metadata

language:
  - en
tags:
  - text2text-generation
  - mednli
datasets:
  - pubmed
  - pmc/open_access
widget:
  - text: >-
      mednli: sentence1: In the ED, initial VS revealed T 98.9, HR 73, BP
      121/90, RR 15, O2 sat 98% on RA. sentence2: The patient is hemodynamically
      stable

SciFive Pubmed+PMC Large on MedNLI

Introduction

Finetuned SciFive Pubmed+PMC Large model achieved state-of-the-art results on MedNLI (Medical Natural Language Inference)

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-large-Pubmed_PMC-MedNLI")  
model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-Pubmed_PMC-MedNLI")
model.cuda()

sent_1 = "In the ED, initial VS revealed T 98.9, HR 73, BP 121/90, RR 15, O2 sat 98% on RA."
sent_2 = "The patient is hemodynamically stable"
text =  f"mednli: sentence1: {sent_1} sentence2: {sent_2}"

encoding = tokenizer.encode_plus(text, padding='max_length', max_length=256, 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=8,
    early_stopping=True
)

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