|
--- |
|
license: mit |
|
base_model: ryantaw/bert-small-finetuned |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: bert-small-finetuned-finetuned |
|
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. --> |
|
|
|
# bert-small-finetuned-finetuned |
|
|
|
This model is a fine-tuned version of [ryantaw/bert-small-finetuned](https://huggingface.co/ryantaw/bert-small-finetuned) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.0767 |
|
- Accuracy: 0.6119 |
|
- F1 Score: 0.6156 |
|
|
|
## 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: 86 |
|
- eval_batch_size: 86 |
|
- 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 Score | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| |
|
| 0.7125 | 1.0 | 18 | 1.0136 | 0.6011 | 0.5997 | |
|
| 0.604 | 2.0 | 36 | 1.0198 | 0.6038 | 0.6058 | |
|
| 0.5421 | 3.0 | 54 | 1.0517 | 0.6065 | 0.6068 | |
|
| 0.4724 | 4.0 | 72 | 1.0767 | 0.6119 | 0.6156 | |
|
| 0.42 | 5.0 | 90 | 1.1184 | 0.5768 | 0.5751 | |
|
| 0.3823 | 6.0 | 108 | 1.1217 | 0.5876 | 0.5881 | |
|
| 0.3312 | 7.0 | 126 | 1.1425 | 0.6065 | 0.6053 | |
|
| 0.3045 | 8.0 | 144 | 1.1760 | 0.6065 | 0.6095 | |
|
| 0.2662 | 9.0 | 162 | 1.2044 | 0.6065 | 0.6090 | |
|
| 0.2403 | 10.0 | 180 | 1.2143 | 0.6011 | 0.6011 | |
|
| 0.2308 | 11.0 | 198 | 1.2394 | 0.5903 | 0.5927 | |
|
| 0.2053 | 12.0 | 216 | 1.2589 | 0.6038 | 0.6068 | |
|
| 0.1808 | 13.0 | 234 | 1.2895 | 0.6065 | 0.6071 | |
|
| 0.1599 | 14.0 | 252 | 1.3144 | 0.6065 | 0.6086 | |
|
| 0.1497 | 15.0 | 270 | 1.3386 | 0.5930 | 0.5951 | |
|
| 0.1383 | 16.0 | 288 | 1.3608 | 0.5903 | 0.5931 | |
|
| 0.1321 | 17.0 | 306 | 1.3624 | 0.5876 | 0.5888 | |
|
| 0.1183 | 18.0 | 324 | 1.3810 | 0.5930 | 0.5945 | |
|
| 0.1196 | 19.0 | 342 | 1.3827 | 0.5903 | 0.5927 | |
|
| 0.1181 | 20.0 | 360 | 1.3805 | 0.5903 | 0.5920 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.0 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.14.6 |
|
- Tokenizers 0.14.1 |
|
|
