Interactively explore your Huggingface dataset with one line of code
The Hugging Face datasets library not only provides access to more than 70k publicly available datasets, but also offers very convenient data preparation pipelines for custom datasets.

Renumics Spotlight allows you to create interactive visualizations to identify critical clusters in your data. Because Spotlight understands the data semantics within Hugging Face datasets, you can get started with just one line of code:

Diffusers welcomes Stable Diffusion 3
Stable Diffusion 3 (SD3), Stability AI’s latest iteration of the Stable Diffusion family of models, is now available on the Hugging Face Hub and can be used with 🧨 Diffusers.

The model released today is Stable Diffusion 3 Medium, with 2B parameters.

As part of this release, we have provided:

Models on the Hub
Diffusers Integration
SD3 Dreambooth and LoRA training scripts

Open-sourcing Knowledge Distillation Code and Weights of SD-Small and SD-Tiny
In recent times, the AI community has witnessed a remarkable surge in the development of larger and more performant language models, such as Falcon 40B, LLaMa-2 70B, Falcon 40B, MPT 30B, and in the imaging domain with models like SD2.1 and SDXL. These advancements have undoubtedly pushed the boundaries of what AI can achieve, enabling highly versatile and state-of-the-art image generation and language understanding capabilities. However, as we marvel at the power and complexity of these models, it is essential to recognize a growing need to make AI models smaller, efficient, and more accessible, particularly by open-sourcing them.

Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e
Generative AI models, such as Stable Diffusion XL (SDXL), enable the creation of high-quality, realistic content with wide-ranging applications. However, harnessing the power of such models presents significant challenges and computational costs. SDXL is a large image generation model whose UNet component is about three times as large as the one in the previous version of the model. Deploying a model like this in production is challenging due to the increased memory requirements, as well as increased inference times. Today, we are thrilled to announce that Hugging Face Diffusers now supports serving SDXL using JAX on Cloud TPUs, enabling high-performance, cost-efficient inference.

Google Cloud TPUs are custom-designed AI accelerators, which are optimized for training and inference of large AI models, including state-of-the-art LLMs and generative AI models such as SDXL. The new Cloud TPU v5e is purpose-built to bring the cost-efficiency and performance required for large-scale AI training and inference. At less than half the cost of TPU v4, TPU v5e makes it possible for more organizations to train and deploy AI models.

🧨 Diffusers JAX integration offers a convenient way to run SDXL on TPU via XLA, and we built a demo to showcase it. You can try it out in this Space or in the playground embedded below:

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Under the hood, this demo runs on several TPU v5e-4 instances (each instance has 4 TPU chips) and takes advantage of parallelization to serve four large 1024×1024 images in about 4 seconds. This time includes format conversions, communications time, and frontend processing; the actual generation time is about 2.3s, as we'll see below!

LoRA training scripts of the world, unite!
A community derived guide to some of the SOTA practices for SD-XL Dreambooth LoRA fine tuning

TL;DR

We combined the Pivotal Tuning technique used on Replicate's SDXL Cog trainer with the Prodigy optimizer used in the Kohya trainer (plus a bunch of other optimizations) to achieve very good results on training Dreambooth LoRAs for SDXL. Check out the training script on diffusers🧨. Try it out on Colab.

If you want to skip the technical talk, you can use all the techniques in this blog and train on Hugging Face Spaces with a simple UI and curated parameters (that you can meddle with).

Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive
Introduction
SD Turbo and SDXL Turbo are two fast generative text-to-image models capable of generating viable images in as little as one step, a significant improvement over the 30+ steps often required with previous Stable Diffusion models. SD Turbo is a distilled version of Stable Diffusion 2.1, and SDXL Turbo is a distilled version of SDXL 1.0. We’ve previously shown how to accelerate Stable Diffusion inference with ONNX Runtime. Not only does ONNX Runtime provide performance benefits when used with SD Turbo and SDXL Turbo, but it also makes the models accessible in languages other than Python, like C# and Java.

Supercharged Searching on the Hugging Face Hub
Open In Colab
The huggingface_hub library is a lightweight interface that provides a programmatic approach to exploring the hosting endpoints Hugging Face provides: models, datasets, and Spaces.

Up until now, searching on the Hub through this interface was tricky to pull off, and there were many aspects of it a user had to "just know" and get accustomed to.

In this article, we will be looking at a few exciting new features added to huggingface_hub to help lower that bar and provide users with a friendly API to search for the models and datasets they want to use without leaving their Jupyter or Python interfaces.

Before we begin, if you do not have the latest version of the huggingface_hub library on your system, please run the following cell:

!pip install huggingface_hub -U

SegMoE: Segmind Mixture of Diffusion Experts
SegMoE is an exciting framework for creating Mixture-of-Experts Diffusion models from scratch! SegMoE is comprehensively integrated within the Hugging Face ecosystem and comes supported with diffusers 🔥!

Among the features and integrations being released today:

Models on the Hub, with their model cards and licenses (Apache 2.0)
Github Repository to create your own MoE-style models.https://github.com/huggingface/blog/blob/main/segmoe.md

GitHub

blog/segmoe.md at main · huggingface/blog

Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.

How Sempre Health is leveraging the Expert Acceleration Program to accelerate their ML roadmap
👋 Hello, friends! We recently sat down with Swaraj Banerjee and Larry Zhang from Sempre Health, a startup that brings behavior-based, dynamic pricing to Healthcare. They are doing some exciting work with machine learning and are leveraging our Expert Acceleration Program to accelerate their ML roadmap.

An example of our collaboration is their new NLP pipeline to automatically classify and respond inbound messages. Since deploying it to production, they have seen more than 20% of incoming messages get automatically handled by this new system 🤯 having a massive impact on their business scalability and team workflow.

In this short video, Swaraj and Larry walk us through some of their machine learning work and share their experience collaborating with our team via the Expert Acceleration Program. Check it out:

<iframe width="100%" style="aspect-ratio: 16 / 9;"src="https://www.youtube.com/embed/QBOTlNJUtdk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
If you'd like to accelerate your machine learning roadmap with the help of our experts, as Swaraj and Larry did, visit hf.co/support to learn more about our Expert Acceleration Program and request a quote.

YouTube

Sempre Health - Expert Acceleration Program Case Study

In this video, Swaraj Banerjee and Larry Zhang from Sempre Health, walk us through their experience of leveraging the Experts Acceleration Program for building an ML system to automatically handle and respond inbound messages. Learn more about the Expert…

Sentence Transformers in the Hugging Face Hub
Over the past few weeks, we've built collaborations with many Open Source frameworks in the machine learning ecosystem. One that gets us particularly excited is Sentence Transformers.

Sentence Transformers is a framework for sentence, paragraph and image embeddings. This allows to derive semantically meaningful embeddings (1) which is useful for applications such as semantic search or multi-lingual zero shot classification. As part of Sentence Transformers v2 release, there are a lot of cool new features:

Sharing your models in the Hub easily.
Widgets and Inference API for sentence embeddings and sentence similarity.
Better sentence-embeddings models available (benchmark and models in the Hub).

Sentiment Analysis on Encrypted Data with Homomorphic Encryption
It is well-known that a sentiment analysis model determines whether a text is positive, negative, or neutral. However, this process typically requires access to unencrypted text, which can pose privacy concerns.

Homomorphic encryption is a type of encryption that allows for computation on encrypted data without needing to decrypt it first. This makes it well-suited for applications where user's personal and potentially sensitive data is at risk (e.g. sentiment analysis of private messages).

This blog post uses the Concrete-ML library, allowing data scientists to use machine learning models in fully homomorphic encryption (FHE) settings without any prior knowledge of cryptography. We provide a practical tutorial on how to use the library to build a sentiment analysis model on encrypted data.

The post covers:

transformers
how to use transformers with XGBoost to perform sentiment analysis
how to do the training
how to use Concrete-ML to turn predictions into predictions over encrypted data
how to deploy to the cloud using a client/server protocol
Last but not least, we’ll finish with a complete demo over Hugging Face Spaces to show this functionality in action.https://github.com/huggingface/blog/blob/main/sentiment-analysis-fhe.md

GitHub

blog/sentiment-analysis-fhe.md at main · huggingface/blog

Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.

Getting Started with Sentiment Analysis using Python
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Sentiment analysis is the automated process of tagging data according to their sentiment, such as positive, negative and neutral. Sentiment analysis allows companies to analyze data at scale, detect insights and automate processes.

In the past, sentiment analysis used to be limited to researchers, machine learning engineers or data scientists with experience in natural language processing. However, the AI community has built awesome tools to democratize access to machine learning in recent years. Nowadays, you can use sentiment analysis with a few lines of code and no machine learning experience at all! 🤯

In this guide, you'll learn everything to get started with sentiment analysis using Python, including:

What is sentiment analysis?
How to use pre-trained sentiment analysis models with Python
How to build your own sentiment analysis model
How to analyze tweets with sentiment analysis
Let's get started! 🚀https://github.com/huggingface/blog/blob/main/sentiment-analysis-python.md

GitHub

blog/sentiment-analysis-python.md at main · huggingface/blog

Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.

Getting Started with Sentiment Analysis on Twitter
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Sentiment analysis is the automatic process of classifying text data according to their polarity, such as positive, negative and neutral. Companies leverage sentiment analysis of tweets to get a sense of how customers are talking about their products and services, get insights to drive business decisions, and identify product issues and potential PR crises early on.

In this guide, we will cover everything you need to learn to get started with sentiment analysis on Twitter. We'll share a step-by-step process to do sentiment analysis, for both, coders and non-coders. If you are a coder, you'll learn how to use the Inference API, a plug & play machine learning API for doing sentiment analysis of tweets at scale in just a few lines of code. If you don't know how to code, don't worry! We'll also cover how to do sentiment analysis with Zapier, a no-code tool that will enable you to gather tweets, analyze them with the Inference API, and finally send the results to Google Sheets ⚡️

Read along or jump to the section that sparks 🌟 your interest:

What is sentiment analysis?
How to do Twitter sentiment analysis with code?
How to do Twitter sentiment analysis without coding?
Buckle up and enjoy the ride! 🤗https://github.com/huggingface/blog/blob/main/sentiment-analysis-twitter.md

GitHub

blog/sentiment-analysis-twitter.md at main · huggingface/blog

Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.

We Raised $100 Million for Open & Collaborative Machine Learning 🚀
Today we have some exciting news to share! Hugging Face has raised $100 Million in Series C funding 🔥🔥🔥 led by Lux Capital with major participations from Sequoia, Coatue and support of existing investors Addition, a_capital, SV Angel, Betaworks, AIX Ventures, Kevin Durant, Rich Kleiman from Thirty Five Ventures, Olivier Pomel (co-founder & CEO at Datadog) and more.

Series C

We've come a long way since we first open sourced PyTorch BERT in 2018 and are just getting started! 🙌

Machine learning is becoming the default way to build technology. When you think about your average day, machine learning is everywhere: from your Zoom background, to searching on Google, to ordering an Uber or writing an email with auto-complete --it's all machine learning.

Hugging Face is now the fastest growing community & most used platform for machine learning! With 100,000 pre-trained models & 10,000 datasets hosted on the platform for NLP,

SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit


SetFitABSA is an efficient technique to detect the sentiment towards specific aspects within the text.

Aspect-Based Sentiment Analysis (ABSA) is the task of detecting the sentiment towards specific aspects within the text. For example, in the sentence, "This phone has a great screen, but its battery is too small", the aspect terms are "screen" and "battery" and the sentiment polarities towards them are Positive and Negative, respectively.

ABSA is widely used by organizations for extracting valuable insights by analyzing customer feedback towards aspects of products or services in various domains. However, labeling training data for ABSA is a tedious task because of the fine-grained nature (token level) of manually identifying aspects within the training samples.

Intel Labs and Hugging Face are excited to introduce SetFitABSA, a framework for few-shot training of domain-specific ABSA models;

SetFit: Efficient Few-Shot Learning Without Prompts


SetFit is significantly more sample efficient and robust to noise than standard fine-tuning.

Few-shot learning with pretrained language models has emerged as a promising solution to every data scientist's nightmare: dealing with data that has few to no labels 😱.

Together with our research partners at Intel Labs and the UKP Lab, Hugging Face is excited to introduce SetFit: an efficient framework for few-shot fine-tuning of Sentence Transformers. SetFit achieves high accuracy with little labeled data - for example, with only 8 labeled examples per class on the Customer Reviews (CR) sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples 🤯!

Compared to other few-shot learning methods, SetFit has several unique features:

SmolLM - blazingly fast and remarkably powerful
TL;DR
This blog post introduces SmolLM, a family of state-of-the-art small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset. It covers data curation, model evaluation, and usage.

Introduction
There is increasing interest in small language models that can operate on local devices. This trend involves techniques such as distillation or quantization to compress large models, as well as training small models from scratch on large datasets. These approaches enable novel applications while dramatically reducing inference costs and improving user privacy.

Microsoft's Phi series, Alibaba's Qwen2 (less than 2B), and Meta's MobileLLM demonstrate that small models can achieve impressive results when designed and trained thoughtfully. However, most of the details about the data curation and training of these models are not publicly available.

In this blog post, we're excited to introduce SmolLM, a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on a meticulously curated high-quality training corpus, which we are releasing as SmolLM-Corpus. Smollm Corpus includes:

Cosmopedia v2: A collection of synthetic textbooks and stories generated by Mixtral (28B tokens)
Python-Edu: educational Python samples from The Stack (4B tokens)
FineWeb-Edu (deduplicated): educational web samples from FineWeb (220B tokens)
Our evaluations demonstrate that SmolLM models outperform other models in their size categories across a diverse set of benchmarks, testing common sense reasoning and world knowledge. In this blog post, we will go over the curation of each subset in the training corpus and then discuss the training and evaluation of SmolLM models.

Snorkel AI x Hugging Face: unlock foundation models for enterprises
This article is a cross-post from an originally published post on April 6, 2023 in Snorkel's blog, by Friea Berg .

As OpenAI releases GPT-4 and Google debuts Bard in beta, enterprises around the world are excited to leverage the power of foundation models. As that excitement builds, so does the realization that most companies and organizations are not equipped to properly take advantage of foundation models.

Foundation models pose a unique set of challenges for enterprises. Their larger-than-ever size makes them difficult and expensive for companies to host themselves, and using off-the-shelf FMs for production use cases could mean poor performance or substantial governance and compliance risks.

Snorkel AI bridges the gap between foundation models and practical enterprise use cases and has yielded impressive results for AI innovators like Pixability. We’re teaming with Hugging Face, best known for its enormous repository of ready-to-use open-source models, to provide enterprises with even more flexibility and choice as they develop AI applications.

Introducing Snowball Fight ☃️, our First ML-Agents Environment
We're excited to share our first custom Deep Reinforcement Learning environment: Snowball Fight 1vs1 🎉. gif

Snowball Fight is a game made with Unity ML-Agents, where you shoot snowballs against a Deep Reinforcement Learning agent. The game is hosted on Hugging Face Spaces.

👉 You can play it online here

Banque des Territoires (CDC Group) x Polyconseil x Hugging Face: Enhancing a Major French Environmental Program with a Sovereign Data Solution
Table of contents
Case Study in English - Banque des Territoires (CDC Group) x Polyconseil x Hugging Face: Enhancing a Major French Environmental Program with a Sovereign Data Solution
Executive summary
The power of RAG to meet environmental objectives
Industrializing while ensuring performance and sovereignty
A modular solution to respond to a dynamic sector
Key Success Factors Success Factors
Case Study in French - Banque des Territoires (Groupe CDC) x Polyconseil x Hugging Face : améliorer un programme environnemental français majeur grâce à une solution data souveraine
Résumé
La puissance du RAG au service d'objectifs environnementaux
Industrialiser en garantissant performance et souveraineté
Une solution modulaire pour répondre au dynamisme du secteur
Facteurs clés de succèshttps://github.com/huggingface/blog/blob/main/sovereign-data-solution-case-study.md