It Ain't Broke So D̶o̶n̶'t̶ F̶i̶x̶ Let's Break It
Jakob takes a look at the limitations of Transformers models and gives us four reasons they will be replaced.
Jakob Uszkoreit is the co-founder of Inceptive. Inceptive designs RNA molecules for vaccines and therapeutics using large-scale deep learning in a tight loop with high throughput experiments with the goal of making RNA-based medicines more accessible, more effective and more broadly applicable. Previously, Jakob worked at Google for more than a decade, leading research and development teams in Google Brain, Research and Search working on deep learning fundamentals, computer vision, language understanding and machine translation.
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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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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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