Update README.md

README.md

@@ -5,28 +5,31 @@ tags:
 model-index:
 - name: distilgpt2-2k_clean_medical_articles_causal_language_model
   results: []
+language:
+- en
+metrics:
+- perplexity
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # distilgpt2-2k_clean_medical_articles_causal_language_model
 
-This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
+This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2).
 It achieves the following results on the evaluation set:
 - Loss: 2.9268
 
 ## Model description
 
-More information needed
+This is a causal language modeling project.
+
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Causal%20Language%20Modeling/2000%20Clean%20Medical%20Articles/2%2C000%20Clean%20Medical%20Articles%20-%20CLM.ipynb
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to demonstrate my ability to solve a complex problem using technology.
 
 ## Training and evaluation data
 
-More information needed
+Dataset Source: https://www.kaggle.com/datasets/trikialaaa/2k-clean-medical-articles-medicalnewstoday
 
 ## Training procedure
 
@@ -49,10 +52,11 @@ The following hyperparameters were used during training:
 | 2.998         | 2.0   | 3982 | 2.9367          |
 | 2.9484        | 3.0   | 5973 | 2.9268          |
 
+Perplexity: 18.67
 
 ### Framework versions
 
 - Transformers 4.26.1
 - Pytorch 1.12.1
 - Datasets 2.9.0
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1