Nicolay Rusnachenko

nicolay-r

https://nicolay-r.github.io/

AI & ML interests

Information Retrieval・Medical Multimodal NLP (🖼+📝) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP

Recent Activity

reacted to DawnC's post with ❤️ 6 minutes ago

🌟 PawMatchAI: Making Breed Selection More Intuitive! 🐕 Excited to share the latest update to this AI-powered companion for finding your perfect furry friend! The breed recommendation system just got a visual upgrade to help you make better decisions. ✨ What's New? Enhanced breed recognition accuracy through strategic model improvements: - Upgraded to a fine-tuned ConvNeXt architecture for superior feature extraction - Implemented progressive layer unfreezing during training - Optimized data augmentation pipeline for better generalization - Achieved 8% improvement in breed classification accuracy 🎯 Key Features: - Smart breed recognition powered by AI - Visual matching scores with intuitive color indicators - Detailed breed comparisons with interactive tooltips - Lifestyle-based recommendations tailored to your needs 💭 Project Vision Combining my passion for AI and pets, this project represents another step toward my goal of creating meaningful AI applications. Each update aims to make the breed selection process more accessible while improving the underlying technology. 👉 Try it now: https://huggingface.co/spaces/DawnC/PawMatchAI Your likes ❤️ on this space fuel this project's growth! #AI #MachineLearning #DeepLearning #Pytorch #ComputerVision See translation

reacted to sayakpaul's post with 🚀 6 minutes ago

Commits speak louder than words 🤪 * 4 new video models * Multiple image models, including SANA & Flux Control * New quantizers -> GGUF & TorchAO * New training scripts Enjoy this holiday-special Diffusers release 🤗 Notes: https://github.com/huggingface/diffusers/releases/tag/v0.32.0

reacted to singhsidhukuldeep's post with 🧠 7 minutes ago

Exciting News in AI: JinaAI Releases JINA-CLIP-v2! The team at Jina AI has just released a groundbreaking multilingual multimodal embedding model that's pushing the boundaries of text-image understanding. Here's why this is a big deal: 🚀 Technical Highlights: - Dual encoder architecture combining a 561M parameter Jina XLM-RoBERTa text encoder and a 304M parameter EVA02-L14 vision encoder - Supports 89 languages with 8,192 token context length - Processes images up to 512×512 pixels with 14×14 patch size - Implements FlashAttention2 for text and xFormers for vision processing - Uses Matryoshka Representation Learning for efficient vector storage ⚡️ Under The Hood: - Multi-stage training process with progressive resolution scaling (224→384→512) - Contrastive learning using InfoNCE loss in both directions - Trained on massive multilingual dataset including 400M English and 400M multilingual image-caption pairs - Incorporates specialized datasets for document understanding, scientific graphs, and infographics - Uses hard negative mining with 7 negatives per positive sample 📊 Performance: - Outperforms previous models on visual document retrieval (52.65% nDCG@5) - Achieves 89.73% image-to-text and 79.09% text-to-image retrieval on CLIP benchmark - Strong multilingual performance across 30 languages - Maintains performance even with 75% dimension reduction (256D vs 1024D) 🎯 Key Innovation: The model solves the long-standing challenge of unifying text-only and multi-modal retrieval systems while adding robust multilingual support. Perfect for building cross-lingual visual search systems! Kudos to the research team at Jina AI for this impressive advancement in multimodal AI!

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1947

📢 So far I noticed that 🧠 reasoning with llm 🤖 in English is tend to be more accurate than in other languages.
However, besides the GoogleTrans and other open transparent translators, I could not find one that could be easy to use solutions to avoid:
1.🔴 Third-party framework installation
2.🔴 Text chunking
3.🔴 support of meta-annotation like spans / objects / etc.

💎 To cope problem of IR from non-english texts, I am happy to share the bulk-translate 0.25.0. 🎊

⭐ https://github.com/nicolay-r/bulk-translate

bulk-translate is a tiny Python 🐍 no-string framework that allows translate series of texts with the pre-annotated fixed-spans that are invariant for translator.

It supports 👨‍💻 API for quick data translation with (optionaly) annotated objects in texts (see figure below) in Python 🐍
I make it accessible as much as possible for RAG and / or LLM-powered app downstreams:
📘 https://github.com/nicolay-r/bulk-translate/wiki

All you have to do is to provide iterator of texts, where each text:
1. ✅ String object
2. ✅ List of strings and nested lists that represent spans (value + any ID data).

🤖 By default I provide a wrapper over googletrans which you can override with your own 🔥
https://github.com/nicolay-r/bulk-translate/blob/master/models/googletrans_310a.py

Post

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📢 If you're working in relation extraction / character network domain, then the following post would be relevant.
Excited to share the most recent milestone on releasing the ARElight 0.25.0 🎊

Core library: https://github.com/nicolay-r/ARElight
Server: https://github.com/nicolay-r/ARElight-server

🔎 What is ARElight? It represents Granular Viewer of Sentiments Between Entities in Massively Large Documents and Collections of Texts.
Shortly speaking, it allows to extract contexts with mentioned object pairs for the related prompting / classification.
In the slides below we illsutrate the ARElight appliation for sentiment classification between object pairs in context.

We exploit DeepPavlov NER modes + GoogleTranslate + BERT-based classifier in the demo. The bash script for launching the quick demo illustrates the application of these components.

The new update provide a series of new features:
✅ SQlite support for storing all the extracted samples
✅ Support of the enhanced GUI for content investigation.
✅ Switch to external no-string projects for NER and Translator

Supplementiary materials:
📜 Paper: https://link.springer.com/chapter/10.1007/978-3-031-56069-9_23

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