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+---
+language: fat
+language_name: Fanti
+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: 4.360
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8158
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-04
+---
+
+# Fanti - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Fanti** 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** | 3.878x | 3.88 | 0.0773% | 470,672 |
+| **16k** | 4.117x | 4.12 | 0.0821% | 443,393 |
+| **32k** | 4.264x | 4.27 | 0.0850% | 428,052 |
+| **64k** | 4.360x 🏆 | 4.36 | 0.0870% | 418,619 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Bishop Herman Nsɔwdo Skuul, a wɔsan frɛ no BIHECO yɛ mbanyin skuul a ɔwɔ Kpando ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁bishop ▁her man ▁nsɔwdo ▁skuul , ▁a ▁wɔsan ▁frɛ ▁no ... (+23 more)` | 33 |
+| 16k | `▁bishop ▁herman ▁nsɔwdo ▁skuul , ▁a ▁wɔsan ▁frɛ ▁no ▁bi ... (+22 more)` | 32 |
+| 32k | `▁bishop ▁herman ▁nsɔwdo ▁skuul , ▁a ▁wɔsan ▁frɛ ▁no ▁bi ... (+22 more)` | 32 |
+| 64k | `▁bishop ▁herman ▁nsɔwdo ▁skuul , ▁a ▁wɔsan ▁frɛ ▁no ▁biheco ... (+20 more)` | 30 |
+
+**Sample 2:** `St. Monica's Senior High School yɛ mbasiafo nsɔwdo skuul a ɔwɔ Mampong wɔ Esuant...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁st . ▁monica ' s ▁senior ▁high ▁school ▁yɛ ▁mbasiafo ... (+12 more)` | 22 |
+| 16k | `▁st . ▁monica ' s ▁senior ▁high ▁school ▁yɛ ▁mbasiafo ... (+12 more)` | 22 |
+| 32k | `▁st . ▁monica ' s ▁senior ▁high ▁school ▁yɛ ▁mbasiafo ... (+12 more)` | 22 |
+| 64k | `▁st . ▁monica ' s ▁senior ▁high ▁school ▁yɛ ▁mbasiafo ... (+12 more)` | 22 |
+
+**Sample 3:** `Sherry Ayittey (wɔwoo no yɛ Ghananyi biochemist, amanyɛnyi na mbasiafo ntamgyina...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁sh er ry ▁ayi t tey ▁( wɔwoo ▁no ▁yɛ ... (+12 more)` | 22 |
+| 16k | `▁sh er ry ▁ayi t tey ▁( wɔwoo ▁no ▁yɛ ... (+10 more)` | 20 |
+| 32k | `▁sherry ▁ayittey ▁( wɔwoo ▁no ▁yɛ ▁ghananyi ▁biochemist , ▁amanyɛnyi ... (+4 more)` | 14 |
+| 64k | `▁sherry ▁ayittey ▁( wɔwoo ▁no ▁yɛ ▁ghananyi ▁biochemist , ▁amanyɛnyi ... (+4 more)` | 14 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.360x compression
+- **Lowest UNK Rate:** 8k with 0.0773% 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 | 4,526 | 12.14 | 15,127 | 23.6% | 52.6% |
+| **2-gram** | Subword | 248 🏆 | 7.95 | 1,938 | 67.4% | 99.6% |
+| **3-gram** | Word | 9,962 | 13.28 | 23,467 | 14.2% | 38.0% |
+| **3-gram** | Subword | 1,776 | 10.79 | 15,671 | 30.6% | 75.8% |
+| **4-gram** | Word | 18,783 | 14.20 | 36,546 | 9.7% | 28.4% |
+| **4-gram** | Subword | 7,938 | 12.95 | 70,574 | 17.1% | 48.4% |
+| **5-gram** | Word | 15,862 | 13.95 | 25,853 | 8.7% | 27.5% |
+| **5-gram** | Subword | 21,806 | 14.41 | 152,670 | 11.1% | 34.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `no mu` | 5,071 |
+| 2 | `mu wɔ` | 3,646 |
+| 3 | `a ɔwɔ` | 3,608 |
+| 4 | `wɔ afe` | 3,273 |
+| 5 | `mu no` | 3,153 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wɔ afe mu` | 1,655 |
+| 2 | `a ɔtɔ do` | 1,549 |
+| 3 | `mu wɔ ghana` | 1,277 |
+| 4 | `mantɔw mu wɔ` | 1,012 |
+| 5 | `afe mu no` | 926 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mantɔw mu wɔ ghana` | 820 |
+| 2 | `a ɔtɔ do anan` | 604 |
+| 3 | `wɔ afe mu no` | 460 |
+| 4 | `mbrahyɛbagua a ɔtɔ do` | 370 |
+| 5 | `a ogyina hɔ ma` | 356 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wɔ mbrahyɛbagua a ɔtɔ do` | 207 |
+| 2 | `a ɔtɔ do anan 4` | 169 |
+| 3 | `a ɔtɔ do anan no` | 167 |
+| 4 | `a ɔtɔ do anan mu` | 155 |
+| 5 | `ghana amansan abatow no mu` | 151 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 136,241 |
+| 2 | `_ a` | 102,913 |
+| 3 | `_ n` | 97,571 |
+| 4 | `a n` | 64,359 |
+| 5 | `o _` | 62,300 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ w ɔ` | 39,784 |
+| 2 | `_ a _` | 32,667 |
+| 3 | `n a _` | 32,487 |
+| 4 | `w ɔ _` | 31,620 |
+| 5 | `_ n o` | 30,963 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ w ɔ _` | 26,115 |
+| 2 | `_ n o _` | 24,203 |
+| 3 | `_ n a _` | 18,686 |
+| 4 | `_ m u _` | 15,392 |
+| 5 | `_ a _ ɔ` | 13,463 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `g h a n a` | 9,543 |
+| 2 | `_ g h a n` | 9,134 |
+| 3 | `_ w ɔ _ a` | 6,691 |
+| 4 | `_ a _ w ɔ` | 6,509 |
+| 5 | `h a n a _` | 6,384 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 248
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~35% 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 | 0.8862 | 1.848 | 5.88 | 39,173 | 11.4% |
+| **1** | Subword | 0.9055 | 1.873 | 6.35 | 857 | 9.5% |
+| **2** | Word | 0.3085 | 1.238 | 1.80 | 229,817 | 69.1% |
+| **2** | Subword | 0.9056 | 1.873 | 5.56 | 5,435 | 9.4% |
+| **3** | Word | 0.1281 | 1.093 | 1.24 | 411,747 | 87.2% |
+| **3** | Subword | 0.8400 | 1.790 | 3.99 | 30,194 | 16.0% |
+| **4** | Word | 0.0556 🏆 | 1.039 | 1.09 | 508,226 | 94.4% |
+| **4** | Subword | 0.6158 | 1.532 | 2.58 | 120,416 | 38.4% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a no ono na ɔyɛ hausa kooko ahorow efi etsifi mantɔwmu grace omaboe maame nna ɔko`
+2. `no odzii nyia ɔnye dwodze a na ɔka ho esian wɔ hɔ na ebien nsɔwdo skuul`
+3. `wɔ ɔberɛfɛw mu maa fomena mpasuar wɔ ablekuma west african bush and entrepreneur citation needed wɔk...`
+
+**Context Size 2:**
+
+1. `no mu a netflix kyerɛwtohɔ no mu no bosoom sanda mu wɔ sunyani polytechnic ɔsanso wɔ mba`
+2. `mu wɔ ghana mbrahyɛbagua ambato mu no wɔpaaw no dɛ house prefect wɔ pickard parker house wɔ`
+3. `a ɔwɔ mpɔtamu hɔ nye pan african forum pan african mbrahyɛbagua no munyi a ɔgyina hɔ ma`
+
+**Context Size 3:**
+
+1. `wɔ afe mu edwuma namoale yɛ kuadwuma ho ɔbenfo agronomist wɔ n edwuma mu lawyer by profession amanyɛ...`
+2. `a ɔtɔ do anan no mbrahyɛbagua a ɔdzi kan a ɔdzii amanyɛsɛm kuw kɛse bi enyim wɔ ghana`
+3. `mu wɔ ghana onyaa ne bachelor of education abɔdzin krataa wɔ ghana institute of journalism na ɔbɔɔ n`
+
+**Context Size 4:**
+
+1. `mantɔw mu wɔ ghana wɔ mbrahyɛbagua a ɔtɔ do akrɔn a ɔwɔ fourth republic no mu wɔ ghana dze`
+2. `a ɔtɔ do anan 4ɔ no mbrahyɛ bagua a ɔtɔ do enum 5 wɔ ghana amansin a ɔtɔ do`
+3. `wɔ afe mu no skuul no hyɛase dze hɔn ho hyɛɛ nkɔmbɔdzi na ɔyɛkyerɛ a mu ahyɛse no nhyiamu`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ahyesak._ɔyinit`
+2. `ana_ɔro_ɔ_muer_f`
+3. `no_a_poonarafamu`
+
+**Context Size 2:**
+
+1. `a_nyimadzii_yɛ_fo`
+2. `_abagen_yɔsoseens`
+3. `_nna_oso_antakyɛb`
+
+**Context Size 3:**
+
+1. `_wɔyɛ_gholicturany`
+2. `_a_ɔkyekunyi_nyim_`
+3. `na_ma_yi_no_no_mum`
+
+**Context Size 4:**
+
+1. `_wɔ_sempɔnhen_ho_ɔs`
+2. `_no_so_boayikuw_no_`
+3. `_na_ɔyɛ_ato_no_ekyi`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (120,416 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 | 18,588 |
+| Total Tokens | 611,715 |
+| Mean Frequency | 32.91 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 474.47 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 33,761 |
+| 2 | no | 29,750 |
+| 3 | wɔ | 26,234 |
+| 4 | mu | 22,593 |
+| 5 | na | 18,784 |
+| 6 | ghana | 8,469 |
+| 7 | do | 7,315 |
+| 8 | dɛ | 7,230 |
+| 9 | ho | 5,744 |
+| 10 | afe | 5,715 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | tampuli | 2 |
+| 2 | gcpp | 2 |
+| 3 | akomeah | 2 |
+| 4 | miif | 2 |
+| 5 | agyapa | 2 |
+| 6 | sdo | 2 |
+| 7 | dzɛmdzi | 2 |
+| 8 | wta | 2 |
+| 9 | slam | 2 |
+| 10 | excision | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1854 |
+| R² (Goodness of Fit) | 0.994799 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 50.5% |
+| Top 1,000 | 77.9% |
+| Top 5,000 | 92.1% |
+| Top 10,000 | 96.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9948 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 50.5% of corpus
+- **Long Tail:** 8,588 words needed for remaining 3.3% 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 | 0.8158 | 0.3399 | N/A | N/A |
+| **mono_64d** | 64 | 0.6643 | 0.2886 | N/A | N/A |
+| **mono_128d** | 128 | 0.2510 | 0.2768 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8158 🏆 | 0.3415 | 0.0320 | 0.1880 |
+| **aligned_64d** | 64 | 0.6643 | 0.2904 | 0.0540 | 0.2820 |
+| **aligned_128d** | 128 | 0.2510 | 0.2769 | 0.0980 | 0.3500 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8158 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3023. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 9.8% 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 | **-0.206** | 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 |
+|--------|----------|
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-fo` | mamfo, skuulfo, nkontaabufo |
+
+### 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 |
+|------|----------|------------------|----------|
+| `yerɛ` | 1.92x | 49 contexts | kyerɛ, ɔkyerɛ, ɛkyerɛ |
+| `yina` | 1.87x | 52 contexts | oyina, gyina, nyina |
+| `gyin` | 1.86x | 44 contexts | egyin, gyina, agyin |
+| `wuma` | 1.82x | 38 contexts | dwuma, adwuma, edwuma |
+| `atio` | 1.94x | 17 contexts | ratio, nation, ratios |
+| `dwum` | 1.76x | 22 contexts | dwuma, adwuma, edwuma |
+| `kuul` | 2.22x | 11 contexts | skuul, skuuls, skuula |
+| `tion` | 1.78x | 17 contexts | nation, action, option |
+| `abat` | 1.97x | 11 contexts | abata, abato, abatoɔ |
+| `bato` | 1.93x | 11 contexts | abato, ambato, abatoɔ |
+| `brah` | 2.28x | 5 contexts | debrah, ibrahim, mbrahyɛ |
+| `pany` | 1.96x | 7 contexts | panyin, mpanyin, opanyin |
+
+### 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 |
+|------|-----------------|------------|------|
+| semeiskiefo | **`semeiskie-fo`** | 4.5 | `semeiskie` |
+| abedziekyirfo | **`abedziekyir-fo`** | 4.5 | `abedziekyir` |
+| britainfo | **`britain-fo`** | 4.5 | `britain` |
+| finlandfo | **`finland-fo`** | 4.5 | `finland` |
+| ekyingyefo | **`ekyingye-fo`** | 4.5 | `ekyingye` |
+| mpanyinfo | **`mpanyin-fo`** | 4.5 | `mpanyin` |
+| edwindzefo | **`edwindze-fo`** | 4.5 | `edwindze` |
+| albaniafo | **`albania-fo`** | 4.5 | `albania` |
+| turkmenfo | **`turkmen-fo`** | 4.5 | `turkmen` |
+| armeniafo | **`armenia-fo`** | 4.5 | `armenia` |
+| dagombafo | **`dagomba-fo`** | 4.5 | `dagomba` |
+| nyimdzefo | **`nyimdze-fo`** | 4.5 | `nyimdze` |
+| konyimdzifo | **`konyimdzi-fo`** | 4.5 | `konyimdzi` |
+| amandzebɔfo | **`amandzebɔ-fo`** | 4.5 | `amandzebɔ` |
+| akandzifo | **`akandzi-fo`** | 4.5 | `akandzi` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Fanti 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 (4.36x) |
+| N-gram | **2-gram** | Lowest perplexity (248) |
+| Markov | **Context-4** | Highest predictability (94.4%) |
+| 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 14:49:17*
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diff --git a/models/tokenizer/fat_tokenizer_8k.model b/models/tokenizer/fat_tokenizer_8k.model
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--- /dev/null
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diff --git a/models/tokenizer/fat_tokenizer_8k.vocab b/models/tokenizer/fat_tokenizer_8k.vocab
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