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README.md

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+---
+language: nap
+language_name: Neapolitan
+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: 3.920
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8038
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Neapolitan - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Neapolitan** 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** | 3.334x | 3.34 | 0.0273% | 157,778 |
+| **16k** | 3.567x | 3.57 | 0.0292% | 147,487 |
+| **32k** | 3.772x | 3.78 | 0.0308% | 139,478 |
+| **64k** | 3.920x 🏆 | 3.93 | 0.0320% | 134,201 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Roccagorga è nu comune 'e crestiane da pruvincia 'e Latina. da pruvincia 'e Lati...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁rocca gor ga ▁è ▁nu ▁comune ▁' e ▁crestiane ▁da ... (+18 more)` | 28 |
+| 16k | `▁rocca gor ga ▁è ▁nu ▁comune ▁' e ▁crestiane ▁da ... (+18 more)` | 28 |
+| 32k | `▁rocca gor ga ▁è ▁nu ▁comune ▁' e ▁crestiane ▁da ... (+18 more)` | 28 |
+| 64k | `▁rocca gorga ▁è ▁nu ▁comune ▁' e ▁crestiane ▁da ▁pruvincia ... (+17 more)` | 27 |
+
+**Sample 2:** `Osini è nu comune 'e 947 crestiane da pruvincia 'e Ogliastra. pruvincia 'e Oglia...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁o sini ▁è ▁nu ▁comune ▁' e ▁ 9 4 ... (+18 more)` | 28 |
+| 16k | `▁o sini ▁è ▁nu ▁comune ▁' e ▁ 9 4 ... (+18 more)` | 28 |
+| 32k | `▁o sini ▁è ▁nu ▁comune ▁' e ▁ 9 4 ... (+18 more)` | 28 |
+| 64k | `▁o sini ▁è ▁nu ▁comune ▁' e ▁ 9 4 ... (+18 more)` | 28 |
+
+**Sample 3:** `Cu 'a canzona Mare verde, Mario Trevi e Milva se piazzajeno 'o siconno posto ô G...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁cu ▁' a ▁canzona ▁mare ▁verde , ▁mario ▁trevi ▁e ... (+16 more)` | 26 |
+| 16k | `▁cu ▁' a ▁canzona ▁mare ▁verde , ▁mario ▁trevi ▁e ... (+16 more)` | 26 |
+| 32k | `▁cu ▁' a ▁canzona ▁mare ▁verde , ▁mario ▁trevi ▁e ... (+15 more)` | 25 |
+| 64k | `▁cu ▁' a ▁canzona ▁mare ▁verde , ▁mario ▁trevi ▁e ... (+13 more)` | 23 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.920x compression
+- **Lowest UNK Rate:** 8k with 0.0273% 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 | 1,553 | 10.60 | 14,483 | 42.3% | 65.9% |
+| **2-gram** | Subword | 233 🏆 | 7.86 | 2,804 | 70.9% | 99.1% |
+| **3-gram** | Word | 1,319 | 10.37 | 17,108 | 45.9% | 70.7% |
+| **3-gram** | Subword | 1,644 | 10.68 | 21,244 | 34.8% | 77.4% |
+| **4-gram** | Word | 1,944 | 10.93 | 26,074 | 40.9% | 70.8% |
+| **4-gram** | Subword | 7,694 | 12.91 | 97,053 | 23.1% | 48.2% |
+| **5-gram** | Word | 2,009 | 10.97 | 18,790 | 35.5% | 73.0% |
+| **5-gram** | Subword | 22,083 | 14.43 | 219,213 | 19.9% | 35.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `categoria comune` | 17,776 |
+| 2 | `pruvincia e` | 16,185 |
+| 3 | `da pruvincia` | 14,509 |
+| 4 | `comune e` | 13,766 |
+| 5 | `comune da` | 11,499 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `da pruvincia e` | 14,465 |
+| 2 | `categoria comune da` | 11,374 |
+| 3 | `è nu comune` | 7,948 |
+| 4 | `nu comune e` | 7,776 |
+| 5 | `e l italia` | 6,831 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `è nu comune e` | 7,772 |
+| 2 | `comune da pruvincia e` | 5,907 |
+| 3 | `categoria comune e l` | 5,901 |
+| 4 | `comune e l italia` | 5,901 |
+| 5 | `categoria comune da pruvincia` | 5,899 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `categoria comune e l italia` | 5,901 |
+| 2 | `categoria comune da pruvincia e` | 5,899 |
+| 3 | `e abitante da pruvincia e` | 3,717 |
+| 4 | `è nu comune e e` | 2,507 |
+| 5 | `nu comune e e abitante` | 2,506 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 256,791 |
+| 2 | `a _` | 167,602 |
+| 3 | `o _` | 102,609 |
+| 4 | `_ c` | 99,915 |
+| 5 | `_ '` | 94,748 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `' e _` | 63,827 |
+| 2 | `_ ' e` | 63,192 |
+| 3 | `n e _` | 60,363 |
+| 4 | `_ c a` | 40,897 |
+| 5 | `e _ d` | 34,274 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ ' e _` | 63,082 |
+| 2 | `u n e _` | 28,433 |
+| 3 | `m u n e` | 27,493 |
+| 4 | `c o m u` | 26,319 |
+| 5 | `o m u n` | 26,310 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `m u n e _` | 27,267 |
+| 2 | `c o m u n` | 26,309 |
+| 3 | `o m u n e` | 26,017 |
+| 4 | `e _ ' e _` | 25,239 |
+| 5 | `a _ ' e _` | 21,668 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 233
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~35% 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 | 0.7129 | 1.639 | 4.16 | 90,826 | 28.7% |
+| **1** | Subword | 0.9473 | 1.928 | 7.06 | 1,028 | 5.3% |
+| **2** | Word | 0.2155 | 1.161 | 1.48 | 376,611 | 78.5% |
+| **2** | Subword | 0.9373 | 1.915 | 5.69 | 7,253 | 6.3% |
+| **3** | Word | 0.0686 | 1.049 | 1.11 | 555,576 | 93.1% |
+| **3** | Subword | 0.8624 | 1.818 | 4.10 | 41,221 | 13.8% |
+| **4** | Word | 0.0236 🏆 | 1.016 | 1.04 | 613,933 | 97.6% |
+| **4** | Subword | 0.6629 | 1.583 | 2.73 | 168,878 | 33.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `e pallone taliano d o salvador grenada o primmo decennio d italia marocco 16 ac 57`
+2. `a suoja se mpara l m giugrafia categoria comune e la štrada rëggiunal 509 e francesco`
+3. `comune da pruvincia e l italia teen angels fall first lady starlight e silenzio cantatore museca`
+
+**Context Size 2:**
+
+1. `categoria comune e crestiane da pruvincia e messina categoria comune da pruvincia e padova categoria...`
+2. `pruvincia e messina categoria comune e 191 e abitante da pruvincia e arrezzo categoria comune da reg...`
+3. `da pruvincia e ancona categoria comune da reggione veneto categoria comune e crestiane da pruvincia ...`
+
+**Context Size 3:**
+
+1. `da pruvincia e teramo è na pruvincia da reggione autonoma da zardegna categoria comune e l italia o`
+2. `categoria comune da pruvincia e rovigo categoria comune da reggione veneto categoria comune e l ital...`
+3. `è nu comune e e abitante da pruvincia e brescia categoria comune da reggione pùglia categoria comune...`
+
+**Context Size 4:**
+
+1. `è nu comune e e abitante da pruvincia e torino categoria comune da pruvincia e brescia categoria com...`
+2. `comune da pruvincia e cuneo categoria comune da pruvincia e pavia categoria comune da pruvincia e ta...`
+3. `categoria comune e l italia nutarelle`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_d_cionettegggop`
+2. `e_'estulliù_25_d`
+3. `a_catrtetru_cali`
+
+**Context Size 2:**
+
+1. `e_l_3:21_'ato_'o_`
+2. `a_canno_23_1_cune`
+3. `o_(quistegordìa_c`
+
+**Context Size 3:**
+
+1. `'e_cano._cano,_and`
+2. `_'e_cchiuvasco_fuj`
+3. `ne_da_pruvincia_da`
+
+**Context Size 4:**
+
+1. `_'e_se_caglie_nòrd_`
+2. `une_rre_casalermo_c`
+3. `mune_'e_veneto_club`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (168,878 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 | 36,283 |
+| Total Tokens | 817,123 |
+| Mean Frequency | 22.52 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 557.45 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | e | 82,690 |
+| 2 | a | 29,343 |
+| 3 | comune | 26,008 |
+| 4 | da | 25,087 |
+| 5 | o | 21,371 |
+| 6 | categoria | 20,079 |
+| 7 | pruvincia | 16,344 |
+| 8 | è | 16,054 |
+| 9 | nu | 12,737 |
+| 10 | l | 10,986 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | boo | 2 |
+| 2 | horror | 2 |
+| 3 | nestate | 2 |
+| 4 | accumula | 2 |
+| 5 | livelli | 2 |
+| 6 | pallòne | 2 |
+| 7 | fàtte | 2 |
+| 8 | orobica | 2 |
+| 9 | dacchessì | 2 |
+| 10 | totàle | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0191 |
+| R² (Goodness of Fit) | 0.998411 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 51.9% |
+| Top 1,000 | 71.4% |
+| Top 5,000 | 84.8% |
+| Top 10,000 | 90.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9984 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 51.9% of corpus
+- **Long Tail:** 26,283 words needed for remaining 9.5% 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 | 0.8038 🏆 | 0.3434 | N/A | N/A |
+| **mono_64d** | 64 | 0.5268 | 0.3001 | N/A | N/A |
+| **mono_128d** | 128 | 0.1336 | 0.3015 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8038 | 0.3363 | 0.0320 | 0.2220 |
+| **aligned_64d** | 64 | 0.5268 | 0.3106 | 0.0660 | 0.2860 |
+| **aligned_128d** | 128 | 0.1336 | 0.2965 | 0.1240 | 0.3880 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8038 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3147. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 12.4% 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 | **1.085** | 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` | suspira, séa, signure |
+| `-c` | cholesterolo, capeto, cë |
+| `-a` | avetrana, accummenzanno, avvène |
+| `-p` | parla, pajise, porcellana |
+| `-m` | mporta, mètte, musolino |
+| `-ca` | capeto, cacciá, cartiere |
+| `-n` | nucliare, nudo, nsediamiente |
+| `-r` | roncalli, racconti, races |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | nucliare, avvène, edifice |
+| `-o` | accummenzanno, nudo, cholesterolo |
+| `-a` | parla, avetrana, porcellana |
+| `-te` | derette, accerette, nsediamiente |
+| `-ne` | avvène, guaglione, tròvene |
+| `-to` | capeto, conquistato, muderato |
+| `-no` | accummenzanno, vomano, musolino |
+| `-i` | aeterni, roncalli, shinji |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ette` | 1.70x | 114 contexts | mette, iette, rette |
+| `tali` | 2.05x | 39 contexts | talia, talian, ëtalia |
+| `zion` | 1.92x | 45 contexts | azione, frazion, azziona |
+| `ione` | 1.92x | 29 contexts | rione, gione, lione |
+| `ggio` | 1.65x | 40 contexts | aggio, ggion, maggio |
+| `gion` | 1.78x | 27 contexts | gione, ggion, légion |
+| `uvin` | 2.19x | 12 contexts | ruvine, pruvinc, pruvinge |
+| `inci` | 1.58x | 26 contexts | incis, vinci, mincio |
+| `eggi` | 1.34x | 46 contexts | leggi, reggie, leggia |
+| `itan` | 1.40x | 37 contexts | titan, titano, aitanic |
+| `ital` | 1.53x | 25 contexts | italy, italo, vitale |
+| `stia` | 1.70x | 17 contexts | ostia, bastia, bestia |
+
+### 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` | `-e` | 290 words | cunzèrve, cruate |
+| `-c` | `-o` | 229 words | completo, cattoleco |
+| `-p` | `-e` | 224 words | perdette, puaése |
+| `-a` | `-e` | 218 words | arretiraje, agge |
+| `-s` | `-e` | 212 words | setteciénde, specialmente |
+| `-c` | `-a` | 177 words | concetta, conca |
+| `-a` | `-o` | 170 words | aspettando, arvero |
+| `-s` | `-o` | 158 words | socio, severino |
+| `-p` | `-o` | 147 words | piccerillo, paleuliteco |
+| `-a` | `-a` | 142 words | ammerecana, agordina |
+
+### 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 |
+|------|-----------------|------------|------|
+| schiavona | **`schiav-o-na`** | 7.5 | `o` |
+| montescheno | **`montesch-e-no`** | 7.5 | `e` |
+| piccolomini | **`piccolom-i-ni`** | 7.5 | `i` |
+| cuntinuato | **`cuntinu-a-to`** | 7.5 | `a` |
+| questione | **`questi-o-ne`** | 7.5 | `o` |
+| davisvideo | **`davisvid-e-o`** | 7.5 | `e` |
+| tenéssene | **`tenés-se-ne`** | 7.5 | `se` |
+| aristofane | **`aristof-a-ne`** | 7.5 | `a` |
+| macchiaiole | **`macchiai-o-le`** | 7.5 | `o` |
+| possebbeletà | **`possebbel-e-tà`** | 7.5 | `e` |
+| ucchiarone | **`ucchiar-o-ne`** | 7.5 | `o` |
+| recensione | **`recensi-o-ne`** | 7.5 | `o` |
+| accuminciaie | **`accumincia-i-e`** | 7.5 | `i` |
+| ascensore | **`ascens-o-re`** | 7.5 | `o` |
+| prubbecato | **`prubbec-a-to`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Neapolitan 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 (3.92x) |
+| N-gram | **2-gram** | Lowest perplexity (233) |
+| Markov | **Context-4** | Highest predictability (97.6%) |
+| 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 14:48:02*

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