eta.embedding.STEmbedder

class STEmbedder(model='sentence-transformers/all-distilroberta-v1', parallelism=False)

Bases: Embedder

An embedder that uses a native SentenceTransformer model to compute embeddings.

Parameters:

• model (str) – The name of a SentenceTransformer model to use.

• parallelism (bool, default=False) – Whether to enable or disable model parallelism.

model

Type:

SentenceTransformer

Methods

embed	Embed a text or list of texts.
score	Score a set of documents relative to a text.

embed(texts)

Embed a text or list of texts.

Parameters:

texts (str or list[str]) – Either a single text string or a list of text strings to embed.

Returns:

The embedding or embeddings computed from the input.

Return type:

list[float] or list[list[float]]

score(text, documents, embeddings=[])

Score a set of documents relative to a text.

Parameters:

• text (str) – A query text to use in computing scores for each document.

• documents (list[str]) – A list of documents to score.

• embeddings (list[list[float]], optional) – If embeddings for the documents have already been precomputed, passing the embeddings as an argument will bypass creating new embeddings for the documents.

Returns:

Scores for each document.

Return type:

list[float]