---
library_name: transformers
base_model: uitnlp/visobert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: visobert-human-tl-seg-seed-6969
  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-tl-seg-seed-6969

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: 0.3765
- Accuracy: 0.8626
- Precision: 0.7099
- Recall: 0.5885
- F1: 0.6157

## 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  | 0.4179          | 0.8439   | 0.6639    | 0.4685 | 0.4911 |
| 0.5071        | 2.0   | 692  | 0.3976          | 0.8582   | 0.7045    | 0.5327 | 0.5664 |
| 0.3754        | 3.0   | 1038 | 0.3962          | 0.8589   | 0.7381    | 0.5336 | 0.5745 |
| 0.3754        | 4.0   | 1384 | 0.3867          | 0.8574   | 0.7182    | 0.5499 | 0.5811 |
| 0.3711        | 5.0   | 1730 | 0.3849          | 0.8615   | 0.7316    | 0.5551 | 0.5903 |
| 0.3634        | 6.0   | 2076 | 0.3894          | 0.8589   | 0.7282    | 0.5462 | 0.5785 |
| 0.3634        | 7.0   | 2422 | 0.3789          | 0.8608   | 0.7043    | 0.5474 | 0.5898 |
| 0.3631        | 8.0   | 2768 | 0.3763          | 0.8623   | 0.7172    | 0.5535 | 0.5932 |
| 0.3551        | 9.0   | 3114 | 0.3728          | 0.8608   | 0.6908    | 0.5678 | 0.6064 |
| 0.3551        | 10.0  | 3460 | 0.3885          | 0.8585   | 0.7107    | 0.5637 | 0.5859 |
| 0.3613        | 11.0  | 3806 | 0.3747          | 0.8612   | 0.7077    | 0.5534 | 0.5947 |
| 0.3518        | 12.0  | 4152 | 0.3765          | 0.8626   | 0.7099    | 0.5885 | 0.6157 |
| 0.3518        | 13.0  | 4498 | 0.3746          | 0.8619   | 0.7195    | 0.5563 | 0.5935 |
| 0.354         | 14.0  | 4844 | 0.3716          | 0.8604   | 0.7016    | 0.5562 | 0.5977 |
| 0.3483        | 15.0  | 5190 | 0.3721          | 0.8612   | 0.7102    | 0.5588 | 0.5985 |
| 0.3516        | 16.0  | 5536 | 0.3739          | 0.8597   | 0.7213    | 0.5375 | 0.5787 |
| 0.3516        | 17.0  | 5882 | 0.3703          | 0.8597   | 0.6851    | 0.5854 | 0.6137 |


### Framework versions

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