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README.md

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+---
+language: nv
+language_name: Navajo
+language_family: american_athabaskan
+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-american_athabaskan
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 3.722
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7658
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Navajo - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Navajo** 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** | 3.313x | 3.32 | 0.7428% | 222,258 |
+| **16k** | 3.483x | 3.49 | 0.7810% | 211,391 |
+| **32k** | 3.612x | 3.62 | 0.8101% | 203,814 |
+| **64k** | 3.722x 🏆 | 3.73 | 0.8346% | 197,818 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Tółání Kʼish Chʼínítʼiʼ Tsé Chʼééchiiʼ yishtłizhii`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁tółání ▁k ʼ ish ▁ch ʼ ínít ʼ i ʼ ... (+7 more)` | 17 |
+| 16k | `▁tółání ▁k ʼ ish ▁ch ʼ ínít ʼ i ʼ ... (+6 more)` | 16 |
+| 32k | `▁tółání ▁k ʼ ish ▁ch ʼ ínít ʼ i ʼ ... (+6 more)` | 16 |
+| 64k | `▁tółání ▁k ʼ ish ▁ch ʼ ínít ʼ i ʼ ... (+6 more)` | 16 |
+
+**Sample 2:** `Naakaii Dootłʼizhii Bikéyahdę́ę́ʼ lókʼaatah naaʼahóóhai Tsiiʼyishbizhí Dineʼé Bi...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁naakaii ▁dootł ʼ izhii ▁bikéyahdę́ę́ ʼ ▁lók ʼ aatah ▁naa ... (+16 more)` | 26 |
+| 16k | `▁naakaii ▁dootł ʼ izhii ▁bikéyahdę́ę́ ʼ ▁lók ʼ aatah ▁naa ... (+16 more)` | 26 |
+| 32k | `▁naakaii ▁dootł ʼ izhii ▁bikéyahdę́ę́ ʼ ▁lók ʼ aatah ▁naa ... (+16 more)` | 26 |
+| 64k | `▁naakaii ▁dootł ʼ izhii ▁bikéyahdę́ę́ ʼ ▁lók ʼ aatah ▁naa ... (+16 more)` | 26 |
+
+**Sample 3:** `Azeeʼ haajinítsoh Azeeʼ haajinítsʼóóz Azeeʼ haajiní łibáhígíí`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁azee ʼ ▁haajiní tsoh ▁azee ʼ ▁haajiní ts ʼ óóz ... (+4 more)` | 14 |
+| 16k | `▁azee ʼ ▁haajiní tsoh ▁azee ʼ ▁haajiní ts ʼ óóz ... (+4 more)` | 14 |
+| 32k | `▁azee ʼ ▁haajinítsoh ▁azee ʼ ▁haajiní ts ʼ óóz ▁azee ... (+3 more)` | 13 |
+| 64k | `▁azee ʼ ▁haajinítsoh ▁azee ʼ ▁haajiníts ʼ óóz ▁azee ʼ ... (+2 more)` | 12 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.722x compression
+- **Lowest UNK Rate:** 8k with 0.7428% 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 | 1,012 | 9.98 | 12,895 | 47.2% | 81.9% |
+| **2-gram** | Subword | 222 🏆 | 7.79 | 1,668 | 72.2% | 99.8% |
+| **3-gram** | Word | 2,466 | 11.27 | 30,460 | 36.6% | 67.1% |
+| **3-gram** | Subword | 858 | 9.74 | 13,690 | 41.6% | 89.2% |
+| **4-gram** | Word | 5,133 | 12.33 | 61,517 | 29.9% | 56.5% |
+| **4-gram** | Subword | 1,964 | 10.94 | 55,169 | 29.2% | 77.2% |
+| **5-gram** | Word | 7,471 | 12.87 | 67,722 | 25.5% | 51.1% |
+| **5-gram** | Subword | 3,279 | 11.68 | 102,677 | 23.7% | 69.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ndaʼałkaahí dóó` | 18,966 |
+| 2 | `dóó ééʼdeetįįhii` | 18,949 |
+| 3 | `ééʼdeetįįhii éí` | 18,878 |
+| 4 | `áádóó éí` | 18,437 |
+| 5 | `dah yikahjí` | 18,133 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ndaʼałkaahí dóó ééʼdeetįįhii` | 18,948 |
+| 2 | `dóó ééʼdeetįįhii éí` | 18,878 |
+| 3 | `dah yikahjí atah` | 18,128 |
+| 4 | `ánoolinígíí dóó bichʼiyąʼ` | 16,794 |
+| 5 | `dóó bichʼiyąʼ díí` | 16,604 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ndaʼałkaahí dóó ééʼdeetįįhii éí` | 18,877 |
+| 2 | `ánoolinígíí dóó bichʼiyąʼ díí` | 16,603 |
+| 3 | `dah yikahjí atah yisdzoh` | 15,997 |
+| 4 | `atah yisdzoh áádóó éí` | 13,441 |
+| 5 | `yikahjí atah yisdzoh áádóó` | 13,428 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dah yikahjí atah yisdzoh áádóó` | 13,428 |
+| 2 | `yikahjí atah yisdzoh áádóó éí` | 13,421 |
+| 3 | `hólǫ́ ndaʼałkaahí dóó ééʼdeetįįhii éí` | 13,312 |
+| 4 | `deiłníigo dayózhí ánoolinígíí dóó bichʼiyąʼ` | 12,295 |
+| 5 | `dayózhí ánoolinígíí dóó bichʼiyąʼ díí` | 12,263 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `í _` | 362,053 |
+| 2 | `_ d` | 273,921 |
+| 3 | `é í` | 184,110 |
+| 4 | `_ é` | 173,881 |
+| 5 | `_ b` | 173,418 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `é í _` | 182,329 |
+| 2 | `_ b i` | 160,761 |
+| 3 | `_ é í` | 154,684 |
+| 4 | `ó ó _` | 132,006 |
+| 5 | `d ó ó` | 123,733 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ é í _` | 154,592 |
+| 2 | `d ó ó _` | 123,699 |
+| 3 | `_ d ó ó` | 98,895 |
+| 4 | `í g í í` | 52,301 |
+| 5 | `g í í _` | 51,425 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d ó ó _` | 98,891 |
+| 2 | `í g í í _` | 51,394 |
+| 3 | `í _ d ó ó` | 48,361 |
+| 4 | `i _ é í _` | 38,726 |
+| 5 | `d ó ó _ é` | 38,444 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 222
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~69% 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 | 0.5447 | 1.459 | 3.56 | 37,020 | 45.5% |
+| **1** | Subword | 1.0994 | 2.143 | 8.42 | 395 | 0.0% |
+| **2** | Word | 0.2649 | 1.202 | 1.82 | 130,895 | 73.5% |
+| **2** | Subword | 1.0039 | 2.005 | 6.61 | 3,325 | 0.0% |
+| **3** | Word | 0.1801 | 1.133 | 1.46 | 235,498 | 82.0% |
+| **3** | Subword | 0.8364 | 1.786 | 3.94 | 21,977 | 16.4% |
+| **4** | Word | 0.1277 🏆 | 1.093 | 1.29 | 339,354 | 87.2% |
+| **4** | Subword | 0.5506 | 1.465 | 2.29 | 86,495 | 44.9% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `éí łigai baʼáádígíí éí kéyah dah ndaaʼeełí łánídę́ę́ʼ tłʼiish dah yikahjí atah yisdzoh áádóó éí chʼi...`
+2. `dóó chʼał dootłʼizhí bikédaayahdi tʼéiyá hólǫ́ ndaʼałkaahí dóó ééʼdeetįįhii éí diłhił shádiʼááh dóó ...`
+3. `dah daalgai bitsiitsʼiin éí nahasdzáán tʼáá díkwíí mm áníłtso bitsʼíís éí yótʼáahdi tsídii tsídígíí ...`
+
+**Context Size 2:**
+
+1. `ndaʼałkaahí dóó ééʼdeetįįhii éí certhilauda benguelensis deiłníigo dayózhí ánoolinígíí dóó bichʼiyąʼ...`
+2. `dóó ééʼdeetįįhii éí euscarthmus rufomarginatus deiłníigo dayózhí ánoolinígíí dóó bichʼiyąʼ díí naʼas...`
+3. `ééʼdeetįįhii éí rhamphiophis oxyrhynchus deiłníigo dayózhí ánoolinígíí dóó bichʼiyąʼ díí tsídii biką...`
+
+**Context Size 3:**
+
+1. `ndaʼałkaahí dóó ééʼdeetįįhii éí dendropsophus koechlini deiłníigo dayózhí ánoolinígíí dóó bichʼiyąʼ ...`
+2. `dóó ééʼdeetįįhii éí ptilopsis leucotis deiłníigo dayózhí ánoolinígíí dóó bichʼiyąʼ díí tłʼiish éí 30...`
+3. `dah yikahjí atah yisdzoh áádóó éí naakaii łizhiní bikéyahdi hólǫ́ ndaʼałkaahí dóó ééʼdeetįįhii éí xe...`
+
+**Context Size 4:**
+
+1. `ndaʼałkaahí dóó ééʼdeetįįhii éí dendrolagus deiłníigo deiyózhí díí nahatʼeʼiitsoh éí 17 ałʼąą ádaatʼ...`
+2. `ánoolinígíí dóó bichʼiyąʼ díí naʼashǫ́ʼii éí 4 5di asdzoh áníłtso bitsʼíís éí chʼilgo dootłʼizh bits...`
+3. `dah yikahjí atah yisdzoh áádóó éí magí bitseeʼ noodǫ́zí bikéyahdi tʼéiyá hólǫ́ ndaʼałkaahí dóó ééʼde...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_béíígaiy_"_yaʼ_`
+2. `i_yąʼééí_ttą́._ée`
+3. `í_éí_éí_tsh_áááʼ`
+
+**Context Size 2:**
+
+1. `í_dóó_atahdę́ę́ʼ_yę́`
+2. `_dóó_bináhooly_oo`
+3. `éí_bitoʼ_atah_yik`
+
+**Context Size 3:**
+
+1. `éí_naaʼałkaahí_éí_`
+2. `_bitłʼaahjí_kélchí`
+3. `_éí_naaznilzhin;_b`
+
+**Context Size 4:**
+
+1. `_éí_naashchʼąąʼ_éí_`
+2. `dóó_éí_hólǫ́._ndaʼał`
+3. `_dóó_ééʼdeetįįhii_é`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 87.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (86,495 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 | 15,109 |
+| Total Tokens | 1,314,110 |
+| Mean Frequency | 86.98 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 1812.30 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | éí | 176,805 |
+| 2 | dóó | 99,009 |
+| 3 | dah | 28,837 |
+| 4 | díí | 25,092 |
+| 5 | bichʼiyąʼ | 23,153 |
+| 6 | áádóó | 21,278 |
+| 7 | ndaʼałkaahí | 19,035 |
+| 8 | ééʼdeetįįhii | 18,949 |
+| 9 | deiłníigo | 18,893 |
+| 10 | atah | 18,728 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | milano | 2 |
+| 2 | príncipe | 2 |
+| 3 | butiama | 2 |
+| 4 | àɖokun | 2 |
+| 5 | yí | 2 |
+| 6 | azɔ | 2 |
+| 7 | àkpɔ̀ | 2 |
+| 8 | gbɔ̀ | 2 |
+| 9 | panafrikan | 2 |
+| 10 | modèle | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.3602 |
+| R² (Goodness of Fit) | 0.987051 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 72.4% |
+| Top 1,000 | 93.5% |
+| Top 5,000 | 97.8% |
+| Top 10,000 | 99.2% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9871 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 72.4% of corpus
+- **Long Tail:** 5,109 words needed for remaining 0.8% 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 | 0.7658 🏆 | 0.3405 | N/A | N/A |
+| **mono_64d** | 64 | 0.6030 | 0.2817 | N/A | N/A |
+| **mono_128d** | 128 | 0.1964 | 0.2867 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7658 | 0.3269 | 0.0120 | 0.1440 |
+| **aligned_64d** | 64 | 0.6030 | 0.2833 | 0.0280 | 0.2120 |
+| **aligned_128d** | 128 | 0.1964 | 0.2859 | 0.0960 | 0.2700 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7658 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3008. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 9.6% 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 | **-0.261** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-a` | allotment, amá, apodora |
+| `-bi` | bikʼa, bichʼoshtsoh, bitsʼáozʼaʼ |
+| `-d` | diastema, dryocalamus, deezlíníidi |
+| `-b` | bílátaʼiitsóóh, bikʼa, bí |
+| `-t` | tséhaagééd, tóńlį́, tʼiistsooítah |
+| `-s` | sylvilagus, sturnira, sturnus |
+| `-n` | natalobatrachus, neomixis, nahonitłʼahii |
+| `-c` | certhiaxis, chʼiltaalzhahii, chʼahí |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | himalayensis, sylvilagus, femoralis |
+| `-us` | sylvilagus, dryocalamus, sturnus |
+| `-í` | wálázhiní, bí, magítʼą́ʼí |
+| `-i` | tséʼałnáoztʼiʼíidi, deezlíníidi, chʼiltaalzhahii |
+| `-a` | sturnira, fuscicauda, bikʼa |
+| `-is` | himalayensis, femoralis, ichthyophis |
+| `-ii` | chʼiltaalzhahii, dáághahii, nahonitłʼahii |
+| `-íí` | yeeyáʼdaałtíʼígíí, díkiwíí, dadijoolígíí |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ikah` | 2.28x | 8 contexts | yikahí, yikahji, yikahjí |
+| `itsʼ` | 1.33x | 31 contexts | bitsʼáh, bitsʼoh, ditsʼoz |
+| `tsʼí` | 1.63x | 14 contexts | tsʼídá, tsʼííh, tsʼímah |
+| `éyah` | 1.67x | 13 contexts | kéyah, kéyahdi, hakéyah |
+| `iłní` | 1.98x | 8 contexts | deiłní, nihiłní, ádeiłní |
+| `sʼíí` | 1.87x | 9 contexts | tsʼííh, bitsʼíí, atsʼíís |
+| `yika` | 2.28x | 5 contexts | yikał, yikahí, yikahji |
+| `kahj` | 2.28x | 5 contexts | yikahji, yikahjí, daakahjí |
+| `kéya` | 1.67x | 9 contexts | kéyah, kéyahdi, hakéyah |
+| `níig` | 1.81x | 7 contexts | níigo, aníigo, aaníigo |
+| `iníg` | 2.05x | 5 contexts | kinígíí, ádinígíí, nízinígíí |
+| `bich` | 1.44x | 11 contexts | bichʼįʼ, bichąąʼ, bichʼil |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-c` | `-s` | 249 words | chrysops, clematis |
+| `-p` | `-s` | 243 words | platymantis, parvirostris |
+| `-d` | `-í` | 213 words | dinilbáhí, dziłghą́ʼí |
+| `-a` | `-s` | 184 words | arvalis, antrozous |
+| `-n` | `-í` | 184 words | naalzheehígíí, naʼazísí |
+| `-s` | `-s` | 156 words | sclerurus, scytodes |
+| `-p` | `-us` | 138 words | perspicillatus, pteruthius |
+| `-c` | `-us` | 131 words | castaneus, chroicocephalus |
+| `-c` | `-a` | 126 words | crocata, cyanoleuca |
+| `-t` | `-í` | 123 words | tłʼohtsʼózí, tłʼohwaaʼí |
+
+### 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 |
+|------|-----------------|------------|------|
+| daʼałhosh | **`daʼałho-s-h`** | 7.5 | `s` |
+| moluccensis | **`moluccen-s-is`** | 7.5 | `s` |
+| daatsʼísí | **`daatsʼí-s-í`** | 7.5 | `s` |
+| sminthopsis | **`sminthop-s-is`** | 7.5 | `s` |
+| barbadensis | **`barbaden-s-is`** | 7.5 | `s` |
+| chʼoshtsoh | **`chʼosht-s-oh`** | 7.5 | `s` |
+| leucopsis | **`leucop-s-is`** | 7.5 | `s` |
+| pretiosus | **`pretio-s-us`** | 7.5 | `s` |
+| dlǫ́ʼiitsoh | **`dlǫ́ʼiit-s-oh`** | 7.5 | `s` |
+| dinilzhinhgo | **`dinilzhin-h-go`** | 7.5 | `h` |
+| mąʼiikʼǫsh | **`mąʼiikʼǫ-s-h`** | 7.5 | `s` |
+| portoricensis | **`portoricen-s-is`** | 7.5 | `s` |
+| natalensis | **`natalen-s-is`** | 7.5 | `s` |
+| yildeełítsoh | **`yildeełít-s-oh`** | 7.5 | `s` |
+| iichʼąhiitsʼósí | **`iichʼąhiitsʼó-s-í`** | 7.5 | `s` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Navajo 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 (3.72x) |
+| N-gram | **2-gram** | Lowest perplexity (222) |
+| Markov | **Context-4** | Highest predictability (87.2%) |
+| 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 16:24:15*

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