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Project 2: Train a Custom Domain-Specific Tokenizer (e.g., for legal or medical texts)
Pitfalls & Tips
- Representativeness beats size: a smaller but well-targeted corpus > massive generic scrape. When creating a domain-specific tokenizer, it's far more effective to use a smaller dataset that accurately represents your specific domain (like legal contracts or medical literature) than to use a huge dataset of general text. This ensures your tokenizer learns the specific vocabulary patterns that matter in your domain rather than being diluted by irrelevant language patterns.
- Case sensitivity: Legal and biomedical text often needs preserving case. Try disabling lowercasing. In domains like law and medicine, capitalization often carries significant meaning (e.g., "Section" vs. "section" in legal documents, or "CD4" vs. "cd4" in medical texts). By default, many tokenization algorithms lowercase all text, which can destroy these important distinctions. Configure your tokenizer to maintain case when working with these specialized domains.
- Numbers & symbols: Decide whether to split "12(b)(6)" or keep as a whole. Consistency helps. Legal citations, chemical formulas, and specialized codes often combine numbers and symbols in ways that are meaningful as complete units. Decide early in your tokenization strategy whether to treat these as single tokens or split them into components. Whatever you choose, apply it consistently throughout your tokenization process to avoid confusing your models.
- Vocab size tuning: Too small → fragmentation; too big → memory footprint. Start 8k–32k for medium corpora. Finding the right vocabulary size is crucial: if it's too small, common domain terms will be split into multiple tokens (fragmentation), reducing efficiency and possibly losing semantic meaning; if it's too large, you'll have a bigger model that requires more memory and might overfit to rare terms. For medium-sized domain-specific datasets, starting with 8,000-32,000 tokens often provides a good balance.
- Measure before/after: Track token counts on real samples you care about (contracts, filings, code files). Always evaluate your tokenizer on representative examples from your actual use case. Count how many tokens are needed to represent key documents before and after training your custom tokenizer. A successful domain-specific tokenizer should represent domain text more efficiently (fewer tokens) than a general-purpose tokenizer, while maintaining or improving representation of key domain concepts.
Pitfalls & Tips
- Representativeness beats size: a smaller but well-targeted corpus > massive generic scrape. When creating a domain-specific tokenizer, it's far more effective to use a smaller dataset that accurately represents your specific domain (like legal contracts or medical literature) than to use a huge dataset of general text. This ensures your tokenizer learns the specific vocabulary patterns that matter in your domain rather than being diluted by irrelevant language patterns.
- Case sensitivity: Legal and biomedical text often needs preserving case. Try disabling lowercasing. In domains like law and medicine, capitalization often carries significant meaning (e.g., "Section" vs. "section" in legal documents, or "CD4" vs. "cd4" in medical texts). By default, many tokenization algorithms lowercase all text, which can destroy these important distinctions. Configure your tokenizer to maintain case when working with these specialized domains.
- Numbers & symbols: Decide whether to split "12(b)(6)" or keep as a whole. Consistency helps. Legal citations, chemical formulas, and specialized codes often combine numbers and symbols in ways that are meaningful as complete units. Decide early in your tokenization strategy whether to treat these as single tokens or split them into components. Whatever you choose, apply it consistently throughout your tokenization process to avoid confusing your models.
- Vocab size tuning: Too small → fragmentation; too big → memory footprint. Start 8k–32k for medium corpora. Finding the right vocabulary size is crucial: if it's too small, common domain terms will be split into multiple tokens (fragmentation), reducing efficiency and possibly losing semantic meaning; if it's too large, you'll have a bigger model that requires more memory and might overfit to rare terms. For medium-sized domain-specific datasets, starting with 8,000-32,000 tokens often provides a good balance.
- Measure before/after: Track token counts on real samples you care about (contracts, filings, code files). Always evaluate your tokenizer on representative examples from your actual use case. Count how many tokens are needed to represent key documents before and after training your custom tokenizer. A successful domain-specific tokenizer should represent domain text more efficiently (fewer tokens) than a general-purpose tokenizer, while maintaining or improving representation of key domain concepts.
Pitfalls & Tips
- Representativeness beats size: a smaller but well-targeted corpus > massive generic scrape. When creating a domain-specific tokenizer, it's far more effective to use a smaller dataset that accurately represents your specific domain (like legal contracts or medical literature) than to use a huge dataset of general text. This ensures your tokenizer learns the specific vocabulary patterns that matter in your domain rather than being diluted by irrelevant language patterns.
- Case sensitivity: Legal and biomedical text often needs preserving case. Try disabling lowercasing. In domains like law and medicine, capitalization often carries significant meaning (e.g., "Section" vs. "section" in legal documents, or "CD4" vs. "cd4" in medical texts). By default, many tokenization algorithms lowercase all text, which can destroy these important distinctions. Configure your tokenizer to maintain case when working with these specialized domains.
- Numbers & symbols: Decide whether to split "12(b)(6)" or keep as a whole. Consistency helps. Legal citations, chemical formulas, and specialized codes often combine numbers and symbols in ways that are meaningful as complete units. Decide early in your tokenization strategy whether to treat these as single tokens or split them into components. Whatever you choose, apply it consistently throughout your tokenization process to avoid confusing your models.
- Vocab size tuning: Too small → fragmentation; too big → memory footprint. Start 8k–32k for medium corpora. Finding the right vocabulary size is crucial: if it's too small, common domain terms will be split into multiple tokens (fragmentation), reducing efficiency and possibly losing semantic meaning; if it's too large, you'll have a bigger model that requires more memory and might overfit to rare terms. For medium-sized domain-specific datasets, starting with 8,000-32,000 tokens often provides a good balance.
- Measure before/after: Track token counts on real samples you care about (contracts, filings, code files). Always evaluate your tokenizer on representative examples from your actual use case. Count how many tokens are needed to represent key documents before and after training your custom tokenizer. A successful domain-specific tokenizer should represent domain text more efficiently (fewer tokens) than a general-purpose tokenizer, while maintaining or improving representation of key domain concepts.
Pitfalls & Tips
- Representativeness beats size: a smaller but well-targeted corpus > massive generic scrape. When creating a domain-specific tokenizer, it's far more effective to use a smaller dataset that accurately represents your specific domain (like legal contracts or medical literature) than to use a huge dataset of general text. This ensures your tokenizer learns the specific vocabulary patterns that matter in your domain rather than being diluted by irrelevant language patterns.
- Case sensitivity: Legal and biomedical text often needs preserving case. Try disabling lowercasing. In domains like law and medicine, capitalization often carries significant meaning (e.g., "Section" vs. "section" in legal documents, or "CD4" vs. "cd4" in medical texts). By default, many tokenization algorithms lowercase all text, which can destroy these important distinctions. Configure your tokenizer to maintain case when working with these specialized domains.
- Numbers & symbols: Decide whether to split "12(b)(6)" or keep as a whole. Consistency helps. Legal citations, chemical formulas, and specialized codes often combine numbers and symbols in ways that are meaningful as complete units. Decide early in your tokenization strategy whether to treat these as single tokens or split them into components. Whatever you choose, apply it consistently throughout your tokenization process to avoid confusing your models.
- Vocab size tuning: Too small → fragmentation; too big → memory footprint. Start 8k–32k for medium corpora. Finding the right vocabulary size is crucial: if it's too small, common domain terms will be split into multiple tokens (fragmentation), reducing efficiency and possibly losing semantic meaning; if it's too large, you'll have a bigger model that requires more memory and might overfit to rare terms. For medium-sized domain-specific datasets, starting with 8,000-32,000 tokens often provides a good balance.
- Measure before/after: Track token counts on real samples you care about (contracts, filings, code files). Always evaluate your tokenizer on representative examples from your actual use case. Count how many tokens are needed to represent key documents before and after training your custom tokenizer. A successful domain-specific tokenizer should represent domain text more efficiently (fewer tokens) than a general-purpose tokenizer, while maintaining or improving representation of key domain concepts.