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PairSoft | AP Automation and Doc ManagementStreamline operations and elevate your team's efficiency with PairSoft. Our AP automation, procurement, and document management solutions eliminate manual processes, cut costs, and free your team to focus on strategic initiatives. Experience our state-of-the-art invoice-to-pay solution, now integrated with advanced AI technology for faster, smarter results. Our customers report a significant 70% reduction in approval times and annual savings of $62,000 in employee hours. At PairSoft, we aim to transform your business operations through automation. Explore the future of automation at pairsoft.com, where you can leverage cutting-edge features like invoice capture, OCR, and comprehensive AP automation to transform your workflow. Whether you are a small business or a large enterprise, our solutions are designed to scale with your needs, providing robust functionality and ease of use. Join the growing number of businesses that trust PairSoft.
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Track time for payroll, billing and productivityBecause time is money, and we understand how challenging it can be to keep track of employee hours. The constant reminder to log timesheets so your business can increase billables, run an accurate payroll and remove the guesswork from project estimates – we get it.
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...To facilitate the use of the R package for the drug discovery community, we also provide a web server called SynergyFinder PLus at http://synergyfinder.org/ that provides a user-friendly interface to enable a more flexible and versatile analysis of drug combination data. Sister websites: https://tangsoftwarelab.shinyapps.io/synergyfinder/ Bioconductor R package: https://www.bioconductor.org/packages/release/bioc/html/synergyfinder.html Citation: Zheng, S.; Wang, W.; Aldahdooh, J.; Malyutina, A.; Shadbahr, T.; Tanoli Z; Pessia, A.; Tang, J. SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets. ...
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PRM800K
800,000 step-level correctness labels on LLM solutions to MATH problem
...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 can contain many step-level labels and rich metadata such as labeler UUIDs, timestamps, generation identifiers, and quality-control flags. Each labeled step can include multiple candidate completions with ratings of -1, 0, or +1, optional human-written corrections (phase 1), and a chosen completion index, along with a final finish reason such as found_error, solution, bad_problem, or give_up. -
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MMClassification
OpenMMLab Image Classification Toolbox and Benchmark
...We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new classifiers. MMClassification mainly uses python files as configs. The design of our configuration file system integrates modularity and inheritance, facilitating users to conduct various experiments. -
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Chinese-LLaMA-Alpaca-2 v2.0
Chinese LLaMA & Alpaca large language model + local CPU/GPU training
This project has open-sourced the Chinese LLaMA model and the Alpaca large model with instruction fine-tuning to further promote the open research of large models in the Chinese NLP community. Based on the original LLaMA , these models expand the Chinese vocabulary and use Chinese data for secondary pre-training, which further improves the basic semantic understanding of Chinese. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, which significantly improves the model's ability to understand and execute instructions. -
Securden Windows Privilege ManagerRemove local administrator rights on Windows servers and endpoints. Seamlessly elevate applications for standard users. Grant time-limited rights on-demand. Control application usage by remote employees through whitelisting and blacklisting.
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MetaTransformer
Meta-Transformer for Unified Multimodal Learning
We're thrilled to present OneLLM, an ensembling Meta-Transformer framework with Multimodal Large Language Models, which performs multimodal joint training, supports more modalities including fMRI, Depth, and Normal Maps, and demonstrates very impressive performances on 25 benchmarks. -
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ThoughtSource
A central, open resource for data and tools
ThoughtSource is a central, open resource and community centered on data and tools for chain-of-thought reasoning in large language models (Wei 2022). Our long-term goal is to enable trustworthy and robust reasoning in advanced AI systems for driving scientific research and medical practice. -
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riscordbot
Discordbot which will keep track of Risk: Global Domination Matches.
This discordbot will be available to all servers who are hosting Risk tournaments, so there is an easy and centralised way to track statistics for players, scores, tournament settings, and much more data that will be nicely output into a userfriendly environment (being plaintext data structured to be viewed in discord-chat, twitch-chat or via a nice designed website.) -
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Anse
Supercharged experience for multiple models such as ChatGPT
...It emphasizes a clean, user-friendly experience and supports different conversation modes (single prompt, continuous dialogue, image generation, etc.). Anse uses client-side storage (IndexDB) to keep session history locally, prioritizing user privacy and avoiding automatic uploads of sensitive chat content. It is responsive and optimized for mobile, supports dark mode, and is designed for deployment in a variety of environments (Vercel, Netlify, Docker, etc.). The architecture includes a plugin/provider system so that new model endpoints or providers can be added easily, making it extensible. ... -
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hloc
Visual localization made easy with hloc
...This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using SfM. Just download the datasets and you're reading to go! The notebook pipeline_InLoc.ipynb shows the steps for localizing with InLoc. It's much simpler since a 3D SfM model is not needed. We show in pipeline_SfM.ipynb how to run 3D reconstruction for an unordered set of images. This generates reference poses, and a nice sparse 3D model suitable for localization with the same pipeline as Aachen. -
Create stunning, professional email signatures in minutesCreate, assign and manage all your employees’ email signatures and add targeted marketing banners. Stop getting worked up about your signatures! Leverage a centralized interface to easily create and manage the email signatures of all your employees. Take advantage of each email to broadcast and amplify your brand. Letsignit helps you regain control over your digital identity. Harmonize 100% of your employee’s email signatures in just a few clicks! 121 professional emails are received and 40 are sent every day by an employee. With Letsignit, turn every email into a powerful communication opportunity: send the right message to the right person at the right time! Innovative more than tech, inspiring more than following. Authentic more than overrated, close more than "think big", trustworthy more than doubtful. Hands-on more than complex, available but yet premium, fun but yet expert.
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AI Explainability 360
Interpretability and explainability of data and machine learning model
...The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. -
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AI-powered enterprise search engine
AI-powered enterprise search engine
...By leveraging natural language processing, Gerev allows users to query information in plain English, making it easier to find answers without needing exact keywords or knowing where the data is stored. The platform indexes content from connected systems rather than relying on their native search capabilities, resulting in faster and more relevant results across large datasets. Gerev is built with a strong emphasis on privacy and control, as it can be fully self-hosted, ensuring that sensitive company data remains. -
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TXM
Unicode XML TEI text analysis platform
TXM is a free and open-source cross-platform Unicode & XML based text analysis environment and graphical client, supporting Windows, Linux and Mac OS X. It can also be used online as a J2EE standard compliant web portal (GWT based) with access control built in. DOWNLOAD LATEST VERSION OF TXM : http://textometrie.ens-lyon.fr/spip.php?rubrique61&lang=en TXM offers a comprehensive range of analysis tools (concordances, collocate search, frequency lists, etc.) based on the powerfull CQP... -
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Lightning Flash
Flash enables you to easily configure and run complex AI recipes
Your PyTorch AI Factory, Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains. In a nutshell, Flash is the production-grade research framework you always dreamed of but didn't have time to build. All data loading in Flash is performed via a from_* classmethod on a DataModule. Which DataModule to use and which from_* methods are available depends on the task you want to perform. For example, for image segmentation where your data is stored in folders, you would use the from_folders method of the SemanticSegmentationData class. ... -
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mindflow
AI-powered CLI git wrapper, boilerplate code generator, chat history
...Interact with chatGPT directly just like on the chatGPT website. We also have chat persistence, so it will remember the previous chat messages. You can provide single or multi-file context to chatGPT by passing in any number of files as a separate argument in the mf chat call. -
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fastMRI
A large open dataset + tools to speed up MRI scans using ML
...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 loaders, subsampling utilities, reconstruction models, and evaluation metrics, supporting both research reproducibility and practical experimentation. It includes reference implementations for key MRI reconstruction architectures such as U-Net and Variational Networks (VarNet), along with example scripts for model training and evaluation using the PyTorch Lightning framework. ... -
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react-llm
Easy-to-use headless React Hooks to run LLMs in the browser with WebGP
Easy-to-use headless React Hooks to run LLMs in the browser with WebGPU. As simple as useLLM(). -
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MMOCR
OpenMMLab Text Detection, Recognition and Understanding Toolbox
...The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction. The modular design of MMOCR enables users to define their own optimizers, data preprocessors, and model components such as backbones, necks and heads as well as losses. Please refer to Getting Started for how to construct a customized model. The toolbox provides a comprehensive set of utilities which can help users assess the performance of models. It includes visualizers which allow visualization of images, ground truths as well as predicted bounding boxes, and a validation tool for evaluating checkpoints. -
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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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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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learn2learn
A PyTorch Library for Meta-learning Research
...It provides reusable components and meta-learning algorithms, making it easier to build, train, and evaluate models that can quickly adapt to new tasks with minimal data. Learn2Learn is widely used in research for tasks such as few-shot classification, reinforcement learning, and optimization. -
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Prime QA
State-of-the-art Multilingual Question Answering research
...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 downloadable. -
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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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It is a powerful data scraping tool that helps you extract information from various online sources. Easily collect data from Google SERP, Maps, Shopify, Zillow, and more. With a user-friendly interface, you can scrape and save data in JSON or Excel formats. Unlock insights from the web effortlessly with scrape-it.cloud API.
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Metaseq
Repo for external large-scale work
...The framework was used internally at Meta to train models like OPT (Open Pre-trained Transformer) and serves as a reference implementation for scaling transformer architectures efficiently across GPUs and nodes. It supports both pretraining and fine-tuning workflows with data pipelines for text, multilingual corpora, and custom tokenization schemes. Metaseq also includes APIs for evaluation, generation, and model serving, enabling seamless transitions from training to inference. -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
...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 processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve. Start scaling your model training with just a few lines of Python code. ...
