Model save

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
+- precision
+- recall
+- f1
+model-index:
+- name: VideoMAE_LSA64_SR_12
+  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_LSA64_SR_12
+
+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: 0.0353
+- Accuracy: 0.9922
+- Precision: 0.9938
+- Recall: 0.9922
+- F1: 0.9921
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- 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: 8640
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 16.1868       | 0.0333  | 288  | 3.7228          | 0.0391   | 0.0138    | 0.0391 | 0.0127 |
+| 13.4364       | 1.0333  | 576  | 2.1449          | 0.4688   | 0.4296    | 0.4688 | 0.3971 |
+| 3.865         | 2.0333  | 864  | 0.5912          | 0.8711   | 0.8732    | 0.8711 | 0.8496 |
+| 1.4285        | 3.0333  | 1152 | 0.3284          | 0.8867   | 0.8762    | 0.8867 | 0.8663 |
+| 0.8195        | 4.0333  | 1440 | 0.1833          | 0.9375   | 0.9276    | 0.9375 | 0.9256 |
+| 0.4318        | 5.0333  | 1728 | 0.0732          | 0.9688   | 0.9756    | 0.9688 | 0.9665 |
+| 0.274         | 6.0333  | 2016 | 0.0440          | 0.9844   | 0.9885    | 0.9844 | 0.9837 |
+| 0.2439        | 7.0333  | 2304 | 0.0661          | 0.9766   | 0.9792    | 0.9766 | 0.9762 |
+| 0.1427        | 8.0333  | 2592 | 0.0970          | 0.9805   | 0.9734    | 0.9805 | 0.9752 |
+| 0.137         | 9.0333  | 2880 | 0.0308          | 0.9961   | 0.9969    | 0.9961 | 0.9960 |
+| 0.1534        | 10.0333 | 3168 | 0.0466          | 0.9883   | 0.9906    | 0.9883 | 0.9881 |
+| 0.122         | 11.0333 | 3456 | 0.1174          | 0.9766   | 0.9784    | 0.9766 | 0.9763 |
+| 0.0199        | 12.0333 | 3744 | 0.0210          | 0.9922   | 0.9922    | 0.9922 | 0.9922 |
+| 0.0184        | 13.0333 | 4032 | 0.0467          | 0.9883   | 0.9917    | 0.9883 | 0.9877 |
+| 0.1376        | 14.0333 | 4320 | 0.0025          | 1.0      | 1.0       | 1.0    | 1.0    |
+| 0.0664        | 15.0333 | 4608 | 0.0323          | 0.9883   | 0.9906    | 0.9883 | 0.9881 |
+| 0.1192        | 16.0333 | 4896 | 0.0807          | 0.9844   | 0.9885    | 0.9844 | 0.9837 |
+| 0.0246        | 17.0333 | 5184 | 0.0387          | 0.9922   | 0.9930    | 0.9922 | 0.9921 |
+| 0.0084        | 18.0333 | 5472 | 0.0339          | 0.9883   | 0.9906    | 0.9883 | 0.9881 |
+| 0.0081        | 19.0333 | 5760 | 0.0353          | 0.9922   | 0.9938    | 0.9922 | 0.9921 |
+
+
+### Framework versions
+
+- Transformers 4.46.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.1.0
+- Tokenizers 0.20.1

model.safetensors

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