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README.md

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+---
+language: vi
+language_name: Vietnamese
+language_family: austroasiatic_vietic
+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-austroasiatic_vietic
+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: 3.900
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8322
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-18
+---
+
+# Vietnamese - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Vietnamese** 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** | 3.647x | 3.65 | 0.1376% | 4,322,437 |
+| **16k** | 3.775x | 3.77 | 0.1424% | 4,176,769 |
+| **32k** | 3.851x | 3.85 | 0.1453% | 4,093,428 |
+| **64k** | 3.900x 🏆 | 3.90 | 0.1471% | 4,042,743 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Siphona scutellata là một loài ruồi trong họ Tachinidae. Chú thích Liên kết ngoà...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁si ph ona ▁sc ut ell ata ▁là ▁một ▁loài ... (+12 more)` | 22 |
+| 16k | `▁si ph ona ▁scut ellata ▁là ▁một ▁loài ▁ruồi ▁trong ... (+9 more)` | 19 |
+| 32k | `▁si ph ona ▁scut ellata ▁là ▁một ▁loài ▁ruồi ▁trong ... (+9 more)` | 19 |
+| 64k | `▁siph ona ▁scutellata ▁là ▁một ▁loài ▁ruồi ▁trong ▁họ ▁tach ... (+7 more)` | 17 |
+
+**Sample 2:** `Kocaali là một xã thuộc huyện Ergani, tỉnh Diyarbakır, Thổ Nhĩ Kỳ. Dân số thời đ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁k oc a ali ▁là ▁một ▁xã ▁thuộc ▁huyện ▁er ... (+31 more)` | 41 |
+| 16k | `▁k oca ali ▁là ▁một ▁xã ▁thuộc ▁huyện ▁er g ... (+29 more)` | 39 |
+| 32k | `▁k oca ali ▁là ▁một ▁xã ▁thuộc ▁huyện ▁er g ... (+28 more)` | 38 |
+| 64k | `▁k oca ali ▁là ▁một ▁xã ▁thuộc ▁huyện ▁erg ani ... (+24 more)` | 34 |
+
+**Sample 3:** `Glipidiomorpha riesei là một loài bọ cánh cứng trong họ Mordellidae. Loài này đư...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁gl ip idi omorpha ▁r ies ei ▁là ▁một ▁loài ... (+24 more)` | 34 |
+| 16k | `▁gl ip idi omorpha ▁r ies ei ▁là ▁một ▁loài ... (+24 more)` | 34 |
+| 32k | `▁gl ip idi omorpha ▁ries ei ▁là ▁một ▁loài ▁bọ ... (+21 more)` | 31 |
+| 64k | `▁gl ip idi omorpha ▁riesei ▁là ▁một ▁loài ▁bọ ▁cánh ... (+19 more)` | 29 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.900x compression
+- **Lowest UNK Rate:** 8k with 0.1376% 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 | 106,320 | 16.70 | 2,695,824 | 10.1% | 25.1% |
+| **2-gram** | Subword | 409 🏆 | 8.67 | 93,876 | 59.2% | 96.0% |
+| **3-gram** | Word | 890,077 | 19.76 | 9,913,320 | 6.8% | 13.5% |
+| **3-gram** | Subword | 2,984 | 11.54 | 411,919 | 25.5% | 66.3% |
+| **4-gram** | Word | 2,796,979 | 21.42 | 22,248,727 | 6.3% | 11.5% |
+| **4-gram** | Subword | 16,513 | 14.01 | 1,959,172 | 13.3% | 41.5% |
+| **5-gram** | Word | 2,571,700 | 21.29 | 19,242,355 | 7.4% | 13.5% |
+| **5-gram** | Subword | 69,615 | 16.09 | 6,377,982 | 8.7% | 27.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `là một` | 1,495,225 |
+| 2 | `chú thích` | 852,707 |
+| 3 | `tham khảo` | 804,096 |
+| 4 | `một loài` | 728,551 |
+| 5 | `trong họ` | 711,111 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `là một loài` | 722,924 |
+| 2 | `liên kết ngoài` | 620,713 |
+| 3 | `loài này được` | 453,066 |
+| 4 | `chú thích liên` | 440,159 |
+| 5 | `thích liên kết` | 440,150 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `chú thích liên kết` | 440,133 |
+| 2 | `thích liên kết ngoài` | 439,810 |
+| 3 | `được mô tả năm` | 384,043 |
+| 4 | `chú thích tham khảo` | 365,017 |
+| 5 | `vật được mô tả` | 363,438 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `chú thích liên kết ngoài` | 439,801 |
+| 2 | `vật được mô tả năm` | 363,377 |
+| 3 | `tả khoa học đầu tiên` | 335,608 |
+| 4 | `khoa học đầu tiên năm` | 309,398 |
+| 5 | `đầu tiên năm chú thích` | 263,309 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ t` | 44,618,705 |
+| 2 | `n g` | 36,466,380 |
+| 3 | `_ c` | 30,008,094 |
+| 4 | `n _` | 29,116,380 |
+| 5 | `g _` | 27,402,011 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g _` | 27,209,259 |
+| 2 | `_ t h` | 17,092,068 |
+| 3 | `_ t r` | 10,431,331 |
+| 4 | `_ c h` | 9,946,202 |
+| 5 | `n h _` | 9,905,520 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g _ t` | 4,408,233 |
+| 2 | `_ v à _` | 3,874,748 |
+| 3 | `_ l à _` | 3,858,257 |
+| 4 | `c ủ a _` | 3,768,746 |
+| 5 | `_ c ủ a` | 3,768,226 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ c ủ a _` | 3,765,562 |
+| 2 | `_ đ ư ợ c` | 3,314,830 |
+| 3 | `đ ư ợ c _` | 3,299,257 |
+| 4 | `_ m ộ t _` | 3,246,287 |
+| 5 | `_ n ă m _` | 3,101,391 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 409
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~27% 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 | 0.7563 | 1.689 | 9.23 | 2,640,968 | 24.4% |
+| **1** | Subword | 1.2157 | 2.323 | 15.79 | 34,963 | 0.0% |
+| **2** | Word | 0.4386 | 1.355 | 3.10 | 24,350,395 | 56.1% |
+| **2** | Subword | 0.5203 | 1.434 | 3.02 | 551,811 | 48.0% |
+| **3** | Word | 0.2736 | 1.209 | 1.81 | 75,436,653 | 72.6% |
+| **3** | Subword | 0.4089 | 1.328 | 2.69 | 1,667,382 | 59.1% |
+| **4** | Word | 0.1518 🏆 | 1.111 | 1.33 | 136,713,102 | 84.8% |
+| **4** | Subword | 0.4863 | 1.401 | 2.89 | 4,478,768 | 51.4% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `là tổng thống như đường sắt bắc iwate grulla morioka thống shad striper rượt đuổi theo`
+2. `và lavrov đã đi hết các xơ cứng trong hiệp hòa thảo loài khác biệt hiệu`
+3. `của mình mang tên đàn đạo đền một mạng nicaragua 3 năm vật hoang mạc thiên`
+
+**Context Size 2:**
+
+1. `là một loài hymenoptera trong họ noctuidae chú thích tham khảo bay kazakhstan không tìm thấy tại`
+2. `chú thích liên kết ngoài vật được mô tả năm vật bolivia vật brasil vật colombia vật`
+3. `một loài bướm đêm trong họ cửu lý hương loài boswellia trong tôn giáo nào giáo dục`
+
+**Context Size 3:**
+
+1. `là một loài bọ cánh cứng trong họ melandryidae loài này được werderm mô tả khoa học năm`
+2. `liên kết ngoài c vật được mô tả năm es hemianemia eximia`
+3. `loài này được baker labat schatz mô tả khoa học đầu tiên năm chú thích tham khảo vật`
+
+**Context Size 4:**
+
+1. `chú thích liên kết ngoài vật được mô tả năm vật đặc hữu đài loan đài loan thuộc nhật`
+2. `vật được mô tả năm vật đặc hữu trung quốc kim lũ mai tai hùm đơn loài vật được`
+3. `khoa học đầu tiên năm chú thích liên kết ngoài vật được mô tả năm đêm indonesia đêm philippines`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_lef_19_kỳ.wanhe`
+2. `n_terìng_đã_phủ_`
+3. `h_"_ayroarọcá_m_`
+
+**Context Size 2:**
+
+1. `_thuệsố_đã_vấn_vù`
+2. `ng_thuộc_nhịu_đầu`
+3. `_của_nh_sác_prit_`
+
+**Context Size 3:**
+
+1. `ng_đã_bị_bệnh_lại_`
+2. `_thắng_của_hampus_`
+3. `_trang_3_joon,_nhữ`
+
+**Context Size 4:**
+
+1. `ng_tăng_ánh_quyết_c`
+2. `_và_những_có_một_cầ`
+3. `_là_volume_shop,_tâ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 84.8% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (4,478,768 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 | 1,088,012 |
+| Total Tokens | 275,589,508 |
+| Mean Frequency | 253.30 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 12931.56 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | là | 3,896,221 |
+| 2 | và | 3,888,002 |
+| 3 | của | 3,770,649 |
+| 4 | năm | 3,541,374 |
+| 5 | được | 3,324,385 |
+| 6 | một | 3,283,880 |
+| 7 | trong | 2,847,858 |
+| 8 | có | 2,266,526 |
+| 9 | các | 2,260,160 |
+| 10 | người | 1,505,528 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | bíchhạnh | 2 |
+| 2 | dâuliên | 2 |
+| 3 | lụanguyễn | 2 |
+| 4 | zeltiq | 2 |
+| 5 | côtobin | 2 |
+| 6 | novitskiy | 2 |
+| 7 | tarelkin | 2 |
+| 8 | 齋堂 | 2 |
+| 9 | zhāitáng | 2 |
+| 10 | chatral | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.5197 |
+| R² (Goodness of Fit) | 0.977671 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 35.9% |
+| Top 1,000 | 79.0% |
+| Top 5,000 | 91.3% |
+| Top 10,000 | 93.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9777 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 35.9% of corpus
+- **Long Tail:** 1,078,012 words needed for remaining 6.4% 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 | 0.8322 | 0.4208 | N/A | N/A |
+| **mono_64d** | 64 | 0.8116 | 0.3302 | N/A | N/A |
+| **mono_128d** | 128 | 0.7892 | 0.2753 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8322 🏆 | 0.4041 | 0.4880 | 0.8640 |
+| **aligned_64d** | 64 | 0.8116 | 0.3384 | 0.7280 | 0.9680 |
+| **aligned_128d** | 128 | 0.7892 | 0.2727 | 0.8360 | 0.9820 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8322 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3403. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 83.6% 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 | **-0.502** | 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 |
+|--------|----------|
+| `-s` | sinothomisus, sportowe, sprogøe |
+| `-t` | thổvàng, trilion, tháitô |
+| `-a` | amorín, aerolindigia, awardchoice |
+| `-m` | minhphạm, mớimtvca, mutungi |
+| `-c` | coccomelia, clacton, clatratum |
+| `-b` | batmagnai, bejt, balep |
+| `-k` | karepura, kỳtriệu, kronthaler |
+| `-ma` | marovt, mayran, marghanna |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | sinothomisus, orestes, trochanteralis |
+| `-a` | coccomelia, karepura, nuichua |
+| `-e` | pilosellae, orée, sportowe |
+| `-n` | oreodendron, gaggabutan, clacton |
+| `-is` | trochanteralis, neoconis, mononalis |
+| `-i` | batmagnai, weinmanntái, eesi |
+| `-us` | sinothomisus, brimidius, eudelus |
+| `-es` | orestes, pseudaspilates, wingates |
+
+### 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 |
+|------|----------|------------------|----------|
+| `atio` | 2.64x | 168 contexts | tatio, natio, fatio |
+| `opte` | 2.62x | 135 contexts | opted, opter, copte |
+| `nter` | 2.01x | 355 contexts | enter, inter, unter |
+| `trưở` | 2.86x | 60 contexts | trưởn, trưởnɡ, trưởng |
+| `tướn` | 2.93x | 45 contexts | tướng, tướngm, 4tướng |
+| `pter` | 2.21x | 106 contexts | ptero, opter, apter |
+| `ceae` | 3.35x | 20 contexts | aceae, ficeae, biceae |
+| `rưởn` | 2.86x | 32 contexts | trưởn, rưởng, trưởnɡ |
+| `huyệ` | 1.59x | 353 contexts | huyệt, huyện, chuyệ |
+| `nhiề` | 2.15x | 75 contexts | nhiền, nhiềy, nhiềm |
+| `uyễn` | 2.16x | 59 contexts | quyễn, duyễn, nuyễn |
+| `huyể` | 2.06x | 28 contexts | chuyể, huyển, thuyểt |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-p` | `-a` | 126 words | pnmburucuya, praeangulata |
+| `-p` | `-s` | 122 words | pedicellatus, polyotis |
+| `-c` | `-s` | 116 words | cicindeloides, constrictiflorus |
+| `-s` | `-a` | 108 words | sungka, serbica |
+| `-c` | `-a` | 103 words | chensa, conardia |
+| `-s` | `-s` | 103 words | sacodes, sulamitis |
+| `-a` | `-s` | 99 words | ardys, airplanes |
+| `-a` | `-a` | 90 words | akassa, attenuatella |
+| `-m` | `-s` | 86 words | matles, moyennes |
+| `-m` | `-a` | 78 words | meryta, mātaatua |
+
+### 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 |
+|------|-----------------|------------|------|
+| tolarucan | **`tolaruc-a-n`** | 7.5 | `a` |
+| kazusensis | **`kazusen-s-is`** | 7.5 | `s` |
+| jāgarābhivamsa | **`jāgarābhivam-s-a`** | 7.5 | `s` |
+| alagappapuram | **`alagappapur-a-m`** | 7.5 | `a` |
+| krickenbach | **`krickenb-a-ch`** | 7.5 | `a` |
+| speculaas | **`specu-la-as`** | 7.5 | `la` |
+| namsskogan | **`namsskog-a-n`** | 7.5 | `a` |
+| mündersbach | **`mündersb-a-ch`** | 7.5 | `a` |
+| quadrisetosus | **`quadriseto-s-us`** | 7.5 | `s` |
+| thắngshonan | **`thắngshon-a-n`** | 7.5 | `a` |
+| atrivenata | **`atrive-na-ta`** | 7.5 | `na` |
+| hochiensis | **`hochien-s-is`** | 7.5 | `s` |
+| outermost | **`outermo-s-t`** | 7.5 | `s` |
+| xuechengensis | **`xuechengen-s-is`** | 7.5 | `s` |
+| mesypochrysa | **`mesypochry-s-a`** | 7.5 | `s` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Vietnamese 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 (3.90x) |
+| N-gram | **2-gram** | Lowest perplexity (409) |
+| Markov | **Context-4** | Highest predictability (84.8%) |
+| 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: 2026-01-18 17:40:28*

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