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Hugging Face and JFrog partner to make AI Security more transparent
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Joseph Robert Turcotte PRO
Fishtiks
AI & ML interests
Roleplaying, lorabration, abliteration, smol models, extensive filtering, unusual datasets, home usage, HPCs for AI, distributed training, and sentience. I got a new computer to put AI on from the start, and intend to develop it. Let me know how people are getting H100s.
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Exciting New Tool for Knowledge Graph Extraction from Plain Text!
I just came across a groundbreaking new tool called KGGen that's solving a major challenge in the AI world - the scarcity of high-quality knowledge graph data.
KGGen is an open-source Python package that leverages language models to extract knowledge graphs (KGs) from plain text. What makes it special is its innovative approach to clustering related entities, which significantly reduces sparsity in the extracted KGs.
The technical approach is fascinating:
1. KGGen uses a multi-stage process involving an LLM (GPT-4o in their implementation) to extract entities and relations from source text
2. It aggregates graphs across sources to reduce redundancy
3. Most importantly, it applies iterative LM-based clustering to refine the raw graph
The clustering stage is particularly innovative - it identifies which nodes and edges refer to the same underlying entities or concepts. This normalizes variations in tense, plurality, stemming, and capitalization (e.g., "labors" clustered with "labor").
The researchers from Stanford and University of Toronto also introduced MINE (Measure of Information in Nodes and Edges), the first benchmark for evaluating KG extractors. When tested against existing methods like OpenIE and GraphRAG, KGGen outperformed them by up to 18%.
For anyone working with knowledge graphs, RAG systems, or KG embeddings, this tool addresses the fundamental challenge of data scarcity that's been holding back progress in graph-based foundation models.
The package is available via pip install kg-gen, making it accessible to everyone. This could be a game-changer for knowledge graph applications!
I just came across a groundbreaking new tool called KGGen that's solving a major challenge in the AI world - the scarcity of high-quality knowledge graph data.
KGGen is an open-source Python package that leverages language models to extract knowledge graphs (KGs) from plain text. What makes it special is its innovative approach to clustering related entities, which significantly reduces sparsity in the extracted KGs.
The technical approach is fascinating:
1. KGGen uses a multi-stage process involving an LLM (GPT-4o in their implementation) to extract entities and relations from source text
2. It aggregates graphs across sources to reduce redundancy
3. Most importantly, it applies iterative LM-based clustering to refine the raw graph
The clustering stage is particularly innovative - it identifies which nodes and edges refer to the same underlying entities or concepts. This normalizes variations in tense, plurality, stemming, and capitalization (e.g., "labors" clustered with "labor").
The researchers from Stanford and University of Toronto also introduced MINE (Measure of Information in Nodes and Edges), the first benchmark for evaluating KG extractors. When tested against existing methods like OpenIE and GraphRAG, KGGen outperformed them by up to 18%.
For anyone working with knowledge graphs, RAG systems, or KG embeddings, this tool addresses the fundamental challenge of data scarcity that's been holding back progress in graph-based foundation models.
The package is available via pip install kg-gen, making it accessible to everyone. This could be a game-changer for knowledge graph applications!
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I've spent most of time working with AI on user-facing apps like Chatbots and TextGen, but today I decided to work on something that I think has a lot of applications for Data Science teams:
ZennyKenny/comment_classification
This Space supports uploading a user CSV and categorizing the fields based on user-defined categories. The applications of AI in production are truly endless. 🚀
This Space supports uploading a user CSV and categorizing the fields based on user-defined categories. The applications of AI in production are truly endless. 🚀
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✨🎉Duality.ai just released a multiclass object detection dataset for YOLOv8, as well as a tutorial on how to create your own multiclass dataset!
Carefully crafted (not GenAI created) synthetic data that ACTUALLY trains a model that works in the physical world.
Create a free FalconEDU account, and download the 1000 image and annotation dataset - https://falcon.duality.ai/secure/documentation/ex3-dataset?sidebarMode=learn
-or-
Follow along with Exercise 3: Multiclass Object Detection to start creating - https://falcon.duality.ai/secure/documentation/ex3-objdetection-multiclass
-or-
Download this Colab notebook to see the data work, no hardware required - https://falcon.duality.ai/secure/documentation/ex3-dataset?sidebarMode=learn
Carefully crafted (not GenAI created) synthetic data that ACTUALLY trains a model that works in the physical world.
Create a free FalconEDU account, and download the 1000 image and annotation dataset - https://falcon.duality.ai/secure/documentation/ex3-dataset?sidebarMode=learn
-or-
Follow along with Exercise 3: Multiclass Object Detection to start creating - https://falcon.duality.ai/secure/documentation/ex3-objdetection-multiclass
-or-
Download this Colab notebook to see the data work, no hardware required - https://falcon.duality.ai/secure/documentation/ex3-dataset?sidebarMode=learn
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🚀 Introducing MOUSE: Space Research Thinking on HuggingFace Spaces
🚀 How to Get Started
ginipick/spaces-research-think
Welcome to **MOUSE: Space Research Thinking** – an innovative HuggingFace Spaces project designed to transform how you analyze and interact with Python code. Whether you're a developer, researcher, or simply passionate about coding, this tool provides state-of-the-art analysis, summarization, and usage guidance, all powered by advanced AI.
---
## 🌟 Key Features
- **Real-Time Code Analysis**
Instantly dissect your Python code to reveal its structure, functionality, and potential applications. Our tool delivers:
- **Background & Necessity**: Understand the context behind the code.
- **Functional Utility & Value**: Highlight core functionalities and benefits.
- **Distinctive Features**: Discover what sets the project apart.
- **Target Audience & Applications**: Identify who can benefit and how.
- **Expected Impact**: Envision the improvements and innovations the code can drive.
🔍
- **Visual File Structure Overview**
Navigate your project with ease! A dynamic tree-view displays your file hierarchy in a clear, intuitive format, allowing you to explore directories and files effortlessly. 🌲
- **Interactive Usage Guide**
Receive step-by-step instructions and practical tips on using the tool effectively. Our AI assistant explains everything in an engaging, user-friendly manner, ensuring a smooth learning curve. 💡
- **AI-Powered Code Chat**
Engage in real-time conversations with our AI. Ask questions, request detailed explanations, or dive deeper into code specifics with a chat interface that makes complex topics accessible. 🤖💬
- **Customizable Experience**
Tailor the analysis to your needs with adjustable parameters like token limits and response temperatures, enabling both concise summaries and in-depth explorations. ⚙️
🚀 How to Get Started
ginipick/spaces-research-think
Welcome to **MOUSE: Space Research Thinking** – an innovative HuggingFace Spaces project designed to transform how you analyze and interact with Python code. Whether you're a developer, researcher, or simply passionate about coding, this tool provides state-of-the-art analysis, summarization, and usage guidance, all powered by advanced AI.
---
## 🌟 Key Features
- **Real-Time Code Analysis**
Instantly dissect your Python code to reveal its structure, functionality, and potential applications. Our tool delivers:
- **Background & Necessity**: Understand the context behind the code.
- **Functional Utility & Value**: Highlight core functionalities and benefits.
- **Distinctive Features**: Discover what sets the project apart.
- **Target Audience & Applications**: Identify who can benefit and how.
- **Expected Impact**: Envision the improvements and innovations the code can drive.
🔍
- **Visual File Structure Overview**
Navigate your project with ease! A dynamic tree-view displays your file hierarchy in a clear, intuitive format, allowing you to explore directories and files effortlessly. 🌲
- **Interactive Usage Guide**
Receive step-by-step instructions and practical tips on using the tool effectively. Our AI assistant explains everything in an engaging, user-friendly manner, ensuring a smooth learning curve. 💡
- **AI-Powered Code Chat**
Engage in real-time conversations with our AI. Ask questions, request detailed explanations, or dive deeper into code specifics with a chat interface that makes complex topics accessible. 🤖💬
- **Customizable Experience**
Tailor the analysis to your needs with adjustable parameters like token limits and response temperatures, enabling both concise summaries and in-depth explorations. ⚙️
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🎉 Exciting news, everyone! I've just released **Thespis-Llama-3.1-8B**, a new language model designed for enhanced roleplaying! ✨️
It's built on Llama-3.1 and fine-tuned with a focus on Theory of Mind reasoning to create more believable and engaging characters. It even learned a few tricks on its own, like adding in-character thought processes! 🧠
Check it out here: Locutusque/Thespis-Llama-3.1-8B
Give it a try and let me know what you think! I'm especially interested in feedback on how well the characters stay in role and if the responses feel natural. Looking forward to seeing what amazing stories you create! ✍️
It's built on Llama-3.1 and fine-tuned with a focus on Theory of Mind reasoning to create more believable and engaging characters. It even learned a few tricks on its own, like adding in-character thought processes! 🧠
Check it out here: Locutusque/Thespis-Llama-3.1-8B
Give it a try and let me know what you think! I'm especially interested in feedback on how well the characters stay in role and if the responses feel natural. Looking forward to seeing what amazing stories you create! ✍️
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Trying something new to keep you ahead of the curve: The 5 AI stories of the week - a weekly curation of the most important AI news you need to know. Do you like it?
For more AI stories and deeper analysis, check out my newsletter: https://open.substack.com/pub/fdaudens/p/ai-competition-heats-up-grok-3-iphone
For more AI stories and deeper analysis, check out my newsletter: https://open.substack.com/pub/fdaudens/p/ai-competition-heats-up-grok-3-iphone
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