Struct Embeddings 

pub struct Embeddings { /* private fields */ }

Trained embeddings with vocabulary — the primary inference interface.

Implementations

impl Embeddings

pub fn vocab_size(&self) -> usize

Number of words in the vocabulary.

assert!(emb.vocab_size() >= 2);

pub fn embedding_dim(&self) -> usize

Embedding dimension.

pub fn get_vector(&self, word: &str) -> Option<&[f32]>

Get the raw embedding vector for a word.

Returns None if the word is not in the vocabulary.

assert!(emb.get_vector("hello").is_some());
assert!(emb.get_vector("nonexistent").is_none());

pub fn similarity(&self, word_a: &str, word_b: &str) -> Result<f32>

Cosine similarity between two words.

Returns a value in [-1.0, 1.0], or an error if either word is not in the vocabulary.

let sim = emb.similarity("cat", "mat").unwrap();
assert!(sim >= -1.0 && sim <= 1.0);

pub fn most_similar(&self, query: &str, top_k: usize) -> Vec<(String, f32)>

Find the top_k most similar words to query.

Returns (word, cosine_similarity) pairs sorted descending. The query word itself is excluded from results.

let nearest = emb.most_similar("cat", 3);
assert!(nearest.len() <= 3);

pub fn analogy( &self, pos_a: &str, neg_a: &str, pos_b: &str, top_k: usize, ) -> Result<Vec<(String, f32)>>

Solve an analogy: pos_a - neg_a + pos_b ≈ result.

Classic example: king - man + woman ≈ queen.

Excludes pos_a, neg_a, and pos_b from the candidate list.

Returns (word, score) pairs sorted by cosine similarity to the query vector.

pub fn words(&self) -> Vec<&str>

Return all words in vocabulary sorted alphabetically.

pub fn save(&self, path: impl AsRef<Path>) -> Result<()>

Save embeddings to JSON.

The file contains both the model weights and vocabulary so it can be loaded independently.

pub fn load(path: impl AsRef<Path>) -> Result<Self>

Load embeddings from a JSON file produced by save.

pub fn save_text_format(&self, path: impl AsRef<Path>) -> Result<()>

Export just the word vectors as a plain-text file (word2vec format).

Format: first line is <vocab_size> <dim>, then one word per line followed by space-separated floats.

pub fn vocab(&self) -> &Vocabulary

Get a reference to the vocabulary.

Trait Implementations

impl Debug for Embeddings

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for Embeddings

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for Embeddings

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.