Building a new tokenizer

HuggingFace ยท Intermediate ยท๐Ÿง  Large Language Models ยท4y ago
Learn how to use the ๐Ÿค— Tokenizers library to build your own tokenizer, train it, then how to use it in the ๐Ÿค— Transformers library. This video is part of the Hugging Face course: http://huggingface.co/course Open in colab to run the code samples: https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/videos/building_tokenizer.ipynb Related videos: - Training a new tokenizer: https://youtu.be/DJimQynXZsQ - Byte Pair Encoding Tokenization: https://youtu.be/HEikzVL-lZU - Unigram Tokenization: https://youtu.be/TGZfZVuF9Yc - WordPiece Tokenization: https://youtu.be/qpv6ms_t_1A Don't have a Hugging Face account? Join now: http://huggingface.co/join Have a question? Checkout the forums: https://discuss.huggingface.co/c/course/20 Subscribe to our newsletter: https://huggingface.curated.co/
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1 The Future of Natural Language Processing
The Future of Natural Language Processing
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 2 Trends in Model Size & Computational Efficiency in NLP
Trends in Model Size & Computational Efficiency in NLP
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 3 Increasing Data Usage in Natural Language Processing
Increasing Data Usage in Natural Language Processing
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 4 In Domain & Out of Domain Generalization in the Future of NLP
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
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
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
Inductive Bias, Common Sense, Continual Learning in The Future of NLP
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 8 Train a Hugging Face Transformers Model with Amazon SageMaker
Train a Hugging Face Transformers Model with Amazon SageMaker
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 9 What is Transfer Learning?
What is Transfer Learning?
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 10 The pipeline function
The pipeline function
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 11 Navigating the Model Hub
Navigating the Model Hub
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 12 Transformer models: Decoders
Transformer models: Decoders
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 13 The Transformer architecture
The Transformer architecture
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 14 Transformer models: Encoder-Decoders
Transformer models: Encoder-Decoders
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 15 Transformer models: Encoders
Transformer models: Encoders
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 16 Keras introduction
Keras introduction
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 17 The push to hub API
The push to hub API
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 18 Fine-tuning with TensorFlow
Fine-tuning with TensorFlow
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 19 Learning rate scheduling with TensorFlow
Learning rate scheduling with TensorFlow
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 20 TensorFlow Predictions and metrics
TensorFlow Predictions and metrics
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 21 Welcome to the Hugging Face course
Welcome to the Hugging Face course
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 22 The tokenization pipeline
The tokenization pipeline
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 23 Supercharge your PyTorch training loop with Accelerate
Supercharge your PyTorch training loop with Accelerate
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 24 The Trainer API
The Trainer API
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 25 Batching inputs together (PyTorch)
Batching inputs together (PyTorch)
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 26 Batching inputs together (TensorFlow)
Batching inputs together (TensorFlow)
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 27 Hugging Face Datasets overview (Pytorch)
Hugging Face Datasets overview (Pytorch)
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 28 Hugging Face Datasets overview (Tensorflow)
Hugging Face Datasets overview (Tensorflow)
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 29 What is dynamic padding?
What is dynamic padding?
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 30 What happens inside the pipeline function? (PyTorch)
What happens inside the pipeline function? (PyTorch)
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 31 What happens inside the pipeline function? (TensorFlow)
What happens inside the pipeline function? (TensorFlow)
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 32 Instantiate a Transformers model (PyTorch)
Instantiate a Transformers model (PyTorch)
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 33 Instantiate a Transformers model (TensorFlow)
Instantiate a Transformers model (TensorFlow)
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 34 Preprocessing sentence pairs (PyTorch)
Preprocessing sentence pairs (PyTorch)
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 35 Preprocessing sentence pairs (TensorFlow)
Preprocessing sentence pairs (TensorFlow)
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 36 Write your training loop in PyTorch
Write your training loop in PyTorch
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 37 Managing a repo on the Model Hub
Managing a repo on the Model Hub
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 38 Chapter 1 Live Session with Sylvain
Chapter 1 Live Session with Sylvain
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 39 Chapter 2 Live Session with Lewis
Chapter 2 Live Session with Lewis
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 40 The push to hub API
The push to hub API
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 41 Chapter 2 Live Session with Sylvain
Chapter 2 Live Session with Sylvain
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 42 Chapter 3 live sessions with Lewis (PyTorch)
Chapter 3 live sessions with Lewis (PyTorch)
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 43 Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
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 44 Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
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 45 Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
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 46 Chapter 4 live sessions with Omar
Chapter 4 live sessions with Omar
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 47 Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
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 48 Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
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 49 Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
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 50 [Webinar] How to add machine learning capabilities with just a few lines of code
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 51 Hugging Face + Zapier Demo Video
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 52 Hugging Face + Google Sheets Demo
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 53 Hugging Face Infinity Launch - 09/28
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 54 Build and Deploy a Machine Learning App in 2 Minutes
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 55 Hugging Face Infinity - GPU Walkthrough
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 56 Otto - ๐Ÿค— Infinity Case Study
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 57 Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it
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 58 Workshop: Going Production: Deploying, Scaling & Monitoring Hugging Face Transformer models
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 59 ๐Ÿค— Tasks: Causal Language Modeling
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 60 ๐Ÿค— Tasks: Masked Language Modeling
๐Ÿค— Tasks: Masked Language Modeling
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