---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: phobert-base-v2-70k-khduoi
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# phobert-base-v2-70k-khduoi

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: 0.6366
- Accuracy: 0.9152
- F1: 0.9155

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
| No log        | 0.2909  | 500   | 0.2524          | 0.8975   | 0.8969 |
| No log        | 0.5817  | 1000  | 0.2447          | 0.9009   | 0.8999 |
| No log        | 0.8726  | 1500  | 0.2319          | 0.9018   | 0.9015 |
| 0.263         | 1.1635  | 2000  | 0.2539          | 0.9060   | 0.9063 |
| 0.263         | 1.4543  | 2500  | 0.2433          | 0.9017   | 0.9029 |
| 0.263         | 1.7452  | 3000  | 0.2358          | 0.9100   | 0.9099 |
| 0.2084        | 2.0361  | 3500  | 0.2755          | 0.9044   | 0.9059 |
| 0.2084        | 2.3269  | 4000  | 0.2547          | 0.9102   | 0.9100 |
| 0.2084        | 2.6178  | 4500  | 0.2223          | 0.9109   | 0.9125 |
| 0.2084        | 2.9087  | 5000  | 0.2189          | 0.9150   | 0.9150 |
| 0.1729        | 3.1995  | 5500  | 0.2825          | 0.9101   | 0.9112 |
| 0.1729        | 3.4904  | 6000  | 0.2663          | 0.9110   | 0.9121 |
| 0.1729        | 3.7813  | 6500  | 0.2367          | 0.9157   | 0.9165 |
| 0.1448        | 4.0721  | 7000  | 0.2891          | 0.9118   | 0.9121 |
| 0.1448        | 4.3630  | 7500  | 0.3180          | 0.9042   | 0.9060 |
| 0.1448        | 4.6539  | 8000  | 0.2441          | 0.9117   | 0.9126 |
| 0.1448        | 4.9447  | 8500  | 0.2638          | 0.9142   | 0.9145 |
| 0.1234        | 5.2356  | 9000  | 0.3499          | 0.9130   | 0.9141 |
| 0.1234        | 5.5265  | 9500  | 0.3086          | 0.9123   | 0.9135 |
| 0.1234        | 5.8173  | 10000 | 0.3203          | 0.9141   | 0.9140 |
| 0.1033        | 6.1082  | 10500 | 0.3234          | 0.9170   | 0.9173 |
| 0.1033        | 6.3991  | 11000 | 0.3367          | 0.9095   | 0.9105 |
| 0.1033        | 6.6899  | 11500 | 0.3402          | 0.9157   | 0.9159 |
| 0.1033        | 6.9808  | 12000 | 0.3843          | 0.9107   | 0.9111 |
| 0.0904        | 7.2717  | 12500 | 0.3559          | 0.9182   | 0.9182 |
| 0.0904        | 7.5625  | 13000 | 0.3646          | 0.9079   | 0.9096 |
| 0.0904        | 7.8534  | 13500 | 0.3392          | 0.9130   | 0.9137 |
| 0.0785        | 8.1443  | 14000 | 0.4064          | 0.9155   | 0.9164 |
| 0.0785        | 8.4351  | 14500 | 0.4013          | 0.9126   | 0.9135 |
| 0.0785        | 8.7260  | 15000 | 0.4351          | 0.9124   | 0.9135 |
| 0.0701        | 9.0169  | 15500 | 0.4190          | 0.9158   | 0.9161 |
| 0.0701        | 9.3077  | 16000 | 0.4567          | 0.9116   | 0.9126 |
| 0.0701        | 9.5986  | 16500 | 0.4230          | 0.9147   | 0.9147 |
| 0.0701        | 9.8895  | 17000 | 0.3956          | 0.9148   | 0.9150 |
| 0.0599        | 10.1803 | 17500 | 0.4854          | 0.9133   | 0.9135 |
| 0.0599        | 10.4712 | 18000 | 0.4958          | 0.9156   | 0.9158 |
| 0.0599        | 10.7621 | 18500 | 0.4552          | 0.9148   | 0.9146 |
| 0.0536        | 11.0529 | 19000 | 0.4678          | 0.9160   | 0.9163 |
| 0.0536        | 11.3438 | 19500 | 0.4802          | 0.9142   | 0.9135 |
| 0.0536        | 11.6347 | 20000 | 0.5360          | 0.9130   | 0.9133 |
| 0.0536        | 11.9255 | 20500 | 0.5305          | 0.9133   | 0.9137 |
| 0.0464        | 12.2164 | 21000 | 0.5413          | 0.9115   | 0.9122 |
| 0.0464        | 12.5073 | 21500 | 0.4867          | 0.9150   | 0.9155 |
| 0.0464        | 12.7981 | 22000 | 0.5100          | 0.9147   | 0.9153 |
| 0.0446        | 13.0890 | 22500 | 0.5750          | 0.9161   | 0.9157 |
| 0.0446        | 13.3799 | 23000 | 0.5742          | 0.9174   | 0.9172 |
| 0.0446        | 13.6707 | 23500 | 0.5790          | 0.9142   | 0.9146 |
| 0.0446        | 13.9616 | 24000 | 0.5476          | 0.9151   | 0.9150 |
| 0.0374        | 14.2525 | 24500 | 0.5621          | 0.9160   | 0.9163 |
| 0.0374        | 14.5433 | 25000 | 0.5633          | 0.9140   | 0.9146 |
| 0.0374        | 14.8342 | 25500 | 0.5496          | 0.9148   | 0.9152 |
| 0.0341        | 15.1251 | 26000 | 0.5869          | 0.9138   | 0.9142 |
| 0.0341        | 15.4159 | 26500 | 0.5901          | 0.9142   | 0.9141 |
| 0.0341        | 15.7068 | 27000 | 0.5548          | 0.9154   | 0.9158 |
| 0.0303        | 15.9977 | 27500 | 0.5832          | 0.9141   | 0.9136 |
| 0.0303        | 16.2885 | 28000 | 0.6070          | 0.9148   | 0.9157 |
| 0.0303        | 16.5794 | 28500 | 0.6208          | 0.9159   | 0.9162 |
| 0.0303        | 16.8703 | 29000 | 0.6134          | 0.9137   | 0.9143 |
| 0.0273        | 17.1611 | 29500 | 0.6021          | 0.9166   | 0.9168 |
| 0.0273        | 17.4520 | 30000 | 0.6063          | 0.9150   | 0.9153 |
| 0.0273        | 17.7429 | 30500 | 0.5942          | 0.9135   | 0.9142 |
| 0.0254        | 18.0337 | 31000 | 0.6073          | 0.9150   | 0.9155 |
| 0.0254        | 18.3246 | 31500 | 0.6304          | 0.9165   | 0.9167 |
| 0.0254        | 18.6155 | 32000 | 0.6121          | 0.9155   | 0.9157 |
| 0.0254        | 18.9063 | 32500 | 0.6087          | 0.9153   | 0.9156 |
| 0.0221        | 19.1972 | 33000 | 0.6234          | 0.9147   | 0.9151 |
| 0.0221        | 19.4881 | 33500 | 0.6312          | 0.9145   | 0.9149 |
| 0.0221        | 19.7789 | 34000 | 0.6366          | 0.9152   | 0.9155 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1