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Tutorial 12: add introduction (#2798)

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* Tutorial 12: add introduction

* PR review for Tutorial 12: add introduction

* Update Documentation & Code Style

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Vladimir Blagojevic 2022-07-13 17:44:19 +02:00 committed by GitHub
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docs/_src/tutorials/tutorials/12.md
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@ -11,6 +11,8 @@ id: "tutorial12md"
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial12_LFQA.ipynb)
Follow this tutorial to learn how to build and use a pipeline for Long-Form Question Answering (LFQA). LFQA is a variety of the generative question answering task. LFQA systems query large document stores for relevant information and then use this information to generate accurate, multi-sentence answers. In a regular question answering system, the retrieved documents related to the query (context passages) act as source tokens for extracted answers. In an LFQS system, context passages provide the context the system uses to generate original, abstractive, long-form answers.
### Prepare environment
#### Colab: Enable the GPU runtime

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tutorials/Tutorial12_LFQA.ipynb
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"source": [
"# Long-Form Question Answering\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial12_LFQA.ipynb)"
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial12_LFQA.ipynb)\n",
"\n",
"Follow this tutorial to learn how to build and use a pipeline for Long-Form Question Answering (LFQA). LFQA is a variety of the generative question answering task. LFQA systems query large document stores for relevant information and then use this information to generate accurate, multi-sentence answers. In a regular question answering system, the retrieved documents related to the query (context passages) act as source tokens for extracted answers. In an LFQS system, context passages provide the context the system uses to generate original, abstractive, long-form answers."
]
},
{
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},
"nbformat": 4,
"nbformat_minor": 0
}
}