mlfont-notosansjp: Range-Compact MLFont Slicing for Noto Sans JP (Japanese)

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Overview

Pretrained MLFont machine-learning-guided font slicing model and WOFF2 slice bundle for Noto Sans JP (Japanese) (NotoSansJP-Regular.otf, 16,663 Unicode codepoints partitioned into 62 range-compact slices).

• Paper: MLFont: Machine Learning-Driven Font Subsetting and Optimal Unicode-Range Slicing for Low-Latency Web Typography (Quan Wang and Yiran Mao)
• Open-Source Repository: https://github.com/wq2012/mlfont
• PyPI Package: https://pypi.org/project/mlfont/

Training Configuration

• Target Font: NotoSansJP-Regular.otf (16,663 Unicode codepoints)
• Training & Evaluation Corpus: wikimedia/wikipedia (20231101, languages: ja), 1,600 training articles and 400 uniformly sampled testing articles
• Slicing Architecture: Tiered Document-Frequency Slicing + Online Variational LDA & Deep Neural Network Topic Model (DNNTM) + Range-Compact Genetic Algorithm (mlfont preset: japanese)
• Total Slices: 62 slices (8,447 total CSS unicode-range intervals)
• Physical WOFF2 Bundle Size: 4.70 MB / 4811.8 KB (4,927,316 bytes) across 62 .woff2 files (companion CSS: 93.3 KB)
• Training Wall-Time: 47.4 seconds on CPU/GPU

Model Files Included

• model.safetensors: Learned topic-codepoint weight matrices (lambda_matrix, dnntm_lambda_matrix), codepoints, remaining_codepoints, frequency_array, and dna_assignment in SafeTensors format
• strategy.json: Range-Compact GA SlicingStrategy definition and hyperparameters
• olda_strategy.json: Online LDA + Greedy SlicingStrategy definition
• dnntm_strategy.json: DNNTM (Entropy Difference Loss) + Greedy SlicingStrategy definition
• test_samples.json: Exact held-out evaluation codepoint sets (400 test pages)
• config.json: Model and font bundle summary metadata (including per-slice WOFF2 byte sizes)
• font_sliced.css: Production-ready @font-face stylesheet with compact unicode-range descriptors
• woff2/*.woff2: 62 physical WOFF2 font slices generated via fontTools.subset
• nam/*.nam: 62 Google Fonts .nam codepoint subset files
• evaluation_metrics.json: Full benchmark metrics across all 7 network/session profiles

Benchmark Performance (400 Wikipedia Test Pages, Session Length L = 10)

Strategy	Slices	CSS Ranges	Missing (Eval / Font)	GIN-2g Mean / P95 (ms)	GIN-3g Mean / P95 (ms)	DSL Mean / P95 (ms)	Session Util (%)
single_slice	1	4830	0 / 0	94.75 / 701.82	23.72 / 175.72	4.73 / 35.06	7.62%
equal_bin_62	62	4876	0 / 0	95.38 / 698.56	25.58 / 187.34	4.60 / 33.66	8.56%
random_slice_62	62	16450	0 / 0	114.88 / 893.64	30.81 / 239.66	5.53 / 43.06	7.62%
language_slice	10	5617	43 / 7632	45.20 / 305.76	11.42 / 77.00	2.25 / 15.22	15.84%
most_500_500_equal_bin_60	62	5975	0 / 0	76.50 / 387.35	20.50 / 103.64	3.69 / 18.69	8.92%
online_lda_topics60_greedy	62	8888	0 / 0	34.87 / 201.00	9.31 / 53.61	1.68 / 9.71	21.31%
dnntm_topics60_greedy	62	8710	0 / 0	34.80 / 188.72	9.30 / 50.47	1.68 / 9.12	20.88%
range_compact_most500_500_topics60 (This Model)	62	8447	0 / 0	37.13 / 214.37	9.91 / 57.09	1.79 / 10.37	19.79%

Usage

Install mlfont:

pip install mlfont huggingface_hub

Load the pretrained bundle (Range-Compact GA, Online LDA, and DNNTM) in Python:

import mlfont

bundle = mlfont.load_pretrained("wq2012/mlfont-notosansjp")
print("GA:", bundle.strategy.name, "ranges:", bundle.strategy.count_unicode_ranges())
print("LDA:", bundle.olda_strategy.name, "ranges:", bundle.olda_strategy.count_unicode_ranges())
print("DNNTM:", bundle.dnntm_strategy.name, "ranges:", bundle.dnntm_strategy.count_unicode_ranges())

Citation

@article{wang2017learning,
  title={Learning Better Font Slicing Strategies from Data},
  author={Wang, Quan and Mao, Yiran},
  journal={Technical Disclosure Commons, Defensive Publications Series},
  volume={906},
  year={2017}
}