---
library_name: transformers
base_model: uitnlp/visobert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: visobert_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. -->

# visobert_v1

This model is a fine-tuned version of [uitnlp/visobert](https://huggingface.co/uitnlp/visobert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4766
- Accuracy: 0.9337
- Precision Macro: 0.8527
- Recall Macro: 0.8055
- F1 Macro: 0.8251
- F1 Weighted: 0.9316

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
| 0.377         | 1.0   | 90   | 0.2037          | 0.9406   | 0.9012          | 0.7694       | 0.8068   | 0.9350      |
| 0.1659        | 2.0   | 180  | 0.2094          | 0.9356   | 0.8396          | 0.8232       | 0.8309   | 0.9348      |
| 0.0966        | 3.0   | 270  | 0.2278          | 0.9381   | 0.8463          | 0.8165       | 0.8298   | 0.9367      |
| 0.0696        | 4.0   | 360  | 0.2619          | 0.9318   | 0.8438          | 0.7756       | 0.8003   | 0.9280      |
| 0.0468        | 5.0   | 450  | 0.3120          | 0.9324   | 0.8362          | 0.8128       | 0.8234   | 0.9313      |
| 0.0337        | 6.0   | 540  | 0.3576          | 0.9311   | 0.8376          | 0.7912       | 0.8103   | 0.9287      |
| 0.0244        | 7.0   | 630  | 0.3796          | 0.9292   | 0.8428          | 0.7816       | 0.8051   | 0.9261      |
| 0.019         | 8.0   | 720  | 0.4309          | 0.9349   | 0.8612          | 0.8070       | 0.8286   | 0.9327      |
| 0.0094        | 9.0   | 810  | 0.4022          | 0.9337   | 0.8565          | 0.8134       | 0.8318   | 0.9319      |
| 0.0098        | 10.0  | 900  | 0.4181          | 0.9349   | 0.8534          | 0.8062       | 0.8259   | 0.9329      |
| 0.0039        | 11.0  | 990  | 0.4484          | 0.9330   | 0.8542          | 0.8091       | 0.8281   | 0.9311      |
| 0.0028        | 12.0  | 1080 | 0.4580          | 0.9349   | 0.8554          | 0.8106       | 0.8294   | 0.9330      |
| 0.0028        | 13.0  | 1170 | 0.4554          | 0.9318   | 0.8613          | 0.7998       | 0.8242   | 0.9292      |
| 0.0031        | 14.0  | 1260 | 0.4575          | 0.9330   | 0.8579          | 0.8009       | 0.8237   | 0.9306      |
| 0.0018        | 15.0  | 1350 | 0.4547          | 0.9356   | 0.8617          | 0.8068       | 0.8291   | 0.9333      |
| 0.0004        | 16.0  | 1440 | 0.4631          | 0.9343   | 0.8455          | 0.8182       | 0.8305   | 0.9331      |
| 0.0006        | 17.0  | 1530 | 0.4642          | 0.9356   | 0.8542          | 0.8152       | 0.8319   | 0.9339      |
| 0.0008        | 18.0  | 1620 | 0.4736          | 0.9343   | 0.8534          | 0.8141       | 0.8311   | 0.9326      |
| 0.0014        | 19.0  | 1710 | 0.4753          | 0.9337   | 0.8527          | 0.8055       | 0.8251   | 0.9316      |
| 0.0009        | 20.0  | 1800 | 0.4766          | 0.9337   | 0.8527          | 0.8055       | 0.8251   | 0.9316      |


### Framework versions

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