pre dataset

README.md

@@ -1,121 +1,80 @@
 ---
-license: "CC-BY-4.0"
+license: cc-by-4.0
 tags:
-  - arabic
-  - nli
-  - embedding
-  - legal
-  - semantic-similarity
-  - sentence-transformers
+- arabic
+- nli
+- embedding
+- legal
+- sentence-transformers
 language:
-  - Arabic
-dataset_name: "Arabic Legal & Semantic Embedding Dataset"
-pretty_name: "Arabic Legal Embedding & NLI Dataset"
+- ar
+dataset_name: arabic-semantic-embedding-dataset
+pretty_name: Arabic Legal Embedding & NLI Dataset
 task_categories:
-  - semantic-similarity
-  - natural-language-inference
+- sentence-similarity
+- text-classification
+task_ids:
+- semantic-similarity-scoring
+- natural-language-inference
 size_categories:
-  - 1K<n<10K
+- 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`.
+The **Arabic Legal Embedding Dataset** is a high-quality dataset designed for:
 
-This dataset is suitable for **training Arabic sentence embeddings**, **legal text analysis**, and **sentence similarity tasks**.
+- Semantic Similarity
+- Natural Language Inference (NLI)
+- Sentence Embedding Training
 
----
+It focuses on **Arabic legal, administrative, and general-purpose text**, enabling the development of models that understand nuanced meanings in real-world Arabic language scenarios.
 
-## 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
+Each example consists of two sentences (`sentence_a`, `sentence_b`) with:
+- A **similarity score** (0 → 1)
+- A **semantic relation label**
 
 ---
 
+## Key Features
 
+- Multiple semantic relations:
+  - `summary`
+  - `synonym`
+  - `paraphrase`
+  - `contradiction`
+  - `weak`
+  - `hard_negative`
+  - `entailment`
+  - `passive`
 
-# Arabic Legal Embedding Dataset (NLI & Semantic Similarity)
+- Fine-grained similarity scoring (continuous values)
 
-## 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.
+- Domain coverage:
+  - Legal
+  - Administrative
+  - Conversational Arabic
+  - General knowledge
 
-## 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.  
+## Dataset Structure
 
-- **Legal & Administrative Focus**: Ideal for training **embeddings, NLI models, or semantic search systems** for Arabic legal documents, contracts, administrative texts, and general-purpose applications.
+| Field        | Type  | Description |
+|--------------|------|-------------|
+| sentence_a   | str  | First sentence |
+| sentence_b   | str  | Second sentence |
+| score        | float| Semantic similarity (0–1) |
+| relation     | str  | Type of relation |
 
-## 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.
+{"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"}