---
base_model: huawei-noah/TinyBERT_General_4L_312D
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Tiny_Bert_Cupstone
  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. -->

# Tiny_Bert_Cupstone

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: 0.3333
- Accuracy: 0.8550

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.524         | 0.2   | 500   | 0.4015          | 0.8318   |
| 0.4268        | 0.4   | 1000  | 0.4274          | 0.8279   |
| 0.39          | 0.6   | 1500  | 0.3743          | 0.8502   |
| 0.3674        | 0.8   | 2000  | 0.3333          | 0.8550   |
| 0.3687        | 1.0   | 2500  | 0.3836          | 0.8585   |
| 0.3489        | 1.2   | 3000  | 0.3927          | 0.8548   |
| 0.3193        | 1.41  | 3500  | 0.3938          | 0.8669   |
| 0.3525        | 1.61  | 4000  | 0.3717          | 0.8753   |
| 0.3327        | 1.81  | 4500  | 0.4589          | 0.8573   |
| 0.3276        | 2.01  | 5000  | 0.3676          | 0.8791   |
| 0.285         | 2.21  | 5500  | 0.4196          | 0.8811   |
| 0.2757        | 2.41  | 6000  | 0.3973          | 0.8777   |
| 0.277         | 2.61  | 6500  | 0.4198          | 0.8805   |
| 0.2834        | 2.81  | 7000  | 0.4955          | 0.8739   |
| 0.338         | 3.01  | 7500  | 0.4383          | 0.8844   |
| 0.2499        | 3.21  | 8000  | 0.4745          | 0.8785   |
| 0.2405        | 3.41  | 8500  | 0.4794          | 0.8854   |
| 0.2648        | 3.61  | 9000  | 0.4576          | 0.8844   |
| 0.2379        | 3.81  | 9500  | 0.4395          | 0.8886   |
| 0.2343        | 4.01  | 10000 | 0.5088          | 0.8791   |
| 0.2011        | 4.22  | 10500 | 0.5272          | 0.8781   |
| 0.2198        | 4.42  | 11000 | 0.5235          | 0.8765   |
| 0.2343        | 4.62  | 11500 | 0.5019          | 0.8844   |
| 0.194         | 4.82  | 12000 | 0.5227          | 0.8791   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1