Transformers training and inference
The process of training and utilizing Transformers for prediction is similar to other deep learning models. The training process involves creating the Transformer architecture, initializing weights, passing training data, and updating weights until the desired level of accuracy is achieved. Transformer models store both architecture and trained weights, which can sometimes be gigabytes in size. The prediction process with a Transformer model includes loading the saved model, encoding and vectorizing input, passing through an encoding-decoding pipeline, and using softmax to predict tokens.
Theories:
Transformer architecture:
- Number of encoder and decoder layers
- Number of attention heads
- Feedforward network architecture
- Normalization techniques
Training process:
- Desired level of accuracy
Transformer model size:
- Can sometimes be gigabytes in size
Prediction process:
- Tokenization
- Softmax layer
Advantages and disadvantages:
Advantages:
- Applicable to various tasks in natural language processing (NLP)
- Performs well on large datasets
- Ability to learn long-range dependencies between words
Disadvantages:
- Requires significant computational resources and memory
- Ineffective with limited training data.
Transformer architecture:
NLP tasks:
Training process:
Prediction methodologies:
Advantages of Transformers:
Disadvantages of Transformers:
Long-range dependencies in NLP:
Computational resources in deep learning:
Artificial intelligence in product marketing:
Understanding the strengths and limitations of tokenization and vectorization in NLP:
