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+---
+language: mos
+language_name: Mossi
+language_family: atlantic_gur
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-atlantic_gur
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 3.679
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8275
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Mossi - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Mossi** Wikipedia data.
+We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
+
+## 📋 Repository Contents
+
+### Models & Assets
+
+- Tokenizers (8k, 16k, 32k, 64k)
+- N-gram models (2, 3, 4, 5-gram)
+- Markov chains (context of 1, 2, 3, 4 and 5)
+- Subword N-gram and Markov chains
+- Embeddings in various sizes and dimensions (aligned and unaligned)
+- Language Vocabulary
+- Language Statistics
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Analysis and Evaluation
+
+- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
+- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
+- [3. Markov Chain Evaluation](#3-markov-chain-evaluation)
+- [4. Vocabulary Analysis](#4-vocabulary-analysis)
+- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation)
+- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
+- [7. Summary & Recommendations](#7-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![Tokenizer Compression](visualizations/tokenizer_compression.png)
+
+![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+
+![Tokenizer OOV](visualizations/tokenizer_oov.png)
+
+![Total Tokens](visualizations/tokenizer_total_tokens.png)
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 3.339x | 3.34 | 0.2504% | 875,853 |
+| **16k** | 3.492x | 3.49 | 0.2618% | 837,545 |
+| **32k** | 3.594x | 3.59 | 0.2695% | 813,821 |
+| **64k** | 3.679x 🏆 | 3.68 | 0.2759% | 794,952 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Ne Wẽnd yʋʋre, Nimbaan-zoetb-naaba, Nin-zēnga nimbaan-zoeta`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ne ▁wẽnd ▁yʋʋre , ▁nimbaan - zoetb - naaba , ... (+6 more)` | 16 |
+| 16k | `▁ne ▁wẽnd ▁yʋʋre , ▁nimbaan - zoetb - naaba , ... (+6 more)` | 16 |
+| 32k | `▁ne ▁wẽnd ▁yʋʋre , ▁nimbaan - zoetb - naaba , ... (+6 more)` | 16 |
+| 64k | `▁ne ▁wẽnd ▁yʋʋre , ▁nimbaan - zoetb - naaba , ... (+6 more)` | 16 |
+
+**Sample 2:** `Sɩngda ne Wẽnd yʋʋre, ãndũni Nimbaan-Zoetb-Naaba la laahir Nimbaan-Zoet-Naaba`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁sɩngda ▁ne ▁wẽnd ▁yʋʋre , ▁ãndũni ▁nimbaan - zoetb - ... (+8 more)` | 18 |
+| 16k | `▁sɩngda ▁ne ▁wẽnd ▁yʋʋre , ▁ãndũni ▁nimbaan - zoetb - ... (+8 more)` | 18 |
+| 32k | `▁sɩngda ▁ne ▁wẽnd ▁yʋʋre , ▁ãndũni ▁nimbaan - zoetb - ... (+8 more)` | 18 |
+| 64k | `▁sɩngda ▁ne ▁wẽnd ▁yʋʋre , ▁ãndũni ▁nimbaan - zoetb - ... (+8 more)` | 18 |
+
+**Sample 3:** `Ne Wẽnd yʋʋre, Nimbaan-zoetb-naaba, Nin-zēnga nimbaan-zoeta`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ne ▁wẽnd ▁yʋʋre , ▁nimbaan - zoetb - naaba , ... (+6 more)` | 16 |
+| 16k | `▁ne ▁wẽnd ▁yʋʋre , ▁nimbaan - zoetb - naaba , ... (+6 more)` | 16 |
+| 32k | `▁ne ▁wẽnd ▁yʋʋre , ▁nimbaan - zoetb - naaba , ... (+6 more)` | 16 |
+| 64k | `▁ne ▁wẽnd ▁yʋʋre , ▁nimbaan - zoetb - naaba , ... (+6 more)` | 16 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.679x compression
+- **Lowest UNK Rate:** 8k with 0.2504% unknown tokens
+- **Trade-off:** Larger vocabularies improve compression but increase model size
+- **Recommendation:** 32k vocabulary provides optimal balance for production use
+
+---
+## 2. N-gram Model Evaluation
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 3,615 | 11.82 | 20,744 | 29.4% | 59.7% |
+| **2-gram** | Subword | 273 🏆 | 8.09 | 2,796 | 65.9% | 99.1% |
+| **3-gram** | Word | 13,336 | 13.70 | 43,968 | 14.2% | 38.3% |
+| **3-gram** | Subword | 1,923 | 10.91 | 21,422 | 32.4% | 73.3% |
+| **4-gram** | Word | 40,697 | 15.31 | 90,918 | 7.5% | 22.5% |
+| **4-gram** | Subword | 8,329 | 13.02 | 100,573 | 19.4% | 48.8% |
+| **5-gram** | Word | 44,157 | 15.43 | 75,214 | 6.3% | 18.6% |
+| **5-gram** | Subword | 22,381 | 14.45 | 221,121 | 13.6% | 36.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sẽn yaa` | 13,134 |
+| 2 | `b sẽn` | 12,171 |
+| 3 | `tɩ b` | 8,032 |
+| 4 | `a sẽn` | 6,522 |
+| 5 | `na n` | 6,461 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n na n` | 2,771 |
+| 2 | `sẽn boond tɩ` | 2,500 |
+| 3 | `sẽn na n` | 2,163 |
+| 4 | `b sẽn da` | 2,127 |
+| 5 | `sẽn wa n` | 1,587 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `b sẽn boond tɩ` | 1,290 |
+| 2 | `sẽn na yɩl n` | 905 |
+| 3 | `b sẽn na n` | 842 |
+| 4 | `a sẽn wa n` | 720 |
+| 5 | `sull ning sẽn get` | 574 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `parliament of the 4th republic` | 465 |
+| 2 | `of the 4th republic of` | 464 |
+| 3 | `the 4th republic of ghana` | 464 |
+| 4 | `b sẽn na n maan` | 315 |
+| 5 | `sẽn yaa zaalem n yit` | 311 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 226,091 |
+| 2 | `n _` | 141,998 |
+| 3 | `_ s` | 119,072 |
+| 4 | `_ n` | 113,003 |
+| 5 | `_ t` | 93,570 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `s ẽ n` | 63,951 |
+| 2 | `ẽ n _` | 63,904 |
+| 3 | `_ s ẽ` | 63,741 |
+| 4 | `_ a _` | 59,840 |
+| 5 | `_ n _` | 52,363 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `s ẽ n _` | 63,824 |
+| 2 | `_ s ẽ n` | 63,514 |
+| 3 | `_ y a a` | 30,361 |
+| 4 | `y a a _` | 29,963 |
+| 5 | `_ l a _` | 23,119 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ s ẽ n _` | 63,440 |
+| 2 | `_ y a a _` | 29,891 |
+| 3 | `s ẽ n _ y` | 17,024 |
+| 4 | `_ y ʋ ʋ m` | 16,370 |
+| 5 | `b _ s ẽ n` | 16,118 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 273
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~36% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.7703 | 1.706 | 5.14 | 57,332 | 23.0% |
+| **1** | Subword | 0.8648 | 1.821 | 5.86 | 1,399 | 13.5% |
+| **2** | Word | 0.3065 | 1.237 | 1.90 | 294,230 | 69.4% |
+| **2** | Subword | 0.8276 | 1.775 | 5.18 | 8,196 | 17.2% |
+| **3** | Word | 0.1679 | 1.123 | 1.37 | 557,321 | 83.2% |
+| **3** | Subword | 0.8333 | 1.782 | 4.05 | 42,425 | 16.7% |
+| **4** | Word | 0.0944 🏆 | 1.068 | 1.17 | 763,192 | 90.6% |
+| **4** | Subword | 0.6301 | 1.548 | 2.63 | 171,784 | 37.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a dickson sɩnga a sẽn yaa kiris neda log koglgã pʋga neb 0 5 b tall`
+2. `sẽn be zĩig a yɩ pipi pipi wã taoor soab a sẽn mik tɩ palmɛtã b`
+3. `n pa vɩ ghana karẽn biiga la a yãame tɩ b sẽn wa a piliin sẽn`
+
+**Context Size 2:**
+
+1. `sẽn yaa rap sẽn be volta tẽnga ghana a keem soaba ra yii na baooda taaba yuuya`
+2. `b sẽn tõe n lebg n wa ne yell sẽn boond tɩ segã b sẽn paam n`
+3. `tɩ b pa bas tɩ b ra boond b lame tɩ pa yɩ sõma n tõe n`
+
+**Context Size 3:**
+
+1. `n na n sõng ghana tẽnga neb tɩ b yũ a ne fɩɩmã zĩig buud wʋsg na n`
+2. `sẽn boond tɩ étni wã wɛɛngẽ kamã rutenberg yɩɩ tẽn zẽms taab karen saamb hekima university college s...`
+3. `sẽn na n zĩnd afcon sẽn zĩnd kameroõ wãpʋgẽ b vɩɩmã a oteng gyasi yaa kiris ned 1`
+
+**Context Size 4:**
+
+1. `b sẽn boond tɩ fõndã yaa fõnd sẽn yaa bẽnd sẽn yaa agaricales tɩ b yaa bẽnda la b`
+2. `sẽn na yɩl n bas a jin ganggang n kẽng a kang ganggangã ye b sẽn maan tʋʋm teedã`
+3. `b sẽn na n tõog a zabrã yʋʋm a yiib sẽn zĩnd senegal tẽnga tʋʋm kaoodbã taoor soaba sẽn`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_oorvo-bã,_wĩ-br`
+2. `amulg_b_rinee_r_`
+3. `n_tẽngerorẽn_nan`
+
+**Context Size 2:**
+
+1. `a_tɩ_tõnd_zãgd_wa`
+2. `n_yʋʋmd_wã_yaa_n_`
+3. `_scul_ham_sẽngané`
+
+**Context Size 3:**
+
+1. `sẽn_da_gov.gh._yʋʋ`
+2. `ẽn_tãag_anda_zĩis_`
+3. `_sẽn_na_sã_la_sẽn_`
+
+**Context Size 4:**
+
+1. `sẽn_yɩɩl_n_to-to_no`
+2. `_sẽn_da_tẽnga_la_ki`
+3. `_yaa_woto_lisga_a_t`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 90.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (171,784 contexts)
+- **Recommendation:** Context-3 or Context-4 for text generation
+
+---
+## 4. Vocabulary Analysis
+
+![Zipf's Law](visualizations/zipf_law.png)
+
+![Top Words](visualizations/top20_words.png)
+
+![Coverage Curve](visualizations/vocab_coverage.png)
+
+### Statistics
+
+| Metric | Value |
+|--------|-------|
+| Vocabulary Size | 25,483 |
+| Total Tokens | 1,059,645 |
+| Mean Frequency | 41.58 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 835.14 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 70,107 |
+| 2 | sẽn | 63,849 |
+| 3 | n | 55,318 |
+| 4 | b | 41,576 |
+| 5 | yaa | 30,095 |
+| 6 | wã | 26,687 |
+| 7 | la | 24,541 |
+| 8 | tɩ | 18,168 |
+| 9 | ne | 14,910 |
+| 10 | be | 10,303 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | grup | 2 |
+| 2 | pamiat | 2 |
+| 3 | kɛlẽ | 2 |
+| 4 | geroy | 2 |
+| 5 | yɛlm | 2 |
+| 6 | ayensu | 2 |
+| 7 | folu | 2 |
+| 8 | storms | 2 |
+| 9 | kabah | 2 |
+| 10 | ayirevire | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2282 |
+| R² (Goodness of Fit) | 0.997023 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 57.5% |
+| Top 1,000 | 81.7% |
+| Top 5,000 | 92.6% |
+| Top 10,000 | 96.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9970 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 57.5% of corpus
+- **Long Tail:** 15,483 words needed for remaining 3.9% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.8275 🏆 | 0.3352 | N/A | N/A |
+| **mono_64d** | 64 | 0.6882 | 0.2965 | N/A | N/A |
+| **mono_128d** | 128 | 0.2573 | 0.2728 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8275 | 0.3501 | 0.0400 | 0.2040 |
+| **aligned_64d** | 64 | 0.6882 | 0.2969 | 0.0880 | 0.3240 |
+| **aligned_128d** | 128 | 0.2573 | 0.2710 | 0.1100 | 0.3980 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8275 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3037. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 11.0% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.486** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-s` | supreme, spaans, svētki |
+| `-a` | adiku, artiste, ampem |
+| `-k` | kʋʋlem, kʋgs, karshon |
+| `-b` | buginese, blige, brobby |
+| `-t` | tuud, tradition, tre |
+| `-p` | pseudostem, parlamentã, ppiri |
+| `-m` | micronesia, mate, molard |
+| `-ma` | mate, malɛɛzi, mante |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | citifmonline, supreme, artiste |
+| `-a` | micronesia, natalia, zaba |
+| `-s` | kʋgs, laws, earphones |
+| `-n` | oleson, tradition, văn |
+| `-ã` | lillã, parlamentã, baoobã |
+| `-i` | yendi, ppiri, malɛɛzi |
+| `-r` | görenler, glamour, tõor |
+| `-o` | folklórico, instituto, klymenko |
+
+### 6.3 Bound Stems (Lexical Roots)
+
+Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.
+
+| Stem | Cohesion | Substitutability | Examples |
+|------|----------|------------------|----------|
+| `aand` | 2.29x | 31 contexts | maand, naand, vaand |
+| `inis` | 1.96x | 27 contexts | minisr, pinisi, phinis |
+| `aren` | 2.46x | 12 contexts | karen, arena, kareng |
+| `oore` | 1.97x | 16 contexts | boore, poore, moore |
+| `kãse` | 1.95x | 15 contexts | kãsem, kãseng, kãsems |
+| `akat` | 2.23x | 10 contexts | wakat, wakato, wakatã |
+| `tame` | 2.15x | 11 contexts | votame, kɩtame, getame |
+| `atio` | 1.95x | 14 contexts | nation, nations, station |
+| `poli` | 1.90x | 15 contexts | polis, politk, police |
+| `oond` | 1.96x | 13 contexts | moond, boond, boondd |
+| `olit` | 2.06x | 10 contexts | politk, polity, politic |
+| `amen` | 2.30x | 7 contexts | ameng, amenfi, amenga |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-a` | `-s` | 53 words | alfreds, anas |
+| `-a` | `-e` | 52 words | ascultare, atske |
+| `-s` | `-e` | 46 words | sokre, suzanne |
+| `-m` | `-s` | 44 words | marsalis, morris |
+| `-s` | `-s` | 43 words | sɩns, seychelles |
+| `-m` | `-a` | 42 words | moroccoa, menga |
+| `-a` | `-n` | 42 words | abelian, agyeman |
+| `-p` | `-s` | 40 words | poems, pʋʋs |
+| `-a` | `-a` | 39 words | arzɛka, adisa |
+| `-k` | `-a` | 37 words | koata, kõta |
+
+### 6.5 Recursive Morpheme Segmentation
+
+Using **Recursive Hierarchical Substitutability**, we decompose complex words into their constituent morphemes. This approach handles nested affixes (e.g., `prefix-prefix-root-suffix`).
+
+| Word | Suggested Split | Confidence | Stem |
+|------|-----------------|------------|------|
+| nicholson | **`nichol-s-on`** | 7.5 | `s` |
+| neuigkeiten | **`neuigkeit-e-n`** | 7.5 | `e` |
+| geleneksel | **`geleneks-e-l`** | 7.5 | `e` |
+| charreadas | **`charread-a-s`** | 7.5 | `a` |
+| ekonomiya | **`ekonomi-y-a`** | 7.5 | `y` |
+| ukrainien | **`ukraini-e-n`** | 7.5 | `e` |
+| condiment | **`condi-me-nt`** | 7.5 | `me` |
+| unopposed | **`unoppo-s-ed`** | 7.5 | `s` |
+| sertipikat | **`sertipik-a-t`** | 7.5 | `a` |
+| valensians | **`valensi-an-s`** | 6.0 | `valensi` |
+| ecoregions | **`e-co-regions`** | 6.0 | `regions` |
+| karẽnsaamb | **`ka-r-ẽnsaamb`** | 4.5 | `ẽnsaamb` |
+| laureates | **`laureat-es`** | 4.5 | `laureat` |
+| koordinatɛɛr | **`ko-ordinatɛɛr`** | 4.5 | `ordinatɛɛr` |
+| monographs | **`monograph-s`** | 4.5 | `monograph` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Mossi shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (3.68x) |
+| N-gram | **2-gram** | Lowest perplexity (273) |
+| Markov | **Context-4** | Highest predictability (90.6%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 12:34:58*
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diff --git a/models/tokenizer/mos_tokenizer_32k.model b/models/tokenizer/mos_tokenizer_32k.model
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diff --git a/models/tokenizer/mos_tokenizer_32k.vocab b/models/tokenizer/mos_tokenizer_32k.vocab
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diff --git a/models/tokenizer/mos_tokenizer_64k.model b/models/tokenizer/mos_tokenizer_64k.model
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diff --git a/models/tokenizer/mos_tokenizer_64k.vocab b/models/tokenizer/mos_tokenizer_64k.vocab
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diff --git a/models/tokenizer/mos_tokenizer_8k.vocab b/models/tokenizer/mos_tokenizer_8k.vocab
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