10.39 | 1200 | 0.6434          | 0.9231   | 0.9061   | 0.9038 | 0.9086    |
| 0.5451        | 11.26 | 1300 | 0.6397          | 0.9231   | 0.9095   | 0.9042 | 0.9162    |
| 0.5315        | 12.12 | 1400 | 0.6552          | 0.9174   | 0.9044   | 0.9128 | 0.8993    |
| 0.5182        | 12.99 | 1500 | 0.6483          | 0.9206   | 0.9009   | 0.9063 | 0.8973    |
| 0.5078        | 13.85 | 1600 | 0.6534          | 0.9222   | 0.9110   | 0.9115 | 0.9149    |
| 0.498         | 14.72 | 1700 | 0.6493          | 0.9255   | 0.9080   | 0.9016 | 0.9152    |
| 0.4998        | 15.58 | 1800 | 0.6451          | 0.9244   | 0.9073   | 0.9146 | 0.9009    |
| 0.49          | 16.45 | 1900 | 0.6693          | 0.9174   | 0.9018   | 0.9039 | 0.9019    |
| 0.4882        | 17.32 | 2000 | 0.6501          | 0.9236   | 0.9057   | 0.9101 | 0.9023    |
| 0.4859        | 18.18 | 2100 | 0.6591          | 0.9258   | 0.9110   | 0.9126 | 0.9113    |


### Framework versions

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