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

# NLP_Capstone

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: 0.3176
- Accuracy: 0.8671

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5286        | 0.2   | 500   | 0.4169          | 0.8251   |
| 0.4299        | 0.4   | 1000  | 0.4137          | 0.8332   |
| 0.3856        | 0.6   | 1500  | 0.3714          | 0.8512   |
| 0.3692        | 0.8   | 2000  | 0.3176          | 0.8671   |
| 0.3604        | 1.0   | 2500  | 0.3869          | 0.8635   |
| 0.3457        | 1.2   | 3000  | 0.4126          | 0.8631   |
| 0.3291        | 1.41  | 3500  | 0.4272          | 0.8675   |
| 0.3481        | 1.61  | 4000  | 0.3754          | 0.8775   |
| 0.3253        | 1.81  | 4500  | 0.4293          | 0.8649   |
| 0.3306        | 2.01  | 5000  | 0.3807          | 0.8789   |
| 0.2849        | 2.21  | 5500  | 0.4291          | 0.8803   |
| 0.2824        | 2.41  | 6000  | 0.4058          | 0.8797   |
| 0.279         | 2.61  | 6500  | 0.4521          | 0.8761   |
| 0.2944        | 2.81  | 7000  | 0.4986          | 0.8747   |
| 0.3347        | 3.01  | 7500  | 0.4364          | 0.8815   |
| 0.2622        | 3.21  | 8000  | 0.5368          | 0.8703   |
| 0.2494        | 3.41  | 8500  | 0.4795          | 0.8854   |
| 0.2645        | 3.61  | 9000  | 0.4795          | 0.8864   |
| 0.243         | 3.81  | 9500  | 0.4570          | 0.8874   |
| 0.2399        | 4.01  | 10000 | 0.5219          | 0.8795   |
| 0.2103        | 4.22  | 10500 | 0.5325          | 0.8775   |
| 0.2196        | 4.42  | 11000 | 0.5629          | 0.8729   |
| 0.2494        | 4.62  | 11500 | 0.5087          | 0.8826   |
| 0.1968        | 4.82  | 12000 | 0.5332          | 0.8779   |


### Framework versions

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