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+visualizations/performance_dashboard.png filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+language: hyw
+language_name: Western Armenian
+language_family: armenian
+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-armenian
+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: 4.072
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8364
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Western Armenian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Western Armenian** 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** | 3.186x | 3.19 | 0.2260% | 265,032 |
+| **16k** | 3.511x | 3.52 | 0.2491% | 240,461 |
+| **32k** | 3.812x | 3.82 | 0.2705% | 221,471 |
+| **64k** | 4.072x 🏆 | 4.08 | 0.2889% | 207,332 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `թուական, ոչ նահանջ տարի, 17րդ դարու 89րդ տարին է Դէպքեր Ծնունդներ 16px 120px Մոն...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 7 րդ ... (+28 more)` | 38 |
+| 16k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 7 րդ ... (+27 more)` | 37 |
+| 32k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 7 րդ ... (+26 more)` | 36 |
+| 64k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 7 րդ ... (+26 more)` | 36 |
+
+**Sample 2:** `թուական, ոչ նահանջ տարի, 17րդ դարու 69րդ տարին է Դէպքեր Անծանօթ ամսաթիւով՝ Օսման...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 7 րդ ... (+30 more)` | 40 |
+| 16k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 7 րդ ... (+27 more)` | 37 |
+| 32k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 7 րդ ... (+27 more)` | 37 |
+| 64k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 7 րդ ... (+26 more)` | 36 |
+
+**Sample 3:** `թուական, ոչ նահանջ տարի, 16րդ դարու 70րդ տարին է Դէպքեր Ծնունդներ 16px 120px Սեպ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 6 րդ ... (+33 more)` | 43 |
+| 16k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 6 րդ ... (+33 more)` | 43 |
+| 32k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 6 րդ ... (+32 more)` | 42 |
+| 64k | `▁թուական , ▁ոչ ▁նահանջ ▁տարի , ▁ 1 6 րդ ... (+30 more)` | 40 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.072x compression
+- **Lowest UNK Rate:** 8k with 0.2260% 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 | 39,006 | 15.25 | 104,315 | 8.5% | 23.6% |
+| **2-gram** | Subword | 392 🏆 | 8.61 | 9,402 | 59.8% | 96.3% |
+| **3-gram** | Word | 62,599 | 15.93 | 104,125 | 4.4% | 14.0% |
+| **3-gram** | Subword | 3,216 | 11.65 | 76,613 | 26.7% | 64.9% |
+| **4-gram** | Word | 113,012 | 16.79 | 154,212 | 2.7% | 9.7% |
+| **4-gram** | Subword | 17,012 | 14.05 | 379,918 | 14.1% | 37.8% |
+| **5-gram** | Word | 73,099 | 16.16 | 96,541 | 3.1% | 11.9% |
+| **5-gram** | Subword | 58,403 | 15.83 | 935,209 | 8.5% | 24.3% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `եղած է` | 5,851 |
+| 2 | `մէջ կը` | 5,372 |
+| 3 | `եւ կը` | 4,686 |
+| 4 | `որ կը` | 4,419 |
+| 5 | `ինչպէս նաեւ` | 3,890 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ծանօթագրութիւններ արտաքին յղումներ` | 1,448 |
+| 2 | `տեղի կ ունենայ` | 1,025 |
+| 3 | `տե ս նաեւ` | 875 |
+| 4 | `լոյս տեսած է` | 799 |
+| 5 | `կենսագրութիւն ծնած է` | 685 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `հ մ ը մ` | 449 |
+| 2 | `կենսագրական գիծեր ծնած է` | 395 |
+| 3 | `թուական ոչ նահանջ տարի` | 330 |
+| 4 | `մ ը մ ի` | 326 |
+| 5 | `նախնական կրթութիւնը ստացած է` | 273 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `հ մ ը մ ի` | 320 |
+| 2 | `տարին է դէպքեր ծնունդներ 16px` | 223 |
+| 3 | `աղբիւրներ հայ հանրագիտակ հ մկրտիչ` | 148 |
+| 4 | `է դէպքեր ծնունդներ 16px մահեր` | 147 |
+| 5 | `դէպքեր ծնունդներ 16px մահեր 16px` | 147 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ո ւ` | 1,154,928 |
+| 2 | `ա ն` | 1,111,749 |
+| 3 | `ն _` | 917,764 |
+| 4 | `ե ր` | 685,736 |
+| 5 | `ա ր` | 622,298 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ա ն _` | 382,604 |
+| 2 | `ն ե ր` | 314,903 |
+| 3 | `ր ո ւ` | 295,790 |
+| 4 | `ա կ ա` | 277,056 |
+| 5 | `ո ւ թ` | 265,013 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ա կ ա ն` | 217,395 |
+| 2 | `կ ա ն _` | 159,498 |
+| 3 | `ե ր ո ւ` | 152,660 |
+| 4 | `ո ւ թ ի` | 140,761 |
+| 5 | `թ ի ւ ն` | 139,607 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ա կ ա ն _` | 153,426 |
+| 2 | `ո ւ թ ի ւ` | 138,505 |
+| 3 | `ւ թ ի ւ ն` | 138,493 |
+| 4 | `ո ւ թ ե ա` | 106,369 |
+| 5 | `ն ե ր ո ւ` | 105,374 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 392
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~24% 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 | 0.8813 | 1.842 | 6.94 | 388,915 | 11.9% |
+| **1** | Subword | 0.5894 | 1.505 | 6.80 | 3,661 | 41.1% |
+| **2** | Word | 0.2276 | 1.171 | 1.57 | 2,695,260 | 77.2% |
+| **2** | Subword | 0.9532 | 1.936 | 6.36 | 24,911 | 4.7% |
+| **3** | Word | 0.0665 | 1.047 | 1.11 | 4,212,072 | 93.4% |
+| **3** | Subword | 0.8254 | 1.772 | 4.29 | 158,342 | 17.5% |
+| **4** | Word | 0.0190 🏆 | 1.013 | 1.03 | 4,666,447 | 98.1% |
+| **4** | Subword | 0.6377 | 1.556 | 2.87 | 679,042 | 36.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `եւ ճոն չիփ ինք արդէն այդ քայլը շրջանը տասը գիրքերը գրուած հայերէն անգլերէն որ կ առնէ`
+2. `է թուականներուն ըստ ժողովրդական արդիստի կոչման հայաստանի հանրապետութեան կառավարութեան սակայն ան իր մ...`
+3. `կը պատսպարէ քանատական կողմէն երգը ձայնագրուած է պաքուի 1 րդ կաթողիկոս խորէն տէր կիւրեղեան տայանա 8`
+
+**Context Size 2:**
+
+1. `եղած է հայ արշակունիներու արքայատոհմէն եղած է 100 հազարէն աւելի հայ պատանիներ ժամանակագրութիւն 15 փե...`
+2. `մէջ կը գտնուին քիմքին ամբողջ տարածքին մեծ կաղնիի անտառները արգելոցին զարդը կը կազմեն իր աշխատութիւնն...`
+3. `եւ կը վերադառնայ երուսաղէմ կ երթան գերեզման անուշաբոյր իւղերով օծելու քրիստոսի մարմինը իր մտահոգութի...`
+
+**Context Size 3:**
+
+1. `ծանօթագրութիւններ արտաքին յղումներ այսօր երջանկության միջազգային օրն է ըստ վիճակագրութեան գիւղացի կա...`
+2. `տեղի կ ունենայ վիզի վիրաբուժական գործողութիւններու ընթացքին եւ յատկապէս քնանալէ առաջ ստորեւ կը ներկա...`
+3. `տե ս նաեւ արեւմտահայաստանի եւ արեւմտահայութեան հարցերու ուսումնասիրութեան կեդրոն ռատիօ եան անկախ հայ...`
+
+**Context Size 4:**
+
+1. `հ մ ը մ ի արժանեաց շքանշանով խօսքեր հ մ ը մ ի կեդրոնական վարչութիւնը գնահատելով բազմավաստակ եղբօր եր...`
+2. `կենսագրական գիծեր ծնած է կիրովականի մէջ այժմ վանաձոր կը լուսաբանէր շրջանի արդիւնաբերական եւ գիւղատնտ...`
+3. `թուական ոչ նահանջ տարի 19րդ դարու վերջին 100րդ տարին է դէպքեր անստոյգ ամսաթիւով արթուր էվանս քնոսոսի...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_մէջութեմն_կակ՛ա`
+2. `ան_արհան_կաւորը_`
+3. `նց։_գոքիսուրուրժ`
+
+**Context Size 2:**
+
+1. `ութեանցնել_ան)_հա`
+2. `անները_պատրուածան`
+3. `ն_պարին_մէտ_հաւար`
+
+**Context Size 3:**
+
+1. `ան_տարբերոս,_17-րդ`
+2. `ները:_ան_կատարապետ`
+3. `րութիւններով։_հօրը`
+
+**Context Size 4:**
+
+1. `ականուազակ»_(«retur`
+2. `կան_կրթակայն_համար։`
+3. `երու_գուստեղծագործո`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (679,042 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 | 163,300 |
+| Total Tokens | 4,777,649 |
+| Mean Frequency | 29.26 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 646.35 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | եւ | 137,926 |
+| 2 | է | 124,125 |
+| 3 | կը | 121,199 |
+| 4 | մէջ | 80,008 |
+| 5 | կ | 34,717 |
+| 6 | որ | 32,222 |
+| 7 | են | 28,873 |
+| 8 | իր | 27,391 |
+| 9 | մը | 25,115 |
+| 10 | ու | 24,951 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | հաւատուի | 2 |
+| 2 | ricœur | 2 |
+| 3 | ցամաքամերձ | 2 |
+| 4 | մունք | 2 |
+| 5 | munch | 2 |
+| 6 | համաժամեցուած | 2 |
+| 7 | measurable | 2 |
+| 8 | թհ | 2 |
+| 9 | indelible | 2 |
+| 10 | պրոդիուսր | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9253 |
+| R² (Goodness of Fit) | 0.997096 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 26.8% |
+| Top 1,000 | 48.9% |
+| Top 5,000 | 67.7% |
+| Top 10,000 | 76.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9971 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 26.8% of corpus
+- **Long Tail:** 153,300 words needed for remaining 23.9% 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 | 0.8364 | 0.3396 | N/A | N/A |
+| **mono_64d** | 64 | 0.7779 | 0.2555 | N/A | N/A |
+| **mono_128d** | 128 | 0.7701 | 0.1665 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8364 🏆 | 0.3355 | 0.0500 | 0.2460 |
+| **aligned_64d** | 64 | 0.7779 | 0.2492 | 0.0600 | 0.2960 |
+| **aligned_128d** | 128 | 0.7701 | 0.1670 | 0.1080 | 0.4300 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8364 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2522. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 10.8% 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 | **-0.511** | 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 |
+|--------|----------|
+| `-ա` | ասեղնագործէին, ապականիչ, անդամակցութիւնը |
+| `-մ` | մոնկոլիան, մտերիմը, մազմզուկներու |
+| `-մա` | մազմզուկներու, մաֆին, մատնութիւն |
+| `-հա` | հաւաքուածները, հաւնած, հակազգային |
+| `-ս` | սթեմֆորտ, սարոս, սարուխանեանը |
+| `-կ` | կայազորի, կայքէջի, կատուն |
+| `-հ` | հաւաքուածները, հաւնած, հակազգային |
+| `-պ` | պոգոդինի, պեշավար, պատսպարուի |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ն` | բառարանագիտական, բակերն, հակազգային |
+| `-ան` | բառարանագիտական, քառորդական, տուրիստական |
+| `-ը` | գուլպաները, զգայարանքը, հաւաքուածները |
+| `-ի` | կայազորի, ռատիօհեռասփռումի, կայքէջի |
+| `-ին` | հակազգային, օրֆէասին, ասեղնագործէին |
+| `-ու` | նոյնականացնելու, փրոֆեսորներու, ուսուցանելու |
+| `-ւն` | կատուն, եկամուտներուն, գնողներուն |
+| `-ւ` | ինտերակտիւ, նոյնականացնելու, փրոֆեսորներու |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ներո` | 1.91x | 133 contexts | ներով, ներող, ներու |
+| `ութե` | 1.66x | 232 contexts | լութեր, ութեան, հութեն |
+| `աստա` | 1.66x | 159 contexts | հաստա, փաստա, դաստա |
+| `աննե` | 1.51x | 178 contexts | բաններ, մկաններ, սպաններ |
+| `րակա` | 1.48x | 146 contexts | րական, փրակա, պրական |
+| `ուակ` | 1.95x | 43 contexts | առուակ, սրուակի, առուակի |
+| `ակու` | 1.42x | 179 contexts | մակու, ակուլ, կակուղ |
+| `ւթեա` | 1.97x | 41 contexts | ութեան, ծութեան, րութեան |
+| `ամար` | 1.47x | 135 contexts | քամար, ամարը, համար |
+| `ագրո` | 1.55x | 84 contexts | ծրագրով, զագրոսի, բնագրով |
+| `ւթիւ` | 1.70x | 48 contexts | ութիւն, ութիւնը, էութիւն |
+| `ններ` | 1.47x | 88 contexts | ֆիններ, իոններ, բաններ |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ա` | `-ն` | 244 words | արածեցման, ամուսնանան |
+| `-մ` | `-ն` | 158 words | մտերիմներէն, մեծերին |
+| `-կ` | `-ն` | 109 words | կիսաքոչուորական, կրետացիին |
+| `-ա` | `-ը` | 95 words | առաջնութիւնները, արականը |
+| `-ա` | `-ի` | 89 words | արիացի, արամանի |
+| `-ս` | `-ն` | 88 words | ստեղներուն, սաթամեանին |
+| `-պ` | `-ն` | 88 words | պարթոն, պատմալեզուագրական |
+| `-ա` | `-ան` | 85 words | արածեցման, ամուսնանան |
+| `-մ` | `-ի` | 84 words | մահտեսեանի, մաղրեպի |
+| `-հա` | `-ն` | 80 words | համաստեղութիւն, հաւաքականին |
+
+### 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 |
+|------|-----------------|------------|------|
+| ծանօթացնել | **`ծանօթաց-ն-ել`** | 7.5 | `ն` |
+| երկրաչափութիւնը | **`երկրաչափութիւ-ն-ը`** | 7.5 | `ն` |
+| ախտապատճառներն | **`ախտապատճառն-եր-ն`** | 7.5 | `եր` |
+| հովիտներով | **`հովիտն-եր-ով`** | 7.5 | `եր` |
+| քէհեայեանը | **`քէհեայեա-ն-ը`** | 7.5 | `ն` |
+| ճէպէճեանի | **`ճէպէճե-ան-ի`** | 7.5 | `ան` |
+| ձգաններուն | **`ձգաններ-ու-ն`** | 7.5 | `ու` |
+| միաբանութիւններ | **`միաբանութիւն-ն-եր`** | 7.5 | `ն` |
+| հանրապետութենէն | **`հանրապետութե-ն-էն`** | 7.5 | `ն` |
+| իմաստասիրութենէն | **`իմաստասիրութե-ն-էն`** | 7.5 | `ն` |
+| տեղանունների | **`տեղանունն-եր-ի`** | 7.5 | `եր` |
+| հարեւաններ | **`հարեւան-ն-եր`** | 7.5 | `ն` |
+| բերբերյանի | **`բերբերյ-ան-ի`** | 7.5 | `ան` |
+| աջակիցներ | **`աջակից-ն-եր`** | 7.5 | `ն` |
+| մթութիւնը | **`մթութիւ-ն-ը`** | 7.5 | `ն` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Western Armenian 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 (4.07x) |
+| N-gram | **2-gram** | Lowest perplexity (392) |
+| Markov | **Context-4** | Highest predictability (98.1%) |
+| 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 03:56:09*

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