---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-sarco-aumentado
  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. -->

# videomae-base-sarco-aumentado

This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3437
- Accuracy: 0.3958

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 320

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7603        | 0.1031 | 33   | 1.5047          | 0.5263   |
| 0.0509        | 1.1031 | 66   | 2.6909          | 0.5263   |
| 0.0422        | 2.1031 | 99   | 3.1231          | 0.5263   |
| 0.0085        | 3.1031 | 132  | 3.3052          | 0.5263   |
| 0.0016        | 4.1031 | 165  | 3.3200          | 0.5263   |
| 0.0011        | 5.1031 | 198  | 3.3733          | 0.5263   |
| 0.0008        | 6.1031 | 231  | 3.3552          | 0.5263   |
| 0.0007        | 7.1031 | 264  | 3.3927          | 0.5263   |
| 0.0007        | 8.1031 | 297  | 3.4122          | 0.5263   |
| 0.0007        | 9.0719 | 320  | 3.4094          | 0.5263   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.0.1+cu117
- Datasets 3.1.0
- Tokenizers 0.20.3