Update README.md

README.md

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 ---
 license: mit
+datasets:
+- qiaojin/PubMedQA
+language:
+- en
+tags:
+- medical
 ---
+
+*Author - Hayden Beadles*
+
+This model is meant to evaluate the results of creating an Encoder / Decoder generative model using BERT. 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 BERT'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 BERTs base training gives it any advantages.