useTextEmbeddings

Text Embedding is the process of converting text into a numerical representation. This representation can be used for various natural language processing tasks, such as semantic search, text classification, and clustering.

caution

It is recommended to use models provided by us, which are available at our Hugging Face repository. You can also use constants shipped with our library.

Reference

import {
  useTextEmbeddings,
  ALL_MINILM_L6_V2,
  ALL_MINILM_L6_V2_TOKENIZER,
} from 'react-native-executorch';

const model = useTextEmbeddings({
  modelSource: ALL_MINILM_L6_V2,
  tokenizerSource: ALL_MINILM_L6_V2_TOKENIZER,
});

try {
  const embedding = await model.forward('Hello World!');
} catch (error) {
  console.error(error);
}

Arguments

modelSource A string that specifies the location of the model binary. For more information, take a look at loading models page.

tokenizerSource A string that specifies the location of the tokenizer JSON file.

preventLoad? - Boolean that can prevent automatic model loading (and downloading the data if you load it for the first time) after running the hook.

Returns

Field	Type	Description
forward	(input: string) => Promise<number[]>	Executes the model's forward pass, where input is a text that will be embedded.
error	string | null	Contains the error message if the model failed to load.
isGenerating	boolean	Indicates whether the model is currently processing an inference.
isReady	boolean	Indicates whether the model has successfully loaded and is ready for inference.
downloadProgress	number	Represents the download progress as a value between 0 and 1.

Running the model

To run the model, you can use the forward method. It accepts one argument, which is a string representing the text you want to embed. The function returns a promise, which can resolve either to an error or an array of numbers representing the embedding.

info

The returned embedding vector is normalized, meaning that its length is equal to 1. This allows for easier comparison of vectors using cosine similarity, just calculate the dot product of two vectors to get the cosine similarity score.

Example

import {
  useTextEmbeddings,
  ALL_MINILM_L6_V2,
  ALL_MINILM_L6_V2_TOKENIZER,
} from 'react-native-executorch';

const dotProduct = (a: number[], b: number[]) =>
  a.reduce((sum, val, i) => sum + val * b[i], 0);

function App() {
  const model = useTextEmbeddings({
    modelSource: ALL_MINILM_L6_V2,
    tokenizerSource: ALL_MINILM_L6_V2_TOKENIZER,
  });

  ...

  try {
    const helloWorldEmbedding = await model.forward('Hello World!');
    const goodMorningEmbedding = await model.forward('Good Morning!');

    // The embeddings are normalized, so we can use dot product to calculate cosine similarity
    const similarity = dotProduct(
      helloWorldEmbedding,
      goodMorningEmbedding
    );

    console.log(`Cosine similarity: ${similarity}`);
  } catch (error) {
    console.error(error);
  }

  ...
}

Supported models

Model	Language	Max Tokens	Embedding Dimensions	Description
all-MiniLM-L6-v2	English	256	384	All-round model tuned for many use-cases. Trained on a large and diverse dataset of over 1 billion training pairs.
all-mpnet-base-v2	English	384	768	All-round model tuned for many use-cases. Trained on a large and diverse dataset of over 1 billion training pairs.
multi-qa-MiniLM-L6-cos-v1	English	511	384	This model was tuned for semantic search: Given a query/question, it can find relevant passages. It was trained on a large and diverse set of (question, answer) pairs.
multi-qa-mpnet-base-dot-v1	English	512	768	This model was tuned for semantic search: Given a query/question, it can find relevant passages. It was trained on a large and diverse set of (question, answer) pairs.

Max Tokens - the maximum number of tokens that can be processed by the model. If the input text exceeds this limit, it will be truncated.

Embedding Dimensions - the size of the output embedding vector. This is the number of dimensions in the vector representation of the input text.

Benchmarks

Model size

Model	XNNPACK [MB]
ALL_MINILM_L6_V2	91
ALL_MPNET_BASE_V2	438
MULTI_QA_MINILM_L6_COS_V1	91
MULTI_QA_MPNET_BASE_DOT_V1	438

Memory usage

Model	Android (XNNPACK) [MB]	iOS (XNNPACK) [MB]
ALL_MINILM_L6_V2	150	190
ALL_MPNET_BASE_V2	520	470
MULTI_QA_MINILM_L6_COS_V1	160	225
MULTI_QA_MPNET_BASE_DOT_V1	540	500

Inference time

warning

Times presented in the tables are measured as consecutive runs of the model. Initial run times may be up to 2x longer due to model loading and initialization.

Model	iPhone 16 Pro (XNNPACK) [ms]	iPhone 14 Pro Max (XNNPACK) [ms]	iPhone SE 3 (XNNPACK) [ms]	Samsung Galaxy S24 (XNNPACK)	OnePlus 12 (XNNPACK) [ms]
ALL_MINILM_L6_V2	53	69	78	60	65
ALL_MPNET_BASE_V2	352	423	478	521	527
MULTI_QA_MINILM_L6_COS_V1	135	166	180	158	165
MULTI_QA_MPNET_BASE_DOT_V1	503	598	680	694	743