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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 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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