metadata

language:
  - en
tags:
  - token-classification
  - text-classification
  - question-answering
  - text2text-generation
  - text-generation
datasets:
  - pubmed

SciFive Pubmed Base

Introduction

Paper: SciFive: a text-to-text transformer model for biomedical literature

Authors: Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet

How to use

For more details, do check out our Github repo.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-base-Pubmed")  
model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-base-Pubmed")

sentence = "Identification of APC2 , a homologue of the adenomatous polyposis coli tumour suppressor ."
text = sentence + " </s>"

encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")

outputs = model.generate(
    input_ids=input_ids, attention_mask=attention_masks,
    max_length=256,
    early_stopping=True
)

for output in outputs:
    line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
    print(line)