MTEB: Massive Text Embedding Benchmark

MTEB is a massive benchmark for measuring the performance of text embedding models on diverse embedding tasks. The πŸ₯‡ leaderboard provides a holistic view of the best text embedding models out there on a variety of tasks. The πŸ“ paper gives background on the tasks and datasets in MTEB and analyzes leaderboard results! The πŸ’» Github    

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From 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 πŸ€— Accelerate’s light wrapper around pytorch.distributed that also helps ensure the code can be run    

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Evaluating Language Model Bias with πŸ€— 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 […]

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Training Stable Diffusion with Dreambooth using 🧨 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. 🧨 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 […]

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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    

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