init dataset

CONTRIBUTING.md

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+# Contributing to Arabic Dataset
+
+## How to contribute
+
+1. Clone the dataset:
+   git clone https://huggingface.co/datasets/YOUR_USERNAME/YOUR_DATASET
+
+2. Add new examples to:
+   data/train.jsonl
+
+3. Follow this format:
+{"sentence_a": "...", "sentence_b": "...", "score": 0.9, "relation": "paraphrase"}
+
+4. Commit and push changes
+
+---
+
+## Rules
+
+- Arabic language only
+- No duplicate sentences
+- Score must match relation
+- Avoid weak or meaningless sentences
+
+---
+
+## Relations
+
+- paraphrase
+- synonym
+- summary
+- contradiction
+- weak
+- hard_negative
+- entailment
+- passive

README.md

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 ---
-license: cc-by-4.0
+license: "CC-BY-4.0"
+tags:
+  - arabic
+  - nli
+  - embedding
+  - legal
+  - semantic-similarity
+  - sentence-transformers
+language:
+  - Arabic
+dataset_name: "Arabic Legal & Semantic Embedding Dataset"
+pretty_name: "Arabic Legal Embedding & NLI Dataset"
+task_categories:
+  - semantic-similarity
+  - natural-language-inference
+size_categories:
+  - 1K<n<10K
 ---
+# Arabic Legal & Semantic Embedding Dataset
+
+## Overview
+This dataset contains Arabic sentences prepared for **semantic similarity**, **embedding training**, and **natural language inference (NLI)**. It is designed for training models that understand legal, administrative, and general-purpose Arabic text.
+
+Each example consists of two sentences (`sentence_a` and `sentence_b`) along with:
+- a `score` indicating their semantic similarity (0–1)
+- a `relation` describing their type: `summary`, `synonym`, `paraphrase`, `contradiction`, `weak`, `hard_negative`, `entailment`, `passive`.
+
+This dataset is suitable for **training Arabic sentence embeddings**, **legal text analysis**, and **sentence similarity tasks**.
+
+---
+
+## Dataset Structure
+- `sentence_a` (str): First sentence  
+- `sentence_b` (str): Second sentence  
+- `score` (float): Semantic similarity score between 0 and 1  
+- `relation` (str): Type of relation. Possible values:
+  - `summary`: one sentence summarizes the other  
+  - `synonym`: sentences have the same meaning  
+  - `paraphrase`: rephrased version  
+  - `contradiction`: sentences contradict each other  
+  - `weak`: weak semantic similarity  
+  - `hard_negative`: very different but potentially confusing sentences  
+  - `entailment`: one sentence logically entails the other  
+  - `passive`: passive voice version
+
+---
+
+
+
+# Arabic Legal Embedding Dataset (NLI & Semantic Similarity)
+
+## Overview
+The **Arabic Legal Embedding Dataset** is a high-quality dataset designed for **semantic similarity**, **Natural Language Inference (NLI)**, and **sentence embedding tasks** in Arabic. It focuses on **legal, administrative, and general-purpose text**, providing a rich set of sentence pairs annotated with semantic relationships. This dataset enables building **robust AI models** capable of understanding nuanced meanings in Arabic legal and administrative contexts.
+
+## Key Features
+- **Semantic Annotations**: Each sentence pair includes a `relation` indicating its semantic type:  
+  - `summary` – concise summary of the first sentence  
+  - `synonym` – paraphrases or semantically equivalent sentences  
+  - `paraphrase` – sentences reworded differently but retaining the same meaning  
+  - `contradiction` – sentences with opposing meanings  
+  - `weak` – loosely related or contextually weak sentences  
+  - `hard_negative` – sentences appearing similar but semantically unrelated  
+  - `entailment` – one sentence logically follows from the other  
+  - `passive` – rephrasing in passive voice  
+
+- **Similarity Scores**: Each pair is scored between 0 and 1, quantifying semantic similarity.  
+
+- **Legal & Administrative Focus**: Ideal for training **embeddings, NLI models, or semantic search systems** for Arabic legal documents, contracts, administrative texts, and general-purpose applications.
+
+## Dataset Structure
+| Field | Type | Description |
+|-------|------|-------------|
+| `sentence_a` | str | First sentence |
+| `sentence_b` | str | Second sentence |
+| `score` | float | Semantic similarity score (0–1) |
+| `relation` | str | Type of relation (`summary`, `synonym`, `paraphrase`, `contradiction`, `weak`, `hard_negative`, `entailment`, `passive`) |
+
+## Example Entries
+```json
+{
+  "sentence_a": "يرجى التوجه إلى كاتب الجلسة لوضع توقيعك في السجل الرسمي الذي يثبت حضورك للدفاع اليوم.",
+  "sentence_b": "يجب التوقيع على إثبات حضور الجلسة في المحضر الرسمي لضمان الحقوق الإجرائية.",
+  "score": 0.93,
+  "relation": "summary"
+}
+{
+  "sentence_a": "إحنا محتاجين مترجم محلف عشان نترجم المستندات دي.",
+  "sentence_b": "إحنا محتاجين خبير فني يترجم الرموز اللي موجودة في كشوف الحسابات.",
+  "score": 0.58,
+  "relation": "hard_negative"
+}
+{
+  "sentence_a": "الجلسة كانت زحمة جداً وما قدرناش نتكلم غير كلمتين.",
+  "sentence_b": "شُهد ازدحام شديد في الجلسة ولم يُسمح إلا بحديث مقتضب.",
+  "score": 0.85,
+  "relation": "passive"
+}
+{
+  "sentence_a": "عم نحاول نتواصل مع المندوب بس تلفونه مغلق.",
+  "sentence_b": "هناك صعوبة مؤقتة في الوصول لجهة التوصيل المسؤولة.",
+  "score": 0.82,
+  "relation": "entailment"
+}
+{
+  "sentence_a": "عم نحاول نتواصل مع المندوب بس تلفونه مغلق.",
+  "sentence_b": "هناك صعوبة مؤقتة في الوصول لجهة التواصل المسؤولة.",
+  "score": 0.82,
+  "relation": "paraphrase"
+}
+
+## Open Contribution
+
+This dataset is open for contributions.
+
+You can:
+- Clone the repository
+- Add new examples
+- Submit improvements
+
+See CONTRIBUTING.md for details.