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+---
+language: lmo
+language_name: Lombard
+language_family: romance_galloitalic
+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_galloitalic
+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: 3.475
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8136
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Lombard - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Lombard** 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** | 2.899x | 2.90 | 0.1988% | 276,191 |
+| **16k** | 3.111x | 3.11 | 0.2133% | 257,402 |
+| **32k** | 3.306x | 3.31 | 0.2267% | 242,211 |
+| **64k** | 3.475x 🏆 | 3.48 | 0.2382% | 230,431 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `a l'è un comun de la Cechia, part de la Moravia de Sota e del distret de Hodonín...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁a ▁l ' è ▁un ▁comun ▁de ▁la ▁cechia , ... (+18 more)` | 28 |
+| 16k | `▁a ▁l ' è ▁un ▁comun ▁de ▁la ▁cechia , ... (+16 more)` | 26 |
+| 32k | `▁a ▁l ' è ▁un ▁comun ▁de ▁la ▁cechia , ... (+16 more)` | 26 |
+| 64k | `▁a ▁l ' è ▁un ▁comun ▁de ▁la ▁cechia , ... (+16 more)` | 26 |
+
+**Sample 2:** `El 872 a l'è 'n ann del secol quell de noeuv. Cossa l'è sucedud Chi l'è che l'è ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁el ▁ 8 7 2 ▁a ▁l ' è ▁' ... (+33 more)` | 43 |
+| 16k | `▁el ▁ 8 7 2 ▁a ▁l ' è ▁' ... (+33 more)` | 43 |
+| 32k | `▁el ▁ 8 7 2 ▁a ▁l ' è ▁' ... (+33 more)` | 43 |
+| 64k | `▁el ▁ 8 7 2 ▁a ▁l ' è ▁' ... (+33 more)` | 43 |
+
+**Sample 3:** `Superfice: 6.334 km² Popolazzion (ISTAT 606.413 ab. Densità: 96 ab./km² Numer de...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁superfice : ▁ 6 . 3 3 4 ▁km 2 ... (+50 more)` | 60 |
+| 16k | `▁superfice : ▁ 6 . 3 3 4 ▁km 2 ... (+49 more)` | 59 |
+| 32k | `▁superfice : ▁ 6 . 3 3 4 ▁km 2 ... (+48 more)` | 58 |
+| 64k | `▁superfice : ▁ 6 . 3 3 4 ▁km 2 ... (+48 more)` | 58 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.475x compression
+- **Lowest UNK Rate:** 8k with 0.1988% 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 | 7,760 | 12.92 | 122,388 | 29.0% | 53.4% |
+| **2-gram** | Subword | 268 🏆 | 8.07 | 6,535 | 68.2% | 98.7% |
+| **3-gram** | Word | 14,354 | 13.81 | 199,723 | 22.6% | 48.6% |
+| **3-gram** | Subword | 2,089 | 11.03 | 52,666 | 30.7% | 72.9% |
+| **4-gram** | Word | 20,963 | 14.36 | 321,119 | 20.2% | 45.6% |
+| **4-gram** | Subword | 10,897 | 13.41 | 280,505 | 17.5% | 44.7% |
+| **5-gram** | Word | 15,543 | 13.92 | 228,095 | 20.6% | 47.3% |
+| **5-gram** | Subword | 37,324 | 15.19 | 760,735 | 11.8% | 32.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l è` | 175,964 |
+| 2 | `de la` | 121,062 |
+| 3 | `a l` | 80,762 |
+| 4 | `alter proget` | 33,969 |
+| 5 | `de l` | 33,487 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a l è` | 71,526 |
+| 2 | `l è un` | 32,278 |
+| 3 | `è un comun` | 23,691 |
+| 4 | `l è n` | 19,224 |
+| 5 | `el g ha` | 18,949 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a l è un` | 30,858 |
+| 2 | `l è un comun` | 23,691 |
+| 3 | `è un comun de` | 15,254 |
+| 4 | `un comun de la` | 15,236 |
+| 5 | `l è n cümü` | 14,678 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a l è un comun` | 23,684 |
+| 2 | `l è un comun de` | 15,254 |
+| 3 | `è un comun de la` | 15,236 |
+| 4 | `cont una popolazzion de abitant` | 13,027 |
+| 5 | `una popolazzion de abitant riferiment` | 12,935 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 1,432,799 |
+| 2 | `_ d` | 1,057,415 |
+| 3 | `e _` | 1,006,725 |
+| 4 | `d e` | 886,199 |
+| 5 | `_ l` | 709,001 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 789,525 |
+| 2 | `d e _` | 528,733 |
+| 3 | `e l _` | 383,347 |
+| 4 | `l a _` | 338,250 |
+| 5 | `_ l a` | 295,669 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 515,677 |
+| 2 | `_ l a _` | 273,207 |
+| 3 | `_ d e l` | 205,525 |
+| 4 | `d e l _` | 203,248 |
+| 5 | `d e _ l` | 169,714 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e l _` | 198,355 |
+| 2 | `_ d e _ l` | 168,983 |
+| 3 | `_ l ' è _` | 164,316 |
+| 4 | `e _ l a _` | 145,472 |
+| 5 | `d e _ l a` | 122,850 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 268
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~33% 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 | 0.8496 | 1.802 | 5.71 | 320,846 | 15.0% |
+| **1** | Subword | 0.9820 | 1.975 | 7.44 | 2,274 | 1.8% |
+| **2** | Word | 0.3082 | 1.238 | 1.84 | 1,817,237 | 69.2% |
+| **2** | Subword | 0.9539 | 1.937 | 6.24 | 16,914 | 4.6% |
+| **3** | Word | 0.1317 | 1.096 | 1.27 | 3,319,649 | 86.8% |
+| **3** | Subword | 0.8409 | 1.791 | 4.52 | 105,502 | 15.9% |
+| **4** | Word | 0.0581 🏆 | 1.041 | 1.10 | 4,172,728 | 94.2% |
+| **4** | Subword | 0.7003 | 1.625 | 3.13 | 476,503 | 30.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de bourg en fransés arrondissements e in del comun del regn d abitant riferiment lista di`
+2. `l è staa fat tort che la u s cieta del dipartimènt de 215 alter proget`
+3. `la stiria waidhofen an und freude ich dich dass dese en g ha na popolasiù de`
+
+**Context Size 2:**
+
+1. `l è de 4 23 test immanuel casto musega keen horror vacui feat romina falconi che a`
+2. `de la serie a l éra csì trascüra e csì da póch che federìco i el re`
+3. `a l è iniziàa in del pleistocene poeu soeu poeu giò 3 milion de alber qe l`
+
+**Context Size 3:**
+
+1. `a l è un comun di isole balear cont una popolazzion de abitant riferiment hacienda es alter proget`
+2. `l è un paes de l asia del pakistan`
+3. `è un comun del distret de hradec králové e del distret de jura nord vaudois in del canton`
+
+**Context Size 4:**
+
+1. `a l è un cumün svizzer del canton türgovia la süperfiss del teritori del cumün l è de 2`
+2. `l è un comun del distret de prešov in la region de trnava ligam de foeura sit ofizzial alter`
+3. `è un comun de la provincia de noara giamò in del el tö part a una manifestaziun ligada ai`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_sò_denal'ètetom`
+2. `a_str_mü_dinteme`
+3. `essöva_gàn_m_d,_`
+
+**Context Size 2:**
+
+1. `a_giù_doregia_l'è`
+2. `_denaa_a_de_abeci`
+3. `e_imèntù_del_noli`
+
+**Context Size 3:**
+
+1. `_de_15_mederàl_bib`
+2. `de_altèsa_movincia`
+3. `el_gh'era,_elegh_u`
+
+**Context Size 4:**
+
+1. `_de_l'onda_dentan_d`
+2. `_la_red_hd_-_gattag`
+3. `_del_cannon._person`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (476,503 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 | 144,217 |
+| Total Tokens | 7,040,353 |
+| Mean Frequency | 48.82 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 2201.90 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 519,158 |
+| 2 | l | 332,263 |
+| 3 | la | 287,692 |
+| 4 | del | 200,975 |
+| 5 | è | 195,911 |
+| 6 | a | 181,605 |
+| 7 | el | 167,836 |
+| 8 | e | 158,973 |
+| 9 | in | 125,242 |
+| 10 | che | 81,914 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | platamone | 2 |
+| 2 | ludvik | 2 |
+| 3 | zorzut | 2 |
+| 4 | alojz | 2 |
+| 5 | gradnik | 2 |
+| 6 | böhmstetten | 2 |
+| 7 | monegasche | 2 |
+| 8 | diaconești | 2 |
+| 9 | chichinsci | 2 |
+| 10 | şerbănești | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0675 |
+| R² (Goodness of Fit) | 0.999638 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 54.0% |
+| Top 1,000 | 72.1% |
+| Top 5,000 | 82.9% |
+| Top 10,000 | 87.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9996 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 54.0% of corpus
+- **Long Tail:** 134,217 words needed for remaining 12.7% 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 | 0.8136 | 0.3373 | N/A | N/A |
+| **mono_64d** | 64 | 0.7985 | 0.2665 | N/A | N/A |
+| **mono_128d** | 128 | 0.7531 | 0.2072 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8136 🏆 | 0.3393 | 0.0920 | 0.3580 |
+| **aligned_64d** | 64 | 0.7985 | 0.2646 | 0.1660 | 0.5380 |
+| **aligned_128d** | 128 | 0.7531 | 0.2006 | 0.2320 | 0.5780 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8136 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2693. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 23.2% 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 | **0.446** | 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 |
+|--------|----------|
+| `-s` | suldà, sedimm, serraj |
+| `-a` | alfanumerich, antiege, apinac |
+| `-c` | capitana, centralizzazzion, concentrich |
+| `-p` | percepìd, pròssima, pizzà |
+| `-ca` | capitana, cabardes, cambo |
+| `-b` | beve, broeulla, buildings |
+| `-m` | mésage, mysteries, mia |
+| `-d` | dificila, dàl, dreits |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | capitana, ghiffa, vallinfreda |
+| `-n` | granon, repulsion, eisenbahn |
+| `-e` | beve, antiege, häme |
+| `-i` | liebenbergii, percassi, kiuruvesi |
+| `-o` | riuso, malvito, quagliuzzo |
+| `-s` | vachères, mysteries, mauvais |
+| `-t` | nètt, tunet, fònoisolant |
+| `-on` | granon, repulsion, centralizzazzion |
+
+### 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 |
+|------|----------|------------------|----------|
+| `zzio` | 2.50x | 36 contexts | azzion, lazzio, dozzion |
+| `rovi` | 2.01x | 57 contexts | rovin, rovid, trovi |
+| `itan` | 1.73x | 84 contexts | titan, ritan, gaitan |
+| `stre` | 1.63x | 106 contexts | èstre, stret, strel |
+| `lter` | 1.80x | 61 contexts | òlter, älter, olter |
+| `ifer` | 1.85x | 49 contexts | cifer, zifer, riferì |
+| `inci` | 1.56x | 98 contexts | vinci, incis, incin |
+| `perf` | 1.94x | 39 contexts | perfet, perfid, perfèt |
+| `popo` | 2.31x | 21 contexts | popoi, popoj, popov |
+| `istr` | 1.57x | 93 contexts | istra, nistra, distro |
+| `omun` | 2.09x | 29 contexts | comun, comune, comunn |
+| `tret` | 2.23x | 23 contexts | stret, trets, strett |
+
+### 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` | `-a` | 182 words | consideràda, calabiana |
+| `-s` | `-a` | 140 words | satyagraha, südtirulesa |
+| `-p` | `-a` | 135 words | porta, provenienza |
+| `-a` | `-a` | 96 words | apiifolia, ajaa |
+| `-c` | `-o` | 79 words | collecchio, cosimo |
+| `-c` | `-e` | 77 words | cadore, cunoniaceae |
+| `-s` | `-n` | 74 words | stagion, stallikon |
+| `-c` | `-n` | 74 words | cardinalin, cunserven |
+| `-d` | `-a` | 71 words | diavolezza, dulia |
+| `-b` | `-a` | 64 words | bicicleta, balaustra |
+
+### 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 |
+|------|-----------------|------------|------|
+| independentisem | **`independentis-e-m`** | 7.5 | `e` |
+| büsserach | **`büsser-a-ch`** | 7.5 | `a` |
+| desvilupar | **`desvilup-a-r`** | 7.5 | `a` |
+| sudcorean | **`sudco-re-an`** | 7.5 | `re` |
+| ingrendient | **`ingrendi-e-nt`** | 7.5 | `e` |
+| desgrazzia | **`de-s-grazzia`** | 7.5 | `grazzia` |
+| monterrei | **`monterr-e-i`** | 7.5 | `e` |
+| beutelsbach | **`beutelsb-a-ch`** | 7.5 | `a` |
+| pianzanda | **`pianza-n-da`** | 7.5 | `n` |
+| compagnii | **`compagn-i-i`** | 7.5 | `i` |
+| marchesan | **`marches-a-n`** | 7.5 | `a` |
+| scrivania | **`scriva-n-ia`** | 7.5 | `n` |
+| recustrüii | **`recustrü-i-i`** | 7.5 | `i` |
+| principiar | **`princip-ia-r`** | 6.0 | `princip` |
+| modernitaa | **`moderni-ta-a`** | 6.0 | `moderni` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Lombard 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 (3.47x) |
+| N-gram | **2-gram** | Lowest perplexity (268) |
+| Markov | **Context-4** | Highest predictability (94.2%) |
+| 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 11:37:13*
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new file mode 100644
index 0000000000000000000000000000000000000000..a53016a1fb21f446945bb11b885c48ac76cea54d
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diff --git a/models/tokenizer/lmo_tokenizer_8k.vocab b/models/tokenizer/lmo_tokenizer_8k.vocab
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