Metadata-Version: 2.3
Name: swarmauri_embedding_nmf
Version: 0.6.1.dev16
Summary: NMF Embedding for Swarmauri.
License: Apache-2.0
Author: Jacob Stewart
Author-email: jacob@swarmauri.com
Requires-Python: >=3.10,<3.13
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: scikit-learn (>=1.4.2,<2.0.0)
Requires-Dist: swarmauri_base (>=0.6.1.dev16,<0.7.0)
Requires-Dist: swarmauri_core (>=0.6.1.dev16,<0.7.0)
Requires-Dist: swarmauri_standard (>=0.6.1.dev3,<0.7.0)
Project-URL: Repository, http://github.com/swarmauri/swarmauri-sdk
Description-Content-Type: text/markdown

![Swarmauri Logo](https://res.cloudinary.com/dbjmpekvl/image/upload/v1730099724/Swarmauri-logo-lockup-2048x757_hww01w.png)

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---

# NMF Embedding Package

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

## Installation

```bash
pip install swarmauri_embedding_nmf
```

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

```python
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](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.


