readme: ---
  inference:
    parameters:
      do_sample: True
      max_length: 500
      top_p: 0.9
      top_k: 20
      temperature: 1
      num_return_sequences: 10
      
  widget:
  - text: "abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
    example_title: "BERT abstract"
  ---
  
  ```
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
  
  model = AutoModelForSeq2SeqLM.from_pretrained("ArvinZhuang/BiTAG-t5-large")
  tokenizer = AutoTokenizer.from_pretrained("ArvinZhuang/BiTAG-t5-large")
  
  text = "abstract: [your abstract]"  # use 'title:' as the prefix for title_to_abs task.
  input_ids = tokenizer.encode(text, return_tensors='pt')
  
  outputs = model.generate(
      input_ids,
      do_sample=True,
      max_length=500,
      top_p=0.9,
      top_k=20,
      temperature=1,
      num_return_sequences=10,
  )
  
  print("Output:\n" + 100 * '-')
  for i, output in enumerate(outputs):
      print("{}: {}".format(i+1, tokenizer.decode(output, skip_special_tokens=True)))
  
  ```
  GitHub: https://github.com/ArvinZhuang/BiTAG