readme: ---
  license: apache-2.0
  library_name: tfhub
  language: en
  tags:
  - text
  - sentence-similarity
  - use
  - universal-sentence-encoder
  - dan
  - tensorflow
  ---
  
  ## Model name: universal-sentence-encoder
  ## Description adapted from [TFHub](https://tfhub.dev/google/universal-sentence-encoder/4)
  
  # Overview
  
  The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.
  
  The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs. It is trained on a variety of data sources and a variety of tasks with the aim of dynamically accommodating a wide variety of natural language understanding tasks. The input is variable length English text and the output is a 512 dimensional vector. We apply this model to the [STS benchmark](https://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) for semantic similarity, and the results can be seen in the [example notebook](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb) made available. The universal-sentence-encoder model is trained with a deep averaging network (DAN) encoder.
  
  To learn more about text embeddings, refer to the [TensorFlow Embeddings](https://www.tensorflow.org/tutorials/text/word_embeddings) documentation. Our encoder differs from word level embedding models in that we train on a number of natural language prediction tasks that require modeling the meaning of word sequences rather than just individual words. Details are available in the paper "Universal Sentence Encoder" [1].
  
  ## Universal Sentence Encoder family
  
  There are several versions of universal sentence encoder models trained with different goals including size/performance multilingual, and fine-grained question answer retrieval.
  
  - [Universal Sentence Encoder family](https://tfhub.dev/google/collections/universal-sentence-encoder/1)
  
  ### Example use
  
  
  ### Using TF Hub and HF Hub
  ```
  model_path = snapshot_download(repo_id="Dimitre/universal-sentence-encoder")
  model =  KerasLayer(handle=model_path)
  embeddings = model([
      "The quick brown fox jumps over the lazy dog.",
      "I am a sentence for which I would like to get its embedding"])
      
  print(embeddings)
  
  # The following are example embedding output of 512 dimensions per sentence
  # Embedding for: The quick brown fox jumps over the lazy dog.
  # [-0.03133016 -0.06338634 -0.01607501, ...]
  # Embedding for: I am a sentence for which I would like to get its embedding.
  # [0.05080863 -0.0165243   0.01573782, ...]
  ```
  
  ### Using [TF Hub fork](https://github.com/dimitreOliveira/hub)
  ```
  model = pull_from_hub(repo_id="Dimitre/universal-sentence-encoder")
  embeddings = model([
      "The quick brown fox jumps over the lazy dog.",
      "I am a sentence for which I would like to get its embedding"])
  
  print(embeddings)
  
  # The following are example embedding output of 512 dimensions per sentence
  # Embedding for: The quick brown fox jumps over the lazy dog.
  # [-0.03133016 -0.06338634 -0.01607501, ...]
  # Embedding for: I am a sentence for which I would like to get its embedding.
  # [0.05080863 -0.0165243   0.01573782, ...]
  ```
  
  This module is about 1GB. Depending on your network speed, it might take a while to load the first time you run inference with it. After that, loading the model should be faster as modules are cached by default ([learn more about caching](https://www.tensorflow.org/hub/tf2_saved_model)). Further, once a module is loaded to memory, inference time should be relatively fast.
  
  ### Preprocessing
  
  The module does not require preprocessing the data before applying the module, it performs best effort text input preprocessing inside the graph.
  
  # Semantic Similarity
  
  ![Semantic Similarity Graphic](https://www.gstatic.com/aihub/tfhub/universal-sentence-encoder/example-similarity.png)
  
  Semantic similarity is a measure of the degree to which two pieces of text carry the same meaning. This is broadly useful in obtaining good coverage over the numerous ways that a thought can be expressed using language without needing to manually enumerate them.
  
  Simple applications include improving the coverage of systems that trigger behaviors on certain keywords, phrases or utterances. [This section of the notebook](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb#scrollTo=BnvjATdy64eR) shows how to encode text and compare encoding distances as a proxy for semantic similarity.
  
  # Classification
  ![Text Classification Graphic](https://www.gstatic.com/aihub/tfhub/universal-sentence-encoder/example-classification.png)
  
  [This notebook](https://colab.research.google.com/github/tensorflow/hub/blob/master/docs/tutorials/text_classification_with_tf_hub.ipynb) shows how to train a simple binary text classifier on top of any TF-Hub module that can e