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I think these may the last remarks for the nightly docs. Only two minor things actually.
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walterhenry 2020-10-19 11:11:32 +02:00
parent 75b7f86383
commit db24dc5614
2 changed files with 2 additions and 2 deletions
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website/docs/usage/101/_vectors-similarity.md
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@ -115,7 +115,7 @@ print(french_fries, "<->", burgers, french_fries.similarity(burgers))
Computing similarity scores can be helpful in many situations, but it's also Computing similarity scores can be helpful in many situations, but it's also
important to maintain **realistic expectations** about what information it can important to maintain **realistic expectations** about what information it can
provide. Words can be related to each over in many ways, so a single provide. Words can be related to each other in many ways, so a single
"similarity" score will always be a **mix of different signals**, and vectors "similarity" score will always be a **mix of different signals**, and vectors
trained on different data can produce very different results that may not be trained on different data can produce very different results that may not be
useful for your purpose. Here are some important considerations to keep in mind: useful for your purpose. Here are some important considerations to keep in mind:

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website/docs/usage/visualizers.md
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@ -257,7 +257,7 @@ output_path.open("w", encoding="utf-8").write(svg)
Since each visualization is generated as a separate SVG, exporting `.svg` files Since each visualization is generated as a separate SVG, exporting `.svg` files
only works if you're rendering **one single doc** at a time. (This makes sense – only works if you're rendering **one single doc** at a time. (This makes sense –
after all, each visualization should be a standalone graphic.) So instead of after all, each visualization should be a standalone graphic.) So instead of
rendering all `Doc`s at one, loop over them and export them separately. rendering all `Doc`s at once, loop over them and export them separately.
</Infobox> </Infobox>