Update README.md

README.md

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 *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.
+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.