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README.md

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+---
+language: jbo
+language_name: Lojban
+language_family: constructed_other
+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-constructed_other
+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: 2.964
+  - name: best_isotropy
+    type: isotropy
+    value: 0.2678
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Lojban - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Lojban** 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** | 2.856x | 2.86 | 0.0265% | 740,723 |
+| **16k** | 2.911x | 2.91 | 0.0270% | 726,775 |
+| **32k** | 2.964x 🏆 | 2.97 | 0.0275% | 713,753 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `le si'o dekna'a cu gradu lo veldetri lo niltei i lo dekna'a cu nanca li 10`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁le ▁si ' o ▁dekna ' a ▁cu ▁gradu ▁lo ... (+14 more)` | 24 |
+| 16k | `▁le ▁si ' o ▁dekna ' a ▁cu ▁gradu ▁lo ... (+14 more)` | 24 |
+| 32k | `▁le ▁si ' o ▁dekna ' a ▁cu ▁gradu ▁lo ... (+14 more)` | 24 |
+
+**Sample 2:** `lo zdotu'a goi zy. cu barda tumla .i zy cu pamoi le'i tumla leka barda .i zy. cu...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lo ▁zdotu ' a ▁goi ▁zy . ▁cu ▁barda ▁tumla ... (+31 more)` | 41 |
+| 16k | `▁lo ▁zdotu ' a ▁goi ▁zy . ▁cu ▁barda ▁tumla ... (+31 more)` | 41 |
+| 32k | `▁lo ▁zdotu ' a ▁goi ▁zy . ▁cu ▁barda ▁tumla ... (+31 more)` | 41 |
+
+**Sample 3:** `da poi ce'u du ka'o goi ko'a zo'u li ka'o te'a re du li ni'u pa .i je ko'a cu re...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁da ▁poi ▁ce ' u ▁du ▁ka ' o ▁goi ... (+30 more)` | 40 |
+| 16k | `▁da ▁poi ▁ce ' u ▁du ▁ka ' o ▁goi ... (+30 more)` | 40 |
+| 32k | `▁da ▁poi ▁ce ' u ▁du ▁ka ' o ▁goi ... (+30 more)` | 40 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 2.964x compression
+- **Lowest UNK Rate:** 8k with 0.0265% 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 | 263 | 8.04 | 5,763 | 71.1% | 90.0% |
+| **2-gram** | Subword | 150 🏆 | 7.23 | 1,249 | 81.8% | 99.9% |
+| **3-gram** | Word | 426 | 8.73 | 11,175 | 65.5% | 84.7% |
+| **3-gram** | Subword | 631 | 9.30 | 9,433 | 58.0% | 87.9% |
+| **4-gram** | Word | 1,152 | 10.17 | 31,022 | 54.5% | 73.7% |
+| **4-gram** | Subword | 1,589 | 10.63 | 41,211 | 49.2% | 73.9% |
+| **5-gram** | Word | 1,669 | 10.70 | 33,007 | 49.2% | 68.6% |
+| **5-gram** | Subword | 2,683 | 11.39 | 80,410 | 44.9% | 68.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de i` | 19,178 |
+| 2 | `la o` | 17,721 |
+| 3 | `a cu` | 17,142 |
+| 4 | `ke a` | 16,638 |
+| 5 | `noi ke` | 16,409 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `noi ke a` | 16,408 |
+| 2 | `ke a cu` | 16,375 |
+| 3 | `i de i` | 16,359 |
+| 4 | `la o zoi` | 16,326 |
+| 5 | `zoi noi ke` | 15,958 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `noi ke a cu` | 16,335 |
+| 2 | `zoi noi ke a` | 15,958 |
+| 3 | `cu jbena i de` | 10,133 |
+| 4 | `jbena i de i` | 10,133 |
+| 5 | `ke a cu merko` | 8,277 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `zoi noi ke a cu` | 15,957 |
+| 2 | `cu jbena i de i` | 10,133 |
+| 3 | `noi ke a cu merko` | 8,276 |
+| 4 | `ke a cu merko ke` | 7,065 |
+| 5 | `i de i lo la` | 6,474 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i _` | 97,095 |
+| 2 | `o _` | 78,639 |
+| 3 | `u _` | 72,524 |
+| 4 | `a _` | 66,871 |
+| 5 | `_ l` | 65,646 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `c u _` | 39,185 |
+| 2 | `_ c u` | 39,177 |
+| 3 | `_ l a` | 35,334 |
+| 4 | `_ z o` | 33,172 |
+| 5 | `z o i` | 32,926 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ c u _` | 38,551 |
+| 2 | `_ z o i` | 32,836 |
+| 3 | `o i . _` | 32,436 |
+| 4 | `z o i .` | 32,435 |
+| 5 | `_ . i _` | 20,318 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `z o i . _` | 32,435 |
+| 2 | `_ z o i .` | 32,422 |
+| 3 | `d e ' i _` | 19,209 |
+| 4 | `_ d e ' i` | 19,179 |
+| 5 | `a _ c u _` | 17,854 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 150
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~68% 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 | 0.4807 | 1.395 | 3.36 | 24,999 | 51.9% |
+| **1** | Subword | 0.8928 | 1.857 | 5.71 | 606 | 10.7% |
+| **2** | Word | 0.2439 | 1.184 | 1.71 | 83,598 | 75.6% |
+| **2** | Subword | 0.8298 | 1.777 | 5.00 | 3,459 | 17.0% |
+| **3** | Word | 0.1180 | 1.085 | 1.28 | 142,297 | 88.2% |
+| **3** | Subword | 0.8915 | 1.855 | 3.94 | 17,283 | 10.8% |
+| **4** | Word | 0.0638 🏆 | 1.045 | 1.18 | 181,290 | 93.6% |
+| **4** | Subword | 0.5626 | 1.477 | 2.30 | 67,967 | 43.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `i de i de i ckaji lo mutce farvi co turni cu jbena i 7 la`
+2. `cu brito ke a cu brito ke xeldraci gasnu cu mrobi o zoi noi ke xeldraci`
+3. `la xamast la gaimast la gaimast la somast la o zoi noi ke a cu sfe`
+
+**Context Size 2:**
+
+1. `de i 31 la pamast la o zoi dirk bogarde zoi noi ke a cu merko skina`
+2. `la o zoi buddy bolden zoi noi ke a cu merko ke xeldraci gasnu cu jbena i`
+3. `a cu brito ke xeldraci gasnu cu jbena i de i 24 la vomast cu 15moi djedi`
+
+**Context Size 3:**
+
+1. `noi ke a cu merko ke xeldraci gasnu cu jbena i de i 14 la cimast i de`
+2. `ke a cu dotco ke xeldraci gasnu cu jbena i de i 13 la cimast la o zoi`
+3. `i de i 4 la remast cu 21moi djedi fi o masti lo rebjukma i i de i`
+
+**Context Size 4:**
+
+1. `noi ke a cu merko ke xeldraci gasnu cu jbena i de i 27 la gaimast la o zoi`
+2. `zoi noi ke a cu brito ke xeldraci gasnu cu jbena i de i 25 la zemast la o`
+3. `cu jbena i de i lo la o zoi jason statham zoi noi ke a cu cimoi masti i`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_lagast._li_t_e_`
+2. `i_xe'au_ja_zoike`
+3. `ast._._keloifino`
+
+**Context Size 2:**
+
+1. `i_51_la'o_ke'i_be`
+2. `o_smu_cu_la_barga`
+3. `u_ke'a_cu_cu_jics`
+
+**Context Size 3:**
+
+1. `cu_cu_je_na_.i_kie`
+2. `_cu_mrobi'o_dju_sr`
+3. `_la_zei_.i_darxi_k`
+
+**Context Size 4:**
+
+1. `_cu_mrobi'o_to_mrob`
+2. `_zoi._noi_ke'a_cu_m`
+3. `oi._ai_se_casnu_cu_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (67,967 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 | 10,828 |
+| Total Tokens | 529,379 |
+| Mean Frequency | 48.89 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 936.81 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | i | 43,370 |
+| 2 | cu | 38,594 |
+| 3 | la | 34,021 |
+| 4 | zoi | 32,918 |
+| 5 | o | 29,624 |
+| 6 | ke | 29,615 |
+| 7 | a | 21,084 |
+| 8 | de | 19,406 |
+| 9 | lo | 19,206 |
+| 10 | noi | 17,016 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | correspondente | 2 |
+| 2 | sitio | 2 |
+| 3 | oficial | 2 |
+| 4 | sperma | 2 |
+| 5 | sexual | 2 |
+| 6 | health | 2 |
+| 7 | linguistics | 2 |
+| 8 | olympiad | 2 |
+| 9 | iol | 2 |
+| 10 | pragmatika | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1384 |
+| R² (Goodness of Fit) | 0.986369 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 80.8% |
+| Top 1,000 | 92.3% |
+| Top 5,000 | 97.6% |
+| Top 10,000 | 99.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9864 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 80.8% of corpus
+- **Long Tail:** 828 words needed for remaining 0.3% 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 | 0.2678 | 0.4864 | N/A | N/A |
+| **mono_64d** | 64 | 0.0649 | 0.4754 | N/A | N/A |
+| **mono_128d** | 128 | 0.0083 | 0.4760 | N/A | N/A |
+| **aligned_32d** | 32 | 0.2678 🏆 | 0.4767 | 0.0100 | 0.0780 |
+| **aligned_64d** | 64 | 0.0649 | 0.4612 | 0.0080 | 0.0760 |
+| **aligned_128d** | 128 | 0.0083 | 0.4657 | 0.0120 | 0.0860 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.2678 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4736. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 1.2% 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 | **0.004** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-s` | seljalge, sunyaev, selpoi |
+| `-c` | cangan, carlos, crepu |
+| `-m` | major, mesurier, mccardie |
+| `-b` | blackmore, bedelia, burmeister |
+| `-k` | kitaro, klaus, ki |
+| `-t` | trefi, téa, tunka |
+| `-p` | pristmen, patchen, pairnu |
+| `-r` | ritli, rossi, riemer |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | nintendos, eros, carlos |
+| `-n` | pristmen, whitman, cangan |
+| `-e` | blackmore, seljalge, émilie |
+| `-i` | farvi, selpoi, ritli |
+| `-a` | bedelia, fipma, guttera |
+| `-u` | crepu, camgu, dotybau |
+| `-r` | major, burmeister, dar |
+| `-o` | kitaro, xrabo, sembello |
+
+### 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 |
+|------|----------|------------------|----------|
+| `jinm` | 1.87x | 15 contexts | jinme, jinmrne, jinmrni |
+| `selc` | 1.69x | 12 contexts | selci, selce, selcu |
+| `selp` | 1.75x | 10 contexts | selpe, selpa, selpo |
+| `skeg` | 1.88x | 6 contexts | skegau, eskegau, xumskegau |
+| `ygau` | 1.40x | 12 contexts | sagygau, popygau, micygau |
+| `anti` | 1.47x | 9 contexts | manti, ranti, canti |
+| `rgau` | 1.31x | 11 contexts | orgau, irgau, argau |
+| `arna` | 1.34x | 5 contexts | rarna, barna, garna |
+| `atni` | 1.53x | 3 contexts | ratni, catni, datni |
+| `cmac` | 1.36x | 3 contexts | cmaci, ocmaci, cmacypre |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-s` | `-i` | 68 words | sanji, skoselti |
+| `-s` | `-a` | 50 words | simkansa, selka |
+| `-m` | `-n` | 49 words | marian, milton |
+| `-m` | `-s` | 48 words | manatus, maksimianus |
+| `-s` | `-s` | 47 words | sabines, sulaues |
+| `-c` | `-e` | 47 words | cemtruje, catnrkonsule |
+| `-s` | `-n` | 44 words | sn, shepperton |
+| `-c` | `-n` | 42 words | chan, copenhagen |
+| `-t` | `-i` | 41 words | terkagni, truci |
+| `-b` | `-n` | 38 words | brannan, beauchemin |
+
+### 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 |
+|------|-----------------|------------|------|
+| erlandson | **`erland-s-on`** | 7.5 | `s` |
+| naknolraitru | **`na-k-nolraitru`** | 7.5 | `nolraitru` |
+| danielson | **`daniel-s-on`** | 7.5 | `s` |
+| humphries | **`humphr-i-es`** | 7.5 | `i` |
+| andersson | **`anders-s-on`** | 7.5 | `s` |
+| gustafson | **`gustaf-s-on`** | 7.5 | `s` |
+| spaskegau | **`s-pa-skegau`** | 6.0 | `skegau` |
+| françoise | **`françois-e`** | 4.5 | `françois` |
+| dominikan | **`dominik-an`** | 4.5 | `dominik` |
+| tedyskegau | **`te-d-yskegau`** | 4.5 | `yskegau` |
+| colasanto | **`co-la-santo`** | 4.5 | `santo` |
+| antioxeias | **`antioxei-as`** | 4.5 | `antioxei` |
+| jefferson | **`jeffers-on`** | 4.5 | `jeffers` |
+| esperantos | **`esperanto-s`** | 4.5 | `esperanto` |
+| dimitrios | **`dimitri-os`** | 4.5 | `dimitri` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Lojban shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **32k BPE** | Best compression (2.96x) |
+| N-gram | **2-gram** | Lowest perplexity (150) |
+| Markov | **Context-4** | Highest predictability (93.6%) |
+| 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 05:55:02*

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