---
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