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README.md

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+---
+language: mi
+language_name: Māori
+language_family: austronesian_polynesian
+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-austronesian_polynesian
+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: 3.987
+  - name: best_isotropy
+    type: isotropy
+    value: 0.5498
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Māori - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Māori** 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** | 3.637x | 3.64 | 0.0513% | 150,109 |
+| **16k** | 3.798x | 3.81 | 0.0536% | 143,743 |
+| **32k** | 3.931x | 3.94 | 0.0554% | 138,904 |
+| **64k** | 3.987x 🏆 | 3.99 | 0.0562% | 136,949 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Ko Tūnihia (reo Ārapi: الجمهورية التونسية, al-Jumhūrīyah at-Tūnisīyah) he whenua...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ko ▁tū nihia ▁( reo ▁ārapi : ▁ال ج م ... (+45 more)` | 55 |
+| 16k | `▁ko ▁tūnihia ▁( reo ▁ārapi : ▁ال ج مهورية ▁ال ... (+39 more)` | 49 |
+| 32k | `▁ko ▁tūnihia ▁( reo ▁ārapi : ▁الجمهورية ▁التونسية , ▁al ... (+27 more)` | 37 |
+| 64k | `▁ko ▁tūnihia ▁( reo ▁ārapi : ▁الجمهورية ▁التونسية , ▁al ... (+27 more)` | 37 |
+
+**Sample 2:** `Ko Kōkiri Ahitereiria Ataahua () te waiata a whenua mo Ahitereiria.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ko ▁kōkiri ▁ahitereiria ▁ata ahua ▁() ▁te ▁waiata ▁a ▁whenua ... (+3 more)` | 13 |
+| 16k | `▁ko ▁kōkiri ▁ahitereiria ▁ataahua ▁() ▁te ▁waiata ▁a ▁whenua ▁mo ... (+2 more)` | 12 |
+| 32k | `▁ko ▁kōkiri ▁ahitereiria ▁ataahua ▁() ▁te ▁waiata ▁a ▁whenua ▁mo ... (+2 more)` | 12 |
+| 64k | `▁ko ▁kōkiri ▁ahitereiria ▁ataahua ▁() ▁te ▁waiata ▁a ▁whenua ▁mo ... (+2 more)` | 12 |
+
+**Sample 3:** `Ko Kiri Te Kanawa he kaiwaiata rongonui nō Aotearoa.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ko ▁kiri ▁te ▁kana wa ▁he ▁kaiwaiata ▁rongonui ▁nō ▁aotearoa ... (+1 more)` | 11 |
+| 16k | `▁ko ▁kiri ▁te ▁kanawa ▁he ▁kaiwaiata ▁rongonui ▁nō ▁aotearoa .` | 10 |
+| 32k | `▁ko ▁kiri ▁te ▁kanawa ▁he ▁kaiwaiata ▁rongonui ▁nō ▁aotearoa .` | 10 |
+| 64k | `▁ko ▁kiri ▁te ▁kanawa ▁he ▁kaiwaiata ▁rongonui ▁nō ▁aotearoa .` | 10 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.987x compression
+- **Lowest UNK Rate:** 8k with 0.0513% 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 | 705 | 9.46 | 6,245 | 49.9% | 87.5% |
+| **2-gram** | Subword | 171 🏆 | 7.42 | 2,075 | 79.6% | 99.6% |
+| **3-gram** | Word | 1,013 | 9.98 | 9,926 | 41.4% | 85.1% |
+| **3-gram** | Subword | 945 | 9.88 | 12,961 | 39.4% | 88.7% |
+| **4-gram** | Word | 1,172 | 10.19 | 16,021 | 40.6% | 83.8% |
+| **4-gram** | Subword | 2,943 | 11.52 | 50,169 | 24.5% | 71.2% |
+| **5-gram** | Word | 1,030 | 10.01 | 12,463 | 41.9% | 86.0% |
+| **5-gram** | Subword | 5,494 | 12.42 | 88,213 | 18.8% | 62.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `o te` | 12,376 |
+| 2 | `ko te` | 7,593 |
+| 3 | `i te` | 7,520 |
+| 4 | `ki te` | 6,736 |
+| 5 | `takiwā o` | 5,380 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `toitū te whenua` | 4,800 |
+| 2 | `kite i te` | 3,310 |
+| 3 | `he mea kite` | 3,304 |
+| 4 | `mea kite i` | 3,304 |
+| 5 | `new zealand he` | 3,271 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mea kite i te` | 3,304 |
+| 2 | `he mea kite i` | 3,304 |
+| 3 | `zealand he mea kite` | 3,271 |
+| 4 | `new zealand he mea` | 3,271 |
+| 5 | `toitū te whenua land` | 3,270 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `he mea kite i te` | 3,304 |
+| 2 | `zealand he mea kite i` | 3,271 |
+| 3 | `new zealand he mea kite` | 3,271 |
+| 4 | `toitū te whenua land information` | 3,270 |
+| 5 | `land information new zealand he` | 3,270 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t` | 130,423 |
+| 2 | `e _` | 120,072 |
+| 3 | `i _` | 95,061 |
+| 4 | `a _` | 80,419 |
+| 5 | `t e` | 80,353 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `t e _` | 67,768 |
+| 2 | `_ t e` | 62,829 |
+| 3 | `_ o _` | 33,303 |
+| 4 | `i _ t` | 32,362 |
+| 5 | `e _ t` | 32,097 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t e _` | 61,846 |
+| 2 | `i _ t e` | 21,978 |
+| 3 | `o _ t e` | 20,819 |
+| 4 | `t e _ t` | 18,461 |
+| 5 | `_ h e _` | 17,269 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i _ t e _` | 21,833 |
+| 2 | `o _ t e _` | 20,672 |
+| 3 | `_ t e _ t` | 17,983 |
+| 4 | `t e _ t a` | 12,754 |
+| 5 | `_ o _ t e` | 12,383 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 171
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~62% 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 | 0.6767 | 1.598 | 3.84 | 28,167 | 32.3% |
+| **1** | Subword | 0.9035 | 1.871 | 6.22 | 1,068 | 9.6% |
+| **2** | Word | 0.2309 | 1.174 | 1.56 | 107,287 | 76.9% |
+| **2** | Subword | 0.7978 | 1.738 | 4.37 | 6,632 | 20.2% |
+| **3** | Word | 0.1002 | 1.072 | 1.19 | 166,148 | 90.0% |
+| **3** | Subword | 0.7225 | 1.650 | 3.30 | 28,939 | 27.7% |
+| **4** | Word | 0.0444 🏆 | 1.031 | 1.08 | 195,678 | 95.6% |
+| **4** | Subword | 0.5194 | 1.433 | 2.19 | 95,255 | 48.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `te haina here turi te ope hōia ka puta ai ki toitū te kaihautū whenua he`
+2. `o te he rite tēnei mō te takiwā ēnei whare matā pākawa pungatara s g ghost`
+3. `ko ngā rā tonu te reo pākehā kaihautū whenua e ai ki ā nuku whiringa ā`
+
+**Context Size 2:**
+
+1. `o te awa garonne ko bordeaux reo wīwī bordeaux bɔʁdo reo occitan vairas te tāone nui tirohia`
+2. `ko te he tau o te wai pounamu ko ōtepoti te tāone matua o aotearoa brainyhistory 999`
+3. `i te reo pākehā he wāhi nohoia e te tangata engari kāore anō kia tae te nui`
+
+**Context Size 3:**
+
+1. `toitū te whenua he nohanga he locality rānei ki te reo pākehā he wāhi nohoia e te tangata`
+2. `kite i te o waitaha`
+3. `he mea kite i te o waikato en list of sgt frog characters garuru platoon ja ガルル小隊 プルル看護長`
+
+**Context Size 4:**
+
+1. `he mea kite i te o te moana a toi he takiwā o aotearoa kei te ika a māui`
+2. `mea kite i te o te whanga nui a tara smith s p history and traditions of the maoris`
+3. `new zealand he mea kite i te o te tai poutini kei te uru o te wai pounamu ko`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_hanohonuhiai_tu`
+2. `a_he_ke_i_bo_the`
+3. `i_in_mat,_o_a_ia`
+
+**Context Size 2:**
+
+1. `_tionei._torahing`
+2. `e_whi_whitū_tāorm`
+3. `i_aotu_whe_wi_he_`
+
+**Context Size 3:**
+
+1. `te_paenga_o_ngā_pu`
+2. `_te_“matahi_i_ki_a`
+3. `_o_tāone_tāone_noh`
+
+**Context Size 4:**
+
+1. `_te_reo_huru_whirin`
+2. `i_te_ai_i_te_tokera`
+3. `o_te_papaki_te_rohe`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (95,255 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 | 11,670 |
+| Total Tokens | 572,993 |
+| Mean Frequency | 49.10 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 801.57 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | te | 64,133 |
+| 2 | o | 34,097 |
+| 3 | ko | 21,829 |
+| 4 | he | 18,921 |
+| 5 | i | 16,028 |
+| 6 | ki | 12,979 |
+| 7 | ngā | 9,360 |
+| 8 | e | 9,113 |
+| 9 | whenua | 8,565 |
+| 10 | a | 8,027 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | kaitono | 2 |
+| 2 | dansk | 2 |
+| 3 | ˈtænˀsk | 2 |
+| 4 | tenemākareo | 2 |
+| 5 | pākehāhej | 2 |
+| 6 | fra | 2 |
+| 7 | joāeyeshvad | 2 |
+| 8 | hedder | 2 |
+| 9 | lycopersicum | 2 |
+| 10 | tomato | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2239 |
+| R² (Goodness of Fit) | 0.987898 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 72.0% |
+| Top 1,000 | 91.2% |
+| Top 5,000 | 97.1% |
+| Top 10,000 | 99.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9879 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 72.0% of corpus
+- **Long Tail:** 1,670 words needed for remaining 0.6% 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 | 0.5498 🏆 | 0.3626 | N/A | N/A |
+| **mono_64d** | 64 | 0.1805 | 0.3661 | N/A | N/A |
+| **mono_128d** | 128 | 0.0211 | 0.3761 | N/A | N/A |
+| **aligned_32d** | 32 | 0.5498 | 0.3657 | 0.0260 | 0.1840 |
+| **aligned_64d** | 64 | 0.1805 | 0.3550 | 0.0380 | 0.2240 |
+| **aligned_128d** | 128 | 0.0211 | 0.3770 | 0.0480 | 0.2580 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.5498 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3671. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 4.8% 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 | **0.386** | 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 |
+|--------|----------|
+| `-t` | tupono, taputapuatea, taraire |
+| `-p` | pupuhi, pekanga, patukirikiri |
+| `-m` | microsoft, momona, metcalf |
+| `-k` | kāreti, kairangahau, kakabai |
+| `-ma` | mashhad, marge, manukorihi |
+| `-h` | honiara, homai, hūtāne |
+| `-a` | arapohue, ano, ahiahi |
+| `-ta` | taputapuatea, taraire, taradale |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | honiara, pekanga, complexa |
+| `-i` | homai, pupuhi, kāreti |
+| `-e` | shore, hūtāne, arapohue |
+| `-ia` | whakatakotohia, whakatuwheratia, incisapaesia |
+| `-s` | reunionnais, carpodetus, press |
+| `-ga` | pekanga, pānuitanga, patunga |
+| `-n` | levin, susan, princeton |
+| `-o` | werokoko, ano, tupono |
+
+### 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 |
+|------|----------|------------------|----------|
+| `inga` | 1.83x | 42 contexts | hinga, ringa, huinga |
+| `angi` | 1.91x | 28 contexts | rangi, tangi, angitū |
+| `whak` | 1.96x | 25 contexts | whaka, whakia, whakaū |
+| `rang` | 1.56x | 58 contexts | range, rangi, ranga |
+| `hang` | 1.83x | 28 contexts | hangā, hanga, hangai |
+| `akat` | 2.00x | 20 contexts | akatea, whakatō, whakatū |
+| `enga` | 1.71x | 34 contexts | henga, renga, awenga |
+| `onga` | 1.84x | 24 contexts | longa, ponga, tonga |
+| `aita` | 1.70x | 19 contexts | taita, vaita, whaita |
+| `taut` | 1.78x | 14 contexts | tautau, tautoro, tautuhi |
+| `ngat` | 1.50x | 19 contexts | ngati, ngata, ngatea |
+| `whan` | 1.81x | 9 contexts | whano, whanga, whanau |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-t` | `-a` | 203 words | temuka, tākaka |
+| `-p` | `-a` | 187 words | pūhonoiika, parawhenua |
+| `-k` | `-a` | 158 words | kopinga, kētia |
+| `-m` | `-a` | 123 words | maramara, mandiraja |
+| `-h` | `-a` | 122 words | hōhipera, henga |
+| `-t` | `-i` | 117 words | tāpoi, tuatini |
+| `-r` | `-a` | 109 words | rubra, robusta |
+| `-a` | `-a` | 93 words | ahumoana, akarana |
+| `-k` | `-i` | 89 words | kuki, koheriki |
+| `-m` | `-i` | 83 words | moanaui, mangaiti |
+
+### 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 |
+|------|-----------------|------------|------|
+| waikāretu | **`wa-i-kāretu`** | 7.5 | `kāretu` |
+| matapouri | **`ma-ta-pouri`** | 7.5 | `pouri` |
+| whakaratohia | **`whakarato-hi-a`** | 7.5 | `hi` |
+| tamarangi | **`ta-ma-rangi`** | 7.5 | `rangi` |
+| ngātokowaru | **`ngātokow-a-ru`** | 7.5 | `a` |
+| whakatūnga | **`whakatū-ng-a`** | 7.5 | `ng` |
+| huasolanum | **`hu-a-solanum`** | 7.5 | `solanum` |
+| ulaanbaatar | **`ulaanbaat-a-r`** | 7.5 | `a` |
+| joāeyeshvad | **`joāeyeshv-a-d`** | 7.5 | `a` |
+| kaipūtaiao | **`ka-i-pūtaiao`** | 7.5 | `pūtaiao` |
+| korerotia | **`korero-ti-a`** | 7.5 | `ti` |
+| azərbaycan | **`azərbayc-a-n`** | 7.5 | `a` |
+| tohatohahia | **`tohatoha-hi-a`** | 7.5 | `hi` |
+| rokohanga | **`ro-ko-hanga`** | 7.5 | `hanga` |
+| taharangi | **`ta-ha-rangi`** | 7.5 | `rangi` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Māori 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 (3.99x) |
+| N-gram | **2-gram** | Lowest perplexity (171) |
+| Markov | **Context-4** | Highest predictability (95.6%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 11:44:01*

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