Business Context

LexCore, a contract analytics platform, wants to automatically extract key entities from commercial agreements so legal ops teams can review obligations faster. You need to fine-tune a HuggingFace transformer model for a custom NER pipeline that identifies contract-specific entities.

Data

The training corpus contains 48,000 annotated clauses from MSAs, NDAs, DPAs, and vendor agreements. Documents are in English, with occasional OCR noise from scanned PDFs. Text length ranges from 20 to 900 tokens per clause (median: 140). Labels follow BIO tagging and include PARTY, EFFECTIVE_DATE, TERM, RENEWAL_NOTICE, GOVERNING_LAW, PAYMENT_TERM, LIABILITY_CAP, and O. The label distribution is imbalanced: O dominates, while entities such as LIABILITY_CAP and RENEWAL_NOTICE appear in fewer than 4% of clauses.

Success Criteria

A strong solution should achieve entity-level macro F1 >= 0.84, with F1 >= 0.90 on PARTY and EFFECTIVE_DATE, and stable performance on long clauses with nested legal phrasing.

Constraints

• Must train on a single A10/T4-class GPU
• Inference should support batch processing of 500K clauses/day
• The approach must preserve token-to-label alignment after subword tokenization
• Output must be auditable for legal review workflows

Requirements

1. Build a complete fine-tuning workflow using a HuggingFace transformer for custom NER.
2. Explain preprocessing for BIO labels, OCR cleanup, and subword alignment.
3. Implement training, validation, and entity-level evaluation in Python.
4. Describe model selection, hyperparameters, and handling of class imbalance.
5. Show how you would analyze errors such as boundary mistakes, fragmented entities, and confusion between TERM and RENEWAL_NOTICE.