Build a Domain-Specific Named-Entity Recognizer for Legal Contracts
Overview
What this challenge is about.
Build a Domain-Specific Named-Entity Recognizer for Legal Contracts. Advanced challenge in code. Writing production code that solves real engineering problem...
The Brief
What you'll do, and what you'll demonstrate.
Train a domain-tuned NER for 9 contract entity types with per-entity F1 reported on a 200-contract held-out set.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Adapt pretrained NER to a domain-specific entity schema
- Apply sliding-window strategies for long-document NER
- Evaluate sequence-labeling models per-entity and per-document
- Document an NER model for downstream product integration
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Named Entity Recognition
Apply named entity recognition to solve real industry problems and demonstrate production-level capability.
- Sequence Labeling
Apply sequence labeling to solve real industry problems and demonstrate production-level capability.
- Domain Adaptation
Apply domain adaptation to solve real industry problems and demonstrate production-level capability.
- Transformers
Apply transformers to solve real industry problems and demonstrate production-level capability.
- Long Document Nlp
Apply long document nlp to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
NLP Engineer
Domain-adapted NER on long documents is the bread-and-butter of NLP engineers at legal-tech, fintech compliance, and healthcare AI companies.
This challenge sharpens
- named-entity-recognition
- sequence-labeling
- domain-adaptation
Machine Learning Engineer
Shipping a trained NER with annotation guidelines + an integration spec is the end-to-end MLE work that vertical AI consultancies need on every engagement.
This challenge sharpens
- transformers
- pytorch
- long-document-nlp
Applied AI Scientist
Translating NER research methods into a domain-specific product capability is exactly the applied-AI work done in legal-tech and verticalized AI.
This challenge sharpens
- named-entity-recognition
- domain-adaptation
- transformers