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README.md

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+---
+language: om
+language_name: Oromo
+language_family: cushitic
+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-cushitic
+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: 4.912
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8881
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Oromo - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Oromo** 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** | 3.925x | 3.93 | 0.7743% | 539,824 |
+| **16k** | 4.303x | 4.30 | 0.8489% | 492,401 |
+| **32k** | 4.614x | 4.62 | 0.9103% | 459,184 |
+| **64k** | 4.912x 🏆 | 4.91 | 0.9692% | 431,282 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Giinii-Bisaa’uu biyya Afrikaa keessa jirtu. President: Umaró Umbaló Sissoko Prim...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁giinii - b isaa ’ uu ▁biyya ▁afrikaa ▁keessa ▁jirtu ... (+26 more)` | 36 |
+| 16k | `▁giinii - b isaa ’ uu ▁biyya ▁afrikaa ▁keessa ▁jirtu ... (+21 more)` | 31 |
+| 32k | `▁giinii - b isaa ’ uu ▁biyya ▁afrikaa ▁keessa ▁jirtu ... (+19 more)` | 29 |
+| 64k | `▁giinii - bisaa ’ uu ▁biyya ▁afrikaa ▁keessa ▁jirtu . ... (+11 more)` | 21 |
+
+**Sample 2:** `Godinni Qeellam Wallagaa kan argamu Oromiyaa keessatti. Wabii Oromiyaa Qellami w...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁godinni ▁q eell am ▁wall agaa ▁kan ▁argamu ▁oromiyaa ▁keessatti ... (+15 more)` | 25 |
+| 16k | `▁godinni ▁qeell am ▁wall agaa ▁kan ▁argamu ▁oromiyaa ▁keessatti . ... (+13 more)` | 23 |
+| 32k | `▁godinni ▁qeellam ▁wallagaa ▁kan ▁argamu ▁oromiyaa ▁keessatti . ▁wabii ▁oromiyaa ... (+8 more)` | 18 |
+| 64k | `▁godinni ▁qeellam ▁wallagaa ▁kan ▁argamu ▁oromiyaa ▁keessatti . ▁wabii ▁oromiyaa ... (+6 more)` | 16 |
+
+**Sample 3:** `Indoneeshiyaan biyya Eshiyaa bahaatti argamtu. Indoneeshiyaan odolotaarraa kan u...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ind on eesh iyaan ▁biyya ▁eshiyaa ▁bahaatti ▁argamtu . ▁ind ... (+25 more)` | 35 |
+| 16k | `▁indon eesh iyaan ▁biyya ▁eshiyaa ▁bahaatti ▁argamtu . ▁indon eesh ... (+22 more)` | 32 |
+| 32k | `▁indoneeshiyaan ▁biyya ▁eshiyaa ▁bahaatti ▁argamtu . ▁indoneeshiyaan ▁odol ot aarraa ... (+18 more)` | 28 |
+| 64k | `▁indoneeshiyaan ▁biyya ▁eshiyaa ▁bahaatti ▁argamtu . ▁indoneeshiyaan ▁odolotaarraa ▁kan ▁uummamte ... (+16 more)` | 26 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.912x compression
+- **Lowest UNK Rate:** 8k with 0.7743% 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 | 5,143 | 12.33 | 11,123 | 19.4% | 44.4% |
+| **2-gram** | Subword | 213 🏆 | 7.73 | 2,915 | 73.1% | 99.4% |
+| **3-gram** | Word | 6,773 | 12.73 | 11,742 | 15.4% | 34.2% |
+| **3-gram** | Subword | 1,522 | 10.57 | 17,267 | 31.9% | 80.6% |
+| **4-gram** | Word | 27,247 | 14.73 | 32,157 | 4.6% | 12.9% |
+| **4-gram** | Subword | 7,489 | 12.87 | 73,751 | 15.1% | 48.6% |
+| **5-gram** | Word | 24,982 | 14.61 | 27,382 | 3.0% | 11.0% |
+| **5-gram** | Subword | 24,392 | 14.57 | 164,495 | 9.3% | 29.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ta u` | 3,055 |
+| 2 | `yoo ta` | 2,546 |
+| 3 | `ta e` | 1,182 |
+| 4 | `danda a` | 874 |
+| 5 | `kan akka` | 737 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `yoo ta u` | 2,255 |
+| 2 | `haa ta u` | 342 |
+| 3 | `ta u malee` | 285 |
+| 4 | `yeroo baay ee` | 252 |
+| 5 | `of keessaa qaba` | 224 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `haa ta u malee` | 282 |
+| 2 | `yoo ta u kunis` | 213 |
+| 3 | `qabu yoo ta u` | 167 |
+| 4 | `ta uu danda a` | 135 |
+| 5 | `tokko yoo ta u` | 128 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kan qabu yoo ta u` | 103 |
+| 2 | `keessaa tokko yoo ta u` | 91 |
+| 3 | `kan qaban yoo ta u` | 61 |
+| 4 | `of keessaa qabu yoo ta` | 41 |
+| 5 | `keessaa qabu yoo ta u` | 41 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a a` | 191,675 |
+| 2 | `a _` | 157,464 |
+| 3 | `a n` | 95,295 |
+| 4 | `i _` | 91,344 |
+| 5 | `n _` | 80,089 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a a _` | 71,357 |
+| 2 | `a n _` | 40,890 |
+| 3 | `a a n` | 34,146 |
+| 4 | `i i _` | 34,015 |
+| 5 | `t t i` | 25,977 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `t t i _` | 21,400 |
+| 2 | `a a n _` | 17,797 |
+| 3 | `_ k a n` | 16,358 |
+| 4 | `e e s s` | 15,993 |
+| 5 | `a t t i` | 15,383 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a t t i _` | 13,078 |
+| 2 | `e e s s a` | 12,757 |
+| 3 | `_ k a n _` | 11,199 |
+| 4 | `k e e s s` | 10,958 |
+| 5 | `_ k e e s` | 10,805 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 213
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~30% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.7695 | 1.705 | 4.74 | 79,366 | 23.1% |
+| **1** | Subword | 1.4423 | 2.718 | 10.26 | 768 | 0.0% |
+| **2** | Word | 0.1820 | 1.134 | 1.36 | 375,403 | 81.8% |
+| **2** | Subword | 0.8632 | 1.819 | 4.70 | 7,870 | 13.7% |
+| **3** | Word | 0.0437 | 1.031 | 1.07 | 508,948 | 95.6% |
+| **3** | Subword | 0.7482 | 1.680 | 3.52 | 36,941 | 25.2% |
+| **4** | Word | 0.0138 🏆 | 1.010 | 1.02 | 541,591 | 98.6% |
+| **4** | Subword | 0.5894 | 1.505 | 2.53 | 129,933 | 41.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `fi niwutiroonii kan maatilee gurguddaa macroscopic anatomy fi wantoota qoyyobiyyoo hojjatu ta ee war...`
+2. `kan maddu hedduu kan akka barachuu irra taxon dha innis dheerina ujummoo dhigaa ademma kunis yaada`
+3. `ta uun leenjisaa yoo ta u guuttiiqoota poozatiiviidhaan chaarjii saffisaan hir isa sirrii irratti ka...`
+
+**Context Size 2:**
+
+1. `ta u malee siwaahiliin ummatta hanga million 40 guddatte haa ta u kan oromiyaa dabalatee heera mataa`
+2. `yoo ta u xurbaa alfaa lamaa fi isaa ol walitti makamuun narvii lafee dugda of keessaa qabu`
+3. `ta e garuu kofni isaa sirrii ta een fide namoonni jireenya godaansaa dhiisanii ganda dhaabbataa uumm...`
+
+**Context Size 3:**
+
+1. `yoo ta u hiikni isaanii kofa sirrii jechuudha rektaangiliin rogoonni isaa afran dheerinni isaa walfa...`
+2. `haa ta u malee rakkoon haaraan dhufe cunqursaa lakkoofsaa tyranny of numbers naannee walxaxaa ijaaru...`
+3. `ta u malee walii galtee dhabuun isaa fi birgaadeer jeneraal abdulkaariim qaasim gidduutti uumameen m...`
+
+**Context Size 4:**
+
+1. `haa ta u malee addeessi lafa irraa fagoo jira fageenyi inni lafarraa qabus kilomeetira 384 000 ol ta...`
+2. `yoo ta u kunis dhuudhaa haaraa dhaabbatummaa saffisa ifaa fi dhuudhaa duraan beekamaa ture dhuudhaa ...`
+3. `qabu yoo ta u yeroo baay ee meeshaalee bulchiinsaatiin kan qindaa anidha jijjiiramni adeemsa kana ke...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a_awalmbarseeers`
+2. `_ga_isufin_saan_`
+3. `idalee_haaaammi_`
+
+**Context Size 2:**
+
+1. `aan._dhaniilixa_n`
+2. `a_gamu_dhummaaloo`
+3. `anii_dhaa_laawudh`
+
+**Context Size 3:**
+
+1. `aa_biran._see_itti`
+2. `an_dina_maloojiiti`
+3. `aane_galuuf_fakkee`
+
+**Context Size 4:**
+
+1. `tti_buuf_itti_oomis`
+2. `aan_fakkasummataa_q`
+3. `_kan_ariiroo_mancaa`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.6% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (129,933 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 | 33,399 |
+| Total Tokens | 552,518 |
+| Mean Frequency | 16.54 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 147.85 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | fi | 12,823 |
+| 2 | kan | 11,436 |
+| 3 | ta | 8,265 |
+| 4 | akka | 6,344 |
+| 5 | keessatti | 5,232 |
+| 6 | u | 4,498 |
+| 7 | yoo | 4,135 |
+| 8 | hin | 3,898 |
+| 9 | yeroo | 3,580 |
+| 10 | tokko | 3,377 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | prezidaantii | 2 |
+| 2 | kasasiyoonaa | 2 |
+| 3 | feltrinelli | 2 |
+| 4 | rizaabii | 2 |
+| 5 | raamyaan | 2 |
+| 6 | rootarii | 2 |
+| 7 | eegsa | 2 |
+| 8 | alta | 2 |
+| 9 | kuusii | 2 |
+| 10 | maalamaa | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0202 |
+| R² (Goodness of Fit) | 0.995340 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 32.2% |
+| Top 1,000 | 59.7% |
+| Top 5,000 | 79.9% |
+| Top 10,000 | 87.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9953 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 32.2% of corpus
+- **Long Tail:** 23,399 words needed for remaining 12.3% 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 | 0.8881 🏆 | 0.3121 | N/A | N/A |
+| **mono_64d** | 64 | 0.7740 | 0.2857 | N/A | N/A |
+| **mono_128d** | 128 | 0.1998 | 0.2448 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8881 | 0.3137 | 0.0140 | 0.1340 |
+| **aligned_64d** | 64 | 0.7740 | 0.2922 | 0.0300 | 0.1800 |
+| **aligned_128d** | 128 | 0.1998 | 0.2360 | 0.0660 | 0.2500 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8881 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2807. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 6.6% 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 | **-0.633** | 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` | alex, amaaradhaan, ambaa |
+| `-s` | silocones, soovieyeet, sexual |
+| `-b` | beekamaafi, baatriin, balaaleffata |
+| `-d` | duruma, document, dheedee |
+| `-m` | milaa, moment, materials |
+| `-ma` | materials, massachusetts, malcolm |
+| `-ba` | baatriin, balaaleffata, baasti |
+| `-g` | garraan, guutuu, guute |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | milaa, qilleensarra, duruma |
+| `-i` | beekamaafi, keetoonoonni, miiti |
+| `-n` | tekinooloojiin, garraan, cimaadhaan |
+| `-ii` | naqanii, giriikii, bismazii |
+| `-aa` | milaa, wiifaa, inaariyaa |
+| `-an` | garraan, cimaadhaan, firoottan |
+| `-e` | caamee, kaaye, dheedee |
+| `-ti` | miiti, baasti, totti |
+
+### 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 |
+|------|----------|------------------|----------|
+| `araa` | 1.85x | 101 contexts | haraa, karaa, qaraa |
+| `rraa` | 1.90x | 85 contexts | erraa, urraa, orraa |
+| `mmaa` | 2.12x | 47 contexts | ummaa, ammaa, lummaa |
+| `eess` | 1.68x | 124 contexts | eessa, keessa, eessoo |
+| `gudd` | 2.12x | 34 contexts | guddo, gudda, guddae |
+| `aala` | 1.58x | 114 contexts | yaala, jaala, gaala |
+| `arga` | 1.75x | 66 contexts | marga, argan, argaa |
+| `rrat` | 2.24x | 25 contexts | irrati, urratti, arratti |
+| `okko` | 2.26x | 24 contexts | tokko, tokkof, tokkos |
+| `ratt` | 1.90x | 45 contexts | iratti, barattu, biratti |
+| `chuu` | 1.77x | 50 contexts | achuu, kichuu, glchuu |
+| `jedh` | 2.23x | 20 contexts | jedhu, jedhe, jedha |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-b` | `-a` | 163 words | balbala, biyyootaa |
+| `-h` | `-a` | 142 words | hubachaa, harkisuudha |
+| `-a` | `-a` | 141 words | arba, araboota |
+| `-d` | `-a` | 137 words | dhooqa, dandeessa |
+| `-a` | `-i` | 132 words | ashaabi, argamni |
+| `-d` | `-i` | 129 words | dargageessi, dirreetti |
+| `-m` | `-a` | 128 words | milliyoona, molekuloota |
+| `-s` | `-a` | 121 words | shaffaaxa, shayxaana |
+| `-d` | `-n` | 115 words | daawwannaan, dirreewwan |
+| `-g` | `-a` | 110 words | genera, gamaa |
+
+### 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 |
+|------|-----------------|------------|------|
+| burtukaanaa | **`burtuka-an-aa`** | 7.5 | `an` |
+| funfannaa | **`funfan-n-aa`** | 7.5 | `n` |
+| dhalattoonni | **`dhalattoon-n-i`** | 7.5 | `n` |
+| aannannoo | **`aannan-n-oo`** | 7.5 | `n` |
+| tambiixni | **`tambiix-n-i`** | 7.5 | `n` |
+| fooyyessanii | **`fooyyess-an-ii`** | 7.5 | `an` |
+| yaadoonni | **`yaadoon-n-i`** | 7.5 | `n` |
+| academies | **`academ-i-es`** | 7.5 | `i` |
+| karibiyaanii | **`karibiya-an-ii`** | 7.5 | `an` |
+| enciclopèdia | **`enciclopèd-i-a`** | 7.5 | `i` |
+| laakkawanii | **`laakkaw-an-ii`** | 7.5 | `an` |
+| jijjiramni | **`jijjiram-n-i`** | 7.5 | `n` |
+| raajeffannoo | **`raajeffan-n-oo`** | 7.5 | `n` |
+| unionoromia | **`unionorom-i-a`** | 7.5 | `i` |
+| raadiyoona | **`raadiyoo-n-a`** | 7.5 | `n` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Oromo 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 (4.91x) |
+| N-gram | **2-gram** | Lowest perplexity (213) |
+| Markov | **Context-4** | Highest predictability (98.6%) |
+| 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 16:39:48*

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