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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