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🔨 Fix spelling error 'likley' → 'likely' #451

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🔨 Fix spelling error 'likley' → 'likely' #451

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ae28393
🔨 Fix spelling error 'likley' → 'likely'
dunnkers Feb 7, 2022
56c05d3
🔨 Fix spelling error 'donwstream' → 'downstream'
dunnkers Feb 7, 2022
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4 changes: 2 additions & 2 deletions docs/examples/featurization.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -241,7 +241,7 @@
"source": [
"### Using feature hashing\n",
"\n",
"In fact, the `StringLookup` layer allows us to configure multiple OOV indices. If we do that, any raw value that is not in the vocabulary will be deterministically hashed to one of the OOV indices. The more such indices we have, the less likley it is that two different raw feature values will hash to the same OOV index. Consequently, if we have enough such indices the model should be able to train about as well as a model with an explicit vocabulary without the disdvantage of having to maintain the token list."
"In fact, the `StringLookup` layer allows us to configure multiple OOV indices. If we do that, any raw value that is not in the vocabulary will be deterministically hashed to one of the OOV indices. The more such indices we have, the less likeley it is that two different raw feature values will hash to the same OOV index. Consequently, if we have enough such indices the model should be able to train about as well as a model with an explicit vocabulary without the disdvantage of having to maintain the token list."
]
},
{
Expand Down Expand Up @@ -584,7 +584,7 @@
"source": [
"This looks correct: the layer is tokenizing titles into individual words.\n",
"\n",
"To finish the processing, we now need to embed the text. Because each title contains multiple words, we will get multiple embeddings for each title. For use in a donwstream model these are usually compressed into a single embedding. Models like RNNs or Transformers are useful here, but averaging all the words' embeddings together is a good starting point."
"To finish the processing, we now need to embed the text. Because each title contains multiple words, we will get multiple embeddings for each title. For use in a downstream model these are usually compressed into a single embedding. Models like RNNs or Transformers are useful here, but averaging all the words' embeddings together is a good starting point."
]
},
{
Expand Down