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50 lines
1.9 KiB
Markdown
50 lines
1.9 KiB
Markdown
---
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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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``` |