readme: ---
  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)](https://paperswithcode.com/sota/natural-language-inference-on-mednli)
  
  Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598)
  
  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](https://github.com/justinphan3110/SciFive). 
  ```python
  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)
  ```