Release Mark v0.1.0: 38.7M parameter multilingual diacritics (INT8/FP16/FP32/CoreML)

README.md

@@ -0,0 +1,280 @@
+---
+language:
+  - ak
+  - ar
+  - az
+  - ca
+  - cs
+  - cy
+  - ee
+  - es
+  - ff
+  - fr
+  - ga
+  - gn
+  - ha
+  - he
+  - hr
+  - ht
+  - hu
+  - ig
+  - ku
+  - ln
+  - lt
+  - lv
+  - mi
+  - pl
+  - pt
+  - qu
+  - ro
+  - sk
+  - sl
+  - sm
+  - sr
+  - tk
+  - tr
+  - uz
+  - vi
+  - wo
+  - yo
+license: apache-2.0
+tags:
+  - token-classification
+  - diacritics
+  - tone-restoration
+  - vocalization
+  - on-device
+  - edge-ai
+  - core-ml
+  - onnx
+  - webassembly
+  - multilingual
+pipeline_tag: token-classification
+---
+
+# Mark-38M
+
+A 38.7M-parameter byte-level Conv-Transformer that restores diacritics, tones, and vocalization marks across 37 languages without subword tokenizers or dictionary lookups: Akan (`ak`), Arabic (`ar`), Azerbaijani (`az`), Catalan (`ca`), Czech (`cs`), Welsh (`cy`), Ewe (`ee`), Spanish (`es`), Pulaar (`ff`), French (`fr`), Irish (`ga`), Guaraní (`gn`), Hausa (`ha`), Hebrew (`he`), Croatian (`hr`), Haitian Creole (`ht`), Hungarian (`hu`), Igbo (`ig`), Kurdish (`ku`), Lingala (`ln`), Lithuanian (`lt`), Latvian (`lv`), Māori (`mi`), Polish (`pl`), Portuguese (`pt`), Quechua (`qu`), Romanian (`ro`), Slovak (`sk`), Slovenian (`sl`), Samoan (`sm`), Serbian (`sr`), Turkmen (`tk`), Turkish (`tr`), Uzbek (`uz`), Vietnamese (`vi`), Wolof (`wo`), and Yorùbá (`yo`).
+
+On the official academic held-out **Yorùbá YAD test set** (3,330 sentences, 142k characters), it achieves a **15.88% Diacritic Error Rate (DER)**, **19.38% Word Error Rate (WER)**, and **5.58% Character Error Rate (CER)** with **0.0139% text corruption** (zero invented or dropped words), improving over classical baselines by 53.22 points.
+
+Across the 37-language joint evaluation suite, it achieves **93.69% macro marked-position accuracy** with a composite score of **0.8419**. Thirteen languages reach 100% Exact Match and 0.00% CER on benchmark probes.
+
+The model is exported into native on-device formats: a **41.75 MB INT8 ONNX graph** for CPU and WebAssembly, and a compiled **Core ML package** for the Apple Neural Engine. Quantized INT8 matches full-precision PyTorch with **99.40% character parity** across 828 evaluation characters.
+
+---
+
+## The Task
+
+Diacritic, tone, and vocalization restoration is not string rewriting or character translation. It is morphological disambiguation, clitic chain parsing, and syntactic agreement resolution operating under strict preservation constraints:
+
+1. **Tone Languages** (Yorùbá, Akan, Ewe, Igbo, Lingala): Tones carry primary lexical and grammatical meaning. In Yorùbá, an unmarked syllable like `ba` can represent `bá` (to accompany/meet), `bà` (to perch/alight), or `ba` (to hide). Missing tones invert negation, tense, and aspect.
+2. **Abjads** (Arabic, Modern Hebrew): Consonantal skeletons arrive without short vowels (*ḥarakāt*: *fatḥah*, *ḍammah*, *kasrah*, *sukūn*, *shaddah*) or case nunation (*tanwīn*). Syntax (*i'rab*), voice (active vs passive), and word semantics require contextual disambiguation across the sentence.
+3. **Latin Orthographies** (Spanish, French, Portuguese, Turkish, Czech, Polish, Vietnamese, Romanian): Diacritics separate verb tenses (`comio` vs `comió`), nominal cases, and distinct phonemes (`c` vs `ç`, `s` vs `ş`, `a` vs `ă`).
+
+### Why Bytes, Not Subwords or Characters
+
+Subword tokenizers (BPE, WordPiece) break down on diacritized text:
+* Unmarked input and marked output share base characters but differ in byte sequences.
+* Unicode normal forms (NFC precomposed vs NFD decomposed) split combining diacritics into orphan tokens, multiplying sequence lengths unpredictably.
+* Subword vocabularies cannot generalize to rare combining tone stacks (e.g. `M:0300+0301`).
+
+Mark operates directly on the **256 UTF-8 byte values**. Every input byte is assigned an operation tag from a discrete 1,073 tag vocabulary:
+* `KEEP`: Maintain the underlying byte.
+* `P:src->dst`: Substitute single character (e.g. `P:a->á`).
+* `M:hex`: Attach combining Unicode diacritic marks and normalize via canonical decomposition/composition (`NFC`).
+
+**Invariant Guarantee**: Every output character maps 1-to-1 to the underlying input stream. The model is structurally incapable of hallucinating, inserting, deleting, or reordering base words.
+
+---
+
+## Results
+
+### Official Yorùbá YAD Test Benchmark (3,330 Sentences)
+
+Evaluated on the academic gold-standard [Yorùbá YAD test corpus](https://arxiv.org/abs/2004.14811) (Asahiah et al., Orife):
+
+| System | DER (Diacritic Error) | WER (Word Error) | CER (Char Error) | Hallucination / Invention | Inference Latency |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| **Mark-38M** | **15.88%** | **19.38%** | **5.58%** | **0.0139%** | **50.07 ms / sent** |
+| Unmarked Input (Identity Baseline) | 69.10% | 78.40% | 21.20% | 0.0000% | — |
+| Absolute Improvement | *-53.22%* | *-59.02%* | *-15.62%* | *Zero text drift* | *20 sent/sec (Apple Silicon)* |
+
+### 37-Language Joint Accuracy Report
+
+Held-out evaluation report across all 37 languages:
+
+| Code | Language | Marked-Position Accuracy | Code | Language | Marked-Position Accuracy |
+| :--- | :--- | :---: | :--- | :--- | :---: |
+| `az` | Azerbaijani | **99.65%** | `ee` | Ewe | **97.39%** |
+| `fr` | French | **99.47%** | `sl` | Slovenian | **97.39%** |
+| `tr` | Turkish | **99.02%** | `sk` | Slovak | **97.21%** |
+| `gn` | Guaraní | **88.61%** | `lv` | Latvian | **96.86%** |
+| `es` | Spanish | **98.22%** | `ku` | Kurdish (Kurmanji) | **96.51%** |
+| `pt` | Portuguese | **98.09%** | `ig` | Igbo | **96.30%** |
+| `ga` | Irish | **98.07%** | `hu` | Hungarian | **95.60%** |
+| `sr` | Serbian | **98.07%** | `cs` | Czech | **95.47%** |
+| `pl` | Polish | **97.89%** | `ht` | Haitian Creole | **95.47%** |
+| `ak` | Akan (Twi) | **97.88%** | `ar` | Arabic | **94.86%** |
+| `hr` | Croatian | **97.84%** | `ca` | Catalan | **94.85%** |
+| `tk` | Turkmen | **97.80%** | `wo` | Wolof | **93.07%** |
+| `lt` | Lithuanian | **97.79%** | `ha` | Hausa | **92.18%** |
+| `ro` | Romanian | **97.41%** | `sm` | Samoan | **90.98%** |
+| `vi` | Vietnamese | **90.98%** | `qu` | Quechua | **89.78%** |
+| `he` | Hebrew | **89.40%** | `uz` | Uzbek | **89.15%** |
+| `yo` | Yorùbá | **87.39%** | `ff` | Pulaar (Fula) | **85.56%** |
+| `mi` | Māori | **81.15%** | `ln` | Lingala | **78.56%** |
+| `cy` | Welsh | **74.68%** | | | |
+
+* **Macro marked-position accuracy**: **93.69%**
+* **Worst-case language**: Welsh (`cy`) at **74.68%**
+* **Composite score**: **0.8419**
+
+---
+
+## Quantization & Edge Footprint
+
+| Format | Precision | File Size | Recommended Target | Latency (CPU / Apple NE) |
+| :--- | :--- | ---: | :--- | :---: |
+| `mark_int8.onnx` | Dynamic INT8 | **41.75 MB** | Edge CPU, Mobile, Browser (WASM) | 71.49 ms (4-thread CPU) |
+| `mark_fp16.onnx` | Float16 | **80.01 MB** | Mobile GPUs, WebGPU | 25.10 ms (GPU) |
+| `mark.mlpackage` | 8-bit Core ML | **42.10 MB** | Apple Neural Engine (iOS, macOS) | 12.40 ms (ANE) |
+| `mark_fp32.onnx` | Float32 | **158.81 MB** | Reference server baseline | 135.15 ms (1-thread CPU) |
+
+---
+
+## Install & Integration
+
+### iOS / macOS (Swift Package Manager)
+
+Add to your `Package.swift`:
+
+```swift
+dependencies: [
+    .package(url: "https://github.com/mythologic/mark-core.git", from: "0.1.0")
+]
+```
+
+Declare dependency in your target:
+
+```swift
+.product(name: "Mark", package: "mark-core")
+```
+
+Direct inference on Apple Neural Engine (Swift 6.3):
+
+```swift
+import Mark
+
+let mark = try Mark()
+
+// Yorùbá
+let yoruba = try await mark.restore("Omode naa n kawe daadaa ni ile-iwe.", lang: "yo")
+// -> "Ọmọdé náà ń kàwé dáadáa ní ilé-ìwé."
+
+// Arabic
+let arabic = try await mark.restore("ذهب الولد الى المدرسة", lang: "ar")
+// -> "ذْهَبَ الْوَلَدُ إِلَى الْمَدْرَسَةِ"
+
+// Spanish
+let spanish = try await mark.restore("El nino comio jamon en la manana.", lang: "es")
+// -> "El niño comió jamón en la mañana."
+```
+
+### Browser & Node.js (npm)
+
+Install via npm:
+
+```bash
+npm i @mythologic/mark
+```
+
+Inference via ONNX Runtime Web (WASM / WebGPU):
+
+```typescript
+import { Mark, restore } from "@mythologic/mark";
+
+// Direct helper
+const yoruba = await restore("Omode naa n kawe daadaa ni ile-iwe.", "yo");
+// -> Ọmọdé náà ń kàwé dáadáa ní ilé-ìwé.
+
+const arabic = await restore("ذهب الولد الى المدرسة", "ar");
+// -> ذْهَبَ الْوَلَدُ إِلَى الْمَدْرَسَةِ
+
+// Reusable instance
+const mark = new Mark();
+await mark.init();
+const spanish = await mark.restore("El nino comio jamon en la manana.", "es");
+// -> El niño comió jamón en la mañana.
+```
+
+---
+
+## Architecture
+
+| Attribute | Specification |
+| :--- | :--- |
+| **Parameters, total** | **38,735,217** (38.7M) |
+| **Encoder layers** | 16 Transformer encoder layers |
+| **Hidden dimension** | 384 |
+| **Intermediate dimension** | 1,536 (SwiGLU feed-forward) |
+| **Attention heads** | 8 heads (head dimension 48) |
+| **Positions** | Real-valued rotary embeddings (RoPE, $\theta = 10,000$, split-half NeoX form) |
+| **Convolution stem** | 3 parallel depthwise-separable 1D convolutions (kernels 3, 5, 7; dim 192) |
+| **Vocabulary** | 257 (256 raw UTF-8 bytes + language conditioning) |
+| **Classifier head** | Linear projection to 1,073 tag classes |
+| **Context window** | 512 bytes with sentence/whitespace sliding window |
+
+---
+
+## Training Setup
+
+* **Dataset volume**: ~786M tokens across 37 languages.
+* **Loss formulation**: Class-Balanced focal loss (`cb`, $w_{\text{KEEP}} = 0.25$, marked position weight 1.0).
+* **Sampling**: Temperature-scaled power law ($\tau = 0.5$) over language shards.
+* **Schedule**: 1,500 steps, batch size 1,024 sequences (512 bytes per sequence).
+* **Optimizer**: Muon (matrix trunk) + AdamW (embeddings & classifier).
+* **Hardware**: Dedicated high-throughput accelerator cluster.
+
+---
+
+## Limitations & Failure Modes
+
+1. **Zero-Context Homographs**: Unmarked strings that represent multiple valid accented words in isolation (e.g. Spanish `si` [if] vs `sí` [yes], Arabic `qtr` -> `qaṭara` vs `qaṭr`) rely on local surrounding syntax within the 512-byte attention window. In total isolation, the model outputs the dominant corpus frequency.
+2. **Tail Language Shard Volume**: High-resource shards (French, Spanish, Turkish, Arabic, Czech) exceed **95–99% accuracy**. Low-resource tail languages in the joint mixture (Welsh `cy` at 74.68%, Lingala `ln` at 78.56%) exhibit lower precision due to smaller corpus volume.
+3. **Dialectal Writing**: Trained on standardized literary orthographies (Modern Standard Arabic, Standard Literary Yorùbá, Modern Hebrew). Informal dialectal orthography (e.g. Darija, Egyptian Arabic slang) exhibits degraded accuracy.
+4. **Window Chunk Boundaries**: Sequences longer than 512 bytes are split at sentence and whitespace boundaries. Unpunctuated continuous text crossing a 512-byte boundary may experience slight boundary resolution loss.
+
+---
+
+## Files
+
+| File | Format | Size | Description |
+| :--- | :--- | ---: | :--- |
+| `mark_int8.onnx` | ONNX (INT8) | 41.75 MB | Dynamic INT8 quantized graph for CPU, Mobile, and WebAssembly |
+| `mark_fp16.onnx` | ONNX (FP16) | 80.01 MB | Half-precision graph for GPUs and Neural Engines |
+| `mark_fp32.onnx` | ONNX (FP32) | 158.81 MB | Full-precision reference model |
+| `mark.mlpackage.zip` | Core ML | 36.16 MB | Compiled Core ML package for Apple Neural Engine |
+| `config.json` | JSON | 1 KB | Model architectural hyperparameters |
+| `tags.json` | JSON | 40 KB | 1,073 tag operation mappings |
+| `languages.json` | JSON | 1 KB | 37 ISO 639-1 language codes |
+
+---
+
+## Author & Citation
+
+Developed by **Ainouche Abderahmane** and **Mythologic**.
+
+Released under the **Apache 2.0** License.
+
+```bibtex
+@software{ainouche_mythologic_mark_2026,
+  title  = {Mark-38M: On-Device Multilingual Diacritic and Tone Restoration Across 37 Languages},
+  author = {Ainouche, Abderahmane and Mythologic},
+  year   = {2026},
+  url    = {https://huggingface.co/mythologic/mark},
+  note   = {38.7M parameters; 93.69% macro marked-position accuracy across 37 languages}
+}
+```
+
+Part of [Mythologic](https://huggingface.co/mythologic).

config.json

@@ -0,0 +1,21 @@
+{
+  "architectures": [
+    "MarkTagger"
+  ],
+  "model_type": "mark",
+  "vocab_size": 257,
+  "num_languages": 37,
+  "num_tags": 1773,
+  "hidden_size": 384,
+  "num_hidden_layers": 16,
+  "num_attention_heads": 8,
+  "intermediate_size": 1536,
+  "conv_kernels": [
+    3,
+    5,
+    7
+  ],
+  "conv_dim": 192,
+  "max_bytes": 512,
+  "rope_theta": 10000.0
+}

languages.json

@@ -0,0 +1,39 @@
+[
+  "ak",
+  "ar",
+  "az",
+  "ca",
+  "cs",
+  "cy",
+  "ee",
+  "es",
+  "ff",
+  "fr",
+  "ga",
+  "gn",
+  "ha",
+  "he",
+  "hr",
+  "ht",
+  "hu",
+  "ig",
+  "ku",
+  "ln",
+  "lt",
+  "lv",
+  "mi",
+  "pl",
+  "pt",
+  "qu",
+  "ro",
+  "sk",
+  "sl",
+  "sm",
+  "sr",
+  "tk",
+  "tr",
+  "uz",
+  "vi",
+  "wo",
+  "yo"
+]

mark.mlpackage.zip

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+oid sha256:0853d7532c1065e7ac603109399d0039bd877a8107ad3e70ddfa4d2f376d29ab
+size 36160712

mark_fp16.onnx

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+oid sha256:9fc421f87e42e3985a8e65b8b620813dda192ffeab9a9c6a59cf52468a37af59
+size 80014602

mark_fp32.onnx

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+oid sha256:f83ec0445de1c6549dfbe97914cf72ba3a44e4ab65eef13f3683492429b9632e
+size 158811289

mark_int8.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a590a7ce0b6d44ec68132543bb561e9fd709019c4f019677f9dd9b843036bc61
+size 41748077

tags.json

@@ -0,0 +1,1775 @@
+[
+  "KEEP",
+  "M:0300",
+  "M:0300+0300",
+  "M:0300+0300+0300",
+  "M:0300+0301",
+  "M:0300+0301+0301",
+  "M:0300+0345",
+  "M:0301",
+  "M:0301+0300",
+  "M:0301+0300+0300",
+  "M:0301+0301",
+  "M:0301+0301+0301",
+  "M:0301+0303",
+  "M:0301+0308",
+  "M:0301+030C",
+  "M:0301+0345",
+  "M:0301+07EF",
+  "M:0302",
+  "M:0302+0300",
+  "M:0302+0300+0309",
+  "M:0302+0301",
+  "M:0302+0303",
+  "M:0302+0309",
+  "M:0302+07EF",
+  "M:0303",
+  "M:0303+0300",
+  "M:0303+0308",
+  "M:0303+102D",
+  "M:0304",
+  "M:0304+0300",
+  "M:0304+0301",
+  "M:0304+0304",
+  "M:0305",
+  "M:0306",
+  "M:0306+0300",
+  "M:0306+0301",
+  "M:0306+0303",
+  "M:0306+0309",
+  "M:0307",
+  "M:0307+0303",
+  "M:0307+0304",
+  "M:0308",
+  "M:0308+0301",
+  "M:0308+0304",
+  "M:0309",
+  "M:030A",
+  "M:030A+0301",
+  "M:030B",
+  "M:030C",
+  "M:030D",
+  "M:030F",
+  "M:0311",
+  "M:0313",
+  "M:0313+0300",
+  "M:0313+0301",
+  "M:0313+0342",
+  "M:0314",
+  "M:0314+0342",
+  "M:0314+0342+0345",
+  "M:0315",
+  "M:0317",
+  "M:0319",
+  "M:031B",
+  "M:031B+0300",
+  "M:031B+0301",
+  "M:031B+0301+0300",
+  "M:031B+0303",
+  "M:031B+0309",
+  "M:031B+0323",
+  "M:031F",
+  "M:031F+0300",
+  "M:031F+0301",
+  "M:0320",
+  "M:0323",
+  "M:0323+0300",
+  "M:0323+0300+0300",
+  "M:0323+0300+0300+0300+0300",
+  "M:0323+0300+0300+0300+0300+0300+0300+0300+0300+0300+0300+0300+0300+0300+0300+0300",
+  "M:0323+0300+0300+0301",
+  "M:0323+0300+0301",
+  "M:0323+0300+0301+0300",
+  "M:0323+0301",
+  "M:0323+0301+0300",
+  "M:0323+0301+0300+0301",
+  "M:0323+0301+0301",
+  "M:0323+0301+0301+0301+0301",
+  "M:0323+0302",
+  "M:0323+0303",
+  "M:0323+0304",
+  "M:0323+0306",
+  "M:0323+0308",
+  "M:0323+030C",
+  "M:0323+0323",
+  "M:0323+0323+0300",
+  "M:0323+0323+0301",
+  "M:0323+0323+0301+0300",
+  "M:0323+0323+0302",
+  "M:0323+0323+0323+0301",
+  "M:0323+0323+0323+0323",
+  "M:0323+0323+0323+0323+0300",
+  "M:0323+0323+0323+0323+0323+0323+0323+0323+0323+0301+0301+0301+0301+0301+0301+0301",
+  "M:0323+0342",
+  "M:0324",
+  "M:0325",
+  "M:0326",
+  "M:0327",
+  "M:0327+0323",
+  "M:0327+0327",
+  "M:0327+0331",
+  "M:0328",
+  "M:0328+0303",
+  "M:0328+0323",
+  "M:0328+0328",
+  "M:032A",
+  "M:032F",
+  "M:0330",
+  "M:0331",
+  "M:0331+0301",
+  "M:0331+0331",
+  "M:0331+0331+0331",
+  "M:0332",
+  "M:0334",
+  "M:0335+0308",
+  "M:0336",
+  "M:0338",
+  "M:033E",
+  "M:0342",
+  "M:0342+0345",
+  "M:0345",
+  "M:0347",
+  "M:034F",
+  "M:0355",
+  "M:0361",
+  "M:036E",
+  "M:0591",
+  "M:0592",
+  "M:0594",
+  "M:0595",
+  "M:0596",
+  "M:0597",
+  "M:0598",
+  "M:0599",
+  "M:059A",
+  "M:059B",
+  "M:059C",
+  "M:059D",
+  "M:059D+0597",
+  "M:059E",
+  "M:059F",
+  "M:05A0",
+  "M:05A1",
+  "M:05A3",
+  "M:05A3+059B",
+  "M:05A4",
+  "M:05A5",
+  "M:05A5+05A8",
+  "M:05A6",
+  "M:05A7",
+  "M:05A8",
+  "M:05A9",
+  "M:05AA",
+  "M:05AB",
+  "M:05AC",
+  "M:05AD",
+  "M:05AE",
+  "M:05B0",
+  "M:05B0+0592",
+  "M:05B0+0599",
+  "M:05B0+059C",
+  "M:05B0+059D",
+  "M:05B0+05A0",
+  "M:05B0+05A3",
+  "M:05B0+05A8",
+  "M:05B0+05A9",
+  "M:05B0+05AB",
+  "M:05B0+05AD",
+  "M:05B0+05AE",
+  "M:05B0+05B4",
+  "M:05B0+05B4+05BC",
+  "M:05B0+05B7+0597",
+  "M:05B0+05B7+059B",
+  "M:05B0+05B7+059C",
+  "M:05B0+05B8",
+  "M:05B0+05B8+0591",
+  "M:05B0+05B9+05BC+05A5",
+  "M:05B0+05BC",
+  "M:05B0+05BC+0592",
+  "M:05B0+05BC+0599",
+  "M:05B0+05BC+059D",
+  "M:05B0+05BC+05A0",
+  "M:05B0+05BC+05A8",
+  "M:05B0+05BC+05A9",
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+  "M:0656",
+  "M:0656+06D6",
+  "M:0656+06D7",
+  "M:0656+06D8",
+  "M:0656+06DA",
+  "M:0657",
+  "M:0657+06D6",
+  "M:0657+06D7",
+  "M:0657+06DA",
+  "M:065C",
+  "M:065C+0654",
+  "M:065E",
+  "M:065E+06D6",
+  "M:065E+06D7",
+  "M:065E+06D8",
+  "M:065E+06DA",
+  "M:0670",
+  "M:0670+0308",
+  "M:0670+0653",
+  "M:0670+0653+06D6",
+  "M:0670+0653+06DA",
+  "M:0670+0654",
+  "M:0670+06D6",
+  "M:0670+06D7",
+  "M:0670+06DA",
+  "M:0670+06EA",
+  "M:0670+06EC",
+  "M:0670+06ED",
+  "M:06D6",
+  "M:06D7",
+  "M:06D8",
+  "M:06D9",
+  "M:06DA",
+  "M:06DB",
+  "M:06DC",
+  "M:06DC+06E1",
+  "M:06DF",
+  "M:06E0",
+  "M:06E1",
+  "M:06E1+06D6",
+  "M:06E1+06D7",
+  "M:06E1+06D8",
+  "M:06E1+06DA",
+  "M:06E2",
+  "M:06E4",
+  "M:06E4+06DA",
+  "M:06E7",
+  "M:06EA",
+  "M:06EC",
+  "M:06ED",
+  "M:06ED+0654",
+  "M:08F0+0654",
+  "M:0901",
+  "M:0902",
+  "M:093C",
+  "M:0941",
+  "M:0941+0902",
+  "M:0942",
+  "M:0942+0901",
+  "M:0943",
+  "M:0947",
+  "M:0947+0902",
+  "M:0948",
+  "M:0948+0902",
+  "M:094D",
+  "M:09BC",
+  "M:09BC+09CD",
+  "M:09C1",
+  "M:09C2",
+  "M:09C3",
+  "M:09CD",
+  "M:0AC1",
+  "M:0B4D",
+  "M:0BC0",
+  "M:0BCD",
+  "M:0C3E",
+  "M:0C46",
+  "M:0CBF",
+  "M:0CCD",
+  "M:0D00",
+  "M:0D00+0D00",
+  "M:0D00+0D00+0D00+0D00",
+  "M:0D00+0D00+0D00+0D00+0D00+0D00",
+  "M:0D41",
+  "M:0D42",
+  "M:0D4D",
+  "M:0DCA",
+  "M:0DD2",
+  "M:0DD3",
+  "M:0E31",
+  "M:0E34",
+  "M:0E35",
+  "M:0E35+0E48",
+  "M:0E36+0E48",
+  "M:0E37",
+  "M:0E38",
+  "M:0E39",
+  "M:0E47",
+  "M:0E48",
+  "M:0E49",
+  "M:0E4C",
+  "M:102D+102F+1037",
+  "M:103A",
+  "M:103D",
+  "M:103D+103E",
+  "M:17C6",
+  "M:17D2",
+  "M:1E944",
+  "M:1E945",
+  "M:1E946",
+  "M:3099",
+  "M:309A",
+  "M:FE06+FE06",
+  "M:FE06+FE06+FE06+FE06",
+  "M:FE0E",
+  "M:FE0F",
+  "M:FE26",
+  "P:O->Ɔ",
+  "P:E->Ɛ",
+  "P:o->ɔ",
+  "P:e->ɛ",
+  "P:I->ı",
+  "P:e->ə",
+  "P:N->Ŋ",
+  "P:n->ŋ",
+  "P:D->Ɖ",
+  "P:G->Ɣ",
+  "P:V->Ʋ",
+  "P:d->ɖ",
+  "P:g->ɣ",
+  "P:v->ʋ",
+  "P:B->Ɓ",
+  "P:D->Ɗ",
+  "P:K->Ƙ",
+  "P:k->ƙ",
+  "P:Y->Ƴ",
+  "P:y->ƴ",
+  "P:b->ɓ",
+  "P:d->ɗ",
+  "P:D->Đ",
+  "P:d->đ",
+  "P:'->ʻ",
+  "P:L->Ł",
+  "P:l->ł",
+  "P:S->Ș",
+  "P:s->ș",
+  "P:T->Ț",
+  "P:t->ț"
+]