Exploring helpful uses for BERT in your browser with Tensorflow.js

Our findings

• — Question: “Can it get wet?” Answer: “submersion in up to 1m of water for 30min”
  
• : — Question: “How tall is it” Answer: “280 feet in height”
  
• — Asking “What is the pitcher made of?” Returned “Ice mode pulses at staggered intervals to uniformly crush a pitcher of ice in seconds” instead of the answer “BPA-free polycarbonate pitcher”
  
• can be used to build a system that can answer users’ questions in natural language. It was created using a pre-trained (Stanford Question Answering Dataset). This is a new method of pre-training language representations which obtain state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. We are pleased to announce that this model is . This means privacy is protected and no text from the website you are analyzing is ever sent to any server for classification.
  

  TensorFlow.js BERT API

  Using the model is super easy. Take a look at the following code snippet:
  
  JAVASCRIPT Kopieren

  <!-- Load TensorFlow.js. This is required to use the qna model. -->
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"> </script>
  <!-- Load the qna model. -->
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/qna"> </script>
  
  <!-- Place your code in the script tag below. You can also use an external .js file -->
  <script>
    // Notice there is no 'import' statement. 'qna' and 'tf' is
    // available on the index-page because of the script tag above.
    // Load the model.
    qna.load().then(model => {
      model.findAnswers(question, passage).then(answers => {
        console.log('Answers: ', answers);
      });
    });
  </script>

  As you can see, the first two lines load the TensorFlow.js library and the Q&A (question and answer) model from our hosted scripts, so we can perform Q&A search. This only needs to be called once - the model will stay loaded whilst it is kept in memory. We can then repeatedly call findAnswers() to which we pass two strings. The first is the question the user wishes to ask, and the second is the text within which we want to search (for example the text on the page). We will then get back a results object of the following structure:
  
  JAVASCRIPT Kopieren

  [
    {
      text: string,
      score: number,
      startIndex: number,
      endIndex: number
    }
  ]

  You get back an array of objects representing parts of the passage that best answer the question, along with a score representing its confidence of it being correct. We also get the indexes of the answer text for easy locating where the answer text is in the context string. That’s all there is to it! With this data you can now highlight the found text, return some richer results, or whatever creative idea you may choose to implement.
  If you would like to try the MobileBERT Q&A model yourself we are happy to share that it is now open sourced and can be found on our Github repository here. If you make something you are proud of, share it with the TensorFlow.js team on social using #MadeWithTFJS - we would love to see what you create.
  Vollständiger Original-Bericht

  Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.

  ↗ Original-Artikel auf blog.tensorflow.org lesen