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+---
+language: tet
+language_name: Tetum
+language_family: austronesian_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-austronesian_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: 4.079
+  - name: best_isotropy
+    type: isotropy
+    value: 0.2388
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Tetum - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Tetum** 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** | 3.685x | 3.69 | 0.0920% | 220,741 |
+| **16k** | 3.897x | 3.90 | 0.0973% | 208,698 |
+| **32k** | 4.079x 🏆 | 4.08 | 0.1018% | 199,418 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Paraná mak sai estadu iha Brazíl. Populasaun ema Ligasaun Ba Li'ur Governo do Es...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁par aná ▁mak ▁sai ▁estadu ▁iha ▁brazíl . ▁populasaun ▁ema ... (+18 more)` | 28 |
+| 16k | `▁paraná ▁mak ▁sai ▁estadu ▁iha ▁brazíl . ▁populasaun ▁ema ▁ligasaun ... (+16 more)` | 26 |
+| 32k | `▁paraná ▁mak ▁sai ▁estadu ▁iha ▁brazíl . ▁populasaun ▁ema ▁ligasaun ... (+16 more)` | 26 |
+
+**Sample 2:** `Mekanika (Lian Latina mechanicus, husi Lian Yunani Mechanikos, ema ne'ebe espesi...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mekanika ▁( lian ▁la tina ▁me ch an ic us ... (+24 more)` | 34 |
+| 16k | `▁mekanika ▁( lian ▁latina ▁mechan ic us , ▁husi ▁lian ... (+18 more)` | 28 |
+| 32k | `▁mekanika ▁( lian ▁latina ▁mechanicus , ▁husi ▁lian ▁yunani ▁mechanikos ... (+12 more)` | 22 |
+
+**Sample 3:** `Inkscape hanesan Aplikasaun editor ba imajem ne'ebe ho kodigu nakloke iha lisens...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁in ks cape ▁hanesan ▁aplikasaun ▁ed itor ▁ba ▁imajem ▁ne ... (+15 more)` | 25 |
+| 16k | `▁in ks cape ▁hanesan ▁aplikasaun ▁editor ▁ba ▁imajem ▁ne ' ... (+11 more)` | 21 |
+| 32k | `▁inkscape ▁hanesan ▁aplikasaun ▁editor ▁ba ▁imajem ▁ne ' ebe ▁ho ... (+9 more)` | 19 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 4.079x compression
+- **Lowest UNK Rate:** 8k with 0.0920% 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 | 1,400 | 10.45 | 5,366 | 42.9% | 71.5% |
+| **2-gram** | Subword | 284 🏆 | 8.15 | 1,827 | 67.5% | 99.3% |
+| **3-gram** | Word | 1,275 | 10.32 | 6,153 | 49.9% | 70.9% |
+| **3-gram** | Subword | 2,144 | 11.07 | 13,149 | 25.6% | 72.8% |
+| **4-gram** | Word | 1,739 | 10.76 | 10,529 | 49.1% | 63.6% |
+| **4-gram** | Subword | 8,921 | 13.12 | 53,309 | 14.4% | 45.0% |
+| **5-gram** | Word | 1,049 | 10.03 | 7,279 | 55.7% | 71.0% |
+| **5-gram** | Subword | 20,481 | 14.32 | 103,985 | 10.6% | 35.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ne e` | 2,579 |
+| 2 | `ne ebé` | 2,254 |
+| 3 | `iha tinan` | 1,036 |
+| 4 | `timor leste` | 973 |
+| 5 | `lorosa e` | 966 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `timór lorosa e` | 863 |
+| 2 | `ba li ur` | 806 |
+| 3 | `ligasaun ba li` | 803 |
+| 4 | `timor leste nian` | 553 |
+| 5 | `ne e iha` | 542 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ligasaun ba li ur` | 803 |
+| 2 | `iha timór lorosa e` | 486 |
+| 3 | `da républica mit dem` | 440 |
+| 4 | `républica mit dem diploma` | 440 |
+| 5 | `jornal da républica mit` | 439 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `da républica mit dem diploma` | 440 |
+| 2 | `jornal da républica mit dem` | 439 |
+| 3 | `ida iha timór lorosa e` | 439 |
+| 4 | `ur sensus fo fila fali` | 438 |
+| 5 | `mit dem diploma ministerial n` | 438 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 55,311 |
+| 2 | `a n` | 26,720 |
+| 3 | `n _` | 25,228 |
+| 4 | `_ n` | 24,195 |
+| 5 | `e _` | 21,839 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _` | 10,780 |
+| 2 | `h a _` | 10,572 |
+| 3 | `i h a` | 10,489 |
+| 4 | `i a _` | 9,335 |
+| 5 | `_ i h` | 9,184 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i h a _` | 10,318 |
+| 2 | `_ i h a` | 9,183 |
+| 3 | `a u n _` | 6,940 |
+| 4 | `_ n i a` | 6,849 |
+| 5 | `s a u n` | 6,166 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ i h a _` | 9,098 |
+| 2 | `s a u n _` | 5,780 |
+| 3 | `_ s i r a` | 4,434 |
+| 4 | `a s a u n` | 4,363 |
+| 5 | `_ n i a n` | 3,879 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 284
+- **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 | 0.8183 | 1.763 | 4.67 | 28,115 | 18.2% |
+| **1** | Subword | 1.1849 | 2.274 | 9.58 | 386 | 0.0% |
+| **2** | Word | 0.2239 | 1.168 | 1.48 | 130,951 | 77.6% |
+| **2** | Subword | 1.0621 | 2.088 | 6.47 | 3,691 | 0.0% |
+| **3** | Word | 0.0716 | 1.051 | 1.13 | 192,808 | 92.8% |
+| **3** | Subword | 0.8599 | 1.815 | 3.88 | 23,852 | 14.0% |
+| **4** | Word | 0.0258 🏆 | 1.018 | 1.04 | 216,360 | 97.4% |
+| **4** | Subword | 0.5884 | 1.504 | 2.40 | 92,496 | 41.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `iha okos hosi ligasaun ba dook liu tan aplikasiaun simples konsistente no hetan ona kartaun natál`
+2. `ne ebé afirma konkluzaun ka siénsia sira tenta seluk ne e haklakar an liu hanesan programa`
+3. `no sosiál isabel de daroca td duxambé tanzánia td taxkent v de amor do escuta nian`
+
+**Context Size 2:**
+
+1. `ne e mós bele funsiona nu udar interiór nia kontinentál ho nuanse sira foho sira hotu sei`
+2. `ne ebé mak marka prezensa iha sira nia komunikasaun ba malu bele mos aumenta e bele realiza`
+3. `iha tinan total populasaun hamutuk área 97 37 km vinilale mak sai sidade kapitál seuta estremadura s...`
+
+**Context Size 3:**
+
+1. `timór lorosa e nian fatu lulik mak sai sidade inan ba giana populasaun 200 000 abit`
+2. `ligasaun ba li ur sensus fo fila fali tetun pdf 8 6 mb referensia munisípiu timor leste nian`
+3. `ba li ur iktiolojia`
+
+**Context Size 4:**
+
+1. `ligasaun ba li ur sensus fo fila fali tetun pdf 8 6 mb seeds of life suco information sheets`
+2. `iha timór lorosa e suku ne e iha postu administrativu watucarbau munisípiu vikeke iha tinan total po...`
+3. `républica mit dem diploma ministerial n 199 09 portugiesisch pdf 323 kb ligasaun ba li ur wikipédia ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_a_n_la_bozatór_`
+2. `ay_nan_simaraica`
+3. `icçõe_bamo_a,_tg`
+
+**Context Size 2:**
+
+1. `a_psainfo_hos,_wi`
+2. `anansusi_ca_anyea`
+3. `n_semindo_lu_stro`
+
+**Context Size 3:**
+
+1. `an_niança_cola_fáb`
+2. `ha_ami_lia_sendári`
+3. `iha_progracts_lor=`
+
+**Context Size 4:**
+
+1. `iha_roma_mit_democr`
+2. `_iha_moris_iha_kata`
+3. `aun_su_entransa._f-`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.4% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (92,496 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 | 12,756 |
+| Total Tokens | 256,639 |
+| Mean Frequency | 20.12 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 164.32 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | iha | 9,917 |
+| 2 | ne | 5,971 |
+| 3 | no | 5,164 |
+| 4 | ba | 4,578 |
+| 5 | sira | 4,433 |
+| 6 | e | 4,309 |
+| 7 | nian | 4,134 |
+| 8 | nia | 3,341 |
+| 9 | ho | 2,906 |
+| 10 | ida | 2,823 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | injeta | 2 |
+| 2 | injesaun | 2 |
+| 3 | stiko | 2 |
+| 4 | rezervatóriu | 2 |
+| 5 | konfirmadu | 2 |
+| 6 | profilaxe | 2 |
+| 7 | 中华人民共和国国家卫生健康委员会 | 2 |
+| 8 | uttar | 2 |
+| 9 | pradesh | 2 |
+| 10 | pántanu | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1160 |
+| R² (Goodness of Fit) | 0.992469 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 46.0% |
+| Top 1,000 | 75.4% |
+| Top 5,000 | 91.9% |
+| Top 10,000 | 97.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9925 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 46.0% of corpus
+- **Long Tail:** 2,756 words needed for remaining 2.1% 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 | 0.2388 | 0.4660 | N/A | N/A |
+| **mono_64d** | 64 | 0.0465 | 0.4453 | N/A | N/A |
+| **mono_128d** | 128 | 0.0060 | 0.4698 | N/A | N/A |
+| **aligned_32d** | 32 | 0.2388 🏆 | 0.4494 | 0.0280 | 0.1680 |
+| **aligned_64d** | 64 | 0.0465 | 0.4460 | 0.0280 | 0.1920 |
+| **aligned_128d** | 128 | 0.0060 | 0.4501 | 0.0340 | 0.2000 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.2388 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.4544. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 3.4% 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 | **1.010** | 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 |
+|--------|----------|
+| `-a` | agosto, asosia, acumau |
+| `-s` | sigla, sleep, simples |
+| `-m` | manulai, metan, markadór |
+| `-k` | knananuk, konvite, krioulu |
+| `-ma` | manulai, markadór, mamuk |
+| `-b` | berliu, bazeada, belém |
+| `-p` | polimentadu, penalidade, pandang |
+| `-l` | leburema, livru, lollipop |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | bazeada, ispánia, leburema |
+| `-u` | polimentadu, berliu, impulsu |
+| `-n` | metan, gestaun, union |
+| `-e` | penalidade, opole, konvite |
+| `-s` | simples, sukumatias, prepirenéus |
+| `-un` | gestaun, turkomenistaun, kirgizistaun |
+| `-o` | agosto, bailoro, pelo |
+| `-ia` | ispánia, sekundária, podlakia |
+
+### 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 |
+|------|----------|------------------|----------|
+| `asau` | 1.75x | 24 contexts | sasau, asaun, rasaun |
+| `ente` | 1.68x | 26 contexts | enter, sente, gente |
+| `ment` | 1.65x | 22 contexts | mental, mentál, aumentu |
+| `aran` | 1.59x | 23 contexts | naran, laran, maran |
+| `entu` | 1.78x | 15 contexts | eventu, bentuk, century |
+| `isau` | 1.66x | 15 contexts | bisau, misaun, lisaun |
+| `orma` | 1.50x | 16 contexts | forma, norma, formas |
+| `idad` | 1.68x | 10 contexts | idade, cidade, sidade |
+| `nist` | 1.47x | 10 contexts | ministro, amnistia, ministry |
+| `ensi` | 1.40x | 11 contexts | ensinu, ensino, ensina |
+| `stra` | 1.36x | 11 contexts | stray, strange, estraga |
+| `istr` | 1.38x | 10 contexts | distritu, ministro, ministry |
+
+### 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` | `-a` | 102 words | alexandria, américa |
+| `-k` | `-a` | 96 words | kassa, kompana |
+| `-p` | `-a` | 96 words | póvoa, portuguesa |
+| `-k` | `-u` | 87 words | kompañeiru, kriadu |
+| `-m` | `-a` | 83 words | manega, medisina |
+| `-s` | `-a` | 75 words | sosa, sida |
+| `-k` | `-n` | 69 words | kukun, kedan |
+| `-a` | `-u` | 67 words | asesu, adversáriu |
+| `-s` | `-o` | 64 words | sukucarlito, são |
+| `-p` | `-n` | 59 words | pokémon, prizaun |
+
+### 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 |
+|------|-----------------|------------|------|
+| listening | **`listen-i-ng`** | 7.5 | `i` |
+| konstituinte | **`konstitui-n-te`** | 7.5 | `n` |
+| bahalarauain | **`bahalarau-a-in`** | 7.5 | `a` |
+| haturalan | **`hatur-al-an`** | 7.5 | `al` |
+| tradusaun | **`tradus-a-un`** | 7.5 | `a` |
+| maubaralissa | **`maubaralis-s-a`** | 7.5 | `s` |
+| administrasaun | **`administra-sa-un`** | 7.5 | `sa` |
+| honorável | **`honoráv-e-l`** | 7.5 | `e` |
+| deskrisaun | **`deskris-a-un`** | 7.5 | `a` |
+| sobrevivente | **`sobrevive-n-te`** | 7.5 | `n` |
+| computing | **`comput-i-ng`** | 7.5 | `i` |
+| calataiud | **`calatai-u-d`** | 7.5 | `u` |
+| dokumentasuan | **`dokumentas-u-an`** | 7.5 | `u` |
+| evolusaun | **`evolus-a-un`** | 7.5 | `a` |
+| prehistory | **`p-re-history`** | 6.0 | `history` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Tetum 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 | **32k BPE** | Best compression (4.08x) |
+| N-gram | **2-gram** | Lowest perplexity (284) |
+| Markov | **Context-4** | Highest predictability (97.4%) |
+| 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: 2026-01-11 00:39:26*
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diff --git a/models/tokenizer/tet_tokenizer_32k.vocab b/models/tokenizer/tet_tokenizer_32k.vocab
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