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+---
+language: fon
+language_name: Fon
+language_family: atlantic_kwa
+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_kwa
+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: 4.124
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6254
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-04
+---
+
+# Fon - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Fon** 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** | 3.633x | 3.64 | 0.1627% | 178,834 |
+| **16k** | 3.846x | 3.85 | 0.1723% | 168,913 |
+| **32k** | 4.057x | 4.06 | 0.1817% | 160,142 |
+| **64k** | 4.124x 🏆 | 4.13 | 0.1847% | 157,541 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Koffi Danger, ɔ́ nyí malànhwlɛ̀nvlɛ́tɔ́ Benɛɛ tɔn ɖé wɛ bɔ è jì i ɖò ɖò Gbɔ̀xikɔ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁koffi ▁dan ger , ▁ɔ́ ▁nyí ▁malànhwlɛ̀nvlɛ́ tɔ́ ▁benɛɛ ▁tɔn ... (+19 more)` | 29 |
+| 16k | `▁koffi ▁danger , ▁ɔ́ ▁nyí ▁malànhwlɛ̀nvlɛ́ tɔ́ ▁benɛɛ ▁tɔn ▁ɖé ... (+18 more)` | 28 |
+| 32k | `▁koffi ▁danger , ▁ɔ́ ▁nyí ▁malànhwlɛ̀nvlɛ́ tɔ́ ▁benɛɛ ▁tɔn ▁ɖé ... (+18 more)` | 28 |
+| 64k | `▁koffi ▁danger , ▁ɔ́ ▁nyí ▁malànhwlɛ̀nvlɛ́ tɔ́ ▁benɛɛ ▁tɔn ▁ɖé ... (+18 more)` | 28 |
+
+**Sample 2:** `Kuwanwangu nyi glekɔxwe ɖokpo nǔ tokpɔnlavi Kwaba tɔn nú tokpɔnla Natitingu tɔn ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ku wan wan gu ▁nyi ▁glekɔxwe ▁ɖokpo ▁nǔ ▁tokpɔnlavi ▁kwaba ... (+12 more)` | 22 |
+| 16k | `▁kuwanwangu ▁nyi ▁glekɔxwe ▁ɖokpo ▁nǔ ▁tokpɔnlavi ▁kwaba ▁tɔn ▁nú ▁tokpɔnla ... (+9 more)` | 19 |
+| 32k | `▁kuwanwangu ▁nyi ▁glekɔxwe ▁ɖokpo ▁nǔ ▁tokpɔnlavi ▁kwaba ▁tɔn ▁nú ▁tokpɔnla ... (+9 more)` | 19 |
+| 64k | `▁kuwanwangu ▁nyi ▁glekɔxwe ▁ɖokpo ▁nǔ ▁tokpɔnlavi ▁kwaba ▁tɔn ▁nú ▁tokpɔnla ... (+9 more)` | 19 |
+
+**Sample 3:** `Ablu ɔ hwenu e minyɔ̀ alo weziza han ɔ wɛ nɔ nyi mɔ̌. Xixa tɔn`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ab lu ▁ɔ ▁hwenu ▁e ▁min yɔ̀ ▁alo ▁weziza ▁han ... (+8 more)` | 18 |
+| 16k | `▁ablu ▁ɔ ▁hwenu ▁e ▁minyɔ̀ ▁alo ▁weziza ▁han ▁ɔ ▁wɛ ... (+6 more)` | 16 |
+| 32k | `▁ablu ▁ɔ ▁hwenu ▁e ▁minyɔ̀ ▁alo ▁weziza ▁han ▁ɔ ▁wɛ ... (+6 more)` | 16 |
+| 64k | `▁ablu ▁ɔ ▁hwenu ▁e ▁minyɔ̀ ▁alo ▁weziza ▁han ▁ɔ ▁wɛ ... (+6 more)` | 16 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.124x compression
+- **Lowest UNK Rate:** 8k with 0.1627% 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 | 1,671 | 10.71 | 7,538 | 38.1% | 71.7% |
+| **2-gram** | Subword | 265 🏆 | 8.05 | 2,254 | 68.9% | 98.7% |
+| **3-gram** | Word | 2,808 | 11.46 | 12,455 | 33.4% | 62.3% |
+| **3-gram** | Subword | 1,585 | 10.63 | 14,789 | 35.7% | 77.3% |
+| **4-gram** | Word | 3,755 | 11.87 | 19,739 | 32.3% | 58.3% |
+| **4-gram** | Subword | 5,749 | 12.49 | 55,463 | 22.8% | 55.5% |
+| **5-gram** | Word | 2,983 | 11.54 | 15,474 | 34.1% | 61.1% |
+| **5-gram** | Subword | 12,261 | 13.58 | 96,928 | 17.0% | 44.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `tɔn mɛ` | 7,028 |
+| 2 | `mɛ ɖo` | 3,347 |
+| 3 | `tɔn lɛ` | 2,790 |
+| 4 | `mɛ e` | 2,133 |
+| 5 | `dodo tɔn` | 1,886 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `tɔn mɛ ɖo` | 2,782 |
+| 2 | `jì é ɖěè` | 1,274 |
+| 3 | `ayi e jì` | 1,171 |
+| 4 | `tɔn mɛ é` | 1,170 |
+| 5 | `e jì é` | 1,168 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ayi e jì é` | 1,167 |
+| 2 | `e jì é ɖěè` | 1,157 |
+| 3 | `e ɖěè mɛ e` | 1,134 |
+| 4 | `gbɛtɔ e ɖěè mɛ` | 1,133 |
+| 5 | `tɔn mɛ ɖo benɛ` | 1,090 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ayi e jì é ɖěè` | 1,156 |
+| 2 | `gbɛtɔ e ɖěè mɛ e` | 1,133 |
+| 3 | `benɛ ayi e jì é` | 1,064 |
+| 4 | `ɖo benɛ ayi e jì` | 1,060 |
+| 5 | `mɛ ɖo benɛ ayi e` | 1,060 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n _` | 58,568 |
+| 2 | `o _` | 46,161 |
+| 3 | `_ t` | 45,106 |
+| 4 | `ɔ n` | 41,894 |
+| 5 | `_ ɖ` | 36,979 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ɔ n _` | 27,349 |
+| 2 | `t ɔ n` | 25,832 |
+| 3 | `_ t ɔ` | 24,140 |
+| 4 | `_ ɖ o` | 19,620 |
+| 5 | `ɖ o _` | 17,028 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t ɔ n` | 23,518 |
+| 2 | `t ɔ n _` | 22,408 |
+| 3 | `_ ɖ o _` | 16,782 |
+| 4 | `_ m ɛ _` | 10,812 |
+| 5 | `k p ɔ n` | 8,817 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t ɔ n _` | 20,896 |
+| 2 | `_ t o k p` | 8,408 |
+| 3 | `t o k p ɔ` | 8,400 |
+| 4 | `o k p ɔ n` | 8,400 |
+| 5 | `t ɔ n _ m` | 7,246 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 265
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~45% 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 | 0.7272 | 1.655 | 4.51 | 24,791 | 27.3% |
+| **1** | Subword | 1.2806 | 2.429 | 14.66 | 265 | 0.0% |
+| **2** | Word | 0.2756 | 1.210 | 1.70 | 111,357 | 72.4% |
+| **2** | Subword | 1.1501 | 2.219 | 7.00 | 3,884 | 0.0% |
+| **3** | Word | 0.1152 | 1.083 | 1.21 | 188,520 | 88.5% |
+| **3** | Subword | 0.7806 | 1.718 | 3.61 | 27,160 | 21.9% |
+| **4** | Word | 0.0471 🏆 | 1.033 | 1.08 | 227,466 | 95.3% |
+| **4** | Subword | 0.5178 | 1.432 | 2.22 | 98,034 | 48.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `tɔn mɛ wli hwe ɔ huzu tokpɔnlavi agɔnkanmɛ tɔn bo ɖyɔ ɛ ylɔ ɛ ɖo yovogbè`
+2. `ɖo tokpɔn alibori e nɔ̀ kpénukún tovixixa wǔ é kpo hɛnnu mɛ bo nɔ nyì do`
+3. `e ɖo lé e é mɛ xwédo 1 lɛ nukɔnnɔtɔ hwɛxo tɔn ayi e yovo hwan`
+
+**Context Size 2:**
+
+1. `tɔn mɛ ɖò totaligbé gbadahweji benɛɛtò tɔn lɛ mi na mɔ xogbè to ɔ tɔn ɖo tantɔn`
+2. `mɛ ɖo benɛ ayi e jì é ɖěè lěè akpɔkpɔ ɖé ɖe ɔ è sɔ ɛ ɖɛmɛnu`
+3. `mɛ e lɛ́zun gletoxo do sɛ̀nxwĭ jí sin azan ayizin 6 xwejisùn léxwé tɔn mɛ toxoɖɔgbɛ tɔn`
+
+**Context Size 3:**
+
+1. `tɔn mɛ ɖo atacora e lɛ́ nyi gletoxo do sɛ̀nxwĭ jí sin azan ayizin 6 xwejisùn lé xwélé`
+2. `jì é ɖěè zinvie ɖo tokpɔnlavi zinvié tɔn mɛ ɖo benɛɛto mɛ bo nyi sɔmi sɔmi ɖɛ̌mɛnu lɛ`
+3. `ayi e jì é ɖěè tokpɔnlávì tayaku tɔn ɔ nyi tokpɔnlavi ɖokpo ɖo wò 10 ě ɖo tokpɔnla`
+
+**Context Size 4:**
+
+1. `ayi e jì é ɖěè dovogon ɖo tokpɔnlavi zogbodomey tɔn mɛ ɖo zou e lɛ́ nyí gletoxo ɖò sɛ̀nxwí`
+2. `e jì é ɖěè bouhanrou ɖo tokpɔnlavi gomparou tɔn mɛ ɖo alibori e lɛ́ nyi gletoxo ɖo sɛ̀nxwĭ jí`
+3. `e ɖěè mɛ e axɔsuxwe insae instad e nɔ̀n kpé nunkún tovixixa wǔ é lɛn xɔta 248 nǔ gbɛtɔ`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_be,._ɖo_ɖěè_e"_`
+2. `nɔn_kuɖoudoku_to`
+3. `o_é_mbe_gblɛn_ɖò`
+
+**Context Size 2:**
+
+1. `n_kan)_xɔtan_è_ɖo`
+2. `o_tɔnla_akanɖie_ɖ`
+3. `_tokpé_dodo_tɛntr`
+
+**Context Size 3:**
+
+1. `ɔn_atlant_dolore_t`
+2. `tɔn_ɖó_azinkpo_ɔ,_`
+3. `_tɔn_ɔ_tɔn_léxwé_d`
+
+**Context Size 4:**
+
+1. `_tɔn._ɖo_tokpɔn_atu`
+2. `tɔn_lɛ_sin_azǎn_20ɔ`
+3. `_ɖo_tokpɔnlavi_tɔn,`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (98,034 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 | 11,148 |
+| Total Tokens | 363,048 |
+| Mean Frequency | 32.57 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 405.71 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | tɔn | 23,451 |
+| 2 | ɖo | 16,822 |
+| 3 | e | 15,001 |
+| 4 | mɛ | 14,011 |
+| 5 | é | 10,488 |
+| 6 | ɔ | 10,251 |
+| 7 | lɛ | 8,160 |
+| 8 | nyi | 5,259 |
+| 9 | nɔ | 5,214 |
+| 10 | ɖò | 4,492 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | rust | 2 |
+| 2 | gnu | 2 |
+| 3 | programme | 2 |
+| 4 | java | 2 |
+| 5 | api | 2 |
+| 6 | columns | 2 |
+| 7 | break | 2 |
+| 8 | inside | 2 |
+| 9 | avoid | 2 |
+| 10 | greek | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1833 |
+| R² (Goodness of Fit) | 0.993854 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 63.7% |
+| Top 1,000 | 86.2% |
+| Top 5,000 | 95.8% |
+| Top 10,000 | 99.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9939 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 63.7% of corpus
+- **Long Tail:** 1,148 words needed for remaining 0.6% 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 | 0.6254 🏆 | 0.3950 | N/A | N/A |
+| **mono_64d** | 64 | 0.3309 | 0.3691 | N/A | N/A |
+| **mono_128d** | 128 | 0.0582 | 0.3829 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6254 | 0.3991 | 0.0100 | 0.1180 |
+| **aligned_64d** | 64 | 0.3309 | 0.3687 | 0.0300 | 0.1420 |
+| **aligned_128d** | 128 | 0.0582 | 0.3777 | 0.0520 | 0.2300 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.6254 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3821. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 5.2% 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 | **0.364** | High formulaic/idiomatic 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 |
+|--------|----------|
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-mɛ` | akwɛnyanumɛ, mimɛ, wùnmɛ |
+
+### 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 |
+|------|----------|------------------|----------|
+| `okpo` | 1.55x | 21 contexts | xokpo, yokpo, lokpo |
+| `ɖokp` | 1.57x | 16 contexts | ɖokpɔ, ɖokpò, ɖokpó |
+| `ɔnyi` | 1.72x | 12 contexts | sɔnyi, lɔnyiji, ɖɔnyitɔ |
+| `plɔn` | 1.72x | 12 contexts | kplɔn, kplɔnnǔ, kplɔnyi |
+| `mɛnu` | 1.74x | 10 contexts | dɛmɛnu, wemɛnu, ɖɛmɛnu |
+| `ntɔn` | 1.41x | 16 contexts | tantɔn, tǎntɔn, xɔntɔn |
+| `ligb` | 1.67x | 9 contexts | aligbo, taligbé, taligbe |
+| `pɔnl` | 1.58x | 10 contexts | kpɔnla, tokpɔnlá, tòkpɔnlà |
+| `hwen` | 1.42x | 13 contexts | hwenù, hwenu, hwenú |
+| `igbe` | 1.53x | 10 contexts | jigbe, yigbe, igbere |
+| `ukun` | 1.53x | 9 contexts | wukun, nukun, bukunbé |
+| `tokp` | 1.59x | 8 contexts | tokpn, tokpo, tokpa |
+
+### 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.
+
+*No significant affix co-occurrences detected.*
+
+
+### 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 |
+|------|-----------------|------------|------|
+| liberiatòmɛ | **`liberiatò-mɛ`** | 4.5 | `liberiatò` |
+| gabɔntomɛ | **`gabɔnto-mɛ`** | 4.5 | `gabɔnto` |
+| jɔwunjɔjamɛ | **`jɔwunjɔja-mɛ`** | 4.5 | `jɔwunjɔja` |
+| flanségbèmɛ | **`flanségbè-mɛ`** | 4.5 | `flanségbè` |
+| kplekplemɛ | **`kplekple-mɛ`** | 4.5 | `kplekple` |
+| flanségbémɛ | **`flanségbé-mɛ`** | 4.5 | `flanségbé` |
+| senegaltòmɛ | **`senegaltò-mɛ`** | 4.5 | `senegaltò` |
+| flansetomɛ | **`flanseto-mɛ`** | 4.5 | `flanseto` |
+| kplékplémɛ | **`kplékplé-mɛ`** | 4.5 | `kplékplé` |
+| avɔɖesinukunmɛ | **`avɔɖesinukun-mɛ`** | 1.5 | `avɔɖesinukun` |
+| zogbodomɛ | **`zogbodo-mɛ`** | 1.5 | `zogbodo` |
+| nùkplɔnmɛ | **`nùkplɔn-mɛ`** | 1.5 | `nùkplɔn` |
+| kotoklomɛ | **`kotoklo-mɛ`** | 1.5 | `kotoklo` |
+| adakplamɛ | **`adakpla-mɛ`** | 1.5 | `adakpla` |
+| azɔnzunmɛ | **`azɔnzun-mɛ`** | 1.5 | `azɔnzun` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Fon shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.12x) |
+| N-gram | **2-gram** | Lowest perplexity (265) |
+| Markov | **Context-4** | Highest predictability (95.3%) |
+| 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: 2026-01-04 14:47:03*
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diff --git a/models/tokenizer/fon_tokenizer_32k.model b/models/tokenizer/fon_tokenizer_32k.model
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diff --git a/models/tokenizer/fon_tokenizer_32k.vocab b/models/tokenizer/fon_tokenizer_32k.vocab
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diff --git a/models/tokenizer/fon_tokenizer_64k.vocab b/models/tokenizer/fon_tokenizer_64k.vocab
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diff --git a/models/tokenizer/fon_tokenizer_8k.model b/models/tokenizer/fon_tokenizer_8k.model
new file mode 100644
index 0000000000000000000000000000000000000000..660cd019c8b1e9e9e3b2277260f16bc28332380d
--- /dev/null
+++ b/models/tokenizer/fon_tokenizer_8k.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/tokenizer/fon_tokenizer_8k.vocab b/models/tokenizer/fon_tokenizer_8k.vocab
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