Natural Language Processing and Transformers in TensorFlow
Language Modeling Foundations
Sequence modeling
Language modeling estimates the probability of token sequences . TensorFlow implementations require careful tokenization, masking, batching, and memory management. Advanced NLP systems also require considerations around context length, decoding strategy, and evaluation beyond simple accuracy.
What does causal masking in a language model prevent?
Causal masking ensures autoregressive models only attend to past and current tokens.
Correct answer: The model from seeing future tokens
Why is tokenization an important modeling choice?
Tokenization shapes the input representation and the difficulty of the learning problem.
Correct answer: It determines how text is segmented into discrete units and affects vocabulary size, efficiency, and performance.
Transformer Architecture
Core transformer components
A transformer block combines multi-head self-attention, feed-forward layers, normalization, and residual connections. Positional information may be injected through absolute or relative schemes. In practice, training stability depends on initialization, normalization placement, learning rates, and sequence packing strategies.
Scaling insight
Transformer efficiency often depends more on memory bandwidth and attention implementation than on theoretical parameter count alone.
NLP implementation concerns
| Concern | Why it matters |
|---|---|
| Masking | Prevents information leakage in autoregressive tasks |
| Padding | Maintains batch efficiency with variable-length text |
| Vocabulary design | Balances granularity and sequence length |
| Decoding strategy | Affects generation quality and diversity |
What is the role of self-attention in a transformer?
Self-attention enables context-dependent representation learning across the sequence.
Correct answer: It lets tokens interact with all other tokens in the context
Name one common decoding strategy for text generation.
Beam search is one common method, though sampling-based approaches are also widely used.
Correct answer: Beam search