#Information Extraction

LangChain Extract provides an effective solution for extracting text and file data using FastAPI, LangChain, and PostgreSQL. It features a FastAPI web server that supports the creation of customizable extractors through JSON Schema. With integration into LangChain, it enhances data processing capabilities, suitable for various data extraction scenarios. The REST API and OpenAPI Documentation facilitate ease of access, while the demo service and continuous development highlight its viability for creating custom applications.

Explore GoLLIE, a Large Language Model designed to excel in zero-shot information extraction through adherence to annotation guidelines. This model supports creating dynamic annotation schemas and goes beyond existing knowledge with detailed definitions. GoLLIE's improved performance is available to the public on the HuggingFace Hub, with comprehensive instructions for installation, usage, and dataset generation, aiding customization in information extraction tasks.

The project leverages the Hugging Face Transformers library to support a range of NLP tasks, facilitating seamless loading and training of transformer models. It enables easy dataset interchange for task-specific model training across domains such as text matching, information extraction, prompt engineering, and more. Detailed guidance and tool integration, including a tokenizer viewer, are provided. This resource supports diverse learning methods, enhancing NLP model customization without excessive promotional language.

Explore a new method of interacting with YouTube videos via AI-driven dialogues. By entering a video URL, the tool enables instant responses and the ability to query and extract information from the video's content. The Chat-Youtube project offers user-friendly setup guidelines and supports multiple platforms, with Replit and Streamlit versions on the horizon. Stay informed by following Anil Chandra Naidu Matcha for more resources, including YouTube tutorials. Experience this AI interaction yourself through our demo link.

This repository focuses on Event Extraction in Natural Language Processing, covering methods from pattern matching to unsupervised learning. It includes resources like AutoSlog, LIEP, and REES, which enhance semantic lexicons with bootstrapping and pattern recognition techniques. The repository supports tasks in various domains, such as terrorism and stock market analysis, offering effective information extraction solutions.

Explore a comprehensive range of academic papers on generative information extraction using Large Language Models (LLMs). This curated collection includes recent studies on topics such as named entity recognition, relation extraction, and event extraction. Access innovative methodologies like supervised fine-tuning, few-shot, and zero-shot learning, along with data augmentation and constrained decoding. The repository invites contributions from academics and offers a detailed survey of LLMs in generative information extraction. Keep current with the latest papers and access useful datasets to advance research in the information extraction domain.

This resource offers carefully curated study notes and materials for natural language processing (NLP) interview preparation. It covers a broad array of interview questions across various NLP domains and provides thorough insights into algorithms such as Hidden Markov Model (HMM), Maximum Entropy Markov Model (MEMM), and Conditional Random Fields (CRF). Designed to support both novices and experienced professionals, the project addresses crucial topics like named entity recognition, relationship extraction, event extraction, and pre-training methods like TF-IDF and Word2Vec. Each section presents typical interview questions, explanations, and solutions, forming a comprehensive reference for NLP enthusiasts preparing for technical interviews.

The KnowLM framework aids in the development of informed Large Language Models, emphasizing data processing, pre-training, fine-tuning, and knowledge enhancement. The model zoo includes adaptable models such as ZhiXi and OneKE for straightforward implementation. Core features entail instruction handling through EasyInstruct, knowledge modification with EasyEdit, and hallucination identification via EasyDetect. Regular updates in model weights ensure support for ongoing advancements accessible via HuggingFace, suitable for users focused on extracting information and knowledge.