---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PhoBert_Lexical_Dataset51KBoDuoiWithNewLexical_15epoch
results: []
---
[
](https://wandb.ai/truongthequocdung1810-esti/huggingface/runs/835pibam)
[](https://wandb.ai/truongthequocdung1810-esti/huggingface/runs/835pibam)
# PhoBert_Lexical_Dataset51KBoDuoiWithNewLexical_15epoch
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: 0.7848
- Accuracy: 0.8402
- F1: 0.8394
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
| No log | 0.2445 | 200 | 0.5985 | 0.7254 | 0.7177 |
| No log | 0.4890 | 400 | 0.5662 | 0.7448 | 0.7339 |
| No log | 0.7335 | 600 | 0.5751 | 0.7506 | 0.7453 |
| No log | 0.9780 | 800 | 0.5838 | 0.7475 | 0.7442 |
| 0.3505 | 1.2225 | 1000 | 0.5789 | 0.7646 | 0.7597 |
| 0.3505 | 1.4670 | 1200 | 0.5104 | 0.7825 | 0.7796 |
| 0.3505 | 1.7115 | 1400 | 0.5660 | 0.7764 | 0.7708 |
| 0.3505 | 1.9560 | 1600 | 0.5603 | 0.7826 | 0.7764 |
| 0.2546 | 2.2005 | 1800 | 0.5996 | 0.7753 | 0.7732 |
| 0.2546 | 2.4450 | 2000 | 0.5646 | 0.7895 | 0.7829 |
| 0.2546 | 2.6895 | 2200 | 0.5774 | 0.7837 | 0.7791 |
| 0.2546 | 2.9340 | 2400 | 0.5415 | 0.7909 | 0.7873 |
| 0.2084 | 3.1785 | 2600 | 0.5848 | 0.7894 | 0.7866 |
| 0.2084 | 3.4230 | 2800 | 0.5447 | 0.8004 | 0.7960 |
| 0.2084 | 3.6675 | 3000 | 0.6179 | 0.7749 | 0.7729 |
| 0.2084 | 3.9120 | 3200 | 0.5892 | 0.7961 | 0.7945 |
| 0.1784 | 4.1565 | 3400 | 0.6669 | 0.7839 | 0.7830 |
| 0.1784 | 4.4010 | 3600 | 0.5824 | 0.7925 | 0.7913 |
| 0.1784 | 4.6455 | 3800 | 0.6170 | 0.8012 | 0.7974 |
| 0.1784 | 4.8900 | 4000 | 0.6482 | 0.7891 | 0.7891 |
| 0.1516 | 5.1345 | 4200 | 0.6269 | 0.8023 | 0.8006 |
| 0.1516 | 5.3790 | 4400 | 0.6487 | 0.8008 | 0.7990 |
| 0.1516 | 5.6235 | 4600 | 0.5999 | 0.8124 | 0.8094 |
| 0.1516 | 5.8680 | 4800 | 0.6239 | 0.8108 | 0.8091 |
| 0.1286 | 6.1125 | 5000 | 0.6551 | 0.8110 | 0.8094 |
| 0.1286 | 6.3570 | 5200 | 0.6106 | 0.8194 | 0.8155 |
| 0.1286 | 6.6015 | 5400 | 0.6491 | 0.8108 | 0.8110 |
| 0.1286 | 6.8460 | 5600 | 0.5692 | 0.8270 | 0.8247 |
| 0.11 | 7.0905 | 5800 | 0.6259 | 0.8263 | 0.8232 |
| 0.11 | 7.3350 | 6000 | 0.6865 | 0.8164 | 0.8154 |
| 0.11 | 7.5795 | 6200 | 0.7079 | 0.8170 | 0.8166 |
| 0.11 | 7.8240 | 6400 | 0.6968 | 0.8158 | 0.8152 |
| 0.0933 | 8.0685 | 6600 | 0.6568 | 0.8285 | 0.8265 |
| 0.0933 | 8.3130 | 6800 | 0.6513 | 0.8309 | 0.8297 |
| 0.0933 | 8.5575 | 7000 | 0.6665 | 0.8331 | 0.8317 |
| 0.0933 | 8.8020 | 7200 | 0.6384 | 0.8259 | 0.8244 |
| 0.0816 | 9.0465 | 7400 | 0.7175 | 0.8271 | 0.8264 |
| 0.0816 | 9.2910 | 7600 | 0.7558 | 0.8187 | 0.8185 |
| 0.0816 | 9.5355 | 7800 | 0.6997 | 0.8281 | 0.8269 |
| 0.0816 | 9.7800 | 8000 | 0.7126 | 0.8298 | 0.8290 |
| 0.0717 | 10.0244 | 8200 | 0.6864 | 0.8388 | 0.8377 |
| 0.0717 | 10.2689 | 8400 | 0.7268 | 0.8319 | 0.8311 |
| 0.0717 | 10.5134 | 8600 | 0.6949 | 0.8398 | 0.8388 |
| 0.0717 | 10.7579 | 8800 | 0.7236 | 0.8393 | 0.8386 |
| 0.063 | 11.0024 | 9000 | 0.6981 | 0.8396 | 0.8383 |
| 0.063 | 11.2469 | 9200 | 0.8012 | 0.8271 | 0.8273 |
| 0.063 | 11.4914 | 9400 | 0.7489 | 0.8283 | 0.8278 |
| 0.063 | 11.7359 | 9600 | 0.7293 | 0.8358 | 0.8349 |
| 0.063 | 11.9804 | 9800 | 0.7780 | 0.8336 | 0.8330 |
| 0.0554 | 12.2249 | 10000 | 0.7075 | 0.8457 | 0.8445 |
| 0.0554 | 12.4694 | 10200 | 0.7652 | 0.8370 | 0.8361 |
| 0.0554 | 12.7139 | 10400 | 0.7281 | 0.8453 | 0.8439 |
| 0.0554 | 12.9584 | 10600 | 0.7350 | 0.8471 | 0.8458 |
| 0.0508 | 13.2029 | 10800 | 0.7775 | 0.8396 | 0.8390 |
| 0.0508 | 13.4474 | 11000 | 0.7786 | 0.8385 | 0.8376 |
| 0.0508 | 13.6919 | 11200 | 0.7719 | 0.8400 | 0.8392 |
| 0.0508 | 13.9364 | 11400 | 0.7847 | 0.8356 | 0.8349 |
| 0.0431 | 14.1809 | 11600 | 0.7546 | 0.8446 | 0.8435 |
| 0.0431 | 14.4254 | 11800 | 0.7576 | 0.8482 | 0.8469 |
| 0.0431 | 14.6699 | 12000 | 0.7831 | 0.8391 | 0.8383 |
| 0.0431 | 14.9144 | 12200 | 0.7848 | 0.8402 | 0.8394 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1