| --- |
| 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 |
|
|
