---
library_name: transformers
base_model: FPTAI/vibert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vi-bert-base_v1
  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. -->

# vi-bert-base_v1

This model is a fine-tuned version of [FPTAI/vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4995
- Accuracy: 0.9292
- Precision Macro: 0.8368
- Recall Macro: 0.7769
- F1 Macro: 0.8000
- F1 Weighted: 0.9259

## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
| 0.5604        | 1.0   | 90   | 0.2596          | 0.9128   | 0.9000          | 0.6666       | 0.6788   | 0.8972      |
| 0.2258        | 2.0   | 180  | 0.2182          | 0.9286   | 0.8216          | 0.8017       | 0.8109   | 0.9275      |
| 0.1532        | 3.0   | 270  | 0.2312          | 0.9198   | 0.7940          | 0.7902       | 0.7919   | 0.9195      |
| 0.123         | 4.0   | 360  | 0.2432          | 0.9311   | 0.8607          | 0.8000       | 0.8238   | 0.9286      |
| 0.0785        | 5.0   | 450  | 0.2592          | 0.9255   | 0.8450          | 0.7784       | 0.8037   | 0.9222      |
| 0.0628        | 6.0   | 540  | 0.3075          | 0.9280   | 0.8358          | 0.7765       | 0.7993   | 0.9247      |
| 0.0457        | 7.0   | 630  | 0.3155          | 0.9255   | 0.8118          | 0.7996       | 0.8053   | 0.9247      |
| 0.034         | 8.0   | 720  | 0.3924          | 0.9248   | 0.8212          | 0.7656       | 0.7870   | 0.9213      |
| 0.0271        | 9.0   | 810  | 0.3776          | 0.9242   | 0.8211          | 0.7782       | 0.7957   | 0.9216      |
| 0.0207        | 10.0  | 900  | 0.4209          | 0.9274   | 0.8067          | 0.8094       | 0.8080   | 0.9275      |
| 0.0189        | 11.0  | 990  | 0.4373          | 0.9255   | 0.7988          | 0.7957       | 0.7971   | 0.9252      |
| 0.0145        | 12.0  | 1080 | 0.4010          | 0.9349   | 0.8392          | 0.8228       | 0.8304   | 0.9341      |
| 0.0083        | 13.0  | 1170 | 0.4337          | 0.9242   | 0.8237          | 0.7988       | 0.8100   | 0.9228      |
| 0.004         | 14.0  | 1260 | 0.4571          | 0.9318   | 0.8491          | 0.7828       | 0.8080   | 0.9285      |
| 0.0081        | 15.0  | 1350 | 0.4862          | 0.9286   | 0.8298          | 0.7857       | 0.8035   | 0.9261      |
| 0.0027        | 16.0  | 1440 | 0.4788          | 0.9280   | 0.8348          | 0.7924       | 0.8103   | 0.9258      |
| 0.0029        | 17.0  | 1530 | 0.4797          | 0.9305   | 0.8339          | 0.7903       | 0.8085   | 0.9281      |
| 0.003         | 18.0  | 1620 | 0.4877          | 0.9280   | 0.8238          | 0.7807       | 0.7984   | 0.9253      |
| 0.0013        | 19.0  | 1710 | 0.4966          | 0.9286   | 0.8363          | 0.7765       | 0.7996   | 0.9253      |
| 0.0014        | 20.0  | 1800 | 0.4995          | 0.9292   | 0.8368          | 0.7769       | 0.8000   | 0.9259      |


### Framework versions

- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4