Datasets:

Lucius-Morningstar
/

enron-correspondence-dedup

license: other
task_categories:
  - text-classification
language:
  - en
tags:
  - legal
  - enron
  - email
  - correspondence
  - document-classification
  - evaluation
  - deduplicated
  - ground-truth
  - sentiment-analysis
  - topic-classification
  - llm-mailroom
pretty_name: Enron Correspondence Deduplicated (Enriched GT, Agent-Blind Default)
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: blind/train.jsonl
      - split: test
        path: blind/test.jsonl
  - config_name: ground_truth
    data_files:
      - split: train
        path: ground_truth/train.jsonl
      - split: test
        path: ground_truth/test.jsonl

Enron Correspondence Deduplicated (Enriched GT, Agent-Blind Default)

The deduplicated, ground-truth-enriched companion to Lucius-Morningstar/enron-correspondence: exact-duplicate bodies removed from the cleaned CMU Enron corpus (517,390 rows in -> 247,523 unique-text rows out, 269,867 duplicates dropped; first occurrence wins on maildir-path order; empty bodies never deduped against each other).

⚠️ Two-config layout: agents get NO answers by default

This dataset ships TWO configs. default is agent-blind — it carries the email content plus routing metadata and ZERO ground-truth columns. The answer keys live in the separate ground_truth config, keyed 1:1 on filename.

from datasets import load_dataset

# what a sorting/extraction agent may see:
blind = load_dataset("Lucius-Morningstar/enron-correspondence-dedup")

# what the scorer joins against (explicit opt-in):
gt = load_dataset("Lucius-Morningstar/enron-correspondence-dedup",
                  "ground_truth", split="test")

config	splits	columns
`default`	train 222,572 / test 24,951	filename, subject, text, split, metadata
`ground_truth`	train 222,572 / test 24,951	filename, expected, expected_subclass, label_evidence, content_topic, topic_evidence, sentiment_score, sentiment_label, sentiment_evidence, split

Ground truth is hidden from the default config so automated agents cannot be tipped off; humans can still audit every label in the viewer by switching to the ground_truth config. This is separation of concerns, NOT encryption — the Hub is public and any deliberate download can fetch both configs.

Ground-truth dimensions

doc_type / subclass (expected, expected_subclass, label_evidence) — heuristic form taxonomy from the shared correspondence_subclasses labeler: attorney_demand, demand, email, letter, meeting_request, memo, notice, press_release.
content_topic (content_topic, topic_evidence) — WHAT the message body is about: an 11-key priority-scored marker taxonomy (content_topics.py): legal_contracts, regulatory, finance_earnings, energy_market, hr_personnel, it_systems, travel_logistics, marketing_clients, announcements, scheduling.
sentiment (sentiment_score ∈ [-1, 1], sentiment_label ∈ negative/neutral/positive, sentiment_evidence) — deterministic lexicon polarity over the subject + forwarded-tail-stripped body (sentiment_scorer.py), negation/intensifier-aware, politeness-formula controlled.

All three dimensions are HEURISTIC ground truth (deterministic pure functions, human-reviewed via spot checks where noted) — not hand annotations. Honest gaps: single-topic assignment for multi-topic emails; head-window scanning (~2000 chars); lexicon sentiment cannot read sarcasm or long-range context — treat scores as weak labels/routing priors. Attorney detection relies on domain/name lists; voicemail cannot occur in this text-only corpus.

Splits

Per-row split follows the family rule md5(filename) % 10 == 0 -> test (~10%), recomputed and asserted row-by-row at build time. Filename-keyed, so dedup/enrichment cannot change any surviving row's split. Coverage: train 222,572 / test 24,951.

Provenance

Built by llm-entity-extraction scripts/datasets/publish_enron_correspondence_dedup.py (KANBAN-079, 2026-08-23T18:52:37+00:00) from the sha256-verified full-corpus export (LFS 0554a5973935…). Labelers: Enron-Evaluation-Environment scripts/ (correspondence_subclasses.py, content_topics.py, sentiment_scorer.py). Source: CMU Enron Email Dataset (cleaned maildir); dedup rule scripts/dedupe.py::body_hash. Research-use license — treat personally identifying content accordingly.