---
library_name: transformers
license: agpl-3.0
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: 7k-PhoContent-10304
  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. -->

# 7k-PhoContent-10304

This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2320
- Accuracy: 0.9419
- F1: 0.9150
- Precision: 0.9233
- Recall: 0.9074

## 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: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7443        | 2.6144  | 100  | 0.2005          | 0.9360   | 0.9056 | 0.9183    | 0.8945 |
| 0.348         | 5.2288  | 200  | 0.2175          | 0.9360   | 0.9024 | 0.9329    | 0.8792 |
| 0.348         | 7.8431  | 300  | 0.1926          | 0.9419   | 0.9128 | 0.9342    | 0.8952 |
| 0.1555        | 10.4575 | 400  | 0.2010          | 0.9457   | 0.9197 | 0.9344    | 0.9069 |
| 0.0984        | 13.0719 | 500  | 0.2211          | 0.9302   | 0.8967 | 0.9106    | 0.8846 |
| 0.0984        | 15.6863 | 600  | 0.2338          | 0.9322   | 0.8999 | 0.9124    | 0.8889 |
| 0.065         | 18.3007 | 700  | 0.2320          | 0.9419   | 0.9150 | 0.9233    | 0.9074 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0