autointent.modules.scoring.LinearScorer#

class autointent.modules.scoring.LinearScorer(embedder_config=None, cv=3, seed=0)#

Bases: autointent.modules.base.BaseScorer

Scoring module for linear classification using logistic regression.

This module uses embeddings generated from a transformer model to train a logistic regression classifier for intent classification.

Parameters:

  • embedder_config (autointent.configs.EmbedderConfig | str | dict[str, Any] | None) – Config of the embedder model

  • cv (int) – Number of cross-validation folds, defaults to 3

  • n_jobs – Number of parallel jobs for cross-validation, defaults to None

  • seed (int) – Random seed for reproducibility, defaults to 0

Example:#

from autointent.modules import LinearScorer
scorer = LinearScorer(
    embedder_config="sergeyzh/rubert-tiny-turbo", cv=2
)
utterances = ["hello", "goodbye", "allo", "sayonara"]
labels = [0, 1, 0, 1]
scorer.fit(utterances, labels)
test_utterances = ["hi", "bye"]
probabilities = scorer.predict(test_utterances)
print(probabilities)

[[0.50000032 0.49999968]
 [0.50000032 0.49999968]]
name = 'linear'#

Name of the module.

supports_multiclass = True#

Whether the module supports multiclass classification

supports_multilabel = True#

Whether the module supports multilabel classification

cv = 3#
seed = 0#
embedder_config#
classmethod from_context(context, embedder_config=None)#

Create a LinearScorer instance using a Context object.

Parameters:
Return type:

LinearScorer

get_embedder_config()#

Get the name of the embedder.

Returns:

Embedder name

Return type:

dict[str, Any]

fit(utterances, labels)#

Train the logistic regression classifier.

Parameters:

  • utterances (list[str]) – List of training utterances

  • labels (autointent.custom_types.ListOfLabels) – List of labels corresponding to the utterances

Raises:

ValueError – If the vector index mismatches the provided utterances

Return type:

None

predict(utterances)#

Predict probabilities for the given utterances.

Parameters:

utterances (list[str]) – List of query utterances

Returns:

Array of predicted probabilities for each class

Return type:

numpy.typing.NDArray[Any]

clear_cache()#

Clear cached data in memory used by the embedder.

Return type:

None