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英文字典中文字典相关资料:


  • Semantris - Google Search
    Semantris is a word association game powered by machine learning
  • About - Semantic Experiences - Google Search
    Semantris Semantris is a word association game that uses this same technology Each time you enter a clue, the AI looks at all the words in play and chooses the ones it thinks are most related
  • Semantic Experiences - Google Search
    Semantic Experiences lets you get hands-on with games and experiments that showcase advances in natural language understanding
  • Semantic Experiences
    Semantic Experiences lets you get hands-on with games and experiments that showcase advances in natural language understanding
  • For Developers - Semantic Experiences - Google Search
    In Semantris, the list of words we're showing are hand-curated and reviewed To the extent possible, we've excluded topics and entities that we think particularly invite unwanted associations, or can easily complement them as inputs
  • Semantic Reactor - Semantic Experiences - Google Search
    If you’re interested in making games that take advantage of Semantic ML, you should take a look at Semantris, which uses the same technology to detect word associations You can experiment with the words it uses in the Semantic Reactor
  • Research at Google
    Research Freedom Google Brain team members set their own research agenda, with the team as a whole maintaining a portfolio of projects across different time horizons and levels of risk
  • Research at Google
    Our People Google is an engineering organization unlike any other Because so much of what we do hasn't been done before, the line between research and product development is wonderfully blurred
  • Google Colab
    This notebook is open with private outputs Outputs will not be saved You can disable this in Notebook settings
  • Google Colab
    There are three interesting bits of information in the above model printout Those are: max_seq_length is 256 That means that the maximum number of tokens (like words) that can be encoded into a single vector embedding is 256 Anything beyond this must be truncated word_embedding_dimension is 1024 This number is the dimensionality of vectors output by this model





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