Upload all models and assets for kus (latest)

.gitattributes

@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+visualizations/embedding_similarity.png filter=lfs diff=lfs merge=lfs -text
+visualizations/embedding_tsne_multilingual.png filter=lfs diff=lfs merge=lfs -text
+visualizations/performance_dashboard.png filter=lfs diff=lfs merge=lfs -text
+visualizations/position_encoding_comparison.png filter=lfs diff=lfs merge=lfs -text
+visualizations/tsne_sentences.png filter=lfs diff=lfs merge=lfs -text
+visualizations/tsne_words.png filter=lfs diff=lfs merge=lfs -text
+visualizations/zipf_law.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,769 @@
+---
+language: kus
+language_name: Kusaal
+language_family: atlantic_gur
+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-atlantic_gur
+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: 3.674
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8088
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Kusaal - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Kusaal** 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** | 3.399x | 3.40 | 0.0986% | 862,718 |
+| **16k** | 3.563x | 3.56 | 0.1034% | 823,082 |
+| **32k** | 3.674x 🏆 | 3.68 | 0.1066% | 798,260 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Wadmaan anɛ zi’eni tisi o sʋ’ʋlim dim wadmaanib yin. O anɛ onɛ paasi gɔsid wada ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁wadmaan ▁anɛ ▁zi ’ eni ▁tisi ▁o ▁sʋ ’ ʋlim ... (+19 more)` | 29 |
+| 16k | `▁wadmaan ▁anɛ ▁zi ’ eni ▁tisi ▁o ▁sʋ ’ ʋlim ... (+19 more)` | 29 |
+| 32k | `▁wadmaan ▁anɛ ▁zi ’ eni ▁tisi ▁o ▁sʋ ’ ʋlim ... (+18 more)` | 28 |
+
+**Sample 2:** `Nɔraŋ anɛ yin bunkɔnbid la yinne. Buudi There several types cockerels Nyɔɔd ther...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁nɔ ra ŋ ▁anɛ ▁yin ▁bun kɔn bid ▁la ▁yinne ... (+29 more)` | 39 |
+| 16k | `▁nɔ ra ŋ ▁anɛ ▁yin ▁bun kɔnbid ▁la ▁yinne . ... (+22 more)` | 32 |
+| 32k | `▁nɔ ra ŋ ▁anɛ ▁yin ▁bunkɔnbid ▁la ▁yinne . ▁buudi ... (+13 more)` | 23 |
+
+**Sample 3:** `Ndebugri Akparipoka Patience anɛ pu'a kanɛ yit Zebilla su'ulum. o anɛ karinsaam ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ndebugri ▁ak p ari po ka ▁pa ti ence ▁anɛ ... (+26 more)` | 36 |
+| 16k | `▁ndebugri ▁ak p ari poka ▁pati ence ▁anɛ ▁pu ' ... (+22 more)` | 32 |
+| 32k | `▁ndebugri ▁akparipoka ▁patience ▁anɛ ▁pu ' a ▁kanɛ ▁yit ▁zebilla ... (+16 more)` | 26 |
+
+
+### Key Findings
+
+- **Best Compression:** 32k achieves 3.674x compression
+- **Lowest UNK Rate:** 8k with 0.0986% 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 | 7,522 | 12.88 | 26,351 | 18.4% | 46.2% |
+| **2-gram** | Subword | 293 🏆 | 8.20 | 2,565 | 64.8% | 99.0% |
+| **3-gram** | Word | 23,120 | 14.50 | 51,608 | 10.0% | 27.1% |
+| **3-gram** | Subword | 2,318 | 11.18 | 22,043 | 27.6% | 70.2% |
+| **4-gram** | Word | 53,599 | 15.71 | 91,269 | 6.5% | 17.1% |
+| **4-gram** | Subword | 11,494 | 13.49 | 102,940 | 13.6% | 41.9% |
+| **5-gram** | Word | 48,864 | 15.58 | 71,111 | 5.8% | 15.3% |
+| **5-gram** | Subword | 35,256 | 15.11 | 239,430 | 7.9% | 28.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ka ba` | 6,977 |
+| 2 | `la ni` | 5,343 |
+| 3 | `ka o` | 4,336 |
+| 4 | `o da` | 3,608 |
+| 5 | `la ka` | 3,126 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `tusa ayi nɛ` | 1,924 |
+| 2 | `yʋʋm tusa ayi` | 1,743 |
+| 3 | `from the original` | 1,206 |
+| 4 | `the original on` | 1,172 |
+| 5 | `archived from the` | 1,171 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `yʋʋm tusa ayi nɛ` | 1,517 |
+| 2 | `archived from the original` | 1,171 |
+| 3 | `from the original on` | 1,167 |
+| 4 | `yʋʋm tusir kɔbiswai nɛ` | 792 |
+| 5 | `tusa ayi nɛ piinɛ` | 526 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `archived from the original on` | 1,132 |
+| 2 | `yʋʋm tusa ayi nɛ piinɛ` | 501 |
+| 3 | `ma asim tiig na adɔɔg` | 369 |
+| 4 | `yʋʋm tusa ayi nɛ pisi` | 323 |
+| 5 | `parliament of the 4th republic` | 287 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 206,888 |
+| 2 | `a n` | 104,100 |
+| 3 | `_ n` | 103,608 |
+| 4 | `_ k` | 87,930 |
+| 5 | `ɛ _` | 84,875 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n ɛ _` | 65,600 |
+| 2 | `_ k a` | 52,937 |
+| 3 | `_ l a` | 49,510 |
+| 4 | `k a _` | 42,042 |
+| 5 | `_ b a` | 38,532 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k a _` | 41,238 |
+| 2 | `_ l a _` | 32,031 |
+| 3 | `_ n ɛ _` | 28,130 |
+| 4 | `a n ɛ _` | 21,450 |
+| 5 | `_ b a _` | 20,894 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k a _ b` | 9,122 |
+| 2 | `_ l a _ n` | 9,113 |
+| 3 | `k a _ b a` | 8,038 |
+| 4 | `i _ n ɛ _` | 7,949 |
+| 5 | `a _ b a _` | 7,848 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 293
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~28% 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 | 0.8171 | 1.762 | 5.91 | 58,990 | 18.3% |
+| **1** | Subword | 0.8560 | 1.810 | 7.50 | 727 | 14.4% |
+| **2** | Word | 0.3267 | 1.254 | 1.89 | 348,345 | 67.3% |
+| **2** | Subword | 1.0600 | 2.085 | 6.97 | 5,454 | 0.0% |
+| **3** | Word | 0.1478 | 1.108 | 1.28 | 656,351 | 85.2% |
+| **3** | Subword | 0.9345 | 1.911 | 4.45 | 37,990 | 6.6% |
+| **4** | Word | 0.0633 🏆 | 1.045 | 1.10 | 839,904 | 93.7% |
+| **4** | Subword | 0.6543 | 1.574 | 2.76 | 169,223 | 34.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `la linɛ kɛ ka ba wadmaan kʋk daan ka nintaŋ wʋsa dʋ ʋs nɛ atan la`
+2. `ka biig yesu ken ninsabilis pua bɔɔd saʋŋ nɛ ka ba bʋgʋdnɛ wʋʋ bahamas nɛ sigir`
+3. `nɛ widi tɛŋ da gaŋi o nɛ saam la tisif la as dim yinne la pigisid`
+
+**Context Size 2:**
+
+1. `ka ba gban e ye o an wadmaan la yɛl o ye reggae na ab la asʋg`
+2. `la ni unesco intangible cultural heritage gbaʋŋin list gⴢsim nɛŋa ya as 23 enok yʋma wʋsa da`
+3. `ka o tiraan alhassan abdul majeed waris abu danladi adama fofana bismark adjei boateng clinton antwi...`
+
+**Context Size 3:**
+
+1. `tusa ayi nɛ kɔbisnaasi nɛ pisyuobʋ nɛ ayuobʋ mɛ da bɛ ndc ka da maal ka alim la`
+2. `yʋʋm tusa ayi nε ayuobu la ni nε an dinε an yiiga mʋ asʋg dinε ka on mεŋ`
+3. `from the original on 24 june retrieved 23 june o da diya ka bas onɛ da zin i`
+
+**Context Size 4:**
+
+1. `yʋʋm tusa ayi nɛ piinɛ nii la emelia brobbey at 3g awards primenewsghana 14 november retrieved 1 dec...`
+2. `archived from the original on 17 february retrieved 24 october ga nɛ akan mɔr nwɛnɛm di anɛ pian ʋk`
+3. `from the original on november 26 retrieved june 9 ceres da paas nwɛn ɛ nwɛn ɛdib sama banɛ nam`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_da_y),_bɛɛ_za_p`
+2. `a_pa_zum_demboct`
+3. `i_bɛ_an_wʋʋʋŋ_mo`
+
+**Context Size 2:**
+
+1. `a_natricat_nal_sɔ`
+2. `anbi_yinni._aceas`
+3. `_nε_ka_sʋŋinɛ_ba_`
+
+**Context Size 3:**
+
+1. `nɛ_piinsaal_ni_lig`
+2. `_ka_gɔsidib_nwa_dɔ`
+3. `_lationsowusa_pamm`
+
+**Context Size 4:**
+
+1. `_ka_pa'ali_onɛ_bɛ_k`
+2. `_la_pʋʋgin_(at_sɔ’_`
+3. `_nɛ_o_tis_winstitue`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.7% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (169,223 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 | 27,663 |
+| Total Tokens | 1,043,706 |
+| Mean Frequency | 37.73 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 541.33 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | la | 45,978 |
+| 2 | ka | 42,536 |
+| 3 | nɛ | 29,431 |
+| 4 | o | 23,467 |
+| 5 | ba | 22,327 |
+| 6 | da | 21,036 |
+| 7 | na | 12,286 |
+| 8 | an | 11,857 |
+| 9 | ye | 11,591 |
+| 10 | ni | 10,171 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | coursera | 2 |
+| 2 | udacity | 2 |
+| 3 | unib | 2 |
+| 4 | abʋ | 2 |
+| 5 | samnya | 2 |
+| 6 | din1 | 2 |
+| 7 | giinlbanɛ | 2 |
+| 8 | luosi | 2 |
+| 9 | kᴐnba | 2 |
+| 10 | gbilifʋ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2228 |
+| R² (Goodness of Fit) | 0.996032 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 48.3% |
+| Top 1,000 | 77.8% |
+| Top 5,000 | 91.0% |
+| Top 10,000 | 95.2% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9960 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 48.3% of corpus
+- **Long Tail:** 17,663 words needed for remaining 4.8% 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 | 0.8088 | 0.3514 | N/A | N/A |
+| **mono_64d** | 64 | 0.6903 | 0.3126 | N/A | N/A |
+| **mono_128d** | 128 | 0.2166 | 0.2849 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8088 🏆 | 0.3494 | 0.0500 | 0.2260 |
+| **aligned_64d** | 64 | 0.6903 | 0.3077 | 0.0700 | 0.2960 |
+| **aligned_128d** | 128 | 0.2166 | 0.2779 | 0.1000 | 0.3960 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8088 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3140. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 10.0% 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 | **-0.455** | 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` | adazum, adviser, aimee |
+| `-s` | saae, sakurasokore, stroke |
+| `-b` | bugur, bit, bʋʋsi |
+| `-t` | title, trichiasis, tɛŋzʋŋ |
+| `-k` | karibiig, kalbelias, kumiodori |
+| `-d` | donkornpptano, districts, dudley |
+| `-m` | mclellan, mahamanational, mט |
+| `-p` | pastor, palami, paamim |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | kalbelias, gerklaus, bars |
+| `-n` | mclellan, fɔn, gbedemahjun |
+| `-a` | xia, lʋgkaŋa, flea |
+| `-e` | saae, sakurasokore, title |
+| `-i` | bʋʋsi, kumiodori, palami |
+| `-d` | bond, dʋgʋd, kirid |
+| `-m` | zaam, paamim, adazum |
+| `-r` | bugur, pastor, hamburger |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ligi` | 1.64x | 96 contexts | aligi, ligid, iligi |
+| `atio` | 2.07x | 20 contexts | spatio, nation, nations |
+| `akur` | 2.29x | 13 contexts | sakur, sakuri, sakura |
+| `ieba` | 2.16x | 15 contexts | sieba, isieba, ɛsieba |
+| `ʋʋgi` | 1.87x | 21 contexts | yʋʋgi, bʋʋgi, tʋʋgi |
+| `dmaa` | 2.33x | 9 contexts | wadmaan, wadmaanɛ, wadmaani |
+| `ɔbis` | 2.37x | 8 contexts | kɔbis, bɔbis, kɔbisa |
+| `tion` | 1.81x | 16 contexts | option, nation, motion |
+| `yinn` | 1.89x | 14 contexts | yinni, yinna, yinnɛ |
+| `aasi` | 1.42x | 35 contexts | baasi, laasi, kaasi |
+| `iswa` | 2.21x | 7 contexts | piswai, piiswai, kↄbiswai |
+| `istr` | 1.76x | 12 contexts | listra, distric, distrit |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-a` | `-a` | 58 words | antoa, andrea |
+| `-p` | `-s` | 51 words | pancras, photographers |
+| `-s` | `-a` | 48 words | sakurwinneba, starsdormaa |
+| `-s` | `-n` | 48 words | southwestern, singaporean |
+| `-s` | `-s` | 45 words | scientists, situations |
+| `-a` | `-e` | 44 words | alangde, agree |
+| `-a` | `-s` | 42 words | afʋtis, addis |
+| `-a` | `-n` | 38 words | asaallin, aan |
+| `-s` | `-e` | 37 words | samme, sakureffiduase |
+| `-n` | `-a` | 37 words | nwama, nifa |
+
+### 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 |
+|------|-----------------|------------|------|
+| inbaanlim | **`i-n-baanlim`** | 7.5 | `baanlim` |
+| cleveland | **`clevel-an-d`** | 7.5 | `an` |
+| organising | **`organis-i-ng`** | 7.5 | `i` |
+| tempʋʋdin | **`tempʋʋ-d-in`** | 7.5 | `d` |
+| sanpielig | **`sa-n-pielig`** | 7.5 | `pielig` |
+| summalisim | **`su-m-malisim`** | 7.5 | `malisim` |
+| kugbaanlig | **`ku-g-baanlig`** | 7.5 | `baanlig` |
+| constituencies | **`constituenc-i-es`** | 7.5 | `i` |
+| governing | **`govern-i-ng`** | 7.5 | `i` |
+| officially | **`official-l-y`** | 7.5 | `l` |
+| anastasia | **`anasta-s-ia`** | 7.5 | `s` |
+| oxherding | **`oxher-di-ng`** | 7.5 | `di` |
+| sʋnpɛɛnni | **`sʋnpɛɛn-n-i`** | 7.5 | `n` |
+| wadmaannam | **`wadmaan-n-am`** | 7.5 | `n` |
+| regionnam | **`region-n-am`** | 7.5 | `n` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Kusaal 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 | **32k BPE** | Best compression (3.67x) |
+| N-gram | **2-gram** | Lowest perplexity (293) |
+| Markov | **Context-4** | Highest predictability (93.7%) |
+| 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 08:44:17*

models/embeddings/aligned/kus_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:989e65f02b3f891bc3d03291b2303e8a852cee37d08ca5e22952889395147529
+size 1038650651

models/embeddings/aligned/kus_128d.meta.json

@@ -0,0 +1 @@
+{"lang": "kus", "dim": 128, "max_seq_len": 512, "is_aligned": true}

models/embeddings/aligned/kus_128d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:289e4c16dfe0635a647cd5305cd0931c19deb722a6d89f891fd506c46a7cf213
+size 65664

models/embeddings/aligned/kus_128d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "kus",
+  "dimension": 128,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 5959,
+  "vocab_size": 14080
+}

models/embeddings/aligned/kus_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab7e215f4bd5799cec2100bfa8ff6cba8751a776efc1ac71e54988f58f9f8343
+size 259837211

models/embeddings/aligned/kus_32d.meta.json

@@ -0,0 +1 @@
+{"lang": "kus", "dim": 32, "max_seq_len": 512, "is_aligned": true}

models/embeddings/aligned/kus_32d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd3baefb772a55b48e12ac91a6b5003286a7c3e5928e0df09952cb6797d6184b
+size 4224

models/embeddings/aligned/kus_32d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "kus",
+  "dimension": 32,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 5959,
+  "vocab_size": 14080
+}

models/embeddings/aligned/kus_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a67565025c506b7b03fff6ae420ee30238f6e330de42d631abc9513389126360
+size 519441691

models/embeddings/aligned/kus_64d.meta.json

@@ -0,0 +1 @@
+{"lang": "kus", "dim": 64, "max_seq_len": 512, "is_aligned": true}

models/embeddings/aligned/kus_64d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1dd04a3622bb4c0dfbd956de14bae3caf2f5533a99aa5bd20c1b37a568fd6de1
+size 16512

models/embeddings/aligned/kus_64d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "kus",
+  "dimension": 64,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 5959,
+  "vocab_size": 14080
+}

models/embeddings/monolingual/kus_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:989e65f02b3f891bc3d03291b2303e8a852cee37d08ca5e22952889395147529
+size 1038650651

models/embeddings/monolingual/kus_128d.meta.json

@@ -0,0 +1 @@
+{"lang": "kus", "dim": 128, "max_seq_len": 512, "is_aligned": false}

models/embeddings/monolingual/kus_128d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "kus",
+  "dimension": 128,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 128,
+    "threads": 5
+  },
+  "vocab_size": 14080
+}

models/embeddings/monolingual/kus_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab7e215f4bd5799cec2100bfa8ff6cba8751a776efc1ac71e54988f58f9f8343
+size 259837211

models/embeddings/monolingual/kus_32d.meta.json

@@ -0,0 +1 @@
+{"lang": "kus", "dim": 32, "max_seq_len": 512, "is_aligned": false}

models/embeddings/monolingual/kus_32d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "kus",
+  "dimension": 32,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 32,
+    "threads": 5
+  },
+  "vocab_size": 14080
+}

models/embeddings/monolingual/kus_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a67565025c506b7b03fff6ae420ee30238f6e330de42d631abc9513389126360
+size 519441691

models/embeddings/monolingual/kus_64d.meta.json

@@ -0,0 +1 @@
+{"lang": "kus", "dim": 64, "max_seq_len": 512, "is_aligned": false}

models/embeddings/monolingual/kus_64d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "kus",
+  "dimension": 64,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 64,
+    "threads": 5
+  },
+  "vocab_size": 14080
+}

models/subword_markov/kus_markov_ctx1_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:375d4a63a324ddd3aa600f243eb9c48505b9f427422f8b6ba51f40c043591245
+size 50348

models/subword_markov/kus_markov_ctx1_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "subword",
+  "language": "kus",
+  "unique_contexts": 727,
+  "total_transitions": 5376721
+}

models/subword_markov/kus_markov_ctx2_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56e1f24c7b01842eeb80d86691ea258fa3e9155bdf8e441d4892af45ae1aeeb5
+size 311100

models/subword_markov/kus_markov_ctx2_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "subword",
+  "language": "kus",
+  "unique_contexts": 5454,
+  "total_transitions": 5375221
+}

models/subword_markov/kus_markov_ctx3_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b310b56ab10483cfc317283bcf39a30ed19aa6e4c95a6b66d113b0c227f8e0f5
+size 1272292

models/subword_markov/kus_markov_ctx3_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 3,
+  "variant": "subword",
+  "language": "kus",
+  "unique_contexts": 37990,
+  "total_transitions": 5373721
+}

models/subword_markov/kus_markov_ctx4_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:86fae4f35682051af8aa363c0a6afd6bd7b65194b46fb14a2b4e91f7d9091892
+size 3608945

models/subword_markov/kus_markov_ctx4_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 4,
+  "variant": "subword",
+  "language": "kus",
+  "unique_contexts": 169223,
+  "total_transitions": 5372221
+}

models/subword_ngram/kus_2gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d51170cc9f7a637b44565f8b51564d71501f6f5043276b48e9921d29ae7c2139
+size 34940

models/subword_ngram/kus_2gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 2,
+  "variant": "subword",
+  "language": "kus",
+  "unique_ngrams": 2565,
+  "total_ngrams": 5376721
+}

models/subword_ngram/kus_3gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7337e7e943de79f92cfe0f62a3173151d53e6b2c6c91e5acff921e8fc1f7f6f0
+size 264353

models/subword_ngram/kus_3gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 3,
+  "variant": "subword",
+  "language": "kus",
+  "unique_ngrams": 22043,
+  "total_ngrams": 5375221
+}

models/subword_ngram/kus_4gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2491b908d4aaa73d5522237c910abad95de5357b2118c5855dd146aa4d3ebc4e
+size 1170948

models/subword_ngram/kus_4gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 4,
+  "variant": "subword",
+  "language": "kus",
+  "unique_ngrams": 102940,
+  "total_ngrams": 5373721
+}

models/subword_ngram/kus_5gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cf0aaf33cf5597c6032cb515c812d9a222ad24b7d76cd39fd65360b62727568
+size 2783167

models/subword_ngram/kus_5gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "kus",
+  "unique_ngrams": 239430,
+  "total_ngrams": 5372221
+}

models/tokenizer/kus_tokenizer_16k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ccf30d4021a438df22e1ac3e0a13ce62ee42010202d1e3f48281bfdea12a54f0
+size 501871

models/tokenizer/kus_tokenizer_32k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b7a5ff73c055ac393aaefdbf791712b97547adcd96e7b192c7b41a29c8ec08c
+size 782293

models/tokenizer/kus_tokenizer_8k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a978dde9e1bdddbc04a46c05680fe1d203cf0b59c59b6c0e964e993695c5fe13
+size 372350

models/vocabulary/kus_vocabulary.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c7a541dfa3d74cbb9f18343f91f63472482da539db30f75d0085759cf90c4ca
+size 463028

models/vocabulary/kus_vocabulary_metadata.json

@@ -0,0 +1,17 @@
+{
+  "language": "kus",
+  "vocabulary_size": 27663,
+  "variant": "full",
+  "statistics": {
+    "type_token_ratio": 0.054892954413537524,
+    "coverage": {
+      "top_100": 0.4686695390751738,
+      "top_1000": 0.7548816061674927,
+      "top_5000": 0.8837409400068461,
+      "top_10000": 0.9242309237844354
+    },
+    "hapax_count": 31350,
+    "hapax_ratio": 0.5312388795689086,
+    "total_documents": 1500
+  }
+}

models/word_markov/kus_markov_ctx1_word.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15e783c968fb3e1818e71c24595388302a252fcbef16822f512ac41d014bda84
+size 2397191

models/word_markov/kus_markov_ctx1_word_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "word",
+  "language": "kus",
+  "unique_contexts": 58990,
+  "total_transitions": 1073556
+}