| datasets: | |
| - squad | |
| tags: | |
| - question-generation | |
| widget: | |
| - text: "<hl> 42 <hl> is the answer to life, the universe and everything. </s>" | |
| - text: "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>" | |
| - text: "Simple is better than <hl> complex <hl>. </s>" | |
| license: mit | |
| ## T5 for question-generation | |
| This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. | |
| You can play with the model using the inference API, just highlight the answer spans with `<hl>` tokens and end the text with `</s>`. For example | |
| `<hl> 42 <hl> is the answer to life, the universe and everything. </s>` | |
| For more deatils see [this](https://github.com/patil-suraj/question_generation) repo. | |
| ### Model in action 🚀 | |
| You'll need to clone the [repo](https://github.com/patil-suraj/question_generation). | |
| [](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb) | |
| ```python3 | |
| from pipelines import pipeline | |
| nlp = pipeline("question-generation") | |
| nlp("42 is the answer to life, universe and everything.") | |
| => [{'answer': '42', 'question': 'What is the answer to life, universe and everything?'}] | |
| ``` |