From 2a7e333d9a5d74c4fcdadeed372dd926734b2402 Mon Sep 17 00:00:00 2001 From: Vladimir Blagojevic Date: Wed, 13 Jul 2022 17:44:19 +0200 Subject: [PATCH] Tutorial 12: add introduction (#2798) * Tutorial 12: add introduction * PR review for Tutorial 12: add introduction * Update Documentation & Code Style Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- docs/_src/tutorials/tutorials/12.md | 2 ++ tutorials/Tutorial12_LFQA.ipynb | 6 ++++-- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/_src/tutorials/tutorials/12.md b/docs/_src/tutorials/tutorials/12.md index 93156caac..8cfbf8324 100644 --- a/docs/_src/tutorials/tutorials/12.md +++ b/docs/_src/tutorials/tutorials/12.md @@ -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 diff --git a/tutorials/Tutorial12_LFQA.ipynb b/tutorials/Tutorial12_LFQA.ipynb index ceca8bf61..a11dc9aa0 100644 --- a/tutorials/Tutorial12_LFQA.ipynb +++ b/tutorials/Tutorial12_LFQA.ipynb @@ -8,7 +8,9 @@ "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." ] }, { @@ -335,4 +337,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +}