Update README.md

README.md

@@ -1,3 +1,22 @@
 ---
 license: mit
+datasets:
+- qiaojin/PubMedQA
+language:
+- en
+pipeline_tag: text2text-generation
+tags:
+- medical
 ---
+
+*Author - Hayden Beadles*
+
+This model is meant to evaluate the results of creating an Encoder / Decoder generative model using SciBERT. The model is then finetuned on 30000 samples of the PubMedQA dataset. Instead of being finetuned
+on the columns **question** and **final_answer**, where **final_answer** is a set of yes / no answers, we instead fine tune on the more challenging **long_answer** column, which gives a short answer
+to the question. 
+
+The model was fine-tuned over 3 epochs, using the Adam learning rate scheduler, with a max length of 128 tokens. 
+
+The results are to help gauge SciBERT's abilities to answer (generate an answer) directly to a question, with no context provided. It is meant to evaluate the overall models training and attention towards
+a more focused topic, to see if SciBERTs base training gives it any advantages. 
+