HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
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2412.18925
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Nicolay Rusnachenko
nicolay-r
AI & ML interests
Information Retrieval・Medical Multimodal NLP (🖼+📝) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP
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nicolay-r's activity
upvoted
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19 days ago
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642
📢 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
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
20 days ago
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 🐍👌
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davanstrien's
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22 days ago
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3166
🇸🇰 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
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
- 3 replies
posted
an
update
22 days ago
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2126
📢 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
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
reacted to
ginipick's
post with 🔥
22 days ago
Post
5152
🎬 Revolutionize Your Video Creation
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Superior Technology 💫
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Intelligent Sound System: Automatically generates perfect audio by analyzing video mood
Multimodal Framework: Advanced AI integrating image, text, and audio analysis
Outstanding Performance 🎯
Ultra-High Resolution: 4K video quality with bfloat16 acceleration
Real-Time Optimization: 3x faster processing with PyTorch GPU acceleration
Smart Sound Matching: Real-time audio effects based on scene transitions and motion
Exceptional Features ✨
Custom Audio Creation: Natural soundtrack matching video tempo and rhythm
Intelligent Watermarking: Adaptive watermark adjusting to video characteristics
Multilingual Support: Precise translation engine powered by Helsinki-NLP
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Product Promotion: Dynamic promotional videos highlighting product features
Educational Content: Interactive learning materials with enhanced engagement
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Experience the video revolution with Dokdo Multimodal, where anyone can create professional-quality content from a single image. Elevate your content with perfectly synchronized video and audio that captivates your audience! 🎨
Start creating stunning videos that stand out from the crowd - whether you're a marketer, educator, content creator, or business owner. Join the future of AI-powered video creation today!
ginipick/Dokdo-multimodal
#VideoInnovation #AITechnology #PremiumContent #MarketingSolution
🔊 Please turn on your sound for the best viewing experience!
Dokdo Multimodal AI Transform a single image into a stunning video with perfect audio harmony! 🚀
Superior Technology 💫
Advanced Flow Matching: Smoother video transitions surpassing Kling and Sora
Intelligent Sound System: Automatically generates perfect audio by analyzing video mood
Multimodal Framework: Advanced AI integrating image, text, and audio analysis
Outstanding Performance 🎯
Ultra-High Resolution: 4K video quality with bfloat16 acceleration
Real-Time Optimization: 3x faster processing with PyTorch GPU acceleration
Smart Sound Matching: Real-time audio effects based on scene transitions and motion
Exceptional Features ✨
Custom Audio Creation: Natural soundtrack matching video tempo and rhythm
Intelligent Watermarking: Adaptive watermark adjusting to video characteristics
Multilingual Support: Precise translation engine powered by Helsinki-NLP
Versatile Applications 🌟
Social Media Marketing: Create engaging shorts for Instagram and YouTube
Product Promotion: Dynamic promotional videos highlighting product features
Educational Content: Interactive learning materials with enhanced engagement
Portfolio Enhancement: Professional-grade videos showcasing your work
Experience the video revolution with Dokdo Multimodal, where anyone can create professional-quality content from a single image. Elevate your content with perfectly synchronized video and audio that captivates your audience! 🎨
Start creating stunning videos that stand out from the crowd - whether you're a marketer, educator, content creator, or business owner. Join the future of AI-powered video creation today!
ginipick/Dokdo-multimodal
#VideoInnovation #AITechnology #PremiumContent #MarketingSolution
🔊 Please turn on your sound for the best viewing experience!
- 1 reply
reacted to
as-cle-bert's
post with 🔥👍
22 days ago
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2173
I got my GitHub Wrapped for 2024 today!🥂
Get yours here on HuggingFace 👉 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!🎉
Get yours here on HuggingFace 👉 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!🎉
-
- 1 reply
reacted to
AkimfromParis's
post with 🚀
22 days ago
Post
1669
🇺🇸 🇨🇦 🇬🇧 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
🇩🇪 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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- 11 replies
reacted to
Muhammadreza's
post with 👀
22 days ago
Post
1770
Does huggingface support URN/AIR system (like what we have in CivitAI). If yes, how can we access it?
- 3 replies
reacted to
Duskfallcrew's
post with 👍
22 days ago
Post
1068
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.
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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- 8 replies