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  • 1
    FastRAG

    FastRAG

    Efficient Retrieval Augmentation and Generation Framework

    fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines, incorporating state-of-the-art LLMs and Information Retrieval. fastRAG is designed to empower researchers and developers with a comprehensive tool set for advancing retrieval augmented generation.
    Downloads: 2 This Week
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  • 2
    TorchDistill

    TorchDistill

    A coding-free framework built on PyTorch

    ...In addition to knowledge distillation, this framework helps you design and perform general deep learning experiments (WITHOUT coding) for reproducible deep learning studies. i.e., it enables you to train models without teachers simply by excluding teacher entries from a declarative yaml config file.
    Downloads: 0 This Week
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  • 3
    ExtractThinker

    ExtractThinker

    ExtractThinker is a Document Intelligence library for LLMs

    ExtractThinker is a tool designed to facilitate the extraction and analysis of information from various data sources, aiding in data processing and knowledge discovery.
    Downloads: 0 This Week
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  • 4
    STORM

    STORM

    An LLM-powered knowledge curation system that researches topics

    STORM is an open-source virtual assistant framework developed by Stanford's OVAL lab. It is designed for creating natural language interfaces and assistants that can interact with APIs, databases, and services in a modular way.
    Downloads: 0 This Week
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  • 5
    Wikipedia2Vec

    Wikipedia2Vec

    A tool for learning vector representations of words and entities

    Wikipedia2Vec is an embedding learning tool that creates word and entity vector representations from Wikipedia, enabling NLP models to leverage structured and contextual knowledge.
    Downloads: 0 This Week
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  • 6
    funNLP

    funNLP

    Resources, corpora, and tools for Chinese natural language processing

    FunNLP is a large, curated collection of resources, corpora, and tools for Chinese natural language processing (NLP). It aggregates datasets, lexicons, wordlists, sentiment dictionaries, knowledge graphs, and pretrained model references, serving as a one-stop resource hub for Chinese NLP practitioners. The repository is organized into categories such as sentiment analysis, text classification, named entity recognition, knowledge graphs, and various lexicons (e.g. sensitive words, emotion dictionaries, stopwords). It also includes links to academic papers, open-source model implementations, and practical utilities like word segmentation or text cleaning scripts. ...
    Downloads: 0 This Week
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  • 7
    Machine Learning PyTorch Scikit-Learn

    Machine Learning PyTorch Scikit-Learn

    Code Repository for Machine Learning with PyTorch and Scikit-Learn

    Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing...
    Downloads: 3 This Week
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  • 8
    TextBrewer

    TextBrewer

    A PyTorch-based knowledge distillation toolkit

    TextBrewer is a PyTorch-based model distillation toolkit for natural language processing. It includes various distillation techniques from both NLP and CV field and provides an easy-to-use distillation framework, which allows users to quickly experiment with the state-of-the-art distillation methods to compress the model with a relatively small sacrifice in the performance, increasing the inference speed and reducing the memory usage.
    Downloads: 0 This Week
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  • 9
    DeepLearn

    DeepLearn

    Implementation of research papers on Deep Learning+ NLP+ CV in Python

    Welcome to DeepLearn. This repository contains an implementation of the following research papers on NLP, CV, ML, and deep learning. The required dependencies are mentioned in requirement.txt. I will also use dl-text modules for preparing the datasets. If you haven't use it, please do have a quick look at it. CV, transfer learning, representation learning.
    Downloads: 0 This Week
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