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4 18 21

Nicolay Rusnachenko

nicolay-r
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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

posted an update 3 days ago
📢 Through the 2024 we attempting in advancing opinion mining by proposing evaluation which involves explanations! A while ago we launched RuOpinionNE-2024 aimed at extraction of sentiment opinions with spans (as explanations) from mass media news written in Russian language. The formed competition is at the final stage on codalab platform: 📊 https://codalab.lisn.upsaclay.fr/competitions/20244 🔎 What we already observe? For the two type of sentiment labels (positive and negative), our recent findings were that the top performing submission results in F1=0.34 while the baseline LLM approach results in F1=0.17 (see screenshot of the leaderboard below 📸) ⏰️ We finally launch the final stage with a decent amount of submissions which lasts until 15th of January 2025. 🙌 Everyone who wish to evaluate most recent advances on explainable opinion mining during the final stage are welcome! Codalab main page: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details More details on github: https://github.com/dialogue-evaluation/RuOpinionNE-2024
replied to as-cle-bert's post 4 days ago
I got my GitHub Wrapped for 2024 today!🥂 Get yours here on HuggingFace 👉 https://huggingface.co/spaces/as-cle-bert/what-a-git-year GitHub repo with the code to reproduce it 👉 https://github.com/AstraBert/what-a-git-year Hope that everybody had a Git year!🎉
reacted to davanstrien's post with ❤️ 7 days ago
🇸🇰 Hovorte po slovensky? Help build better AI for Slovak! We only need 90 more annotations to include Slovak in the next Hugging Face FineWeb2-C dataset (https://huggingface.co/datasets/data-is-better-together/fineweb-c) release! Your contribution will help create better language models for 5+ million Slovak speakers. Annotate here: https://huggingface.co/spaces/data-is-better-together/fineweb-c. Read more about why we're doing it: https://huggingface.co/blog/davanstrien/fineweb2-community
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nicolay-r's activity

posted an update 3 days ago
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📢 Through the 2024 we attempting in advancing opinion mining by proposing evaluation which involves explanations!

A while ago we launched RuOpinionNE-2024 aimed at extraction of sentiment opinions with spans (as explanations) from mass media news written in Russian language. The formed competition is at the final stage on codalab platform:
📊 https://codalab.lisn.upsaclay.fr/competitions/20244

🔎 What we already observe? For the two type of sentiment labels (positive and negative), our recent findings were that the top performing submission results in F1=0.34 while the baseline LLM approach results in F1=0.17 (see screenshot of the leaderboard below 📸)

⏰️ We finally launch the final stage with a decent amount of submissions which lasts until
15th of January 2025.

🙌 Everyone who wish to evaluate most recent advances on explainable opinion mining during the final stage are welcome!

Codalab main page:
https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details
More details on github:
https://github.com/dialogue-evaluation/RuOpinionNE-2024
replied to as-cle-bert's post 4 days ago
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Just got mine via hf forms 🔥 I like the concept of fetching topics across several respos is interestng. That's how I found myself of being focused on developing llm-based nlp frameworks that are 94% purely in Python 🐍👌
reacted to davanstrien's post with ❤️ 7 days ago
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🇸🇰 Hovorte po slovensky? Help build better AI for Slovak!

We only need 90 more annotations to include Slovak in the next Hugging Face FineWeb2-C dataset ( data-is-better-together/fineweb-c) release!

Your contribution will help create better language models for 5+ million Slovak speakers.

Annotate here: data-is-better-together/fineweb-c.

Read more about why we're doing it: https://huggingface.co/blog/davanstrien/fineweb2-community
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posted an update 7 days ago
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2053
📢 Deligted to share the most recent milestone on quick deployment of Named Entity Recognition (NER) in Gen-AI powered systems.

Releasing the bulk-ner 0.25.0 which represent a tiny framework that would save you time for deploing NER with any model.

💎 Why is this important? In the era of GenAI the handling out textual output might be challenging. Instead, recognizing named-entities via domain-oriented systems for your donwstream LLM would be preferable option.

📦: https://pypi.org/project/bulk-ner/0.25.0/
🌟: https://github.com/nicolay-r/bulk-ner

I noticed that the direct adaptaion of the LM for NER would result in spending signifcant amount of time on formatting your texts according to the NER-model needs.
In particular:
1. Processing CONLL format with B-I-O tags from model outputs
2. Input trimming: long input content might not be completely fitted

To cope with these problems, in version 0.25.0 I made a huge steps forward by providing:
✅ 🐍 Python API support: see screenshot below for a quick deployment (see screenshot below 📸)
✅ 🪶 No-string: dependencies are now clear, so it is purely Python implementation for API calls.
✅ 👌 Simplified output formatting: we use lists to represent texts with inner lists that refer to annotated objects (see screenshot below 📸)

📒 We have a colab for a quick start here (or screenshot for bash / Python API 📸)
https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb

👏 The code for pipeline deployment is taken from the AREkit project:
https://github.com/nicolay-r/AREkit
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🇺🇸 🇨🇦 🇬🇧 Nobel Prize winners against USSR & Japanese AI pioneers ☭🇯🇵

🇩🇪 Prof. Jürgen Schmidhuber:  “The #NobelPrize in Physics 2024 for Hopfield & Hinton turns out to be a Nobel Prize for plagiarism. They republished methodologies developed in #Ukraine and #Japan by Ivakhnenko and Amari in the 1960s & 1970s, as well as other techniques, without citing the original inventors.”

1965 - First Deep Learning - USSR ☭ (Ukraine 🇺🇦 now)
Ivakhnenko and Lapa introduced the first deep learning in deep MLPs that learn internal representations of input data.

1967/68 - Deep Learning by Stochastic Gradient Descent - Japan 🇯🇵
Shun-Ichi Amari trained MLPs with many layers in non-incremental end-to-end fashion from scratch by stochastic gradient descent (SGD).

1969 - Rectified linear unit - Japan 🇯🇵
In 1969, Kunihiko Fukushima introduced ReLU in the context of visual feature extraction in hierarchical neural networks.

1970 - Backpropagation - Finland 🇫🇮 😃
In 1970, Seppo Linnainmaa was the first the reverse mode of automatic differentiation, now known as backpropagation.

1972 - Recurrent Neural Network - Japan 🇯🇵
In 1972, Shun-Ichi Amari published a learning recurrent neural network based on Lenz-Ising model (Amari's net was later called the "Hopfield network". Hopfield republished in 1982, without citing Amari papers.)

1979 - First Convolutional neural network - Japan 🇯🇵
CNN architecture was introduced in 1979 by Kunihiko Fukushima, also known as Neocognitron.

https://people.idsia.ch/~juergen/deep-learning-history.html#AMH2
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Does huggingface support URN/AIR system (like what we have in CivitAI). If yes, how can we access it?
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Merry Christmas! 🎄 Open sourced a small TTS model at hexgrad/Kokoro-82M
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Just finally ... added my HF backup tool to an HF repo... after two years roughly of making this -
It's inspired KINDA by Camenduru but his one stopped working and i wish i had the original code for it so i could reformat the A111 extension he had...

Becausei 'm TRYING to make an A111 extension and maybe a custom comfyUI node:

Duskfallcrew/Huggingface_Backup

I originally patched this from Everydream2trainer and some other stuff.
So the credits stay.

I'm amazed at this lol.
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#001 | A journey into open-source Hugging Face Models on Azure AI

December is the month for New Year resolutions - and this year I am determined to write more on Hugging Face. I kept putting this off thinking I wanted to have time to craft perfect long-form articles, but then I discovered we can do quick posts. So why wait till January?

I am a PhD, a Polyglot, a Parent, a Visual Storyteller, a Community Builder - and an AI Advocate at Microsoft. However, if I look back on my 25+ years in tech, what I love most is to help people learn by making complex concepts feel more accessible and actionable regardless of your background or expertise. And in 2025, I want to use a #NityaLearnsAI tagline as a way to share my learning journey, explore the vast space of AI tools and technologies, amplify our open-source community and put the fun back in fundamentals. I hope you find it useful and will join me!

My first post is on this Microsoft Ignite theater session delivered in Nov:
https://ignite.microsoft.com/en-US/sessions/THR502?source=sessions It was not recorded but can find the slides here: https://speakerdeck.com/nitya/thr502-journey-into-open-source-hugging-face-models-on-azure-ai - and the illustrated guide attached below summarizes the talk in one big picture.

At the core, this is about my growing interest in **Model Choice** and learning more about not just frontier models but the much larger ecosystem of open-source variants and the community creators who build them. See:

1. Oct / The Future of AI is model choice / https://techcommunity.microsoft.com/blog/aiplatformblog/the-future-of-ai-is-model-choice---from-structured-process-to-seamless-platform/4284091
2. Sep / HF Models Recap / https://techcommunity.microsoft.com/blog/aiplatformblog/new-hugging-face-models-on-azure-ai-phi-3-variants-from
3. Aug / HF Models Recap / https://techcommunity.microsoft.com/blog/aiplatformblog/new-hugging-face-models-on-azure-ai-multilingual-slm-and-biomed--july-2024-updat/4211881
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