End of training

README.md

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+---
+library_name: transformers
+license: cc-by-nc-4.0
+base_model: MCG-NJU/videomae-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: videomae-base-finetuned-xd-violence-binary
+  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-xd-violence-binary
+
+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: 0.6208
+- Accuracy: 0.6674
+- F1: 0.7483
+- Precision: 0.7108
+- Recall: 0.7900
+- Specificity: 0.4625
+- True Positives: 17801
+- True Negatives: 6233
+- False Positives: 7244
+- False Negatives: 4733
+
+## 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: 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: 1684
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Specificity | True Positives | True Negatives | False Positives | False Negatives |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----------:|:--------------:|:--------------:|:---------------:|:---------------:|
+| 0.6787        | 0.2506 | 422  | 0.7039          | 0.5679   | 0.5938 | 0.7211    | 0.5047 | 0.6736      | 11374          | 9078           | 4399            | 11160           |
+| 0.6389        | 1.2506 | 844  | 0.6784          | 0.5828   | 0.5991 | 0.7513    | 0.4981 | 0.7243      | 11225          | 9761           | 3716            | 11309           |
+| 0.6126        | 2.2506 | 1266 | 0.6322          | 0.6599   | 0.7373 | 0.7135    | 0.7626 | 0.4881      | 17185          | 6578           | 6899            | 5349            |
+| 0.6762        | 3.2482 | 1684 | 0.6208          | 0.6674   | 0.7483 | 0.7108    | 0.7900 | 0.4625      | 17801          | 6233           | 7244            | 4733            |
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.1.0+cu118
+- Datasets 3.6.0
+- Tokenizers 0.21.1