Showing 5 open source projects for "python-ldap"

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  • Easily build robust connections between Salesforce and any platform Icon
    Easily build robust connections between Salesforce and any platform

    We help companies using Salesforce connect their data with a no-code Salesforce-native solution.

    Like having Postman inside Salesforce! Declarative Webhooks allows users to quickly and easily configure bi-directional integrations between Salesforce and external systems using a point-and-click interface. No coding is required, making it a fast and efficient and as a native solution, Declarative Webhooks seamlessly integrates with Salesforce platform features such as Flow, Process Builder, and Apex. You can also leverage the AI Integration Agent feature to automatically build your integration templates by providing it with links to API documentation.
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  • Bullseye Locations: Store Locator Software Icon
    Bullseye Locations: Store Locator Software

    The locator that closes more sales

    In the competitive world of building materials, digital marketers need a contractor or dealer locator that does more than display a phone number and map. Bullseye’s advanced locator provides tools for improving customer engagement, capturing leads, tracking prospects, driving dealer loyalty and increasing sales conversion.
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  • 1
    imodelsX

    imodelsX

    Interpretable prompting and models for NLP

    Interpretable prompting and models for NLP (using large language models). Generates a prompt that explains patterns in data (Official) Explain the difference between two distributions. Find a natural-language prompt using input-gradients. Fit a better linear model using an LLM to extract embeddings. Fit better decision trees using an LLM to expand features. Finetune a single linear layer on top of LLM embeddings. Use these just a like a sci-kit-learn model. During training, they fit better...
    Downloads: 0 This Week
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  • 2
    solo-learn

    solo-learn

    Library of self-supervised methods for visual representation

    A library of self-supervised methods for visual representation learning powered by Pytorch Lightning. A library of self-supervised methods for unsupervised visual representation learning powered by PyTorch Lightning. We aim at providing SOTA self-supervised methods in a comparable environment while, at the same time, implementing training tricks. The library is self-contained, but it is possible to use the models outside of solo-learn.
    Downloads: 0 This Week
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  • 3
    CTranslate2

    CTranslate2

    Fast inference engine for Transformer models

    CTranslate2 is a C++ and Python library for efficient inference with Transformer models. The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many advanced optimizations: layer fusion, padding removal, batch reordering, in-place operations, caching mechanism, etc. ...
    Downloads: 7 This Week
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  • 4
    Hyperformer

    Hyperformer

    Hypergraph Transformer for Skeleton-based Action Recognition

    This is the official implementation of our paper "Hypergraph Transformer for Skeleton-based Action Recognition." Skeleton-based action recognition aims to recognize human actions given human joint coordinates with skeletal interconnections. By defining a graph with joints as vertices and their natural connections as edges, previous works successfully adopted Graph Convolutional networks (GCNs) to model joint co-occurrences and achieved superior performance. More recently, a limitation of...
    Downloads: 0 This Week
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  • The Game-Changing URL Builder For Your Marketing Campaigns Icon
    The Game-Changing URL Builder For Your Marketing Campaigns

    Saves time by automating campaign conventions and UTM Link creation

    CampaignTrackly is a SaaS-based platform that automates the process of adding tracking tags to digital links. The platform enables businesses to build a centralized, consistent and standardized link-tracking strategy in a simple and cost-effective way. It removes ambiguities, simplifies processes and empowers marketers to be in control of their campaign setup and reporting process without the need to use IT code or manual operations. Its ease of use promotes consistency and high adoption rates among platform users, resulting in enhanced insights into the customer journeys and marketing budget spend, which in turn, helps businesses optimize their promotions to increase revenue streams. The platform boasts over 45 automation features across standard Google Analytics, Adobe and Custom tags, as well as, extensive tag library management capabilities, friendly reporting functions, sophisticated team access level management and more.
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  • 5
    Neuro-comma

    Neuro-comma

    Punctuation restoration production-ready model for Russian language

    ...The Library doesn't use any high-level frameworks, such as PyTorch-lightning or Keras, to reduce the level entry threshold. Feel free to fork this repo and edit model or dataset classes for your purposes. Our team always uses the latest version and features of Python. We started with Python 3.9, but realized, that there is no FastAPI image for Python 3.9. There is several PRs in image repositories, but no response from maintainers. So we decided to change code which we use in production to work with the 3.8 version of Python. In some functions we have 3.9 code, but we still use them, these functions are needed only for development purposes.
    Downloads: 0 This Week
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