monsoon-nlp (Nick Doiron)

reacted to daavoo's post with 👀 10 days ago

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🤖 🗺️Pushed an update to support processing entire areas (i.e. a city) in https://github.com/mozilla-ai/osm-ai-helper.

I have mapped and contributed to https://www.openstreetmap.org all(?) the swimming pools around my hometown, taking about 1h to process (+15 min verification) in a free Colab GPU🚀

Try it yourself: mozilla-ai/osm-ai-helper

And check the https://github.com/mozilla-ai/osm-ai-helper to find the demo notebooks.

reacted to clem's post with 🚀 12 days ago

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We just crossed 1,500,000 public models on Hugging Face (and 500k spaces, 330k datasets, 50k papers). One new repository is created every 15 seconds. Congratulations all!

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reacted to Yehor's post with 👍 14 days ago

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Published some datasets for researchers in Ukrainian NLP from my project https://ua-lawyer.com (Q&A platform in Ukraine):

Datasets:
- ua-l/topics
- ua-l/topics-train-test
- ua-l/topics-text-label

Model:
- https://huggingface.co/ua-l/topics-classifier

Space:
- ua-l/topics-classifier-demo

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replied to ashercn97's post 15 days ago

I would say, sort by "Mean (task)" and pick one of those. Or if you can, compare three of the best on your data. That holds unless you need a longer context, or you are in medical or similar field where there are domain-specific models

posted an update 15 days ago

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Genetic counselors help patients get 🧬 tests and understand their results. They need to study inheritance of several conditions, statistics, and patient care 🤓⚕️. I compiled 225 multiple-choice questions for the ABGC exam into a dataset: monsoon-nlp/genetic-counselor-multiple-choice
Llama 3.1 8B Instruct gets a 51% score.
I'm also creating a dataset of real-world open-ended questions (starting with Reddit) and am open to contributors

reacted to MohamedRashad's post with 🧠 about 1 month ago

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Today is a big day for the Arabic Language,

We have Navid-AI/The-Arabic-Rag-Leaderboard,
an Update for OALL/Open-Arabic-LLM-Leaderboard
and the release of atlasia/darija-chatbot-arena

All of this announcements was under 12 hours of time 🤯

reacted to davanstrien's post with ❤️ 3 months 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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reacted to MohamedRashad's post with 🚀 4 months ago

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A while back i shared this model MohamedRashad/arabic-small-nougat that was a finetune from facebook/nougat-small for the Arabic Language.

Today this humble project has been scaled with new models, new datasets, new space, and a new paper

Check everything throught this collection here:
MohamedRashad/arabic-nougat-673a3f540bd92904c9b92a8e

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reacted to fdaudens's post with 🤗 4 months ago

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🦋 Hug the butterfly! You can now add your Bluesky handle to your Hugging Face profile! ✨

reacted to m-ric's post with 😎 5 months ago

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I'm very proud to have supported @CGIAR and @Digigreen in making http://Farmer.chat, an app that supports 20k smallholder farmers on a daily basis 🌾

There are ~500 million smallholder farmers globally, playing a critical role in global food security. Having access to accurate information is essential for them.

💬 An “agricultural extension service” offers technical advice on agriculture, and also supplies farmers with the necessary inputs and services to support their agricultural production.

But agriculture extension agents are not in large enough numbers to cope with all the requests, especially in countries like Kenya, India, Ethiopia, and Nigeria.

🚀 So the team set out to build an app called http://Farmer.Chat, to provide an agricultural extension service, by building on the immense knowledge accumulated by CGIAR.

✨ The app is technically impressive: behind the Whatsapp-type UX, an agent interprets the user's intent, and identifies which tool to call to best answer their request: weather API, RAG on a CGIAR-provided knowledge base, market data, etc. The RAG on the knowledge base is in itself a work of art.

🎯 A key part of building such a complex system is to be able to evaluate it properly. During our bi-weekly sessions with the team, I could support them in implementing the method called "LLM-as-a-judge" to tackle this problem.

It worked really well : thanks to the amazing work of the team, the app now successfully answered over 300 thousand requests, in 6 different languages, and it keeps growing!

➡️ @Vinsingh , @rajgreen and I just wrote a blog post to describe how the app works, especially the LLM-as-a-judge system!

Read it here 👉 https://huggingface.co/blog/digital-green-llm-judge

reacted to Tonic's post with 👀 5 months ago

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🙋🏻‍♂️ hey there folks ,

really enjoying sharing cool genomics and protein datasets on the hub these days , check out our cool new org : https://huggingface.co/seq-to-pheno

scroll down for the datasets, still figuring out how to optimize for discoverability , i do think on that part it will be better than zenodo[dot}org , it would be nice to write a tutorial about that and compare : we already have more downloads than most zenodo datasets from famous researchers !

reacted to nyuuzyou's post with 👀 5 months ago

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🎙 Introducing LiveATC Recordings (Partial 2024-08-26) Dataset - nyuuzyou/liveatc

Dataset highlights:

- 21,172 air traffic control audio recordings from LiveATC.net for August 26, 2024
- Multilingual content, primarily in English with potential for other languages
- Each entry includes: audio file, ICAO airport code, facility type, date, and time
- Contains original MP3 files stored in .tar.zst archives, organized by ICAO airport code
- Data covers various airports and ATC facilities worldwide
- Subject to LiveATC.net's Terms of Use for personal, non-commercial use only

The dataset can be used for audio classification, automatic speech recognition, and analysis of air traffic control communications. The inclusion of recordings from multiple airports allows for comparative analysis across different locations and facility types.

reacted to Tonic's post with 👀 6 months ago

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🙋🏻‍♂️ Hey there folks ,

🦎Salamandra release by @mvillegas and team
@BSC_CNS https://huggingface.co/BSC-LT is absolutely impressive so far !

perhaps the largest single training dataset of high quality text to date of 7.8 trillion tokens in 35 European languages and code.

the best part : the data was correctly licenced so it's actually future-proof!

the completions model is really creative and instruct fine tuned version is very good also.

now you can use such models for multi-lingual enterprise applications with further finetunes , long response generation, structured outputs (coding) also works.

check out 👇🏻
the collection : BSC-LT/salamandra-66fc171485944df79469043a
the repo : https://github.com/langtech-bsc/salamandra
7B-Instruct demo : Tonic/Salamandra-7B

reacted to clem's post with 🚀 6 months ago

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Very few people realize that most of the successful AI startups got successful because they were focused on open science and open-source for at least their first few years. To name but a few, OpenAI (GPT, GPT2 was open-source), Runway & Stability (stable diffusion), Cohere, Mistral and of course Hugging Face!

The reasons are not just altruistic, it's also because sharing your science and your models pushes you to build AI faster (which is key in a fast-moving domain like AI), attracts the best scientists & engineers and generates much more visibility, usage and community contributions than if you were 100% closed-source. The same applies to big tech companies as we're seeing with Meta and Google!

More startups and companies should release research & open-source AI, it's not just good for the world but also increases their probability of success!

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reacted to pain's post with ❤️ 6 months ago

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We have published an excellent paper for Arabic CLIP model.

Paper link:
https://aclanthology.org/2024.arabicnlp-1.9/

More information in this website:
https://arabic-clip.github.io/Arabic-CLIP/

All datasets, models, and demo are published to Huggingface:
https://huggingface.co/Arabic-Clip

The codes are published to github:
https://github.com/Arabic-Clip/Arabic-CLIP

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reacted to ezgikorkmaz's post with 👀 6 months ago

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If you are interested in adversarial machine learning and AI safety, find my curated reading list below:

GitHub: https://github.com/EzgiKorkmaz/adversarial-reinforcement-learning

reacted to Tonic's post with 🚀 7 months ago

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🙋🏻‍♂️hey there folks ,

✒️InkubaLM has been trained from scratch using 1.9 billion tokens of data for five African languages, along with English and French data, totaling 2.4 billion tokens of data. It is capable of understanding and generating content in five African languages: Swahili, Yoruba, Hausa, isiZulu, and isiXhosa, as well as English and French.

model lelapa/InkubaLM-0.4B
demo Tonic/Inkuba-0.4B

reacted to ucsahin's post with 🔥 8 months ago

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🚀 Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! 🇹🇷🖼️

I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, it’s easy to load, fine-tune, and use—no external libraries needed!

Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model.

🎉 Explore it now:

- Model: ucsahin/TraVisionLM-base
- Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Object Detection Finetune: ucsahin/TraVisionLM-Object-Detection-ft

Let’s push Turkish visual language processing forward!

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🚀 TraVisionLM: Türünün İlk Örneği Türkçe Görsel Dil Modelini Sunuyorum! 🇹🇷🖼️

TraVisionLM modelini Hugging Face'te yayınladım! 875M parametre ile bu hafif ve verimli model, görüntüye dayalı Türkçe talimatları işlemek için tasarlandı. Transformers kütüphanesiyle tamamen uyumlu, yüklemesi, eğitmesi ve kullanması çok kolay—dış kütüphane gerekmez!

Tek başıma geliştirdiğim TraVisionLM, düşük kaynaklı dillerde araştırmalar için sağlam bir temel sunuyor. Geliştirmeye devam ederken geri bildirimlerinizi bekliyorum.

🎉 Hemen keşfedin:

- Model: ucsahin/TraVisionLM-base
- Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Obje Tespiti İnce Ayarı: ucsahin/TraVisionLM-Object-Detection-ft

Türkçe görsel dil işleme sınırlarını birlikte zorlayalım!

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reacted to merve's post with 🔥 8 months ago

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New smol-vision tutorial dropped: QLoRA fine-tuning IDEFICS3-Llama 8B on VQAv2 🐶

Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook 📖
Fine-tuning notebook: https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb
Resulting model: merve/idefics3llama-vqav2

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reacted to merve's post with 🚀 8 months ago

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Idefics3-Llama is out! 💥💥
Model: HuggingFaceM4/Idefics3-8B-Llama3
Demo: HuggingFaceM4/idefics3

It's a multimodal model based on Llama 3.1 that accepts an arbitrary number of interleaved images with text with a huge context window (10k tokens!) ✨

Supported by Hugging Face transformers 🤗

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Nick Doiron

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