Upload all models and assets for ln (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 +1,773 @@
+---
+language: ln
+language_name: Lingala
+language_family: bantu_central
+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-bantu_central
+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: 4.485
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7328
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Lingala - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Lingala** 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** | 3.555x | 3.56 | 0.4106% | 154,152 |
+| **16k** | 3.898x | 3.91 | 0.4502% | 140,596 |
+| **32k** | 4.214x | 4.22 | 0.4867% | 130,050 |
+| **64k** | 4.485x 🏆 | 4.49 | 0.5181% | 122,181 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Linux ezalí litámbwisi-mokonzi nsɔ́mí na kompíta. Ezalí ofelé. Tála mpé Ubuntu F...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lin ux ▁ezalí ▁litámbwisi - mokonzi ▁nsɔ́ mí ▁na ▁kompíta ... (+12 more)` | 22 |
+| 16k | `▁linux ▁ezalí ▁litámbwisi - mokonzi ▁nsɔ́mí ▁na ▁kompíta . ▁ezalí ... (+6 more)` | 16 |
+| 32k | `▁linux ▁ezalí ▁litámbwisi - mokonzi ▁nsɔ́mí ▁na ▁kompíta . ▁ezalí ... (+6 more)` | 16 |
+| 64k | `▁linux ▁ezalí ▁litámbwisi - mokonzi ▁nsɔ́mí ▁na ▁kompíta . ▁ezalí ... (+6 more)` | 16 |
+
+**Sample 2:** `Bogota ezalí mbóka-mokonzi ya Kolombi. Ekélami Bato ya bwanya Mazita o libanda M...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁bo go ta ▁ezalí ▁mbóka - mokonzi ▁ya ▁kolombi . ... (+12 more)` | 22 |
+| 16k | `▁bo gota ▁ezalí ▁mbóka - mokonzi ▁ya ▁kolombi . ▁ekélami ... (+11 more)` | 21 |
+| 32k | `▁bogota ▁ezalí ▁mbóka - mokonzi ▁ya ▁kolombi . ▁ekélami ▁bato ... (+10 more)` | 20 |
+| 64k | `▁bogota ▁ezalí ▁mbóka - mokonzi ▁ya ▁kolombi . ▁ekélami ▁bato ... (+10 more)` | 20 |
+
+**Sample 3:** `Jean-Claude Kalonji azalí bugumési ya Kalamú. Bapɔnákí yě na mokɔlɔ ya 8 yúli na...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁jean - claude ▁kalonji ▁azalí ▁bugumési ▁ya ▁kalamú . ▁bapɔnákí ... (+13 more)` | 23 |
+| 16k | `▁jean - claude ▁kalonji ▁azalí ▁bugumési ▁ya ▁kalamú . ▁bapɔnákí ... (+13 more)` | 23 |
+| 32k | `▁jean - claude ▁kalonji ▁azalí ▁bugumési ▁ya ▁kalamú . ▁bapɔnákí ... (+13 more)` | 23 |
+| 64k | `▁jean - claude ▁kalonji ▁azalí ▁bugumési ▁ya ▁kalamú . ▁bapɔnákí ... (+13 more)` | 23 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.485x compression
+- **Lowest UNK Rate:** 8k with 0.4106% 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 | 5,174 | 12.34 | 14,092 | 19.7% | 50.0% |
+| **2-gram** | Subword | 245 🏆 | 7.94 | 2,735 | 70.5% | 98.9% |
+| **3-gram** | Word | 10,269 | 13.33 | 21,481 | 13.0% | 36.2% |
+| **3-gram** | Subword | 1,757 | 10.78 | 18,976 | 33.9% | 74.6% |
+| **4-gram** | Word | 25,188 | 14.62 | 42,798 | 8.7% | 23.1% |
+| **4-gram** | Subword | 8,040 | 12.97 | 81,004 | 19.5% | 48.2% |
+| **5-gram** | Word | 20,070 | 14.29 | 32,202 | 9.6% | 24.7% |
+| **5-gram** | Subword | 22,299 | 14.44 | 167,515 | 12.5% | 34.8% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mpo na` | 3,151 |
+| 2 | `na ye` | 2,560 |
+| 3 | `ya ba` | 1,630 |
+| 4 | `ezali na` | 1,494 |
+| 5 | `kongó kinsásá` | 1,464 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `na mokolo ya` | 659 |
+| 2 | `ya kongó kinsásá` | 654 |
+| 3 | `na ye ya` | 586 |
+| 4 | `démocratique du congo` | 541 |
+| 5 | `na kati ya` | 531 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `république démocratique du congo` | 515 |
+| 2 | `ya bomoi ya bato` | 441 |
+| 3 | `biografi ya bomoi ya` | 415 |
+| 4 | `moto ya politiki ya` | 263 |
+| 5 | `banote mpe ba références` | 244 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `biografi ya bomoi ya bato` | 411 |
+| 2 | `ya république démocratique du congo` | 234 |
+| 3 | `azali moto ya politiki ya` | 210 |
+| 4 | `mbúla na manáka ya glégwalè` | 191 |
+| 5 | `tǒ mbúla na manáka ya` | 191 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 195,411 |
+| 2 | `_ m` | 88,286 |
+| 3 | `y a` | 72,891 |
+| 4 | `_ y` | 72,395 |
+| 5 | `_ n` | 71,895 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ y a` | 66,739 |
+| 2 | `y a _` | 65,868 |
+| 3 | `n a _` | 53,277 |
+| 4 | `_ n a` | 50,800 |
+| 5 | `a _ m` | 37,316 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ y a _` | 62,045 |
+| 2 | `_ n a _` | 48,326 |
+| 3 | `a _ y a` | 15,957 |
+| 4 | `y a _ m` | 13,666 |
+| 5 | `i _ y a` | 12,708 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ y a _` | 13,303 |
+| 2 | `_ y a _ m` | 12,930 |
+| 3 | `i _ y a _` | 12,251 |
+| 4 | `i _ n a _` | 12,016 |
+| 5 | `o _ y a _` | 11,394 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 245
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~35% 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 | 0.7987 | 1.740 | 4.74 | 49,624 | 20.1% |
+| **1** | Subword | 1.2616 | 2.398 | 11.18 | 493 | 0.0% |
+| **2** | Word | 0.2666 | 1.203 | 1.67 | 234,159 | 73.3% |
+| **2** | Subword | 1.0538 | 2.076 | 6.33 | 5,503 | 0.0% |
+| **3** | Word | 0.1127 | 1.081 | 1.21 | 389,544 | 88.7% |
+| **3** | Subword | 0.8061 | 1.748 | 3.86 | 34,806 | 19.4% |
+| **4** | Word | 0.0501 🏆 | 1.035 | 1.07 | 468,155 | 95.0% |
+| **4** | Subword | 0.5954 | 1.511 | 2.51 | 134,153 | 40.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ya kinsásá bokúli bonganga diacre hermès fɛ́tí 9 yúli 7 décembre na yango col de france`
+2. `na molongo ya moto botángi bapu m conservatoire et les autres mouvements de boeck larcier départemen...`
+3. `mpe photographies mosala na akendaki kotala bakonzi ya bobandisi lingomba ya kongó kinsásá o mobu az...`
+
+**Context Size 2:**
+
+1. `mpo na ba congolais lokola azali député national ya microfinance ya droit na université libre ya kin...`
+2. `na ye laurence ndong aponamaki directeur adjoint ya assemblée constituante oyo azali na mbongo ya mo...`
+3. `ya ba saisons mibale azalaki ministre ya république démocratique ya congo ya franc ya congo kinshasa...`
+
+**Context Size 3:**
+
+1. `na mokolo ya 12 sanza ya minéi mobu ya bomoyi ya lucie eyenga mituya ya miké wa kongó`
+2. `na ye ya mwasi lokola mama na ye mpo na koluka ekimelo na crète lokola esanga yango ezalaki`
+3. `na kati ya relation na ye ntango vidéo moko na ba provinces oyo ezwami naino te na mokili`
+
+**Context Size 4:**
+
+1. `république démocratique du congo wuta mobu mpe aponamaki ministre d etat ya bilenge mpe bana 5 na nk...`
+2. `ya bomoi ya bato libota mpe bomwana anita mwarabu abotami o mokɔlɔ 30 sanza ya zomi na moko console`
+3. `biografi ya bomoi ya bato mosala ya politiki banote mpe ba références wa kongó kinsásá na na kinsásá...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_o_yaswe_ya_mi_p`
+2. `ai_na_catuvi_wol`
+3. `ouve_kilazangiom`
+
+**Context Size 2:**
+
+1. `a_na_miteyelselá-`
+2. `_mabili_lotdi._._`
+3. `ya_na_lo_ya_2h2o_`
+
+**Context Size 3:**
+
+1. `_ya_basi_o_kabimin`
+2. `ya_baye_bazalí_na_`
+3. `na_ezalí_engango:_`
+
+**Context Size 4:**
+
+1. `_ya_12_sɛtɛ́mbɛ_na_b`
+2. `_na_kongo:_dick_mpe`
+3. `a_yangomba_na_commi`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (134,153 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 | 23,757 |
+| Total Tokens | 563,773 |
+| Mean Frequency | 23.73 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 530.15 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ya | 62,155 |
+| 2 | na | 48,882 |
+| 3 | mpe | 9,425 |
+| 4 | oyo | 6,953 |
+| 5 | ba | 6,454 |
+| 6 | ezali | 4,114 |
+| 7 | o | 4,086 |
+| 8 | mpé | 3,857 |
+| 9 | ye | 3,508 |
+| 10 | mpo | 3,484 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | utroque | 2 |
+| 2 | iure | 2 |
+| 3 | latran | 2 |
+| 4 | nyon | 2 |
+| 5 | buvandji | 2 |
+| 6 | buvanji | 2 |
+| 7 | g10 | 2 |
+| 8 | mboulignaoh | 2 |
+| 9 | onkô | 2 |
+| 10 | jula | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0891 |
+| R² (Goodness of Fit) | 0.993943 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 44.8% |
+| Top 1,000 | 71.3% |
+| Top 5,000 | 87.8% |
+| Top 10,000 | 93.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9939 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 44.8% of corpus
+- **Long Tail:** 13,757 words needed for remaining 6.3% 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 | 0.7328 🏆 | 0.3467 | N/A | N/A |
+| **mono_64d** | 64 | 0.3440 | 0.3258 | N/A | N/A |
+| **mono_128d** | 128 | 0.0954 | 0.3221 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7328 | 0.3472 | 0.0340 | 0.2280 |
+| **aligned_64d** | 64 | 0.3440 | 0.3261 | 0.0520 | 0.2520 |
+| **aligned_128d** | 128 | 0.0954 | 0.3189 | 0.0740 | 0.3160 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7328 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3312. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 7.4% 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 | **-0.290** | 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 |
+|--------|----------|
+| `-m` | musées, mes, monani |
+| `-b` | bilelo, balapolo, balaboratware |
+| `-ba` | balapolo, balaboratware, bazwá |
+| `-ma` | mayelemaya, madívi, malonga |
+| `-a` | above, attention, apparition |
+| `-s` | spectre, spekilos, statut |
+| `-mo` | monani, mokɛle, mondúle |
+| `-e` | entrepreneuriat, ekɛ́sɛ́ní, empompo |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | above, diarrhee, spectre |
+| `-a` | libóta, kimbanda, kopelisa |
+| `-i` | fpi, pulutugɛ́shi, katalani |
+| `-s` | peintures, musées, mes |
+| `-es` | peintures, musées, mes |
+| `-ki` | ekokisaki, ebandamaki, mbeki |
+| `-n` | attention, girkin, apparition |
+| `-o` | bilelo, kopo, balapolo |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ongo` | 1.65x | 88 contexts | yongo, bongo, mongo |
+| `anga` | 1.49x | 126 contexts | banga, vanga, kanga |
+| `anda` | 1.40x | 106 contexts | manda, sanda, fanda |
+| `tion` | 1.88x | 28 contexts | nation, action, option |
+| `zalí` | 2.10x | 19 contexts | azalí, ézalí, izalí |
+| `enge` | 1.67x | 41 contexts | wenge, kenge, penge |
+| `ambo` | 1.61x | 46 contexts | yambo, mambo, tambo |
+| `bong` | 1.62x | 44 contexts | bongo, bongó, bongò |
+| `alak` | 1.45x | 66 contexts | alakí, salaka, palaki |
+| `atio` | 2.02x | 19 contexts | nation, ization, station |
+| `osal` | 1.76x | 28 contexts | tosala, mosala, kosala |
+| `maka` | 1.72x | 30 contexts | makau, makasi, makabo |
+
+### 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` | `-i` | 222 words | bomanyoli, bowéi |
+| `-ko` | `-a` | 196 words | komilakisa, kopusa |
+| `-b` | `-a` | 159 words | bakáa, bulambemba |
+| `-e` | `-i` | 157 words | emonaneli, eyebisamaki |
+| `-a` | `-i` | 129 words | aluki, atalelami |
+| `-m` | `-i` | 123 words | minéyi, musuni |
+| `-e` | `-a` | 122 words | etika, esálaka |
+| `-m` | `-a` | 102 words | madeira, makota |
+| `-a` | `-ki` | 83 words | aluki, atombolaki |
+| `-m` | `-e` | 82 words | mwange, michelle |
+
+### 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 |
+|------|-----------------|------------|------|
+| publicité | **`public-i-té`** | 7.5 | `i` |
+| esengelaka | **`esengel-a-ka`** | 7.5 | `a` |
+| moipolitik | **`mo-i-politik`** | 7.5 | `politik` |
+| présidentiel | **`présidenti-e-l`** | 7.5 | `e` |
+| quasiment | **`quasim-e-nt`** | 7.5 | `e` |
+| kominanola | **`kominan-o-la`** | 7.5 | `o` |
+| elandamaki | **`elandam-a-ki`** | 7.5 | `a` |
+| électricité | **`électric-i-té`** | 7.5 | `i` |
+| millettia | **`millett-i-a`** | 7.5 | `i` |
+| débarquement | **`débarquem-e-nt`** | 7.5 | `e` |
+| heuvelmans | **`heuvelm-a-ns`** | 7.5 | `a` |
+| balandelaki | **`balandel-a-ki`** | 7.5 | `a` |
+| continent | **`contin-e-nt`** | 7.5 | `e` |
+| epesameli | **`epesam-e-li`** | 7.5 | `e` |
+| balingaki | **`ba-linga-ki`** | 6.0 | `linga` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Lingala 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 (4.49x) |
+| N-gram | **2-gram** | Lowest perplexity (245) |
+| Markov | **Context-4** | Highest predictability (95.0%) |
+| 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 11:16:42*

models/embeddings/aligned/ln_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f21b41e2d502c046103392bd4e30a23ae5e7e92f9f332f669e5c51c0dbc704b
+size 1035296690

models/embeddings/aligned/ln_128d.meta.json

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

models/embeddings/aligned/ln_128d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c2958cbb62927960cd1097a9f88ef060c9f365d6da362f8020838063aa3eaa0
+size 65664

models/embeddings/aligned/ln_128d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "ln",
+  "dimension": 128,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 4408,
+  "vocab_size": 10853
+}

models/embeddings/aligned/ln_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21050cc4effc3acaf419405f203850563d6bd0a5edf3132b274a16a09af0a402
+size 258961586

models/embeddings/aligned/ln_32d.meta.json

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

models/embeddings/aligned/ln_32d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f3b09749baaf87cec01313f2eaa7ef038b8d2a6173fb35d63a8572231984111
+size 4224

models/embeddings/aligned/ln_32d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "ln",
+  "dimension": 32,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 4408,
+  "vocab_size": 10853
+}

models/embeddings/aligned/ln_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6af60f3c72015e303acbaea5aa9e30c1cf56dd50212613db046794605ef1d20
+size 517739954

models/embeddings/aligned/ln_64d.meta.json

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

models/embeddings/aligned/ln_64d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:440969be300546b0ed2bf34f8cc32b814b1b032b3d605afdd3383db539e2e470
+size 16512

models/embeddings/aligned/ln_64d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "ln",
+  "dimension": 64,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 4408,
+  "vocab_size": 10853
+}

models/embeddings/monolingual/ln_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f21b41e2d502c046103392bd4e30a23ae5e7e92f9f332f669e5c51c0dbc704b
+size 1035296690

models/embeddings/monolingual/ln_128d.meta.json

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

models/embeddings/monolingual/ln_128d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "ln",
+  "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": 10853
+}

models/embeddings/monolingual/ln_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21050cc4effc3acaf419405f203850563d6bd0a5edf3132b274a16a09af0a402
+size 258961586

models/embeddings/monolingual/ln_32d.meta.json

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

models/embeddings/monolingual/ln_32d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "ln",
+  "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": 10853
+}

models/embeddings/monolingual/ln_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6af60f3c72015e303acbaea5aa9e30c1cf56dd50212613db046794605ef1d20
+size 517739954

models/embeddings/monolingual/ln_64d.meta.json

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

models/embeddings/monolingual/ln_64d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "ln",
+  "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": 10853
+}

models/subword_markov/ln_markov_ctx1_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac1f0bee037519edb8381caf53e9c7a659ebdae62eb78541cf33ba695a68218d
+size 47396

models/subword_markov/ln_markov_ctx1_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "subword",
+  "language": "ln",
+  "unique_contexts": 493,
+  "total_transitions": 3528081
+}

models/subword_markov/ln_markov_ctx2_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:157b2b76d2bf2c0bb9063f3b3aa3e1c21e56a61a5ae8b841f675ed6d3772322c
+size 271995

models/subword_markov/ln_markov_ctx2_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "subword",
+  "language": "ln",
+  "unique_contexts": 5503,
+  "total_transitions": 3523456
+}

models/subword_markov/ln_markov_ctx3_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64232af2e000353fc86c5da920f41a852803486cf6fba9e95d3681b81aaa3e76
+size 1007485

models/subword_markov/ln_markov_ctx3_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 3,
+  "variant": "subword",
+  "language": "ln",
+  "unique_contexts": 34806,
+  "total_transitions": 3518831
+}

models/subword_markov/ln_markov_ctx4_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b02d6ba99751aef99209c67d279af933effc15b922f33e083fa157af509e241f
+size 2645581

models/subword_markov/ln_markov_ctx4_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 4,
+  "variant": "subword",
+  "language": "ln",
+  "unique_contexts": 134153,
+  "total_transitions": 3514206
+}

models/subword_ngram/ln_2gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c3bd8c7a58edc3387272fbff0b562c200a6bb664d217f5a6042733070a442a2
+size 36602

models/subword_ngram/ln_2gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 2,
+  "variant": "subword",
+  "language": "ln",
+  "unique_ngrams": 2735,
+  "total_ngrams": 3528081
+}

models/subword_ngram/ln_3gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76dc771b2e64eab255086c42647d308ad09e23c987bf12c6cdb0fd97e4e832dd
+size 230352

models/subword_ngram/ln_3gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 3,
+  "variant": "subword",
+  "language": "ln",
+  "unique_ngrams": 18976,
+  "total_ngrams": 3523456
+}

models/subword_ngram/ln_4gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6de73a162d6dc09f30c7e97a2a9fc2e49298c085fda535500c5712f7a286854d
+size 941552

models/subword_ngram/ln_4gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 4,
+  "variant": "subword",
+  "language": "ln",
+  "unique_ngrams": 81004,
+  "total_ngrams": 3518831
+}

models/subword_ngram/ln_5gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23a267bda9befc5b9d75cd72be5f63e3ed6951e2ba273ffb7fe54f360f2a64d4
+size 1950081

models/subword_ngram/ln_5gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "ln",
+  "unique_ngrams": 167515,
+  "total_ngrams": 3514206
+}

models/tokenizer/ln_tokenizer_16k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:147bd0e646a31835d161a4f1a7dc4223963233dbbf2dd8f2ee24d63f589a145f
+size 509663

models/tokenizer/ln_tokenizer_32k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:984996b6368effdb478720a8d47996c0cd200142e07bc38a9e8fac37630040f4
+size 793640

models/tokenizer/ln_tokenizer_64k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c024be921ae74ccd15c655c6d28d646ed92a467a277cfeacb4c562f5055f77f2
+size 1378608

models/tokenizer/ln_tokenizer_8k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:104ccb3303cdc2a1511aca3458379fa298bacf11921121d9dd878983f7d6f1e1
+size 375440

models/vocabulary/ln_vocabulary.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c6c1a97f86df2a26d7d477016767d017a0b9892c05b750ff3ac54e8399c16ab
+size 402573

models/vocabulary/ln_vocabulary_metadata.json

@@ -0,0 +1,17 @@
+{
+  "language": "ln",
+  "vocabulary_size": 23757,
+  "variant": "full",
+  "statistics": {
+    "type_token_ratio": 0.08438498774977322,
+    "coverage": {
+      "top_100": 0.4287189399526946,
+      "top_1000": 0.681107522232678,
+      "top_5000": 0.8392973710759005,
+      "top_10000": 0.8959671744788356
+    },
+    "hapax_count": 26012,
+    "hapax_ratio": 0.522654664550222,
+    "total_documents": 4625
+  }
+}