---
language:
- en
license: apache-2.0
tags:
- named-entity-recognition
- legal
- contracts
- information-extraction
- ner
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: ContractNER
size_categories:
- 1K<n<10K
---

# ContractNER Dataset

Developed at [**Agile Lab**](https://www.agilelab.it/) by **Luca Sorrentino** and **Annalisa Belia**, with **Irene Donato** as project lead.

---

Named entity recognition dataset built from real legal contracts sourced from **SEC EDGAR** filings. Designed to train and evaluate models for fine-grained entity extraction from contract text.

Used to fine-tune [lucasorrentino/Contractner](https://huggingface.co/lucasorrentino/Contractner), which achieves **72.5% micro F1** on the test split.

## Dataset Structure

### Splits

| Split | Samples | Annotations |
| :--- | ---: | ---: |
| train | 3,082 | ~14,000 |
| test | 158 | ~730 |

**Total:** 3,240 annotated contract chunks after stratified reduction from ~5,000 raw segments.

### Data Format

Each line is a JSON object with the following fields:

```json
{
  "id": "3669",
  "text": "On the Closing Date, the Company agrees to sell up to $2.5 million of Shares...",
  "ner": [[60, 64, "Principal"], [188, 198, "Address"]],
  "tokenized_text": ["On", "the", "Closing", "Date", ",", "the", "Company", "..."]
}
```

| Field | Type | Description |
| :--- | :--- | :--- |
| `id` | string | Unique sample identifier |
| `text` | string | Raw contract text chunk |
| `ner` | list of `[start_tok, end_tok, label]` | Token-indexed entity spans (inclusive) |
| `tokenized_text` | list of strings | Whitespace-tokenized words |

Spans in `ner` use **token indices** into `tokenized_text`. To convert to character offsets, align tokens back to the original `text`.

## Entity Schema

18 entity types covering the main elements of commercial contracts:

### Document Metadata
| Label | Description | Example |
| :--- | :--- | :--- |
| `EffectiveDate` | Contract start date | `"January 1, 2026"` |
| `TerminationDate` | Contract end or expiration date | `"December 31, 2027"` |
| `RenewalTerm` | Renewal periods or conditions | `"automatically renews for one-year terms"` |
| `Title` | Official document title | `"EMPLOYMENT AGREEMENT"` |

### Actors & Roles
| Label | Description | Example |
| :--- | :--- | :--- |
| `Parties` | Legal entities entering the agreement | `"Tech Solutions Inc."` |
| `Role` | Professional titles and positions | `"Chief Technology Officer"` |

### Contact Information
| Label | Description | Example |
| :--- | :--- | :--- |
| `Address` | Physical addresses | `"1540 Broadway, New York, NY 10036"` |
| `PII_Ref` | Personal identifiable information references | `"(212) 555-0100"` |

### Financial Values
| Label | Description | Example |
| :--- | :--- | :--- |
| `Salary` | Compensation amounts (with currency) | `"$250,000.00"` |
| `Price` | Goods or services prices | `"$1,500 per unit"` |
| `Principal` | Loan principal amounts | `"$2.5 million"` |
| `Shares` | Stock or equity quantities | `"10,000 shares"` |
| `Percentage` | Percentage values | `"50%"` |
| `Ratio` | Financial ratios | `"1.25:1"` |
| `Rent` | Lease or rental amounts | `"$8,500 per month"` |

### Legal and Regulatory
| Label | Description | Example |
| :--- | :--- | :--- |
| `Court` | Judicial bodies and tribunals | `"State of Delaware"` |
| `Act` | Legislative acts and laws | `"Securities Exchange Act of 1934"` |
| `Regulation` | Regulatory references | `"Rule 10b5-1"` |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("lucasorrentino/ContractNER")

# Access train and test splits
train = dataset["train"]
test  = dataset["test"]

# Example: inspect annotations
sample = train[0]
print(sample["text"][:200])
for start_tok, end_tok, label in sample["ner"]:
    span = sample["tokenized_text"][start_tok:end_tok + 1]
    print(f"  {label}: {' '.join(span)}")
```

### Loading with GLiNER

This dataset pairs directly with [lucasorrentino/Contractner](https://huggingface.co/lucasorrentino/Contractner):

```python
from gliner import GLiNER
from datasets import load_dataset

model   = GLiNER.from_pretrained("lucasorrentino/Contractner")
dataset = load_dataset("lucasorrentino/ContractNER")

labels = [
    "Parties", "EffectiveDate", "Role", "Salary", "TerminationDate",
    "Principal", "Percentage", "Act", "Regulation", "Title"
]

sample   = dataset["test"][0]
entities = model.predict_entities(sample["text"], labels, threshold=0.9)
for e in entities:
    print(f"{e['text']} => {e['label']} ({e['score']:.2f})")
```

## Source Data

Contracts sourced from **SEC EDGAR** public filings (U.S. Securities and Exchange Commission). Original annotation schema based on Adibhatla et al. (2023) — ContractNER corpus.

**Preprocessing:**
- Removed `RevolvingCredit` class (too rare and ambiguous)
- Stratified reduction to balance class representation
- 80/20 train/test split

## License

Apache 2.0