---
license: other
license_name: research-only-enron-corpus
task_categories:
- text-classification
language:
- en
language_creators:
- found
multilinguality:
- monolingual
annotations_creators:
- machine-generated
source_datasets:
- extended
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: parquet/blind/train/train-*.parquet
  - split: test
    path: parquet/blind/test/test-*.parquet
- config_name: ground_truth
  data_files:
  - split: train
    path: parquet/ground_truth/train/train-*.parquet
  - split: test
    path: parquet/ground_truth/test/test-*.parquet
---

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

The **deduplicated, ground-truth-enriched** Enron correspondence benchmark:
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). This dataset is the core evaluation corpus for the
[LLM Mailroom](https://github.com/Exios66/llm-mailroom) agent-sorting stack.

## ⚠️ 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`.

```python
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.

## File layout

Both configs point at pre-sharded **parquet** (`parquet/<config>/<split>/*.parquet`,
zstd). The original `blind/*.jsonl` and `ground_truth/*.jsonl` files remain in
the repo unchanged — row-for-row identical to the parquet shards (same order,
same schema; verified 2026-08-23) — for pipelines that prefer line-delimited
JSON. The single-file 548 MB `blind/train.jsonl` crashed the Dataset Viewer's
conversion worker (`JobManagerCrashedError`); serving parquet directly removes
that conversion step entirely.

A machine-readable build manifest (`manifest.txt`) records schema version,
row counts, and the dedup/enrichment provenance chain.

## Ground-truth dimensions

1. **doc_type / subclass** (`expected`, `expected_subclass`,
   `label_evidence`) — heuristic form taxonomy from the shared
   [`correspondence_subclasses`](https://github.com/Exios66/Enron-Evaluation-Environment/blob/main/scripts/correspondence_subclasses.py)
   labeler: attorney_demand, demand, email, letter, meeting_request, memo, notice, press_release.
2. **content_topic** (`content_topic`, `topic_evidence`) — WHAT the message
   body is about: an 11-key priority-scored marker taxonomy
   ([`content_topics.py`](https://github.com/Exios66/Enron-Evaluation-Environment/blob/main/scripts/content_topics.py)):
   legal_contracts, regulatory, finance_earnings, energy_market, hr_personnel, it_systems, travel_logistics, marketing_clients, announcements, scheduling.
3. **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`](https://github.com/Exios66/Enron-Evaluation-Environment/blob/main/scripts/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.

## Source data & original download

All content derives from the **CMU Enron Email Dataset** — Bryan Klimt and
Yiming Yang, Carnegie Mellon University, 2004. Original public download
source: **https://www.cs.cmu.edu/~enron/**

- Roughly 500,000 messages from ~150 Enron employees, released by the Federal
  Energy Regulatory Commission during the fraud investigation and cleaned and
  published for research by CMU.
- We ingest the cleaned `maildir` variant; each row preserves its provenance
  in `metadata.source` (`cmu_enron_maildir`), `metadata.custodian`,
  `metadata.folder`, and `metadata.message_id`.
- The full-corpus export step (sha256-verified, 517,390 rows) was produced by
  [`publish_enron_correspondence.py`](https://github.com/Exios66/llm-entity-extraction/blob/main/scripts/datasets/publish_enron_correspondence.py);
  dedup uses `body_hash` from
  [`scripts/dedupe.py`](https://github.com/Exios66/Enron-Evaluation-Environment/blob/main/scripts/dedupe.py)
  in Enron-Evaluation-Environment.

## Licensing and appropriate use

- Released for **research use**, consistent with the original CMU Enron
  release terms (recorded per-row in `metadata.license`: "Enron corpus —
  released for research use"). Hub license badge: `research-only-enron-corpus`
  (custom, `license: other`).
- The corpus contains **real personally identifying information** (names,
  addresses, phone numbers) of Enron employees and correspondents. Treat all
  rows as sensitive research data: no redistribution of raw PII outside
  research contexts, and no use in production or consumer-facing systems.
- Label columns are deterministic heuristics (see honest-gaps notes above),
  suitable as routing priors and weak supervision — not as gold human
  annotation.
- Provided as-is, with no warranty of any kind. Downstream users are
  responsible for complying with the original CMU/FERC terms.

## Related projects

- [LLM Mailroom](https://github.com/Exios66/llm-mailroom) — the agentic
  email-triage system this benchmark evaluates; the blind/ground-truth split
  exists precisely so sorting agents can be scored without label leakage.
- [Enron-Evaluation-Environment](https://github.com/Exios66/Enron-Evaluation-Environment) —
  labelers (`correspondence_subclasses.py`, `content_topics.py`,
  `sentiment_scorer.py`) and the dedup rule (`scripts/dedupe.py::body_hash`).
- [llm-entity-extraction](https://github.com/Exios66/llm-entity-extraction) —
  dataset publishing pipelines (`scripts/datasets/publish_enron_*.py`).
- Companion Hub datasets from the same family:
  [legalbench-full](https://huggingface.co/datasets/Lucius-Morningstar/legalbench-full),
  [docclass-merged](https://huggingface.co/datasets/Lucius-Morningstar/docclass-merged),
  [mailroom-cuad-contracts](https://huggingface.co/datasets/Lucius-Morningstar/mailroom-cuad-contracts),
  [mailroom-cuad-contracts-full](https://huggingface.co/datasets/Lucius-Morningstar/mailroom-cuad-contracts-full).

Note: an earlier companion repo `Lucius-Morningstar/enron-correspondence` (the
pre-dedup full corpus) is no longer published on the Hub; reproduce it with
`publish_enron_correspondence.py` instead.

## Citation

If you use this dataset, please cite both the dataset and the underlying
corpus:

```bibtex
@misc{morningstar2026enroncorrespondencededup,
  title        = {Enron Correspondence Deduplicated (Enriched GT, Agent-Blind Default)},
  author       = {Lucius-Morningstar},
  year         = {2026},
  month        = {August},
  howpublished = {\url{https://huggingface.co/datasets/Lucius-Morningstar/enron-correspondence-dedup}},
}

@inproceedings{klimt2004enron,
  title     = {The Enron Corpus: A New Dataset for Email Classification Research},
  author    = {Klimt, Bryan and Yang, Yiming},
  booktitle = {European Conference on Machine Learning (ECML 2004)},
  pages     = {217--226},
  year      = {2004}
}
```

## Provenance

Built by [`llm-entity-extraction`](https://github.com/Exios66/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 and dedup rule: Enron-Evaluation-Environment
`scripts/` (`correspondence_subclasses.py`, `content_topics.py`,
`sentiment_scorer.py`, `dedupe.py`). Source: CMU Enron Email Dataset (cleaned
maildir). Parquet shards added 2026-08-23 (same-day viewer fix, verified
row-for-row against the JSONL originals).

## Maintenance

Issues and fixes: [llm-entity-extraction issues](https://github.com/Exios66/llm-entity-extraction/issues)
or contact [@Lucius-Morningstar](https://huggingface.co/Lucius-Morningstar)
on the Hub.