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--- |
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datasets: |
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- squad |
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tags: |
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- question-generation |
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widget: |
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- text: "generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>" |
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- text: "question: What is 42 context: 42 is the answer to life, the universe and everything. </s>" |
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license: mit |
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--- |
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## T5 for multi-task QA and QG |
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This is multi-task [t5-base](https://arxiv.org/abs/1910.10683) model trained for question answering and answer aware question generation tasks. |
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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>` |
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You can play with the model using the inference API. Here's how you can use it |
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For QG |
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`generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>` |
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For QA |
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`question: What is 42 context: 42 is the answer to life, the universe and everything. </s>` |
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For more deatils see [this](https://github.com/patil-suraj/question_generation) repo. |
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### Model in action 🚀 |
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You'll need to clone the [repo](https://github.com/patil-suraj/question_generation). |
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[](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb) |
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```python3 |
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from pipelines import pipeline |
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nlp = pipeline("multitask-qa-qg", model="valhalla/t5-base-qa-qg-hl") |
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# to generate questions simply pass the text |
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nlp("42 is the answer to life, the universe and everything.") |
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=> [{'answer': '42', 'question': 'What is the answer to life, the universe and everything?'}] |
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# for qa pass a dict with "question" and "context" |
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nlp({ |
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"question": "What is 42 ?", |
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"context": "42 is the answer to life, the universe and everything." |
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}) |
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=> 'the answer to life, the universe and everything' |
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``` |
