Easy-to-use and powerful LLM and SLM library with awesome model zoo.
- Updated
May 13, 2025 - Python
Easy-to-use and powerful LLM and SLM library with awesome model zoo.
Snips Python library to extract meaning from text
[EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction
Deep neural network to extract intelligent information from invoice documents.
Chinese Named Entity Recognition with IDCNN/biLSTM+CRF, and Relation Extraction with biGRU+2ATT 中文实体识别与关系提取
Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts) @ NAACL 2024
LLM(😽)
This Script will help you to gather information about your victim or friend.
The online version is temporarily unavailable because we cannot afford the key. You can clone and run it locally. Note: we set defaul openai key. If keys exceed plan and are invalid, please tell us. The response speed depends on openai. ( sometimes, the official is too crowded and slow)
[ACL 2020] A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
AIL framework - Analysis Information framework
Uscrapper Vanta: Dive deeper into the web with this powerful open-source tool. Extract valuable insights with ease and efficiency, from both surface and deep web sources. Empower your data mining and analysis with Vanta's advanced capabilities. Fast, reliable, and user-friendly, Uscrapper Vanta is the ultimate choice for researchers and analysts.
Knowledge triples extraction and knowledge base construction based on dependency syntax for open domain text.
RomBuster is a router exploitation tool that allows to disclosure network router admin password.
Medical Concept Annotation Tool
PyTorch solution of named entity recognition task Using Google AI's pre-trained BERT model.
🏥 Medical Text Mining and Information Extraction with spaCy
AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models
Attention Guided Graph Convolutional Networks for Relation Extraction (authors' PyTorch implementation for the ACL19 paper)
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