---
library_name: transformers
license: agpl-3.0
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: phobert_45k_boduoi_test10k
  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_45k_boduoi_test10k

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: 0.4654
- Accuracy: 0.8876
- F1: 0.8882

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3644        | 1.0   | 1407  | 0.2934          | 0.8707   | 0.8722 |
| 0.2799        | 2.0   | 2814  | 0.2776          | 0.8774   | 0.8792 |
| 0.2362        | 3.0   | 4221  | 0.2986          | 0.8839   | 0.8854 |
| 0.1977        | 4.0   | 5628  | 0.3291          | 0.8893   | 0.8890 |
| 0.1665        | 5.0   | 7035  | 0.3141          | 0.8903   | 0.8906 |
| 0.1363        | 6.0   | 8442  | 0.3616          | 0.8892   | 0.8898 |
| 0.1146        | 7.0   | 9849  | 0.3969          | 0.8887   | 0.8888 |
| 0.1016        | 8.0   | 11256 | 0.4377          | 0.8890   | 0.8896 |
| 0.0906        | 9.0   | 12663 | 0.4637          | 0.8875   | 0.8882 |
| 0.0819        | 10.0  | 14070 | 0.4654          | 0.8876   | 0.8882 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1