---
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
  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

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: 0.4704
- Accuracy: 0.7815
- 0 Precision: 0.7484
- 0 Recall: 0.8149
- 0 F1-score: 0.7803
- 0 Support: 6322.0
- 1 Precision: 0.8170
- 1 Recall: 0.7510
- 1 F1-score: 0.7827
- 1 Support: 6957.0
- Accuracy F1-score: 0.7815
- Macro avg Precision: 0.7827
- Macro avg Recall: 0.7830
- Macro avg F1-score: 0.7815
- Macro avg Support: 13279.0
- Weighted avg Precision: 0.7844
- Weighted avg Recall: 0.7815
- Weighted avg F1-score: 0.7815
- 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: 2060

### 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|
| 0.6282        | 0.2005 | 413  | 0.6101          | 0.6848   | 0.7561      | 0.4991   | 0.6012     | 6322.0    | 0.6522      | 0.8537   | 0.7395     | 6957.0    | 0.6848            | 0.7041              | 0.6764           | 0.6704             | 13279.0           | 0.7016                 | 0.6848              | 0.6737                | 13279.0              |
| 0.6569        | 1.2005 | 826  | 0.5357          | 0.7290   | 0.7392      | 0.6655   | 0.7004     | 6322.0    | 0.7213      | 0.7867   | 0.7526     | 6957.0    | 0.7290            | 0.7303              | 0.7261           | 0.7265             | 13279.0           | 0.7298                 | 0.7290              | 0.7277                | 13279.0              |
| 0.5064        | 2.2005 | 1239 | 0.4839          | 0.7687   | 0.7517      | 0.7680   | 0.7597     | 6322.0    | 0.7849      | 0.7694   | 0.7771     | 6957.0    | 0.7687            | 0.7683              | 0.7687           | 0.7684             | 13279.0           | 0.7691                 | 0.7687              | 0.7688                | 13279.0              |
| 0.4293        | 3.2005 | 1652 | 0.5120          | 0.7518   | 0.6850      | 0.8861   | 0.7727     | 6322.0    | 0.8589      | 0.6297   | 0.7267     | 6957.0    | 0.7518            | 0.7719              | 0.7579           | 0.7497             | 13279.0           | 0.7761                 | 0.7518              | 0.7486                | 13279.0              |
| 0.421         | 4.1981 | 2060 | 0.4704          | 0.7815   | 0.7484      | 0.8149   | 0.7803     | 6322.0    | 0.8170      | 0.7510   | 0.7827     | 6957.0    | 0.7815            | 0.7827              | 0.7830           | 0.7815             | 13279.0           | 0.7844                 | 0.7815              | 0.7815                | 13279.0              |


### Framework versions

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