📺
blog.tensorflow.org
is a new plugin for ) on your own data, right from a spreadsheet.
Where do these numbers come from? They’re learned by a machine learning model through many bits of conversational and language data. By showing all those examples, the model learns which words tend to occur in the same spots in sentences.
Consider these two sentences:
- “My mother gave birth to a son.”
- “My mother gave birth to a daughter.”
Semantic Reactor: Prototype using NLP in a Google Sheet
Alright, now onto the fun part: Building things! There are three NLP models available in the Semantic Reactor:- that can run entirely within a webpage.
- - A full-sized Universal Sentence Encoder model trained on question/answer pairs in 16 languages.
- Semantic Similarity: How similar are two blocks of text?
Great for applications where you can anticipate what users might ask, like an FAQ bot. (Many customer service bots use semantic similarity to help deliver good answers to users.) - Input / Response: How good of a response is one block of text to another?
Useful for when you have a large, and constantly changing, set of texts and you don’t know what users might ask. For instance, model.
As mentioned, there are lots of great uses for NLU tech, and more interesting applications come out almost everyday. Every digital assistant, customer service bot, and search engine is likely using some flavor of machine learning. Smart Reply and Smart Compose in Gmail are two well-used features that make good use of semantic tech.
However, it’s fun and helpful to play with the tech within applications where the quality demands aren’t so high, where failure is okay and even entertaining. To that end, we’ve used the same tech that’s within the Semantic Reactor to create a couple of example games. uses semantic similarity.
Playing those two games, and finding out where they work and where they don’t, might give you ideas on what experiences you might create., a word-association game powered by word embeddings. is a simple game powered by NLU and available as open source code. (It’s also playable , a former game designer at Double Fine who now works with Stadia. She used Semantic Reactor to prototype a video game world that infers how the environment should react to player inputs using ML. Check out our conversation ) considers all of the possible ways the game might respond: - “Fox turns on lights.“
- “Fox turns on radio.“
- “Fox move to you.“
- “Fox brings you mug.“
Let’s see how you might build something like Anna’s game with Semantic Reactor (for all the nitty gritties of the fox demo, check out her Clicking “Start” will open a panel that allows you to type in an input and hit “React”:.
Let’s take a look at how to use those models in JavaScript, so that you can convert your spreadsheet prototype into a working app.
1 - Create a new Node project and install the module:2 - Create a new file (use_demo.js) and require the library:TEXTnpm init
npm install @tensorflow/tfjs @tensorflow-models/universal-sentence-encoder3 - Load the model:TEXTrequire('@tensorflow/tfjs');
const encoder = require('@tensorflow-models/universal-sentence-encoder');4 - Encode your sentences and query:TEXTconst model = await encoder.loadQnA();5 - Voila! You’ve transformed your responses and query into vectors. Unfortunately, vectors are just points in space. To rank the responses, you’ll want to compute the distance between those points (you can do this by computing the between points):TEXTconst input = {
queries: \["I want some coffee"\],
responses: \[
"I grab a ball",
"I go to you",
"I play with a ball",
"I go to school.",
"I go to the mug.",
"I bring you the mug."
\]
};
const embeddings = await model.embed(input);If you run this code, you should see output like:TEXT//zipWith :: (a -> b -> c) -> \[a\] -> \[b\] -> \[c\]
const zipWith =
(f, xs, ys) => {
const ny = ys.length;
return (xs.length .map((x, i) => f(x, ys\[i\]));
}
// Calculate the dot product of two vector arrays.
const dotProduct = (xs, ys) => {
const sum = xs => xs ? xs.reduce((a, b) => a + b, 0) : undefined;
return xs.length === ys.length ?
sum(zipWith((a, b) => a * b, xs, ys))
: undefined;
}Check out the full code sample .TEXT[
{ response: 'I grab a ball', score: 10.788130270345432 },
{ response: 'I go to you', score: 11.597091717283469 },
{ response: 'I play with a ball', score: 9.346379028479209 },
{ response: 'I go to school.', score: 10.130473646521292 },
{ response: 'I go to the mug.', score: 12.475453722603106 },
{ response: 'I bring you the mug.', score: 13.229019199245684 }
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