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--- |
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base_model: microsoft/mdeberta-v3-base |
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datasets: |
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- massive |
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library_name: transformers |
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license: mit |
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metrics: |
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- accuracy |
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- f1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: scenario-NON-KD-SCR-D2_data-AmazonScience_massive_all_1_166 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: massive |
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type: massive |
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config: all_1.1 |
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split: validation |
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args: all_1.1 |
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metrics: |
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- type: accuracy |
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value: 0.8145597638957206 |
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name: Accuracy |
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- type: f1 |
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value: 0.7901247763851288 |
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name: F1 |
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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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# scenario-NON-KD-SCR-D2_data-AmazonScience_massive_all_1_166 |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9580 |
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- Accuracy: 0.8146 |
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- F1: 0.7901 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 66 |
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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 | F1 | |
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|:-------------:|:------:|:------:|:---------------:|:--------:|:------:| |
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| 1.2987 | 0.2672 | 5000 | 1.2851 | 0.6566 | 0.5593 | |
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| 0.9658 | 0.5344 | 10000 | 0.9963 | 0.7328 | 0.6604 | |
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| 0.808 | 0.8017 | 15000 | 0.8786 | 0.7661 | 0.7077 | |
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| 0.5621 | 1.0689 | 20000 | 0.8689 | 0.7807 | 0.7298 | |
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| 0.5504 | 1.3361 | 25000 | 0.8296 | 0.7897 | 0.7417 | |
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| 0.5334 | 1.6033 | 30000 | 0.8176 | 0.7940 | 0.7494 | |
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| 0.5188 | 1.8706 | 35000 | 0.7691 | 0.8056 | 0.7619 | |
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| 0.3426 | 2.1378 | 40000 | 0.8369 | 0.8057 | 0.7691 | |
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| 0.3362 | 2.4050 | 45000 | 0.8393 | 0.8053 | 0.7663 | |
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| 0.355 | 2.6722 | 50000 | 0.8216 | 0.8075 | 0.7742 | |
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| 0.3452 | 2.9394 | 55000 | 0.8262 | 0.8108 | 0.7767 | |
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| 0.2176 | 3.2067 | 60000 | 0.9217 | 0.8061 | 0.7754 | |
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| 0.2271 | 3.4739 | 65000 | 0.9242 | 0.8093 | 0.7800 | |
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| 0.2366 | 3.7411 | 70000 | 0.9183 | 0.8127 | 0.7835 | |
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| 0.2095 | 4.0083 | 75000 | 0.9789 | 0.8126 | 0.7802 | |
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| 0.146 | 4.2756 | 80000 | 1.0693 | 0.8097 | 0.7796 | |
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| 0.1526 | 4.5428 | 85000 | 1.0715 | 0.8119 | 0.7845 | |
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| 0.1606 | 4.8100 | 90000 | 1.0722 | 0.8150 | 0.7869 | |
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| 0.0916 | 5.0772 | 95000 | 1.2010 | 0.8157 | 0.7908 | |
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| 0.0957 | 5.3444 | 100000 | 1.2828 | 0.8112 | 0.7826 | |
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| 0.1065 | 5.6117 | 105000 | 1.2375 | 0.8146 | 0.7856 | |
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| 0.0993 | 5.8789 | 110000 | 1.2607 | 0.8141 | 0.7877 | |
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| 0.0583 | 6.1461 | 115000 | 1.4788 | 0.8122 | 0.7847 | |
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| 0.0688 | 6.4133 | 120000 | 1.4891 | 0.8117 | 0.7881 | |
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| 0.0673 | 6.6806 | 125000 | 1.5137 | 0.8129 | 0.7840 | |
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| 0.0781 | 6.9478 | 130000 | 1.5225 | 0.8110 | 0.7867 | |
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| 0.0453 | 7.2150 | 135000 | 1.6409 | 0.8132 | 0.7860 | |
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| 0.0487 | 7.4822 | 140000 | 1.6796 | 0.8125 | 0.7872 | |
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| 0.041 | 7.7495 | 145000 | 1.7356 | 0.8121 | 0.7864 | |
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| 0.035 | 8.0167 | 150000 | 1.7378 | 0.8139 | 0.7905 | |
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| 0.0293 | 8.2839 | 155000 | 1.8422 | 0.8118 | 0.7885 | |
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| 0.0289 | 8.5511 | 160000 | 1.8606 | 0.8126 | 0.7885 | |
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| 0.0271 | 8.8183 | 165000 | 1.8730 | 0.8140 | 0.7899 | |
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| 0.0186 | 9.0856 | 170000 | 1.9062 | 0.8139 | 0.7898 | |
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| 0.0197 | 9.3528 | 175000 | 1.9150 | 0.8142 | 0.7895 | |
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| 0.0274 | 9.6200 | 180000 | 1.9638 | 0.8133 | 0.7891 | |
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| 0.018 | 9.8872 | 185000 | 1.9580 | 0.8146 | 0.7901 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.19.1 |
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