---
library_name: transformers
license: agpl-3.0
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PhoBert_Lexical_Dataset45K
  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_Dataset45K

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: 0.4753
- Accuracy: 0.8913
- F1: 0.8822

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
| No log        | 0.2841  | 200   | 0.3590          | 0.8315   | 0.8226 |
| No log        | 0.5682  | 400   | 0.2883          | 0.8752   | 0.8666 |
| No log        | 0.8523  | 600   | 0.2859          | 0.8760   | 0.8676 |
| 0.3584        | 1.1364  | 800   | 0.2665          | 0.8822   | 0.8733 |
| 0.3584        | 1.4205  | 1000  | 0.2749          | 0.8817   | 0.8661 |
| 0.3584        | 1.7045  | 1200  | 0.2749          | 0.8788   | 0.8721 |
| 0.3584        | 1.9886  | 1400  | 0.2721          | 0.8915   | 0.8796 |
| 0.2564        | 2.2727  | 1600  | 0.2601          | 0.8897   | 0.8823 |
| 0.2564        | 2.5568  | 1800  | 0.2470          | 0.8930   | 0.8829 |
| 0.2564        | 2.8409  | 2000  | 0.2494          | 0.8958   | 0.8862 |
| 0.2235        | 3.125   | 2200  | 0.2616          | 0.8944   | 0.8848 |
| 0.2235        | 3.4091  | 2400  | 0.2578          | 0.8887   | 0.8816 |
| 0.2235        | 3.6932  | 2600  | 0.2665          | 0.8905   | 0.8816 |
| 0.2235        | 3.9773  | 2800  | 0.2510          | 0.8934   | 0.8850 |
| 0.1979        | 4.2614  | 3000  | 0.2717          | 0.8906   | 0.8827 |
| 0.1979        | 4.5455  | 3200  | 0.2817          | 0.8849   | 0.8784 |
| 0.1979        | 4.8295  | 3400  | 0.2865          | 0.8835   | 0.8770 |
| 0.1693        | 5.1136  | 3600  | 0.2833          | 0.8921   | 0.8840 |
| 0.1693        | 5.3977  | 3800  | 0.2817          | 0.8950   | 0.8856 |
| 0.1693        | 5.6818  | 4000  | 0.3206          | 0.8837   | 0.8772 |
| 0.1693        | 5.9659  | 4200  | 0.3095          | 0.8921   | 0.8805 |
| 0.1468        | 6.25    | 4400  | 0.3032          | 0.8925   | 0.8819 |
| 0.1468        | 6.5341  | 4600  | 0.3090          | 0.8896   | 0.8805 |
| 0.1468        | 6.8182  | 4800  | 0.3135          | 0.8948   | 0.8854 |
| 0.128         | 7.1023  | 5000  | 0.3523          | 0.8838   | 0.8770 |
| 0.128         | 7.3864  | 5200  | 0.3495          | 0.8956   | 0.8850 |
| 0.128         | 7.6705  | 5400  | 0.3480          | 0.8956   | 0.8859 |
| 0.128         | 7.9545  | 5600  | 0.3398          | 0.8909   | 0.8829 |
| 0.1086        | 8.2386  | 5800  | 0.3459          | 0.8930   | 0.8844 |
| 0.1086        | 8.5227  | 6000  | 0.3390          | 0.8954   | 0.8864 |
| 0.1086        | 8.8068  | 6200  | 0.3383          | 0.8952   | 0.8861 |
| 0.0943        | 9.0909  | 6400  | 0.3674          | 0.8928   | 0.8825 |
| 0.0943        | 9.375   | 6600  | 0.3632          | 0.8954   | 0.8860 |
| 0.0943        | 9.6591  | 6800  | 0.3776          | 0.8889   | 0.8794 |
| 0.0943        | 9.9432  | 7000  | 0.3761          | 0.8935   | 0.8846 |
| 0.0838        | 10.2273 | 7200  | 0.3892          | 0.8930   | 0.8837 |
| 0.0838        | 10.5114 | 7400  | 0.4170          | 0.8882   | 0.8800 |
| 0.0838        | 10.7955 | 7600  | 0.4109          | 0.8920   | 0.8823 |
| 0.0718        | 11.0795 | 7800  | 0.4371          | 0.8902   | 0.8808 |
| 0.0718        | 11.3636 | 8000  | 0.4116          | 0.8906   | 0.8820 |
| 0.0718        | 11.6477 | 8200  | 0.4400          | 0.8880   | 0.8802 |
| 0.0718        | 11.9318 | 8400  | 0.4392          | 0.8889   | 0.8809 |
| 0.0621        | 12.2159 | 8600  | 0.4430          | 0.8919   | 0.8825 |
| 0.0621        | 12.5    | 8800  | 0.4558          | 0.8904   | 0.8819 |
| 0.0621        | 12.7841 | 9000  | 0.4548          | 0.8930   | 0.8840 |
| 0.0572        | 13.0682 | 9200  | 0.4681          | 0.8904   | 0.8816 |
| 0.0572        | 13.3523 | 9400  | 0.4781          | 0.8891   | 0.8799 |
| 0.0572        | 13.6364 | 9600  | 0.4710          | 0.8900   | 0.8816 |
| 0.0572        | 13.9205 | 9800  | 0.4691          | 0.8887   | 0.8800 |
| 0.0514        | 14.2045 | 10000 | 0.4772          | 0.8912   | 0.8823 |
| 0.0514        | 14.4886 | 10200 | 0.4740          | 0.8893   | 0.8806 |
| 0.0514        | 14.7727 | 10400 | 0.4753          | 0.8913   | 0.8822 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1