--- 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