---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: metadata-cls-no-gov-8k-v3
  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/gi7dm5g5)
# metadata-cls-no-gov-8k-v3

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: 0.3064
- Accuracy: 0.9515
- F1: 0.8155

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 0.5565        | 1.6393  | 200  | 0.1942          | 0.9472   | 0.7911 |
| 0.1619        | 3.2787  | 400  | 0.1935          | 0.9404   | 0.7817 |
| 0.1275        | 4.9180  | 600  | 0.1903          | 0.9430   | 0.8019 |
| 0.0768        | 6.5574  | 800  | 0.2192          | 0.9489   | 0.8016 |
| 0.0579        | 8.1967  | 1000 | 0.2350          | 0.9455   | 0.7866 |
| 0.0477        | 9.8361  | 1200 | 0.2572          | 0.9498   | 0.7952 |
| 0.0358        | 11.4754 | 1400 | 0.2823          | 0.9413   | 0.7938 |
| 0.0277        | 13.1148 | 1600 | 0.2704          | 0.9464   | 0.8096 |
| 0.0233        | 14.7541 | 1800 | 0.2868          | 0.9481   | 0.7951 |
| 0.0139        | 16.3934 | 2000 | 0.3026          | 0.9438   | 0.7965 |
| 0.0125        | 18.0328 | 2200 | 0.3034          | 0.9489   | 0.8035 |
| 0.0085        | 19.6721 | 2400 | 0.3064          | 0.9515   | 0.8155 |


### Framework versions

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