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---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: cls-comment-phobert-base-v2-v2.2
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-v2.2
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: 0.3163
- Accuracy: 0.9317
- F1 Score: 0.8875
- Recall: 0.8777
- Precision: 0.9015
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 1.663 | 1.05 | 100 | 1.4870 | 0.5041 | 0.1117 | 0.1667 | 0.0840 |
| 1.294 | 2.11 | 200 | 0.9956 | 0.6975 | 0.3911 | 0.3900 | 0.4902 |
| 0.898 | 3.16 | 300 | 0.6779 | 0.8232 | 0.5499 | 0.5697 | 0.5354 |
| 0.6411 | 4.21 | 400 | 0.5164 | 0.8568 | 0.5740 | 0.5895 | 0.5613 |
| 0.5031 | 5.26 | 500 | 0.4106 | 0.8938 | 0.7181 | 0.7114 | 0.7319 |
| 0.38 | 6.32 | 600 | 0.3474 | 0.9096 | 0.8326 | 0.8145 | 0.8739 |
| 0.2927 | 7.37 | 700 | 0.3110 | 0.9142 | 0.8598 | 0.8455 | 0.8810 |
| 0.2532 | 8.42 | 800 | 0.3046 | 0.9188 | 0.8702 | 0.8551 | 0.8881 |
| 0.2049 | 9.47 | 900 | 0.2851 | 0.9218 | 0.8689 | 0.8539 | 0.8902 |
| 0.1785 | 10.53 | 1000 | 0.2802 | 0.9251 | 0.8769 | 0.8561 | 0.9045 |
| 0.1511 | 11.58 | 1100 | 0.2875 | 0.9231 | 0.8744 | 0.8748 | 0.8770 |
| 0.1392 | 12.63 | 1200 | 0.2811 | 0.9264 | 0.8775 | 0.8597 | 0.9005 |
| 0.1166 | 13.68 | 1300 | 0.2757 | 0.9248 | 0.8751 | 0.8746 | 0.8786 |
| 0.1087 | 14.74 | 1400 | 0.2727 | 0.9258 | 0.8804 | 0.8761 | 0.8858 |
| 0.0918 | 15.79 | 1500 | 0.2862 | 0.9284 | 0.8830 | 0.8712 | 0.8988 |
| 0.0824 | 16.84 | 1600 | 0.2915 | 0.9291 | 0.8833 | 0.8689 | 0.9009 |
| 0.0745 | 17.89 | 1700 | 0.2994 | 0.9291 | 0.8797 | 0.8796 | 0.8847 |
| 0.0743 | 18.95 | 1800 | 0.3092 | 0.9254 | 0.8783 | 0.8686 | 0.8910 |
| 0.0636 | 20.0 | 1900 | 0.3142 | 0.9291 | 0.8811 | 0.8743 | 0.8916 |
| 0.0605 | 21.05 | 2000 | 0.3099 | 0.9291 | 0.8823 | 0.8700 | 0.8974 |
| 0.0501 | 22.11 | 2100 | 0.3163 | 0.9317 | 0.8875 | 0.8777 | 0.9015 |
| 0.0519 | 23.16 | 2200 | 0.3290 | 0.9297 | 0.8837 | 0.8692 | 0.9011 |
| 0.0464 | 24.21 | 2300 | 0.3406 | 0.9274 | 0.8805 | 0.8772 | 0.8872 |
| 0.0432 | 25.26 | 2400 | 0.3305 | 0.9284 | 0.8810 | 0.8775 | 0.8876 |
| 0.0404 | 26.32 | 2500 | 0.3378 | 0.9294 | 0.8826 | 0.8785 | 0.8901 |
| 0.0416 | 27.37 | 2600 | 0.3436 | 0.9284 | 0.8830 | 0.8726 | 0.8977 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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