Create README.md

README.md

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+---
+language: en
+tags:
+- question-answering
+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
+{TBA
+}
+```