Best AI tools for< Process Raw Text >

Vmaker is an AI video editor and screen recorder that revolutionizes the video editing process by leveraging artificial intelligence technology. It offers a wide range of features such as auto-adding videos, images, and GIFs, background music based on video mood, stickers, text animation, smart zoom, transitions, auto subtitles in multiple languages, intro and outro generation, and more. Vmaker aims to simplify the video editing workflow and empower users to create professional-looking videos effortlessly. It caters to content creators, marketers, YouTubers, and learning and development teams, providing them with a comprehensive tool for enhancing their video content.

Smartrazor is an AI-powered video editing tool designed for YouTubers and content creators to streamline the editing process. It automates repetitive tasks, such as clipping raw footage and enhancing video quality, allowing users to focus on creative aspects of content creation. With a user-friendly interface and compatibility with industry-standard editing software, Smartrazor aims to save time and improve editing efficiency for creators of 'talking head' style videos.

ScribblePad AI is an AI-powered content creation tool that helps users translate their raw thoughts and ideas into well-structured content for platforms like LinkedIn, blogs, and Twitter. It allows users to record their thoughts, upload audio, and receive structured content quickly and effortlessly. With features like efficiency, creativity, and versatility, ScribblePad AI is designed to cater to professionals, bloggers, and social media enthusiasts, enabling them to amplify their voice and engage their audience effectively.

Sensay is a platform that specializes in creating digital AI Replicas, offering cutting-edge cloning technology to simplify the process of developing humanlike AI Replicas. These Replicas are designed to preserve and share wisdom, catering to various needs such as dementia care, custom solutions, education, and fan engagement. Sensay ensures the creation of personalized Replicas that mimic individual personalities for realistic interactions, with a focus on continuous learning and enhancing interaction quality over time. The platform also delves into ethical and philosophical implications, emphasizing privacy protection, consent, and the exploration of identity concepts.

Ocrolus is an intelligent document automation software that leverages AI-driven document processing automation with Human-in-the-Loop. It helps in classifying, capturing, detecting, and analyzing various types of documents to streamline processes and make faster and more accurate financial decisions. The software is designed to assist in tasks such as income verification, fraud detection, cash flow analysis, and business process automation across different industries.

BiteSyzed is an AI-powered video repurposing tool that transforms long videos into viral clips 10 times faster. The platform uses cutting-edge AI technology to automatically analyze and edit raw footage, extract captivating moments, and create cohesive video clips. Users can upload videos from YouTube, export clips in different aspect ratios, and share them with their audience effortlessly. Bitesyzed simplifies the video editing process by automating the creation of viral clips with AI-generated descriptions and hashtags, saving time and resources. The application is designed to help users create more engaging video content with minimal effort, catering to a wide range of users from content creators to marketers.

Boolvideo is an AI-powered video editing tool that simplifies the video editing process by automating the editing tasks. Users can create professional-looking videos by simply inputting their raw footage and letting the AI algorithm handle the editing. With Boolvideo, users can save time and effort in creating engaging video content for various purposes such as social media, marketing, and personal projects.

AutoPod is an AI-powered software tool designed for editing video podcasts and shows automatically. It offers a seamless and efficient solution for content creators to enhance their video content without the need for manual editing. With AutoPod, users can easily transform their raw footage into polished and professional-looking videos in a matter of minutes. The tool leverages advanced AI algorithms to streamline the editing process and deliver high-quality results. Whether you are a beginner or an experienced content creator, AutoPod provides a user-friendly interface that simplifies the editing workflow and helps you save time and effort.

Columns is an AI tool designed to automate data storytelling. It helps users in creating compelling narratives and visualizations from their data without the need for manual intervention. With Columns, users can easily transform raw data into engaging stories, making data analysis more accessible and impactful. The tool offers a user-friendly interface and a range of customization options to tailor the storytelling process to individual needs.

Process Street is an AI-powered platform that helps businesses streamline their processes and improve operational efficiency. It offers features such as workflows automation, data unification, document sharing, and AI transformation. With Process Street, users can create, track, and complete tasks efficiently, make data-driven decisions, and automate repetitive tasks using generative AI. The platform also provides analytics to track key performance indicators and ensure consistent adherence to procedures. Process Street is trusted by top companies to revolutionize workflow management and drive productivity and growth.

Process Street is a powerful checklist, workflow, and SOP software that is designed to streamline and automate business processes. It offers a wide range of features such as workflows, projects, data sets, forms, and pages to help organizations organize and manage their operations efficiently. With AI capabilities, Process Street can transform manual processes, boost productivity, and empower decision-making with analytics. The platform also provides integrations with various tools for maximum efficiency.

Super.AI provides Intelligent Document Processing (IDP) solutions powered by Large Language Models (LLMs) and human-in-the-loop (HITL) capabilities. It automates document processing tasks such as data extraction, classification, and redaction, enabling businesses to streamline their workflows and improve accuracy. Super.AI's platform leverages cutting-edge AI models from providers like Amazon, Google, and OpenAI to handle complex documents, ensuring high-quality outputs. With its focus on accuracy, flexibility, and scalability, Super.AI caters to various industries, including financial services, insurance, logistics, and healthcare.

Smace is an AI-powered SaaS platform designed to enhance process implementation efficiency. It offers features such as enhanced process collaboration, automated workflows and integration, streamlined task management, and data-driven decision support. Smace aims to bridge the gap between process design and execution, promoting team efficiency, streamlined collaboration, and advanced integration.

Greenhouse is an AI-powered applicant tracking software and hiring platform that offers smart hiring tools to streamline the hiring process. It provides features such as AI tools for sourcing, texting solutions, and feature upgrades to help connect teams and propel success. Greenhouse is designed to help companies hire fairly and purposefully, offering expertise and advice to maximize hiring ROI and support business growth at any stage.

Expertia AI is an AI-powered hiring partner that leverages advanced algorithms and machine learning to streamline the recruitment process. It offers a comprehensive suite of tools to assist HR professionals in sourcing, screening, and selecting top talent efficiently. By automating repetitive tasks and providing data-driven insights, Expertia AI helps companies make informed hiring decisions and improve overall recruitment outcomes.

Meshy is a free 3D AI model generator that empowers artists, game developers, and creators to bring their visions to life with a toolkit for creating 3D models in minutes. It offers powerful AI generation tools, lightning speed modeling, PBR maps, versatile art styles, and user-friendly interface. Meshy allows users to convert text to 3D, images to 3D models, and upload existing 3D models to transform words into textures. With multilingual support, API integration, and various export options, Meshy provides a seamless 3D workflow for users to unleash their creativity like never before.

HireQuotient is an end-to-end recruitment automation platform for non-tech hiring. It supports talent leaders in sourcing, screening, and interviewing top talent in an inclusive way. The platform offers features such as AI-recommended candidate filters, multi-channel outreach automation, and seamless navigation for hiring. HireQuotient's AI tool revolutionizes talent discovery processes, providing warmth, efficiency, and optimization to organizations' people functions.

PyjamaHR is a leading AI-powered Applicant Tracking System (ATS) and recruitment software designed to streamline the hiring process for businesses of all sizes. It offers advanced features such as source management, candidate evaluation, collaboration tools, and AI-powered candidate tests to enhance the efficiency and effectiveness of the recruitment process. With a user-friendly interface and robust security measures, PyjamaHR is a trusted solution for managing talent acquisition and improving hiring outcomes.

MyEssayWriter.ai is an AI-powered essay writing tool that offers advanced features to help students generate high-quality essays efficiently. The tool is designed to save time, improve writing skills, and provide unique and plagiarism-free content. With a user-friendly interface and customizable essays, MyEssayWriter.ai aims to revolutionize the writing process for students worldwide.

La Growth Machine is a multichannel sales automation tool that helps users import and enrich leads, automate conversions, manage leads, and analyze performances. It offers features such as LinkedIn Voice Messages, multichannel inbox, calls, automation of actions and messages, AI-powered writing assistance, campaign analysis, lead management, and more. La Growth Machine streamlines operational processes, enhances performance, and centralizes data in one place. With a focus on multi-channel prospecting, the tool aims to increase conversations and opportunities for users. Trusted by over 10,000 professionals, La Growth Machine provides a seamless experience for reaching out to leads across various platforms.

This repository contains examples of how to use the Hume API with different frameworks and languages. It includes examples for Empathic Voice Interface (EVI) and Expression Measurement API. The EVI examples cover custom language models, modal, Next.js integration, Vue integration, Hume Python SDK, and React integration. The Expression Measurement API examples include models for face, language, burst, and speech, with implementations in Python and Typescript using frameworks like Next.js.

The LLM-Aided OCR Project is an advanced system that enhances Optical Character Recognition (OCR) output by leveraging natural language processing techniques and large language models. It offers features like PDF to image conversion, OCR using Tesseract, error correction using LLMs, smart text chunking, markdown formatting, duplicate content removal, quality assessment, support for local and cloud-based LLMs, asynchronous processing, detailed logging, and GPU acceleration. The project provides detailed technical overview, text processing pipeline, LLM integration, token management, quality assessment, logging, configuration, and customization. It requires Python 3.12+, Tesseract OCR engine, PDF2Image library, PyTesseract, and optional OpenAI or Anthropic API support for cloud-based LLMs. The installation process involves setting up the project, installing dependencies, and configuring environment variables. Users can place a PDF file in the project directory, update input file path, and run the script to generate post-processed text. The project optimizes processing with concurrent processing, context preservation, and adaptive token management. Configuration settings include choosing between local or API-based LLMs, selecting API provider, specifying models, and setting context size for local LLMs. Output files include raw OCR output and LLM-corrected text. Limitations include performance dependency on LLM quality and time-consuming processing for large documents.

MicroLens is a content-driven micro-video recommendation dataset at scale. It provides a large dataset with multimodal data, including raw text, images, audio, video, and video comments, for tasks such as multi-modal recommendation, foundation model building, and fairness recommendation. The dataset is available in two versions: MicroLens-50K and MicroLens-100K, with extracted features for multimodal recommendation tasks. Researchers can access the dataset through provided links and reach out to the corresponding author for the complete dataset. The repository also includes codes for various algorithms like VideoRec, IDRec, and VIDRec, each implementing different video models and baselines.

NineRec is a benchmark dataset suite for evaluating transferable recommendation models. It provides datasets for pre-training and transfer learning in recommender systems, focusing on multimodal and foundation model tasks. The dataset includes user-item interactions, item texts in multiple languages, item URLs, and raw images. Researchers can use NineRec to develop more effective and efficient methods for pre-training recommendation models beyond end-to-end training. The dataset is accompanied by code for dataset preparation, training, and testing in PyTorch environment.

qlora-pipe is a pipeline parallel training script designed for efficiently training large language models that cannot fit on one GPU. It supports QLoRA, LoRA, and full fine-tuning, with efficient model loading and the ability to load any dataset that Axolotl can handle. The script allows for raw text training, resuming training from a checkpoint, logging metrics to Tensorboard, specifying a separate evaluation dataset, training on multiple datasets simultaneously, and supports various models like Llama, Mistral, Mixtral, Qwen-1.5, and Cohere (Command R). It handles pipeline- and data-parallelism using Deepspeed, enabling users to set the number of GPUs, pipeline stages, and gradient accumulation steps for optimal utilization.

STaRK is a large-scale semi-structure retrieval benchmark on Textual and Relational Knowledge Bases. It provides natural-sounding and practical queries crafted to incorporate rich relational information and complex textual properties, closely mirroring real-life scenarios. The benchmark aims to assess how effectively large language models can handle the interplay between textual and relational requirements in queries, using three diverse knowledge bases constructed from public sources.

Azure Functions OpenAI Extension is a project that adds support for OpenAI LLM (GPT-3.5-turbo, GPT-4) bindings in Azure Functions. It provides NuGet packages for various functionalities like text completions, chat completions, assistants, embeddings generators, and semantic search. The project requires .NET 6 SDK or greater, Azure Functions Core Tools v4.x, and specific settings in Azure Function or local settings for development. It offers features like text completions, chat completion, assistants with custom skills, embeddings generators for text relatedness, and semantic search using vector databases. The project also includes examples in C# and Python for different functionalities.

pg_vectorize is a Postgres extension that automates text to embeddings transformation, enabling vector search and LLM applications with minimal function calls. It integrates with popular LLMs, provides workflows for vector search and RAG, and automates Postgres triggers for updating embeddings. The tool is part of the VectorDB Stack on Tembo Cloud, offering high-level APIs for easy initialization and search.

This repository presents the findings of the paper "Compression Represents Intelligence Linearly". The study reveals a strong linear correlation between the intelligence of LLMs, as measured by benchmark scores, and their ability to compress external text corpora. Compression efficiency, derived from raw text corpora, serves as a reliable evaluation metric that is linearly associated with model capabilities. The repository includes the compression corpora used in the paper, code for computing compression efficiency, and data collection and processing pipelines.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

This repository contains the code to prepare the data for the Amber 7B language model. The final training data comes from three sources: RedPajama V1, RefinedWeb, and StarCoderData. The data preparation involves downloading untokenized data, tokenizing the data using the Huggingface tokenizer, concatenating tokens into 2048 token sequences, merging datasets, and splitting the merged dataset into 360 chunks. Each tokenized data chunk is a jsonl file containing samples with 2049 tokens. The repository provides scripts for downloading datasets, tokenizing and concatenating sequences, validating data, and merging subsets into chunks.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

LLM Datasets is a repository containing high-quality datasets, tools, and concepts for LLM fine-tuning. It provides datasets with characteristics like accuracy, diversity, and complexity to train large language models for various tasks. The repository includes datasets for general-purpose, math & logic, code, conversation & role-play, and agent & function calling domains. It also offers guidance on creating high-quality datasets through data deduplication, data quality assessment, data exploration, and data generation techniques.

LLM-Zero-to-Hundred is a repository showcasing various applications of LLM chatbots and providing insights into training and fine-tuning Language Models. It includes projects like WebGPT, RAG-GPT, WebRAGQuery, LLM Full Finetuning, RAG-Master LLamaindex vs Langchain, open-source-RAG-GEMMA, and HUMAIN: Advanced Multimodal, Multitask Chatbot. The projects cover features like ChatGPT-like interaction, RAG capabilities, image generation and understanding, DuckDuckGo integration, summarization, text and voice interaction, and memory access. Tutorials include LLM Function Calling and Visualizing Text Vectorization. The projects have a general structure with folders for README, HELPER, .env, configs, data, src, images, and utils.

Reflection-Tuning is a project focused on improving the quality of instruction-tuning data through a reflection-based method. It introduces Selective Reflection-Tuning, where the student model can decide whether to accept the improvements made by the teacher model. The project aims to generate high-quality instruction-response pairs by defining specific criteria for the oracle model to follow and respond to. It also evaluates the efficacy and relevance of instruction-response pairs using the r-IFD metric. The project provides code for reflection and selection processes, along with data and model weights for both V1 and V2 methods.

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Optimizes production processes for improved efficiency and quality.

Optimizes business operations through process enhancements.

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Improves operational efficiency by optimizing processes and reducing waste.

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Optimizes manufacturing processes for efficiency and quality.

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Process Documentation for serving GPTs.

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Structured process for prioritizing marketing tasks based on strategic alignment. Outputs in Eisenhower, RACI and other methodologies.

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Insurance providers collect and process personal health, financial, and property information, making it crucial to implement comprehensive data protection strategies.

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