---
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: 0.4880
- Accuracy: 0.8851
- F1: 0.8858

## 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        | 0.2841  | 200   | 0.3476          | 0.8474   | 0.8494 |
| No log        | 0.5682  | 400   | 0.3035          | 0.8692   | 0.8703 |
| No log        | 0.8523  | 600   | 0.3132          | 0.8654   | 0.8674 |
| 0.3678        | 1.1364  | 800   | 0.2832          | 0.8797   | 0.8797 |
| 0.3678        | 1.4205  | 1000  | 0.2805          | 0.8828   | 0.8815 |
| 0.3678        | 1.7045  | 1200  | 0.2622          | 0.8862   | 0.8874 |
| 0.3678        | 1.9886  | 1400  | 0.2751          | 0.8898   | 0.8889 |
| 0.2662        | 2.2727  | 1600  | 0.2627          | 0.8873   | 0.8886 |
| 0.2662        | 2.5568  | 1800  | 0.2514          | 0.8945   | 0.8940 |
| 0.2662        | 2.8409  | 2000  | 0.2433          | 0.8941   | 0.8949 |
| 0.219         | 3.125   | 2200  | 0.2600          | 0.8914   | 0.8922 |
| 0.219         | 3.4091  | 2400  | 0.2563          | 0.8933   | 0.8937 |
| 0.219         | 3.6932  | 2600  | 0.2592          | 0.8909   | 0.8919 |
| 0.219         | 3.9773  | 2800  | 0.2390          | 0.8969   | 0.8974 |
| 0.1865        | 4.2614  | 3000  | 0.2925          | 0.8820   | 0.8834 |
| 0.1865        | 4.5455  | 3200  | 0.2922          | 0.8871   | 0.8883 |
| 0.1865        | 4.8295  | 3400  | 0.2975          | 0.8838   | 0.8854 |
| 0.1591        | 5.1136  | 3600  | 0.2935          | 0.8920   | 0.8926 |
| 0.1591        | 5.3977  | 3800  | 0.2860          | 0.8893   | 0.8901 |
| 0.1591        | 5.6818  | 4000  | 0.2943          | 0.8939   | 0.8940 |
| 0.1591        | 5.9659  | 4200  | 0.2898          | 0.8970   | 0.8973 |
| 0.1394        | 6.25    | 4400  | 0.3268          | 0.8886   | 0.8886 |
| 0.1394        | 6.5341  | 4600  | 0.3399          | 0.8804   | 0.8820 |
| 0.1394        | 6.8182  | 4800  | 0.3353          | 0.8887   | 0.8894 |
| 0.1159        | 7.1023  | 5000  | 0.3313          | 0.8950   | 0.8956 |
| 0.1159        | 7.3864  | 5200  | 0.3496          | 0.8955   | 0.8956 |
| 0.1159        | 7.6705  | 5400  | 0.3509          | 0.8920   | 0.8925 |
| 0.1159        | 7.9545  | 5600  | 0.3534          | 0.8842   | 0.8855 |
| 0.1005        | 8.2386  | 5800  | 0.3529          | 0.8932   | 0.8936 |
| 0.1005        | 8.5227  | 6000  | 0.3641          | 0.8914   | 0.8916 |
| 0.1005        | 8.8068  | 6200  | 0.3572          | 0.8907   | 0.8911 |
| 0.0838        | 9.0909  | 6400  | 0.4026          | 0.8873   | 0.8873 |
| 0.0838        | 9.375   | 6600  | 0.4049          | 0.8869   | 0.8876 |
| 0.0838        | 9.6591  | 6800  | 0.4024          | 0.8808   | 0.8822 |
| 0.0838        | 9.9432  | 7000  | 0.4161          | 0.8874   | 0.8886 |
| 0.0732        | 10.2273 | 7200  | 0.4098          | 0.8881   | 0.8885 |
| 0.0732        | 10.5114 | 7400  | 0.4010          | 0.8880   | 0.8885 |
| 0.0732        | 10.7955 | 7600  | 0.4166          | 0.8890   | 0.8891 |
| 0.0616        | 11.0795 | 7800  | 0.4317          | 0.8853   | 0.8859 |
| 0.0616        | 11.3636 | 8000  | 0.4323          | 0.8878   | 0.8884 |
| 0.0616        | 11.6477 | 8200  | 0.4550          | 0.8862   | 0.8872 |
| 0.0616        | 11.9318 | 8400  | 0.4509          | 0.8882   | 0.8890 |
| 0.0547        | 12.2159 | 8600  | 0.4463          | 0.8871   | 0.8877 |
| 0.0547        | 12.5    | 8800  | 0.4705          | 0.8842   | 0.8849 |
| 0.0547        | 12.7841 | 9000  | 0.4663          | 0.8876   | 0.8880 |
| 0.0472        | 13.0682 | 9200  | 0.4825          | 0.8867   | 0.8876 |
| 0.0472        | 13.3523 | 9400  | 0.4796          | 0.8858   | 0.8864 |
| 0.0472        | 13.6364 | 9600  | 0.4856          | 0.8853   | 0.8862 |
| 0.0472        | 13.9205 | 9800  | 0.4896          | 0.8840   | 0.8851 |
| 0.0433        | 14.2045 | 10000 | 0.4810          | 0.8859   | 0.8864 |
| 0.0433        | 14.4886 | 10200 | 0.4900          | 0.8842   | 0.8851 |
| 0.0433        | 14.7727 | 10400 | 0.4880          | 0.8851   | 0.8858 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1