---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PhoBert_Dataset59KCoDuoi
  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_Dataset59KCoDuoi

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: 0.2804
- Accuracy: 0.9582
- F1: 0.9583

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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        | 0.5115  | 200  | 0.1696          | 0.9382   | 0.9389 |
| No log        | 1.0230  | 400  | 0.1521          | 0.9538   | 0.9539 |
| No log        | 1.5345  | 600  | 0.1459          | 0.9524   | 0.9528 |
| 0.1561        | 2.0460  | 800  | 0.1362          | 0.9557   | 0.9558 |
| 0.1561        | 2.5575  | 1000 | 0.1355          | 0.9577   | 0.9581 |
| 0.1561        | 3.0691  | 1200 | 0.1271          | 0.9617   | 0.9618 |
| 0.1561        | 3.5806  | 1400 | 0.1243          | 0.9620   | 0.9621 |
| 0.0913        | 4.0921  | 1600 | 0.1298          | 0.9598   | 0.9601 |
| 0.0913        | 4.6036  | 1800 | 0.1216          | 0.9617   | 0.9619 |
| 0.0913        | 5.1151  | 2000 | 0.1332          | 0.9607   | 0.9608 |
| 0.0913        | 5.6266  | 2200 | 0.1444          | 0.9609   | 0.9610 |
| 0.065         | 6.1381  | 2400 | 0.1573          | 0.9608   | 0.9609 |
| 0.065         | 6.6496  | 2600 | 0.1477          | 0.9608   | 0.9610 |
| 0.065         | 7.1611  | 2800 | 0.1572          | 0.9610   | 0.9611 |
| 0.065         | 7.6726  | 3000 | 0.1521          | 0.9605   | 0.9606 |
| 0.047         | 8.1841  | 3200 | 0.1523          | 0.9596   | 0.9598 |
| 0.047         | 8.6957  | 3400 | 0.1935          | 0.9591   | 0.9591 |
| 0.047         | 9.2072  | 3600 | 0.1751          | 0.9576   | 0.9577 |
| 0.047         | 9.7187  | 3800 | 0.1979          | 0.9573   | 0.9573 |
| 0.0335        | 10.2302 | 4000 | 0.1905          | 0.9592   | 0.9592 |
| 0.0335        | 10.7417 | 4200 | 0.1870          | 0.9582   | 0.9582 |
| 0.0335        | 11.2532 | 4400 | 0.1940          | 0.9585   | 0.9586 |
| 0.0335        | 11.7647 | 4600 | 0.1787          | 0.9549   | 0.9551 |
| 0.0268        | 12.2762 | 4800 | 0.2240          | 0.9602   | 0.9602 |
| 0.0268        | 12.7877 | 5000 | 0.2161          | 0.9572   | 0.9572 |
| 0.0268        | 13.2992 | 5200 | 0.2160          | 0.9542   | 0.9544 |
| 0.0268        | 13.8107 | 5400 | 0.2299          | 0.9553   | 0.9555 |
| 0.0177        | 14.3223 | 5600 | 0.2551          | 0.9579   | 0.9580 |
| 0.0177        | 14.8338 | 5800 | 0.2355          | 0.9576   | 0.9578 |
| 0.0177        | 15.3453 | 6000 | 0.2467          | 0.9587   | 0.9589 |
| 0.0177        | 15.8568 | 6200 | 0.2507          | 0.9593   | 0.9594 |
| 0.0142        | 16.3683 | 6400 | 0.2482          | 0.9580   | 0.9581 |
| 0.0142        | 16.8798 | 6600 | 0.2600          | 0.9599   | 0.9600 |
| 0.0142        | 17.3913 | 6800 | 0.2682          | 0.9566   | 0.9568 |
| 0.0142        | 17.9028 | 7000 | 0.2782          | 0.9573   | 0.9574 |
| 0.0094        | 18.4143 | 7200 | 0.2826          | 0.9572   | 0.9573 |
| 0.0094        | 18.9258 | 7400 | 0.2783          | 0.9583   | 0.9584 |
| 0.0094        | 19.4373 | 7600 | 0.2819          | 0.9583   | 0.9584 |
| 0.0094        | 19.9488 | 7800 | 0.2804          | 0.9582   | 0.9583 |


### Framework versions

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