|
|
--- |
|
|
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) |
|
|
|
