---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
- Multiple Choice
metrics:
- accuracy
model-index:
- name: bert-base-uncased-e_CARE
  results: []
datasets:
- 12ml/e-CARE
language:
- en
pipeline_tag: question-answering
---

# bert-base-uncased-e_CARE

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: 1.7677
- Accuracy: 0.7212

## 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/e-CARE/e_CARE_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/12ml/e-CARE

**Histogram of Input Lengths**

![Histogram of Input Lengths](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Multiple%20Choice/e-CARE/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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5637        | 1.0   | 1571 | 0.5282          | 0.7244   |
| 0.345         | 2.0   | 3142 | 0.6667          | 0.7320   |
| 0.1098        | 3.0   | 4713 | 1.3113          | 0.7257   |
| 0.0212        | 4.0   | 6284 | 1.8194          | 0.7225   |
| 0.0185        | 5.0   | 7855 | 1.7677          | 0.7212   |

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3