From Paper to Product – How we implemented BERT | Christoph Henkelmann

Training BERT for German

• To train BERT for German, it’s necessary to have a large dataset
• The dataset used was a mixture of Wikipedia and Project Gutenberg texts
• A pre-trained BERT model was then used for transfer learning

Implementing BERT in Product Development

• The company implemented BERT as a solution for their product development
• It helped to reduce the time and effort required to create a product
• The product now runs on consumer hardware, including a 100 euro gaming card

Unsupervised Training

• The pre-training process took four days for the Google pre-training paper
• The self-attention layer is a crucial part of the BERT architecture
• BERT can learn to predict a prefix and a suffix

Key Takeaways

• BERT is a powerful tool for natural language processing
• Transfer learning is a key concept in BERT
• Unsupervised training is necessary for BERT to learn about language structure
• The company was able to implement BERT in their product development, reducing time and effort
• BERT can be used for a variety of tasks, including sentiment analysis and text classification