token-morph-metrics

To evaluate Arabic tokenizers' morphological boundary adherence, analyze tokenization output files using Python scripts to compute intrinsic metrics like morpheme integrity, root preservation, and fragmentation ratios. This yields detailed linguistic fidelity scores deterministically without external dependencies.

Arabic morphological tokenization evaluation uses intrinsic metrics such as root integrity, pattern preservation, morpheme boundary accuracy, clitic separation, and fragmentation ratios. These metrics quantify how well a tokenizer maintains linguistic structures across subword, character, and byte-level architectures.

Test tokenizer architectures for Arabic morphology preservation without downstream tasks by running an analysis script on your tokenizer output files. This computes intrinsic metrics deterministically, comparing subword, character, and byte-level models without requiring external dependencies.

Yes, you can compare different Arabic subword tokenization strategies deterministically by testing across multiple tokenizer families. The analysis relies on intrinsic metrics computed locally via Python tools, ensuring fair comparisons without external model interference.

No, you do not need external NLP models to assess clitic separation in Arabic tokenizers. The Skill calculates clitic separation ratios and other intrinsic morphological metrics using only Python tools and your test data, ensuring a self-contained evaluation.

The best way to measure root preservation in Arabic tokenization is by computing root integrity ratios from your tokenizer output files. This intrinsic metric evaluates how effectively a tokenizer isolates and maintains Arabic root structures.