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README.md

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+---
+language: guc
+language_name: Wayuu
+language_family: american_arawak
+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-american_arawak
+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: 5.025
+  - name: best_isotropy
+    type: isotropy
+    value: 0.4101
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Wayuu - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Wayuu** 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** | 3.998x | 4.00 | 0.1729% | 272,950 |
+| **16k** | 4.380x | 4.38 | 0.1895% | 249,121 |
+| **32k** | 4.743x | 4.75 | 0.2052% | 230,070 |
+| **64k** | 5.025x 🏆 | 5.03 | 0.2173% | 217,163 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Alhuliya Sawara Arabu Demokratika (RASD), nisqaqa Aphrikapi huk mama llaqtam, ll...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁al hu li ya ▁sawa ra ▁ara bu ▁de mo ... (+26 more)` | 36 |
+| 16k | `▁al hu liya ▁sawa ra ▁ara bu ▁de mo k ... (+15 more)` | 25 |
+| 32k | `▁alhuliya ▁sawara ▁ara bu ▁demokratika ▁( rasd ), ▁nisqaqa ▁aphrikapi ... (+7 more)` | 17 |
+| 64k | `▁alhuliya ▁sawara ▁arabu ▁demokratika ▁( rasd ), ▁nisqaqa ▁aphrikapi ▁huk ... (+6 more)` | 16 |
+
+**Sample 2:** `Tü Mma'ipakat Toliima (Alijunaiki: Departamento Tolima) jiia wanee mma'ipakat sa...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁tü ▁mma ' ipakat ▁to li ima ▁( alijunaiki : ... (+25 more)` | 35 |
+| 16k | `▁tü ▁mma ' ipakat ▁toliima ▁( alijunaiki : ▁departamento ▁to ... (+23 more)` | 33 |
+| 32k | `▁tü ▁mma ' ipakat ▁toliima ▁( alijunaiki : ▁departamento ▁tolima ... (+22 more)` | 32 |
+| 64k | `▁tü ▁mma ' ipakat ▁toliima ▁( alijunaiki : ▁departamento ▁tolima ... (+22 more)` | 32 |
+
+**Sample 3:** `Tarüjeeta ajuyaajia (alijunaiki: tarjeta de crédito)`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ta rü jee ta ▁aju yaa jia ▁( alijunaiki : ... (+11 more)` | 21 |
+| 16k | `▁ta rüjee ta ▁ajuyaa jia ▁( alijunaiki : ▁ta r ... (+7 more)` | 17 |
+| 32k | `▁tarüjee ta ▁ajuyaajia ▁( alijunaiki : ▁tarjeta ▁de ▁crédito )` | 10 |
+| 64k | `▁tarüjeeta ▁ajuyaajia ▁( alijunaiki : ▁tarjeta ▁de ▁crédito )` | 9 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.025x compression
+- **Lowest UNK Rate:** 8k with 0.1729% 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 | 1,485 | 10.54 | 2,732 | 29.7% | 71.2% |
+| **2-gram** | Subword | 206 🏆 | 7.68 | 1,229 | 73.2% | 99.9% |
+| **3-gram** | Word | 1,502 | 10.55 | 2,148 | 24.9% | 69.9% |
+| **3-gram** | Subword | 1,420 | 10.47 | 7,905 | 32.1% | 80.6% |
+| **4-gram** | Word | 2,089 | 11.03 | 2,754 | 19.8% | 54.1% |
+| **4-gram** | Subword | 6,489 | 12.66 | 32,863 | 16.4% | 49.5% |
+| **5-gram** | Word | 965 | 9.91 | 1,313 | 29.3% | 83.0% |
+| **5-gram** | Subword | 18,510 | 14.18 | 70,413 | 10.3% | 32.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sulu u` | 663 |
+| 2 | `de la` | 582 |
+| 3 | `u tü` | 336 |
+| 4 | `otta müsia` | 255 |
+| 5 | `sukua ipa` | 255 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sulu u tü` | 189 |
+| 2 | `shi ipajee sukua` | 89 |
+| 3 | `ipajee sukua ipa` | 88 |
+| 4 | `no u chi` | 88 |
+| 5 | `tü mma ipakat` | 70 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `shi ipajee sukua ipa` | 88 |
+| 2 | `wanee mma ipakat saakaje` | 63 |
+| 3 | `jiia wanee mma ipakat` | 61 |
+| 4 | `shi ipajee sukuwa ipa` | 53 |
+| 5 | `no u chi juyakai` | 44 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `jiia wanee mma ipakat saakaje` | 61 |
+| 2 | `apünüin shiiki sumaa piama mma` | 32 |
+| 3 | `shiiki sumaa piama mma ipakat` | 32 |
+| 4 | `32 apünüin shiiki sumaa piama` | 32 |
+| 5 | `wanee mma ipakat saakaje 32` | 31 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 43,575 |
+| 2 | `_ s` | 25,414 |
+| 3 | `k a` | 22,304 |
+| 4 | `i n` | 19,881 |
+| 5 | `n a` | 18,513 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i n _` | 11,521 |
+| 2 | `a k a` | 8,886 |
+| 3 | `_ w a` | 8,759 |
+| 4 | `a _ s` | 8,142 |
+| 5 | `_ s ü` | 8,067 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t ü _` | 6,482 |
+| 2 | `a i n _` | 4,294 |
+| 3 | `ü i n _` | 3,910 |
+| 4 | `a y u u` | 3,806 |
+| 5 | `_ s h i` | 3,758 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ w a y u` | 3,416 |
+| 2 | `w a y u u` | 3,413 |
+| 3 | `_ w a n e` | 2,552 |
+| 4 | `n a i n _` | 2,226 |
+| 5 | `a _ t ü _` | 2,171 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 206
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~32% 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 | 0.5726 | 1.487 | 3.22 | 34,317 | 42.7% |
+| **1** | Subword | 1.3184 | 2.494 | 11.14 | 212 | 0.0% |
+| **2** | Word | 0.1658 | 1.122 | 1.33 | 110,087 | 83.4% |
+| **2** | Subword | 1.1483 | 2.217 | 6.13 | 2,362 | 0.0% |
+| **3** | Word | 0.0458 | 1.032 | 1.06 | 145,663 | 95.4% |
+| **3** | Subword | 0.8483 | 1.800 | 3.79 | 14,457 | 15.2% |
+| **4** | Word | 0.0123 🏆 | 1.009 | 1.01 | 154,297 | 98.8% |
+| **4** | Subword | 0.6394 | 1.558 | 2.55 | 54,759 | 36.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `tü niipüsekat ramón paz neima cacerio los ciudadanos no tener cola de la península guajira atunkulee`
+2. `de extinción iniciativa de la primera sesión de aguas marinas pueden no u wakuwa ipa shia`
+3. `wayuu laaulakai`
+
+**Context Size 2:**
+
+1. `sulu u wanee mma yawaasirü sünülia apütaa müsia tü eekalü süpa apünaa mmakalü wajiira soo opünaa nak...`
+2. `de la madre ya que solamente hay garantía de venta en el año cuando se unifican todas`
+3. `u tü wanuikikalü sünain waikale erüin tü nikirajaaka anain naja laje erüin tü ta yataainka jüpüla to...`
+
+**Context Size 3:**
+
+1. `sulu u tü mmakat maliro ulia cha wopumuin chawaishii juyapo ulu otta jouktaleolu u chawaishi kepian ...`
+2. `shi ipajee sukua ipa wayuuirua`
+3. `no u chi juyakai akumajüi jee aashajaai saa u akua ipa tü oushiikat aikalaasü süchoin namaa süikeeyu...`
+
+**Context Size 4:**
+
+1. `wanee mma ipakat saakaje 24 piama shiiki sumaa pienchi mma ipakat yaa ekuwatoorü`
+2. `jiia wanee mma ipakat saakaje 23 piama shiiki sumaa apünüin mma ipakat yaa wenesueela wenesueela`
+3. `shi ipajee sukua ipa otta mürülü`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a_see_ive'ictü_,`
+2. `_ma'waijolulasü_`
+3. `ine_pümona_ayanü`
+
+**Context Size 2:**
+
+1. `a_naiwayuunain_lo`
+2. `_su_ya_jute_recto`
+3. `ka_hiin_o'u_juu_y`
+
+**Context Size 3:**
+
+1. `in_toolomwia;_y_co`
+2. `akaa_pies._eekalü_`
+3. `_wajirapüla_lin_sü`
+
+**Context Size 4:**
+
+1. `_tü_pütchirain_müts`
+2. `ain_naya_sünain_nup`
+3. `üin_shimuunain_jalo`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.8% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (54,759 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 | 11,490 |
+| Total Tokens | 139,617 |
+| Mean Frequency | 12.15 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 94.29 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | tü | 6,686 |
+| 2 | de | 2,914 |
+| 3 | wayuu | 2,549 |
+| 4 | u | 2,074 |
+| 5 | a | 2,048 |
+| 6 | la | 1,823 |
+| 7 | otta | 1,430 |
+| 8 | süpüla | 1,331 |
+| 9 | shia | 1,323 |
+| 10 | sünain | 1,278 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | jintu | 2 |
+| 2 | sünee | 2 |
+| 3 | eekiraja | 2 |
+| 4 | nushi | 2 |
+| 5 | aajat | 2 |
+| 6 | outon | 2 |
+| 7 | joloo | 2 |
+| 8 | lunes | 2 |
+| 9 | nien | 2 |
+| 10 | rimikukai | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9694 |
+| R² (Goodness of Fit) | 0.991868 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 44.8% |
+| Top 1,000 | 70.2% |
+| Top 5,000 | 89.4% |
+| Top 10,000 | 97.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9919 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 44.8% of corpus
+- **Long Tail:** 1,490 words needed for remaining 2.1% 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 | 0.4101 🏆 | 0.4040 | N/A | N/A |
+| **mono_64d** | 64 | 0.0872 | 0.4056 | N/A | N/A |
+| **mono_128d** | 128 | 0.0142 | 0.4419 | N/A | N/A |
+| **aligned_32d** | 32 | 0.4101 | 0.4057 | 0.0200 | 0.1260 |
+| **aligned_64d** | 64 | 0.0872 | 0.4093 | 0.0240 | 0.1680 |
+| **aligned_128d** | 128 | 0.0142 | 0.4428 | 0.0340 | 0.1740 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.4101 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4182. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 3.4% 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 | **1.563** | 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 |
+|--------|----------|
+| `-su` | supone, sukuiappa, supüshuwa |
+| `-wa` | waraittaa, wapüshi, wayü |
+| `-sü` | sümioushe, süsika, sümülüin |
+| `-ka` | kariñas, kanuliakat, kashiiwai |
+| `-an` | antüna, ancestros, anainjanit |
+| `-ma` | mariia, malu, maicao |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | mariia, universitaria, waraittaa |
+| `-n` | nashatüin, neraajüin, llegaron |
+| `-in` | nashatüin, neraajüin, epijain |
+| `-ka` | erajunaka, süsika, isashiika |
+| `-aa` | waraittaa, naa, atunkaa |
+| `-aka` | erajunaka, ipaka, ataka |
+| `-üin` | nashatüin, neraajüin, sümülüin |
+| `-sü` | akanajasü, kojutsü, akumujunüsü |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ajaa` | 1.63x | 48 contexts | rajaa, kajaa, ajaaya |
+| `chik` | 1.40x | 59 contexts | achiku, achiki, süchiku |
+| `ainj` | 1.47x | 47 contexts | ainja, aainja, ainjaa |
+| `inja` | 1.68x | 27 contexts | ainja, aainja, ainjaa |
+| `tuma` | 1.64x | 23 contexts | atuma, tumas, watuma |
+| `akal` | 1.48x | 30 contexts | akalü, jakala, makalü |
+| `uuka` | 1.67x | 18 contexts | suuka, ayuuka, jouukai |
+| `kuwa` | 1.60x | 20 contexts | kuwai, akuwa, nükuwa |
+| `amüi` | 1.65x | 16 contexts | amüin, tamüin, wamüin |
+| `anee` | 1.48x | 22 contexts | wanee, aneerü, taanee |
+| `shik` | 1.35x | 26 contexts | shiki, shika, shikat |
+| `hika` | 1.32x | 26 contexts | shika, shikat, shikaa |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-wa` | `-a` | 107 words | wapüshua, wayuukaluirua |
+| `-su` | `-a` | 105 words | suluupuna, suichikana |
+| `-sü` | `-a` | 96 words | süpulaa, sümüsainka |
+| `-ka` | `-a` | 80 words | kaaraipia, kama |
+| `-ma` | `-a` | 57 words | maalia, maracaaya |
+| `-sü` | `-n` | 56 words | sümuin, sülamain |
+| `-sü` | `-in` | 55 words | sümuin, sülamain |
+| `-an` | `-a` | 47 words | anapajirawaa, anulia |
+| `-su` | `-n` | 45 words | sulapuin, sukumajünuin |
+| `-wa` | `-n` | 41 words | wain, waneeyan |
+
+### 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 |
+|------|-----------------|------------|------|
+| suwanajain | **`su-wa-naja-in`** | 7.5 | `naja` |
+| anashanain | **`an-asha-na-in`** | 7.5 | `asha` |
+| suichikana | **`su-ichi-ka-na`** | 7.5 | `ichi` |
+| pasanainsü | **`pasa-na-in-sü`** | 7.5 | `pasa` |
+| laülayuukana | **`laülayuu-ka-na`** | 6.0 | `laülayuu` |
+| layuukana | **`layuu-ka-na`** | 6.0 | `layuu` |
+| kachirasü | **`ka-chira-sü`** | 6.0 | `chira` |
+| ewaliikana | **`ewalii-ka-na`** | 6.0 | `ewalii` |
+| nüpülainka | **`nüpüla-in-ka`** | 6.0 | `nüpüla` |
+| upayuukana | **`upayuu-ka-na`** | 6.0 | `upayuu` |
+| tepichikana | **`tepichi-ka-na`** | 6.0 | `tepichi` |
+| sugolfoin | **`su-golfo-in`** | 6.0 | `golfo` |
+| sumainwaa | **`su-ma-inwaa`** | 6.0 | `inwaa` |
+| wachukumuinkana | **`wa-chukumu-in-ka-na`** | 6.0 | `chukumu` |
+| kamanakat | **`ka-ma-nakat`** | 6.0 | `nakat` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Wayuu 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 (5.02x) |
+| N-gram | **2-gram** | Lowest perplexity (206) |
+| Markov | **Context-4** | Highest predictability (98.8%) |
+| 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: 2026-01-10 00:33:28*

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