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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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