---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- Multiple Choice
metrics:
- accuracy
pipeline_tag: question-answering
base_model: bert-base-uncased
model-index:
- name: bert-base-uncased-Figurative_Language
  results: []
---

# bert-base-uncased-Figurative_Language

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: 0.7629
- Accuracy: 0.8124

## 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/Figurative%20Language/Figurative%20Language%20-%20Multiple%20Choice%20Using%20BERT.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/nightingal3/fig-qa

**Histogram of Input Lengths**

![Histogram of Input Lengths](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Multiple%20Choice/Figurative%20Language/Images/Histogram%20of%20Input%20Word%20Lengths.png)

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6961        | 1.0   | 539  | 0.6932          | 0.5190   |
| 0.6595        | 2.0   | 1078 | 0.5326          | 0.7214   |
| 0.4647        | 3.0   | 1617 | 0.4604          | 0.7948   |
| 0.2884        | 4.0   | 2156 | 0.6204          | 0.8217   |
| 0.1702        | 5.0   | 2695 | 0.7629          | 0.8124   |

### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3