Best AI tools for< Find Lawyers >
20 - AI tool Sites
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Legal Data
Legal Data is a comprehensive legal research platform developed by lawyers for lawyers. It offers a powerful search feature that covers various legal areas from commercial to criminal law. The platform recognizes synonyms, legalese, and abbreviations, corrects typos, and provides suggestions as you type. Additionally, Legal Data includes an AI-assistant called FlyBot, trained on carefully selected laws and cases, to provide accurate legal answers without fabricating information.
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Lexology
Lexology is a next-generation search tool designed to help users find the right lawyer for their needs. It offers a wide range of resources, including practical analysis, in-depth research tools, primary sources, and expert reports. The platform aims to be a go-to resource for legal professionals and individuals seeking legal expertise.
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Skimming
Skimming is an AI tool that allows users to instantly get answers from PDF, YouTube, audio, and video content. It offers features such as chatting with documents, websites, audio, and video, custom prompts, drag and capture functionality, multilingual support, live support, integration with custom APIs, and more. Skimming is trusted by over 100,000 users, including students, researchers, YouTubers, podcasters, teachers, lawyers, and professionals. The tool simplifies document reading, data analysis, and information retrieval, making it a valuable resource for various industries and individuals.
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Legalyze.ai
Legalyze.ai is an AI application designed for lawyers to streamline document review processes and automate the creation of medical chronologies. The platform uses AI to summarize and extract key points from case documents, providing rapid insights and answers to specific questions. With features like document summaries, rapid case review, and fact-finding capabilities, Legalyze.ai aims to save time and enhance efficiency in legal practices.
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Ask AI Lawyer
Ask AI Lawyer is a free online legal information service that utilizes advanced artificial intelligence technology to provide answers to legal questions within 5 minutes. It is powered by OpenAI's GPT models and offers general legal information on various topics such as traffic laws, business start-ups, tax law, family law, business contracts, employment law, landlord/tenant matters, wills and trusts, and real estate law. The AI analyzes user questions, cross-references them with a database of legal information, and provides comprehensive answers. Answers are published publicly under the "Latest Questions" section for everyone to view. The service is designed to provide quick and easy access to legal information but should not be considered as a substitute for professional legal advice.
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Grok
Grok is an AI-powered search engine that helps you find the documents you need quickly and easily. With Grok, you can search across all of your files, emails, and chats, and get instant results. Grok also uses AI to learn your search habits and preferences, so the more you use it, the better it gets at finding the documents you need.
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Keylight AI
Keylight AI is an AI-powered solution designed to help users efficiently find information within their documents. It offers lightning-fast searches, precision accuracy, a user-friendly interface, customizable prompts, and ensures secure and confidential document handling. Ideal for professionals across various industries, Keylight AI revolutionizes document search by providing quick and efficient navigation. Users can boost their productivity and save time with this innovative tool.
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Hotseat AI
Hotseat AI is a legal research assistant that allows users to search through legal documents and find expert-level quotes matching their queries in seconds. It offers semantic search capabilities, metadata extraction, and the ability to search over both public and private documents. The tool is currently in private beta with a focus on EU regulations related to tech, fintech, banking, and financial services.
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docAnalyzer.ai
docAnalyzer.ai is an intelligent document analysis tool that allows users to have easy and intelligent conversations with their documents. It is powered by cutting-edge AI research and state-of-the-art embeddings, which ensures superior document analysis and dynamic interactions with PDFs. docAnalyzer.ai is easy to use, privacy-conscious, and secure, and it offers a number of features that make it a valuable tool for anyone who works with documents.
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DiscoveryAi
DiscoveryAi is an AI-powered research assistant that helps you find and organize the information you need to make better decisions. With DiscoveryAi, you can quickly and easily search through a vast database of articles, news, and other content to find the information you need. You can also use DiscoveryAi to organize your research into folders and notes, and to collaborate with others on research projects.
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Czat.ai
Czat.ai is a Polish chat application powered by artificial intelligence that aims to provide answers to any question, solve problems, and help users achieve their goals. With over 25 expert companions available, users can engage in conversations on a wide range of topics, from career advice to personal development and even entertainment. The application offers practical solutions, quick responses, and creative ideas to assist users in their daily lives. It also emphasizes simplicity, accessibility, and user-friendly interactions, making advanced AI technology available to everyone.
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Talking Tree
Talking Tree is an AI-powered document management tool designed for legal professionals to digitize and manage legal documents efficiently. It offers advanced OCR technology and a custom RAG architecture to convert printed and handwritten text into searchable structured data. The platform enables users to find information, draft agreements, and analyze legacy documents with unprecedented speed and accuracy. Talking Tree provides a secure and user-friendly interface with multilingual support, making it a valuable resource for legal research and document management.
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Find AI
Find AI is an AI-powered search engine that provides users with advanced search capabilities to unlock contact details and gain more accurate insights. The platform caters to individuals and companies looking to research people, companies, startups, founders, and more. Users can access email addresses and premium search features to explore a wide range of data related to various industries and sectors. Find AI offers a user-friendly interface and efficient search algorithms to deliver relevant results in a timely manner.
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Find your next book
Find your next book is an AI-powered librarian that provides personalized book recommendations based on your preferences. It uses advanced algorithms to analyze your reading history, interests, and other factors to suggest books that you're likely to enjoy. The platform offers a wide range of genres and authors to choose from, making it easy to find your next favorite read.
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Find Your AIs
Find Your AIs is an AI directory website that showcases a wide range of AI tools and applications. It offers a platform for users to explore and discover various AI-powered solutions across different categories such as digital wellness, marketing, text-to-image generation, resume customization, and more. The website aims to connect users with innovative AI technologies to enhance their daily lives and work efficiency.
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Find My Remote
Find My Remote is an AI-powered job search platform that streamlines the job hunting process by leveraging artificial intelligence to find and structure job postings from various ATS platforms. Users can set their job preferences, receive personalized job matches, and save time by applying to curated job listings. The platform offers exclusive job opportunities not typically found on popular job search websites like LinkedIn. With features such as job discovery, application tracking, and faster application process, Find My Remote aims to revolutionize the way job seekers find and apply for jobs.
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Find New AI
Find New AI is a comprehensive platform offering a variety of AI tools and efficiency solutions for different purposes such as SEO, content creation, marketing, link building, image manipulation, and more. The website provides reviews, tutorials, and guides on utilizing AI software effectively to enhance productivity and creativity in various domains.
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Find My Size
Find My Size is a web application that provides personalized size recommendations for exclusive deals at hundreds of top retailers. Users can input their measurements and preferences to receive tailored suggestions for clothing items that will fit them perfectly. The platform aims to enhance the online shopping experience by helping customers find the right size and style without the need for multiple returns. Find My Size collaborates with various retailers to offer a wide range of products across different categories, including active & sportswear, young contemporary, business & workwear, lingerie & sleepwear, outerwear, maternity wear, plus size apparel, and swimwear.
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Realtor.com®
Realtor.com® is a real estate platform that provides a wide range of services for buying, renting, and selling homes. The website offers listings for homes, apartments, and houses for rent, as well as tools for pre-approval, home value estimation, and browsing properties in various locations. Users can find information on new listings, price reductions, open houses, recently sold properties, new constructions, and more. Realtor.com® also provides resources for real estate professionals and homeowners, including market trends, educational materials, and networking opportunities.
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Prolific
Prolific is a platform that allows users to quickly find research participants they can trust. It offers a diverse participant pool, including domain experts and API integration. Prolific ensures high-quality human-powered datasets in less than 2 hours, trusted by over 3000 organizations. The platform is designed for ease of use, with self-serve options and scalability. It provides rich, accurate, and comprehensive responses from engaged participants, verified through manual and algorithmic quality checks.
20 - Open Source AI Tools
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lawyer-llama
Lawyer LLaMA is a large language model that has been specifically trained on legal data, including Chinese laws, regulations, and case documents. It has been fine-tuned on a large dataset of legal questions and answers, enabling it to understand and respond to legal inquiries in a comprehensive and informative manner. Lawyer LLaMA is designed to assist legal professionals and individuals with a variety of law-related tasks, including: * **Legal research:** Quickly and efficiently search through vast amounts of legal information to find relevant laws, regulations, and case precedents. * **Legal analysis:** Analyze legal issues, identify potential legal risks, and provide insights on how to proceed. * **Document drafting:** Draft legal documents, such as contracts, pleadings, and legal opinions, with accuracy and precision. * **Legal advice:** Provide general legal advice and guidance on a wide range of legal matters, helping users understand their rights and options. Lawyer LLaMA is a powerful tool that can significantly enhance the efficiency and effectiveness of legal research, analysis, and decision-making. It is an invaluable resource for lawyers, paralegals, law students, and anyone else who needs to navigate the complexities of the legal system.
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llms-interview-questions
This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.
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warc-gpt
WARC-GPT is an experimental retrieval augmented generation pipeline for web archive collections. It allows users to interact with WARC files, extract text, generate text embeddings, visualize embeddings, and interact with a web UI and API. The tool is highly customizable, supporting various LLMs, providers, and embedding models. Users can configure the application using environment variables, ingest WARC files, start the server, and interact with the web UI and API to search for content and generate text completions. WARC-GPT is designed for exploration and experimentation in exploring web archives using AI.
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TinyTroupe
TinyTroupe is an experimental Python library that leverages Large Language Models (LLMs) to simulate artificial agents called TinyPersons with specific personalities, interests, and goals in simulated environments. The focus is on understanding human behavior through convincing interactions and customizable personas for various applications like advertisement evaluation, software testing, data generation, project management, and brainstorming. The tool aims to enhance human imagination and provide insights for better decision-making in business and productivity scenarios.
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ComfyUI-HunyuanVideo-Nyan
ComfyUI-HunyuanVideo-Nyan is a repository that provides tools for manipulating the attention of LLM models, allowing users to shuffle the AI's attention and cause confusion. The repository includes a Nerdy Transformer Shuffle node that enables users to mess with the LLM's attention layers, providing a workflow for installation and usage. It also offers a new SAE-informed Long-CLIP model with high accuracy, along with recommendations for CLIP models. Users can find detailed instructions on how to use the provided nodes to scale CLIP & LLM factors and create high-quality nature videos. The repository emphasizes compatibility with other related tools and provides insights into the functionality of the included nodes.
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ai-enablement-stack
The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.
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zenu
ZeNu is a high-performance deep learning framework implemented in pure Rust, featuring a pure Rust implementation for safety and performance, GPU performance comparable to PyTorch with CUDA support, a simple and intuitive API, and a modular design for easy extension. It supports various layers like Linear, Convolution 2D, LSTM, and optimizers such as SGD and Adam. ZeNu also provides device support for CPU and CUDA (NVIDIA GPU) with CUDA 12.3 and cuDNN 9. The project structure includes main library, automatic differentiation engine, neural network layers, matrix operations, optimization algorithms, CUDA implementation, and other support crates. Users can find detailed implementations like MNIST classification, CIFAR10 classification, and ResNet implementation in the examples directory. Contributions to ZeNu are welcome under the MIT License.
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LLMsKnow
LLMs Know More Than They Show is a repository containing code to reproduce the results in the paper. It includes scripts to generate model answers, extract exact answers, probe all layers and tokens, probe specific layers and tokens, conduct generalization experiments, perform resampling for error type probing and answer selection experiments, and run other baselines like logprob detection and p_true detection. The repository supports various datasets such as TriviaQA, Movies, HotpotQA, Winobias, Winogrande, NLI, IMDB, Math, and Natural questions. It also provides supported models like Mistral-7B-Instruct-v0.2, Mistral-7B-v0.3, Meta-Llama-3-8B, and Meta-Llama-3-8B-Instruct.
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TriForce
TriForce is a training-free tool designed to accelerate long sequence generation. It supports long-context Llama models and offers both on-chip and offloading capabilities. Users can achieve a 2.2x speedup on a single A100 GPU. TriForce also provides options for offloading with tensor parallelism or without it, catering to different hardware configurations. The tool includes a baseline for comparison and is optimized for performance on RTX 4090 GPUs. Users can cite the associated paper if they find TriForce useful for their projects.
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1.5-Pints
1.5-Pints is a repository that provides a recipe to pre-train models in 9 days, aiming to create AI assistants comparable to Apple OpenELM and Microsoft Phi. It includes model architecture, training scripts, and utilities for 1.5-Pints and 0.12-Pint developed by Pints.AI. The initiative encourages replication, experimentation, and open-source development of Pint by sharing the model's codebase and architecture. The repository offers installation instructions, dataset preparation scripts, model training guidelines, and tools for model evaluation and usage. Users can also find information on finetuning models, converting lit models to HuggingFace models, and running Direct Preference Optimization (DPO) post-finetuning. Additionally, the repository includes tests to ensure code modifications do not disrupt the existing functionality.
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LEADS
LEADS is a lightweight embedded assisted driving system designed to simplify the development of instrumentation, control, and analysis systems for racing cars. It is written in Python and C/C++ with impressive performance. The system is customizable and provides abstract layers for component rearrangement. It supports hardware components like Raspberry Pi and Arduino, and can adapt to various hardware types. LEADS offers a modular structure with a focus on flexibility and lightweight design. It includes robust safety features, modern GUI design with dark mode support, high performance on different platforms, and powerful ESC systems for traction control and braking. The system also supports real-time data sharing, live video streaming, and AI-enhanced data analysis for driver training. LEADS VeC Remote Analyst enables transparency between the driver and pit crew, allowing real-time data sharing and analysis. The system is designed to be user-friendly, adaptable, and efficient for racing car development.
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ai-services
AI Services is a WordPress plugin that provides a centralized infrastructure for integrating AI capabilities into WordPress websites. It allows other plugins to utilize AI services via a common API, making it easier for developers to incorporate AI features without the need to implement separate API layers. The plugin supports various AI services such as Anthropic, Google, and OpenAI, enabling users to choose their preferred service. It simplifies the process of configuring AI APIs and unlocks AI capabilities for smaller plugins or features. The plugin is still in early stages, with ongoing enhancements and improvements planned to streamline API usage and enhance user experience.
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lorax
LoRAX is a framework that allows users to serve thousands of fine-tuned models on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency. It features dynamic adapter loading, heterogeneous continuous batching, adapter exchange scheduling, optimized inference, and is ready for production with prebuilt Docker images, Helm charts for Kubernetes, Prometheus metrics, and distributed tracing with Open Telemetry. LoRAX supports a number of Large Language Models as the base model including Llama, Mistral, and Qwen, and any of the linear layers in the model can be adapted via LoRA and loaded in LoRAX.
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llm-foundry
LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs
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ivy
Ivy is an open-source machine learning framework that enables users to convert code between different ML frameworks and write framework-agnostic code. It allows users to transpile code from one framework to another, making it easy to use building blocks from different frameworks in a single project. Ivy also serves as a flexible framework that breaks free from framework limitations, allowing users to publish code that is interoperable with various frameworks and future frameworks. Users can define trainable modules and layers using Ivy's stateful API, making it easy to build and train models across different backends.
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FinRobot
FinRobot is an open-source AI agent platform designed for financial applications using large language models. It transcends the scope of FinGPT, offering a comprehensive solution that integrates a diverse array of AI technologies. The platform's versatility and adaptability cater to the multifaceted needs of the financial industry. FinRobot's ecosystem is organized into four layers, including Financial AI Agents Layer, Financial LLMs Algorithms Layer, LLMOps and DataOps Layers, and Multi-source LLM Foundation Models Layer. The platform's agent workflow involves Perception, Brain, and Action modules to capture, process, and execute financial data and insights. The Smart Scheduler optimizes model diversity and selection for tasks, managed by components like Director Agent, Agent Registration, Agent Adaptor, and Task Manager. The tool provides a structured file organization with subfolders for agents, data sources, and functional modules, along with installation instructions and hands-on tutorials.
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DiagrammerGPT
DiagrammerGPT is an official implementation of a two-stage text-to-diagram generation framework that utilizes the layout guidance capabilities of LLMs to create accurate open-domain, open-platform diagrams. The tool first generates a diagram plan based on a prompt, which includes dense entities, fine-grained relationships, and precise layouts. Then, it refines the plan iteratively before generating the final diagram. DiagrammerGPT has been used to create various diagrams such as layers of the earth, Earth's position around the sun, and different types of rocks with labels.
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nncf
Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in OpenVINO™ with a minimal accuracy drop. It is designed to work with models from PyTorch, TorchFX, TensorFlow, ONNX, and OpenVINO™. NNCF offers samples demonstrating compression algorithms for various use cases and models, with the ability to add different compression algorithms easily. It supports GPU-accelerated layers, distributed training, and seamless combination of pruning, sparsity, and quantization algorithms. NNCF allows exporting compressed models to ONNX or TensorFlow formats for use with OpenVINO™ toolkit, and supports Accuracy-Aware model training pipelines via Adaptive Compression Level Training and Early Exit Training.
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deepdoctection
**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.
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Web-LLM-Assistant-Llama-cpp
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20 - OpenAI Gpts
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