Tokenizer Training and Operational Best Practices

When to Train a Custom Tokenizer
Pre-trained tokenizers work well for general English text. Train your own when: your domain has specialized vocabulary (medical terms, chemical formulas, code), you need multilingual coverage not in existing tokenizers, or compression ratio on your data is poor (more than 2 tokens per word average).
Training cost is low: a few hours on 10-100GB of text. The risk is getting it wrong and needing to retrain the entire model. Test extensively before committing to a vocabulary.
Training Process
Data selection: Use representative text from your domain. Include rare but important terms. Balance across languages if multilingual. 10GB is minimum; 100GB gives better coverage of rare words.
BPE training: Start with byte-level vocabulary (256 entries). Iteratively merge the most frequent adjacent pairs. Stop when vocabulary reaches target size. More merges = larger vocabulary = better compression but more memory.
Special tokens: Reserve slots for [PAD], [UNK], [CLS], [SEP], [MASK] and any task-specific tokens. Add these before training. You cannot add special tokens later without breaking existing models.
Operational Best Practices
✅ Version Everything: Tokenizer version, vocabulary hash, preprocessing code version. Store together with model checkpoints. You must be able to recreate exact tokenization from any model version.
Vocabulary updates: Never modify vocabulary of a trained model. Adding tokens invalidates learned embeddings. If you need new tokens, train a new model from scratch or use the [UNK] fallback.
Testing: Create a test suite of edge cases: empty strings, single characters, maximum length inputs, Unicode edge cases, adversarial inputs. Run on every tokenizer change. Regression bugs are subtle and costly.
Monitoring: Track token distribution in production. Alert if unknown token rate exceeds 0.1% or if average sequence length changes by more than 10%. Both indicate data distribution shift.

💡 Key Takeaways

✓Train custom tokenizer for specialized vocabulary, multilingual needs, or poor compression ratio

✓BPE training: start with 256 bytes, merge frequent pairs until target vocabulary size

✓Reserve special tokens ([PAD], [UNK], [CLS], [SEP], [MASK]) before training

✓Never modify vocabulary of a trained model; adding tokens invalidates learned embeddings

✓Monitor unknown token rate (alert if >0.1%) and sequence length changes (alert if >10% shift)

📌 Interview Tips

1Explain when to train custom tokenizer: specialized domain, multilingual, or >2 tokens/word average

2Mention versioning: tokenizer version, vocabulary hash, preprocessing code must be stored with model

3For monitoring, alert on unknown token rate >0.1% or sequence length change >10%

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