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---
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 |