---
language:
- sr
license: cc-by-4.0
task_categories:
- token-classification
tags:
- ner
- legal
- conllu
dataset_info:
  features:
  - name: id
    dtype: string
  - name: doc_id
    dtype: string
  - name: tokens
    sequence: string
  - name: ner_tags
    sequence:
      class_label:
        names:
          0: B-ADR
          1: B-COM
          2: B-CONTACT
          3: B-COURT
          4: B-DATE
          5: B-IDCOM
          6: B-IDOTH
          7: B-IDPER
          8: B-IDTAX
          9: B-INST
          10: B-LAW
          11: B-MISC
          12: B-MONEY
          13: B-NUMACC
          14: B-NUMCAR
          15: B-NUMDOC
          16: B-NUMPLOT
          17: B-ORGOTH
          18: B-PER
          19: B-REF
          20: B-TOP
          21: I-ADR
          22: I-COM
          23: I-COURT
          24: I-DATE
          25: I-INST
          26: I-LAW
          27: I-MISC
          28: I-MONEY
          29: I-NUMCAR
          30: I-ORGOTH
          31: I-PER
          32: I-REF
          33: I-TOP
          34: O
---

# Dataset Card for Legal NER dataset

## Dataset Description

This dataset is based on **COMtext.SR.legal**, the first corpus of legal-administrative texts in Serbian that has been manually annotated for **Named Entities (NER)** according to the IOB2 standard.

The corpus was created during 2023. The selection of representative legal texts of various types (contracts, judgments, conclusions, decisions, requests, appeals, rulebooks, laws, decrees, statutes, etc.) was conducted with the assistance of the lawyers.

The corpus includes **79 documents**, comprising **4,762 sentences** or **105,470 tokens**.

## NER Statistics

Out of a total of 105,470 tokens, **14,113 (13.4%)** belong to named entities.

Systematization and frequency of entity types:

| Category | Subcategory | Tag | Count / Percentage | Avg. Length in Tokens |
| :--- | :--- | :--- | :--- | :--- |
| **Persons** | | **PER** | 694 (19.2%) | 1.89 |
| **Locations** | Toponyms | **TOP** | 294 (8.1%) | 1.24 |
| | Addresses | **ADR** | 203 (5.6%) | 6.96 |
| **Organizations** | Courts | **COURT** | 148 (4.1%) | 3.59 |
| | Institutions | **INST** | 395 (10.9%) | 2.75 |
| | Companies | **COM** | 337 (9.3%) | 2.28 |
| | Other Organizations | **ORGOTH** | 97 (2.7%) | 2.27 |
| **Legal Docs** | General Legal Acts | **LAW** | 395 (10.9%) | 9.17 |
| | Individual Legal Acts | **REF** | 227 (6.3%) | 10.52 |
| **Sensitive Data** | Personal ID (JMBG) | **IDPER** | 21 (0.6%) | 1.0 |
| | Company Reg. No. | **IDCOM** | 33 (0.9%) | 1.0 |
| | Tax ID (PIB) | **IDTAX** | 16 (0.4%) | 1.0 |
| | Bank Account No. | **NUMACC** | 6 (0.2%) | 1.0 |
| | ID Card/Passport No. | **NUMDOC** | 9 (0.2%) | 1.0 |
| | Car Plate/Chassis | **NUMCAR** | 6 (0.2%) | 2.67 |
| | Cadastral Plot No. | **NUMPLOT** | 67 (1.9%) | 1.0 |
| | Other IDs | **IDOTH** | 18 (0.5%) | 1.0 |
| | Contact (Email, Phone) | **CONTACT** | 8 (0.2%) | 1.0 |
| **Time/Money** | Dates | **DATE** | 352 (9.7%) | 4.1 |
| | Monetary Amounts | **MONEY** | 246 (6.8%) | 2.32 |
| **Other** | | **MISC** | 39 (1.1%) | 5.0 |

## Structure & Usage

The data has been converted from CoNLL-U format to Hugging Face columnar format.

### Train/Test Split

The split into training and testing sets was performed using the **GroupShuffleSplit** strategy on the `doc_id` attribute. This guarantees that sentences from the same document do not appear simultaneously in both sets, preventing data leakage and ensuring realistic model evaluation.

### Usage Example

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("gradientflow/legal-ner")

# Display the first example from the train set
print(dataset['train'][0])

# Display all available tags
print(dataset['train'].features['ner_tags'].feature.names)
```

## Source Data
The original annotated corpus is available on GitHub:
* [COMtext.SR](https://github.com/ICEF-NLP/COMtext.SR/tree/main/data)