On Invariance Penalties for Risk Minimization
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2106.09777
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AI & ML interests
Make all hub models available for conversion to ONNX format.
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kenobiย
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3
papers
20 days ago
Aurelien-Morganย
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an
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27 days ago
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318
Hey, I went to Hangzhou to talk about
The recording just got released. Go check it out !
https://www.youtube.com/watch?v=nmrMachM5aM
Slides are there :
https://docs.google.com/presentation/d/1hnAzHJ0SbeAOtGJir-iH84RBtXT1OxVT/
retrain-pipelines at the GOSIM Foundation's conference last september.The recording just got released. Go check it out !
https://www.youtube.com/watch?v=nmrMachM5aM
Slides are there :
https://docs.google.com/presentation/d/1hnAzHJ0SbeAOtGJir-iH84RBtXT1OxVT/
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- 2 replies
gagan3012ย
authored
a
paper
3 months ago
MaziyarPanahiย
authored
a
paper
5 months ago
wannaphongย
authored
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5 months ago
MaziyarPanahiย
authored
a
paper
5 months ago
Post
586
Qwen 3 Coder is a personal attack to k2, and I love it.
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...
Qwen ftw!
DECENTRALIZE DECENTRALIZE DECENTRALIZE
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...
Qwen ftw!
DECENTRALIZE DECENTRALIZE DECENTRALIZE
MaziyarPanahiย
posted
an
update
6 months ago
Post
12954
๐งฌ Breaking news in Clinical AI: Introducing the OpenMed NER Model Discovery App on Hugging Face ๐ฌ
OpenMed is back! ๐ฅ Finding the right biomedical NER model just became as precise as a PCR assay!
I'm thrilled to unveil my comprehensive OpenMed Named Entity Recognition Model Discovery App that puts 384 specialized biomedical AI models at your fingertips.
๐ฏ Why This Matters in Healthcare AI:
Traditional clinical text mining required hours of manual model evaluation. My Discovery App instantly connects researchers, clinicians, and data scientists with the exact NER models they need for their biomedical entity extraction tasks.
๐ฌ What You Can Discover:
โ Pharmacological Models - Extract "chemical compounds", "drug interactions", and "pharmaceutical" entities from clinical notes
โ Genomics & Proteomics - Identify "DNA sequences", "RNA transcripts", "gene variants", "protein complexes", and "cell lines"
โ Pathology & Disease Detection - Recognize "pathological formations", "cancer types", and "disease entities" in medical literature
โ Anatomical Recognition - Map "anatomical systems", "tissue types", "organ structures", and "cellular components"
โ Clinical Entity Extraction - Detect "organism species", "amino acids", 'protein families", and "multi-tissue structures"
๐ก Advanced Features:
๐ Intelligent Entity Search - Find models by specific biomedical entities (e.g., "Show me models detecting CHEM + DNA + Protein")
๐ฅ Domain-Specific Filtering - Browse by Oncology, Pharmacology, Genomics, Pathology, Hematology, and more
๐ Model Architecture Insights - Compare BERT, RoBERTa, and DeBERTa implementations
โก Real-Time Search - Auto-filtering as you type, no search buttons needed
๐จ Clinical-Grade UI - Beautiful, intuitive interface designed for medical professionals
Ready to revolutionize your biomedical NLP pipeline?
๐ Try it now: OpenMed/openmed-ner-models
๐งฌ Built with: Gradio, Transformers, Advanced Entity Mapping
OpenMed is back! ๐ฅ Finding the right biomedical NER model just became as precise as a PCR assay!
I'm thrilled to unveil my comprehensive OpenMed Named Entity Recognition Model Discovery App that puts 384 specialized biomedical AI models at your fingertips.
๐ฏ Why This Matters in Healthcare AI:
Traditional clinical text mining required hours of manual model evaluation. My Discovery App instantly connects researchers, clinicians, and data scientists with the exact NER models they need for their biomedical entity extraction tasks.
๐ฌ What You Can Discover:
โ Pharmacological Models - Extract "chemical compounds", "drug interactions", and "pharmaceutical" entities from clinical notes
โ Genomics & Proteomics - Identify "DNA sequences", "RNA transcripts", "gene variants", "protein complexes", and "cell lines"
โ Pathology & Disease Detection - Recognize "pathological formations", "cancer types", and "disease entities" in medical literature
โ Anatomical Recognition - Map "anatomical systems", "tissue types", "organ structures", and "cellular components"
โ Clinical Entity Extraction - Detect "organism species", "amino acids", 'protein families", and "multi-tissue structures"
๐ก Advanced Features:
๐ Intelligent Entity Search - Find models by specific biomedical entities (e.g., "Show me models detecting CHEM + DNA + Protein")
๐ฅ Domain-Specific Filtering - Browse by Oncology, Pharmacology, Genomics, Pathology, Hematology, and more
๐ Model Architecture Insights - Compare BERT, RoBERTa, and DeBERTa implementations
โก Real-Time Search - Auto-filtering as you type, no search buttons needed
๐จ Clinical-Grade UI - Beautiful, intuitive interface designed for medical professionals
Ready to revolutionize your biomedical NLP pipeline?
๐ Try it now: OpenMed/openmed-ner-models
๐งฌ Built with: Gradio, Transformers, Advanced Entity Mapping
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- 5 replies
gagan3012ย
authored
a
paper
7 months ago
Post
3093
deepseek-ai/DeepSeek-R1-0528
This is the end
This is the end
- 1 reply
hazemessamย
authored
2
papers
7 months ago
gagan3012ย
authored
a
paper
7 months ago
Aurelien-Morganย
posted
an
update
8 months ago
Post
461
Hey, I'll be presenting
@retrain-pipelines
and almighty function-calling at the Hugging Face Paris HQ, you guys.
Monday evening. Lightning-talk style. With AI Tinkerers.
Come hang !
https://paris.aitinkerers.org/p/ai-tinkerers-paris-ai21-labs-takeover-on-may-19th
https://huggingface.co/blog/Aurelien-Morgan/the-almighty-function-caller
Monday evening. Lightning-talk style. With AI Tinkerers.
Come hang !
https://paris.aitinkerers.org/p/ai-tinkerers-paris-ai21-labs-takeover-on-may-19th
https://huggingface.co/blog/Aurelien-Morgan/the-almighty-function-caller
Post
374
Expansion of Global and Dense Open Embeddings Dataset of Earth ๐
We updated our previous embeddings release with three models MMEarth and DeCUR-S2, DeCUR-S1 of the Major TOM embeddings dataset, developed in collaboration with CloudFerro S.A. asterisk labs and ฮฆ-lab, European Space Agency - ESA. Together with @mikonvergence , Jฤdrzej S. Bojanowski, we extend the open-access collection of open dataset of Copernicus embeddings built at global scale, providing dense coverage across the entire acquisition area of Sentinel-1 and Sentinel-2 sensors.
Total embedding resources after the update:
- 51 TB of AI-embeddings generated from processed Sentinel data,
- over 40 billion embedding vectors,
- processing of 147 TB of raw satellite data,
- analysis covering more than 15 million Sentinel-1 and Sentinel-2 scenes and more than 16 trillion pixels.
This project delivers open and free vectorized expansions of Major TOM datasets available on CREODIAS and Hugging Face, setting a new standard for embedding releases and enabling lightweight, scalable ingestion of Earth Observation (EO) data for countless applications.
Datasets:
Major-TOM/Core-S2L2A-MMEarth
Major-TOM/Core-S2L1C-DeCUR
Major-TOM/Core-S1RTC-DeCUR
#EarthObservation #AI #CloudFerro #asterisklabs #ESA
We updated our previous embeddings release with three models MMEarth and DeCUR-S2, DeCUR-S1 of the Major TOM embeddings dataset, developed in collaboration with CloudFerro S.A. asterisk labs and ฮฆ-lab, European Space Agency - ESA. Together with @mikonvergence , Jฤdrzej S. Bojanowski, we extend the open-access collection of open dataset of Copernicus embeddings built at global scale, providing dense coverage across the entire acquisition area of Sentinel-1 and Sentinel-2 sensors.
Total embedding resources after the update:
- 51 TB of AI-embeddings generated from processed Sentinel data,
- over 40 billion embedding vectors,
- processing of 147 TB of raw satellite data,
- analysis covering more than 15 million Sentinel-1 and Sentinel-2 scenes and more than 16 trillion pixels.
This project delivers open and free vectorized expansions of Major TOM datasets available on CREODIAS and Hugging Face, setting a new standard for embedding releases and enabling lightweight, scalable ingestion of Earth Observation (EO) data for countless applications.
Datasets:
Major-TOM/Core-S2L2A-MMEarth
Major-TOM/Core-S2L1C-DeCUR
Major-TOM/Core-S1RTC-DeCUR
#EarthObservation #AI #CloudFerro #asterisklabs #ESA
Aurelien-Morganย
posted
an
update
8 months ago
Post
3156
The Almighty function-caller
How would you like to build smart GenAi infrastructure ?
Give extensive tools memory to your edge agentic system,
And optimize the resources it takes to run yet a high-performance set of agents ?
We came up with a novel approach to function-calling at scale for smart companies and corporate-grade use-cases.
Read our full-fledged blog article on this here on Hugging Face :
https://huggingface.co/blog/Aurelien-Morgan/the-almighty-function-caller
How would you like to build smart GenAi infrastructure ?
Give extensive tools memory to your edge agentic system,
And optimize the resources it takes to run yet a high-performance set of agents ?
We came up with a novel approach to function-calling at scale for smart companies and corporate-grade use-cases.
Read our full-fledged blog article on this here on Hugging Face :
https://huggingface.co/blog/Aurelien-Morgan/the-almighty-function-caller
Aurelien-Morganย
posted
an
update
8 months ago
Post
677
retrain-pipelines 0.1.2 finally dropped. It comes with a hot Hugging Face Hub integration. Go check it out. We have 2 articles about it coming up. One already fully written so, be on the lookout !@retrain-pipelines
Also, I'll be volunteering at GOSIM AI Paris 2025. If you're interested in chatting, hmu.
