transformers safetensors encoder-decoder text2text-generation medical en dataset:qiaojin/PubMedQA license:mit autotrain_compatible endpoints_compatible region:us
---
datasets:
- qiaojin/PubMedQA
language:
- en
license: mit
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. 

