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Fix link to colab notebook in tutorial 16 (#1802)

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* Fix link to colab notebook in tutorial 16

* Add latest docstring and tutorial changes

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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Julian Risch 2021-11-24 13:19:20 +01:00 committed by GitHub
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docs/_src/tutorials/tutorials/16.md
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@ -9,7 +9,7 @@ id: "tutorial16md"
# Extending your Metadata using DocumentClassifiers at Index Time
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial8_Preprocessing.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/Tutorial16_Document_Classifier_at_Index_Time.ipynb)
With DocumentClassifier it's possible to automatically enrich your documents with categories, sentiments, topics or whatever metadata you like. This metadata could be used for efficient filtering or further processing. Say you have some categories your users typically filter on. If the documents are tagged manually with these categories, you could automate this process by training a model. Or you can leverage the full power and flexibility of zero shot classification. All you need to do is pass your categories to the classifier, no labels required. This tutorial shows how to integrate it in your indexing pipeline.

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tutorials/Tutorial16_Document_Classifier_at_Index_Time.ipynb
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@ -6,7 +6,7 @@
"source": [
"# Extending your Metadata using DocumentClassifiers at Index Time\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/Tutorial8_Preprocessing.ipynb)\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/Tutorial16_Document_Classifier_at_Index_Time.ipynb)\n",
"\n",
"With DocumentClassifier it's possible to automatically enrich your documents with categories, sentiments, topics or whatever metadata you like. This metadata could be used for efficient filtering or further processing. Say you have some categories your users typically filter on. If the documents are tagged manually with these categories, you could automate this process by training a model. Or you can leverage the full power and flexibility of zero shot classification. All you need to do is pass your categories to the classifier, no labels required. This tutorial shows how to integrate it in your indexing pipeline."
]