Showing 2650 open source projects for "x-windows"

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  • 1
    Prime QA

    Prime QA

    State-of-the-art Multilingual Question Answering research

    PrimeQA is a public open source repository that enables researchers and developers to train state-of-the-art models for question answering (QA). By using PrimeQA, a researcher can replicate the experiments outlined in a paper published in the latest NLP conference while also enjoying the capability to download pre-trained models (from an online repository) and run them on their own custom data. PrimeQA is built on top of the Transformers toolkit and uses datasets and models that are directly...
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  • 2
    fastMRI

    fastMRI

    A large open dataset + tools to speed up MRI scans using ML

    fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data...
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  • 3
    PromethAI

    PromethAI

    Open-source framework that gives you AI Agents

    PromethAI-Backend is a backend framework for AI-driven automation and knowledge extraction. It is designed to integrate with large language models (LLMs) to provide AI-enhanced workflows, including content generation, summarization, and data analysis.
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  • 4
    CausalNex

    CausalNex

    A Python library that helps data scientists to infer causation

    CausalNex is a Python library that uses Bayesian Networks to combine machine learning and domain expertise for causal reasoning. You can use CausalNex to uncover structural relationships in your data, learn complex distributions, and observe the effect of potential interventions.
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  • 5
    AI-powered enterprise search engine

    AI-powered enterprise search engine

    AI-powered enterprise search engine

    AI-powered enterprise search engine is an open-source, AI-powered enterprise search engine designed to help organizations quickly locate and retrieve information scattered across multiple internal tools, documents, and communication platforms. It enables users to search across sources such as Slack, Confluence, Jira, Google Drive, and other enterprise systems, consolidating fragmented knowledge into a single, unified search experience. By leveraging natural language processing, Gerev allows...
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  • 6
    pyts

    pyts

    A Python package for time series classification

    pyts is a Python package dedicated to time series classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of several time series classification algorithms. The package comes up with many unit tests and continuous integration ensures new code integration and backward compatibility. The package is distributed under the 3-clause BSD license.
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  • 7
    Poe Bot Protocol
    Poe is a platform for interacting with AI-based bots. That includes well-known chat bots like OpenAI's ChatGPT and Anthropic's Claude, but anyone can create their own bot by implementing the protocol described in this repository.
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  • 8
    CORL

    CORL

    High-quality single-file implementations of SOTA Offline

    CORL (Collection of Reinforcement Learning Environments for Control Tasks) is a modular and extensible set of high-quality reinforcement learning environments focused on continuous control and robotics. It aims to offer standardized environments suitable for benchmarking state-of-the-art RL algorithms in control tasks, including physics-based simulations and custom-designed scenarios.
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  • 9
    doccano client

    doccano client

    A simple client for doccano API

    doccano-client is a simple client wrapper for the doccano API. We're introducing a newly revamped Doccano API Client that features more Pythonic interaction as well as more testing and documentation. It also adds more regulated compatibility with specific Doccano release versions.
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  • 10
    lora-svc

    lora-svc

    Singing voice change based on whisper, lora for singing voice clone

    singing voice change based on whisper, and lora for singing voice clone. You will feel the beauty of the code from this project. Uni-SVC main branch is for singing voice clone based on whisper with speaker encoder and speaker adapter. Uni-SVC main target is to develop lora for SVC. With lora, maybe clone a singer just need 10 stence after 10 minutes train. Each singer is a plug-in of the base model.
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  • 11
    Horovod

    Horovod

    Distributed training framework for TensorFlow, Keras, PyTorch, etc.

    Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data...
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  • 12
    Metaseq

    Metaseq

    Repo for external large-scale work

    Metaseq is a flexible, high-performance framework for training and serving large-scale sequence models, such as language models, translation systems, and instruction-tuned LLMs. Built on top of PyTorch, it provides distributed training, model sharding, mixed-precision computation, and memory-efficient checkpointing to support models with hundreds of billions of parameters. The framework was used internally at Meta to train models like OPT (Open Pre-trained Transformer) and serves as a...
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  • 13
    langchain-prefect

    langchain-prefect

    Tools for using Langchain with Prefect

    Large Language Models (LLMs) are interesting and useful  -  building apps that use them responsibly feels like a no-brainer. Tools like Langchain make it easier to build apps using LLMs. We need to know details about how our apps work, even when we want to use tools with convenient abstractions that may obfuscate those details. Prefect is built to help data people build, run, and observe event-driven workflows wherever they want. It provides a framework for creating deployments on a whole...
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  • 14

    Image to Text

    Convert an image to text to spot intelligible words.

    The program will convert to text an image, such as a photo , with the purpose of analyzing it to spot intelligible words. Use the program with photos of clouds, sea, soil, vegetation or any other photo of natural or man-made semi-homogeneous configuration, to reveal the hidden universal-philosophical messages of the image. You can also use it on photos of people or art pieces to have a psychological insight of the person portrayed or of the image author. The resulting text will be a long...
    Downloads: 2 This Week
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  • 15
    PRM800K

    PRM800K

    800,000 step-level correctness labels on LLM solutions to MATH problem

    PRM800K is a process supervision dataset accompanying the paper Let’s Verify Step by Step, providing 800,000 step-level correctness labels on model-generated solutions to problems from the MATH dataset. The repository releases the raw labels and the labeler instructions used in two project phases, enabling researchers to study how human raters graded intermediate reasoning. Data are stored as newline-delimited JSONL files tracked with Git LFS, where each line is a full solution sample that...
    Downloads: 2 This Week
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  • 16
    ParlAI

    ParlAI

    A framework for training and evaluating AI models

    ParlAI is a comprehensive research platform for building, training, and evaluating dialogue agents across a wide variety of tasks and datasets. It provides a unified interface—agents, teachers, and worlds—so the same model can be trained on multi-turn chit-chat, question answering, task-oriented dialogue, retrieval, or safety-focused datasets without changing core code. The library integrates tightly with PyTorch and supports both generative and retrieval-augmented models, along with...
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  • 17
    TensorFlow Ranking

    TensorFlow Ranking

    Learning to rank in TensorFlow

    TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. Commonly used loss functions including pointwise, pairwise, and listwise losses. Commonly used ranking metrics like Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). Multi-item (also known as groupwise) scoring functions. LambdaLoss implementation for direct ranking metric optimization. Unbiased Learning-to-Rank from biased feedback data. We envision that this library...
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  • 18
    Spektral

    Spektral

    Graph Neural Networks with Keras and Tensorflow 2

    ...Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects. Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. Other Linux distros should work as well. The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements.
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  • 19
    DiT (Diffusion Transformers)

    DiT (Diffusion Transformers)

    Official PyTorch Implementation of "Scalable Diffusion Models"

    DiT (Diffusion Transformer) is a powerful architecture that applies transformer-based modeling directly to diffusion generative processes for high-quality image synthesis. Unlike CNN-based diffusion models, DiT represents the diffusion process in the latent space and processes image tokens through transformer blocks with learned positional encodings, offering scalability and superior sample quality. The model architecture parallels large language models but for image tokens—each block...
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  • 20

    Lumi-HSP

    This is an AI language model that can predict Heart failure or stroke

    Using thsi AI model, you can predict the chances of heart stroke and heart failure. HIGLIGHTS : 1. Accuracy of this model is 95% 2. This model uses the powerful Machine Learning algorithm "GradientBoosting" for predicting the outcomes. 3. An easy to use model and accessible to everyone.
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  • 21
    TensorFlow Documentation

    TensorFlow Documentation

    TensorFlow documentation

    An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples.
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  • 22
    DALL-E in Pytorch

    DALL-E in Pytorch

    Implementation / replication of DALL-E, OpenAI's Text to Image

    Implementation / replication of DALL-E (paper), OpenAI's Text to Image Transformer, in Pytorch. It will also contain CLIP for ranking the generations. Kobiso, a research engineer from Naver, has trained on the CUB200 dataset here, using full and deepspeed sparse attention. You can also skip the training of the VAE altogether, using the pretrained model released by OpenAI! The wrapper class should take care of downloading and caching the model for you auto-magically. You can also use the...
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  • 23
    AB3DMOT

    AB3DMOT

    Official Python Implementation for "3D Multi-Object Tracking

    AB3DMOT is a real-time 3D multi-object tracking framework designed for applications such as autonomous driving and robotics perception. The system processes detection results from 3D object detectors that analyze LiDAR point clouds and uses them to track multiple objects across consecutive frames. Its tracking pipeline relies on a combination of classical algorithms, including a Kalman filter for state estimation and the Hungarian algorithm for data association between detected objects and...
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  • 24
    TF2DeepFloorplan

    TF2DeepFloorplan

    TF2 Deep FloorPlan Recognition using a Multi-task Network

    TF2 Deep FloorPlan Recognition using a Multi-task Network with Room-boundary-Guided Attention. Enable tensorboard, quantization, flask, tflite, docker, github actions and google colab. This repo contains a basic procedure to train and deploy the DNN model suggested by the paper 'Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention'. It rewrites the original codes from zlzeng/DeepFloorplan into newer versions of Tensorflow and Python.
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  • 25
    VAMS

    VAMS

    Virtual Assistant Maintenance System

    Virtual Assistant Maintenance System also knowns as VAMS is an AI software application, that helps users with some computer maintenance issues. Application Requirements: Operating System: Windows 8.1/10 /11 Processor: Intel Core i5 or equivalent RAM: 4GB or higher Free Disk Space: 500MB
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