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+---
+language: hif
+language_name: Fiji Hindi
+language_family: indoaryan_fiji
+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-indoaryan_fiji
+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: 4.228
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8158
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Fiji Hindi - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Fiji Hindi** 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** | 3.785x | 3.79 | 0.0809% | 234,998 |
+| **16k** | 4.011x | 4.02 | 0.0857% | 221,746 |
+| **32k** | 4.156x | 4.16 | 0.0888% | 214,028 |
+| **64k** | 4.228x 🏆 | 4.23 | 0.0903% | 210,369 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Khandeshi bhasa ek Indo-European bhasa hae jisme India ke Maharashtra state ke 1...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁k hand es hi ▁bhasa ▁ek ▁indo - european ▁bhasa ... (+32 more)` | 42 |
+| 16k | `▁khand es hi ▁bhasa ▁ek ▁indo - european ▁bhasa ▁hae ... (+27 more)` | 37 |
+| 32k | `▁khand eshi ▁bhasa ▁ek ▁indo - european ▁bhasa ▁hae ▁jisme ... (+25 more)` | 35 |
+| 64k | `▁khand eshi ▁bhasa ▁ek ▁indo - european ▁bhasa ▁hae ▁jisme ... (+23 more)` | 33 |
+
+**Sample 2:** `Elören ek gaon hae jon Turkey ke Bolu praant ke Gerede district me hae. Elören k...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁el ören ▁ek ▁gaon ▁hae ▁jon ▁turkey ▁ke ▁bolu ▁praant ... (+22 more)` | 32 |
+| 16k | `▁el ören ▁ek ▁gaon ▁hae ▁jon ▁turkey ▁ke ▁bolu ▁praant ... (+22 more)` | 32 |
+| 32k | `▁el ören ▁ek ▁gaon ▁hae ▁jon ▁turkey ▁ke ▁bolu ▁praant ... (+22 more)` | 32 |
+| 64k | `▁elören ▁ek ▁gaon ▁hae ▁jon ▁turkey ▁ke ▁bolu ▁praant ▁ke ... (+20 more)` | 30 |
+
+**Sample 3:** `Palia Kalan bhaarat mein Uttar Pradesh ke Municipal board hain. References Prade...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁pal ia ▁kal an ▁bhaarat ▁mein ▁uttar ▁pradesh ▁ke ▁municipal ... (+6 more)` | 16 |
+| 16k | `▁pal ia ▁kal an ▁bhaarat ▁mein ▁uttar ▁pradesh ▁ke ▁municipal ... (+6 more)` | 16 |
+| 32k | `▁pal ia ▁kalan ▁bhaarat ▁mein ▁uttar ▁pradesh ▁ke ▁municipal ▁board ... (+5 more)` | 15 |
+| 64k | `▁pal ia ▁kalan ▁bhaarat ▁mein ▁uttar ▁pradesh ▁ke ▁municipal ▁board ... (+5 more)` | 15 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.228x compression
+- **Lowest UNK Rate:** 8k with 0.0809% 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 | 6,213 | 12.60 | 22,149 | 21.0% | 50.1% |
+| **2-gram** | Subword | 263 🏆 | 8.04 | 3,336 | 67.9% | 99.2% |
+| **3-gram** | Word | 10,451 | 13.35 | 32,506 | 17.2% | 41.0% |
+| **3-gram** | Subword | 2,210 | 11.11 | 22,191 | 26.4% | 71.8% |
+| **4-gram** | Word | 18,375 | 14.17 | 56,140 | 15.8% | 34.2% |
+| **4-gram** | Subword | 11,729 | 13.52 | 106,944 | 14.3% | 40.3% |
+| **5-gram** | Word | 14,491 | 13.82 | 42,977 | 17.8% | 36.0% |
+| **5-gram** | Subword | 36,295 | 15.15 | 256,262 | 9.3% | 28.4% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ke gaon` | 3,298 |
+| 2 | `hae ii` | 3,135 |
+| 3 | `me banaa` | 2,853 |
+| 4 | `ii film` | 2,821 |
+| 5 | `ke ek` | 2,370 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ke gaon ke` | 1,619 |
+| 2 | `gaon ke gaon` | 1,618 |
+| 3 | `ek me banaa` | 1,425 |
+| 4 | `banaa rahaa ii` | 1,402 |
+| 5 | `rahaa ii film` | 1,398 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ke gaon ke gaon` | 1,618 |
+| 2 | `banaa rahaa ii film` | 1,394 |
+| 3 | `rahaa ii film me` | 1,380 |
+| 4 | `ke direction me banaa` | 1,378 |
+| 5 | `direction me banaa rahaa` | 1,377 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `banaa rahaa ii film me` | 1,377 |
+| 2 | `ke direction me banaa rahaa` | 1,377 |
+| 3 | `me banaa rahaa ii film` | 1,364 |
+| 4 | `direction me banaa rahaa ii` | 1,363 |
+| 5 | `acting kare rahin external link` | 968 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 215,179 |
+| 2 | `_ k` | 118,527 |
+| 3 | `h a` | 109,485 |
+| 4 | `a n` | 94,117 |
+| 5 | `a _` | 90,974 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `k e _` | 78,323 |
+| 2 | `_ k e` | 70,674 |
+| 3 | `_ m e` | 42,082 |
+| 4 | `_ h a` | 35,377 |
+| 5 | `m e _` | 31,901 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k e _` | 66,724 |
+| 2 | `_ m e _` | 27,033 |
+| 3 | `_ h a e` | 24,843 |
+| 4 | `_ r a h` | 20,874 |
+| 5 | `_ a u r` | 19,225 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ a u r _` | 18,842 |
+| 2 | `_ r a h a` | 16,026 |
+| 3 | `r a h a a` | 15,421 |
+| 4 | `_ h a e .` | 15,329 |
+| 5 | `h a e . _` | 14,766 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 263
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~28% 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 | 0.7772 | 1.714 | 4.95 | 83,282 | 22.3% |
+| **1** | Subword | 0.8893 | 1.852 | 5.71 | 2,227 | 11.1% |
+| **2** | Word | 0.2435 | 1.184 | 1.59 | 410,746 | 75.7% |
+| **2** | Subword | 0.6909 | 1.614 | 4.11 | 12,721 | 30.9% |
+| **3** | Word | 0.0951 | 1.068 | 1.18 | 650,872 | 90.5% |
+| **3** | Subword | 0.7145 | 1.641 | 3.67 | 52,201 | 28.5% |
+| **4** | Word | 0.0428 🏆 | 1.030 | 1.07 | 760,827 | 95.7% |
+| **4** | Subword | 0.6343 | 1.552 | 2.75 | 191,335 | 36.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ke border kare rahin kuchh sau sau isse barra chaand pe dher town nagar palika hain`
+2. `me bharti hoe gais rahaa uu philosophiae naturalis principia mathematica likhis rahaa ghatna guadelo...`
+3. `hae ocean aur minister hae jiske rewa suva ke kendr ke american actress ke direction me`
+
+**Context Size 2:**
+
+1. `hae ii film usa me khela gais rahaa iske jaada kar ke hatais rahaa apartheid ek afrikaans`
+2. `me banaa english film hae ii sab county heritage me lia rahaa ii film germany me bhais`
+3. `ii film india me karaa jaawe hae duusra websites cia world factbook central intelligence agency foru...`
+
+**Context Size 3:**
+
+1. `ke gaon ke gaon bihar ke gaon bahaari jorr references ke gaon ke gaon ke gaon bihar ke`
+2. `ek me banaa english film hae ii film canada me michel jetté ke direction me banaa rahaa ii`
+3. `banaa rahaa ii film me sam worthington liam neeson ralph fiennes edgar ramírez acting kare the sandh...`
+
+**Context Size 4:**
+
+1. `banaa rahaa ii film me jonathan daniel brown kenny wormald aaron yoo ron perlman acting kare rahin e...`
+2. `rahaa ii film me larry rahin cable guy owen wilson michael caine emily mortimer acting kare rahin sa...`
+3. `ke direction me banaa rahaa ii film me jill clayburgh amelia heinle adam kaufman austin lysy acting ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_kilaeet,_bhanti`
+2. `ae)_l_tn,_tevadi`
+3. `eoe_(r_con_otenc`
+
+**Context Size 2:**
+
+1. `e_me_shaad,_al_sh`
+2. `_ke_dvincenve_ban`
+3. `haagence_ginv_bar`
+
+**Context Size 3:**
+
+1. `ke_bakhstandhmada_`
+2. `_ke_nource)_sive_p`
+3. `_me_hasanga_iske_j`
+
+**Context Size 4:**
+
+1. `_ke_logan_ke_ki_uu_`
+2. `_me_lautoka_0-0_0-0`
+3. `_hae._āndhra_projec`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.7% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (191,335 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 | 36,370 |
+| Total Tokens | 971,297 |
+| Mean Frequency | 26.71 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 466.12 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ke | 67,375 |
+| 2 | me | 28,710 |
+| 3 | hae | 24,635 |
+| 4 | aur | 18,902 |
+| 5 | rahaa | 15,337 |
+| 6 | ek | 13,483 |
+| 7 | se | 11,961 |
+| 8 | the | 10,559 |
+| 9 | ii | 10,014 |
+| 10 | of | 9,683 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | mahajanapadas | 2 |
+| 2 | kikatas | 2 |
+| 3 | brihadratha | 2 |
+| 4 | gangaridae | 2 |
+| 5 | prasioi | 2 |
+| 6 | asokas | 2 |
+| 7 | excavations | 2 |
+| 8 | pāṭali | 2 |
+| 9 | sutta | 2 |
+| 10 | chhetraphal | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0911 |
+| R² (Goodness of Fit) | 0.997141 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 42.5% |
+| Top 1,000 | 69.1% |
+| Top 5,000 | 85.2% |
+| Top 10,000 | 91.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9971 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 42.5% of corpus
+- **Long Tail:** 26,370 words needed for remaining 8.9% 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 | 0.8158 | 0.3455 | N/A | N/A |
+| **mono_64d** | 64 | 0.6008 | 0.3053 | N/A | N/A |
+| **mono_128d** | 128 | 0.1730 | 0.2933 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8158 🏆 | 0.3433 | 0.0800 | 0.3760 |
+| **aligned_64d** | 64 | 0.6008 | 0.2939 | 0.1640 | 0.5060 |
+| **aligned_128d** | 128 | 0.1730 | 0.3011 | 0.2060 | 0.5720 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8158 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3137. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 20.6% 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 | **0.267** | 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` | sampati, satha, scheer |
+| `-a` | airspeed, administrators, avery |
+| `-b` | balavu, bright, bonaire |
+| `-ma` | mace, mahmoud, mayawati |
+| `-m` | mère, munia, mace |
+| `-sa` | sampati, satha, sanvaadadaata |
+| `-p` | patakatha, parrii, prasith |
+| `-ba` | balavu, balcılar, barisan |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | jawaan, haddiyaan, bunun |
+| `-s` | galaxies, nepals, administrators |
+| `-e` | shakeshafte, mère, karke |
+| `-a` | patakatha, virendra, tuva |
+| `-r` | scheer, oper, rahikpur |
+| `-on` | lebanon, davaon, definition |
+| `-an` | jawaan, haddiyaan, lillian |
+| `-t` | bright, environment, piedmont |
+
+### 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 |
+|------|----------|------------------|----------|
+| `aara` | 2.02x | 49 contexts | taara, saara, maara |
+| `tion` | 1.92x | 39 contexts | action, motion, option |
+| `anaa` | 1.84x | 40 contexts | ganaa, manaa, hanaa |
+| `atio` | 1.96x | 29 contexts | patio, ratio, nation |
+| `ctio` | 1.93x | 21 contexts | action, actions, faction |
+| `arat` | 1.44x | 50 contexts | marat, parat, carat |
+| `ecti` | 1.86x | 18 contexts | section, lection, election |
+| `indi` | 1.74x | 19 contexts | bindi, hindi, indic |
+| `ence` | 1.87x | 15 contexts | fence, pence, hence |
+| `mber` | 1.77x | 16 contexts | amber, ember, timber |
+| `nati` | 1.80x | 15 contexts | unnati, banati, nation |
+| `renc` | 1.82x | 14 contexts | french, trench, örencik |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-p` | `-n` | 77 words | puraanan, penelitian |
+| `-s` | `-n` | 69 words | sampann, shailiyon |
+| `-p` | `-s` | 68 words | primates, planets |
+| `-s` | `-a` | 66 words | sarma, sakata |
+| `-s` | `-r` | 56 words | shoemaker, screenwriter |
+| `-p` | `-a` | 55 words | pandya, pratibaddhata |
+| `-a` | `-s` | 52 words | aras, anegnos |
+| `-s` | `-s` | 49 words | status, strauss |
+| `-s` | `-e` | 48 words | seville, sale |
+| `-a` | `-a` | 47 words | ashéninka, aba |
+
+### 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 |
+|------|-----------------|------------|------|
+| chitrkalaa | **`chitrka-la-a`** | 7.5 | `la` |
+| prateekon | **`pratee-k-on`** | 7.5 | `k` |
+| developing | **`develop-i-ng`** | 7.5 | `i` |
+| oxidizing | **`oxidiz-i-ng`** | 7.5 | `i` |
+| gyllenhaal | **`gyllenh-a-al`** | 7.5 | `a` |
+| zonguldak | **`zonguld-a-k`** | 7.5 | `a` |
+| constance | **`const-an-ce`** | 7.5 | `an` |
+| reactants | **`react-an-ts`** | 7.5 | `an` |
+| lagaataar | **`lagaa-ta-ar`** | 7.5 | `ta` |
+| boliviano | **`bolivi-an-o`** | 7.5 | `an` |
+| americans | **`americ-an-s`** | 7.5 | `an` |
+| metaphysical | **`me-ta-physical`** | 7.5 | `physical` |
+| sukumaran | **`su-kumar-an`** | 6.0 | `kumar` |
+| javascript | **`ja-va-script`** | 6.0 | `script` |
+| krishneel | **`krishn-ee-l`** | 6.0 | `krishn` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Fiji Hindi 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 (4.23x) |
+| N-gram | **2-gram** | Lowest perplexity (263) |
+| Markov | **Context-4** | Highest predictability (95.7%) |
+| 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 02:32:56*
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new file mode 100644
index 0000000000000000000000000000000000000000..3b201c243fdd5b52b5ffe17b7e99e475aa81af06
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index 0000000000000000000000000000000000000000..8625ef660a94116ecce77cf1cfb6f5d38d56d289
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diff --git a/models/tokenizer/hif_tokenizer_32k.vocab b/models/tokenizer/hif_tokenizer_32k.vocab
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diff --git a/models/vocabulary/hif_vocabulary.parquet b/models/vocabulary/hif_vocabulary.parquet
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diff --git a/models/vocabulary/hif_vocabulary_metadata.json b/models/vocabulary/hif_vocabulary_metadata.json
new file mode 100644
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--- /dev/null
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+{
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+  }
+}
\ No newline at end of file
diff --git a/models/word_markov/hif_markov_ctx1_word.parquet b/models/word_markov/hif_markov_ctx1_word.parquet
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