metadata

license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
  - multiple_choice
metrics:
  - accuracy
model-index:
  - name: bert-base-uncased-Vitamin_C_Fact_Verification
    results: []
datasets:
  - tasksource/bigbench
language:
  - en
pipeline_tag: question-answering

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