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dev7halo

HaloKim

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liked a dataset 15 days ago

Malikeh1375/code-switching-tokenizer-robustness

new activity 19 days ago

kakaocorp/kanana-1.5-8b-instruct-2505:kanana vllm mcp template, jinja

new activity about 2 months ago

naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B:Thanks for sharing !

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dev7halo's activity

liked a dataset 15 days ago

Malikeh1375/code-switching-tokenizer-robustness

Viewer • Updated 18 days ago • 960 • 240 • 1

New activity in kakaocorp/kanana-1.5-8b-instruct-2505 19 days ago

kanana vllm mcp template, jinja

#1 opened 19 days ago by

dev7halo

New activity in naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B about 2 months ago

Thanks for sharing !

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#2 opened about 2 months ago by

dev7halo

upvoted a paper about 2 months ago

LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs

Paper • 2504.14655 • Published Apr 20 • 19

reacted to openfree's post with 🔥 about 2 months ago

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📊 Papers Impact: Instant AI Grading for Your Research Papers! 🚀

🌟 Introduction
Hello, AI research community! 🎉
Introducing Papers Impact - the revolutionary AI tool that automatically grades and predicts the potential impact of research papers! 🧠💡

VIDraft/PapersImpact

✨ Key Feature: Instant Paper Grading
The core functionality is brilliantly simple: Just enter an arXiv paper ID or URL, and our AI instantly analyzes and grades the paper's potential academic impact! No need to read through the entire paper yourself - our system automatically evaluates the title and abstract to generate a normalized impact score between 0 and 1.
🎯 How It Works

Enter Paper ID or URL: Simply paste an arXiv ID (e.g., "2504.11651") or full URL
Automatic Fetching: The system retrieves the paper's title and abstract
AI Analysis: Our advanced LLaMA-based transformer model analyzes the content
Instant Grading: Receive an impact score and corresponding letter grade in seconds!

💡 Who Can Benefit?

🔬 Researchers: Pre-assess your paper before submission
📚 Students: Quickly gauge the quality of papers for literature reviews
🏫 Educators: Objectively evaluate student research
📊 Research Managers: Prioritize which papers to read in depth
🧩 Journal Editors: Get an AI second opinion on submissions

🚀 Technical Details
Our model is trained on an extensive dataset of published papers in CS.CV, CS.CL, and CS.AI fields, using NDCG optimization with Sigmoid activation and MSE loss. It's been rigorously cross-validated against historical citation data to ensure accurate impact predictions.