Upload all models and assets for lij (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 +1,775 @@
+---
+language: lij
+language_name: Ligurian
+language_family: romance_galloitalic
+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-romance_galloitalic
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 3.659
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8072
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Ligurian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Ligurian** 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** | 3.021x | 3.02 | 0.0824% | 601,973 |
+| **16k** | 3.271x | 3.27 | 0.0892% | 556,020 |
+| **32k** | 3.488x | 3.49 | 0.0951% | 521,472 |
+| **64k** | 3.659x 🏆 | 3.66 | 0.0998% | 497,043 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Togo (nomme ofiçiâ: République Togolaise) stato de l'Africa çentro oççidentâ ind...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁to go ▁( nomme ▁ofiçiâ : ▁république ▁to gola ise ... (+24 more)` | 34 |
+| 16k | `▁to go ▁( nomme ▁ofiçiâ : ▁république ▁to gola ise ... (+24 more)` | 34 |
+| 32k | `▁to go ▁( nomme ▁ofiçiâ : ▁république ▁to gola ise ... (+24 more)` | 34 |
+| 64k | `▁togo ▁( nomme ▁ofiçiâ : ▁république ▁to golaise ) ▁stato ... (+21 more)` | 31 |
+
+**Sample 2:** `Fæti Euröpa Àzia Àfrica América Arte Costruçión Inovaçión Nasciûi Mòrti Âtri pro...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁fæti ▁euröpa ▁àzia ▁àfrica ▁américa ▁arte ▁costruçión ▁inovaçión ▁nasciûi ▁mòrti ... (+6 more)` | 16 |
+| 16k | `▁fæti ▁euröpa ▁àzia ▁àfrica ▁américa ▁arte ▁costruçión ▁inovaçión ▁nasciûi ▁mòrti ... (+6 more)` | 16 |
+| 32k | `▁fæti ▁euröpa ▁àzia ▁àfrica ▁américa ▁arte ▁costruçión ▁inovaçión ▁nasciûi ▁mòrti ... (+6 more)` | 16 |
+| 64k | `▁fæti ▁euröpa ▁àzia ▁àfrica ▁américa ▁arte ▁costruçión ▁inovaçión ▁nasciûi ▁mòrti ... (+6 more)` | 16 |
+
+**Sample 3:** `Fæti Euröpa Àzia Àfrica Arte Costruçión Inovaçión Nasciûi Mòrti Âtri progètti 62...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁fæti ▁euröpa ▁àzia ▁àfrica ▁arte ▁costruçión ▁inovaçión ▁nasciûi ▁mòrti ▁âtri ... (+5 more)` | 15 |
+| 16k | `▁fæti ▁euröpa ▁àzia ▁àfrica ▁arte ▁costruçión ▁inovaçión ▁nasciûi ▁mòrti ▁âtri ... (+5 more)` | 15 |
+| 32k | `▁fæti ▁euröpa ▁àzia ▁àfrica ▁arte ▁costruçión ▁inovaçión ▁nasciûi ▁mòrti ▁âtri ... (+5 more)` | 15 |
+| 64k | `▁fæti ▁euröpa ▁àzia ▁àfrica ▁arte ▁costruçión ▁inovaçión ▁nasciûi ▁mòrti ▁âtri ... (+5 more)` | 15 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.659x compression
+- **Lowest UNK Rate:** 8k with 0.0824% 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 | 8,853 | 13.11 | 49,924 | 26.3% | 45.2% |
+| **2-gram** | Subword | 320 🏆 | 8.32 | 4,859 | 63.9% | 98.1% |
+| **3-gram** | Word | 14,009 | 13.77 | 67,505 | 24.0% | 39.1% |
+| **3-gram** | Subword | 2,727 | 11.41 | 37,721 | 26.1% | 68.2% |
+| **4-gram** | Word | 20,217 | 14.30 | 101,414 | 23.3% | 36.5% |
+| **4-gram** | Subword | 15,550 | 13.92 | 176,973 | 11.9% | 37.8% |
+| **5-gram** | Word | 9,572 | 13.22 | 64,332 | 30.4% | 45.0% |
+| **5-gram** | Subword | 56,888 | 15.80 | 437,893 | 6.7% | 23.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a l` | 19,797 |
+| 2 | `o l` | 18,061 |
+| 3 | `l é` | 14,515 |
+| 4 | `l è` | 13,540 |
+| 5 | `de l` | 9,867 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a l é` | 6,311 |
+| 2 | `o l é` | 6,083 |
+| 3 | `o l è` | 4,965 |
+| 4 | `a l è` | 4,710 |
+| 5 | `pòsti de interèsse` | 3,216 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `stöia pòsti de interèsse` | 3,068 |
+| 2 | `fæti euröpa àzia àfrica` | 3,016 |
+| 3 | `de interèsse architetûe religiôze` | 2,952 |
+| 4 | `pòsti de interèsse architetûe` | 2,952 |
+| 5 | `interèsse architetûe religiôze architetûe` | 2,898 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `pòsti de interèsse architetûe religiôze` | 2,952 |
+| 2 | `de interèsse architetûe religiôze architetûe` | 2,898 |
+| 3 | `arte costruçión inovaçión nasciûi mòrti` | 2,888 |
+| 4 | `stöia pòsti de interèsse architetûe` | 2,882 |
+| 5 | `giögrafîa stöia pòsti de interèsse` | 2,868 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 413,980 |
+| 2 | `e _` | 401,203 |
+| 3 | `_ d` | 286,230 |
+| 4 | `o _` | 266,322 |
+| 5 | `_ c` | 191,071 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 118,475 |
+| 2 | `d e _` | 110,988 |
+| 3 | `_ i n` | 87,620 |
+| 4 | `_ a _` | 84,437 |
+| 5 | `_ l '` | 74,032 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 102,062 |
+| 2 | `_ i n t` | 38,995 |
+| 3 | `_ d a _` | 38,425 |
+| 4 | `a _ d e` | 29,461 |
+| 5 | `_ i n _` | 28,157 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _ d e _` | 26,234 |
+| 2 | `_ a _ l '` | 17,187 |
+| 3 | `e _ d e _` | 16,809 |
+| 4 | `ç i ó n _` | 16,721 |
+| 5 | `_ o _ l '` | 15,543 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 320
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~24% 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 | 0.8200 | 1.765 | 4.97 | 178,552 | 18.0% |
+| **1** | Subword | 1.0889 | 2.127 | 9.46 | 1,196 | 0.0% |
+| **2** | Word | 0.2946 | 1.227 | 1.73 | 885,169 | 70.5% |
+| **2** | Subword | 1.0226 | 2.032 | 6.45 | 11,315 | 0.0% |
+| **3** | Word | 0.1100 | 1.079 | 1.19 | 1,528,711 | 89.0% |
+| **3** | Subword | 0.8102 | 1.753 | 4.13 | 72,907 | 19.0% |
+| **4** | Word | 0.0425 🏆 | 1.030 | 1.07 | 1,822,768 | 95.7% |
+| **4** | Subword | 0.6391 | 1.557 | 2.85 | 301,052 | 36.1% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `a sùd e ço ch u ciàn nòbile do regno de mattê bernabè çigâa meliaduce cicala`
+2. `de fronte orientâ lengua do segnô de conponiménti in testimunianse da o 16 sec o l`
+3. `l arçipélago de vétria fraçión de ciù ò a a i 99 finale de frànsa 531`
+
+**Context Size 2:**
+
+1. `a l à in nómme fâso ò in sce téia gàlata musêo do mâ neigro comme goernao`
+2. `o l impediva che i pelêujanti pelleuiante o sûnnòu da pelleuia seggian di cacciueì da oxelletti e`
+3. `l é scrîta j e a e a elaborâ a ricostruçión de pröto léngoe chi léngoe dravìdiche`
+
+**Context Size 3:**
+
+1. `a l é conpréiza fra o triónfo de idêe rivoluçionâie coscì cómme o sécolo dòppo inta marìnn a`
+2. `o l é ascì n importante çentro commerçiâ edûcatïo e coltûà a l è na sitò a mêza`
+3. `o l è stæto fondoö o 28 dexembre stöia âtri progètti do gruppo italico giulia`
+
+**Context Size 4:**
+
+1. `stöia pòsti de interèsse architetûe religiôze architetûe civîli economîa coltûa manifestaçioîn fèste...`
+2. `fæti euröpa àzia àfrica arte costruçión inovaçión nasciûi mòrti 036`
+3. `pòsti de interèsse architetûe religiôze architetûe civîli economîa coltûa manifestaçioîn fèste e fêe...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_deno_sspenco_me`
+2. `ao_dantel'umié_c`
+3. `i_àu_e_ve_an_'as`
+
+**Context Size 2:**
+
+1. `a_gioîn_òcenovita`
+2. `e_vìttormâ_sciü_i`
+3. `_da_e_d'o_viancio`
+
+**Context Size 3:**
+
+1. `_de_cian_cuntròllo`
+2. `de_de_paruz)_o_pre`
+3. `_in_livia_àzia_tòc`
+
+**Context Size 4:**
+
+1. `_de_"reusa_dellese_`
+2. `_interèsse_arche_in`
+3. `_da_manicu,_nun_int`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.7% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (301,052 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 | 80,296 |
+| Total Tokens | 2,142,871 |
+| Mean Frequency | 26.69 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 840.07 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | a | 140,267 |
+| 2 | de | 102,749 |
+| 3 | l | 78,988 |
+| 4 | o | 78,535 |
+| 5 | e | 69,403 |
+| 6 | da | 49,709 |
+| 7 | in | 31,558 |
+| 8 | i | 27,140 |
+| 9 | u | 26,568 |
+| 10 | do | 24,863 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | bashkitë | 2 |
+| 2 | savais | 2 |
+| 3 | attends | 2 |
+| 4 | humaine | 2 |
+| 5 | conne | 2 |
+| 6 | promesses | 2 |
+| 7 | naufrages | 2 |
+| 8 | belsen | 2 |
+| 9 | margòt | 2 |
+| 10 | antisemìtiche | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9823 |
+| R² (Goodness of Fit) | 0.998916 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 49.7% |
+| Top 1,000 | 66.4% |
+| Top 5,000 | 79.5% |
+| Top 10,000 | 85.2% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9989 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 49.7% of corpus
+- **Long Tail:** 70,296 words needed for remaining 14.8% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.8072 🏆 | 0.3095 | N/A | N/A |
+| **mono_64d** | 64 | 0.7236 | 0.2526 | N/A | N/A |
+| **mono_128d** | 128 | 0.3751 | 0.2268 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8072 | 0.3052 | 0.0180 | 0.1580 |
+| **aligned_64d** | 64 | 0.7236 | 0.2558 | 0.0520 | 0.2720 |
+| **aligned_128d** | 128 | 0.3751 | 0.2313 | 0.1120 | 0.4000 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.8072 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2635. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 11.2% 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 | **0.908** | High formulaic/idiomatic 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` | spontaneamente, sciûto, sanctum |
+| `-a` | archiòlogo, apatico, alverniate |
+| `-c` | cavour, cuštřuia, cessiun |
+| `-p` | partensa, pianeti, pecchi |
+| `-m` | müàggia, meòçia, maschî |
+| `-ca` | cavour, caden, caratterizzæ |
+| `-d` | devota, dorso, dicitur |
+| `-b` | belinda, borzonasca, berga |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-a` | inmensa, oryza, devota |
+| `-o` | dorso, archiòlogo, grano |
+| `-e` | ànche, tutâle, spontaneamente |
+| `-i` | olandéixi, novelli, pianeti |
+| `-n` | gabìnn, finsen, trœuvan |
+| `-u` | scrìtu, rilasciòu, scumpartìu |
+| `-te` | spontaneamente, alverniate, frequénte |
+| `-ia` | müàggia, cuštřuia, guardia |
+
+### 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 |
+|------|----------|------------------|----------|
+| `çion` | 1.98x | 61 contexts | açion, leçion, seçion |
+| `ment` | 1.67x | 123 contexts | mente, menti, mentæ |
+| `açió` | 2.28x | 26 contexts | açión, façión, naçión |
+| `açio` | 1.61x | 71 contexts | façio, açion, laçio |
+| `çión` | 2.18x | 22 contexts | açión, seçión, leçión |
+| `nter` | 1.72x | 51 contexts | nterò, inter, interä |
+| `rovi` | 1.74x | 45 contexts | rovie, rovinn, rovine |
+| `stru` | 1.52x | 75 contexts | austru, mestru, castru |
+| `rchi` | 1.48x | 65 contexts | archi, ærchi, erchi |
+| `raçi` | 1.52x | 48 contexts | graçia, oraçio, graçie |
+| `taçi` | 1.53x | 40 contexts | staçión, staçion, staçiun |
+| `hite` | 2.05x | 13 contexts | white, architetu, architetü |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-c` | `-a` | 168 words | carbònica, chionea |
+| `-c` | `-o` | 136 words | càspio, caldaréllo |
+| `-c` | `-e` | 128 words | circe, crìste |
+| `-s` | `-a` | 121 words | servia, svevia |
+| `-s` | `-e` | 117 words | scenette, særavàlle |
+| `-p` | `-e` | 112 words | prutešte, provvidde |
+| `-p` | `-o` | 111 words | petto, prononçiao |
+| `-p` | `-a` | 109 words | puiia, preminença |
+| `-s` | `-o` | 103 words | spartio, satûrno |
+| `-a` | `-a` | 103 words | achela, atella |
+
+### 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 |
+|------|-----------------|------------|------|
+| prescidiâ | **`prescid-i-â`** | 7.5 | `i` |
+| continoava | **`contino-a-va`** | 7.5 | `a` |
+| economìsta | **`economì-s-ta`** | 7.5 | `s` |
+| imprezaio | **`imprez-a-io`** | 7.5 | `a` |
+| attribuii | **`attribu-i-i`** | 7.5 | `i` |
+| gianfranco | **`gi-an-franco`** | 7.5 | `franco` |
+| consonanti | **`consona-n-ti`** | 7.5 | `n` |
+| élémentaire | **`élémenta-i-re`** | 7.5 | `i` |
+| manifèsti | **`manifè-s-ti`** | 7.5 | `s` |
+| travagiòu | **`travag-i-òu`** | 7.5 | `i` |
+| mangiâvan | **`mangiâ-va-n`** | 6.0 | `mangiâ` |
+| parallela | **`par-alle-la`** | 6.0 | `alle` |
+| borgorato | **`borgo-ra-to`** | 6.0 | `borgo` |
+| incontrao | **`in-contra-o`** | 6.0 | `contra` |
+| codevilla | **`co-de-villa`** | 6.0 | `villa` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Ligurian shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+> **Note on Idiomaticity:** The high Idiomaticity Gap suggests a large number of frequent multi-word expressions or formulaic sequences that are statistically distinct from their component parts.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (3.66x) |
+| N-gram | **2-gram** | Lowest perplexity (320) |
+| Markov | **Context-4** | Highest predictability (95.7%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 10:57:38*

models/embeddings/aligned/lij_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a12775541e2635dd245123f4eae6a4fedc395e264f7c6eda52d03afcef47217
+size 1060269058

models/embeddings/aligned/lij_128d.meta.json

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

models/embeddings/aligned/lij_128d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf6b4e3f52bba5b1712efb60c58bffa0f212230d5cf55cb36f374c4dfded634f
+size 65664

models/embeddings/aligned/lij_128d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "lij",
+  "dimension": 128,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 6383,
+  "vocab_size": 34826
+}

models/embeddings/aligned/lij_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:386c7b04528a88a858e5a76521d09deebea7f1bc1bcbc03b1d3a5c2c6e6d68bc
+size 265522690

models/embeddings/aligned/lij_32d.meta.json

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

models/embeddings/aligned/lij_32d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d45c71fd82e787feacf62f4f115893ac819932792c6c355b7a563247f8cd6a1
+size 4224

models/embeddings/aligned/lij_32d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "lij",
+  "dimension": 32,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 6383,
+  "vocab_size": 34826
+}

models/embeddings/aligned/lij_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c0c5c426be3baa26549aea9bbe21f6184634e73ed2e8e0d93ac12060ac11077e
+size 530438146

models/embeddings/aligned/lij_64d.meta.json

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

models/embeddings/aligned/lij_64d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4523a4f97143f66bf47fb856b06379c6ed363729f35d9528a4cd6a70efe19bb0
+size 16512

models/embeddings/aligned/lij_64d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "lij",
+  "dimension": 64,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 6383,
+  "vocab_size": 34826
+}

models/embeddings/monolingual/lij_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a12775541e2635dd245123f4eae6a4fedc395e264f7c6eda52d03afcef47217
+size 1060269058

models/embeddings/monolingual/lij_128d.meta.json

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

models/embeddings/monolingual/lij_128d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "lij",
+  "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": 34826
+}

models/embeddings/monolingual/lij_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:386c7b04528a88a858e5a76521d09deebea7f1bc1bcbc03b1d3a5c2c6e6d68bc
+size 265522690

models/embeddings/monolingual/lij_32d.meta.json

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

models/embeddings/monolingual/lij_32d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "lij",
+  "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": 34826
+}

models/embeddings/monolingual/lij_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c0c5c426be3baa26549aea9bbe21f6184634e73ed2e8e0d93ac12060ac11077e
+size 530438146

models/embeddings/monolingual/lij_64d.meta.json

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

models/embeddings/monolingual/lij_64d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "lij",
+  "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": 34826
+}

models/subword_markov/lij_markov_ctx1_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a88c8f5e313d25f29d8598a36428f8bb73c2b0af8e79eae675c2c8b918bedce4
+size 92104

models/subword_markov/lij_markov_ctx1_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "subword",
+  "language": "lij",
+  "unique_contexts": 1196,
+  "total_transitions": 12114708
+}

models/subword_markov/lij_markov_ctx2_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce645bee5922f2942066bc3c8d18cc4d8ba93e0cdb28f04e0556bd40b4db0da8
+size 595106

models/subword_markov/lij_markov_ctx2_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "subword",
+  "language": "lij",
+  "unique_contexts": 11315,
+  "total_transitions": 12103554
+}

models/subword_markov/lij_markov_ctx3_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:711cf09ee855f7d8927540598ddc2b6484236010e181e78f3185852d921c4e8f
+size 2240994

models/subword_markov/lij_markov_ctx3_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 3,
+  "variant": "subword",
+  "language": "lij",
+  "unique_contexts": 72907,
+  "total_transitions": 12092400
+}

models/subword_markov/lij_markov_ctx4_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b19cab4f273d4528d06483af05a6eb92841703885b20838034522e5bf4887d4d
+size 6692561

models/subword_markov/lij_markov_ctx4_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 4,
+  "variant": "subword",
+  "language": "lij",
+  "unique_contexts": 301052,
+  "total_transitions": 12081246
+}

models/subword_ngram/lij_2gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37dfe4260cb4b327fd0325db1bf918b2960e8e68d497993f9e336a8849c0cd06
+size 65509

models/subword_ngram/lij_2gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 2,
+  "variant": "subword",
+  "language": "lij",
+  "unique_ngrams": 4859,
+  "total_ngrams": 12114708
+}

models/subword_ngram/lij_3gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebc81393214ebb98f470f42e6ff7b3f8222c9c73272f80b749339c7d73485c93
+size 485528

models/subword_ngram/lij_3gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 3,
+  "variant": "subword",
+  "language": "lij",
+  "unique_ngrams": 37721,
+  "total_ngrams": 12103554
+}

models/subword_ngram/lij_4gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c0b1ff4733679b17857a525f114c4bdf46dd637f1b90652033b3aef55541620
+size 2049663

models/subword_ngram/lij_4gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 4,
+  "variant": "subword",
+  "language": "lij",
+  "unique_ngrams": 176973,
+  "total_ngrams": 12092400
+}

models/subword_ngram/lij_5gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c0567c0f0549b416e18b44868ec647e7ce56e3ccbb2f0596aa46b3f41c8e85d8
+size 5250751

models/subword_ngram/lij_5gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "lij",
+  "unique_ngrams": 437893,
+  "total_ngrams": 12081246
+}

models/tokenizer/lij_tokenizer_16k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76c703492337a2f6b9ffaca7b3f90995dc68acf40084bc1e7b79da4a9811bf73
+size 506449

models/tokenizer/lij_tokenizer_32k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d25513909e9d8bee965f21a967894ffbbab7ba56c7f443498bafe52854f66e8
+size 780446

models/tokenizer/lij_tokenizer_64k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3de197e5b196c1bdb500da48c531357aee9c9f3f0737b9d0a7a67f4dce181726
+size 1367136

models/tokenizer/lij_tokenizer_8k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e53c163ba090606447c437b6829d229452f130bccf99f52af3cc26b38faf64ac
+size 372013

models/vocabulary/lij_vocabulary.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3bd8ec6615db08057d6ec537780dab9183e88158f945e3c7d3b7968137ca304f
+size 1405702

models/vocabulary/lij_vocabulary_metadata.json

@@ -0,0 +1,17 @@
+{
+  "language": "lij",
+  "vocabulary_size": 80296,
+  "variant": "full",
+  "statistics": {
+    "type_token_ratio": 0.07969855367337514,
+    "coverage": {
+      "top_100": 0.47531116212556246,
+      "top_1000": 0.6348242790118664,
+      "top_5000": 0.7598727464589203,
+      "top_10000": 0.8143334248023933
+    },
+    "hapax_count": 98324,
+    "hapax_ratio": 0.5504646736087784,
+    "total_documents": 11154
+  }
+}