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README.md

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+---
+language: pcm
+language_name: Nigerian Pidgin
+language_family: germanic_west_anglofrisian
+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-germanic_west_anglofrisian
+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: 4.488
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6433
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Nigerian Pidgin - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Nigerian Pidgin** 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** | 3.887x | 3.89 | 0.0679% | 407,967 |
+| **16k** | 4.155x | 4.16 | 0.0726% | 381,687 |
+| **32k** | 4.347x | 4.35 | 0.0759% | 364,797 |
+| **64k** | 4.488x 🏆 | 4.49 | 0.0784% | 353,400 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Ikot Ibok na dey Nigerian village in the Etinan local government area of Akwa Ib...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ikot ▁ib ok ▁na ▁dey ▁nigerian ▁village ▁in ▁the ▁etinan ... (+8 more)` | 18 |
+| 16k | `▁ikot ▁ib ok ▁na ▁dey ▁nigerian ▁village ▁in ▁the ▁etinan ... (+8 more)` | 18 |
+| 32k | `▁ikot ▁ib ok ▁na ▁dey ▁nigerian ▁village ▁in ▁the ▁etinan ... (+8 more)` | 18 |
+| 64k | `▁ikot ▁ibok ▁na ▁dey ▁nigerian ▁village ▁in ▁the ▁etinan ▁local ... (+7 more)` | 17 |
+
+**Sample 2:** `Jigawa State na one of di 36 state for Naija. Di governor of di state na Badaru ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁j iga wa ▁state ▁na ▁one ▁of ▁di ▁ 3 ... (+20 more)` | 30 |
+| 16k | `▁jigawa ▁state ▁na ▁one ▁of ▁di ▁ 3 6 ▁state ... (+17 more)` | 27 |
+| 32k | `▁jigawa ▁state ▁na ▁one ▁of ▁di ▁ 3 6 ▁state ... (+16 more)` | 26 |
+| 64k | `▁jigawa ▁state ▁na ▁one ▁of ▁di ▁ 3 6 ▁state ... (+16 more)` | 26 |
+
+**Sample 3:** `Greensleeves na kultural song of som pipul in Ingland. Di song "What Child is th...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁gre ens le ev es ▁na ▁kult ural ▁song ▁of ... (+32 more)` | 42 |
+| 16k | `▁gre ens le eves ▁na ▁kult ural ▁song ▁of ▁som ... (+30 more)` | 40 |
+| 32k | `▁greensleeves ▁na ▁kultural ▁song ▁of ▁som ▁pipul ▁in ▁ingland . ... (+22 more)` | 32 |
+| 64k | `▁greensleeves ▁na ▁kultural ▁song ▁of ▁som ▁pipul ▁in ▁ingland . ... (+21 more)` | 31 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.488x compression
+- **Lowest UNK Rate:** 8k with 0.0679% 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 | 5,069 | 12.31 | 13,342 | 20.2% | 49.4% |
+| **2-gram** | Subword | 249 🏆 | 7.96 | 1,956 | 69.0% | 99.6% |
+| **3-gram** | Word | 9,350 | 13.19 | 16,820 | 12.8% | 34.7% |
+| **3-gram** | Subword | 2,025 | 10.98 | 14,262 | 26.7% | 73.0% |
+| **4-gram** | Word | 14,669 | 13.84 | 22,527 | 10.2% | 25.9% |
+| **4-gram** | Subword | 10,389 | 13.34 | 66,396 | 14.2% | 40.4% |
+| **5-gram** | Word | 8,268 | 13.01 | 11,704 | 12.1% | 31.0% |
+| **5-gram** | Subword | 32,215 | 14.98 | 156,798 | 8.7% | 27.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wey dey` | 2,591 |
+| 2 | `for di` | 2,440 |
+| 3 | `of di` | 2,155 |
+| 4 | `wey dem` | 1,785 |
+| 5 | `dem bon` | 1,401 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dem bon am` | 620 |
+| 2 | `how e tek` | 619 |
+| 3 | `wey dem dey` | 420 |
+| 4 | `wey dem bon` | 382 |
+| 5 | `bon am for` | 369 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dem bon am for` | 356 |
+| 2 | `dem gada di tori` | 337 |
+| 3 | `wey dem bon for` | 337 |
+| 4 | `e tek stat life` | 241 |
+| 5 | `how e tek stat` | 219 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `wie dem gada di tori` | 193 |
+| 2 | `how e tek stat life` | 179 |
+| 3 | `wia dem gada di tori` | 139 |
+| 4 | `e tek stat life an` | 108 |
+| 5 | `di tori abaut pipul life` | 80 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d` | 59,136 |
+| 2 | `n _` | 51,333 |
+| 3 | `e _` | 50,359 |
+| 4 | `_ a` | 49,736 |
+| 5 | `i _` | 45,649 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e y _` | 29,288 |
+| 2 | `_ d e` | 23,834 |
+| 3 | `_ d i` | 23,563 |
+| 4 | `_ f o` | 23,098 |
+| 5 | `o r _` | 23,038 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `f o r _` | 19,631 |
+| 2 | `_ f o r` | 19,386 |
+| 3 | `_ d i _` | 18,549 |
+| 4 | `w e y _` | 13,808 |
+| 5 | `_ w e y` | 13,590 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ f o r _` | 18,549 |
+| 2 | `_ w e y _` | 13,534 |
+| 3 | `_ d e y _` | 12,165 |
+| 4 | `_ d e m _` | 7,432 |
+| 5 | `w e y _ d` | 5,568 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 249
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~27% 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 | 0.9199 | 1.892 | 6.12 | 38,924 | 8.0% |
+| **1** | Subword | 1.3238 | 2.503 | 11.10 | 395 | 0.0% |
+| **2** | Word | 0.3059 | 1.236 | 1.74 | 237,554 | 69.4% |
+| **2** | Subword | 1.0876 | 2.125 | 6.38 | 4,381 | 0.0% |
+| **3** | Word | 0.1110 | 1.080 | 1.18 | 411,842 | 88.9% |
+| **3** | Subword | 0.8589 | 1.814 | 4.11 | 27,929 | 14.1% |
+| **4** | Word | 0.0379 🏆 | 1.027 | 1.05 | 486,600 | 96.2% |
+| **4** | Subword | 0.6482 | 1.567 | 2.73 | 114,736 | 35.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `di buk i rich an som of oxford gardens na one wuman too how e for`
+2. `for na november na im hav bin liv air lahore an octave one wey bi boy`
+3. `wey lead and university of empires for folkmuzik of lanzarote on 8 goals in naijá for`
+
+**Context Size 2:**
+
+1. `wey dey stodi difren difren instrument wey dem dey uze to tek mek buk wey shi dey`
+2. `for di american folklore center`
+3. `of di futbol klub wey di nem na tørris toresen dey bon am for e honor dem`
+
+**Context Size 3:**
+
+1. `dem bon am on 19 august na pesin wey no get promoshon sins david mark tel dem sey`
+2. `how e tek do fashon pared ukah fest stat fashon pared in wen e be 18 years for`
+3. `wey dem dey also call argungu dance festival na one festival inside kebbi state plus including oda n...`
+
+**Context Size 4:**
+
+1. `dem bon am for e bi naijá singa olamide david e bi naijá man pikin akto olamide faison dem`
+2. `wey dem bon for for naija`
+3. `dem gada di tori pipul wuman wey dem bon for wey kpai for pipul politishan abaut pipul life`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_bey_l_s,_m_wene`
+2. `e_deman_pelisoma`
+3. `anetatbofar_pllf`
+
+**Context Size 2:**
+
+1. `_didon,_an_an_bik`
+2. `n_em_ti_pai_dem_h`
+3. `e_bon,_p.shan_shi`
+
+**Context Size 3:**
+
+1. `ey_sout._na_engin_`
+2. `_dey_oyo_e_kar_for`
+3. `_dis_for_unival_an`
+
+**Context Size 4:**
+
+1. `for_babatunder-17_c`
+2. `_for_dey_rili_la_li`
+3. `_di_aablanker_di_pe`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (114,736 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 | 18,108 |
+| Total Tokens | 520,860 |
+| Mean Frequency | 28.76 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 327.08 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | di | 18,819 |
+| 2 | for | 18,818 |
+| 3 | wey | 13,794 |
+| 4 | dey | 12,381 |
+| 5 | of | 12,090 |
+| 6 | e | 11,367 |
+| 7 | an | 9,408 |
+| 8 | na | 9,331 |
+| 9 | dem | 8,867 |
+| 10 | to | 5,138 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | fir | 2 |
+| 2 | feirense | 2 |
+| 3 | invention | 2 |
+| 4 | ahl | 2 |
+| 5 | sunnah | 2 |
+| 6 | broader | 2 |
+| 7 | asg | 2 |
+| 8 | sogato | 2 |
+| 9 | strategies | 2 |
+| 10 | kompinies | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1589 |
+| R² (Goodness of Fit) | 0.993730 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 49.4% |
+| Top 1,000 | 75.8% |
+| Top 5,000 | 91.4% |
+| Top 10,000 | 96.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9937 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 49.4% of corpus
+- **Long Tail:** 8,108 words needed for remaining 3.6% 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 | 0.6433 🏆 | 0.4025 | N/A | N/A |
+| **mono_64d** | 64 | 0.3018 | 0.3833 | N/A | N/A |
+| **mono_128d** | 128 | 0.0480 | 0.3642 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6433 | 0.3875 | 0.0600 | 0.3120 |
+| **aligned_64d** | 64 | 0.3018 | 0.3929 | 0.0980 | 0.3220 |
+| **aligned_128d** | 128 | 0.0480 | 0.3729 | 0.0980 | 0.3400 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.6433 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3839. 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 | **0.149** | 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` | anorda, achievement, adura |
+| `-s` | spanner, seen, system |
+| `-o` | ospital, oloore, odg |
+| `-b` | biginin, bitwin, belfast |
+| `-m` | mcgill, meenin, memba |
+| `-e` | exploits, emeritus, eku |
+| `-k` | kanye, kontris, komunitis |
+| `-t` | tsm, tottenham, tool |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | rhymes, kontris, komunitis |
+| `-n` | patan, meenin, investigation |
+| `-e` | raise, kanye, oloore |
+| `-a` | memba, grandma, anorda |
+| `-on` | investigation, madison, lexikon |
+| `-t` | profit, pct, belfast |
+| `-i` | gidi, jaji, olusi |
+| `-y` | galaxy, newly, fidelity |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ight` | 1.65x | 34 contexts | eight, light, night |
+| `ther` | 1.71x | 28 contexts | there, other, rather |
+| `tion` | 1.63x | 26 contexts | motion, option, action |
+| `ment` | 1.47x | 31 contexts | mento, menta, mental |
+| `atio` | 1.71x | 17 contexts | ratio, nation, nations |
+| `esho` | 1.57x | 21 contexts | mesho, naesho, neshon |
+| `kont` | 1.55x | 19 contexts | kontat, kontan, kontro |
+| `isho` | 1.37x | 26 contexts | pisho, bishop, pishon |
+| `liti` | 1.64x | 14 contexts | realiti, politis, abiliti |
+| `nter` | 1.52x | 17 contexts | enter, inter, hunter |
+| `ress` | 1.52x | 16 contexts | press, aress, tress |
+| `asho` | 1.51x | 16 contexts | ashok, vashon, ashoka |
+
+### 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` | `-e` | 72 words | ajiwere, alive |
+| `-s` | `-s` | 65 words | scelles, somtimes |
+| `-p` | `-s` | 61 words | patterns, plaets |
+| `-a` | `-s` | 52 words | aktivis, aleros |
+| `-a` | `-a` | 52 words | anorda, ahoada |
+| `-k` | `-n` | 45 words | kitchen, kabon |
+| `-s` | `-e` | 45 words | spotlite, shake |
+| `-a` | `-n` | 44 words | akan, alabukun |
+| `-o` | `-e` | 42 words | ogbe, okezie |
+| `-a` | `-i` | 41 words | abdullahi, alli |
+
+### 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 |
+|------|-----------------|------------|------|
+| panafrican | **`pa-n-african`** | 7.5 | `african` |
+| peaceland | **`peace-la-nd`** | 7.5 | `la` |
+| aristotle | **`aristo-t-le`** | 7.5 | `t` |
+| orijinali | **`orijin-al-i`** | 7.5 | `al` |
+| friesland | **`fries-la-nd`** | 7.5 | `la` |
+| seventeen | **`sevente-e-n`** | 7.5 | `e` |
+| producing | **`produc-i-ng`** | 7.5 | `i` |
+| williamson | **`william-s-on`** | 7.5 | `s` |
+| bestseller | **`be-st-seller`** | 7.5 | `seller` |
+| musicians | **`music-ia-ns`** | 6.0 | `music` |
+| yunivasiti | **`yunivasit-i`** | 4.5 | `yunivasit` |
+| activists | **`activist-s`** | 4.5 | `activist` |
+| chartered | **`charter-ed`** | 4.5 | `charter` |
+| celebrities | **`celebriti-es`** | 4.5 | `celebriti` |
+| festivals | **`festival-s`** | 4.5 | `festival` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Nigerian Pidgin 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 (4.49x) |
+| N-gram | **2-gram** | Lowest perplexity (249) |
+| Markov | **Context-4** | Highest predictability (96.2%) |
+| 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 17:35:04*

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