56
HuggingDiscussions
🏢
Join discussions on Hugging Face Hub
xwxwxw
xwxwxw12
·
AI & ML interests
None yet
Recent Activity
liked
a Space
6 days ago
huggingface/HuggingDiscussions
published
a Space
6 days ago
xwxwxw12/ohieby
published
a Space
6 days ago
xwxwxw12/ohiey
Organizations
xwxwxw12's activity
liked
a
Space
6 days ago
published
2
Spaces
6 days ago
updated
a
Space
7 days ago
published
a
Space
7 days ago
🚩 Report: Ethical issue(s)
#52 opened 7 days ago
by
xwxwxw12
reacted to
singhsidhukuldeep's
post with 👍
7 days ago
Post
6697
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!
replied to
mlmPenguin's
post
7 days ago
published
a
Space
about 2 months ago
liked
a
Space
about 2 months ago
updated
a
Space
about 2 months ago
reacted to
alibabasglab's
post with 👍
about 2 months ago
Post
5295
🎉 ClearerVoice-Studio New Feature: Speech Super-Resolution with MossFormer2 ! 🚀
We’re excited to announce that ClearerVoice-Studio now supports speech super-resolution, powered by our latest MossFormer2-based model!
What’s New?
🔊 Convert Low-Resolution to High-Resolution Audio:
Transform low-resolution audio (effective sampling rate ≥ 16 kHz) into crystal-clear, high-resolution audio at 48 kHz.
🤖 Cutting-Edge Technology:
Leverages the MossFormer2 model plus HiFi-GAN, optimised for generating high-quality audio with enhanced perceptual clarity.
🎧 Enhanced Listening Experience:
Perfect for speech enhancement, content restoration, and high-fidelity audio applications.
🌟 Try It Out!
Upgrade to the latest version of ClearerVoice-Studio (https://github.com/modelscope/ClearerVoice-Studio) to experience this powerful feature. Check out the updated documentation and examples in our repository.
Let us know your thoughts, feedback, or feature requests in the Issues section.
We’re excited to announce that ClearerVoice-Studio now supports speech super-resolution, powered by our latest MossFormer2-based model!
What’s New?
🔊 Convert Low-Resolution to High-Resolution Audio:
Transform low-resolution audio (effective sampling rate ≥ 16 kHz) into crystal-clear, high-resolution audio at 48 kHz.
🤖 Cutting-Edge Technology:
Leverages the MossFormer2 model plus HiFi-GAN, optimised for generating high-quality audio with enhanced perceptual clarity.
🎧 Enhanced Listening Experience:
Perfect for speech enhancement, content restoration, and high-fidelity audio applications.
🌟 Try It Out!
Upgrade to the latest version of ClearerVoice-Studio (https://github.com/modelscope/ClearerVoice-Studio) to experience this powerful feature. Check out the updated documentation and examples in our repository.
Let us know your thoughts, feedback, or feature requests in the Issues section.