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