---
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: 0.2985
- Accuracy: 0.9307
- F1: 0.9312

## 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  | 0.1511          | 0.9390   | 0.9395 |
| No log        | 2.0460  | 400  | 0.1571          | 0.9351   | 0.9356 |
| No log        | 3.0691  | 600  | 0.1750          | 0.9340   | 0.9347 |
| 0.15          | 4.0921  | 800  | 0.1731          | 0.9334   | 0.9339 |
| 0.15          | 5.1151  | 1000 | 0.1912          | 0.9321   | 0.9326 |
| 0.15          | 6.1381  | 1200 | 0.2302          | 0.9273   | 0.9283 |
| 0.15          | 7.1611  | 1400 | 0.2180          | 0.9325   | 0.9330 |
| 0.0918        | 8.1841  | 1600 | 0.2620          | 0.9292   | 0.9300 |
| 0.0918        | 9.2072  | 1800 | 0.2363          | 0.9326   | 0.9329 |
| 0.0918        | 10.2302 | 2000 | 0.2687          | 0.9243   | 0.9252 |
| 0.0918        | 11.2532 | 2200 | 0.2621          | 0.9312   | 0.9317 |
| 0.0608        | 12.2762 | 2400 | 0.2741          | 0.9306   | 0.9312 |
| 0.0608        | 13.2992 | 2600 | 0.2614          | 0.9302   | 0.9306 |
| 0.0608        | 14.3223 | 2800 | 0.2830          | 0.9317   | 0.9321 |
| 0.0608        | 15.3453 | 3000 | 0.2874          | 0.9285   | 0.9291 |
| 0.0429        | 16.3683 | 3200 | 0.2844          | 0.9318   | 0.9323 |
| 0.0429        | 17.3913 | 3400 | 0.2836          | 0.9310   | 0.9313 |
| 0.0429        | 18.4143 | 3600 | 0.2885          | 0.9309   | 0.9312 |
| 0.0429        | 19.4373 | 3800 | 0.2985          | 0.9307   | 0.9312 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1