---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: cls-comment-phobert-base-v2-v3.2.1
  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-v3.2.1

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: 0.4657
- Accuracy: 0.9407
- F1 Score: 0.9319
- Recall: 0.9326
- Precision: 0.9319

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 1.8571        | 0.8696  | 100  | 1.6996          | 0.3986   | 0.0814   | 0.1429 | 0.0569    |
| 1.552         | 1.7391  | 200  | 1.2878          | 0.6150   | 0.2552   | 0.2860 | 0.2738    |
| 1.1701        | 2.6087  | 300  | 0.9309          | 0.7746   | 0.5380   | 0.5249 | 0.5797    |
| 0.8958        | 3.4783  | 400  | 0.7468          | 0.8371   | 0.6099   | 0.6121 | 0.6113    |
| 0.7463        | 4.3478  | 500  | 0.6540          | 0.8641   | 0.6758   | 0.6741 | 0.7556    |
| 0.6489        | 5.2174  | 600  | 0.5884          | 0.8866   | 0.7502   | 0.7443 | 0.7611    |
| 0.5604        | 6.0870  | 700  | 0.5297          | 0.9010   | 0.8350   | 0.8196 | 0.9060    |
| 0.4907        | 6.9565  | 800  | 0.4928          | 0.9171   | 0.8962   | 0.8769 | 0.9190    |
| 0.4428        | 7.8261  | 900  | 0.4692          | 0.9220   | 0.9048   | 0.8958 | 0.9170    |
| 0.4086        | 8.6957  | 1000 | 0.4600          | 0.9236   | 0.9073   | 0.9183 | 0.8978    |
| 0.3892        | 9.5652  | 1100 | 0.4530          | 0.9293   | 0.9156   | 0.9159 | 0.9156    |
| 0.3659        | 10.4348 | 1200 | 0.4574          | 0.9258   | 0.9154   | 0.9258 | 0.9071    |
| 0.3577        | 11.3043 | 1300 | 0.4533          | 0.9288   | 0.9159   | 0.9177 | 0.9152    |
| 0.338         | 12.1739 | 1400 | 0.4454          | 0.9339   | 0.9203   | 0.9285 | 0.9128    |
| 0.3302        | 13.0435 | 1500 | 0.4539          | 0.9312   | 0.9179   | 0.9172 | 0.9196    |
| 0.3186        | 13.9130 | 1600 | 0.4533          | 0.9320   | 0.9220   | 0.9146 | 0.9298    |
| 0.3146        | 14.7826 | 1700 | 0.4485          | 0.9356   | 0.9246   | 0.9224 | 0.9281    |
| 0.3093        | 15.6522 | 1800 | 0.4557          | 0.9326   | 0.9194   | 0.9125 | 0.9291    |
| 0.3019        | 16.5217 | 1900 | 0.4684          | 0.9290   | 0.9169   | 0.9234 | 0.9128    |
| 0.2985        | 17.3913 | 2000 | 0.4545          | 0.9347   | 0.9248   | 0.9238 | 0.9259    |
| 0.2959        | 18.2609 | 2100 | 0.4689          | 0.9334   | 0.9220   | 0.9208 | 0.9249    |
| 0.2891        | 19.1304 | 2200 | 0.4558          | 0.9386   | 0.9262   | 0.9180 | 0.9360    |
| 0.2905        | 20.0    | 2300 | 0.4590          | 0.9358   | 0.9227   | 0.9163 | 0.9308    |
| 0.2875        | 20.8696 | 2400 | 0.4797          | 0.9307   | 0.9193   | 0.9146 | 0.9268    |
| 0.2812        | 21.7391 | 2500 | 0.4697          | 0.9356   | 0.9247   | 0.9257 | 0.9242    |
| 0.2789        | 22.6087 | 2600 | 0.4668          | 0.9380   | 0.9255   | 0.9250 | 0.9271    |
| 0.2785        | 23.4783 | 2700 | 0.4671          | 0.9383   | 0.9293   | 0.9301 | 0.9289    |
| 0.2773        | 24.3478 | 2800 | 0.4657          | 0.9391   | 0.9293   | 0.9274 | 0.9328    |
| 0.2814        | 25.2174 | 2900 | 0.4702          | 0.9361   | 0.9259   | 0.9285 | 0.9244    |
| 0.2744        | 26.0870 | 3000 | 0.4732          | 0.9353   | 0.9274   | 0.9290 | 0.9273    |
| 0.2772        | 26.9565 | 3100 | 0.4676          | 0.9388   | 0.9281   | 0.9301 | 0.9264    |
| 0.2736        | 27.8261 | 3200 | 0.4661          | 0.9394   | 0.9281   | 0.9242 | 0.9325    |
| 0.2754        | 28.6957 | 3300 | 0.4746          | 0.9367   | 0.9257   | 0.9233 | 0.9288    |
| 0.2717        | 29.5652 | 3400 | 0.4688          | 0.9380   | 0.9283   | 0.9255 | 0.9315    |
| 0.27          | 30.4348 | 3500 | 0.4697          | 0.9388   | 0.9304   | 0.9307 | 0.9308    |
| 0.2674        | 31.3043 | 3600 | 0.4668          | 0.9391   | 0.9274   | 0.9311 | 0.9240    |
| 0.2693        | 32.1739 | 3700 | 0.4657          | 0.9407   | 0.9319   | 0.9326 | 0.9319    |
| 0.2685        | 33.0435 | 3800 | 0.4672          | 0.9402   | 0.9298   | 0.9304 | 0.9297    |
| 0.268         | 33.9130 | 3900 | 0.4668          | 0.9410   | 0.9317   | 0.9311 | 0.9328    |
| 0.272         | 34.7826 | 4000 | 0.4654          | 0.9402   | 0.9310   | 0.9300 | 0.9325    |


### Framework versions

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