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+---
+language: lt
+language_name: Lithuanian
+language_family: baltic
+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-baltic
+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: 4.757
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8202
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-14
+---
+
+# Lithuanian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Lithuanian** 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** | 3.661x | 3.66 | 0.1102% | 2,105,079 |
+| **16k** | 4.090x | 4.09 | 0.1231% | 1,884,341 |
+| **32k** | 4.453x | 4.45 | 0.1340% | 1,730,520 |
+| **64k** | 4.757x 🏆 | 4.76 | 0.1431% | 1,620,062 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Vilkonys – kaimas Panevėžio rajono savivaldybėje, 2 km nuo Raguvos. Gyventojai Š...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁vilk on ys ▁– ▁kaimas ▁panevėžio ▁rajono ▁savivaldybėje , ▁ ... (+11 more)` | 21 |
+| 16k | `▁vilk onys ▁– ▁kaimas ▁panevėžio ▁rajono ▁savivaldybėje , ▁ 2 ... (+10 more)` | 20 |
+| 32k | `▁vilk onys ▁– ▁kaimas ▁panevėžio ▁rajono ▁savivaldybėje , ▁ 2 ... (+9 more)` | 19 |
+| 64k | `▁vilk onys ▁– ▁kaimas ▁panevėžio ▁rajono ▁savivaldybėje , ▁ 2 ... (+9 more)` | 19 |
+
+**Sample 2:** `Džufros savivaldybė () – Libijos savivaldybė šalies centrinėje dalyje, Sacharos ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁dž u f ros ▁savivaldybė ▁() ▁– ▁lib ijos ▁savivaldybė ... (+18 more)` | 28 |
+| 16k | `▁dž uf ros ▁savivaldybė ▁() ▁– ▁lib ijos ▁savivaldybė ▁šalies ... (+15 more)` | 25 |
+| 32k | `▁dž uf ros ▁savivaldybė ▁() ▁– ▁libijos ▁savivaldybė ▁šalies ▁centrinėje ... (+12 more)` | 22 |
+| 64k | `▁dž uf ros ▁savivaldybė ▁() ▁– ▁libijos ▁savivaldybė ▁šalies ▁centrinėje ... (+12 more)` | 22 |
+
+**Sample 3:** `Dūdoriškiai – viensėdis Biržų rajono savivaldybėje, 6 km į vakarus nuo Pabiržės....`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁dū do rišk iai ▁– ▁viensėdis ▁biržų ▁rajono ▁savivaldybėje , ... (+15 more)` | 25 |
+| 16k | `▁dū do rišk iai ▁– ▁viensėdis ▁biržų ▁rajono ▁savivaldybėje , ... (+15 more)` | 25 |
+| 32k | `▁dū do rišk iai ▁– ▁viensėdis ▁biržų ▁rajono ▁savivaldybėje , ... (+15 more)` | 25 |
+| 64k | `▁dū do riškiai ▁– ▁viensėdis ▁biržų ▁rajono ▁savivaldybėje , ▁ ... (+12 more)` | 22 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.757x compression
+- **Lowest UNK Rate:** 8k with 0.1102% 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 | 196,143 | 17.58 | 951,489 | 6.6% | 16.9% |
+| **2-gram** | Subword | 347 🏆 | 8.44 | 16,291 | 61.2% | 98.5% |
+| **3-gram** | Word | 370,705 | 18.50 | 1,291,283 | 4.1% | 12.0% |
+| **3-gram** | Subword | 3,304 | 11.69 | 131,904 | 20.0% | 64.1% |
+| **4-gram** | Word | 774,264 | 19.56 | 2,157,224 | 3.3% | 9.4% |
+| **4-gram** | Subword | 21,025 | 14.36 | 755,683 | 8.5% | 31.1% |
+| **5-gram** | Word | 702,338 | 19.42 | 1,649,947 | 3.1% | 8.5% |
+| **5-gram** | Subword | 92,546 | 16.50 | 2,569,308 | 4.6% | 17.5% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `nuo m` | 80,598 |
+| 2 | `taip pat` | 61,102 |
+| 3 | `m m` | 49,221 |
+| 4 | `km į` | 40,737 |
+| 5 | `g m` | 39,476 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `rajono savivaldybės gyvenvietės` | 21,760 |
+| 2 | `šaltiniai rajono savivaldybės` | 18,042 |
+| 3 | `gyventojai šaltiniai rajono` | 15,946 |
+| 4 | `pr m e` | 15,933 |
+| 5 | `0 0 0` | 11,090 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `šaltiniai rajono savivaldybės gyvenvietės` | 17,791 |
+| 2 | `gyventojai šaltiniai rajono savivaldybės` | 15,570 |
+| 3 | `m pr m e` | 9,199 |
+| 4 | `0 0 0 0` | 6,658 |
+| 5 | `į šiaurės rytus nuo` | 6,349 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `gyventojai šaltiniai rajono savivaldybės gyvenvietės` | 15,554 |
+| 2 | `km į šiaurės rytus nuo` | 5,574 |
+| 3 | `km į šiaurės vakarus nuo` | 4,918 |
+| 4 | `0 0 0 0 0` | 4,143 |
+| 5 | `general information about the player` | 2,888 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `s _` | 8,108,761 |
+| 2 | `o _` | 4,732,529 |
+| 3 | `i n` | 4,492,534 |
+| 4 | `. _` | 4,477,090 |
+| 5 | `a s` | 3,925,055 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `o s _` | 2,303,630 |
+| 2 | `a s _` | 2,033,580 |
+| 3 | `_ p a` | 1,580,889 |
+| 4 | `i n i` | 1,432,110 |
+| 5 | `a i _` | 1,247,013 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ m . _` | 1,006,887 |
+| 2 | `_ i r _` | 1,000,574 |
+| 3 | `i j o s` | 645,380 |
+| 4 | `j o s _` | 630,230 |
+| 5 | `t a s _` | 487,967 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i j o s _` | 560,980 |
+| 2 | `. _ m . _` | 345,387 |
+| 3 | `b u v o _` | 340,153 |
+| 4 | `_ b u v o` | 328,906 |
+| 5 | `l i e t u` | 311,837 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 347
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~17% 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 | 1.0333 | 2.047 | 10.88 | 1,497,676 | 0.0% |
+| **1** | Subword | 1.0306 | 2.043 | 6.87 | 9,691 | 0.0% |
+| **2** | Word | 0.2905 | 1.223 | 1.79 | 16,264,065 | 71.0% |
+| **2** | Subword | 0.6783 | 1.600 | 4.61 | 66,441 | 32.2% |
+| **3** | Word | 0.0931 | 1.067 | 1.18 | 29,101,762 | 90.7% |
+| **3** | Subword | 0.7377 | 1.667 | 4.28 | 306,205 | 26.2% |
+| **4** | Word | 0.0375 🏆 | 1.026 | 1.06 | 34,194,540 | 96.3% |
+| **4** | Subword | 0.7065 | 1.632 | 3.54 | 1,310,778 | 29.3% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `m m new york reprint der lehre von trips as ratas paskutinis vicekaralius vėliau tiškevičiaus logois...`
+2. `ir žagarės vidurinės mokyklos patalpose aušros pradžios rokas beliukevičius g m rugpjūtį jo sūnų dže...`
+3. `buvo prilyginti taip pat deltuvos žemė šiose pareigose g m atvyko į pasaulio lietuvių tarybos narių`
+
+**Context Size 2:**
+
+1. `nuo m kursuoja trollino 15 ac šaltiniai miestai miestai apygardos apygardos vakarinius krantus skala...`
+2. `taip pat katalikiškos pakraipos šv sebastijono atvaizdas andrėja mantenja mirė m balandžio 18 m bala...`
+3. `m m gruodžio 11 d vilnius smuikininkas pedagogas vargonininkas chorvedys muzikos mokytojas lankydama...`
+
+**Context Size 3:**
+
+1. `šaltiniai rajono savivaldybės gyvenvietės miesto dalys`
+2. `gyventojai šaltiniai rajono savivaldybės geležinkelio stotys kultūros vertybės geležinkelio stotys s...`
+3. `pr m e galėjo būti knoso uostas įkūrimas dabartinį heraklioną 824 m įkūrė saracėnai išvaryti iš anda...`
+
+**Context Size 4:**
+
+1. `gyventojai šaltiniai rajono savivaldybės gyvenvietės kaimai aukštaitijos nacionaliniame parke kaimai`
+2. `m pr m e jų indėlis į matematiką astronomiją ir mediciną būdamas 16 metų pagarsėjo išgydęs bucharos ...`
+3. `0 0 0 0 0 0 0 1 0 4 1 1 0 ii grupė komandatšk rungt laim lyg`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_vičingauve_us_į`
+2. `i_g_vilie_ją_aly`
+3. `adiuvosłakaikari`
+
+**Context Size 2:**
+
+1. `s_kopiniai_važoda`
+2. `o_bel_patvė_citas`
+3. `ingotentrauliteli`
+
+**Context Size 3:**
+
+1. `os_karius_sodų_būt`
+2. `as_ii_panų_siniais`
+3. `_pastame_garalianč`
+
+**Context Size 4:**
+
+1. `_m._laipėdoje_yra_k`
+2. `_ir_dvejų_žvaigždę_`
+3. `ijos_karoliuojas,_a`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,310,778 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 | 719,992 |
+| Total Tokens | 41,466,357 |
+| Mean Frequency | 57.59 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 2237.16 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | m | 1,249,293 |
+| 2 | ir | 1,005,030 |
+| 3 | buvo | 329,252 |
+| 4 | į | 322,738 |
+| 5 | nuo | 252,061 |
+| 6 | d | 244,466 |
+| 7 | iš | 222,420 |
+| 8 | su | 217,987 |
+| 9 | 1 | 212,417 |
+| 10 | yra | 198,413 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | triumphal | 2 |
+| 2 | sawano | 2 |
+| 3 | taisen | 2 |
+| 4 | iryu | 2 |
+| 5 | nzk | 2 |
+| 6 | lorensavo | 2 |
+| 7 | gauchos | 2 |
+| 8 | architekturze | 2 |
+| 9 | sztuce | 2 |
+| 10 | łubieński | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9393 |
+| R² (Goodness of Fit) | 0.994466 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 23.0% |
+| Top 1,000 | 44.0% |
+| Top 5,000 | 62.7% |
+| Top 10,000 | 70.6% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9945 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 23.0% of corpus
+- **Long Tail:** 709,992 words needed for remaining 29.4% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.8202 🏆 | 0.3473 | N/A | N/A |
+| **mono_64d** | 64 | 0.8013 | 0.2803 | N/A | N/A |
+| **mono_128d** | 128 | 0.7515 | 0.2219 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8202 | 0.3470 | 0.1200 | 0.4580 |
+| **aligned_64d** | 64 | 0.8013 | 0.2841 | 0.3160 | 0.7180 |
+| **aligned_128d** | 128 | 0.7515 | 0.2188 | 0.4260 | 0.7780 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8202 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2832. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 42.6% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.511** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-s` | stambinant, smutki, sanio |
+| `-a` | abaujaus, atidėliojimą, antigono |
+| `-ma` | maškė, maršruto, maceika |
+| `-k` | kišenes, kaljan, khan |
+| `-m` | maškė, mágica, mergystės |
+| `-ka` | kaljan, kastilija, kalanti |
+| `-p` | praktikuoti, partenopė, pajė |
+| `-pa` | partenopė, pajė, paitensis |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | kišenes, goblets, hallas |
+| `-as` | hallas, anamas, kuartas |
+| `-i` | praktikuoti, smutki, trūki |
+| `-is` | alramis, paitensis, refrakcinis |
+| `-o` | antigono, sanio, sliesoraičio |
+| `-e` | užantyje, geheimnisse, šturmane |
+| `-a` | mágica, arhitektūra, susuka |
+| `-ai` | antikomunistiniai, užkuriai, laurinavičiai |
+
+### 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 |
+|------|----------|------------------|----------|
+| `inių` | 1.80x | 283 contexts | ainių, žinių, ninių |
+| `etuv` | 2.37x | 59 contexts | betuvė, rietuvę, sietuvą |
+| `yven` | 1.82x | 148 contexts | lyven, gyvenu, gyveną |
+| `inim` | 1.53x | 306 contexts | minim, dinim, minime |
+| `gyve` | 1.86x | 92 contexts | gyvenu, gyveną, gyvenc |
+| `iaur` | 1.66x | 139 contexts | iauri, žiaurų, siaure |
+| `tinė` | 1.34x | 421 contexts | etinė, matinė, vatinė |
+| `ltin` | 1.42x | 229 contexts | altin, altino, baltin |
+| `ausi` | 1.47x | 173 contexts | kausi, gausi, ausim |
+| `tuvo` | 1.98x | 45 contexts | tuvos, tuvoje, bytuvo |
+| `ajon` | 1.66x | 80 contexts | fajon, rajon, pajon |
+| `ietu` | 1.54x | 104 contexts | vietu, kietu, lietu |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-p` | `-s` | 299 words | pingus, plėtotos |
+| `-s` | `-s` | 261 words | slaugymas, statybiniais |
+| `-k` | `-s` | 206 words | kreatininas, komarnicos |
+| `-a` | `-s` | 202 words | ajagozas, apsukrus |
+| `-b` | `-s` | 142 words | bubles, begėdis |
+| `-d` | `-s` | 128 words | dramblys, džinhanas |
+| `-p` | `-i` | 124 words | piktindamiesi, pirkiniui |
+| `-m` | `-s` | 114 words | monofizitais, meniškais |
+| `-s` | `-i` | 97 words | stasiūniečiai, segmentuojasi |
+| `-s` | `-o` | 85 words | slomo, skaitytojo |
+
+### 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 |
+|------|-----------------|------------|------|
+| stankiškiuose | **`stankiškiuo-s-e`** | 7.5 | `s` |
+| pertekusi | **`perteku-s-i`** | 7.5 | `s` |
+| ekranuose | **`ekranuo-s-e`** | 7.5 | `s` |
+| bačkonyse | **`bačkony-s-e`** | 7.5 | `s` |
+| ramonuose | **`ramonuo-s-e`** | 7.5 | `s` |
+| komentaruose | **`komentaruo-s-e`** | 7.5 | `s` |
+| hidnotrija | **`hidnotr-i-ja`** | 7.5 | `i` |
+| šaudyklose | **`šaudyklo-s-e`** | 7.5 | `s` |
+| nuomojosi | **`nuomojo-s-i`** | 7.5 | `s` |
+| suvynioja | **`suvyni-o-ja`** | 7.5 | `o` |
+| riboženkliai | **`riboženkl-i-ai`** | 7.5 | `i` |
+| potvarkiuose | **`potvarkiuo-s-e`** | 7.5 | `s` |
+| antigvosą | **`antigvo-s-ą`** | 7.5 | `s` |
+| užutekiuose | **`užutekiuo-s-e`** | 7.5 | `s` |
+| muitinėse | **`muitinė-s-e`** | 7.5 | `s` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Lithuanian 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 (4.76x) |
+| N-gram | **2-gram** | Lowest perplexity (347) |
+| Markov | **Context-4** | Highest predictability (96.3%) |
+| 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: 2026-01-14 22:52:53*
diff --git a/lt_morph_tokenizer.json b/lt_morph_tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..f3f547f9f3e72e217eb43d7fd82027c58455ead0
--- /dev/null
+++ b/lt_morph_tokenizer.json
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diff --git a/models/tokenizer/lt_tokenizer_8k.model b/models/tokenizer/lt_tokenizer_8k.model
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diff --git a/models/tokenizer/lt_tokenizer_8k.vocab b/models/tokenizer/lt_tokenizer_8k.vocab
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