Metadata-Version: 2.1
Name: deepneighbor
Version: 0.2.2
Summary: embedding-based item nearest neighborhoods extraction
Home-page: https://github.com/LouisBIGDATA/deepneighbor
Author: Yufeng Wang
Author-email: louiswang524@gmail.com
License: UNKNOWN
Description: # DeepNeighbor
        
        [![Python Versions](https://img.shields.io/pypi/pyversions/deepneighbor.svg)](https://pypi.org/project/deepneighbor)
        [![PyPI Version](https://img.shields.io/pypi/v/deepneighbor.svg)](https://pypi.org/project/deepneighbor)
        
        ---
        
        DeepNeighbor is a **Easy-to-use**,**Modular** and **Extendible** package of deep-learning based models along with lots of core components layers which can be used to easily build custom models.You can use any complex model with
        <br>`model.train()`，which generates embeddings for users and items (Deep),
        <br> and `model.search()`, which looks for Approximate nearest neighbor for seed user/item (Neighbor) .
        
        ```python
        from deepneighbor.embed import Embed
        
        model = Embed(data)
        model.train()
        model.search(seed = 'Louis', k=10)
        ```
        
Keywords: embedding,information retrieval,deep learning,torch,tensor,pytorch,nearest neighbor
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.4
Description-Content-Type: text/markdown
