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---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PhoBert_Dataset59KBoDuoi
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_Dataset59KBoDuoi
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: 0.3898
- Accuracy: 0.8943
- F1: 0.8949
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- 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 | 1.0230 | 200 | 0.3239 | 0.8676 | 0.8657 |
| No log | 2.0460 | 400 | 0.2895 | 0.8761 | 0.8750 |
| No log | 3.0691 | 600 | 0.2810 | 0.8862 | 0.8865 |
| 0.2918 | 4.0921 | 800 | 0.2887 | 0.8842 | 0.8856 |
| 0.2918 | 5.1151 | 1000 | 0.2770 | 0.8938 | 0.8945 |
| 0.2918 | 6.1381 | 1200 | 0.3323 | 0.8837 | 0.8856 |
| 0.2918 | 7.1611 | 1400 | 0.3013 | 0.8935 | 0.8942 |
| 0.1744 | 8.1841 | 1600 | 0.3146 | 0.8919 | 0.8935 |
| 0.1744 | 9.2072 | 1800 | 0.3165 | 0.8977 | 0.8978 |
| 0.1744 | 10.2302 | 2000 | 0.3452 | 0.8889 | 0.8903 |
| 0.1744 | 11.2532 | 2200 | 0.3487 | 0.8956 | 0.8964 |
| 0.1208 | 12.2762 | 2400 | 0.3420 | 0.8956 | 0.8963 |
| 0.1208 | 13.2992 | 2600 | 0.3441 | 0.8983 | 0.8984 |
| 0.1208 | 14.3223 | 2800 | 0.3713 | 0.8962 | 0.8966 |
| 0.1208 | 15.3453 | 3000 | 0.3696 | 0.8962 | 0.8968 |
| 0.0881 | 16.3683 | 3200 | 0.3812 | 0.8957 | 0.8964 |
| 0.0881 | 17.3913 | 3400 | 0.3824 | 0.8952 | 0.8958 |
| 0.0881 | 18.4143 | 3600 | 0.3838 | 0.8975 | 0.8978 |
| 0.0881 | 19.4373 | 3800 | 0.3898 | 0.8943 | 0.8949 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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