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README.md

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+---
+language: sw
+language_name: Swahili
+language_family: bantu_eastern
+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-bantu_eastern
+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: 4.845
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8185
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Swahili - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Swahili** 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** | 4.023x | 4.02 | 0.1861% | 762,689 |
+| **16k** | 4.373x | 4.37 | 0.2022% | 701,655 |
+| **32k** | 4.646x | 4.65 | 0.2149% | 660,356 |
+| **64k** | 4.845x 🏆 | 4.85 | 0.2241% | 633,306 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Eduardo Cerda (1 Januari – 19 Februari alikuwa mwanasiasa kutoka Chile aliyehudu...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁edu ar do ▁cer da ▁( 1 ▁januari ▁– ▁ ... (+15 more)` | 25 |
+| 16k | `▁edu ardo ▁cer da ▁( 1 ▁januari ▁– ▁ 1 ... (+13 more)` | 23 |
+| 32k | `▁eduardo ▁cer da ▁( 1 ▁januari ▁– ▁ 1 9 ... (+12 more)` | 22 |
+| 64k | `▁eduardo ▁cer da ▁( 1 ▁januari ▁– ▁ 1 9 ... (+12 more)` | 22 |
+
+**Sample 2:** `ni mji wa Afrika Kusini katika jimbo la Limpopo. Mwaka ulikuwa na wakazi 150,637...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ni ▁mji ▁wa ▁afrika ▁kusini ▁katika ▁jimbo ▁la ▁li mpo ... (+27 more)` | 37 |
+| 16k | `▁ni ▁mji ▁wa ▁afrika ▁kusini ▁katika ▁jimbo ▁la ▁limpopo . ... (+25 more)` | 35 |
+| 32k | `▁ni ▁mji ▁wa ▁afrika ▁kusini ▁katika ▁jimbo ▁la ▁limpopo . ... (+25 more)` | 35 |
+| 64k | `▁ni ▁mji ▁wa ▁afrika ▁kusini ▁katika ▁jimbo ▁la ▁limpopo . ... (+25 more)` | 35 |
+
+**Sample 3:** `Calvados ni département au department la Basse-Normandie ya Ufaransa. Mji mkuu w...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁cal va dos ▁ni ▁dé partement ▁au ▁department ▁la ▁ba ... (+22 more)` | 32 |
+| 16k | `▁cal va dos ▁ni ▁département ▁au ▁department ▁la ▁ba sse ... (+21 more)` | 31 |
+| 32k | `▁cal va dos ▁ni ▁département ▁au ▁department ▁la ▁basse - ... (+20 more)` | 30 |
+| 64k | `▁calva dos ▁ni ▁département ▁au ▁department ▁la ▁basse - normandie ... (+16 more)` | 26 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.845x compression
+- **Lowest UNK Rate:** 8k with 0.1861% 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 | 32,577 | 14.99 | 236,638 | 11.9% | 32.7% |
+| **2-gram** | Subword | 217 🏆 | 7.76 | 8,173 | 70.6% | 99.4% |
+| **3-gram** | Word | 76,791 | 16.23 | 451,861 | 10.5% | 26.1% |
+| **3-gram** | Subword | 1,772 | 10.79 | 58,649 | 33.0% | 75.0% |
+| **4-gram** | Word | 139,824 | 17.09 | 795,817 | 10.4% | 23.7% |
+| **4-gram** | Subword | 9,836 | 13.26 | 327,130 | 17.7% | 45.5% |
+| **5-gram** | Word | 100,216 | 16.61 | 591,148 | 11.6% | 26.1% |
+| **5-gram** | Subword | 36,485 | 15.15 | 1,045,354 | 10.1% | 30.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `orodha ya` | 42,976 |
+| 2 | `viungo vya` | 33,564 |
+| 3 | `vya nje` | 33,274 |
+| 4 | `mkoa wa` | 28,247 |
+| 5 | `mwaka wa` | 26,864 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `viungo vya nje` | 33,145 |
+| 2 | `ya mito ya` | 18,487 |
+| 3 | `orodha ya mito` | 16,786 |
+| 4 | `orodha ya watakatifu` | 13,847 |
+| 5 | `pia orodha ya` | 13,762 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `orodha ya mito ya` | 16,782 |
+| 2 | `tanbihi viungo vya nje` | 13,442 |
+| 3 | `tazama pia orodha ya` | 13,414 |
+| 4 | `viungo vya nje geonames` | 11,683 |
+| 5 | `vya nje geonames org` | 11,683 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `viungo vya nje geonames org` | 11,683 |
+| 2 | `tanbihi viungo vya nje geonames` | 11,453 |
+| 3 | `vya nje geonames org ya` | 8,508 |
+| 4 | `pia orodha ya mito ya` | 5,727 |
+| 5 | `tazama pia orodha ya mito` | 5,721 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 5,814,475 |
+| 2 | `w a` | 2,144,347 |
+| 3 | `i _` | 1,912,486 |
+| 4 | `_ k` | 1,852,377 |
+| 5 | `_ m` | 1,569,760 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `w a _` | 1,284,455 |
+| 2 | `a _ k` | 1,094,934 |
+| 3 | `_ w a` | 1,053,327 |
+| 4 | `y a _` | 1,017,628 |
+| 5 | `_ y a` | 970,536 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ y a _` | 848,634 |
+| 2 | `_ w a _` | 582,979 |
+| 3 | `_ n a _` | 505,684 |
+| 4 | `a _ w a` | 409,317 |
+| 5 | `a _ y a` | 354,524 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ y a _` | 321,570 |
+| 2 | `i _ y a _` | 263,973 |
+| 3 | `_ k a t i` | 252,483 |
+| 4 | `a _ n a _` | 243,482 |
+| 5 | `a t i k a` | 235,918 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 217
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~30% 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 | 0.8924 | 1.856 | 8.08 | 460,401 | 10.8% |
+| **1** | Subword | 1.0657 | 2.093 | 6.66 | 3,978 | 0.0% |
+| **2** | Word | 0.2982 | 1.230 | 1.99 | 3,713,903 | 70.2% |
+| **2** | Subword | 0.8028 | 1.745 | 4.94 | 26,486 | 19.7% |
+| **3** | Word | 0.1311 | 1.095 | 1.29 | 7,371,068 | 86.9% |
+| **3** | Subword | 0.7744 | 1.710 | 4.25 | 130,800 | 22.6% |
+| **4** | Word | 0.0548 🏆 | 1.039 | 1.10 | 9,467,105 | 94.5% |
+| **4** | Subword | 0.7269 | 1.655 | 3.45 | 556,331 | 27.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ya afrika asia william watson alijiunga baada ya kati ya kwimba katika ziwa victoria msaada wa`
+2. `wa uzo august anthony gonsalvez filamu mwanamitindo na uchovu dalili wakati wa ii wa kisayansi anaan...`
+3. `na kinanda trevor grace ni kifaa kinachotumika hasa sindhi rasool gangi marejeo walio na mama yake`
+
+**Context Size 2:**
+
+1. `orodha ya mito ya wilaya ya kwimba za mkoa wa manyara nchini tanzania kilele kina urefu wa`
+2. `viungo vya nje lugha ya kuhispania lilirekodiwa tar 21 juni ni mtamgazaji wa runinga na hali mbalimb...`
+3. `vya nje lugha ya kiduke katika glottolog lugha ya kilatini ingawa kuna makundi ya madawa yaliyodhami...`
+
+**Context Size 3:**
+
+1. `viungo vya nje geonames org vya tanzania vya bahari ya hindi ya kwale`
+2. `ya mito ya mkoa wa mara tanzania kaskazini ambao maji yake yanaingia katika ziwa viktoria na hatimay...`
+3. `orodha ya mito ya burundi mito mirefu ya afrika tanbihi viungo vya nje ya uholanzi wa groningen`
+
+**Context Size 4:**
+
+1. `orodha ya mito ya kaunti ya makueni tanbihi viungo vya nje kuhusu papa telesphorus katika kamusi ele...`
+2. `tazama pia orodha ya mito ya uganda orodha ya mito ya wilaya ya kyenjojo tanbihi viungo vya nje geon...`
+3. `tanbihi viungo vya nje geonames org wa bururi n tanganyika kongo`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a_an,7593_naakwa`
+2. `_kaje_zar._maadu`
+3. `i_5_fa_lahife_hi`
+
+**Context Size 2:**
+
+1. `a_na_ncina_wann_y`
+2. `wat)"_afuanteadem`
+3. `i_gui_vike_la_mag`
+
+**Context Size 3:**
+
+1. `wa_katikation_hola`
+2. `a_kufu_wa_kice_ana`
+3. `_walihus_tolea_,_1`
+
+**Context Size 4:**
+
+1. `_ya_cha_jina_wa_tan`
+2. `_wa_kenya_ya_alihes`
+3. `_na_kuwa_matolikio_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 94.5% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (556,331 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 | 215,305 |
+| Total Tokens | 13,265,910 |
+| Mean Frequency | 61.61 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 2704.96 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ya | 849,701 |
+| 2 | wa | 583,407 |
+| 3 | na | 508,022 |
+| 4 | katika | 214,000 |
+| 5 | kwa | 192,923 |
+| 6 | ni | 157,481 |
+| 7 | za | 140,350 |
+| 8 | la | 133,736 |
+| 9 | mwaka | 92,204 |
+| 10 | kama | 74,978 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | xqc | 2 |
+| 2 | cenat | 2 |
+| 3 | thandiwe | 2 |
+| 4 | msimango | 2 |
+| 5 | technocrats | 2 |
+| 6 | explosively | 2 |
+| 7 | paracinema | 2 |
+| 8 | sphingonotus | 2 |
+| 9 | tmetonota | 2 |
+| 10 | vosseleriana | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1454 |
+| R² (Goodness of Fit) | 0.992293 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 39.6% |
+| Top 1,000 | 66.1% |
+| Top 5,000 | 81.9% |
+| Top 10,000 | 87.0% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9923 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 39.6% of corpus
+- **Long Tail:** 205,305 words needed for remaining 13.0% 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 | 0.8185 | 0.3407 | N/A | N/A |
+| **mono_64d** | 64 | 0.8034 | 0.2947 | N/A | N/A |
+| **mono_128d** | 128 | 0.7557 | 0.2285 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8185 🏆 | 0.3482 | 0.2100 | 0.6260 |
+| **aligned_64d** | 64 | 0.8034 | 0.2933 | 0.3440 | 0.7440 |
+| **aligned_128d** | 128 | 0.7557 | 0.2305 | 0.4480 | 0.7880 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8185 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2893. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 44.8% 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 | **-0.139** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-ki` | kimuru, kievant, kinachoaminiwa |
+| `-a` | akisomea, alipoumia, amino |
+| `-ma` | markazi, mabingwadurufile, matuidi |
+| `-m` | mukato, markazi, mabingwadurufile |
+| `-wa` | wanaodaiwa, wanazozitumia, wanaokoma |
+| `-s` | seann, stepfather, sosi |
+| `-k` | kujengewa, kimuru, kievant |
+| `-ka` | kamwene, kapitolias, kakora |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | zilianguka, kujengewa, akisomea |
+| `-i` | pokezi, dodati, markazi |
+| `-wa` | kujengewa, imechanganywa, wanaodaiwa |
+| `-e` | kamwene, hue, kikwere |
+| `-s` | ldcs, kimarangis, wells |
+| `-ia` | alipoumia, wanazozitumia, alimhakikishia |
+| `-o` | mukato, amino, khutso |
+| `-n` | gybrian, liberalization, grindin |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ikuw` | 2.17x | 92 contexts | ikuwo, ikuwa, uikuwa |
+| `fany` | 1.88x | 165 contexts | fanya, mfanya, fanywa |
+| `iung` | 2.05x | 86 contexts | viung, viungu, jiunge |
+| `akat` | 1.78x | 160 contexts | akate, akata, zakat |
+| `liku` | 1.97x | 96 contexts | aliku, likud, likuyu |
+| `mwan` | 2.14x | 62 contexts | mwang, mwana, mwano |
+| `ikan` | 1.98x | 82 contexts | ikana, kikanu, onikan |
+| `reje` | 1.87x | 74 contexts | rejeo, rejea, arejee |
+| `kwen` | 2.07x | 44 contexts | nkwen, kwenu, kweny |
+| `ekan` | 2.00x | 45 contexts | lekan, dekan, kekana |
+| `lish` | 1.67x | 93 contexts | lisha, lisht, lishe |
+| `utok` | 2.04x | 38 contexts | utoke, kutok, utoka |
+
+### 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` | 407 words | ameendesha, akoma |
+| `-wa` | `-a` | 277 words | waliwaita, wakisukumwa |
+| `-k` | `-a` | 272 words | kutotulia, kilichofunganishwa |
+| `-ki` | `-a` | 136 words | kilichofunganishwa, kipama |
+| `-ki` | `-i` | 103 words | kifupifupi, kichaudangsi |
+| `-m` | `-i` | 90 words | mwambaoni, msanidi |
+| `-wa` | `-i` | 78 words | walivyoishi, wamamaluki |
+| `-a` | `-wa` | 78 words | akahifadhiwa, ahesabiwa |
+| `-m` | `-a` | 73 words | mkesia, magaia |
+| `-a` | `-i` | 71 words | akipoi, amkabidhi |
+
+### 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 |
+|------|-----------------|------------|------|
+| charleroi | **`charler-o-i`** | 7.5 | `o` |
+| wamebaini | **`wameba-i-ni`** | 7.5 | `i` |
+| srilankan | **`srilank-a-n`** | 7.5 | `a` |
+| anisopliae | **`anisopli-a-e`** | 7.5 | `a` |
+| amcharrat | **`amcharr-a-t`** | 7.5 | `a` |
+| vegetarian | **`vegetari-a-n`** | 7.5 | `a` |
+| nyamashishi | **`nyamashi-s-hi`** | 7.5 | `s` |
+| fregatidae | **`fregatid-a-e`** | 7.5 | `a` |
+| tunapandanet | **`tunapandan-e-t`** | 7.5 | `e` |
+| alioutaka | **`aliout-a-ka`** | 7.5 | `a` |
+| humheshimu | **`hu-m-heshimu`** | 7.5 | `heshimu` |
+| inayopaswa | **`inayopa-s-wa`** | 7.5 | `s` |
+| ametawala | **`ameta-wa-la`** | 7.5 | `wa` |
+| chillianwala | **`chillian-wa-la`** | 7.5 | `wa` |
+| anchietas | **`anchie-ta-s`** | 7.5 | `ta` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Swahili shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.84x) |
+| N-gram | **2-gram** | Lowest perplexity (217) |
+| Markov | **Context-4** | Highest predictability (94.5%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-11 00:35:41*

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