---
license: mit
task_categories:
  - text-classification
language:
  - en
tags:
  - synthetic
  - ediscovery
  - document-review
  - legal-tech
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data.jsonl
---

# Apex Telecommunications Document Relevance Review (Synthetic)

A fully synthetic eDiscovery / internal-investigation dataset built to test whether an AI agent can
perform first-pass **relevance coding** on a realistic, adversarial document population.

## The scenario

Apex Telecommunications, Inc. suspects Senior Sales Manager Michael Carter of sharing confidential
pricing and customer information with a competitor, Northstar Communications, before and during his
departure from the company. The dataset contains 138 documents (emails, chat logs, meeting notes, and
attachments) spanning the six-month investigation period (Jan 1 - Jun 30, 2025).

**All people, companies, and events are fictional.**

## Why it's adversarial

The document set is deliberately constructed to defeat naive keyword-matching approaches:

- **Keyword traps** — the competitor's name appears in clearly irrelevant documents, and a
  similarly-named but unrelated company ("Northgate" vs. "Northstar") is designed to trigger false
  positives.
- **Authorship bias** — several benign documents are authored by the subject of the investigation.
- **Document families** — one relevant document's evidence lives entirely in an attachment referenced
  by a generic "see attached" email.
- **Buried evidence** — a genuinely relevant admission is nested inside a multi-level forwarded email
  chain.
- **Genuine ambiguity** — a set of documents are honestly unclear even to a human reviewer, testing
  whether a reviewer (human or AI) has the judgment to escalate rather than force a confident guess.

## Labels

Each document is labeled with one of three classes:

| Label | Count | Meaning |
|---|---|---|
| `Relevant` | 30 | Contains evidence of disclosure, competitor communication, intent, or concealment |
| `Not Relevant` | 80 | Routine, unrelated business communication |
| `Needs Human Review` | 28 | Genuinely ambiguous — should be escalated, not guessed |

## Files

- `data.jsonl` — one row per document: `doc_id`, `text`, `date`, `author`, `recipient`, `cc`, `type`,
  `subject`, `label`, `reason`. This is the file the Hugging Face Dataset Viewer will render and what
  `load_dataset()` picks up automatically.
- `documents/` — the original raw documents as individual `.txt` files, in the format an agent would
  actually be handed (headers + body), for use in agentic/tool-use evaluations rather than plain
  classification.
- `document_metadata.csv` — the metadata table on its own.
- `ground_truth.csv` — the label/reason table on its own.
- `review_protocol.docx` — the relevance criteria reviewers (human or AI) were asked to apply.
- `score_output.py` — scores a model's predictions (`ai_output.csv`) against `ground_truth.csv`,
  reporting accuracy, precision, recall, and F1, with `Needs Human Review` treated as a correct
  escalation rather than a miss.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("Vrishab80/apex-document-relevance-review")
print(ds["train"][0])
```

## Intended use

Built as a course project testing whether a frontier AI agent can perform accurate, well-reasoned
first-pass document relevance review. Useful as a small benchmark for agentic document review,
legal-tech / eDiscovery tooling evaluation, or prompt-engineering experiments around grounded
classification with an abstain option.