---
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](https://huggingface.co/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