zjunlp/DeepKE

Features

• Knowledge Graph Construction Tools - Provides a comprehensive framework for building structured knowledge graphs by extracting entities, relations, and events from text.
• Entity and Relation Extraction - Detects and categorizes semantic relationships between entities to transform raw text into structured triples.
• Joint Extractions - Implements a joint extraction process that identifies named entities and their relationships in a single structured output.
• Event Extraction - Identifies event types, trigger words, and argument roles from unstructured text to structure event-based data.
• Knowledge Graph Extraction - Provides a toolkit for extracting entities, relations, and events to build structured knowledge graphs.
• LLM Tool Calling - Utilizes large language models as tool callers through a standardized context protocol to automate data extraction.
• Named Entity Recognition - Implements systems for identifying and classifying named entities using supervised, few-shot, and multimodal models.
• Information Extraction Frameworks - Provides a complete pipeline for converting raw datasets into structured instructions and serving models via API.
• Schema-Driven Extraction - Maps unstructured text to predefined structured formats and task descriptions for domain-specific knowledge extraction.
• LLM-Driven Data Extractors - Leverages large language models as tool callers to automate the transformation of unstructured content into structured formats.
• LLM-to-Structured Data Converters - Employs large language models as tool callers to drive the extraction of specific knowledge into structured formats.
• Custom Model Training - Provides a training pipeline for entity recognition and relation extraction models using customized datasets.
• Distributed GPU Training - Supports spreading neural network training across multiple graphics cards to increase speed and memory capacity.
• Distributed Memory Optimizers - Optimizes model memory through quantization and weight distribution across hardware accelerators to support larger parameter counts.
• Distributed Training - Provides capabilities to train large neural networks across multiple GPUs to handle large datasets and memory requirements.
• Schema-Driven Extraction - Implements a comprehensive system for identifying entities, relations, and events from bilingual text using predefined schemas.
• Relation Extraction Training - Provides a training framework for deep learning models to identify semantic relations between entities.
• Multi-GPU Distribution - Optimizes model memory by splitting weights across multiple graphics cards and using quantization.
• Distant Supervision Pipelines - Leverages existing high-quality triple files to automatically generate relation labels for raw text.
• Tool-Protocol Standardizations - Uses a standardized context protocol to allow language models to act as tool callers for knowledge extraction.
• Training Data Annotations - Offers a system for producing annotated datasets through manual labeling and automated dictionary matching.
• Model Fine-Tuning - Supports fine-tuning deep learning model weights and hyperparameters for domain-specific information extraction.
• Relation Classification - Categorizes semantic relationships between detected entities across document-level and multimodal scenarios.
• Relation Labeling - Provides an automated supervision system for assigning relationship labels to reduce manual training data annotation.
• Instruction-Based Conversions - Transforms raw extraction datasets into structured formats featuring task descriptions and dynamic schemas.
• Model Inference Deployments - Deploys trained extraction models as high-performance API endpoints for real-time inference requests.
• Data Annotation - Generative knowledge graph construction using code models.
• Evaluation and Benchmarking - Instruction-based information extraction dataset for Chinese.
• In Context Learning - Generative knowledge graph construction using code models.
• Named Entity Recognition - Collaborative domain-prefix tuning for cross-domain entity recognition.
• Natural Language Processing - Toolkit for knowledge graph extraction and relation classification.
• Relation Extraction - Unleashing model power for few-shot relation extraction.
• Task-Specific Prompting - Lightweight generative framework for low-resource NER.
• Universal Information Extraction - Code language model for generative knowledge graph construction.
• Zero-Shot Prompting - Generative knowledge graph construction using code models.

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