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About Universal Sentence Encoder
The document presents two models for encoding sentences into embedding vectors that can be used for transfer learning in other NLP tasks: a transformer-based model and a deep averaging network (DAN) model. The transformer model uses attention to produce context-aware word representations that are summed to create a fixed-length sentence embedding. This model achieves the best transfer performance but has high compute costs. The DAN model averages word embeddings in the sentence to create the sentence embedding, providing a more efficient alternative. Both models are implemented in TensorFlow and made publicly available on TF Hub.
Detailed Information
| Author: | ['viterbi kkk'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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