MTEB: Massive Text Embedding Benchmark
MTEB is a massive benchmark for measuring the performance of text embedding models on diverse embedding tasks. The ๐ฅ leaderboard provides a holistic view of the best text embedding models out there on a variety of tasks. The ๐ paper gives background on the tasks and datasets in MTEB and analyzes leaderboard results! The ๐ป Github
Read moreFrom PyTorch DDP to Accelerate to Trainer, mastery of distributed training with ease
This tutorial assumes you have a basic understanding of PyTorch and how to train a simple model. It will showcase training on multiple GPUs through a process called Distributed Data Parallelism (DDP) through three different levels of increasing abstraction: Native PyTorch DDP through the pytorch.distributed module Utilizing ๐ค Accelerate’s light wrapper around pytorch.distributed that also helps ensure the code can be run
Read moreEvaluating Language Model Bias with ๐ค Evaluate
While the size and capabilities of large language models have drastically increased over the past couple of years, so too has the concern around biases imprinted into these models and their training data. In fact, many popular language models have been found to be biased against specific religions and genders, which can result in the promotion of discriminatory ideas and the perpetuation of harms against marginalized groups. To help the community explore these kinds of biases and strengthen our understanding […]
Read moreAccelerate your models with ๐ค Optimum Intel and OpenVINO
Last July, we announced that Intel and Hugging Face would collaborate on building state-of-the-art yet simple hardware acceleration tools for Transformer models. โ Today, we are very happy to announce that we added
Read moreFine-Tune Whisper For Multilingual ASR with ๐ค Transformers
In this blog, we present a step-by-step guide on fine-tuning Whisper for any multilingual ASR dataset using Hugging Face ๐ค Transformers. This blog provides in-depth explanations of the Whisper model, the Common Voice dataset and the theory behind fine-tuning, with accompanying code cells to execute the data preparation and fine-tuning steps. For a more
Read moreTraining Stable Diffusion with Dreambooth using ๐งจ Diffusers
Dreambooth is a technique to teach new concepts to Stable Diffusion using a specialized form of fine-tuning. Some people have been using it with a few of their photos to place themselves in fantastic situations, while others are using it to incorporate new styles. ๐งจ Diffusers provides a Dreambooth training script. It doesn’t take long to train, but it’s hard to select the right set of hyperparameters and it’s easy to overfit. We conducted a lot of experiments to analyze […]
Read moreGenerating Human-level Text with Contrastive Search in Transformers ๐ค
1. Introduction: Natural language generation (i.e. text generation) is one of the core tasks in natural language processing (NLP). In this blog, we introduce the current state-of-the-art decoding method, Contrastive Search, for neural
Read moreSentiment 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
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