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+---
+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*
diff --git a/ln_morph_tokenizer.json b/ln_morph_tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..6afd930e3fff6aeaeda2302a6b959052a44a425b
--- /dev/null
+++ b/ln_morph_tokenizer.json
@@ -0,0 +1,50109 @@
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+++ b/models/subword_ngram/ln_5gram_subword_metadata.json
@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "ln",
+  "unique_ngrams": 167515,
+  "total_ngrams": 3514206
+}
\ No newline at end of file
diff --git a/models/tokenizer/ln_tokenizer_16k.model b/models/tokenizer/ln_tokenizer_16k.model
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index 0000000000000000000000000000000000000000..502784ef3cbc7a0c5817ec4ff0336e81638608e1
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diff --git a/models/tokenizer/ln_tokenizer_16k.vocab b/models/tokenizer/ln_tokenizer_16k.vocab
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diff --git a/models/tokenizer/ln_tokenizer_32k.model b/models/tokenizer/ln_tokenizer_32k.model
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diff --git a/models/tokenizer/ln_tokenizer_32k.vocab b/models/tokenizer/ln_tokenizer_32k.vocab
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+▁mimpe	-39121
+▁novos	-39122
+▁rampe	-39123
+katampe	-39124
+pertome	-39125
+richard	-39126
+undelak	-39127
+▁awondo	-39128
+▁esangí	-39129
+▁ezwâmí	-39130
+▁ghenda	-39131
+▁ghorra	-39132
+▁kitaba	-39133
+▁kitaku	-39134
+▁kɛ́dɛ́	-39135
+▁martyn	-39136
+▁quitta	-39137
+▁trompe	-39138
+bokokani	-39139
+clonique	-39140
+tanzanie	-39141
+undɛ́lɛ́	-39142
+▁baiboko	-39143
+▁changes	-39144
+▁ebakisi	-39145
+▁emɛ́lɛ́	-39146
+▁etɛ́lɛ́	-39147
+▁eulalie	-39148
+▁extract	-39149
+▁kampete	-39150
+▁kitagan	-39151
+▁kɛ́dɛ́n	-39152
+▁lavenir	-39153
+▁lilɔngɔ	-39154
+▁machard	-39155
+▁mikɔ́lɔ	-39156
+▁médecin	-39157
+▁ranieri	-39158
+▁tanoshi	-39159
+actualité	-39160
+pulutugal	-39161
+támbolaka	-39162
+ɛ́lɛ́jika	-39163
+▁bawutaki	-39164
+▁bundelak	-39165
+▁béthanie	-39166
+▁credited	-39167
+▁kopiquer	-39168
+▁liyeboli	-39169
+▁mayɛ́lɛ́	-39170
+▁mikokísí	-39171
+▁mikéléké	-39172
+▁milekíli	-39173
+▁mindéngé	-39174
+▁mineraux	-39175
+▁minzɔtɔ́	-39176
+▁minérais	-39177
+▁miquelon	-39178
+▁miqueuse	-39179
+▁mitangan	-39180
+▁mitanona	-39181
+▁mitsubis	-39182
+▁mitunakí	-39183
+▁mitutama	-39184
+▁mitíkala	-39185
+▁miyamore	-39186
+▁mobotisi	-39187
+▁mobótaka	-39188
+▁mofútisi	-39189
+▁mokaboli	-39190
+▁mokanisi	-39191
+▁mokókóli	-39192
+▁molekale	-39193
+▁molálaka	-39194
+▁molécule	-39195
+▁molíyaka	-39196
+▁momataki	-39197
+▁mombongi	-39198
+▁mombóngo	-39199
+▁sóó	-39200
+▁tôt	-39201
+▁ugc	-39202
+audit	-39203
+reamw	-39204
+sarea	-39205
+yards	-39206
+▁amsc	-39207
+▁amse	-39208
+▁amvi	-39209
+▁amvu	-39210
+▁guiy	-39211
+▁mɛma	-39212
+▁sómo	-39213
+▁tvya	-39214
+allars	-39215
+limiya	-39216
+reafor	-39217
+rianus	-39218
+seilla	-39219
+äsarea	-39220
+▁annua	-39221
+▁azapa	-39222
+▁dalla	-39223
+▁edefa	-39224
+▁fruto	-39225
+▁gorge	-39226
+▁mayfa	-39227
+▁ndúle	-39228
+▁posté	-39229
+ramomum	-39230
+reamwor	-39231
+ómbitin	-39232
+▁amviko	-39233
+▁amvuko	-39234
+▁anzelu	-39235
+▁azapan	-39236
+▁badiou	-39237
+▁badius	-39238
+▁camden	-39239
+▁distir	-39240
+▁episco	-39241
+▁episkɔ	-39242
+▁guites	-39243
+▁insurg	-39244
+▁kiriwa	-39245
+▁kovisi	-39246
+▁luanga	-39247
+▁parqué	-39248
+▁sexual	-39249
+afaudage	-39250
+derianus	-39251
+rogation	-39252
+rologist	-39253
+tenseite	-39254
+▁amselle	-39255
+▁azapane	-39256
+▁bambata	-39257
+▁basómbi	-39258
+▁bobómbi	-39259
+▁chanard	-39260
+▁chanter	-39261
+▁cäsarea	-39262
+▁flavors	-39263
+▁kovánda	-39264
+▁mayfair	-39265
+▁pallars	-39266
+reamworks	-39267
+seillaise	-39268
+vineyards	-39269
+▁annuaire	-39270
+▁banikina	-39271
+▁citibank	-39272
+▁distirct	-39273
+▁episkɔpɔ	-39274
+▁harkness	-39275
+▁insurgés	-39276
+▁momóyébí	-39277
+▁mondlane	-39278
+▁mondongo	-39279
+▁monogata	-39280
+▁monoxide	-39281
+▁monsters	-39282
+▁monsɔ́bu	-39283
+▁montagne	-39284
+▁monterey	-39285
+▁monzemba	-39286
+▁monzéngá	-39287
+▁mopakolo	-39288
+▁mopasuka	-39289
+▁mopésámí	-39290
+▁moravian	-39291
+▁mortelle	-39292
+▁mosaïque	-39293
+▁mosilaki	-39294
+▁motatoli	-39295
+▁motrices	-39296
+▁mouellet	-39297
+▁moyifran	-39298
+▁mozalaki	-39299
+▁lói	-39300
+tandu	-39301
+wumbu	-39302
+▁iden	-39303
+▁lókó	-39304
+▁lɛki	-39305
+afropa	-39306
+batour	-39307
+mandro	-39308
+ngandu	-39309
+sekele	-39310
+ussets	-39311
+vuandu	-39312
+yeláká	-39313
+▁anapi	-39314
+▁frano	-39315
+▁gyele	-39316
+▁lóngo	-39317
+▁mfini	-39318
+▁nyobé	-39319
+▁nyota	-39320
+▁texaf	-39321
+emakoma	-39322
+milleux	-39323
+usseini	-39324
+▁anacon	-39325
+▁bukele	-39326
+▁bákoma	-39327
+▁latour	-39328
+▁lókótá	-39329
+▁maduda	-39330
+▁mbandu	-39331
+▁omanga	-39332
+▁sonore	-39333
+▁tomáto	-39334
+▁élodie	-39335
+afropavo	-39336
+diengila	-39337
+husseini	-39338
+mandroso	-39339
+ngilanga	-39340
+éléments	-39341
+▁akoláká	-39342
+▁akóláká	-39343
+▁anapipo	-39344
+▁asiláká	-39345
+▁basindi	-39346
+▁batandu	-39347
+▁destour	-39348
+▁fiscale	-39349
+▁franois	-39350
+▁ligreke	-39351
+▁mohumbu	-39352
+▁mowumbu	-39353
+▁okofuta	-39354
+▁sangila	-39355
+▁wangila	-39356
+▁anaconda	-39357
+▁bakelási	-39358
+▁bakokoma	-39359
+▁bâtirons	-39360
+▁courcier	-39361
+▁elimbolí	-39362
+▁kintandu	-39363
+▁mozaïque	-39364
+▁mpandaji	-39365
+▁mpangatɔ	-39366
+▁mpɛmbɛni	-39367
+▁mudikela	-39368
+▁mudimaka	-39369
+▁mufankol	-39370
+▁mukundji	-39371
+▁mulongwe	-39372
+▁mumbanza	-39373
+▁mungombe	-39374
+▁mungongo	-39375
+▁munguldi	-39376
+▁munihire	-39377
+▁munkamba	-39378
+▁munzihir	-39379
+▁musadila	-39380
+▁mutwashi	-39381
+▁muungano	-39382
+▁muyébani	-39383
+▁muzalaka	-39384
+▁muzalakí	-39385
+▁mwangaza	-39386
+▁mwanyimi	-39387
+▁mymovies	-39388
+▁mélanges	-39389
+▁météores	-39390
+▁münsters	-39391
+▁nabisika	-39392
+▁nabopola	-39393
+▁nakoloba	-39394
+▁nakoyeba	-39395
+▁nakómaki	-39396
+▁namabele	-39397
+▁namegabe	-39398
+▁namitika	-39399
+edz	-39400
+grâ	-39401
+oed	-39402
+qed	-39403
+diná	-39404
+gart	-39405
+heed	-39406
+shed	-39407
+ffart	-39408
+freud	-39409
+grisa	-39410
+rodin	-39411
+vakou	-39412
+ziedz	-39413
+zodin	-39414
+▁need	-39415
+▁odin	-39416
+▁reed	-39417
+▁tigr	-39418
+▁дела	-39419
+dináli	-39420
+edison	-39421
+grâces	-39422
+nisaka	-39423
+nische	-39424
+ttgart	-39425
+▁esfar	-39426
+▁iyamu	-39427
+▁maqed	-39428
+▁pikou	-39429
+▁polym	-39430
+▁vista	-39431
+elisami	-39432
+express	-39433
+mɛ́tɛlɛ	-39434
+uretcha	-39435
+zodinma	-39436
+▁abidin	-39437
+▁azalaí	-39438
+▁cadets	-39439
+▁esfarj	-39440
+▁forums	-39441
+▁garnis	-39442
+▁jaheed	-39443
+▁jonghe	-39444
+▁kafuka	-39445
+▁lungen	-39446
+▁mankou	-39447
+▁mokabi	-39448
+▁mokabo	-39449
+▁ntokou	-39450
+▁nɔ́ɛli	-39451
+▁pushed	-39452
+▁ramato	-39453
+▁uziedz	-39454
+▁voltae	-39455
+▁yannis	-39456
+andisaka	-39457
+basaleli	-39458
+provinsi	-39459
+quitaine	-39460
+sɛnisaka	-39461
+▁andreas	-39462
+▁azalaká	-39463
+▁balubas	-39464
+▁biolozí	-39465
+▁borodin	-39466
+▁flavine	-39467
+▁goffart	-39468
+▁iyamule	-39469
+▁nefasch	-39470
+▁polysta	-39471
+▁polyval	-39472
+▁wirksto	-39473
+freudenth	-39474
+landisaka	-39475
+libandeli	-39476
+▁bangungu	-39477
+▁contrats	-39478
+▁iyamulem	-39479
+▁kadináli	-39480
+▁kafukafu	-39481
+▁louvakou	-39482
+▁lungenyi	-39483
+▁mokabeli	-39484
+▁monisaka	-39485
+▁namitiki	-39486
+▁naruhito	-39487
+▁nathuram	-39488
+▁naviundu	-39489
+▁nazalaka	-39490
+▁nazalakí	-39491
+▁naïvetés	-39492
+▁ndietika	-39493
+▁ndongese	-39494
+▁nederlân	-39495
+▁netrebko	-39496
+▁neurophy	-39497
+▁ngaingai	-39498
+▁ngalasso	-39499
+apô	-39500
+uous	-39501
+vell	-39502
+vánd	-39503
+▁rdr	-39504
+▁rdv	-39505
+april	-39506
+bruno	-39507
+cetta	-39508
+fesap	-39509
+lɛ́rɛ	-39510
+ouses	-39511
+unops	-39512
+▁juno	-39513
+▁okpo	-39514
+▁sort	-39515
+▁voiy	-39516
+apport	-39517
+aprili	-39518
+apôtre	-39519
+dimico	-39520
+hihara	-39521
+ristya	-39522
+rovell	-39523
+vándji	-39524
+ándisi	-39525
+▁adiri	-39526
+▁budja	-39527
+▁chiso	-39528
+▁ekáti	-39529
+▁geträ	-39530
+▁koiso	-39531
+▁matoí	-39532
+▁mpuya	-39533
+▁nadja	-39534
+▁noyes	-39535
+▁okáti	-39536
+▁radja	-39537
+▁sorti	-39538
+dgments	-39539
+lestage	-39540
+milɛ́rɛ	-39541
+▁houses	-39542
+▁junost	-39543
+▁kipulu	-39544
+▁mapela	-39545
+▁tajiri	-39546
+▁voiyja	-39547
+antorena	-39548
+concetta	-39549
+mosálisi	-39550
+wuvándji	-39551
+▁akopela	-39552
+▁anduous	-39553
+▁arovell	-39554
+▁baumann	-39555
+▁cofesap	-39556
+▁consomm	-39557
+▁lindimi	-39558
+▁mabánga	-39559
+▁ndokula	-39560
+▁tondimi	-39561
+▁vantage	-39562
+toriennes	-39563
+ɔrmilɛ́rɛ	-39564
+▁getränke	-39565
+▁kahihara	-39566
+▁koisoler	-39567
+▁makúfela	-39568
+▁ngamanda	-39569
+▁ngamiaka	-39570
+▁ngampani	-39571
+▁ngantien	-39572
+▁ngengísá	-39573
+▁nginamau	-39574
+▁ngongolí	-39575
+▁ngwasuma	-39576
+▁nocturne	-39577
+▁nordique	-39578
+▁novelist	-39579
+▁nsimbulu	-39580
+▁ntimasie	-39581
+▁nuytinck	-39582
+▁nyiramba	-39583
+▁nyɔ́nsɔ́	-39584
+▁nzenzɛkɛ	-39585
+▁nzíngíní	-39586
+▁négation	-39587
+▁oguensis	-39588
+▁okendaki	-39589
+▁okokumba	-39590
+▁oleaceae	-39591
+▁oliefera	-39592
+▁oliphant	-39593
+▁olivetti	-39594
+▁olympics	-39595
+▁opinions	-39596
+▁oppenhei	-39597
+▁opprimés	-39598
+▁orfanato	-39599
+crc	-39600
+кка	-39601
+zern	-39602
+ézer	-39603
+ркка	-39604
+▁usb	-39605
+sɛ́ng	-39606
+tizer	-39607
+zerbo	-39608
+zerte	-39609
+▁fisc	-39610
+▁icrc	-39611
+▁klee	-39612
+▁стра	-39613
+baseer	-39614
+cultor	-39615
+inkoto	-39616
+laphus	-39617
+ngbase	-39618
+saurer	-39619
+torfer	-39620
+▁assez	-39621
+▁fisch	-39622
+▁fissu	-39623
+▁lieux	-39624
+▁nerve	-39625
+▁nzung	-39626
+▁sinus	-39627
+▁solva	-39628
+▁speze	-39629
+▁tsaka	-39630
+▁usale	-39631
+baseerd	-39632
+esterwo	-39633
+kilumbu	-39634
+likambo	-39635
+lumbata	-39636
+torsion	-39637
+tétizer	-39638
+▁fauves	-39639
+▁floriu	-39640
+▁joaqui	-39641
+▁kabobo	-39642
+▁losaka	-39643
+▁luzern	-39644
+▁midele	-39645
+▁minigi	-39646
+▁musaka	-39647
+▁mvunzi	-39648
+▁mystic	-39649
+▁oporto	-39650
+▁solvay	-39651
+▁spezet	-39652
+▁usines	-39653
+▁страна	-39654
+celaphus	-39655
+sorciers	-39656
+▁bapɔ́ní	-39657
+▁batóngi	-39658
+▁bizerte	-39659
+▁chester	-39660
+▁crucial	-39661
+▁fischer	-39662
+▁fischho	-39663
+▁florian	-39664
+▁joaquim	-39665
+▁kikombo	-39666
+▁kulumba	-39667
+▁lamɛ́lɛ	-39668
+▁miniers	-39669
+▁minimal	-39670
+▁nerveux	-39671
+▁usaleti	-39672
+▁éliézer	-39673
+ngankombo	-39674
+▁albrecht	-39675
+▁fischhof	-39676
+▁fissures	-39677
+▁grandeur	-39678
+▁grandeza	-39679
+▁kotutana	-39680
+▁osengela	-39681
+▁ostentas	-39682
+▁ouvrages	-39683
+▁overseas	-39684
+▁ozenfant	-39685
+▁ozongela	-39686
+▁palmiers	-39687
+▁pancréas	-39688
+▁panorama	-39689
+▁paradigm	-39690
+▁parcelle	-39691
+▁pasadena	-39692
+▁patronat	-39693
+▁patronne	-39694
+▁paulinho	-39695
+▁peacekee	-39696
+▁pelamoko	-39697
+▁peluches	-39698
+▁penguins	-39699
+coup	-39700
+hamn	-39701
+niak	-39702
+spee	-39703
+ǒnia	-39704
+ndiká	-39705
+raham	-39706
+scoto	-39707
+zniak	-39708
+▁afiz	-39709
+▁jéré	-39710
+outils	-39711
+sɛ́nga	-39712
+▁ekɔta	-39713
+▁halte	-39714
+▁iǒnia	-39715
+▁merck	-39716
+▁merle	-39717
+▁merry	-39718
+▁mowéi	-39719
+▁nandi	-39720
+▁reggo	-39721
+abraham	-39722
+amensis	-39723
+bulungu	-39724
+fréries	-39725
+lourdes	-39726
+neensis	-39727
+scotope	-39728
+ussière	-39729
+▁akosɛ́	-39730
+▁amédée	-39731
+▁angels	-39732
+▁chumbu	-39733
+▁ekondi	-39734
+▁guidon	-39735
+▁madère	-39736
+▁mercre	-39737
+▁mosɔmi	-39738
+▁newham	-39739
+▁tutala	-39740
+ciformis	-39741
+kanensis	-39742
+speeches	-39743
+ventures	-39744
+▁afirika	-39745
+▁bolungu	-39746
+▁guidons	-39747
+▁instant	-39748
+▁jérémie	-39749
+▁mercklé	-39750
+▁mosɔngi	-39751
+▁nandimi	-39752
+▁palmier	-39753
+▁reginal	-39754
+▁restant	-39755
+▁sekondi	-39756
+▁sɛndiká	-39757
+▁tempelo	-39758
+▁victory	-39759
+▁wozniak	-39760
+brazavile	-39761
+ndlourdes	-39762
+urneensis	-39763
+▁beaucoup	-39764
+▁bolungui	-39765
+▁departed	-39766
+▁elcinema	-39767
+▁halteren	-39768
+▁humanite	-39769
+▁libikisi	-39770
+▁mapolome	-39771
+▁perfecta	-39772
+▁perfecto	-39773
+▁perpétue	-39774
+▁personne	-39775
+▁peshanga	-39776
+▁petipeti	-39777
+▁petronas	-39778
+▁philbert	-39779
+▁phytoche	-39780
+▁phytochi	-39781
+▁picoides	-39782
+▁pictogra	-39783
+▁pictoris	-39784
+▁pilotage	-39785
+▁pionnier	-39786
+▁pittsbur	-39787
+▁plaindre	-39788
+▁platnumz	-39789
+▁plymouth	-39790
+▁poetryfo	-39791
+▁poignées	-39792
+▁pointage	-39793
+▁politolo	-39794
+▁polystar	-39795
+▁possible	-39796
+▁potopoto	-39797
+▁pourquoi	-39798
+▁powerful	-39799
+odd	-39800
+tft	-39801
+ckey	-39802
+khan	-39803
+left	-39804
+raft	-39805
+sorã	-39806
+▁)."	-39807
+▁vey	-39808
+aimpu	-39809
+bonde	-39810
+bonza	-39811
+gsell	-39812
+phare	-39813
+pukur	-39814
+simus	-39815
+▁ctft	-39816
+▁léod	-39817
+▁uélé	-39818
+▁veti	-39819
+bouger	-39820
+ffrage	-39821
+firish	-39822
+onimus	-39823
+phagie	-39824
+soyons	-39825
+tresor	-39826
+yumbwe	-39827
+▁besch	-39828
+▁camus	-39829
+▁rabou	-39830
+▁vedec	-39831
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+▁<__.|_|-|_|	-59426
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+hertfordshire	-60364
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+interrogation	-60369
+kompasretning	-60370
+krestjanskaja	-60371
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+niedersachsen	-60375
+nsimaefúngámá	-60376
+orientalmbuji	-60377
+panathinaïkos	-60378
+schungsarbeit	-60379
+staffordshire	-60380
+strombsiopsis	-60381
+technologique	-60382
+universalisme	-60383
+▁abdourahmane	-60384
+▁abengisamaki	-60385
+▁abradacactus	-60386
+▁abundisamaki	-60387
+▁abɔ́tɔ́lɔ́kí	-60388
+▁accidentelle	-60389
+▁accouchement	-60390
+▁actionnaires	-60391
+▁africamuseum	-60392
+▁africavision	-60393
+▁africlutures	-60394
+▁afrocentrist	-60395
+▁afrofischeri	-60396
+▁agricultural	-60397
+▁agroforestry	-60398
+▁albolineatus	-60399
+▁alimbisamaki	-60400
+▁alimentaires	-60401
+▁amoxycilline	-60402
+▁amundalasini	-60403
+▁anciennement	-60404
+▁andimisamaki	-60405
+▁anguilloides	-60406
+▁annanetrebko	-60407
+▁anniversaire	-60408
+▁antananarivo	-60409
+▁anticipation	-60410
+▁antisociales	-60411
+▁antroponymes	-60412
+▁applications	-60413
+▁astigmatisme	-60414
+▁atangisamaki	-60415
+▁attenborough	-60416
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+▁ayanawebzine	-60418
+▁azalijoueuse	-60419
+▁azangamapapu	-60420
+▁azithromycin	-60421
+▁baanabakintu	-60422
+▁babangubangu	-60423
+▁babimisamaki	-60424
+▁baboyankánda	-60425
+▁bainfections	-60426
+▁bakilometele	-60427
+▁bakoelámbela	-60428
+▁bakomikebisa	-60429
+▁bakomokamata	-60430
+▁bakotisamaki	-60431
+▁bakotombwama	-60432
+▁bakɔtisamaki	-60433
+▁bakɛsɛnisaka	-60434
+▁balalisamaka	-60435
+▁balikuddembé	-60436
+▁bamasalelaka	-60437
+▁bamasálelaka	-60438
+▁bambumambuma	-60439
+▁bamibimisaki	-60440
+▁bamobóyeláká	-60441
+▁bamokamatáká	-60442
+▁bamomɛmɛ́kɛ́	-60443
+▁bamoyembelak	-60444
+▁bamusálelaka	-60445
+▁bamɛmɛmɛ́kɛ́	-60446
+▁banningville	-60447
+▁banyokwamaka	-60448
+▁banyokwamaki	-60449
+▁basemjondolo	-60450
+▁basilikelaki	-60451
+▁batambwisami	-60452
+▁batransports	-60453
+▁bazzekuketta	-60454
+▁bedfordshire	-60455
+▁belligerants	-60456
+▁benowidikila	-60457
+▁beschreibung	-60458
+▁betsuzashiki	-60459
+▁bhattacharya	-60460
+▁bibakisamaka	-60461
+▁biodiversité	-60462
+▁biographical	-60463
+▁biomédicales	-60464
+▁bonkolongono	-60465
+▁bopalinginyi	-60466
+▁bopamelamaki	-60467
+▁bosakolamaki	-60468
+▁boskowitzrfi	-60469
+▁bozinganzɔku	-60470
+▁brachysperma	-60471
+▁brachystegia	-60472
+▁brazzavilois	-60473
+▁britanniques	-60474
+▁broadcasting	-60475
+▁burgoumestre	-60476
+▁buuzabalyawo	-60477
+▁bángaladɛ́si	-60478
+▁bɔkɔlɔngɔ́nú	-60479
+▁californicus	-60480
+▁cameronsanza	-60481
+▁camerounaise	-60482
+▁camptosperma	-60483
+▁cartographie	-60484
+▁cavalliensis	-60485
+▁ceniélection	-60486
+▁championnats	-60487
+▁choreographe	-60488
+▁christadelfe	-60489
+▁chrolorquine	-60490
+▁clavulanique	-60491
+▁climatiseurs	-60492
+▁cofondatrice	-60493
+▁collaboratif	-60494
+▁collectibles	-60495
+▁collectivité	-60496
+▁colonialisme	-60497
+▁colonialismo	-60498
+▁colonisateur	-60499
+▁commencement	-60500
+▁communicante	-60501
+▁compilations	-60502
+▁complication	-60503
+▁compositions	-60504
+▁compromising	-60505
+▁compréhesion	-60506
+▁comunautaire	-60507
+▁conglomerate	-60508
+▁congokulture	-60509
+▁connectivité	-60510
+▁conservative	-60511
+▁consignation	-60512
+▁conspiration	-60513
+▁constipation	-60514
+▁constituante	-60515
+▁conséquences	-60516
+▁contempteurs	-60517
+▁contribution	-60518
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+▁cucchiarelli	-60520
+▁cupressaceae	-60521
+▁demografiche	-60522
+▁difficulties	-60523
+▁dinossáurios	-60524
+▁discothèques	-60525
+▁diskographie	-60526
+▁displacement	-60527
+▁distinctives	-60528
+▁dommageables	-60529
+▁douloureuses	-60530
+▁durableesika	-60531
+▁décentralisé	-60532
+▁déconcentrée	-60533
+▁démembrement	-60534
+▁démocratisme	-60535
+▁dénombrement	-60536
+▁dénonciation	-60537
+▁ebongisamaka	-60538
+▁ebongolamákí	-60539
+▁echauffement	-60540
+▁ekokémbisaka	-60541
+▁ekolimbolama	-60542
+▁ekondimisama	-60543
+▁ekɔ́tisamaka	-60544
+▁elandisamaka	-60545
+▁elandisamaki	-60546
+▁elimbomalaka	-60547
+▁eliminatoire	-60548
+▁emblématique	-60549
+▁emotionnelle	-60550
+▁engycephalus	-60551
+▁environement	-60552
+▁epalanganaki	-60553
+▁epalanganina	-60554
+▁epambolamaki	-60555
+▁epidemiologi	-60556
+▁epidemiology	-60557
+▁esambeliselo	-60558
+▁esangisamaka	-60559
+▁esembolamaka	-60560
+▁esembolamaki	-60561
+▁esengisamaka	-60562
+▁esqueceremos	-60563
+▁essentielles	-60564
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+▁etambolisami	-60566
+▁ethnogaphica	-60567
+▁etúkáengumba	-60568
+▁exceptionnel	-60569
+▁exploratrice	-60570
+▁ezongisamaki	-60571
+▁febwáliforum	-60572
+▁ferroviaires	-60573
+▁fibromyalgie	-60574
+▁filamentosus	-60575
+▁filmographie	-60576
+▁fonctionnels	-60577
+▁francebomoto	-60578
+▁francophonix	-60579
+▁friedafrieda	-60580
+▁gemmologique	-60581
+▁glorianzembo	-60582
+▁glottophagie	-60583
+▁gossweilerii	-60584
+▁grammaticale	-60585
+▁gratuitement	-60586
+▁greinerville	-60587
+▁guerrilheiro	-60588
+▁généralement	-60589
+▁généralistes	-60590
+▁habilitation	-60591
+▁hammarskjold	-60592
+▁helicophagus	-60593
+▁hellzapoppin	-60594
+▁hieronymites	-60595
+▁himmoglobule	-60596
+▁hippopotamus	-60597
+▁horticulture	-60598
+▁humanitarian	-60599
+▁humoristique	-60600
+▁hydrocabures	-60601
+▁hypertrophie	-60602
+▁hypoglycémie	-60603
+▁hypostomatus	-60604
+▁hélicoptères	-60605
+▁idéocidaires	-60606
+▁ilifunguliwa	-60607
+▁impenetrable	-60608
+▁implantation	-60609
+▁implications	-60610
+▁inadmissible	-60611
+▁incorporated	-60612
+▁independance	-60613
+▁individuelle	-60614
+▁inflagration	-60615
+▁influenceuse	-60616
+▁informatisés	-60617
+▁initialement	-60618
+▁initiatiques	-60619
+▁innovatrices	-60620
+▁installaties	-60621
+▁instructeurs	-60622
+▁instrumental	-60623
+▁insuffisance	-60624
+▁intellectuel	-60625
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+▁interminable	-60627
+▁internatinal	-60628
+▁intervenants	-60629
+▁intervieweur	-60630
+▁invertebrata	-60631
+▁jacksonville	-60632
+▁jeanlemignon	-60633
+▁jurisconsult	-60634
+▁jurisdiction	-60635
+▁kadiakuikala	-60636
+▁kakéfrédéric	-60637
+▁kalamunkómbó	-60638
+▁kaléidoscope	-60639
+▁kanyabayonga	-60640
+▁kasavubistes	-60641
+▁kelekuelonga	-60642
+▁kilimandjaro	-60643
+▁kimbobalitii	-60644
+▁kobasopelaka	-60645
+▁kodiembikisa	-60646
+▁kolendendela	-60647
+▁komilelalela	-60648
+▁komisepelisa	-60649
+▁komunikasyon	-60650
+▁konstitision	-60651
+▁kopalinginya	-60652
+▁kopekisamaka	-60653
+▁kopromouvoir	-60654
+▁kosakanemela	-60655
+▁kosalaportal	-60656
+▁kosalelamaki	-60657
+▁kosengasenga	-60658
+▁kotelemelaka	-60659
+▁kriegsschule	-60660
+▁kurländische	-60661
+▁kusafirishwa	-60662
+▁lacourtianum	-60663
+▁laurentienne	-60664
+▁lelitteraire	-60665
+▁leopoldville	-60666
+▁lexicologues	-60667
+▁liazerbayani	-60668
+▁libebisibika	-60669
+▁libosanamaka	-60670
+▁libosókomona	-60671
+▁libóngwanaka	-60672
+▁lifalansealt	-60673
+▁limalayalami	-60674
+▁limnothrissa	-60675
+▁lingüistique	-60676
+▁linkɔ́lɔ́tɔ́	-60677
+▁lipordoazali	-60678
+▁lisapolisano	-60679
+▁livländische	-60680
+▁localisation	-60681
+▁longirostris	-60682
+▁luandaangola	-60683
+▁lubukulukumu	-60684
+▁lukiyanovich	-60685
+▁lumindimboli	-60686
+▁lumumbavideo	-60687
+▁mabiangamaka	-60688
+▁macrophyllum	-60689
+▁maditeraneen	-60690
+▁mahengeensis	-60691
+▁makabolamaka	-60692
+▁makasimakasi	-60693
+▁makobalukaka	-60694
+▁malámucarnet	-60695
+▁malángwisaka	-60696
+▁mamamodifier	-60697
+▁mangercratie	-60698
+▁maniemakindu	-60699
+▁manimalwordl	-60700
+▁mapúngupúngu	-60701
+▁marseillaise	-60702
+▁masepelisáká	-60703
+▁matsouanisme	-60704
+▁matuatambula	-60705
+▁mbangumbangu	-60706
+▁megatransect	-60707
+▁melanopterus	-60708
+▁membranaceus	-60709
+▁menstruation	-60710
+▁menstruelles	-60711
+▁meridionalis	-60712
+▁methotrexate	-60713
+▁metropolitan	-60714
+▁michelangelo	-60715
+▁millionnaire	-60716
+▁miraculeuses	-60717
+▁missionaries	-60718
+▁mitémanzembo	-60719
+▁modèleavenue	-60720
+▁momóyébísáká	-60721
+▁mpoyijeanine	-60722
+▁mudimakatshi	-60723
+▁multilingues	-60724
+▁muzikiregard	-60725
+▁mwǐndanzembo	-60726
+▁métamorphose	-60727
+▁méthodologie	-60728
+▁nabopolassar	-60729
+▁nakomikwanza	-60730
+▁nakosepelisa	-60731
+▁nationalisme	-60732
+▁ndeakanyanya	-60733
+▁ngombangomba	-60734
+▁ngánzálángan	-60735
+▁niederlanden	-60736
+▁nigriventris	-60737
+▁ningavtokole	-60738
+▁nitazoxanide	-60739
+▁northeastern	-60740
+▁nsimirenzoto	-60741
+▁numismatique	-60742
+▁numérisation	-60743
+▁numérotation	-60744
+▁nyirambarato	-60745
+▁nzámbénzembo	-60746
+▁obstétricale	-60747
+▁obstétricien	-60748
+▁occupational	-60749
+▁okosanganisa	-60750
+▁opportunités	-60751
+▁ornatissimus	-60752
+▁orthographie	-60753
+▁pacification	-60754
+▁papadopoulos	-60755
+▁paramanietik	-60756
+▁paramaniétik	-60757
+▁paratyphoide	-60758
+▁parkkamwanga	-60759
+▁participants	-60760
+▁pauliniennes	-60761
+▁peacekeeping	-60762
+▁pennsylvania	-60763
+▁pennsylvanie	-60764
+▁pensionnaire	-60765
+▁permanganate	-60766
+▁personnelles	-60767
+▁petekosalatv	-60768
+▁phacochoerus	-60769
+▁philosophers	-60770
+▁photographer	-60771
+▁phylogénique	-60772
+▁pictogrammes	-60773
+▁pinaceaesous	-60774
+▁plansprachen	-60775
+▁polichinelle	-60776
+▁poliomyélite	-60777
+▁polyarthrite	-60778
+▁postmédiéval	-60779
+▁presbyterian	-60780
+▁probablement	-60781
+▁profondément	-60782
+▁progesterone	-60783
+▁progressions	-60784
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+▁propriétaire	-60786
+▁protagoniste	-60787
+▁protestation	-60788
+▁prédestinaki	-60789
+▁préliminaire	-60790
+▁psophocarpus	-60791
+▁psychosocial	-60792
+▁périphérique	-60793
+▁rabindranath	-60794
+▁radiographie	-60795
+▁representaki	-60796
+▁restaurateur	-60797
+▁robbeneiland	-60798
+▁routesonline	-60799
+▁réclamations	-60800
+▁réfractaires	-60801
+▁répresentant	-60802
+▁résidentiels	-60803
+▁résurrection	-60804
+▁santrafilíka	-60805
+▁saskatchewan	-60806
+▁satisfaction	-60807
+▁schaffhausen	-60808
+▁schädeltiere	-60809
+▁scriptmokolo	-60810
+▁sempervirens	-60811
+▁senscritique	-60812
+▁sessiliflora	-60813
+▁sexfasciatus	-60814
+▁shakmanbobak	-60815
+▁shakmanchuck	-60816
+▁shakmanlaura	-60817
+▁shakmanmegan	-60818
+▁shakmanpeter	-60819
+▁sidérurgique	-60820
+▁significatif	-60821
+▁slavistinnen	-60822
+▁sokolámpúnda	-60823
+▁solovyanenko	-60824
+▁soomaaliland	-60825
+▁sophistiqués	-60826
+▁southwestern	-60827
+▁spiritualité	-60828
+▁sputnikovaya	-60829
+▁squiressanza	-60830
+▁statisticien	-60831
+▁stellenbosch	-60832
+▁stochocratie	-60833
+▁stolothrissa	-60834
+▁stratavarius	-60835
+▁stratovolcan	-60836
+▁streptocoque	-60837
+▁surroundings	-60838
+▁surveillance	-60839
+▁swinderianus	-60840
+▁synthétiques	-60841
+▁synthétiseur	-60842
+▁systématique	-60843
+▁tageszeitung	-60844
+▁tanzaniennes	-60845
+▁taxinomiques	-60846
+▁taxonomiques	-60847
+▁thessaloniki	-60848
+▁théologiques	-60849
+▁tokyopériode	-60850
+▁tomilɛ́ngɛlɛ	-60851
+▁traditionnal	-60852
+▁transversale	-60853
+▁tshimankinda	-60854
+▁tèmoingnages	-60855
+▁tétracycline	-60856
+▁unverbrüchli	-60857
+▁uwasilishaji	-60858
+▁vaguedivague	-60859
+▁valorisation	-60860
+▁vanderlinden	-60861
+▁vermiculatus	-60862
+▁westerwolder	-60863
+▁wikipédienne	-60864
+▁wodalitsidwa	-60865
+▁yaliendeshwa	-60866
+▁yamoussoukro	-60867
+▁yerushalayim	-60868
+▁zanzibaricus	-60869
+▁zweisprachig	-60870
+▁évangéliques	-60871
+▁évangélistes	-60872
+acétaminophene	-60873
+administrateur	-60874
+aminoquinoline	-60875
+automatisation	-60876
+basketballeuse	-60877
+biografinkombo	-60878
+cambridgeshire	-60879
+dactylographie	-60880
+démobilisation	-60881
+elisabethville	-60882
+entreprenariat	-60883
+extraordinaire	-60884
+identification	-60885
+immunodéfience	-60886
+interprétation	-60887
+laryngopharynx	-60888
+leicestershire	-60889
+malvengewächse	-60890
+northumberland	-60891
+officiellement	-60892
+orientalmotúya	-60893
+pelmatachromis	-60894
+représentation	-60895
+universitaires	-60896
+worcestershire	-60897
+▁abelakilokumu	-60898
+▁abokolomɔ́kɔ́	-60899
+▁abótámínzembo	-60900
+▁accumulateurs	-60901
+▁adiminisatele	-60902
+▁afrikamobimba	-60903
+▁afrocentrisme	-60904
+▁afrochampions	-60905
+▁afrofuturisme	-60906
+▁aggiornamento	-60907
+▁alboviolaceum	-60908
+▁amilelalelaki	-60909
+▁angolaestação	-60910
+▁antidépressif	-60911
+▁antioxydantes	-60912
+▁apalinginyaka	-60913
+▁arboriculture	-60914
+▁archiduchesse	-60915
+▁architectures	-60916
+▁atroventralis	-60917
+▁avertissement	-60918
+▁babenganamaki	-60919
+▁babobongolani	-60920
+▁baeksperiansi	-60921
+▁bakosámbelaka	-60922
+▁bakotungisama	-60923
+▁bakónzinzembo	-60924
+▁balaboratoire	-60925
+▁balendisamaki	-60926
+▁balifungolaka	-60927
+▁balongolamaki	-60928
+▁bamilóngisaki	-60929
+▁bandakonzambe	-60930
+▁baputrugɛ́shi	-60931
+▁baputulugɛ́si	-60932
+▁basambisamaki	-60933
+▁basketballeur	-60934
+▁batungisamaki	-60935
+▁baudouinville	-60936
+▁bayanganisaka	-60937
+▁bazongisamaki	-60938
+▁bibliothèques	-60939
+▁bienveillante	-60940
+▁blasphémateur	-60941
+▁bokɔ́lɔ́ngúnú	-60942
+▁bomobenganáká	-60943
+▁bopalanganisi	-60944
+▁brusselstimes	-60945
+▁caudovittatus	-60946
+▁charismatique	-60947
+▁chattopadhyay	-60948
+▁chikumbumtima	-60949
+▁chrysophyllum	-60950
+▁cilubaelobami	-60951
+▁circincisions	-60952
+▁circonstances	-60953
+▁civilisatrice	-60954
+▁civilizations	-60955
+▁clarification	-60956
+▁communicateur	-60957
+▁compensations	-60958
+▁complementary	-60959
+▁compréhension	-60960
+▁concentrateur	-60961
+▁conceptualisé	-60962
+▁confoederatio	-60963
+▁conjonctivite	-60964
+▁consciousness	-60965
+▁contemporains	-60966
+▁contractilité	-60967
+▁contributions	-60968
+▁coquihatville	-60969
+▁correspondant	-60970
+▁crosscurrents	-60971
+▁cryptozoology	-60972
+▁dexaméthasone	-60973
+▁diplomatiques	-60974
+▁dissemination	-60975
+▁dolichocarpum	-60976
+▁dominguezsite	-60977
+▁doumbiaarvers	-60978
+▁dévéloppement	-60979
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diff --git a/models/tokenizer/ln_tokenizer_8k.model b/models/tokenizer/ln_tokenizer_8k.model
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diff --git a/models/tokenizer/ln_tokenizer_8k.vocab b/models/tokenizer/ln_tokenizer_8k.vocab
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