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README.md

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+---
+language: gpe
+language_name: Ghanaian Pidgin English
+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: 4.789
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8645
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-09
+---
+
+# Ghanaian Pidgin English - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Ghanaian Pidgin English** 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** | 4.124x | 4.13 | 0.1031% | 720,937 |
+| **16k** | 4.434x | 4.44 | 0.1108% | 670,476 |
+| **32k** | 4.661x | 4.66 | 0.1165% | 637,864 |
+| **64k** | 4.789x 🏆 | 4.79 | 0.1197% | 620,843 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Institutions Abourso CHPs References insyd Ghana insyd Eastern Region (Ghana) pl...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁institutions ▁ab ours o ▁ch ps ▁references ▁insyd ▁ghana ▁insyd ... (+13 more)` | 23 |
+| 16k | `▁institutions ▁ab ours o ▁chps ▁references ▁insyd ▁ghana ▁insyd ▁eastern ... (+12 more)` | 22 |
+| 32k | `▁institutions ▁ab ours o ▁chps ▁references ▁insyd ▁ghana ▁insyd ▁eastern ... (+12 more)` | 22 |
+| 64k | `▁institutions ▁ab ours o ▁chps ▁references ▁insyd ▁ghana ▁insyd ▁eastern ... (+12 more)` | 22 |
+
+**Sample 2:** `References newspapers media insyd Ghana publish insyd Ghana publish insyd Africa`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁references ▁newspapers ▁media ▁insyd ▁ghana ▁publish ▁insyd ▁ghana ▁publish ▁insyd ... (+1 more)` | 11 |
+| 16k | `▁references ▁newspapers ▁media ▁insyd ▁ghana ▁publish ▁insyd ▁ghana ▁publish ▁insyd ... (+1 more)` | 11 |
+| 32k | `▁references ▁newspapers ▁media ▁insyd ▁ghana ▁publish ▁insyd ▁ghana ▁publish ▁insyd ... (+1 more)` | 11 |
+| 64k | `▁references ▁newspapers ▁media ▁insyd ▁ghana ▁publish ▁insyd ▁ghana ▁publish ▁insyd ... (+1 more)` | 11 |
+
+**Sample 3:** `References insyd Ghana insyd Ashanti Region places for Ashanti Region insyd`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁references ▁insyd ▁ghana ▁insyd ▁ashanti ▁region ▁places ▁for ▁ashanti ▁region ... (+1 more)` | 11 |
+| 16k | `▁references ▁insyd ▁ghana ▁insyd ▁ashanti ▁region ▁places ▁for ▁ashanti ▁region ... (+1 more)` | 11 |
+| 32k | `▁references ▁insyd ▁ghana ▁insyd ▁ashanti ▁region ▁places ▁for ▁ashanti ▁region ... (+1 more)` | 11 |
+| 64k | `▁references ▁insyd ▁ghana ▁insyd ▁ashanti ▁region ▁places ▁for ▁ashanti ▁region ... (+1 more)` | 11 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.789x compression
+- **Lowest UNK Rate:** 8k with 0.1031% 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 | 21,240 | 14.37 | 78,160 | 14.3% | 31.9% |
+| **2-gram** | Subword | 267 🏆 | 8.06 | 3,973 | 67.1% | 99.4% |
+| **3-gram** | Word | 53,111 | 15.70 | 117,024 | 7.0% | 18.8% |
+| **3-gram** | Subword | 2,195 | 11.10 | 30,848 | 25.8% | 72.0% |
+| **4-gram** | Word | 94,293 | 16.52 | 171,368 | 5.3% | 13.6% |
+| **4-gram** | Subword | 11,353 | 13.47 | 164,542 | 14.5% | 40.0% |
+| **5-gram** | Word | 63,802 | 15.96 | 106,259 | 5.8% | 14.6% |
+| **5-gram** | Subword | 38,013 | 15.21 | 434,778 | 9.2% | 27.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `of de` | 20,308 |
+| 2 | `for de` | 13,045 |
+| 3 | `insyd de` | 12,862 |
+| 4 | `wey dey` | 10,251 |
+| 5 | `na dem` | 7,893 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `from the original` | 4,522 |
+| 2 | `archived from the` | 4,424 |
+| 3 | `the original on` | 4,295 |
+| 4 | `de university of` | 1,482 |
+| 5 | `references external links` | 1,398 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `archived from the original` | 4,424 |
+| 2 | `from the original on` | 4,295 |
+| 3 | `at the wayback machine` | 842 |
+| 4 | `of de national assembly` | 704 |
+| 5 | `be one of de` | 605 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `archived from the original on` | 4,199 |
+| 2 | `national assembly of south africa` | 578 |
+| 3 | `de national assembly of south` | 560 |
+| 4 | `of de national assembly of` | 550 |
+| 5 | `from the original on retrieved` | 523 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 512,209 |
+| 2 | `_ d` | 373,324 |
+| 3 | `d e` | 362,084 |
+| 4 | `i n` | 287,429 |
+| 5 | `n _` | 274,000 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 304,465 |
+| 2 | `d e _` | 147,839 |
+| 3 | `_ i n` | 103,335 |
+| 4 | `_ o f` | 102,797 |
+| 5 | `o f _` | 98,533 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 134,879 |
+| 2 | `_ o f _` | 96,992 |
+| 3 | `_ f o r` | 70,879 |
+| 4 | `t i o n` | 67,685 |
+| 5 | `_ i n s` | 65,269 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ f o r _` | 62,539 |
+| 2 | `i n s y d` | 58,915 |
+| 3 | `_ i n s y` | 58,082 |
+| 4 | `n s y d _` | 53,327 |
+| 5 | `_ d e n _` | 48,301 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 267
+- **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 | 1.0024 | 2.003 | 9.14 | 112,922 | 0.0% |
+| **1** | Subword | 0.8797 | 1.840 | 6.38 | 1,680 | 12.0% |
+| **2** | Word | 0.3635 | 1.287 | 2.00 | 1,031,914 | 63.6% |
+| **2** | Subword | 0.9207 | 1.893 | 5.68 | 10,718 | 7.9% |
+| **3** | Word | 0.1363 | 1.099 | 1.26 | 2,064,281 | 86.4% |
+| **3** | Subword | 0.8539 | 1.807 | 4.49 | 60,872 | 14.6% |
+| **4** | Word | 0.0524 🏆 | 1.037 | 1.08 | 2,589,043 | 94.8% |
+| **4** | Subword | 0.6904 | 1.614 | 3.06 | 273,196 | 31.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de grand slams for ein birth before he finally establish dis celebration of dakar get one`
+2. `of science for di original on retrieved 13 may 7 6 10 of health science report`
+3. `for de quarterfinals wer na she participate insyd a quarrel between tropical wey don decide am`
+
+**Context Size 2:**
+
+1. `of de prayer hall give students de degree of specialization wey range from 56 for de total`
+2. `for de standard entry times oqt oct paris swimming info world aquatics championshipsfukuoka july mol...`
+3. `insyd de centuries na dem enact by ordering all of ein permanent campus na de average millennial`
+
+**Context Size 3:**
+
+1. `from the original on 27 june on top convention peoples party c p p plus some other arab`
+2. `archived from the original on 13 march retrieved 7 march insyd de ghana premier league club al hilal`
+3. `the original on 29 september de electoral authority come talk say de cave be de original owners as`
+
+**Context Size 4:**
+
+1. `archived from the original on 3 january retrieved 17 may references of education winneba institution...`
+2. `from the original on 11 july retrieved 31 july early life den education dem born pravin gordhan on 1...`
+3. `at the wayback machine cricketarchive retrieved 2 january elizabeth tracing the journey the vice cha...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_om_oon_o-orof_d`
+2. `es_a_fangaplmala`
+3. `al,_3_wirintmptt`
+
+**Context Size 2:**
+
+1. `e_nes_ber's_beent`
+2. `_distrycle_fish_a`
+3. `dento_di_clu_bas_`
+
+**Context Size 3:**
+
+1. `_dey_dey_for_65._e`
+2. `de_politadiye,_buf`
+3. `_infor_de_greem),_`
+
+**Context Size 4:**
+
+1. `_de_wale,_municipal`
+2. `_of_convictories_di`
+3. `_for_south_dis_gran`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.8% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (273,196 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 | 53,888 |
+| Total Tokens | 3,007,969 |
+| Mean Frequency | 55.82 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 1006.84 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 136,329 |
+| 2 | of | 97,116 |
+| 3 | for | 62,865 |
+| 4 | insyd | 58,595 |
+| 5 | den | 48,591 |
+| 6 | dem | 45,328 |
+| 7 | wey | 45,073 |
+| 8 | dey | 39,231 |
+| 9 | be | 34,093 |
+| 10 | ein | 30,298 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | tɔra | 2 |
+| 2 | ntebe | 2 |
+| 3 | principia | 2 |
+| 4 | malingering | 2 |
+| 5 | fdis | 2 |
+| 6 | catlett | 2 |
+| 7 | modif | 2 |
+| 8 | outbursts | 2 |
+| 9 | impulse | 2 |
+| 10 | excoriation | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1693 |
+| R² (Goodness of Fit) | 0.988970 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 41.6% |
+| Top 1,000 | 69.9% |
+| Top 5,000 | 87.3% |
+| Top 10,000 | 92.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9890 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 41.6% of corpus
+- **Long Tail:** 43,888 words needed for remaining 7.4% 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 | 0.8634 | 0.3326 | N/A | N/A |
+| **mono_64d** | 64 | 0.8645 | 0.2673 | N/A | N/A |
+| **mono_128d** | 128 | 0.8465 | 0.1986 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8634 | 0.3488 | 0.2620 | 0.6480 |
+| **aligned_64d** | 64 | 0.8645 🏆 | 0.2624 | 0.4380 | 0.8040 |
+| **aligned_128d** | 128 | 0.8465 | 0.1961 | 0.5700 | 0.8700 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_64d with 0.8645 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2677. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 57.0% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.460** | 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 |
+|--------|----------|
+| `-co` | commendations, consumption, corona |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | étoiles, ibs, seriesjenifas |
+| `-es` | étoiles, cinématographiques, bapes |
+| `-ng` | offsetting, subverting, visiting |
+| `-on` | koomson, rodinson, consumption |
+| `-ed` | administered, categorized, overcrowded |
+| `-ing` | offsetting, subverting, visiting |
+| `-er` | mulder, turnover, longer |
+
+### 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 |
+|------|----------|------------------|----------|
+| `nter` | 1.66x | 48 contexts | unter, inter, enter |
+| `atio` | 1.56x | 49 contexts | natio, ratio, ratios |
+| `tion` | 1.44x | 64 contexts | option, lation, notion |
+| `ment` | 1.51x | 46 contexts | mente, lament, moment |
+| `ican` | 1.96x | 17 contexts | rican, vatican, pelican |
+| `ence` | 1.70x | 27 contexts | pence, fence, hence |
+| `iver` | 1.52x | 35 contexts | hiver, giver, river |
+| `mber` | 1.74x | 21 contexts | mberi, amber, member |
+| `ersi` | 1.78x | 19 contexts | persia, versity, version |
+| `embe` | 1.80x | 18 contexts | embed, lembe, kpembe |
+| `ieve` | 1.83x | 14 contexts | nieve, thieves, achieve |
+| `nive` | 2.19x | 8 contexts | niven, nivera, univen |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-co` | `-s` | 39 words | contributes, conservations |
+| `-co` | `-on` | 16 words | contraception, constitution |
+| `-co` | `-ed` | 13 words | committed, commanded |
+| `-co` | `-ng` | 10 words | counselling, connecting |
+| `-co` | `-ing` | 9 words | counselling, connecting |
+| `-co` | `-es` | 8 words | contributes, comprises |
+| `-co` | `-er` | 5 words | contender, colder |
+
+### 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 |
+|------|-----------------|------------|------|
+| descended | **`descend-ed`** | 4.5 | `descend` |
+| assaulted | **`assault-ed`** | 4.5 | `assault` |
+| requested | **`request-ed`** | 4.5 | `request` |
+| approaching | **`approach-ing`** | 4.5 | `approach` |
+| universes | **`univers-es`** | 4.5 | `univers` |
+| distracted | **`distract-ed`** | 4.5 | `distract` |
+| encompasses | **`encompass-es`** | 4.5 | `encompass` |
+| choreographed | **`choreograph-ed`** | 4.5 | `choreograph` |
+| fermented | **`ferment-ed`** | 4.5 | `ferment` |
+| reprinted | **`reprint-ed`** | 4.5 | `reprint` |
+| abstained | **`abstain-ed`** | 4.5 | `abstain` |
+| transformed | **`transform-ed`** | 4.5 | `transform` |
+| mistresses | **`mistress-es`** | 4.5 | `mistress` |
+| reporting | **`report-ing`** | 4.5 | `report` |
+| entertainer | **`entertain-er`** | 4.5 | `entertain` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Ghanaian Pidgin English 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 (4.79x) |
+| N-gram | **2-gram** | Lowest perplexity (267) |
+| Markov | **Context-4** | Highest predictability (94.8%) |
+| 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: 2026-01-09 23:55:27*

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