---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles
  results: []
language:
- en
metrics:
- perplexity
---

# bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles

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: 1.8322

## Model description

This is a masked language modeling project.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Masked%20Language%20Model/US%20Economic%20News%20Articles/US_Economic_News_Articles_MLM.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://www.kaggle.com/datasets/trikialaaa/2k-clean-medical-articles-medicalnewstoday

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1833        | 1.0   | 2016 | 1.9529          |
| 2.004         | 2.0   | 4032 | 1.9002          |
| 1.941         | 3.0   | 6048 | 1.8600          |

Perplexity: 6.25

### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2