Skip to main content

NMF Embedding for Swarmauri.

Project description

Swarmauri Logo

PyPI - Downloads PyPI - Python Version PyPI - License PyPI - Version


NMF Embedding Package

A Non-negative Matrix Factorization (NMF) based embedding implementation for text data processing in the Swarmauri ecosystem.

Installation

pip install swarmauri_embedding_nmf

Usage

Here's a basic example of how to use the NMF Embedding:

from swarmauri.embeddings.NmfEmbedding import NmfEmbedding

# Initialize the embedder
embedder = NmfEmbedding(n_components=10)

# Example documents
documents = ["This is the first document", "This is the second document", "And this is the third one"]

# Fit and transform the documents
vectors = embedder.fit_transform(documents)

# Transform a new document
new_vector = embedder.infer_vector("This is a new document")

Want to help?

If you want to contribute to swarmauri-sdk, read up on our guidelines for contributing that will help you get started.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

swarmauri_embedding_nmf-0.6.0.tar.gz (6.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

swarmauri_embedding_nmf-0.6.0-py3-none-any.whl (8.0 kB view details)

Uploaded Python 3

File details

Details for the file swarmauri_embedding_nmf-0.6.0.tar.gz.

File metadata

  • Download URL: swarmauri_embedding_nmf-0.6.0.tar.gz
  • Upload date:
  • Size: 6.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.1 CPython/3.12.9 Linux/6.8.0-1021-azure

File hashes

Hashes for swarmauri_embedding_nmf-0.6.0.tar.gz
Algorithm Hash digest
SHA256 fcc4f8b34c9f4fee5d81979af0d62d603b455c4944958503e665dd59e90c59ae
MD5 863ef2b958dc248bb31d2ba3afd6fac2
BLAKE2b-256 3fd544a217e4c730ce3e80672df96e37b73fcb7a995fb849ed6029a36f2534fc

See more details on using hashes here.

File details

Details for the file swarmauri_embedding_nmf-0.6.0-py3-none-any.whl.

File metadata

File hashes

Hashes for swarmauri_embedding_nmf-0.6.0-py3-none-any.whl
Algorithm Hash digest
SHA256 9a61b70291bbcb90a20ac461f40459d1ef8bd6277cef33b55540fe31e68f8e3f
MD5 52f156a4238e89f88a047f60f29182f9
BLAKE2b-256 3ed043aff531154d42f344971975bdfc3e217dedd4f4b8cbdb1bda9b67d5d5b2

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page