Struct rust_bert::pipelines::sentence_embeddings::SentenceEmbeddingsModel

pub struct SentenceEmbeddingsModel { /* private fields */ }

SentenceEmbeddingsModel to perform sentence embeddings

It is made of the following blocks:

• transformer: Base transformer model
• pooling: Pooling layer
• dense (optional): Linear (feed forward) layer
• normalization (optional): Embeddings normalization

Implementations

impl SentenceEmbeddingsModel

pub fn new(config: SentenceEmbeddingsConfig) -> Result<Self, RustBertError>

Build a new SentenceEmbeddingsModel

Arguments

• config - SentenceEmbeddingsConfig object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)

pub fn set_tokenizer_truncation(
    &mut self,
    truncation_strategy: TruncationStrategy
)

Sets the tokenizer’s truncation strategy

pub fn tokenize<S>(&self, inputs: &[S]) -> SentenceEmbeddingsTokenizerOuputwhere
    S: AsRef<str> + Sync,

Tokenizes the inputs

pub fn encode_as_tensor<S>(
    &self,
    inputs: &[S]
) -> Result<SentenceEmbeddingsModelOuput, RustBertError>where
    S: AsRef<str> + Sync,

Computes sentence embeddings, outputs Tensor.

pub fn encode<S>(&self, inputs: &[S]) -> Result<Vec<Embedding>, RustBertError>where
    S: AsRef<str> + Sync,

Computes sentence embeddings.

pub fn encode_with_attention<S>(
    &self,
    inputs: &[S]
) -> Result<(Vec<Embedding>, Vec<AttentionOutput>), RustBertError>where
    S: AsRef<str> + Sync,

Computes sentence embeddings, also outputs AttentionOutputs.