---
library_name: transformers
base_model: huawei-noah/TinyBERT_General_4L_312D
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: 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. -->

# 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: 0.0419
- Accuracy: 0.9919
- Precision: 0.9744
- Recall: 0.9469
- F1 score: 0.9546

## 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1 score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.2112        | 1.0   | 1828  | 0.1824          | 0.9775   | 0.7759    | 0.7784 | 0.7742   |
| 0.1196        | 2.0   | 3656  | 0.1061          | 0.9850   | 0.9060    | 0.8522 | 0.8502   |
| 0.0956        | 3.0   | 5484  | 0.0855          | 0.9874   | 0.9140    | 0.8833 | 0.8858   |
| 0.0823        | 4.0   | 7312  | 0.0708          | 0.9891   | 0.9204    | 0.8982 | 0.9029   |
| 0.0653        | 5.0   | 9140  | 0.0628          | 0.9897   | 0.9313    | 0.8985 | 0.9070   |
| 0.066         | 6.0   | 10968 | 0.0572          | 0.9901   | 0.9206    | 0.9132 | 0.9106   |
| 0.0546        | 7.0   | 12796 | 0.0534          | 0.9907   | 0.9284    | 0.9156 | 0.9187   |
| 0.0486        | 8.0   | 14624 | 0.0511          | 0.9909   | 0.9287    | 0.9184 | 0.9199   |
| 0.0473        | 9.0   | 16452 | 0.0490          | 0.9911   | 0.9732    | 0.9284 | 0.9346   |
| 0.0483        | 10.0  | 18280 | 0.0469          | 0.9914   | 0.9788    | 0.9237 | 0.9344   |
| 0.0435        | 11.0  | 20108 | 0.0451          | 0.9915   | 0.9722    | 0.9323 | 0.9360   |
| 0.0388        | 12.0  | 21936 | 0.0429          | 0.9917   | 0.9757    | 0.9395 | 0.9477   |
| 0.039         | 13.0  | 23764 | 0.0426          | 0.9917   | 0.9758    | 0.9514 | 0.9597   |
| 0.0362        | 14.0  | 25592 | 0.0423          | 0.9918   | 0.9743    | 0.9467 | 0.9542   |
| 0.0357        | 15.0  | 27420 | 0.0419          | 0.9919   | 0.9744    | 0.9469 | 0.9546   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1