readme: ---
  language: 
    - en
  
  tags:
  - token-classification
  - text-classification
  - question-answering
  - text2text-generation
  - text-generation
  
  datasets:
  - pmc/open_access
  ---
  
  # SciFive PMC Large
  
  ## Introduction
  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-PMC")  
  model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-PMC")
  ​
  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)
  ```