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+---
+language: ha
+language_name: Hausa
+language_family: chadic
+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-chadic
+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: 4.398
+ - name: best_isotropy
+ type: isotropy
+ value: 0.8106
+ - name: vocabulary_size
+ type: vocab
+ value: 0
+generated: 2026-01-10
+---
+
+# Hausa - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Hausa** 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
+
+
+
+### 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
+
+
+
+
+
+
+
+
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 3.763x | 3.76 | 0.2087% | 416,305 |
+| **16k** | 4.047x | 4.05 | 0.2245% | 387,089 |
+| **32k** | 4.258x | 4.26 | 0.2362% | 367,890 |
+| **64k** | 4.398x 🏆 | 4.40 | 0.2440% | 356,119 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Luke Ashworth (an haife shi a shekara ta shi ne dan wasan ƙwallon ƙafa ta ƙasar ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁l uke ▁ash worth ▁( an ▁haife ▁shi ▁a ▁shekara ... (+18 more)` | 28 |
+| 16k | `▁l uke ▁ash worth ▁( an ▁haife ▁shi ▁a ▁shekara ... (+18 more)` | 28 |
+| 32k | `▁luke ▁ash worth ▁( an ▁haife ▁shi ▁a ▁shekara ▁ta ... (+17 more)` | 27 |
+| 64k | `▁luke ▁ashworth ▁( an ▁haife ▁shi ▁a ▁shekara ▁ta ▁shi ... (+16 more)` | 26 |
+
+**Sample 2:** `Joshua Ogunlola (an haife shi 19 Afrilu ɗan wasan cricket ne na Najeriya . Ya bu...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁jo shua ▁ogun lo la ▁( an ▁haife ▁shi ▁ ... (+23 more)` | 33 |
+| 16k | `▁joshua ▁ogun lola ▁( an ▁haife ▁shi ▁ 1 9 ... (+21 more)` | 31 |
+| 32k | `▁joshua ▁ogun lola ▁( an ▁haife ▁shi ▁ 1 9 ... (+21 more)` | 31 |
+| 64k | `▁joshua ▁ogun lola ▁( an ▁haife ▁shi ▁ 1 9 ... (+21 more)` | 31 |
+
+**Sample 3:** `Roland Omoruyi (an haife shi 5 ga watan Yuni ɗan damben Najeriya ne. Yayi gasa a...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁r oland ▁om or u yi ▁( an ▁haife ▁shi ... (+22 more)` | 32 |
+| 16k | `▁roland ▁om or u yi ▁( an ▁haife ▁shi ▁ ... (+21 more)` | 31 |
+| 32k | `▁roland ▁om oru yi ▁( an ▁haife ▁shi ▁ 5 ... (+20 more)` | 30 |
+| 64k | `▁roland ▁om oru yi ▁( an ▁haife ▁shi ▁ 5 ... (+20 more)` | 30 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.398x compression
+- **Lowest UNK Rate:** 8k with 0.2087% 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
+
+
+
+
+
+
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 49,621 | 15.60 | 604,355 | 12.3% | 29.9% |
+| **2-gram** | Subword | 196 🏆 | 7.61 | 13,430 | 74.9% | 99.3% |
+| **3-gram** | Word | 290,081 | 18.15 | 1,505,795 | 4.6% | 13.9% |
+| **3-gram** | Subword | 1,547 | 10.60 | 97,163 | 36.1% | 78.3% |
+| **4-gram** | Word | 898,959 | 19.78 | 2,859,421 | 2.8% | 8.4% |
+| **4-gram** | Subword | 8,574 | 13.07 | 534,835 | 17.2% | 50.0% |
+| **5-gram** | Word | 876,152 | 19.74 | 2,080,226 | 2.6% | 7.9% |
+| **5-gram** | Subword | 33,589 | 15.04 | 1,728,117 | 9.7% | 31.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a cikin` | 313,998 |
+| 2 | `tare da` | 141,234 |
+| 3 | `a matsayin` | 130,861 |
+| 4 | `da aka` | 106,305 |
+| 5 | `da kuma` | 89,834 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a shekara ta` | 43,773 |
+| 2 | `ci gaba da` | 25,571 |
+| 3 | `da ba a` | 20,387 |
+| 4 | `an haife shi` | 20,273 |
+| 5 | `afirka ta kudu` | 17,311 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `archived from the original` | 15,473 |
+| 2 | `from the original on` | 15,162 |
+| 3 | `an haife shi a` | 14,183 |
+| 4 | `fassarorin da ba a` | 13,066 |
+| 5 | `masu fassarorin da ba` | 13,066 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `archived from the original on` | 14,682 |
+| 2 | `fassarorin da ba a duba` | 13,066 |
+| 3 | `masu fassarorin da ba a` | 13,066 |
+| 4 | `da ba a duba ba` | 13,065 |
+| 5 | `an haife shi a ranar` | 5,602 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 13,901,672 |
+| 2 | `n _` | 6,669,315 |
+| 3 | `a n` | 6,077,508 |
+| 4 | `a r` | 5,295,640 |
+| 5 | `d a` | 4,369,505 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d a` | 3,204,702 |
+| 2 | `d a _` | 3,036,418 |
+| 3 | `i n _` | 2,924,187 |
+| 4 | `a n _` | 2,144,471 |
+| 5 | `a r _` | 2,066,174 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d a _` | 2,454,989 |
+| 2 | `_ n a _` | 991,541 |
+| 3 | `a _ d a` | 987,768 |
+| 4 | `_ t a _` | 853,598 |
+| 5 | `a _ t a` | 717,349 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ d a _` | 720,468 |
+| 2 | `i k i n _` | 496,368 |
+| 3 | `_ c i k i` | 458,937 |
+| 4 | `a _ t a _` | 441,174 |
+| 5 | `c i k i n` | 435,066 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 196
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~31% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+
+
+
+
+
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.8863 | 1.848 | 10.46 | 661,201 | 11.4% |
+| **1** | Subword | 1.0685 | 2.097 | 6.96 | 7,221 | 0.0% |
+| **2** | Word | 0.3948 | 1.315 | 2.52 | 6,908,013 | 60.5% |
+| **2** | Subword | 0.7292 | 1.658 | 4.69 | 50,274 | 27.1% |
+| **3** | Word | 0.2061 | 1.154 | 1.53 | 17,415,052 | 79.4% |
+| **3** | Subword | 0.7187 | 1.646 | 4.06 | 235,540 | 28.1% |
+| **4** | Word | 0.1035 🏆 | 1.074 | 1.21 | 26,662,755 | 89.6% |
+| **4** | Subword | 0.6831 | 1.606 | 3.40 | 956,556 | 31.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `da sojojin kasar ke iyakance ma aunin cinikayya da alaƙa da duniya cambridge ta kuma wani`
+2. `a kwalejin fort douteuse manazarta nijar da jama a shekara ta bi na wanda aka gudanar`
+3. `na shekara ta everett dutton jump gable ray choto an tsare ta wannan baya kudancin tasman`
+
+**Context Size 2:**
+
+1. `a cikin alal misali ƙwararrun hindu sun nuna cewa suna adawa da shi 23 da kwallaye 26`
+2. `tare da ƙungiyar ƙwallon ƙafa a ƙayyadaddun su ba bisa ka ida ba ta koma tare da`
+3. `a matsayin mai ba da masauki a kowane yanayi taimako ga peter da saint pons de thomières`
+
+**Context Size 3:**
+
+1. `a shekara ta larabci غالية شاكر mawaƙi ne ɗan ƙasar ghana wanda ke taka leda a matsayin ɗan`
+2. `ci gaba da amfani duk da wannan karuwar kwanan nan a cikin ya ya shida na yusufu da`
+3. `da ba a duba ba wasan kwaikwawo ta kudu`
+
+**Context Size 4:**
+
+1. `archived from the original on 4 march retrieved 23 january ita ce shekara ta goma sha tara a saman`
+2. `from the original on retrieved october 1 dajin yana wurin zama ga nau in ruwa da na kogi da`
+3. `an haife shi a shekara ta ɗan siyasan najeriya ne daga jihar yobe a yankin arewa maso gabas cen`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ar_ar_yandu_t_am`
+2. `_chea_ƴa_ctar_ki`
+3. `n_aya_ar_su,_don`
+
+**Context Size 2:**
+
+1. `a_sc_ake_gwa_gayu`
+2. `n_re_que_ta_redea`
+3. `an_in_huga_cikar_`
+
+**Context Size 3:**
+
+1. `_daidaraktanin_tsa`
+2. `da_ya_kuma_na_doka`
+3. `in_mallace_takewac`
+
+**Context Size 4:**
+
+1. `_da_za_manazartar_a`
+2. `_na_mai_don_a_kansa`
+3. `a_da_no._632._an_fo`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 89.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (956,556 contexts)
+- **Recommendation:** Context-3 or Context-4 for text generation
+
+---
+## 4. Vocabulary Analysis
+
+
+
+
+
+
+
+### Statistics
+
+| Metric | Value |
+|--------|-------|
+| Vocabulary Size | 289,201 |
+| Total Tokens | 38,460,059 |
+| Mean Frequency | 132.99 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 6762.57 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | da | 2,472,553 |
+| 2 | a | 1,750,033 |
+| 3 | na | 1,000,437 |
+| 4 | ta | 870,013 |
+| 5 | ya | 735,582 |
+| 6 | kuma | 428,826 |
+| 7 | cikin | 427,094 |
+| 8 | ba | 345,573 |
+| 9 | an | 263,110 |
+| 10 | daga | 256,194 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | lakisha | 2 |
+| 2 | tanish | 2 |
+| 3 | katakanaタニシャ | 2 |
+| 4 | tanishia | 2 |
+| 5 | tinisha | 2 |
+| 6 | tír | 2 |
+| 7 | sunami | 2 |
+| 8 | mamis | 2 |
+| 9 | mywo | 2 |
+| 10 | iyaz | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2631 |
+| R² (Goodness of Fit) | 0.985164 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 43.1% |
+| Top 1,000 | 71.6% |
+| Top 5,000 | 87.4% |
+| Top 10,000 | 91.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9852 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 43.1% of corpus
+- **Long Tail:** 279,201 words needed for remaining 8.6% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+
+
+
+
+
+
+
+
+
+### 5.1 Cross-Lingual Alignment
+
+
+
+
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.8106 | 0.4067 | N/A | N/A |
+| **mono_64d** | 64 | 0.7783 | 0.3527 | N/A | N/A |
+| **mono_128d** | 128 | 0.6921 | 0.2853 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8106 🏆 | 0.3959 | 0.3320 | 0.7500 |
+| **aligned_64d** | 64 | 0.7783 | 0.3627 | 0.5680 | 0.8980 |
+| **aligned_128d** | 128 | 0.6921 | 0.3062 | 0.6520 | 0.9100 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8106 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3516. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 65.2% 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 | **-0.749** | 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 |
+|--------|----------|
+| `-a` | adéọlá, andros, a9 |
+| `-ma` | mahbubani, mackandal, madejski |
+| `-s` | spahis, songulashvili, srw |
+| `-m` | mohie, mufassir, mahbubani |
+| `-n` | nnung, naturist, nogomania |
+| `-b` | bachtarzi, bosley, barbashi |
+| `-k` | kwararawar, kantako, kalaman |
+| `-ba` | bachtarzi, barbashi, balar |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | tsarkakarta, gunilla, ejeagha |
+| `-s` | conscripts, chucks, spahis |
+| `-e` | coatesville, paleotemperature, renfrewshire |
+| `-n` | lallausan, incan, hakannan |
+| `-i` | empangeni, bachtarzi, barbashi |
+| `-r` | kwararawar, balar, mufassir |
+| `-o` | derzhkino, vio, kantako |
+| `-an` | lallausan, incan, hakannan |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ekar` | 2.65x | 71 contexts | ekara, lekar, sekara |
+| `ungi` | 2.31x | 129 contexts | bungi, fungi, lungi |
+| `ngiy` | 2.51x | 74 contexts | ungiya, tangiya, ungiyar |
+| `afir` | 2.80x | 41 contexts | kafir, afire, afira |
+| `heka` | 2.48x | 64 contexts | sheka, bheka, cheka |
+| `atio` | 2.30x | 89 contexts | ratio, patio, natio |
+| `eriy` | 2.31x | 44 contexts | eriyo, eriya, teriy |
+| `anay` | 2.31x | 41 contexts | anayi, anaya, anaye |
+| `nyar` | 2.01x | 54 contexts | nyara, nyari, cinyar |
+| `amfa` | 2.30x | 32 contexts | amfan, camfa, amfar |
+| `arsh` | 1.75x | 95 contexts | warsh, karsh, arsht |
+| `bban` | 2.12x | 42 contexts | abban, dabban, kibban |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-s` | `-a` | 89 words | sonaiya, skikda |
+| `-k` | `-a` | 84 words | kwatankwacinsa, kadiyawa |
+| `-a` | `-a` | 79 words | adaora, aña |
+| `-a` | `-e` | 66 words | alane, aggiunte |
+| `-b` | `-a` | 63 words | brunhilda, barasa |
+| `-s` | `-e` | 59 words | sinninghe, serere |
+| `-ma` | `-a` | 58 words | mashogwawara, maikusa |
+| `-t` | `-a` | 53 words | taila, tcha |
+| `-a` | `-s` | 52 words | aidas, agnews |
+| `-m` | `-a` | 52 words | mujica, musina |
+
+### 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 |
+|------|-----------------|------------|------|
+| omanawanui | **`omanawan-u-i`** | 7.5 | `u` |
+| chickpeas | **`chickpe-a-s`** | 7.5 | `a` |
+| chieveley | **`chievel-e-y`** | 7.5 | `e` |
+| bunamwaya | **`bunamw-a-ya`** | 7.5 | `a` |
+| manawashi | **`ma-na-washi`** | 7.5 | `washi` |
+| zamaninsa | **`zamanin-s-a`** | 7.5 | `s` |
+| tanacikin | **`ta-na-cikin`** | 7.5 | `cikin` |
+| fortalezas | **`fortalez-a-s`** | 7.5 | `a` |
+| bangarensa | **`bangaren-s-a`** | 7.5 | `s` |
+| equalizing | **`equaliz-i-ng`** | 7.5 | `i` |
+| abdulwahid | **`abdulwah-i-d`** | 7.5 | `i` |
+| rangitata | **`rangi-ta-ta`** | 7.5 | `ta` |
+| parkinsons | **`parkins-on-s`** | 6.0 | `parkins` |
+| almajiran | **`al-ma-jiran`** | 6.0 | `jiran` |
+| finalises | **`final-is-es`** | 6.0 | `final` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Hausa 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
+
+
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.40x) |
+| N-gram | **2-gram** | Lowest perplexity (196) |
+| Markov | **Context-4** | Highest predictability (89.6%) |
+| 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 03:18:39*
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