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--- |
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license: mit |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: roberta-finetuned-gesture-prediction-21-classes |
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results: [] |
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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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# roberta-finetuned-gesture-prediction-21-classes |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9312 |
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- Accuracy: 0.8163 |
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- Precision: 0.8090 |
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- Recall: 0.8163 |
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- F1: 0.8108 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 2.2472 | 1.0 | 104 | 1.4263 | 0.7364 | 0.6666 | 0.7364 | 0.6921 | |
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| 1.2677 | 2.0 | 208 | 1.0547 | 0.7888 | 0.7581 | 0.7888 | 0.7638 | |
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| 0.8676 | 3.0 | 312 | 0.9315 | 0.7963 | 0.7775 | 0.7963 | 0.7791 | |
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| 0.6407 | 4.0 | 416 | 0.9022 | 0.8102 | 0.8012 | 0.8102 | 0.7995 | |
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| 0.4926 | 5.0 | 520 | 0.8994 | 0.8120 | 0.8080 | 0.8120 | 0.8016 | |
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| 0.3754 | 6.0 | 624 | 0.9018 | 0.8069 | 0.7999 | 0.8069 | 0.8002 | |
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| 0.3037 | 7.0 | 728 | 0.9048 | 0.8131 | 0.8055 | 0.8131 | 0.8060 | |
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| 0.2499 | 8.0 | 832 | 0.9030 | 0.8161 | 0.8119 | 0.8161 | 0.8117 | |
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| 0.2155 | 9.0 | 936 | 0.9279 | 0.8160 | 0.8088 | 0.8160 | 0.8106 | |
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| 0.2062 | 10.0 | 1040 | 0.9312 | 0.8163 | 0.8090 | 0.8163 | 0.8108 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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