bert-base-uncased-Vitamin_C_Fact_Verification
This model is a fine-tuned version of bert-base-uncased.
It achieves the following results on the evaluation set:
- Loss: 0.6329
- Accuracy: 0.7240
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/Vitamin%20C%20Fact%20Verification/Vitamin_C_Fact_Verification_Multiple_Choice_Using_BERT.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/vitaminc_fact_verification
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6985 | 1.0 | 2170 | 0.6894 | 0.6864 |
| 0.5555 | 2.0 | 4340 | 0.6329 | 0.7240 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
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Model tree for DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification
Base model
google-bert/bert-base-uncased