SentenceTransformer#

class ragger_duck.embedding.SentenceTransformer(model_name_or_path=None, modules=None, device=None, cache_folder=None, use_auth_token=None, batch_size=32, show_progress_bar=True)#

Sentence transformer that embeds sentences to embeddings.

This is a thin wrapper around SentenceTransformer that follows the scikit-learn API and thus can be used inside a scikit-learn pipeline.

Parameters:

model_name_or_pathstr, default=None: If it is a filepath on disc, it loads the model from that path. If it is not a path, it first tries to download a pre-trained SentenceTransformer model. If that fails, tries to construct a model from Huggingface models repository with that name.
modulesIterable of nn.Module, default=None: This parameter can be used to create custom SentenceTransformer models from scratch.
devicestr, default=None: Device (e.g. “cpu”, “cuda”, “mps”) that should be used for computation. If None, checks if a GPU can be used.
cache_folderstr, default=None: Path to store models.
use_auth_tokenbool or str, default=None: HuggingFace authentication token to download private models.
batch_sizeint, default=32: The batch size to use during transform.
show_progress_barbool, default=True: Whether to show a progress bar or not during transform.

Methods

`fit`([X, y])	No-op operation, only validate parameters.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Embed sentences to vectors.

fit(X=None, y=None)#

No-op operation, only validate parameters.

Parameters:

XNone: This parameter is ignored.
yNone: This parameter is ignored.

Returns:

self: The fitted estimator.

fit_transform(X, y=None, **fit_params)#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

transform(X)#

Embed sentences to vectors.

Parameters:

Xstr or Iterable of str or dict or length (n_sentences,)

The sentences to embed.

If str, a single sentence to embed;
If list of str, a list of sentences to embed;
If list of dict, a list of dictionaries with a key “text” that contains the sentence to embed.

Returns:

embeddingndarray of shape (n_sentences, embedding_size): The embedding of the sentences.