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README.md

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+---
+language: mwl
+language_name: Mirandese
+language_family: romance_iberian
+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_iberian
+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: 4.578
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8323
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Mirandese - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Mirandese** 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** | 3.793x | 3.79 | 0.0216% | 2,683,483 |
+| **16k** | 4.139x | 4.14 | 0.0236% | 2,459,597 |
+| **32k** | 4.421x | 4.42 | 0.0252% | 2,302,588 |
+| **64k** | 4.578x 🏆 | 4.58 | 0.0261% | 2,223,729 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Propebela miona ye ua spece de gastrópode de l género Propebela, pertencente la ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁pro pe bela ▁mi ona ▁ye ▁ua ▁spece ▁de ▁gas ... (+16 more)` | 26 |
+| 16k | `▁pro pe bela ▁mi ona ▁ye ▁ua ▁spece ▁de ▁gastrópode ... (+13 more)` | 23 |
+| 32k | `▁pro pe bela ▁mi ona ▁ye ▁ua ▁spece ▁de ▁gastrópode ... (+12 more)` | 22 |
+| 64k | `▁propebela ▁mi ona ▁ye ▁ua ▁spece ▁de ▁gastrópode ▁de ▁l ... (+8 more)` | 18 |
+
+**Sample 2:** `Pingnan ye un cundado de la porbinça Fujian ne la China. Ten ua sobrefiç de km² ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ping nan ▁ye ▁un ▁cundado ▁de ▁la ▁porbinça ▁fujian ▁ne ... (+21 more)` | 31 |
+| 16k | `▁ping nan ▁ye ▁un ▁cundado ▁de ▁la ▁porbinça ▁fujian ▁ne ... (+21 more)` | 31 |
+| 32k | `▁ping nan ▁ye ▁un ▁cundado ▁de ▁la ▁porbinça ▁fujian ▁ne ... (+21 more)` | 31 |
+| 64k | `▁ping nan ▁ye ▁un ▁cundado ▁de ▁la ▁porbinça ▁fujian ▁ne ... (+21 more)` | 31 |
+
+**Sample 3:** `Paízes Baixos ye un paíç localizado na Ouropa. A sua capital ye Amsterdam de la ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁paízes ▁baixos ▁ye ▁un ▁paíç ▁localizado ▁na ▁ouropa . ▁a ... (+10 more)` | 20 |
+| 16k | `▁paízes ▁baixos ▁ye ▁un ▁paíç ▁localizado ▁na ▁ouropa . ▁a ... (+10 more)` | 20 |
+| 32k | `▁paízes ▁baixos ▁ye ▁un ▁paíç ▁localizado ▁na ▁ouropa . ▁a ... (+10 more)` | 20 |
+| 64k | `▁paízes ▁baixos ▁ye ▁un ▁paíç ▁localizado ▁na ▁ouropa . ▁a ... (+7 more)` | 17 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.578x compression
+- **Lowest UNK Rate:** 8k with 0.0216% 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 | 15,343 | 13.91 | 73,697 | 17.8% | 35.6% |
+| **2-gram** | Subword | 225 🏆 | 7.81 | 4,011 | 72.6% | 99.4% |
+| **3-gram** | Word | 43,244 | 15.40 | 99,993 | 7.1% | 21.5% |
+| **3-gram** | Subword | 1,730 | 10.76 | 30,226 | 30.5% | 76.9% |
+| **4-gram** | Word | 83,756 | 16.35 | 139,745 | 4.6% | 13.5% |
+| **4-gram** | Subword | 9,145 | 13.16 | 149,701 | 15.4% | 43.2% |
+| **5-gram** | Word | 53,205 | 15.70 | 77,395 | 5.4% | 14.4% |
+| **5-gram** | Subword | 33,248 | 15.02 | 377,533 | 9.3% | 26.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de l` | 58,173 |
+| 2 | `de la` | 48,036 |
+| 3 | `ne l` | 20,582 |
+| 4 | `de ls` | 12,372 |
+| 5 | `de las` | 10,382 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de l seclo` | 1,892 |
+| 2 | `ls stados ounidos` | 1,436 |
+| 3 | `a partir de` | 1,328 |
+| 4 | `i de l` | 1,327 |
+| 5 | `i de la` | 1,270 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de ls stados ounidos` | 710 |
+| 2 | `i ua poblaçon de` | 453 |
+| 3 | `km i ua poblaçon` | 453 |
+| 4 | `la china ten ua` | 447 |
+| 5 | `china ten ua sobrefiç` | 445 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `km i ua poblaçon de` | 453 |
+| 2 | `china ten ua sobrefiç de` | 445 |
+| 3 | `la china ten ua sobrefiç` | 445 |
+| 4 | `ne la china ten ua` | 342 |
+| 5 | `stados ounidos de la américa` | 309 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 591,904 |
+| 2 | `a _` | 499,400 |
+| 3 | `s _` | 411,342 |
+| 4 | `_ l` | 403,980 |
+| 5 | `d e` | 400,252 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 310,441 |
+| 2 | `d e _` | 308,352 |
+| 3 | `e _ l` | 194,993 |
+| 4 | `_ l a` | 160,851 |
+| 5 | `l a _` | 145,857 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 270,574 |
+| 2 | `d e _ l` | 136,607 |
+| 3 | `_ l a _` | 127,081 |
+| 4 | `e _ l _` | 83,501 |
+| 5 | `e _ l a` | 74,074 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _ l` | 133,195 |
+| 2 | `e _ l a _` | 60,089 |
+| 3 | `d e _ l a` | 59,980 |
+| 4 | `o _ d e _` | 56,259 |
+| 5 | `d e _ l _` | 54,129 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 225
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~26% 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 | 1.0545 | 2.077 | 7.75 | 149,145 | 0.0% |
+| **1** | Subword | 0.8887 | 1.852 | 6.01 | 2,125 | 11.1% |
+| **2** | Word | 0.3376 | 1.264 | 1.92 | 1,155,292 | 66.2% |
+| **2** | Subword | 0.8016 | 1.743 | 4.96 | 12,756 | 19.8% |
+| **3** | Word | 0.1237 | 1.090 | 1.24 | 2,212,454 | 87.6% |
+| **3** | Subword | 0.7949 | 1.735 | 4.10 | 63,188 | 20.5% |
+| **4** | Word | 0.0452 🏆 | 1.032 | 1.07 | 2,748,862 | 95.5% |
+| **4** | Subword | 0.6515 | 1.571 | 2.89 | 258,945 | 34.8% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de participaçon de l príncepe tutmés morriu na mesma família turbenidae apersentan porte las cuostas...`
+2. `l liezi recebírun mais de la stória bai siempre porjetan este al gerar todas las ciéncias`
+3. `la proposiçon cumpuosta por misson apollo fazírun ancursones de la region de trabalhadores renobában...`
+
+**Context Size 2:**
+
+1. `de l testo de l japon residentes strangeiros eilegales besitado an 28 de dezembre de l católicos`
+2. `de la tierra ye to berde cun un sistema político i houmanitário dreitos de ls nomes de`
+3. `ne l sou purmeiro trabalho na astronomie geofísica angenharie eiquenomie etc einicialmente la rebolu...`
+
+**Context Size 3:**
+
+1. `de l seclo xiv i xv antre las percipales obras de la eigreija i sin antermediários repersentantes ó`
+2. `ls stados ounidos an stephen r cobey outor de l yoga eilhes son ls mais amportantes silicatos custit...`
+3. `a partir de anton la reboluçon stendiu se al campo adonde çparou un tiro de canhon i l`
+
+**Context Size 4:**
+
+1. `de ls stados ounidos ne l bietname promobida por lyndon johnson debediu ls amaricanos an campos oupo...`
+2. `km i ua poblaçon de 116 mil ingros an`
+3. `i ua poblaçon de 431 mil ingros an`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_xor_gri_bes,_ca`
+2. `a_",_ye_"birrter`
+3. `ebefrmel_las_gog`
+
+**Context Size 2:**
+
+1. `e_lha_pe,_bólicar`
+2. `a_ambregeiriencia`
+3. `s_oute_l_ra_eisei`
+
+**Context Size 3:**
+
+1. `_de_subre_formas._`
+2. `de_31_de_mera_qu'e`
+3. `e_l_ciclónia_de_l_`
+
+**Context Size 4:**
+
+1. `_de_l_de_an_cente_s`
+2. `de_l_telscópio_lhio`
+3. `_la_sue_tenente,_d.`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (258,945 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 | 74,297 |
+| Total Tokens | 3,042,544 |
+| Mean Frequency | 40.95 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 1358.50 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 272,017 |
+| 2 | l | 154,267 |
+| 3 | la | 129,771 |
+| 4 | i | 87,959 |
+| 5 | an | 48,574 |
+| 6 | que | 42,608 |
+| 7 | ls | 41,935 |
+| 8 | a | 31,842 |
+| 9 | las | 29,271 |
+| 10 | se | 25,391 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | quedó | 2 |
+| 2 | debut | 2 |
+| 3 | haldane | 2 |
+| 4 | xenopus | 2 |
+| 5 | werskey | 2 |
+| 6 | loom | 2 |
+| 7 | bodmer | 2 |
+| 8 | birminghan | 2 |
+| 9 | maureen | 2 |
+| 10 | correspondência | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0129 |
+| R² (Goodness of Fit) | 0.994529 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 45.6% |
+| Top 1,000 | 65.5% |
+| Top 5,000 | 81.7% |
+| Top 10,000 | 87.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9945 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 45.6% of corpus
+- **Long Tail:** 64,297 words needed for remaining 12.1% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.8157 | 0.3421 | N/A | N/A |
+| **mono_64d** | 64 | 0.8323 🏆 | 0.2544 | N/A | N/A |
+| **mono_128d** | 128 | 0.8007 | 0.1810 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8157 | 0.3370 | 0.0960 | 0.3740 |
+| **aligned_64d** | 64 | 0.8323 | 0.2524 | 0.1680 | 0.5300 |
+| **aligned_128d** | 128 | 0.8007 | 0.1744 | 0.2420 | 0.5960 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_64d with 0.8323 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2569. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 24.2% 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** | 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` | ambencíbel, altas, atacante |
+| `-s` | surprende, spaçonabes, seguiren |
+| `-c` | certificación, cruzou, cungestionamientos |
+| `-b` | balioso, bissau, birginia |
+| `-p` | pioneiros, paredones, prague |
+| `-m` | márteres, menimamente, munshiganj |
+| `-ma` | malaquias, matricula, mayas |
+| `-t` | telégrafo, tóxicas, templo |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | pioneiros, márteres, flabonóides |
+| `-o` | etiológico, telégrafo, eisilado |
+| `-a` | gmina, júnia, ria |
+| `-os` | pioneiros, cungestionamientos, canídeos |
+| `-e` | menimamente, çcubre, surprende |
+| `-as` | tóxicas, altas, águas |
+| `-es` | márteres, flabonóides, paredones |
+| `-n` | çporen, certificación, seguiren |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ones` | 2.27x | 105 contexts | mones, cones, pones |
+| `ados` | 2.37x | 66 contexts | lados, fados, dados |
+| `idad` | 2.30x | 59 contexts | idade, lidado, unidad |
+| `ento` | 2.05x | 80 contexts | cento, mento, lento |
+| `çone` | 2.62x | 29 contexts | açones, maçones, raçones |
+| `ista` | 1.91x | 102 contexts | pista, bista, mista |
+| `ient` | 1.97x | 77 contexts | niente, ciento, biento |
+| `tado` | 1.80x | 102 contexts | atado, stado, betado |
+| `amie` | 2.49x | 26 contexts | jamie, tamien, amiens |
+| `dade` | 2.18x | 42 contexts | idade, edades, cidade |
+| `mien` | 2.27x | 35 contexts | miente, tamien, amiens |
+| `ment` | 1.82x | 84 contexts | mento, mente, menta |
+
+### 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` | 247 words | ancenadas, anterspecíficas |
+| `-c` | `-s` | 203 words | cunsequentes, caseiras |
+| `-a` | `-a` | 194 words | angloba, alicia |
+| `-a` | `-o` | 182 words | atípico, assimilado |
+| `-p` | `-s` | 177 words | porgramados, perjuízos |
+| `-s` | `-s` | 167 words | saturadas, surinamés |
+| `-c` | `-o` | 140 words | cometimiento, caindo |
+| `-c` | `-a` | 139 words | cántabra, cunceituada |
+| `-p` | `-a` | 126 words | plaka, pesquisa |
+| `-m` | `-s` | 124 words | mostradas, mosteiros |
+
+### 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 |
+|------|-----------------|------------|------|
+| campanapse | **`campanap-s-e`** | 7.5 | `s` |
+| corumbaenses | **`corumbaen-s-es`** | 7.5 | `s` |
+| machucado | **`machu-ca-do`** | 7.5 | `ca` |
+| cuncluísse | **`cuncluís-s-e`** | 7.5 | `s` |
+| antressando | **`antress-an-do`** | 7.5 | `an` |
+| eilegíaco | **`eilegí-a-co`** | 7.5 | `a` |
+| albergaba | **`alberg-a-ba`** | 7.5 | `a` |
+| alcançasse | **`alcanças-s-e`** | 7.5 | `s` |
+| portucalenses | **`portucalen-s-es`** | 7.5 | `s` |
+| ancluírun | **`ancluí-r-un`** | 7.5 | `r` |
+| ampatando | **`ampat-an-do`** | 7.5 | `an` |
+| neubauten | **`neubau-te-n`** | 7.5 | `te` |
+| asturiense | **`asturien-s-e`** | 7.5 | `s` |
+| banguardista | **`banguardi-s-ta`** | 7.5 | `s` |
+| cumpostelana | **`cumpostel-an-a`** | 7.5 | `an` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Mirandese 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 (4.58x) |
+| N-gram | **2-gram** | Lowest perplexity (225) |
+| Markov | **Context-4** | Highest predictability (95.5%) |
+| 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 13:50:43*

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