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Brigitte Tousignant

BrigitteTousi

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reacted to giadap's post with 🤗 2 days ago

🤗 Just published: "Consent by Design" - exploring how we're building better consent mechanisms across the HF ecosystem! Our research shows open AI development enables: - Community-driven ethical standards - Transparent accountability - Context-specific implementations - Privacy as core infrastructure Check out our Space Privacy Analyzer tool that automatically generates privacy summaries of applications! Effective consent isn't about perfect policies; it's about architectures that empower users while enabling innovation. 🚀 Read more: https://huggingface.co/blog/giadap/consent-by-design

reacted to giadap's post with 🔥 2 days ago

reacted to burtenshaw's post with 🚀 2 days ago

Hacked my presentation building with inference providers, Cohere command a, and sheer simplicity. Use this script if you’re burning too much time on presentations: 🔗 https://github.com/burtenshaw/course_generator/blob/main/scripts/create_presentation.py This is what it does: - uses command a to generates slides and speaker notes based on some material. - it renders the material in remark open format and imports all images, tables, etc - you can then review the slides as markdown and iterate - export to either pdf or pptx using backslide 🚀 Next steps are: add text to speech for the audio and generate a video. This should make Hugging Face educational content scale to a billion AI Learners.

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reacted to giadap's post with 🤗🔥 2 days ago

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1354

🤗 Just published: "Consent by Design" - exploring how we're building better consent mechanisms across the HF ecosystem!

Our research shows open AI development enables:
- Community-driven ethical standards
- Transparent accountability
- Context-specific implementations
- Privacy as core infrastructure

Check out our Space Privacy Analyzer tool that automatically generates privacy summaries of applications!

Effective consent isn't about perfect policies; it's about architectures that empower users while enabling innovation. 🚀

Read more: https://huggingface.co/blog/giadap/consent-by-design

reacted to burtenshaw's post with 🚀 2 days ago

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1293

Hacked my presentation building with inference providers, Cohere command a, and sheer simplicity. Use this script if you’re burning too much time on presentations:

🔗 https://github.com/burtenshaw/course_generator/blob/main/scripts/create_presentation.py

This is what it does:
- uses command a to generates slides and speaker notes based on some material.
- it renders the material in remark open format and imports all images, tables, etc
- you can then review the slides as markdown and iterate
- export to either pdf or pptx using backslide

🚀 Next steps are: add text to speech for the audio and generate a video. This should make Hugging Face educational content scale to a billion AI Learners.

1 reply

upvoted an article 2 days ago

Article

17 Reasons Why Gradio Isn't Just Another UI Library

4 days ago

• 10

reacted to yjernite's post with 🔥 2 days ago

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2955

Today in Privacy & AI Tooling - introducing a nifty new tool to examine where data goes in open-source apps on 🤗

HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data 📚🔍

That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK 🙌

The app works in three stages:
1. Download all code files
2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1)
3. Summarize the app's main functionality and data journeys (screen 2)
4. Build a Privacy TLDR with those inputs

It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints 🤗

Note that this is a POC, lots of exciting work to do to make it more robust, so:
- try it: yjernite/space-privacy
- reach out to collab: yjernite/space-privacy

reacted to clem's post with 🤗 2 days ago

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1264

You can now bill your inference costs from all our inference partners (together, fireworks, fal, sambanova, cerebras, hyperbolic,...) to your Hugging Face organization.

Useful to drive more company-wide usage of AI without the billing headaches!

1 reply

reacted to gavinkhung's post with 🤗 2 days ago

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2089

Want to see machine learning algorithms training?
I made a website: https://gavinkhung.github.io/machine-learning-visualized/

The website implements, visualizes, and mathematically derives machine learning algorithms from first-principles.

Feel free to contribute to this open-source resource: https://github.com/gavinkhung/machine-learning-visualized

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reacted to nyuuzyou's post with 👍 2 days ago

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1452

🦅 EagleSFT Dataset - nyuuzyou/EagleSFT

Collection of 536,231 question-answer pairs featuring:

- Human-posed questions and machine-generated responses for SFT
- Bilingual content in Russian and English with linked IDs
- Derived from 739k+ real user queries, primarily educational topics
- Includes unique IDs and machine-generated category labels

This dataset provides a resource for supervised fine-tuning (SFT) of large language models, cross-lingual research, and understanding model responses to diverse user prompts. Released to the public domain under CC0 1.0 license.

reacted to JLouisBiz's post with 👀 2 days ago

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1379

This is short demonstration of large language model integration into a user's workflow. This is helping to quickly save or capture whatever you have copied to your clipboard. It goes into the database. In your case, it could go to the file. It could be published quickly. You could make a one-click page or one-click document. Eventually, it becomes immediately a note for later use.

https://discord.gg/N2BRPZ2jKb

1 reply

reacted to JunhaoZhuang's post with ❤️ 2 days ago

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2394

We are excited to announce the release of our paper, "Cobra: Efficient Line Art COlorization with BRoAder References," along with the official code! Cobra is a novel efficient long-context fine-grained ID preservation framework for line art colorization, achieving high precision, efficiency, and flexible usability for comic colorization. By effectively integrating extensive contextual references, it transforms black-and-white line art into vibrant illustrations.

We invite you to explore Cobra and share your feedback! You can access the paper and code via the following links: [PDF](https://arxiv.org/abs/2504.12240) and [Project page](https://zhuang2002.github.io/Cobra/). We eagerly anticipate your engagement and support!

Thank you for your interest!

4 replies

reacted to VolodymyrPugachov's post with 🔥 2 days ago

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1628

Introducing BioClinicalBERT-Triage: A Medical Triage Classification Model
I'm excited to share my latest project: a fine-tuned model for medical triage classification!
What is BioClinicalBERT-Triage?
BioClinicalBERT-Triage is a specialized model that classifies patient-reported symptoms into appropriate triage categories. Built on the foundation of emilyalsentzer/Bio_ClinicalBERT, this model helps healthcare providers prioritize patient care by analyzing symptom descriptions and medical history.
Why I Built This
As healthcare systems face increasing demands, efficient triage becomes crucial. This model aims to support healthcare professionals in quickly assessing the urgency of medical situations, particularly in telehealth and high-volume settings.
Model Performance
The model was trained on 42,513 medical symptom descriptions, using an 80:20 train/test split. After 3 epochs of training, the model achieved:

Final training loss: 0.3246
Processing speed: 13.99 samples/second

The loss steadily decreased throughout training:

Early training (epoch 0.24): 0.5796
Mid-training (epoch 1.65): 0.4308
Final (epoch 2.82): 0.3246
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

Limitations & Ethical Considerations
This model is designed to support, not replace, clinical decision-making. It should always be used under the supervision of qualified healthcare professionals. While it performs well on common presentations, it may be less accurate for rare conditions or unusual symptom descriptions.
Try It Out
I'd love to hear your feedback if you use this model in your projects! Check out the full model card here: VolodymyrPugachov/BioClinicalBERT-Triage
#medical #healthcare #bert #nlp #triage #classification

upvoted an article 3 days ago

Article

Cohere on Hugging Face Inference Providers 🔥

4 days ago

• 56

liked a dataset 5 days ago

openai/mrcr

Viewer • Updated 5 days ago • 2.4k • 1.67k • 102

reacted to thomwolf's post with 🤗🔥 5 days ago

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4173

If you've followed the progress of robotics in the past 18 months, you've likely noticed how robotics is increasingly becoming the next frontier that AI will unlock.

At Hugging Face—in robotics and across all AI fields—we believe in a future where AI and robots are open-source, transparent, and affordable; community-built and safe; hackable and fun. We've had so much mutual understanding and passion working with the Pollen Robotics team over the past year that we decided to join forces!

You can already find our open-source humanoid robot platform Reachy 2 on the Pollen website and the Pollen community and people here on the hub at