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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![[Webinar] How to add machine learning capabilities with just a few lines of code](https://i.ytimg.com/vi/p055U0dnEos/mqdefault.jpg)
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The Future of Natural Language Processing
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Trends in Model Size & Computational Efficiency in NLP
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Increasing Data Usage in Natural Language Processing
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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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The pipeline function
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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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Subword-based tokenizers
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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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Tokenizers Overview
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Word-based tokenizers
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Welcome to the Hugging Face course
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The tokenization pipeline
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The Trainer API
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Batching inputs together (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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Day 1 Talks: JAX, Flax & Transformers 🤗
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Day 2 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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Hugging Face + Google Sheets Demo
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