Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—

HuggingFace ยท Intermediate ยท๐Ÿง  Large Language Models ยท4y ago
Day 2 Talks: JAX, Flax & Transformers ๐Ÿค— 0:00:00 Suraj Patil & Patrick von Platen (Hugging Face): How to use JAX/Flax with Transformers 0:26:16 Sabrina J. Mielke (Johns Hopkins University & Hugging Face): From stateful code to purified JAX: how to build your neural net framework 1:01:35 Mostafa Dehghani (Google Brain): Long Range Arena: Benchmarking Efficient Transformers 1:28:05 Rohan Anil (Google Brain): Scalable Second Order Optimization for Deep Learning Find more information about the speakers and the talks here https://github.com/huggingface/transformers/blob/master/examples/research_prโ€ฆ
Watch on YouTube โ†— (saves to browser)

Chapters (4)

Suraj Patil & Patrick von Platen (Hugging Face): How to use JAX/Flax with Transf
26:16 Sabrina J. Mielke (Johns Hopkins University & Hugging Face): From stateful code
1:01:35 Mostafa Dehghani (Google Brain): Long Range Arena: Benchmarking Efficient Transf
1:28:05 Rohan Anil (Google Brain): Scalable Second Order Optimization for Deep Learning

Playlist

Uploads from HuggingFace ยท HuggingFace ยท 50 of 60

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1 The Future of Natural Language Processing
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 2 Trends in Model Size & Computational Efficiency in NLP
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 3 Increasing Data Usage in Natural Language Processing
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 4 In Domain & Out of Domain Generalization in the Future of NLP
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 5 The Limits of NLU & the Rise of NLG in the Future of NLP
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 6 The Lack of Robustness in the Future of NLP
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 7 Inductive Bias, Common Sense, Continual Learning in The Future of NLP
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 8 Train and use a NLP model in 10 mins!
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 9 Automatic text classification in a few lines of code
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 10 Train a Hugging Face Transformers Model with Amazon SageMaker
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 11 What is Transfer Learning?
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 12 The pipeline function
The pipeline function
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 13 Navigating the Model Hub
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 14 Character-based tokenizers
Character-based tokenizers
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 15 Transformer models: Decoders
Transformer models: Decoders
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 16 The Transformer architecture
The Transformer architecture
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 17 Transformer models: Encoder-Decoders
Transformer models: Encoder-Decoders
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 18 Transformer models: Encoders
Transformer models: Encoders
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 19 Keras introduction
Keras introduction
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 20 The push to hub API
The push to hub API
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 21 Subword-based tokenizers
Subword-based tokenizers
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 22 Fine-tuning with TensorFlow
Fine-tuning with TensorFlow
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 23 Learning rate scheduling with TensorFlow
Learning rate scheduling with TensorFlow
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 24 TensorFlow Predictions and metrics
TensorFlow Predictions and metrics
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 25 Tokenizers Overview
Tokenizers Overview
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 26 Word-based tokenizers
Word-based tokenizers
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 27 Welcome to the Hugging Face course
Welcome to the Hugging Face course
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 28 The tokenization pipeline
The tokenization pipeline
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 29 Supercharge your PyTorch training loop with Accelerate
Supercharge your PyTorch training loop with Accelerate
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 30 The Trainer API
The Trainer API
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 31 Batching inputs together (PyTorch)
Batching inputs together (PyTorch)
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 32 Batching inputs together (TensorFlow)
Batching inputs together (TensorFlow)
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 33 Hugging Face Datasets overview (Pytorch)
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 34 Hugging Face Datasets overview (Tensorflow)
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 35 What is dynamic padding?
What is dynamic padding?
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 36 What happens inside the pipeline function? (PyTorch)
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 37 What happens inside the pipeline function? (TensorFlow)
What happens inside the pipeline function? (TensorFlow)
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 38 Instantiate a Transformers model (PyTorch)
Instantiate a Transformers model (PyTorch)
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 39 Instantiate a Transformers model (TensorFlow)
Instantiate a Transformers model (TensorFlow)
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 40 Preprocessing sentence pairs (PyTorch)
Preprocessing sentence pairs (PyTorch)
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 41 Preprocessing sentence pairs (TensorFlow)
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 42 Write your training loop in PyTorch
Write your training loop in PyTorch
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 43 Managing a repo on the Model Hub
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 44 Chapter 1 Live Session with Sylvain
Chapter 1 Live Session with Sylvain
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 45 Chapter 2 Live Session with Lewis
Chapter 2 Live Session with Lewis
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 46 The push to hub API
The push to hub API
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 47 Chapter 2 Live Session with Sylvain
Chapter 2 Live Session with Sylvain
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 48 Chapter 3 live sessions with Lewis (PyTorch)
Chapter 3 live sessions with Lewis (PyTorch)
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 49 Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
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 โ–ถ Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
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 51 Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
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 52 Chapter 4 live sessions with Omar
Chapter 4 live sessions with Omar
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 53 Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
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 54 Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
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 55 Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
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 56 [Webinar] How to add machine learning capabilities with just a few lines of code
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 57 Hugging Face + Zapier Demo Video
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 58 Hugging Face + Google Sheets Demo
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 59 Hugging Face Infinity Launch - 09/28
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 60 Introducing AutoNLP (Trailer)
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5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
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