---
library_name: transformers
base_model: uitnlp/visobert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: visobert-human-finetune-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-finetune-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: 0.3589
- Accuracy: 0.8825
- Precision: 0.7296
- Recall: 0.6876
- F1: 0.7068

## 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  | 0.3514          | 0.8656   | 0.6970    | 0.6739 | 0.6527 |
| 0.3551        | 2.0   | 692  | 0.3589          | 0.8825   | 0.7296    | 0.6876 | 0.7068 |
| 0.1986        | 3.0   | 1038 | 0.4328          | 0.8787   | 0.7072    | 0.7021 | 0.7043 |
| 0.1986        | 4.0   | 1384 | 0.5486          | 0.8829   | 0.7486    | 0.6557 | 0.6838 |
| 0.1016        | 5.0   | 1730 | 0.6443          | 0.8784   | 0.7173    | 0.6937 | 0.7013 |
| 0.0687        | 6.0   | 2076 | 0.6960          | 0.8709   | 0.6958    | 0.6813 | 0.6861 |
| 0.0687        | 7.0   | 2422 | 0.6090          | 0.875    | 0.7156    | 0.6963 | 0.7026 |


### Framework versions

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