Few-shot learning in production
Join researchers from Hugging Face and Intel AI for a workshop on training & deploying few-shot language models!
In this free workshop, you will learn how to:
👩🎓 Train Sentence Transformers with little labeled data using a technique called SetFit
🐭 Compress models with knowledge distillation
🏎 Accelerate inference with quantization and 🤗 Optimum and Intel® Neural Compressor
🐳 Deploy models with Inference Endpoints
For more details about SetFit, check out the library 👉: https://github.com/huggingface/setfit
Watch on YouTube ↗
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The Future of Natural Language Processing
HuggingFace
Trends in Model Size & Computational Efficiency in NLP
HuggingFace
Increasing Data Usage in Natural Language Processing
HuggingFace
In Domain & Out of Domain Generalization in the Future of NLP
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The Limits of NLU & the Rise of NLG in the Future of NLP
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The Lack of Robustness in the Future of NLP
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Inductive Bias, Common Sense, Continual Learning in The Future of NLP
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Train a Hugging Face Transformers Model with Amazon SageMaker
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What is Transfer Learning?
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The pipeline function
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Navigating the Model Hub
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Transformer models: Decoders
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The Transformer architecture
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Transformer models: Encoder-Decoders
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Transformer models: Encoders
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Keras introduction
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The push to hub API
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Fine-tuning with TensorFlow
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Learning rate scheduling with TensorFlow
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TensorFlow Predictions and metrics
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Welcome to the Hugging Face course
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The tokenization pipeline
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Supercharge your PyTorch training loop with Accelerate
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The Trainer API
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Batching inputs together (PyTorch)
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Batching inputs together (TensorFlow)
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Hugging Face Datasets overview (Pytorch)
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Hugging Face Datasets overview (Tensorflow)
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What is dynamic padding?
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What happens inside the pipeline function? (PyTorch)
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What happens inside the pipeline function? (TensorFlow)
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Instantiate a Transformers model (PyTorch)
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Instantiate a Transformers model (TensorFlow)
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Preprocessing sentence pairs (PyTorch)
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Preprocessing sentence pairs (TensorFlow)
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Write your training loop in PyTorch
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Managing a repo on the Model Hub
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Chapter 1 Live Session with Sylvain
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Chapter 2 Live Session with Lewis
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The push to hub API
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Chapter 2 Live Session with Sylvain
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Chapter 3 live sessions with Lewis (PyTorch)
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Day 1 Talks: JAX, Flax & Transformers 🤗
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Day 2 Talks: JAX, Flax & Transformers 🤗
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Day 3 Talks JAX, Flax, Transformers 🤗
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Chapter 4 live sessions with Omar
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Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
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Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
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Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
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[Webinar] How to add machine learning capabilities with just a few lines of code
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Hugging Face + Zapier Demo Video
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Hugging Face + Google Sheets Demo
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Hugging Face Infinity Launch - 09/28
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Build and Deploy a Machine Learning App in 2 Minutes
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Hugging Face Infinity - GPU Walkthrough
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Otto - 🤗 Infinity Case Study
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Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it
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Workshop: Going Production: Deploying, Scaling & Monitoring Hugging Face Transformer models
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🤗 Tasks: Causal Language Modeling
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🤗 Tasks: Masked Language Modeling
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DeepCamp AI