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Challenge

NYC Rental Lease Clause Disguised Fee Anomaly Detector

In response to landlord fee restrictions, non-standard fee clauses have proliferated in residential leases. Build a natural language processing anomaly detector using HuggingFace Transformers to scan lease agreement text and highlight illegal tenant fee surcharges. Achieve 92%+ F1-score on legal phrase classification.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an NLP lease agreement auditor using HuggingFace Transformers to detect disguised broker fee clauses.

Delivery guide

How work is evaluated

Evaluation

Evaluates sentence-level clause anomaly detection accuracy on standard lease agreements.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Flag Prohibited Clause

Must flag non-refundable facilitation fee as prohibited

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Clause Classification F1-Score

F1 score across test dataset of lease clauses • target: 0.93 • range: 0.85-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Fine-tune a DeBERTa or RoBERTa classification model on lease addendums

  • Detect suspicious wording like 'mandatory processing surcharge' or 'application review fee'

  • Extract dollar amounts attached to questionable contract clauses

  • Output risk-ranked audit summaries for tenant advocacy applications

Resources and assets

Reference links and supporting material

Dataset notes

Residential lease addendums and fee schedule text blocks collected across NYC borough rental listings.

Challenge setup

Evaluates sentence-level clause anomaly detection accuracy on standard lease agreements.

Challenge input

JSON with clause_text

HuggingFace Transformers

Provides pre-trained models for sequence classification and NER

Evaluated output

JSON with is_prohibited (boolean), anomaly_category, fee_amount

Evaluation checks
  • Must flag non-refundable facilitation fee as prohibited
  • F1 score across test dataset of lease clauses • target: 0.93 • range: 0.85-1
Configured evaluation evidence
  • Benchmark: LegalNLPBench
  • Clause Classification F1 Score target: 0.93
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Transformers · Required
  • Hugging Face · Optional
  • Kubeflow · Optional

Evaluation contract

  • Flag Prohibited Clause · Weight 1
  • Clause Classification F1-Score · Weight 1

Metrics

  • Clause Classification F1-Score · Target 0.93

Benchmarks

  • LegalNLPBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with clause_text

    Output: JSON with is_prohibited (boolean), anomaly_category, fee_amount

    Execution harness: Python

Evaluation tools

  • HuggingFace Transformers · NLP Framework

Recipe freeze marker

No recipe freeze time is recorded.

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