Update README.md

README.md

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   - split: train
     path: data/train-*
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+
+# Arabic Sentence Segmentation Shared Task 2026
+For details about the shared task, evaluation scripts, leaderboard, and submission guidelines, visit:
+https://www.araseg.aramlab.ai/
+
+
+## Dataset Summary
+
+AraSeg is the first comprehensive benchmark for Arabic sentence segmentation.
+The corpus is designed to support research on sentence segmentation in Modern Standard Arabic (MSA), particularly in settings where punctuation is inconsistent, missing, or noisy.
+AraSeg contains manually annotated documents collected from diverse sources and genres, enabling robust evaluation across different writing styles and domains.
+The benchmark contains manually annotated documents collected from diverse sources and genres, enabling robust evaluation across different writing styles and domains.
+
+---
+## Dataset Structure
+
+### Data Instances
+```
+{'doc_id': 'doc_00b450a96684',
+'text': ['الفصل','الأول','حين','ركبت','السيارة','لم','أكن','أتصور','أنني','أبدأ', ...],
+'labels': [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...]}
+```
+
+### Data Fields
+- **doc_id**: Unique document identifier.
+- **text**: White-space-tokenized document represented as a list of tokens.
+- **labels**: Token-level sentence boundary labels. `1` indicates that a sentence boundary follows the current token, while `0` indicates no boundary.
+
+### Data Splits
+- **train**: 174 documents (10,657 sentences and 128K words).
+- **dev**: 222 documents (12,985 sentences and 164K words).
+- **test**: 262 documents (12,509 sentences and 159K words).
+---
+## Task Definition
+Sentence segmentation is formulated as a binary token classification task. 
+
+Given a sequence of tokens: ```[token_1, token_2, ..., token_n]```, the model predicts for each token whether a sentence boundary follows it.
+For example:
+
+| Token | Label |
+|---|---|
+| ذهب | 0 |
+| الطالب | 0 |
+| إلى | 0 |
+| المدرسة | 1 |
+
+The label `1` indicates that the sentence ends after the token.
+
+---
+
+## Evaluation
+We evaluate systems using boundary-level metrics:
+
+- **Boundary Precision (P):** Percentage of predicted sentence boundaries that are correct.
+- **Boundary Recall (R):** Percentage of gold sentence boundaries correctly identified.
+- **Boundary F1 (F1):** Harmonic mean of precision and recall.
+  
+Metrics are computed at the document level and averaged across the corpus.
+
+---
+
+