bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles

This model is a fine-tuned version of 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