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README.md

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+---
+language: lad
+language_name: Ladino
+language_family: semitic_hebrew
+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-semitic_hebrew
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 4.557
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8013
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Ladino - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Ladino** 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** | 3.622x | 3.62 | 0.1235% | 455,180 |
+| **16k** | 3.981x | 3.98 | 0.1357% | 414,144 |
+| **32k** | 4.311x | 4.31 | 0.1470% | 382,411 |
+| **64k** | 4.557x 🏆 | 4.56 | 0.1553% | 361,808 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `La komarka de Pinares es una komarka de la provinsia de Soria en la junta de Kas...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁la ▁komarka ▁de ▁pin ares ▁es ▁una ▁komarka ▁de ▁la ... (+20 more)` | 30 |
+| 16k | `▁la ▁komarka ▁de ▁pin ares ▁es ▁una ▁komarka ▁de ▁la ... (+19 more)` | 29 |
+| 32k | `▁la ▁komarka ▁de ▁pinares ▁es ▁una ▁komarka ▁de ▁la ▁provinsia ... (+17 more)` | 27 |
+| 64k | `▁la ▁komarka ▁de ▁pinares ▁es ▁una ▁komarka ▁de ▁la ▁provinsia ... (+17 more)` | 27 |
+
+**Sample 2:** `La Wilaya de Tebesa es una wilaya arjelina. Su kapital es Tebesa. de Arjelia`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁la ▁wilaya ▁de ▁te b esa ▁es ▁una ▁wilaya ▁arjelina ... (+10 more)` | 20 |
+| 16k | `▁la ▁wilaya ▁de ▁te b esa ▁es ▁una ▁wilaya ▁arjelina ... (+10 more)` | 20 |
+| 32k | `▁la ▁wilaya ▁de ▁te besa ▁es ▁una ▁wilaya ▁arjelina . ... (+8 more)` | 18 |
+| 64k | `▁la ▁wilaya ▁de ▁tebesa ▁es ▁una ▁wilaya ▁arjelina . ▁su ... (+6 more)` | 16 |
+
+**Sample 3:** `Loeches es un belediye del Komunidad de Madrid. Ver endemas Komunidad Otonoma de...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lo e ches ▁es ▁un ▁belediye ▁del ▁komunidad ▁de ▁madrid ... (+15 more)` | 25 |
+| 16k | `▁lo e ches ▁es ▁un ▁belediye ▁del ▁komunidad ▁de ▁madrid ... (+15 more)` | 25 |
+| 32k | `▁lo eches ▁es ▁un ▁belediye ▁del ▁komunidad ▁de ▁madrid . ... (+14 more)` | 24 |
+| 64k | `▁lo eches ▁es ▁un ▁belediye ▁del ▁komunidad ▁de ▁madrid . ... (+14 more)` | 24 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.557x compression
+- **Lowest UNK Rate:** 8k with 0.1235% 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 | 4,604 | 12.17 | 16,752 | 25.6% | 52.0% |
+| **2-gram** | Subword | 248 🏆 | 7.96 | 3,814 | 71.5% | 98.6% |
+| **3-gram** | Word | 9,419 | 13.20 | 23,823 | 17.0% | 38.9% |
+| **3-gram** | Subword | 1,904 | 10.89 | 23,591 | 30.3% | 75.0% |
+| **4-gram** | Word | 17,892 | 14.13 | 39,193 | 13.5% | 30.2% |
+| **4-gram** | Subword | 9,391 | 13.20 | 97,361 | 15.8% | 45.6% |
+| **5-gram** | Word | 13,967 | 13.77 | 27,943 | 13.4% | 32.0% |
+| **5-gram** | Subword | 27,847 | 14.77 | 203,110 | 9.7% | 31.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de la` | 8,391 |
+| 2 | `en la` | 3,733 |
+| 3 | `la sivdad` | 3,206 |
+| 4 | `de los` | 3,045 |
+| 5 | `en el` | 2,358 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kon grafia ladina` | 2,216 |
+| 2 | `la sivdad de` | 1,675 |
+| 3 | `del estado de` | 1,012 |
+| 4 | `referensias atamientos eksternos` | 997 |
+| 5 | `grafia ladina katēggoría` | 907 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kon grafia ladina katēggoría` | 907 |
+| 2 | `eksternos kon grafia ladina` | 858 |
+| 3 | `atamientos eksternos kon grafia` | 819 |
+| 4 | `es la sivdad de` | 759 |
+| 5 | `referensias atamientos eksternos kon` | 642 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `atamientos eksternos kon grafia ladina` | 819 |
+| 2 | `referensias atamientos eksternos kon grafia` | 642 |
+| 3 | `eksternos kon grafia ladina katēggoría` | 509 |
+| 4 | `kapitala es la sivdad de` | 449 |
+| 5 | `kon grafia ladina katēggoría belediyes` | 303 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 136,043 |
+| 2 | `e _` | 108,724 |
+| 3 | `s _` | 99,726 |
+| 4 | `d e` | 96,629 |
+| 5 | `_ e` | 96,324 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 78,766 |
+| 2 | `d e _` | 60,667 |
+| 3 | `_ l a` | 41,480 |
+| 4 | `e l _` | 39,777 |
+| 5 | `l a _` | 39,678 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 56,438 |
+| 2 | `_ l a _` | 31,063 |
+| 3 | `_ e l _` | 20,949 |
+| 4 | `_ e n _` | 19,353 |
+| 5 | `a _ d e` | 16,872 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _ l` | 14,247 |
+| 2 | `_ d e l _` | 12,864 |
+| 3 | `o _ d e _` | 12,483 |
+| 4 | `a _ d e _` | 12,025 |
+| 5 | `s _ d e _` | 11,131 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 248
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~31% 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 | 0.7341 | 1.663 | 4.31 | 80,061 | 26.6% |
+| **1** | Subword | 1.1710 | 2.252 | 8.23 | 1,285 | 0.0% |
+| **2** | Word | 0.2459 | 1.186 | 1.59 | 344,604 | 75.4% |
+| **2** | Subword | 0.9119 | 1.882 | 4.98 | 10,579 | 8.8% |
+| **3** | Word | 0.0977 | 1.070 | 1.17 | 547,473 | 90.2% |
+| **3** | Subword | 0.7442 | 1.675 | 3.49 | 52,668 | 25.6% |
+| **4** | Word | 0.0388 🏆 | 1.027 | 1.06 | 640,118 | 96.1% |
+| **4** | Subword | 0.5699 | 1.484 | 2.45 | 183,606 | 43.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de su chika komunidad djudia 6 de 1 de un numero 1 11 de querétaro es`
+2. `la turkiya antika esnoga i aztekos el fin de hongos la máale antika fragua mas visitadas`
+3. `el grup de territorio denantes de la sigunda i afrikanos malgrado munchos se topa al sudeste`
+
+**Context Size 2:**
+
+1. `de la libertad san francisco por 51 payises dempués de la india kon grafia ladina katēggoría zionism...`
+2. `en la feria istoria en el 7 de ogusto de el al en ebreo כלכליסט un portmanto`
+3. `la sivdad espanyola en meksiko referensias atamientos eksternos kon grafia ladina kon varias grafias...`
+
+**Context Size 3:**
+
+1. `kon grafia ladina katēggoría belediyes del estado de washington es uno de los 125 belediyes del esta...`
+2. `la sivdad de meksiko en la repuvlika popular kina kon mas de 10 000 a añosa c jeografia`
+3. `del estado de veracruz kultura veracruz es una delas más pobladas dela rusia endagora egziste un enl...`
+
+**Context Size 4:**
+
+1. `kon grafia ladina katēggoría departamentos de guatemala`
+2. `eksternos kon grafia ladina katēggoría istorya de kina`
+3. `atamientos eksternos kon grafia ladina de madrid de madrid kon mas de 1 000 moradores kon asentamien...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_dejurlaya_bamoj`
+2. `a:la_s_tiko_e_e_`
+3. `espe_duvdon_tun_`
+
+**Context Size 2:**
+
+1. `a_en_de_i_audisha`
+2. `e_la_kolde_las_ch`
+3. `s_en_ritot_oy_chi`
+
+**Context Size 3:**
+
+1. `_de_los_fraguatl_o`
+2. `de_–_world’s_way._`
+3. `_la_carle_de_se_in`
+
+**Context Size 4:**
+
+1. `_de_termistion,_gin`
+2. `_la_ser_for_tresénd`
+3. `_el_tresendiya_ay_u`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (183,606 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 | 32,887 |
+| Total Tokens | 724,627 |
+| Mean Frequency | 22.03 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 442.51 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 56,587 |
+| 2 | la | 31,911 |
+| 3 | el | 21,691 |
+| 4 | en | 20,558 |
+| 5 | i | 17,448 |
+| 6 | del | 12,991 |
+| 7 | kon | 11,057 |
+| 8 | es | 10,781 |
+| 9 | los | 9,929 |
+| 10 | ke | 7,038 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | radia | 2 |
+| 2 | syon | 2 |
+| 3 | radiasyon | 2 |
+| 4 | cygnus | 2 |
+| 5 | yoshlar | 2 |
+| 6 | qashqadaryolik | 2 |
+| 7 | ibrat | 2 |
+| 8 | farzandlari | 2 |
+| 9 | oʻzbekcha | 2 |
+| 10 | karluka | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0211 |
+| R² (Goodness of Fit) | 0.997834 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 48.4% |
+| Top 1,000 | 69.1% |
+| Top 5,000 | 84.5% |
+| Top 10,000 | 90.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9978 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 48.4% of corpus
+- **Long Tail:** 22,887 words needed for remaining 9.4% 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 | 0.8013 🏆 | 0.3223 | N/A | N/A |
+| **mono_64d** | 64 | 0.6133 | 0.3071 | N/A | N/A |
+| **mono_128d** | 128 | 0.1352 | 0.2792 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8013 | 0.3333 | 0.0580 | 0.2520 |
+| **aligned_64d** | 64 | 0.6133 | 0.3150 | 0.0740 | 0.3240 |
+| **aligned_128d** | 128 | 0.1352 | 0.2795 | 0.1260 | 0.4300 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8013 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3061. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 12.6% 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 | **0.020** | 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 |
+|--------|----------|
+| `-a` | ashana, afektados, asyatika |
+| `-s` | syeklo, self, soldiers |
+| `-m` | montalvo, mode, mediterráneo |
+| `-k` | kuantos, kolleksioner, kuvrirse |
+| `-t` | tradición, tersio, tributo |
+| `-p` | plano, pearce, polrec |
+| `-b` | beijing, burn, bordj |
+| `-ma` | marks, malayali, martín |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | chafarinas, viejas, afektados |
+| `-a` | ashana, goa, estaba |
+| `-o` | plano, montalvo, mediterráneo |
+| `-os` | afektados, espozos, kuantos |
+| `-n` | occupation, tradición, división |
+| `-es` | estatales, iguales, miques |
+| `-as` | chafarinas, viejas, venideras |
+| `-on` | occupation, foundation, emigration |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ensi` | 1.69x | 66 contexts | pensi, kensi, sensia |
+| `ient` | 1.69x | 46 contexts | siente, orient, viento |
+| `ento` | 1.75x | 34 contexts | lento, vento, tento |
+| `asio` | 1.67x | 40 contexts | nasio, dasio, lasio |
+| `djud` | 1.94x | 20 contexts | djudo, djudía, adjudo |
+| `tado` | 1.50x | 48 contexts | matado, metado, estado |
+| `tern` | 1.77x | 25 contexts | stern, shtern, eterna |
+| `iona` | 1.73x | 26 contexts | lisiona, adisiona, mensiona |
+| `eren` | 1.89x | 19 contexts | keren, serena, ferenc |
+| `ntos` | 1.90x | 17 contexts | santos, pontos, puntos |
+| `graf` | 1.70x | 23 contexts | grafia, grafos, grafía |
+| `entr` | 1.51x | 34 contexts | entre, entry, entró |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-a` | `-s` | 136 words | antikos, ankontrados |
+| `-p` | `-s` | 131 words | pons, puerporasiones |
+| `-a` | `-o` | 130 words | ameyalco, adisionado |
+| `-a` | `-a` | 121 words | ailuropoda, aa |
+| `-m` | `-s` | 119 words | malvinas, materials |
+| `-k` | `-s` | 119 words | konsejos, kolores |
+| `-e` | `-s` | 118 words | establesidas, empieses |
+| `-k` | `-a` | 104 words | kaskadya, kateggoriya |
+| `-e` | `-a` | 104 words | editora, esmirna |
+| `-p` | `-a` | 92 words | preistorya, pionera |
+
+### 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 |
+|------|-----------------|------------|------|
+| respublika | **`re-s-publika`** | 7.5 | `publika` |
+| estatales | **`estat-al-es`** | 7.5 | `al` |
+| ensinyansas | **`ensinyan-s-as`** | 7.5 | `s` |
+| organisar | **`organi-s-ar`** | 7.5 | `s` |
+| entenderse | **`entender-s-e`** | 7.5 | `s` |
+| preistoria | **`p-re-istoria`** | 7.5 | `istoria` |
+| lavoraron | **`lavor-ar-on`** | 7.5 | `ar` |
+| valenzuela | **`valenzu-e-la`** | 7.5 | `e` |
+| tempranas | **`tempr-an-as`** | 7.5 | `an` |
+| espozaron | **`espoz-ar-on`** | 7.5 | `ar` |
+| kolonialo | **`koloni-al-o`** | 7.5 | `al` |
+| apropriado | **`apropri-a-do`** | 7.5 | `a` |
+| parinacota | **`parinac-o-ta`** | 7.5 | `o` |
+| universalo | **`univers-al-o`** | 7.5 | `al` |
+| israelitas | **`israeli-ta-s`** | 7.5 | `ta` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Ladino 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 | **64k BPE** | Best compression (4.56x) |
+| N-gram | **2-gram** | Lowest perplexity (248) |
+| Markov | **Context-4** | Highest predictability (96.1%) |
+| 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 10:17:28*

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