---
license: apache-2.0
base_model: dandelin/vilt-b32-finetuned-vqa
tags:
- generated_from_trainer
model-index:
- name: ViLT_FT_Balanced_Binary_Abstract_Scenes
  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. -->

# ViLT_FT_Balanced_Binary_Abstract_Scenes

This model is a fine-tuned version of [dandelin/vilt-b32-finetuned-vqa](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3521

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6688        | 0.17  | 200  | 1.6769          |
| 1.3841        | 0.34  | 400  | 1.6145          |
| 1.3773        | 0.5   | 600  | 1.5574          |
| 1.3539        | 0.67  | 800  | 1.5374          |
| 1.3458        | 0.84  | 1000 | 1.5044          |
| 1.3653        | 1.01  | 1200 | 1.4956          |
| 1.3222        | 1.18  | 1400 | 1.4968          |
| 1.3362        | 1.34  | 1600 | 1.4855          |
| 1.3557        | 1.51  | 1800 | 1.3809          |
| 1.3207        | 1.68  | 2000 | 1.3806          |
| 1.348         | 1.85  | 2200 | 1.3718          |
| 1.3215        | 2.02  | 2400 | 1.3677          |
| 1.3299        | 2.18  | 2600 | 1.3793          |
| 1.335         | 2.35  | 2800 | 1.3662          |
| 1.3033        | 2.52  | 3000 | 1.3628          |
| 1.3377        | 2.69  | 3200 | 1.3525          |
| 1.3001        | 2.85  | 3400 | 1.3521          |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2