Showing 7 open source projects for "python feature selection"

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  • 1
    Superlinked

    Superlinked

    Superlinked is a Python framework for AI Engineers

    Superlinked is a Python framework designed for AI engineers to build high-performance search and recommendation applications that combine structured and unstructured data.
    Downloads: 6 This Week
    Last Update:
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  • 2
    LightAutoML

    LightAutoML

    Fast and customizable framework for automatic ML model creation

    LightAutoML is an automated machine learning (AutoML) framework optimized for efficient model training and hyperparameter tuning, focusing on both tabular and text data.
    Downloads: 1 This Week
    Last Update:
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  • 3
    Datasets

    Datasets

    Hub of ready-to-use datasets for ML models

    Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep integration...
    Downloads: 5 This Week
    Last Update:
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  • 4
    Transformers-Interpret

    Transformers-Interpret

    Model explainability that works seamlessly with Hugging Face

    Transformers-Interpret is an interpretability tool for Transformer-based NLP models, providing insights into attention mechanisms and feature importance.
    Downloads: 1 This Week
    Last Update:
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  • 5
    Emb-GAM

    Emb-GAM

    An interpretable and efficient predictor using pre-trained models

    Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings...
    Downloads: 1 This Week
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  • 6
    Self-Attentive Parser

    Self-Attentive Parser

    High-accuracy NLP parser with models for 11 languages

    LightAutoML is an automated machine learning (AutoML) framework developed by Sberbank AI Lab, designed to facilitate the development of machine learning models with minimal human intervention.
    Downloads: 1 This Week
    Last Update:
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  • 7
    DeText

    DeText

    A Deep Neural Text Understanding Framework

    DeText is a Deep Text understanding framework for NLP-related ranking, classification, and language generation tasks. It leverages semantic matching using deep neural networks to understand member intents in search and recommender systems. As a general NLP framework, DeText can be applied to many tasks, including search & recommendation ranking, multi-class classification and query understanding tasks.
    Downloads: 1 This Week
    Last Update:
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