BERT demo // Masked language model
📰 Reddit r/deeplearning
Learn to implement a simple masked language model using BERT and NumPy, and understand the basics of configuring and training a neural network
Action Steps
- Import necessary libraries like NumPy
- Configure parameters such as learning rate and maximum epochs
- Initialize model weights and biases using NumPy's random functions
- Prepare training data in the form of input sequences and corresponding labels
- Train the model using the configured parameters and data
Who Needs to Know This
This lesson is beneficial for machine learning engineers and NLP specialists who want to understand the fundamentals of masked language models and how to implement them using popular libraries like NumPy
Key Insight
💡 Masked language models like BERT can be implemented using simple NumPy operations, making it easier to understand and experiment with NLP concepts
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🤖 Implement a simple masked language model using BERT and NumPy! 📊
Key Takeaways
Learn to implement a simple masked language model using BERT and NumPy, and understand the basics of configuring and training a neural network
Full Article
import numpy as np # 1. Configuration & Parameters lr = 0.007 max_epochs = 1000 np.random.seed(42) # Model: W in R^(4x5), b in [0,1]^4, weights ~ N(0, 2) W = np.random.normal(0, 2, (4, 5)) b = np.random.uniform(0, 1, (4,)) data = [ ("Sayori walks to school and finds Daniel at the", "club", 0), ("Yuri takes out her pen and starts writing a mystical forest", "poem", 3), ("I reach Sayo
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