|
--- |
|
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 |
|
|
