vertebrae.extractors.sentence_transformers
Optional sentence-transformers extractor.
Classes
Sentence-transformers embedding extractor. |
Module Contents
- class vertebrae.extractors.sentence_transformers.SentenceTransformerExtractor(name, model_id, batch_size=64, normalize_embeddings=True, device=None, show_progress_bar=False, model_kwargs=None, encode_kwargs=None, checkpoint_paths=None, revision=None, cache_identity=None)[source]
Sentence-transformers embedding extractor.
- Parameters:
name (str) – User-facing extractor name.
model_id (str) – Sentence-transformers model identifier or local path.
batch_size (int) – Batch size passed to model.encode.
normalize_embeddings (bool) – Whether sentence-transformers should normalize outputs.
device (Optional[str]) – Optional device string.
show_progress_bar (bool) – Whether to show sentence-transformers progress output.
model_kwargs (Optional[Dict[str, Any]]) – Extra keyword arguments for SentenceTransformer.
encode_kwargs (Optional[Dict[str, Any]]) – Extra keyword arguments for model.encode.
checkpoint_paths (Optional[Sequence[str]])
revision (Optional[str])
cache_identity (Optional[str])
- fit(X, y=None)[source]
No-op fit for frozen sentence-transformers models.
- Parameters:
X (Any) – Input text samples.
y (Any) – Optional labels.
- Returns:
This extractor.
- Return type:
- transform(X)[source]
Encode text inputs into dense embeddings.
- Parameters:
X (Any) – Sequence of strings.
- Returns:
Dense numeric embedding matrix.
- Raises:
ImportError – If sentence-transformers is not installed.
ValueError – If inputs are not strings or output is invalid.
- Return type:
numpy.ndarray