Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: tokens
-    list: string
-  - name: ner_tags
-    list: int64
-  splits:
-  - name: train
-    num_bytes: 2965530
-    num_examples: 1591
-  - name: validation
-    num_bytes: 329917
-    num_examples: 177
-  - name: test_adversarial
-    num_bytes: 24511
-    num_examples: 59
-  download_size: 3327548
-  dataset_size: 3319958
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test_adversarial
-    path: data/test_adversarial-*
+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