---
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
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# 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: 0.4780
- Accuracy: 0.9383
- F1 Score: 0.9288
- Recall: 0.9294
- Precision: 0.9285

## 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: 0.05

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 1.8639        | 0.8696  | 100  | 1.7088          | 0.4004   | 0.0835   | 0.1438 | 0.1795    |
| 1.5668        | 1.7391  | 200  | 1.3288          | 0.5800   | 0.2172   | 0.2575 | 0.2674    |
| 1.2197        | 2.6087  | 300  | 0.9746          | 0.7668   | 0.5366   | 0.5148 | 0.5820    |
| 0.9384        | 3.4783  | 400  | 0.7674          | 0.8391   | 0.6138   | 0.6267 | 0.6053    |
| 0.7551        | 4.3478  | 500  | 0.6780          | 0.8527   | 0.6284   | 0.6454 | 0.6147    |
| 0.6636        | 5.2174  | 600  | 0.6152          | 0.8684   | 0.6833   | 0.6785 | 0.7626    |
| 0.5767        | 6.0870  | 700  | 0.5487          | 0.8929   | 0.7884   | 0.7698 | 0.8968    |
| 0.5059        | 6.9565  | 800  | 0.5262          | 0.8986   | 0.8665   | 0.8534 | 0.8880    |
| 0.4512        | 7.8261  | 900  | 0.4882          | 0.9195   | 0.9002   | 0.9082 | 0.8928    |
| 0.4098        | 8.6957  | 1000 | 0.4828          | 0.9212   | 0.9111   | 0.9061 | 0.9183    |
| 0.3916        | 9.5652  | 1100 | 0.4685          | 0.9280   | 0.9193   | 0.9140 | 0.9254    |
| 0.373         | 10.4348 | 1200 | 0.4756          | 0.9239   | 0.9145   | 0.9210 | 0.9100    |
| 0.3592        | 11.3043 | 1300 | 0.4597          | 0.9318   | 0.9230   | 0.9203 | 0.9263    |
| 0.3377        | 12.1739 | 1400 | 0.4692          | 0.9304   | 0.9181   | 0.9198 | 0.9175    |
| 0.3299        | 13.0435 | 1500 | 0.4672          | 0.9329   | 0.9244   | 0.9216 | 0.9292    |
| 0.3198        | 13.9130 | 1600 | 0.4619          | 0.9331   | 0.9241   | 0.9225 | 0.9264    |
| 0.3121        | 14.7826 | 1700 | 0.4672          | 0.9331   | 0.9243   | 0.9245 | 0.9249    |
| 0.3053        | 15.6522 | 1800 | 0.4664          | 0.9345   | 0.9216   | 0.9272 | 0.9167    |
| 0.3058        | 16.5217 | 1900 | 0.4655          | 0.9331   | 0.9229   | 0.9221 | 0.9240    |
| 0.2976        | 17.3913 | 2000 | 0.4619          | 0.9356   | 0.9259   | 0.9221 | 0.9299    |
| 0.2975        | 18.2609 | 2100 | 0.4663          | 0.9342   | 0.9255   | 0.9248 | 0.9267    |
| 0.2872        | 19.1304 | 2200 | 0.4737          | 0.9345   | 0.9237   | 0.9194 | 0.9285    |
| 0.2879        | 20.0    | 2300 | 0.4799          | 0.9318   | 0.9201   | 0.9295 | 0.9116    |
| 0.2848        | 20.8696 | 2400 | 0.4843          | 0.9326   | 0.9194   | 0.9309 | 0.9092    |
| 0.2808        | 21.7391 | 2500 | 0.4839          | 0.9326   | 0.9243   | 0.9237 | 0.9259    |
| 0.2798        | 22.6087 | 2600 | 0.4840          | 0.9342   | 0.9240   | 0.9289 | 0.9197    |
| 0.2797        | 23.4783 | 2700 | 0.4770          | 0.9334   | 0.9223   | 0.9246 | 0.9203    |
| 0.2754        | 24.3478 | 2800 | 0.4863          | 0.9318   | 0.9225   | 0.9252 | 0.9212    |
| 0.2752        | 25.2174 | 2900 | 0.4879          | 0.9326   | 0.9243   | 0.9259 | 0.9238    |
| 0.2718        | 26.0870 | 3000 | 0.4788          | 0.9361   | 0.9270   | 0.9244 | 0.9301    |
| 0.2712        | 26.9565 | 3100 | 0.4766          | 0.9356   | 0.9253   | 0.9237 | 0.9273    |
| 0.2714        | 27.8261 | 3200 | 0.4780          | 0.9383   | 0.9288   | 0.9294 | 0.9285    |
| 0.2697        | 28.6957 | 3300 | 0.4857          | 0.9367   | 0.9263   | 0.9286 | 0.9243    |
| 0.2674        | 29.5652 | 3400 | 0.4876          | 0.9348   | 0.9235   | 0.9304 | 0.9174    |
| 0.2681        | 30.4348 | 3500 | 0.4869          | 0.9361   | 0.9262   | 0.9348 | 0.9184    |
| 0.2685        | 31.3043 | 3600 | 0.4931          | 0.9339   | 0.9241   | 0.9279 | 0.9212    |
| 0.2665        | 32.1739 | 3700 | 0.4851          | 0.9339   | 0.9234   | 0.9262 | 0.9211    |
| 0.2703        | 33.0435 | 3800 | 0.4864          | 0.9367   | 0.9263   | 0.9304 | 0.9226    |
| 0.2661        | 33.9130 | 3900 | 0.4849          | 0.9364   | 0.9271   | 0.9319 | 0.9227    |
| 0.2695        | 34.7826 | 4000 | 0.4863          | 0.9361   | 0.9269   | 0.9320 | 0.9223    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1