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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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.
