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Triplex
Overview :
Triplex is an innovative open-source model that transforms large amounts of unstructured data into structured data. Its performance in knowledge graph construction surpasses that of GPT-4, and its cost is only one-tenth of it. By efficiently converting unstructured text into the foundational building blocks of knowledge graphs—semantic triples—it significantly reduces the cost of knowledge graph generation.
Target Users :
The target audience includes enterprises and research institutions that need to build knowledge graphs, especially those that are cost-sensitive and deal with large volumes of unstructured data. Triplex is particularly suitable for data-intensive applications and scenarios requiring rapid extraction of information from text due to its cost-effectiveness and high efficiency.
Use Cases
Companies use Triplex to construct knowledge graphs from customer feedback to improve products and services.
Research institutions utilize Triplex to analyze scientific literature and build knowledge networks within their fields.
Educational institutions employ Triplex to extract knowledge points from textbooks, constructing course association graphs.
Features
Convert unstructured data into structured data.
Construct knowledge graphs, supporting complex and simple relational queries.
Open-source, available on HuggingFace and Ollama.
Utilize DPO and KTO for additional training to enhance model performance.
Train on datasets generated from authoritative data sources to ensure model diversity and robustness.
Integrate with R2R RAG engine and Neo4J for local knowledge graph construction.
Provide documentation and trials for users to quickly get started.
How to Use
Visit the Triplex open-source page to learn about the model's basic information and features.
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Read the documentation to understand how to install and configure the Triplex model.
Prepare unstructured data inputs according to specific requirements.
Use the Triplex model to process data and generate semantic triples.
Integrate the generated triples to construct a knowledge graph.
Utilize the knowledge graph for querying and analysis to extract the desired information.
Adjust the model parameters based on feedback to optimize the knowledge graph construction process.
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