| | --- |
| | library_name: transformers |
| | base_model: huawei-noah/TinyBERT_General_4L_312D |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | model-index: |
| | - name: Structured-NF4-KD-NID |
| | 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. --> |
| |
|
| | # Structured-NF4-KD-NID |
| |
|
| | This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0384 |
| | - Accuracy: 0.9923 |
| | - Precision: 0.9796 |
| | - Recall: 0.9374 |
| | - F1 score: 0.9470 |
| |
|
| | ## 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: 650 |
| | - eval_batch_size: 650 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 20 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 score | |
| | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
| | | 0.217 | 1.0 | 1828 | 0.1859 | 0.9775 | 0.7808 | 0.7811 | 0.7797 | |
| | | 0.1295 | 2.0 | 3656 | 0.1142 | 0.9844 | 0.9142 | 0.8421 | 0.8404 | |
| | | 0.0958 | 3.0 | 5484 | 0.0916 | 0.9864 | 0.9136 | 0.8662 | 0.8699 | |
| | | 0.0846 | 4.0 | 7312 | 0.0779 | 0.9882 | 0.9207 | 0.8902 | 0.8953 | |
| | | 0.0683 | 5.0 | 9140 | 0.0677 | 0.9894 | 0.9246 | 0.9034 | 0.9073 | |
| | | 0.07 | 6.0 | 10968 | 0.0599 | 0.9899 | 0.9259 | 0.9103 | 0.9118 | |
| | | 0.0574 | 7.0 | 12796 | 0.0555 | 0.9904 | 0.9284 | 0.9097 | 0.9156 | |
| | | 0.0503 | 8.0 | 14624 | 0.0541 | 0.9906 | 0.9211 | 0.9185 | 0.9162 | |
| | | 0.048 | 9.0 | 16452 | 0.0498 | 0.9911 | 0.9318 | 0.9185 | 0.9217 | |
| | | 0.0479 | 10.0 | 18280 | 0.0483 | 0.9912 | 0.9353 | 0.9178 | 0.9232 | |
| | | 0.0452 | 11.0 | 20108 | 0.0467 | 0.9914 | 0.9264 | 0.9233 | 0.9213 | |
| | | 0.042 | 12.0 | 21936 | 0.0437 | 0.9917 | 0.9331 | 0.9211 | 0.9244 | |
| | | 0.038 | 13.0 | 23764 | 0.0426 | 0.9918 | 0.9782 | 0.9282 | 0.9366 | |
| | | 0.0358 | 14.0 | 25592 | 0.0414 | 0.9919 | 0.9359 | 0.9226 | 0.9253 | |
| | | 0.0351 | 15.0 | 27420 | 0.0417 | 0.9920 | 0.9785 | 0.9288 | 0.9370 | |
| | | 0.0319 | 16.0 | 29248 | 0.0404 | 0.9921 | 0.9768 | 0.9437 | 0.9518 | |
| | | 0.0302 | 17.0 | 31076 | 0.0391 | 0.9922 | 0.9789 | 0.9319 | 0.9386 | |
| | | 0.0319 | 18.0 | 32904 | 0.0383 | 0.9922 | 0.9801 | 0.9428 | 0.9533 | |
| | | 0.0265 | 19.0 | 34732 | 0.0383 | 0.9923 | 0.9802 | 0.9436 | 0.9542 | |
| | | 0.0277 | 20.0 | 36560 | 0.0384 | 0.9923 | 0.9796 | 0.9374 | 0.9470 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.50.0 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 3.4.1 |
| | - Tokenizers 0.21.1 |
| |
|
