arabic-token-eval

What problem does it solve?

This Skill assesses the effectiveness of various Arabic tokenization approaches, including morphological preservation and segmentation quality, to improve NLP model performance.

Core Features & Use Cases

• Tokenizer Evaluation: Compares subword, character, and morphology-aware tokenizers based on morphology metrics and downstream task results.
• Intrinsic & Morphological Metrics: Calculates root, pattern, morpheme integrity, clitic separation, and fragmentation ratios to analyze tokenization quality.
• Use Case: Researchers can use this Skill to select optimal tokenization strategies for Arabic NLP models, ensuring accurate root extraction and minimal fragmentation.

Quick Start

Use the arabic-token-eval skill to evaluate a new tokenizer on sample Arabic texts and examine morphology scores.