README.md

metadata

license: mit
language:
  - it
  - en
tags:
  - sentence-boundary-detection
  - token-classification
  - legal-nlp
  - multilingual
task_categories:
  - token-classification
pretty_name: SentenceSplitter Dataset
size_categories:
  - 1K<n<10K

SentenceSplitter Dataset

Dataset Description

This dataset is designed for Sentence Boundary Disambiguation (SBD) as a token classification task.

Each sample uses the schema:

• tokens: list of token strings
• ner_tags: list of integer labels aligned with tokens
  • 0 = not end of sentence
  • 1 = end of sentence

The dataset is intended for multilingual SBD, with focus on Italian and English, and includes both domain-specific and adversarial patterns.

Data Sources

The training corpus is created by merging:

1. Professor corpus from sent_split_data.tar.gz
2. MultiLegalSBD legal JSONL corpora
3. Wikipedia (20231101.it, 20231101.en)

Current filtering rules used in data preparation:

• Only professor files ending with -train.sent_split
• Only legal files ending with *train.jsonl

These filters are used to avoid dev/test leakage from source corpora.

Dataset Splits

Published splits in this dataset repo:

• train: 1591 rows
• validation: 177 rows
• test_adversarial: 59 rows

All splits use the same features:

• tokens
• ner_tags

How Splits Are Built

• train and validation are derived from unified_training_dataset with train_test_split(test_size=0.1, seed=42).
• test_adversarial is loaded from comprehensive_test_dataset generated by the project testset pipeline.

Intended Uses

• Training and evaluating SBD models for legal/academic/general text.
• Robustness checks on punctuation-heavy and abbreviation-heavy inputs.
• Benchmarking token-classification approaches for sentence segmentation.

Limitations

• The adversarial split is intentionally difficult and may not represent natural document frequency.
• Source corpora come from different domains and annotation strategies.
• Performance can vary on domains not represented by legal, academic, or encyclopedic text.

Reproducibility Notes

Core preprocessing choices:

• Sliding window size: 128
• Stride: 100
• Whitespace tokenization at dataset construction stage
• Label alignment to token-level EOS boundaries

Recommended practice:

• Use validation for tuning
• Keep test_adversarial for final robustness evaluation