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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
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