---
library_name: transformers
license: agpl-3.0
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: phobert_55k_boduoi_test14k
  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_55k_boduoi_test14k

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: 0.9433
- Accuracy: 0.8078
- F1: 0.8537

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3601        | 1.0   | 1725  | 0.7095          | 0.7082   | 0.7864 |
| 0.2727        | 2.0   | 3450  | 0.6126          | 0.7577   | 0.8202 |
| 0.229         | 3.0   | 5175  | 0.7614          | 0.7444   | 0.8116 |
| 0.1946        | 4.0   | 6900  | 0.6227          | 0.7929   | 0.8439 |
| 0.1633        | 5.0   | 8625  | 0.7921          | 0.7551   | 0.8190 |
| 0.1378        | 6.0   | 10350 | 0.7906          | 0.7750   | 0.8323 |
| 0.1161        | 7.0   | 12075 | 0.8249          | 0.7990   | 0.8480 |
| 0.1           | 8.0   | 13800 | 0.8345          | 0.8257   | 0.8652 |
| 0.0917        | 9.0   | 15525 | 0.8945          | 0.8113   | 0.8560 |
| 0.0852        | 10.0  | 17250 | 0.9433          | 0.8078   | 0.8537 |


### Framework versions

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