Integrate text embedders.

Text embedders allow embedding text into a high-dimensional feature vector representing its semantic meaning, which can then be compared with the feature vector of other texts to evaluate their semantic similarity.

As opposed to text search , the text embedder allows computing the similarity between texts on-the-fly instead of searching through a predefined index built from a corpus.

Use the Task Library TextEmbedder API to deploy your custom text embedder into your mobile apps.

Key features of the TextEmbedder API

• Input text processing, including in-graph or out-of-graph Wordpiece or Sentencepiece tokenizations on input text.

• Built-in utility function to compute the cosine similarity between feature vectors.

Supported text embedder models

The following models are guaranteed to be compatible with the TextEmbedder API.

• The Universal Sentence Encoder TFLite model from TensorFlow Hub

• Custom models that meet the model compatibility requirements .

Run inference in C++

  // Initialization. 
 TextEmbedderOptions 
  
 options 
 : 
 options 
 . 
 mutable_base_options 
 () 
 - 
> mutable_model_file 
 () 
 - 
> set_file_name 
 ( 
 model_path 
 ); 
 std 
 :: 
 unique_ptr<TextEmbedder> 
  
 text_embedder 
  
 = 
  
 TextEmbedder 
 :: 
 CreateFromOptions 
 ( 
 options 
 ). 
 value 
 (); 
 // Run inference with your two inputs, `input_text1` and `input_text2`. 
 const 
  
 EmbeddingResult 
  
 result_1 
  
 = 
  
 text_embedder 
 - 
> Embed 
 ( 
 input_text1 
 ); 
 const 
  
 EmbeddingResult 
  
 result_2 
  
 = 
  
 text_embedder 
 - 
> Embed 
 ( 
 input_text2 
 ); 
 // Compute cosine similarity. 
 double 
  
 similarity 
  
 = 
  
 TextEmbedder 
 :: 
 CosineSimilarity 
 ( 
  
 result_1 
 . 
 embeddings 
 [ 
 0 
 ]. 
 feature_vector 
 () 
  
 result_2 
 . 
 embeddings 
 [ 
 0 
 ]. 
 feature_vector 
 ()); 
 

See the source code for more options to configure TextEmbedder .

Run inference in Python

Step 1: Install TensorFlow Lite Support Pypi package.

You can install the TensorFlow Lite Support Pypi package using the following command:

 pip  
install  
tflite-support 

Step 2: Using the model

  from 
  
 tflite_support.task 
  
 import 
 text 
 # Initialization. 
 text_embedder 
 = 
 text 
 . 
 TextEmbedder 
 . 
 create_from_file 
 ( 
 model_path 
 ) 
 # Run inference on two texts. 
 result_1 
 = 
 text_embedder 
 . 
 embed 
 ( 
 text_1 
 ) 
 result_2 
 = 
 text_embedder 
 . 
 embed 
 ( 
 text_2 
 ) 
 # Compute cosine similarity. 
 feature_vector_1 
 = 
 result_1 
 . 
 embeddings 
 [ 
 0 
 ] 
 . 
 feature_vector 
 feature_vector_2 
 = 
 result_2 
 . 
 embeddings 
 [ 
 0 
 ] 
 . 
 feature_vector 
 similarity 
 = 
 text_embedder 
 . 
 cosine_similarity 
 ( 
 result_1 
 . 
 embeddings 
 [ 
 0 
 ] 
 . 
 feature_vector 
 , 
 result_2 
 . 
 embeddings 
 [ 
 0 
 ] 
 . 
 feature_vector 
 ) 
 

See the source code for more options to configure TextEmbedder .

Example results

Cosine similarity between normalized feature vectors return a score between -1 and 1. Higher is better, i.e. a cosine similarity of 1 means the two vectors are identical.

 Cosine similarity: 0.954312 

Try out the simple CLI demo tool for TextEmbedder with your own model and test data.

Model compatibility requirements

The TextEmbedder API expects a TFLite model with mandatory TFLite Model Metadata .

Three main types of models are supported:

• BERT-based models (see source code for more details):

  • Exactly 3 input tensors (kTfLiteString)

  • IDs tensor, with metadata name "ids",

  • Mask tensor, with metadata name "mask".

  • Segment IDs tensor, with metadata name "segment_ids"

  • Exactly one output tensor (kTfLiteUInt8/kTfLiteFloat32)

  • with N components corresponding to the N dimensions of the returned feature vector for this output layer.

  • Either 2 or 4 dimensions, i.e. [1 x N] or [1 x 1 x 1 x N] .

  • An input_process_units for Wordpiece/Sentencepiece Tokenizer

• Universal Sentence Encoder-based models (see source code for more details):

  • Exactly 3 input tensors (kTfLiteString)

  • Query text tensor, with metadata name "inp_text".

  • Response context tensor, with metadata name "res_context".

  • Response text tensor, with metadata name "res_text".

  • Exactly 2 output tensors (kTfLiteUInt8/kTfLiteFloat32)

  • Query encoding tensor, with metadata name "query_encoding".

  • Response encoding tensor, with metadata name "response_encoding".

  • Both with N components corresponding to the N dimensions of the returned feature vector for this output layer.

  • Both with either 2 or 4 dimensions, i.e. [1 x N] or [1 x 1 x 1 x N] .

• Any text embedder model with:

  • An input text tensor (kTfLiteString)

  • At least one output embedding tensor (kTfLiteUInt8/kTfLiteFloat32)

  • with N components corresponding to the N dimensions of the returned feature vector for this output layer.

  • Either 2 or 4 dimensions, i.e. [1 x N] or [1 x 1 x 1 x N] .