---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: metadata-cls-no-gov-8k-v2
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/nguyenducbao/huggingface/runs/s6iy3mkr)
# metadata-cls-no-gov-8k-v2

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: 0.3052
- Accuracy: 0.9447
- F1: 0.7927

## 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
- 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 0.5649        | 1.6260  | 200  | 0.2215          | 0.9455   | 0.7479 |
| 0.1634        | 3.2520  | 400  | 0.1869          | 0.9438   | 0.8204 |
| 0.1222        | 4.8780  | 600  | 0.2286          | 0.9370   | 0.7837 |
| 0.0808        | 6.5041  | 800  | 0.2174          | 0.9532   | 0.8263 |
| 0.0528        | 8.1301  | 1000 | 0.2440          | 0.9387   | 0.7862 |
| 0.046         | 9.7561  | 1200 | 0.2416          | 0.9472   | 0.8180 |
| 0.0329        | 11.3821 | 1400 | 0.2631          | 0.9464   | 0.7967 |
| 0.0271        | 13.0081 | 1600 | 0.2769          | 0.9481   | 0.8124 |
| 0.0179        | 14.6341 | 1800 | 0.2687          | 0.9506   | 0.8122 |
| 0.0185        | 16.2602 | 2000 | 0.2935          | 0.9438   | 0.7921 |
| 0.0153        | 17.8862 | 2200 | 0.2957          | 0.9455   | 0.7907 |
| 0.0146        | 19.5122 | 2400 | 0.3052          | 0.9447   | 0.7927 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1