---
base_model: vinai/phobert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: UIT-VSFC-PhoBert-CLSModel-v1
  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. -->

# UIT-VSFC-PhoBert-CLSModel-v1

This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2107
- Accuracy: 0.9400
- F1: 0.8137
- Precision: 0.8659
- Recall: 0.7848

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 90   | 0.3109          | 0.9154   | 0.6245 | 0.6099    | 0.6398 |
| No log        | 2.0   | 180  | 0.2242          | 0.9337   | 0.7738 | 0.8546    | 0.7438 |
| No log        | 3.0   | 270  | 0.2107          | 0.9400   | 0.8137 | 0.8659    | 0.7848 |


### Framework versions

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