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videomae-base-finetuned-yt_short_classification-3

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
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  1. README.md +96 -0
  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: cc-by-nc-4.0
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+ base_model: MCG-NJU/videomae-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: videomae-base-finetuned-yt_short_classification-3
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # videomae-base-finetuned-yt_short_classification-3
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+
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+ This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4759
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+ - Accuracy: 0.7968
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+ - 0 Precision: 0.7671
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+ - 0 Recall: 0.8232
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+ - 0 F1-score: 0.7941
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+ - 0 Support: 6322.0
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+ - 1 Precision: 0.8279
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+ - 1 Recall: 0.7729
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+ - 1 F1-score: 0.7994
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+ - 1 Support: 6957.0
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+ - Accuracy F1-score: 0.7968
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+ - Macro avg Precision: 0.7975
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+ - Macro avg Recall: 0.7980
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+ - Macro avg F1-score: 0.7968
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+ - Macro avg Support: 13279.0
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+ - Weighted avg Precision: 0.7989
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+ - Weighted avg Recall: 0.7968
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+ - Weighted avg F1-score: 0.7969
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+ - Weighted avg Support: 13279.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - training_steps: 8240
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+
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+ ### Training results
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+
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+ | 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 |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|
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+ | 0.6086 | 0.0501 | 413 | 0.5774 | 0.7153 | 0.6893 | 0.7320 | 0.7100 | 6322.0 | 0.7420 | 0.7002 | 0.7205 | 6957.0 | 0.7153 | 0.7156 | 0.7161 | 0.7152 | 13279.0 | 0.7169 | 0.7153 | 0.7155 | 13279.0 |
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+ | 0.7001 | 1.0501 | 826 | 0.5860 | 0.7059 | 0.7671 | 0.5487 | 0.6398 | 6322.0 | 0.6742 | 0.8486 | 0.7514 | 6957.0 | 0.7059 | 0.7207 | 0.6987 | 0.6956 | 13279.0 | 0.7184 | 0.7059 | 0.6983 | 13279.0 |
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+ | 0.5946 | 2.0501 | 1239 | 0.5355 | 0.7391 | 0.7899 | 0.6156 | 0.6920 | 6322.0 | 0.7091 | 0.8512 | 0.7737 | 6957.0 | 0.7391 | 0.7495 | 0.7334 | 0.7328 | 13279.0 | 0.7476 | 0.7391 | 0.7348 | 13279.0 |
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+ | 0.5304 | 3.0501 | 1652 | 0.5068 | 0.7592 | 0.7034 | 0.8543 | 0.7716 | 6322.0 | 0.8356 | 0.6727 | 0.7453 | 6957.0 | 0.7592 | 0.7695 | 0.7635 | 0.7585 | 13279.0 | 0.7727 | 0.7592 | 0.7578 | 13279.0 |
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+ | 0.4982 | 4.0501 | 2065 | 0.5257 | 0.7595 | 0.7060 | 0.8481 | 0.7706 | 6322.0 | 0.8311 | 0.6790 | 0.7474 | 6957.0 | 0.7595 | 0.7685 | 0.7636 | 0.7590 | 13279.0 | 0.7715 | 0.7595 | 0.7584 | 13279.0 |
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+ | 0.5006 | 5.0501 | 2478 | 0.4784 | 0.7736 | 0.8036 | 0.6939 | 0.7448 | 6322.0 | 0.7526 | 0.8459 | 0.7965 | 6957.0 | 0.7736 | 0.7781 | 0.7699 | 0.7706 | 13279.0 | 0.7769 | 0.7736 | 0.7719 | 13279.0 |
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+ | 0.4356 | 6.0501 | 2891 | 0.4878 | 0.7772 | 0.7188 | 0.8738 | 0.7887 | 6322.0 | 0.8573 | 0.6894 | 0.7642 | 6957.0 | 0.7772 | 0.7881 | 0.7816 | 0.7765 | 13279.0 | 0.7914 | 0.7772 | 0.7759 | 13279.0 |
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+ | 0.4393 | 7.0501 | 3304 | 0.4555 | 0.7884 | 0.7969 | 0.7455 | 0.7703 | 6322.0 | 0.7815 | 0.8274 | 0.8038 | 6957.0 | 0.7884 | 0.7892 | 0.7864 | 0.7871 | 13279.0 | 0.7889 | 0.7884 | 0.7879 | 13279.0 |
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+ | 0.3447 | 8.0501 | 3717 | 0.4561 | 0.7946 | 0.8046 | 0.7509 | 0.7768 | 6322.0 | 0.7866 | 0.8343 | 0.8097 | 6957.0 | 0.7946 | 0.7956 | 0.7926 | 0.7933 | 13279.0 | 0.7951 | 0.7946 | 0.7940 | 13279.0 |
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+ | 0.4447 | 9.0501 | 4130 | 0.4655 | 0.7793 | 0.7202 | 0.8771 | 0.7910 | 6322.0 | 0.8608 | 0.6904 | 0.7662 | 6957.0 | 0.7793 | 0.7905 | 0.7837 | 0.7786 | 13279.0 | 0.7938 | 0.7793 | 0.7780 | 13279.0 |
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+ | 0.4503 | 10.0501 | 4543 | 0.4822 | 0.7748 | 0.8554 | 0.6343 | 0.7284 | 6322.0 | 0.7309 | 0.9025 | 0.8077 | 6957.0 | 0.7748 | 0.7931 | 0.7684 | 0.7681 | 13279.0 | 0.7902 | 0.7748 | 0.7700 | 13279.0 |
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+ | 0.3794 | 11.0501 | 4956 | 0.5383 | 0.7577 | 0.6870 | 0.9018 | 0.7799 | 6322.0 | 0.8753 | 0.6267 | 0.7304 | 6957.0 | 0.7577 | 0.7812 | 0.7642 | 0.7552 | 13279.0 | 0.7857 | 0.7577 | 0.7540 | 13279.0 |
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+ | 0.3636 | 12.0501 | 5369 | 0.4371 | 0.8049 | 0.7832 | 0.8160 | 0.7993 | 6322.0 | 0.8262 | 0.7947 | 0.8102 | 6957.0 | 0.8049 | 0.8047 | 0.8054 | 0.8047 | 13279.0 | 0.8057 | 0.8049 | 0.8050 | 13279.0 |
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+ | 0.4918 | 13.0501 | 5782 | 0.4571 | 0.8010 | 0.8331 | 0.7278 | 0.7769 | 6322.0 | 0.7781 | 0.8675 | 0.8204 | 6957.0 | 0.8010 | 0.8056 | 0.7976 | 0.7986 | 13279.0 | 0.8043 | 0.8010 | 0.7997 | 13279.0 |
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+ | 0.4904 | 14.0501 | 6195 | 0.4412 | 0.8047 | 0.7801 | 0.8211 | 0.8001 | 6322.0 | 0.8293 | 0.7897 | 0.8090 | 6957.0 | 0.8047 | 0.8047 | 0.8054 | 0.8046 | 13279.0 | 0.8059 | 0.8047 | 0.8048 | 13279.0 |
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+ | 0.2887 | 15.0501 | 6608 | 0.4838 | 0.7829 | 0.7317 | 0.8589 | 0.7902 | 6322.0 | 0.8477 | 0.7138 | 0.7750 | 6957.0 | 0.7829 | 0.7897 | 0.7864 | 0.7826 | 13279.0 | 0.7925 | 0.7829 | 0.7823 | 13279.0 |
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+ | 0.3773 | 16.0501 | 7021 | 0.5072 | 0.7778 | 0.7140 | 0.8896 | 0.7922 | 6322.0 | 0.8708 | 0.6762 | 0.7612 | 6957.0 | 0.7778 | 0.7924 | 0.7829 | 0.7767 | 13279.0 | 0.7961 | 0.7778 | 0.7760 | 13279.0 |
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+ | 0.3193 | 17.0501 | 7434 | 0.4759 | 0.7968 | 0.7671 | 0.8232 | 0.7941 | 6322.0 | 0.8279 | 0.7729 | 0.7994 | 6957.0 | 0.7968 | 0.7975 | 0.7980 | 0.7968 | 13279.0 | 0.7989 | 0.7968 | 0.7969 | 13279.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.3
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+ - Pytorch 2.0.0+cu117
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
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