readme: ---
  language: en
  widget:
  - text: $answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?
  license: apache-2.0
  ---
  
  # macaw-11b
  
  ## Model description
  
  Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of 
  general question answering, 
  showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion, 
  which means it can handle a flexible set of input and output "slots" 
  (question, answer, multiple-choice options, context, and explanation) .
  
  Macaw was built on top of [T5](https://github.com/google-research/text-to-text-transfer-transformer) and comes in 
  three sizes: [macaw-11b](https://huggingface.co/allenai/macaw-11b), [macaw-3b](https://huggingface.co/allenai/macaw-3b), 
  and [macaw-large](https://huggingface.co/allenai/macaw-large), as well as an answer-focused version featured on 
  various leaderboards [macaw-answer-11b](https://huggingface.co/allenai/macaw-answer-11b).
  
  See https://github.com/allenai/macaw for more details.
  
  ## Intended uses & limitations
  
  #### How to use
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  tokenizer = AutoTokenizer.from_pretrained("allenai/macaw-11b")
  model = AutoModelForSeq2SeqLM.from_pretrained("allenai/macaw-11b")
  input_string = "$answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?"
  input_ids = tokenizer.encode(input_string, return_tensors="pt")
  output = model.generate(input_ids, max_length=200)
  
  >>> tokenizer.batch_decode(output, skip_special_tokens=True)
  ['$answer$ = gray ; $mcoptions$ = (A) blue (B) white (C) grey (D) black']
  ```
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{Tafjord2021Macaw,
    title={General-Purpose Question-Answering with {M}acaw},
    author={Oyvind Tafjord and Peter Clark},
    journal={ArXiv},
    year={2021},
    volume={abs/2109.02593}
  }
  ```