sklearn-embeddings 0.1.0

Sentence-Transformer embeddings for scikit-learn

sklearn-embeddings

Overview

sklearn-embeddings is a Python package that integrates sentence-transformer based embeddings with scikit-learn classifiers and clustering algorithms. This allows users to leverage powerful natural language processing capabilities within the familiar scikit-learn framework.

Installation

To install sklearn-embeddings, you can use pip:

pip install sklearn-embeddings

Usage

Here is a simple example of how to use sklearn-embeddings with a scikit-learn classifier:

from sklearn_embeddings.embeddings import SentenceTransformerEmbedding
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline

# Sample data
documents = ["This is a sentence.", "This is another sentence."]
labels = [True, False]

# Create a pipeline with the embedding model and a classifier
pipeline = make_pipeline(
    SentenceTransformerEmbedding(), 
    LogisticRegression()
    )

# Fit the model
pipeline.fit(documents, labels)

# Make predictions
predictions = pipeline.predict(["A new sentence to classify."])