---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: phobert-base-v2-DACN1
  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-base-v2-DACN1

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: 0.5520
- Accuracy: 0.8784
- F1: 0.8781

## 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: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.2782 | 200  | 0.4102          | 0.8061   | 0.8037 |
| No log        | 0.5563 | 400  | 0.3549          | 0.8456   | 0.8461 |
| No log        | 0.8345 | 600  | 0.3583          | 0.8466   | 0.8454 |
| 0.4175        | 1.1127 | 800  | 0.3401          | 0.8537   | 0.8523 |
| 0.4175        | 1.3908 | 1000 | 0.3179          | 0.8639   | 0.8646 |
| 0.4175        | 1.6690 | 1200 | 0.3148          | 0.8687   | 0.8691 |
| 0.4175        | 1.9471 | 1400 | 0.3240          | 0.8574   | 0.8582 |
| 0.3061        | 2.2253 | 1600 | 0.3148          | 0.8734   | 0.8740 |
| 0.3061        | 2.5035 | 1800 | 0.3224          | 0.8742   | 0.8743 |
| 0.3061        | 2.7816 | 2000 | 0.3288          | 0.8678   | 0.8671 |
| 0.2524        | 3.0598 | 2200 | 0.3512          | 0.8767   | 0.8769 |
| 0.2524        | 3.3380 | 2400 | 0.3421          | 0.8798   | 0.8796 |
| 0.2524        | 3.6161 | 2600 | 0.3089          | 0.8795   | 0.8799 |
| 0.2524        | 3.8943 | 2800 | 0.3569          | 0.8718   | 0.8725 |
| 0.2123        | 4.1725 | 3000 | 0.3840          | 0.8747   | 0.8744 |
| 0.2123        | 4.4506 | 3200 | 0.3681          | 0.8729   | 0.8736 |
| 0.2123        | 4.7288 | 3400 | 0.3575          | 0.8725   | 0.8732 |
| 0.1771        | 5.0070 | 3600 | 0.3575          | 0.8793   | 0.8794 |
| 0.1771        | 5.2851 | 3800 | 0.4285          | 0.8758   | 0.8752 |
| 0.1771        | 5.5633 | 4000 | 0.3843          | 0.8778   | 0.8782 |
| 0.1771        | 5.8414 | 4200 | 0.3951          | 0.8780   | 0.8779 |
| 0.1479        | 6.1196 | 4400 | 0.4364          | 0.8734   | 0.8726 |
| 0.1479        | 6.3978 | 4600 | 0.4273          | 0.8753   | 0.8752 |
| 0.1479        | 6.6759 | 4800 | 0.4596          | 0.8786   | 0.8783 |
| 0.1479        | 6.9541 | 5000 | 0.4498          | 0.8784   | 0.8785 |
| 0.1284        | 7.2323 | 5200 | 0.4592          | 0.8793   | 0.8795 |
| 0.1284        | 7.5104 | 5400 | 0.4796          | 0.8755   | 0.8747 |
| 0.1284        | 7.7886 | 5600 | 0.4830          | 0.8729   | 0.8722 |
| 0.1068        | 8.0668 | 5800 | 0.4879          | 0.8789   | 0.8787 |
| 0.1068        | 8.3449 | 6000 | 0.5213          | 0.8767   | 0.8761 |
| 0.1068        | 8.6231 | 6200 | 0.5114          | 0.8769   | 0.8764 |
| 0.1068        | 8.9013 | 6400 | 0.5090          | 0.8778   | 0.8777 |
| 0.0946        | 9.1794 | 6600 | 0.5192          | 0.8800   | 0.8800 |
| 0.0946        | 9.4576 | 6800 | 0.5517          | 0.8753   | 0.8748 |
| 0.0946        | 9.7357 | 7000 | 0.5520          | 0.8784   | 0.8781 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1