---
library_name: transformers
base_model: uitnlp/visobert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: visobert-human-finetune-seed-69
  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-human-finetune-seed-69

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: 0.3651
- Accuracy: 0.8720
- Precision: 0.6884
- Recall: 0.7116
- F1: 0.6966

## 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: 0.0001
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 346  | 0.3459          | 0.8656   | 0.6824    | 0.6750 | 0.6476 |
| 0.3399        | 2.0   | 692  | 0.3651          | 0.8720   | 0.6884    | 0.7116 | 0.6966 |
| 0.1883        | 3.0   | 1038 | 0.3686          | 0.8787   | 0.7074    | 0.6771 | 0.6913 |
| 0.1883        | 4.0   | 1384 | 0.5800          | 0.8720   | 0.7127    | 0.6519 | 0.6594 |
| 0.0913        | 5.0   | 1730 | 0.5507          | 0.8746   | 0.7029    | 0.6860 | 0.6934 |
| 0.0605        | 6.0   | 2076 | 0.6090          | 0.8757   | 0.7007    | 0.6792 | 0.6895 |
| 0.0605        | 7.0   | 2422 | 0.6178          | 0.8821   | 0.7406    | 0.6434 | 0.6830 |


### Framework versions

- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0