---
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-finetuned-yt_short_classification-2
  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-finetuned-yt_short_classification-2

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: 0.4733
- Accuracy: 0.7818
- 0 Precision: 0.7333
- 0 Recall: 0.8515
- 0 F1-score: 0.7880
- 0 Support: 6322.0
- 1 Precision: 0.8419
- 1 Recall: 0.7186
- 1 F1-score: 0.7753
- 1 Support: 6957.0
- Accuracy F1-score: 0.7818
- Macro avg Precision: 0.7876
- Macro avg Recall: 0.7850
- Macro avg F1-score: 0.7817
- Macro avg Support: 13279.0
- Weighted avg Precision: 0.7902
- Weighted avg Recall: 0.7818
- Weighted avg F1-score: 0.7814
- Weighted avg Support: 13279.0

## 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: 16
- eval_batch_size: 16
- 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: 4120

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | Accuracy F1-score | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|
| 0.6122        | 0.1002 | 413  | 0.7143          | 0.6551   | 0.5925      | 0.8828   | 0.7091     | 6322.0    | 0.8080      | 0.4482   | 0.5766     | 6957.0    | 0.6551            | 0.7002              | 0.6655           | 0.6428             | 13279.0           | 0.7054                 | 0.6551              | 0.6396                | 13279.0              |
| 0.6904        | 1.1002 | 826  | 0.5800          | 0.6909   | 0.8170      | 0.4519   | 0.5819     | 6322.0    | 0.6458      | 0.9080   | 0.7548     | 6957.0    | 0.6909            | 0.7314              | 0.6800           | 0.6683             | 13279.0           | 0.7273                 | 0.6909              | 0.6725                | 13279.0              |
| 0.5489        | 2.1002 | 1239 | 0.5122          | 0.7555   | 0.7450      | 0.7395   | 0.7422     | 6322.0    | 0.7648      | 0.7700   | 0.7674     | 6957.0    | 0.7555            | 0.7549              | 0.7547           | 0.7548             | 13279.0           | 0.7554                 | 0.7555              | 0.7554                | 13279.0              |
| 0.4979        | 3.1002 | 1652 | 0.5434          | 0.7443   | 0.6785      | 0.8798   | 0.7662     | 6322.0    | 0.8505      | 0.6212   | 0.7180     | 6957.0    | 0.7443            | 0.7645              | 0.7505           | 0.7421             | 13279.0           | 0.7686                 | 0.7443              | 0.7409                | 13279.0              |
| 0.5141        | 4.1002 | 2065 | 0.4793          | 0.7723   | 0.7482      | 0.7866   | 0.7669     | 6322.0    | 0.7966      | 0.7594   | 0.7775     | 6957.0    | 0.7723            | 0.7724              | 0.7730           | 0.7722             | 13279.0           | 0.7735                 | 0.7723              | 0.7725                | 13279.0              |
| 0.4472        | 5.1002 | 2478 | 0.4673          | 0.7798   | 0.7398      | 0.8290   | 0.7819     | 6322.0    | 0.8255      | 0.7351   | 0.7777     | 6957.0    | 0.7798            | 0.7827              | 0.7820           | 0.7798             | 13279.0           | 0.7847                 | 0.7798              | 0.7797                | 13279.0              |
| 0.4108        | 6.1002 | 2891 | 0.4491          | 0.7952   | 0.7715      | 0.8096   | 0.7901     | 6322.0    | 0.8188      | 0.7821   | 0.8000     | 6957.0    | 0.7952            | 0.7951              | 0.7958           | 0.7950             | 13279.0           | 0.7963                 | 0.7952              | 0.7953                | 13279.0              |
| 0.3756        | 7.1002 | 3304 | 0.4955          | 0.7773   | 0.7472      | 0.8045   | 0.7748     | 6322.0    | 0.8090      | 0.7526   | 0.7798     | 6957.0    | 0.7773            | 0.7781              | 0.7786           | 0.7773             | 13279.0           | 0.7796                 | 0.7773              | 0.7774                | 13279.0              |
| 0.3147        | 8.1002 | 3717 | 0.5889          | 0.7318   | 0.6523      | 0.9348   | 0.7684     | 6322.0    | 0.9023      | 0.5472   | 0.6813     | 6957.0    | 0.7318            | 0.7773              | 0.7410           | 0.7249             | 13279.0           | 0.7833                 | 0.7318              | 0.7228                | 13279.0              |
| 0.4019        | 9.0978 | 4120 | 0.4733          | 0.7818   | 0.7333      | 0.8515   | 0.7880     | 6322.0    | 0.8419      | 0.7186   | 0.7753     | 6957.0    | 0.7818            | 0.7876              | 0.7850           | 0.7817             | 13279.0           | 0.7902                 | 0.7818              | 0.7814                | 13279.0              |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.0.0+cu117
- Datasets 3.1.0
- Tokenizers 0.20.3