---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PhoBert_Lexical_Dataset55K
  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_Lexical_Dataset55K

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: 1.0598
- Accuracy: 0.7970
- F1: 0.8468

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
| No log        | 0.2317  | 200   | 0.5915          | 0.7575   | 0.8196 |
| No log        | 0.4635  | 400   | 0.6149          | 0.7294   | 0.8009 |
| No log        | 0.6952  | 600   | 0.5838          | 0.7335   | 0.8038 |
| No log        | 0.9270  | 800   | 0.4301          | 0.8213   | 0.8606 |
| 0.3511        | 1.1587  | 1000  | 0.4820          | 0.8211   | 0.8608 |
| 0.3511        | 1.3905  | 1200  | 0.6798          | 0.7461   | 0.8126 |
| 0.3511        | 1.6222  | 1400  | 0.6930          | 0.7087   | 0.7868 |
| 0.3511        | 1.8540  | 1600  | 0.6207          | 0.7709   | 0.8292 |
| 0.2551        | 2.0857  | 1800  | 0.5473          | 0.8101   | 0.8545 |
| 0.2551        | 2.3175  | 2000  | 0.5228          | 0.8072   | 0.8526 |
| 0.2551        | 2.5492  | 2200  | 0.5701          | 0.7828   | 0.8371 |
| 0.2551        | 2.7810  | 2400  | 0.5560          | 0.7838   | 0.8378 |
| 0.214         | 3.0127  | 2600  | 0.7375          | 0.7332   | 0.8039 |
| 0.214         | 3.2445  | 2800  | 0.5101          | 0.8126   | 0.8565 |
| 0.214         | 3.4762  | 3000  | 0.5639          | 0.8139   | 0.8574 |
| 0.214         | 3.7080  | 3200  | 0.4802          | 0.8299   | 0.8671 |
| 0.214         | 3.9397  | 3400  | 0.6431          | 0.7773   | 0.8336 |
| 0.1835        | 4.1715  | 3600  | 0.8122          | 0.7347   | 0.8051 |
| 0.1835        | 4.4032  | 3800  | 0.4787          | 0.8506   | 0.8804 |
| 0.1835        | 4.6350  | 4000  | 0.4719          | 0.8476   | 0.8784 |
| 0.1835        | 4.8667  | 4200  | 0.4643          | 0.8419   | 0.8751 |
| 0.157         | 5.0985  | 4400  | 0.6827          | 0.7918   | 0.8432 |
| 0.157         | 5.3302  | 4600  | 0.5695          | 0.8225   | 0.8629 |
| 0.157         | 5.5620  | 4800  | 0.7416          | 0.7831   | 0.8376 |
| 0.157         | 5.7937  | 5000  | 0.7787          | 0.7643   | 0.8253 |
| 0.1337        | 6.0255  | 5200  | 0.6881          | 0.7963   | 0.8462 |
| 0.1337        | 6.2572  | 5400  | 0.6894          | 0.7953   | 0.8456 |
| 0.1337        | 6.4890  | 5600  | 0.6472          | 0.8156   | 0.8587 |
| 0.1337        | 6.7207  | 5800  | 0.5857          | 0.8222   | 0.8626 |
| 0.1337        | 6.9525  | 6000  | 0.6816          | 0.8113   | 0.8559 |
| 0.1154        | 7.1842  | 6200  | 0.9047          | 0.7590   | 0.8216 |
| 0.1154        | 7.4160  | 6400  | 0.7080          | 0.8088   | 0.8541 |
| 0.1154        | 7.6477  | 6600  | 0.6871          | 0.8148   | 0.8581 |
| 0.1154        | 7.8795  | 6800  | 0.7481          | 0.7900   | 0.8422 |
| 0.1002        | 8.1112  | 7000  | 0.7101          | 0.8236   | 0.8637 |
| 0.1002        | 8.3430  | 7200  | 0.8398          | 0.7933   | 0.8443 |
| 0.1002        | 8.5747  | 7400  | 0.7284          | 0.8239   | 0.8640 |
| 0.1002        | 8.8065  | 7600  | 0.7415          | 0.8117   | 0.8561 |
| 0.0843        | 9.0382  | 7800  | 0.8033          | 0.8066   | 0.8529 |
| 0.0843        | 9.2700  | 8000  | 0.8593          | 0.8080   | 0.8536 |
| 0.0843        | 9.5017  | 8200  | 0.8626          | 0.7953   | 0.8456 |
| 0.0843        | 9.7335  | 8400  | 0.7607          | 0.8192   | 0.8609 |
| 0.0843        | 9.9652  | 8600  | 0.9512          | 0.7875   | 0.8406 |
| 0.072         | 10.1970 | 8800  | 0.9709          | 0.7891   | 0.8418 |
| 0.072         | 10.4287 | 9000  | 0.9674          | 0.7948   | 0.8453 |
| 0.072         | 10.6605 | 9200  | 0.9815          | 0.7863   | 0.8399 |
| 0.072         | 10.8922 | 9400  | 1.0347          | 0.7839   | 0.8382 |
| 0.0617        | 11.1240 | 9600  | 1.1195          | 0.7774   | 0.8340 |
| 0.0617        | 11.3557 | 9800  | 1.0192          | 0.7916   | 0.8434 |
| 0.0617        | 11.5875 | 10000 | 0.9594          | 0.8022   | 0.8502 |
| 0.0617        | 11.8192 | 10200 | 1.0892          | 0.7750   | 0.8325 |
| 0.0541        | 12.0510 | 10400 | 1.0634          | 0.7886   | 0.8414 |
| 0.0541        | 12.2827 | 10600 | 1.0198          | 0.8036   | 0.8512 |
| 0.0541        | 12.5145 | 10800 | 1.0040          | 0.8016   | 0.8499 |
| 0.0541        | 12.7462 | 11000 | 1.0208          | 0.7962   | 0.8464 |
| 0.0541        | 12.9780 | 11200 | 1.0329          | 0.7927   | 0.8441 |
| 0.048         | 13.2097 | 11400 | 0.9916          | 0.8033   | 0.8510 |
| 0.048         | 13.4415 | 11600 | 1.0793          | 0.7902   | 0.8424 |
| 0.048         | 13.6732 | 11800 | 1.0424          | 0.7964   | 0.8465 |
| 0.048         | 13.9050 | 12000 | 1.0162          | 0.8018   | 0.8500 |
| 0.0426        | 14.1367 | 12200 | 1.0633          | 0.7936   | 0.8447 |
| 0.0426        | 14.3685 | 12400 | 1.0128          | 0.8022   | 0.8502 |
| 0.0426        | 14.6002 | 12600 | 1.0780          | 0.7924   | 0.8439 |
| 0.0426        | 14.8320 | 12800 | 1.0598          | 0.7970   | 0.8468 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1