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README.md

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+---
+language: ht
+language_name: Haitian Creole
+language_family: romance_creole
+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-romance_creole
+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: 4.271
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7588
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Haitian Creole - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Haitian Creole** 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** | 3.548x | 3.55 | 0.3624% | 230,377 |
+| **16k** | 3.848x | 3.85 | 0.3931% | 212,420 |
+| **32k** | 4.091x | 4.10 | 0.4179% | 199,821 |
+| **64k** | 4.271x 🏆 | 4.28 | 0.4363% | 191,397 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `se yon vil Etazini. Li sitye nan leta Kentucky. Chèf-lye li se ?. Istwa Istwa Po...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁se ▁yon ▁vil ▁etazini . ▁li ▁sitye ▁nan ▁leta ▁kentucky ... (+18 more)` | 28 |
+| 16k | `▁se ▁yon ▁vil ▁etazini . ▁li ▁sitye ▁nan ▁leta ▁kentucky ... (+18 more)` | 28 |
+| 32k | `▁se ▁yon ▁vil ▁etazini . ▁li ▁sitye ▁nan ▁leta ▁kentucky ... (+18 more)` | 28 |
+| 64k | `▁se ▁yon ▁vil ▁etazini . ▁li ▁sitye ▁nan ▁leta ▁kentucky ... (+18 more)` | 28 |
+
+**Sample 2:** `lane nan almanak gregoryen lane nan lòt almanak yo nonm`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lane ▁nan ▁almanak ▁gregoryen ▁lane ▁nan ▁lòt ▁almanak ▁yo ▁nonm` | 10 |
+| 16k | `▁lane ▁nan ▁almanak ▁gregoryen ▁lane ▁nan ▁lòt ▁almanak ▁yo ▁nonm` | 10 |
+| 32k | `▁lane ▁nan ▁almanak ▁gregoryen ▁lane ▁nan ▁lòt ▁almanak ▁yo ▁nonm` | 10 |
+| 64k | `▁lane ▁nan ▁almanak ▁gregoryen ▁lane ▁nan ▁lòt ▁almanak ▁yo ▁nonm` | 10 |
+
+**Sample 3:** `Solit se yon sibstans ki fonn nan yon solisyon. Referans`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁sol it ▁se ▁yon ▁sibstans ▁ki ▁fon n ▁nan ▁yon ... (+4 more)` | 14 |
+| 16k | `▁sol it ▁se ▁yon ▁sibstans ▁ki ▁fonn ▁nan ▁yon ▁solisyon ... (+2 more)` | 12 |
+| 32k | `▁solit ▁se ▁yon ▁sibstans ▁ki ▁fonn ▁nan ▁yon ▁solisyon . ... (+1 more)` | 11 |
+| 64k | `▁solit ▁se ▁yon ▁sibstans ▁ki ▁fonn ▁nan ▁yon ▁solisyon . ... (+1 more)` | 11 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.271x compression
+- **Lowest UNK Rate:** 8k with 0.3624% 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 | 8,092 | 12.98 | 142,856 | 26.4% | 57.0% |
+| **2-gram** | Subword | 263 🏆 | 8.04 | 4,790 | 68.2% | 99.4% |
+| **3-gram** | Word | 7,226 | 12.82 | 216,416 | 28.0% | 62.3% |
+| **3-gram** | Subword | 2,065 | 11.01 | 40,230 | 29.0% | 73.0% |
+| **4-gram** | Word | 6,609 | 12.69 | 326,152 | 29.8% | 66.2% |
+| **4-gram** | Subword | 10,042 | 13.29 | 232,790 | 16.6% | 45.8% |
+| **5-gram** | Word | 3,612 | 11.82 | 221,589 | 33.1% | 73.5% |
+| **5-gram** | Subword | 31,768 | 14.96 | 722,497 | 11.3% | 35.2% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `se yon` | 67,026 |
+| 2 | `istwa istwa` | 34,640 |
+| 3 | `kèk lyen` | 34,549 |
+| 4 | `referans kèk` | 34,144 |
+| 5 | `nan etazini` | 32,194 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `referans kèk lyen` | 33,800 |
+| 2 | `se yon vil` | 31,899 |
+| 3 | `kèk lyen nan` | 24,836 |
+| 4 | `yon vil nan` | 23,512 |
+| 5 | `relasyon ak ayiti` | 23,065 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `referans kèk lyen nan` | 24,833 |
+| 2 | `se yon vil nan` | 23,468 |
+| 3 | `relasyon ant eta sa` | 23,057 |
+| 4 | `ayisyen relasyon ant eta` | 23,056 |
+| 5 | `eta sa epi ayiti` | 23,056 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ant eta sa epi ayiti` | 23,056 |
+| 2 | `relasyon ant eta sa epi` | 23,056 |
+| 3 | `ayisyen relasyon ant eta sa` | 23,056 |
+| 4 | `kominote ayisyen relasyon ant eta` | 23,056 |
+| 5 | `istwa istwa relasyon ak ayiti` | 23,047 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n _` | 1,566,894 |
+| 2 | `a n` | 1,400,762 |
+| 3 | `e _` | 1,352,775 |
+| 4 | `_ a` | 826,760 |
+| 5 | `o n` | 794,450 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _` | 604,651 |
+| 2 | `o n _` | 457,174 |
+| 3 | `_ : _` | 418,125 |
+| 4 | `y o n` | 400,796 |
+| 5 | `_ n a` | 387,040 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n a n _` | 364,441 |
+| 2 | `_ n a n` | 363,482 |
+| 3 | `y o n _` | 363,435 |
+| 4 | `s y o n` | 232,709 |
+| 5 | `a s y o` | 182,198 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ n a n _` | 361,168 |
+| 2 | `s y o n _` | 199,085 |
+| 3 | `a s y o n` | 182,181 |
+| 4 | `_ y o n _` | 136,613 |
+| 5 | `y o n _ a` | 111,148 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 263
+- **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 | 1.0080 | 2.011 | 8.45 | 250,146 | 0.0% |
+| **1** | Subword | 0.9515 | 1.934 | 6.83 | 2,043 | 4.8% |
+| **2** | Word | 0.3036 | 1.234 | 1.83 | 2,109,748 | 69.6% |
+| **2** | Subword | 0.8151 | 1.759 | 5.68 | 13,935 | 18.5% |
+| **3** | Word | 0.1123 | 1.081 | 1.22 | 3,857,526 | 88.8% |
+| **3** | Subword | 0.8180 | 1.763 | 4.75 | 79,027 | 18.2% |
+| **4** | Word | 0.0480 🏆 | 1.034 | 1.08 | 4,709,643 | 95.2% |
+| **4** | Subword | 0.7094 | 1.635 | 3.41 | 374,804 | 29.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `nan mikwofòn elektwomayetik yo te regrèt ke lidè otokratik jouk lè nou yòk new york li`
+2. `de paul belmondo yon vil nan pwovens pinar del rio nan eta sa te rantre nan`
+3. `li yo gide ak bibliyometrik premye woman ki ra kòm luis lazo aktivite li se pi`
+
+**Context Size 2:**
+
+1. `se yon endikatè ph tankou fenolftalein oswa bromotimol ble vignette tès pou lide a soti nan vèb`
+2. `istwa istwa relasyon ak ayiti kominote ayisyen relasyon ant eta sa epi ayiti 6 fevrye gouvènman ayis...`
+3. `referans kèk lyen nan georgie nan etazini li sitye nan leta ilinwa chèf lye li se bèzbòl`
+
+**Context Size 3:**
+
+1. `referans kèk lyen nan new york nan etazini se yon aktris ak chantèz fransèz orijin woumèn li te`
+2. `se yon vil nan eta kawolin dinò nan etazini li te genyen 26 996 abitan nan rejyon windham`
+3. `kèk lyen nan habana nan kiba gade tout gwo vil yo nan kat sa pèsonalite moun sa yo`
+
+**Context Size 4:**
+
+1. `referans kèk lyen nan kawolin dinò nan etazini istwa istwa relasyon ak ayiti kominote ayisyen relasy...`
+2. `se yon vil nan pwovens santiago de cuba nan kiba gade tout gwo vil yo nan kat sa pèsonalite`
+3. `relasyon ant eta sa epi ayiti 6 fevrye gouvènman ayisyen reprann kontak ak otorite kiben 2 chanselye...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_pasyoikshanimyo`
+2. `an_sawen_d'épili`
+3. `e_i_keri_kizonn_`
+
+**Context Size 2:**
+
+1. `n_rartikaskasyo._`
+2. `ans_fevitillies_k`
+3. `e_latî-mageonsema`
+
+**Context Size 3:**
+
+1. `an_antmanuel_marti`
+2. `on_lan_redracebsta`
+3. `_:_maritanizatè_pl`
+
+**Context Size 4:**
+
+1. `nan_li_se_yon_vil_p`
+2. `_nan_wyominote_ayit`
+3. `yon_ak_aktivite_kas`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (374,804 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 | 121,217 |
+| Total Tokens | 8,389,833 |
+| Mean Frequency | 69.21 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 1815.48 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | nan | 363,480 |
+| 2 | de | 183,168 |
+| 3 | li | 156,632 |
+| 4 | yo | 145,429 |
+| 5 | yon | 137,456 |
+| 6 | se | 132,316 |
+| 7 | ak | 125,166 |
+| 8 | sa | 123,752 |
+| 9 | te | 99,980 |
+| 10 | la | 84,358 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | taman | 2 |
+| 2 | meotian | 2 |
+| 3 | billikens | 2 |
+| 4 | stb | 2 |
+| 5 | oden | 2 |
+| 6 | beno | 2 |
+| 7 | olimpija | 2 |
+| 8 | omri | 2 |
+| 9 | duny | 2 |
+| 10 | robiane | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1103 |
+| R² (Goodness of Fit) | 0.998571 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 47.2% |
+| Top 1,000 | 71.5% |
+| Top 5,000 | 84.3% |
+| Top 10,000 | 89.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9986 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 47.2% of corpus
+- **Long Tail:** 111,217 words needed for remaining 10.9% 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 | 0.7588 | 0.3532 | N/A | N/A |
+| **mono_64d** | 64 | 0.7534 | 0.2841 | N/A | N/A |
+| **mono_128d** | 128 | 0.7522 | 0.2432 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7588 🏆 | 0.3565 | 0.0840 | 0.3820 |
+| **aligned_64d** | 64 | 0.7534 | 0.2966 | 0.1500 | 0.5020 |
+| **aligned_128d** | 128 | 0.7522 | 0.2468 | 0.2020 | 0.5860 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7588 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2967. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 20.2% 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 | **1.087** | High formulaic/idiomatic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-a` | anios, answé, alexandrian |
+| `-s` | slimane, scholl, serpico |
+| `-ma` | marell, mariton, marivi |
+| `-m` | marell, métrage, mariton |
+| `-b` | belencita, basse, bretonneau |
+| `-p` | puzzled, polisemi, pwoteyins |
+| `-d` | dezyèm, divinite, delsham |
+| `-c` | clayton, cuétara, cuarto |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | naville, slimane, gigolette |
+| `-s` | anios, pwoteyins, disques |
+| `-n` | clayton, expression, kayman |
+| `-a` | cuétara, belencita, preacha |
+| `-on` | clayton, expression, dèfon |
+| `-es` | disques, personnages, conneries |
+| `-r` | haudecoeur, quarter, messemer |
+| `-t` | joyadet, briat, fiat |
+
+### 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 |
+|------|----------|------------------|----------|
+| `asyo` | 2.56x | 46 contexts | rasyo, rasyon, kasyon |
+| `efer` | 2.56x | 29 contexts | refer, defer, jefery |
+| `ogra` | 1.88x | 95 contexts | òtograf, ekograf, pwogram |
+| `ikas` | 2.84x | 15 contexts | likasi, vikash, efikas |
+| `lasy` | 2.82x | 15 contexts | glasyè, plasye, glasye |
+| `omin` | 1.86x | 65 contexts | comin, komin, bomin |
+| `rans` | 1.85x | 63 contexts | frans, trans, transe |
+| `rela` | 2.10x | 34 contexts | relay, prela, irela |
+| `liti` | 2.02x | 31 contexts | litik, litij, politi |
+| `ayis` | 2.34x | 18 contexts | gayis, kayis, ayisye |
+| `dika` | 2.18x | 21 contexts | odikap, fadika, endikap |
+| `refe` | 2.30x | 17 contexts | refer, grefe, refere |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-c` | `-e` | 91 words | charente, caderousse |
+| `-c` | `-s` | 87 words | coquillages, colins |
+| `-p` | `-e` | 78 words | paratonnerre, pwentiye |
+| `-s` | `-s` | 72 words | sannois, sabines |
+| `-p` | `-s` | 72 words | panis, phénomènes |
+| `-s` | `-e` | 71 words | souffre, sœurette |
+| `-a` | `-e` | 66 words | ampoule, affronte |
+| `-d` | `-e` | 61 words | détente, detache |
+| `-c` | `-n` | 59 words | comparaison, chambrun |
+| `-b` | `-e` | 59 words | burlesque, banalite |
+
+### 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 |
+|------|-----------------|------------|------|
+| champigny | **`champig-n-y`** | 7.5 | `n` |
+| bronchinson | **`bronchin-s-on`** | 7.5 | `s` |
+| illustreret | **`illustrer-e-t`** | 7.5 | `e` |
+| paristonkar | **`paristonk-a-r`** | 7.5 | `a` |
+| réalisateur | **`réalisat-e-ur`** | 7.5 | `e` |
+| amoureuse | **`amoureu-s-e`** | 7.5 | `s` |
+| glorieuses | **`glorieu-s-es`** | 7.5 | `s` |
+| mauricette | **`maurice-t-te`** | 7.5 | `t` |
+| manglehorn | **`mangleho-r-n`** | 7.5 | `r` |
+| merchantville | **`merchantvi-l-le`** | 7.5 | `l` |
+| smithville | **`smithvi-l-le`** | 7.5 | `l` |
+| pedevilla | **`pe-de-villa`** | 7.5 | `villa` |
+| potpourri | **`potpour-r-i`** | 7.5 | `r` |
+| colasanti | **`co-la-santi`** | 7.5 | `santi` |
+| ayikodans | **`ayikod-an-s`** | 7.5 | `an` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Haitian Creole shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.27x) |
+| N-gram | **2-gram** | Lowest perplexity (263) |
+| Markov | **Context-4** | Highest predictability (95.2%) |
+| 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 03:29:01*

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