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Arabic
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+ ---
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+ dataset_info:
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+ features:
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+ - name: semantic
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+ dtype: float64
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+ - name: semantic_rank
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+ dtype: float64
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+ - name: semantic_diff
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+ dtype: float64
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+ - name: lexical1
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+ dtype: float64
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+ - name: lexical1_rank
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+ dtype: float64
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+ - name: lexical1_diff
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+ dtype: float64
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+ - name: lexical2
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+ dtype: float64
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+ - name: lexical2_rank
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+ dtype: float64
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+ - name: lexical2_diff
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+ dtype: float64
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+ - name: entity
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+ dtype: string
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+ - name: src_entity
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+ dtype: string
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+ - name: phonetic
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+ dtype: bool
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+ - name: pos
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+ dtype: string
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+ - name: label
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+ dtype: bool
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+ splits:
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+ - name: train
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+ num_examples: 19405
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train.csv
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+ license: mit
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+ task_categories:
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+ - text-classification
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+ language:
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+ - ar
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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+
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+ # Masrad Dataset
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+
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+ ## Dataset Description
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+
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+ The **Masrad** dataset contains 19,405 examples with features related to semantic similarity, lexical similarity, entity recognition, phonetic matching, and part-of-speech tagging for Arabic text.
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+
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+ ## Features
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+
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+ | Feature | Type | Description |
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+ |---------|------|-------------|
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+ | `semantic` | float | Semantic similarity score |
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+ | `semantic_rank` | float | Rank based on semantic similarity |
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+ | `semantic_diff` | float | Difference from top semantic score |
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+ | `lexical1` | float | First lexical similarity score |
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+ | `lexical1_rank` | float | Rank based on first lexical score |
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+ | `lexical1_diff` | float | Difference from top first lexical score |
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+ | `lexical2` | float | Second lexical similarity score |
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+ | `lexical2_rank` | float | Rank based on second lexical score |
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+ | `lexical2_diff` | float | Difference from top second lexical score |
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+ | `entity` | string | Detected entity type (PER, ORG, LOC, none) |
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+ | `src_entity` | string | Source entity type |
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+ | `phonetic` | bool | Whether phonetic match exists |
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+ | `pos` | string | Part of speech tag |
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+ | `label` | bool | Target label |
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+
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+ ## Usage
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ dataset = load_dataset("U4RASD/Masrad")
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+ ```