Upload README.md

README.md

@@ -0,0 +1,46 @@
+---
+datasets:
+- squadv1
+tags:
+- question-generation
+---
+
+## T5 for multi-task QA and QG
+This is multi-task [t5-base](https://arxiv.org/abs/1910.10683) model trained for question answering and answer aware question generation tasks. 
+
+For question generation the answer spans are highlighted within the text with special highlight tokens (`<hl>`) and prefixed with 'generate question: '. For QA the input is processed like this `question: question_text context: context_text </s>` 
+
+You can play with the model using the inference API. Here's how you can use it
+
+For QG
+
+`generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>`
+
+For QA
+
+`question: What is 42 context: 42 is the answer to life, the universe and everything. </s>`
+
+For more deatils see [this](https://github.com/sabhi27/question_generation) repo.
+
+
+### Model in action 🚀
+
+You'll need to clone the [repo](https://github.com/sabhi27/question_generation).
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sabhi27/question_generation/blob/master/question_generation.ipynb)
+
+```python3
+from pipelines import pipeline
+nlp = pipeline("multitask-qa-qg", model="sabhi/t5-base-qa-qg")
+
+# to generate questions simply pass the text
+nlp("42 is the answer to life, the universe and everything.")
+=> [{'answer': '42', 'question': 'What is the answer to life, the universe and everything?'}]
+
+# for qa pass a dict with "question" and "context"
+nlp({
+    "question": "What is 42 ?",
+    "context": "42 is the answer to life, the universe and everything."
+})
+=> 'the answer to life, the universe and everything'
+```