|
--- |
|
license: cc-by-sa-4.0 |
|
base_model: klue/bert-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: degree-bert-finetuning-2 |
|
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. --> |
|
|
|
# degree-bert-finetuning-2 |
|
|
|
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6113 |
|
- Accuracy: 0.698 |
|
- F1: 0.6968 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
|
| 1.06 | 1.0 | 104 | 0.8187 | 0.61 | 0.6100 | |
|
| 0.849 | 2.0 | 208 | 0.7525 | 0.642 | 0.6415 | |
|
| 0.8117 | 3.0 | 312 | 0.7479 | 0.616 | 0.6044 | |
|
| 0.7757 | 4.0 | 416 | 0.7266 | 0.652 | 0.6489 | |
|
| 0.7638 | 5.0 | 520 | 0.6960 | 0.674 | 0.6742 | |
|
| 0.7412 | 6.0 | 624 | 0.6845 | 0.676 | 0.6739 | |
|
| 0.7338 | 7.0 | 728 | 0.6655 | 0.692 | 0.6922 | |
|
| 0.723 | 8.0 | 832 | 0.6500 | 0.676 | 0.6733 | |
|
| 0.7047 | 9.0 | 936 | 0.6415 | 0.672 | 0.6681 | |
|
| 0.6979 | 10.0 | 1040 | 0.6333 | 0.686 | 0.6852 | |
|
| 0.6911 | 11.0 | 1144 | 0.6360 | 0.684 | 0.6825 | |
|
| 0.6877 | 12.0 | 1248 | 0.6239 | 0.704 | 0.7044 | |
|
| 0.6718 | 13.0 | 1352 | 0.6238 | 0.698 | 0.6978 | |
|
| 0.6732 | 14.0 | 1456 | 0.6257 | 0.678 | 0.6736 | |
|
| 0.6699 | 15.0 | 1560 | 0.6129 | 0.704 | 0.7042 | |
|
| 0.6592 | 16.0 | 1664 | 0.6201 | 0.688 | 0.6853 | |
|
| 0.653 | 17.0 | 1768 | 0.6075 | 0.706 | 0.7062 | |
|
| 0.6528 | 18.0 | 1872 | 0.6099 | 0.704 | 0.7040 | |
|
| 0.6512 | 19.0 | 1976 | 0.6129 | 0.7 | 0.6985 | |
|
| 0.6405 | 20.0 | 2080 | 0.6113 | 0.698 | 0.6968 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.1 |
|
- Pytorch 2.2.0 |
|
- Datasets 2.17.1 |
|
- Tokenizers 0.15.2 |
|
|
