---
library_name: transformers
license: agpl-3.0
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: PhishLang_PhoBERTCNN_10k
  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. -->

# PhishLang_PhoBERTCNN_10k

This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3681
- Accuracy: 0.9075
- F1: 0.9064
- Precision: 0.9103
- Recall: 0.9044

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6931        | 0.8   | 100  | 0.4079          | 0.8915   | 0.8893 | 0.9001    | 0.8859 |
| 0.6931        | 1.6   | 200  | 0.3679          | 0.9055   | 0.9041 | 0.9102    | 0.9015 |
| 0.4782        | 2.4   | 300  | 0.3651          | 0.9015   | 0.9004 | 0.9032    | 0.8989 |
| 0.4782        | 3.2   | 400  | 0.3533          | 0.908    | 0.9070 | 0.9101    | 0.9052 |
| 0.3495        | 4.0   | 500  | 0.3650          | 0.9085   | 0.9068 | 0.9160    | 0.9036 |
| 0.3495        | 4.8   | 600  | 0.3562          | 0.9115   | 0.9102 | 0.9164    | 0.9075 |
| 0.3495        | 5.6   | 700  | 0.3595          | 0.905    | 0.9042 | 0.9052    | 0.9035 |
| 0.3147        | 6.4   | 800  | 0.3666          | 0.902    | 0.9013 | 0.9018    | 0.9009 |
| 0.3147        | 7.2   | 900  | 0.3666          | 0.911    | 0.9097 | 0.9154    | 0.9072 |
| 0.2962        | 8.0   | 1000 | 0.3618          | 0.908    | 0.9070 | 0.9097    | 0.9055 |
| 0.2962        | 8.8   | 1100 | 0.3680          | 0.9095   | 0.9083 | 0.9127    | 0.9062 |
| 0.2962        | 9.6   | 1200 | 0.3681          | 0.9075   | 0.9064 | 0.9103    | 0.9044 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.21.0