fastTextWordEmbedding — Return a compact fastText-style word embedding compatibility model.

fastTextWordEmbedding returns a RunMat wordEmbedding compatibility object with Dimension 300 and a compact built-in English vocabulary.

Syntax

emb = fastTextWordEmbedding

Returns

NameTypeDescription
embAnyWord embedding compatibility object.

Errors

IdentifierWhenMessage
RunMat:fastTextWordEmbedding:InvalidInputInputs do not match the supported fastTextWordEmbedding form.fastTextWordEmbedding received invalid input

How fastTextWordEmbedding works

  • The supported form is the zero-argument emb = fastTextWordEmbedding form.
  • The returned object exposes Dimension and Vocabulary and stores dense vectors in the same implementation-detail property used by word2vec, vec2word, and doc2sequence.
  • The compact model includes curated vectors for common examples such as Italy - Rome + Paris, which maps back to France with vec2word.
  • RunMat does not download MathWorks support packages. The full 1-million-word fastText English 16 Billion Token model remains tracked in the broader Text Analytics compatibility issue.

GPU memory and residency

fastTextWordEmbedding performs host object construction and has no provider kernel.

Examples

Create The Compatibility Model

emb = fastTextWordEmbedding

Expected output:

`emb.Dimension` is 300.

Use With Embedding Lookup

italy = word2vec(emb,"Italy");
rome = word2vec(emb,"Rome");
paris = word2vec(emb,"Paris");
word = vec2word(emb, italy - rome + paris)

Expected output:

`word` is `"France"` for the bundled compact model.

Using fastTextWordEmbedding with coding agents

Open a RunMat example with live inputs, then ask the agent to explain how fastTextWordEmbedding changes the result.

Run a small fastTextWordEmbedding example, explain the result, then change one input and compare the output.

FAQ

Does this include MATLAB's full pretrained fastText support-package corpus?

No. RunMat currently provides a compact compatibility model so scripts can construct and use a wordEmbedding object. Full support-package corpus parity remains open.

Can the result be used with word2vec, vec2word, and doc2sequence?

Yes. The returned object uses the same RunMat wordEmbedding representation as readWordEmbedding and trainWordEmbedding.

Does fastTextWordEmbedding execute on the GPU?

No. It constructs host text/model metadata and dense vectors.

Open-source implementation

Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how fastTextWordEmbedding is executed, line by line, in Rust.

About RunMat

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