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---
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
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