bert-base-uncased-Winowhy
This model is a fine-tuned version of bert-base-uncased.
It achieves the following results on the evaluation set:
- Loss: 0.8005
- Accuracy: 0.7118
Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multiple%20Choice/Winowhy/Winowhy%20-%20Multiple%20Choice%20Using%20BERT.ipynb
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
Training and evaluation data
Dataset Source: https://huggingface.co/datasets/tasksource/bigbench/viewer/winowhy/train
Histogram of Input Lengths
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|
| 0.7028 | 1.0 | 115 | 0.6916 | 0.5371 |
| 0.6119 | 2.0 | 230 | 0.5572 | 0.7031 |
| 0.4959 | 3.0 | 345 | 0.5328 | 0.7118 |
| 0.4537 | 4.0 | 460 | 0.5829 | 0.7118 |
| 0.2275 | 5.0 | 575 | 0.8005 | 0.7118 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
