Upload all models and assets for os (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: os
+language_name: Ossetic
+language_family: iranian_eastern
+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-iranian_eastern
+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: 3.901
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7990
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Ossetic - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Ossetic** 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** | 3.279x | 3.29 | 0.2317% | 140,728 |
+| **16k** | 3.535x | 3.54 | 0.2497% | 130,553 |
+| **32k** | 3.746x | 3.75 | 0.2646% | 123,211 |
+| **64k** | 3.901x 🏆 | 3.91 | 0.2756% | 118,301 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Тахъазты Фидар. Дыгурон-уырыссаг дзырдуат — Аланыстон, (, ) — хуыз.`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁тахъазты ▁фидар . ▁дыгурон - уырыссаг ▁дзырдуат ▁— ▁аланыстон , ... (+5 more)` | 15 |
+| 16k | `▁тахъазты ▁фидар . ▁дыгурон - уырыссаг ▁дзырдуат ▁— ▁аланыстон , ... (+5 more)` | 15 |
+| 32k | `▁тахъазты ▁фидар . ▁дыгурон - уырыссаг ▁дзырдуат ▁— ▁аланыстон , ... (+5 more)` | 15 |
+| 64k | `▁тахъазты ▁фидар . ▁дыгурон - уырыссаг ▁дзырдуат ▁— ▁аланыстон , ... (+5 more)` | 15 |
+
+**Sample 2:** `дон у Ирыстоны, рахиз Цагъаты Анастасия. Ирыстоны топоними. хай. Ирыстоны`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁дон ▁у ▁ирыстоны , ▁рахиз ▁цагъаты ▁анастасия . ▁ирыстоны ▁топоними ... (+4 more)` | 14 |
+| 16k | `▁дон ▁у ▁ирыстоны , ▁рахиз ▁цагъаты ▁анастасия . ▁ирыстоны ▁топоними ... (+4 more)` | 14 |
+| 32k | `▁дон ▁у ▁ирыстоны , ▁рахиз ▁цагъаты ▁анастасия . ▁ирыстоны ▁топоними ... (+4 more)` | 14 |
+| 64k | `▁дон ▁у ▁ирыстоны , ▁рахиз ▁цагъаты ▁анастасия . ▁ирыстоны ▁топоними ... (+4 more)` | 14 |
+
+**Sample 3:** `ХуыбарцЦагаева А. Дз. Топонимия Северной Осетии — Владикавказ: Ир, — с. 623. у н...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁хуы бар ццаг аева ▁а . ▁дз . ▁топонимия ▁северной ... (+24 more)` | 34 |
+| 16k | `▁хуы бар ццаг аева ▁а . ▁дз . ▁топонимия ▁северной ... (+24 more)` | 34 |
+| 32k | `▁хуы бар ццаг аева ▁а . ▁дз . ▁топонимия ▁северной ... (+23 more)` | 33 |
+| 64k | `▁хуы бар ццаг аева ▁а . ▁дз . ▁топонимия ▁северной ... (+22 more)` | 32 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.901x compression
+- **Lowest UNK Rate:** 8k with 0.2317% 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 | 3,788 | 11.89 | 11,541 | 24.3% | 55.4% |
+| **2-gram** | Subword | 406 🏆 | 8.67 | 3,916 | 57.9% | 97.2% |
+| **3-gram** | Word | 2,985 | 11.54 | 11,245 | 29.5% | 61.0% |
+| **3-gram** | Subword | 3,147 | 11.62 | 27,926 | 23.4% | 65.1% |
+| **4-gram** | Word | 4,436 | 12.12 | 19,787 | 27.9% | 56.3% |
+| **4-gram** | Subword | 13,689 | 13.74 | 117,148 | 13.3% | 40.0% |
+| **5-gram** | Word | 3,339 | 11.71 | 15,120 | 31.1% | 60.3% |
+| **5-gram** | Subword | 32,155 | 14.97 | 219,588 | 10.1% | 30.1% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `административон центр` | 3,450 |
+| 2 | `хуссар ирыстоны` | 2,544 |
+| 3 | `у сахар` | 2,437 |
+| 4 | `з д` | 1,539 |
+| 5 | `центр у` | 1,478 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `административон центр у` | 1,464 |
+| 2 | `з д ирон` | 1,320 |
+| 3 | `йæ административон центр` | 1,314 |
+| 4 | `2 аг рауагъд` | 1,181 |
+| 5 | `рауагъд цхинвал республика` | 1,177 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `йæ административон центр у` | 1,306 |
+| 2 | `аг рауагъд цхинвал республика` | 1,177 |
+| 3 | `ирон 2 аг рауагъд` | 1,177 |
+| 4 | `2 аг рауагъд цхинвал` | 1,177 |
+| 5 | `д ирон 2 аг` | 1,177 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `2 аг рауагъд цхинвал республика` | 1,177 |
+| 2 | `ирон 2 аг рауагъд цхинвал` | 1,177 |
+| 3 | `д ирон 2 аг рауагъд` | 1,177 |
+| 4 | `з д ирон 2 аг` | 1,177 |
+| 5 | `аг рауагъд цхинвал республика 372` | 1,167 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ы _` | 115,086 |
+| 2 | `о н` | 67,122 |
+| 3 | `. _` | 57,323 |
+| 4 | `с т` | 50,166 |
+| 5 | `, _` | 48,007 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `о н _` | 33,294 |
+| 2 | `т ы _` | 26,081 |
+| 3 | `_ — _` | 23,993 |
+| 4 | `_ æ м` | 20,395 |
+| 5 | `æ м æ` | 20,225 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ æ м æ` | 20,051 |
+| 2 | `æ м æ _` | 19,557 |
+| 3 | `о н ы _` | 10,876 |
+| 4 | `_ й æ _` | 10,577 |
+| 5 | `с т о н` | 9,896 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ æ м æ _` | 19,383 |
+| 2 | `ы с т о н` | 9,292 |
+| 3 | `с т о н ы` | 8,183 |
+| 4 | `_ а з ы _` | 7,933 |
+| 5 | `р ы с т о` | 7,533 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 406
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~30% 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 | 0.6700 | 1.591 | 4.34 | 68,503 | 33.0% |
+| **1** | Subword | 1.2100 | 2.313 | 9.73 | 927 | 0.0% |
+| **2** | Word | 0.2070 | 1.154 | 1.45 | 292,738 | 79.3% |
+| **2** | Subword | 1.1075 | 2.155 | 6.21 | 9,001 | 0.0% |
+| **3** | Word | 0.0537 | 1.038 | 1.09 | 416,198 | 94.6% |
+| **3** | Subword | 0.8368 | 1.786 | 3.82 | 55,801 | 16.3% |
+| **4** | Word | 0.0178 🏆 | 1.012 | 1.03 | 443,373 | 98.2% |
+| **4** | Subword | 0.5695 | 1.484 | 2.37 | 212,713 | 43.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `æмæ xviii та сæ арараты хох ирыстоны ацыдис райгуырдысты мамсыраты темырболат турчы сахар конгойы ко...`
+2. `у сахар свердловсчы сергийы чызг куы сты 10 48 кандемир kandemir 31 матчы дæр ныззаууат азы`
+3. `йæ административон центр у индонезийы амалхъомады архайын амарыныл йæ линник æмæ дæр кодтой æмæ дугт...`
+
+**Context Size 2:**
+
+1. `хуссар ирыстоны закъон ирон мыггагон ирон иууон номхыгъд сты ирон мыггаг æмæ уырым пага тотыкк абоны...`
+2. `у сахар челябинсчы ис ашайы районы административон центр азы онг уыцы хуыдтой уыцы хонын байдыдтой у...`
+3. `административон центр у сахар ис брабанты провинцийы административон центр аквитанийы`
+
+**Context Size 3:**
+
+1. `административон центр у чарльз таун вирджинийы`
+2. `з д ирон 2 аг рауагъд цхинвал республика 372 с сты ирон мыггаг хъантемыраты алибег æмæ йæ династи`
+3. `йæ административон центр у худжанд`
+
+**Context Size 4:**
+
+1. `йæ административон центр у нагасаки`
+2. `аг рауагъд цхинвал республика 372 с ныхас сост р с кантемирова наукон ред джусойты нафи ир 263 ф сты`
+3. `2 аг рауагъд цхинвал республика 372 с сты ирон мыггаг æмæ сты мыггаг уыд сæ дæр уыд чеселты комы`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_рд._уысистры_р_`
+2. `ардтисовис.и)_лц`
+3. `ы_ф_аданом_цин_с`
+
+**Context Size 2:**
+
+1. `ы_мыгуый_æм_казон`
+2. `он_(райгуырыстор,`
+3. `._аслогонограты_а`
+
+**Context Size 3:**
+
+1. `он_дих_кодтой,_уым`
+2. `ты_империйы_седерл`
+3. `_—_458_с._истас_ко`
+
+**Context Size 4:**
+
+1. `æмæ_симиля»_«жизнью`
+2. `_æмæ_георгийы_азы_2`
+3. `оны_районы,_созыры_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 98.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (212,713 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 | 26,680 |
+| Total Tokens | 558,043 |
+| Mean Frequency | 20.92 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 234.02 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | æмæ | 20,225 |
+| 2 | у | 19,112 |
+| 3 | йæ | 10,772 |
+| 4 | азы | 9,341 |
+| 5 | ирыстоны | 6,756 |
+| 6 | ирон | 6,567 |
+| 7 | сахар | 4,914 |
+| 8 | ис | 4,518 |
+| 9 | и | 4,222 |
+| 10 | районы | 4,121 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | вазыгджындӕр | 2 |
+| 2 | мардан | 2 |
+| 3 | бернет | 2 |
+| 4 | бастроп | 2 |
+| 5 | ллано | 2 |
+| 6 | мейсон | 2 |
+| 7 | лампасас | 2 |
+| 8 | накодочес | 2 |
+| 9 | чирцъинайы | 2 |
+| 10 | какета | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.1191 |
+| R² (Goodness of Fit) | 0.996097 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 41.2% |
+| Top 1,000 | 70.4% |
+| Top 5,000 | 86.6% |
+| Top 10,000 | 92.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9961 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 41.2% of corpus
+- **Long Tail:** 16,680 words needed for remaining 7.6% 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 | 0.7990 🏆 | 0.3569 | N/A | N/A |
+| **mono_64d** | 64 | 0.5337 | 0.3206 | N/A | N/A |
+| **mono_128d** | 128 | 0.1178 | 0.3107 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7990 | 0.3615 | 0.0140 | 0.1100 |
+| **aligned_64d** | 64 | 0.5337 | 0.3182 | 0.0180 | 0.1460 |
+| **aligned_128d** | 128 | 0.1178 | 0.3127 | 0.0540 | 0.2240 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7990 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3301. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 5.4% 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 | **1.006** | 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 |
+|--------|----------|
+| `-с` | сылгоймаджы, суарх, соня |
+| `-а` | архипелаг, абалц, аллонты |
+| `-к` | кларк, куры, кады |
+| `-б` | биту, балаш, бидары |
+| `-м` | медзилаборце, моисеев, мамытты |
+| `-д` | джебетъа, дзау, дауыраты |
+| `-т` | тырсыгом, трансильвани, тъилиана |
+| `-ба` | балаш, бакусыны, батиста |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ы` | сылгоймаджы, рауайы, ратдзысты |
+| `-н` | гурджаан, актуалон, оккупацион |
+| `-йы` | рауайы, пиерийы, росариойы |
+| `-ты` | ратдзысты, аллонты, африканерты |
+| `-он` | актуалон, оккупацион, микрофинансон |
+| `-и` | лариджани, эстели, трансильвани |
+| `-а` | пирканмаа, джебетъа, сота |
+| `-й` | владикавказский, калий, рафыстой |
+
+### 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 |
+|------|----------|------------------|----------|
+| `дтой` | 2.00x | 22 contexts | радтой, ардтой, уыдтой |
+| `ысты` | 1.84x | 25 contexts | ыстыр, фысты, мысты |
+| `кодт` | 1.92x | 21 contexts | кодта, кодтой, кодтон |
+| `ахар` | 1.92x | 20 contexts | сахар, махар, шахар |
+| `дыст` | 1.94x | 18 contexts | цыдысты, уадысты, равдыст |
+| `ыдис` | 1.90x | 17 contexts | уыдис, цыдис, ссыдис |
+| `цент` | 1.89x | 17 contexts | центр, центы, центра |
+| `райо` | 2.16x | 10 contexts | район, райони, районе |
+| `гуыр` | 1.50x | 27 contexts | гуыры, гуырд, агуырд |
+| `айон` | 1.83x | 14 contexts | хайон, район, райони |
+| `истр` | 1.91x | 12 contexts | бистра, истрийы, министр |
+| `ентр` | 2.07x | 8 contexts | центр, центра, центре |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-к` | `-ы` | 203 words | конфедерацийы, кизилюрты |
+| `-с` | `-ы` | 138 words | сконды, слесыры |
+| `-б` | `-ы` | 126 words | банымайыны, бицъоты |
+| `-а` | `-ы` | 118 words | алексейы, азары |
+| `-м` | `-ы` | 112 words | малайзийы, муганы |
+| `-д` | `-ы` | 105 words | димитровы, дзидзайы |
+| `-т` | `-ы` | 100 words | тлаты, туркманчайы |
+| `-г` | `-ы` | 97 words | гроднойы, габысаты |
+| `-п` | `-ы` | 69 words | перуйы, парадоксы |
+| `-к` | `-ты` | 64 words | кизилюрты, къониаты |
+
+### 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 |
+|------|-----------------|------------|------|
+| музыканты | **`музыка-н-ты`** | 7.5 | `н` |
+| комплексной | **`комплекс-н-ой`** | 7.5 | `н` |
+| фыртыфырт | **`фыртыфы-р-т`** | 7.5 | `р` |
+| бердзейнаг | **`бердзей-н-аг`** | 7.5 | `н` |
+| беджызаты | **`беджыз-а-ты`** | 7.5 | `а` |
+| административно | **`административ-н-о`** | 7.5 | `н` |
+| чырыстонады | **`чырыстон-ад-ы`** | 6.0 | `чырыстон` |
+| тырысатыл | **`тырыса-ты-л`** | 6.0 | `тырыса` |
+| куыстадон | **`куыст-ад-он`** | 6.0 | `куыст` |
+| кастилиаг | **`касти-ли-аг`** | 6.0 | `касти` |
+| александрияйы | **`александр-ия-йы`** | 6.0 | `александр` |
+| дзаумайыл | **`дзаума-йы-л`** | 6.0 | `дзаума` |
+| рынчынтыл | **`рынчын-ты-л`** | 6.0 | `рынчын` |
+| информационные | **`информацион-ны-е`** | 6.0 | `информацион` |
+| сальвадораг | **`сальвадор-аг`** | 4.5 | `сальвадор` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Ossetic 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 (3.90x) |
+| N-gram | **2-gram** | Lowest perplexity (406) |
+| Markov | **Context-4** | Highest predictability (98.2%) |
+| 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 17:09:46*

models/embeddings/aligned/os_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5ee7fcabc30f4180048bc6be38044ea73cc80a6f76e289d8f634c79edb37ef3
+size 1035108248

models/embeddings/aligned/os_128d.meta.json

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

models/embeddings/aligned/os_128d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc5aedfad86b9ed3acc47555886d760ba403ed22e28106748e74fb5e1460a08f
+size 65664

models/embeddings/aligned/os_128d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "os",
+  "dimension": 128,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 1423,
+  "vocab_size": 10615
+}

models/embeddings/aligned/os_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5e3972ca9599203faf6667e3445264592750fc3598136fe4c1114c26ca5c6a3
+size 258955928

models/embeddings/aligned/os_32d.meta.json

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

models/embeddings/aligned/os_32d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0de140a3232ced14be95c7453c90d1f770a8015a4d9d32670c072742170bcd8
+size 4224

models/embeddings/aligned/os_32d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "os",
+  "dimension": 32,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 1423,
+  "vocab_size": 10615
+}

models/embeddings/aligned/os_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb4dce22a8954359651a34bc86dc4bf2b994337fa8d0e302a4113bdcb1e08cf8
+size 517673368

models/embeddings/aligned/os_64d.meta.json

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

models/embeddings/aligned/os_64d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3c99b1748a257d89385bef95240f0f89bd68dff1666e1e84d358098428c596e
+size 16512

models/embeddings/aligned/os_64d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "os",
+  "dimension": 64,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 1423,
+  "vocab_size": 10615
+}

models/embeddings/monolingual/os_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5ee7fcabc30f4180048bc6be38044ea73cc80a6f76e289d8f634c79edb37ef3
+size 1035108248

models/embeddings/monolingual/os_128d.meta.json

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

models/embeddings/monolingual/os_128d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "os",
+  "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": 10615
+}

models/embeddings/monolingual/os_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5e3972ca9599203faf6667e3445264592750fc3598136fe4c1114c26ca5c6a3
+size 258955928

models/embeddings/monolingual/os_32d.meta.json

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

models/embeddings/monolingual/os_32d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "os",
+  "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": 10615
+}

models/embeddings/monolingual/os_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb4dce22a8954359651a34bc86dc4bf2b994337fa8d0e302a4113bdcb1e08cf8
+size 517673368

models/embeddings/monolingual/os_64d.meta.json

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

models/embeddings/monolingual/os_64d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "os",
+  "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": 10615
+}

models/subword_markov/os_markov_ctx1_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31b241f868133767c020711658cb36496307986a245e4aeeabab7f1ddb14d07c
+size 71744

models/subword_markov/os_markov_ctx1_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "subword",
+  "language": "os",
+  "unique_contexts": 927,
+  "total_transitions": 4015768
+}

models/subword_markov/os_markov_ctx2_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2c2c18517b3643bcefe0ee3e58e3c0ae83fed77ac83cde2fb4cf44fa22460cf
+size 430160

models/subword_markov/os_markov_ctx2_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "subword",
+  "language": "os",
+  "unique_contexts": 9001,
+  "total_transitions": 4000207
+}

models/subword_markov/os_markov_ctx3_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c71b2fdf4dcbf2d37a51bbbfe30deb49ce7935c2bd633ed8e4bda4459152d10a
+size 1552783

models/subword_markov/os_markov_ctx3_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 3,
+  "variant": "subword",
+  "language": "os",
+  "unique_contexts": 55801,
+  "total_transitions": 3984646
+}

models/subword_markov/os_markov_ctx4_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be9cd33f3eb2bb862afee32ec59dcda97be739267de61f5e3cc91aa78368ee33
+size 4488774

models/subword_markov/os_markov_ctx4_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 4,
+  "variant": "subword",
+  "language": "os",
+  "unique_contexts": 212713,
+  "total_transitions": 3969085
+}

models/subword_ngram/os_2gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57a3acbb9ca2acc1318ad18f47aee618efde46feb5844978df6fb1e8b3bc6f51
+size 53173

models/subword_ngram/os_2gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 2,
+  "variant": "subword",
+  "language": "os",
+  "unique_ngrams": 3916,
+  "total_ngrams": 4015768
+}

models/subword_ngram/os_3gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4af9d138afa5159702f701972b8c070909ffbea7eb7560c3914526fb0560dc9a
+size 372150

models/subword_ngram/os_3gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 3,
+  "variant": "subword",
+  "language": "os",
+  "unique_ngrams": 27926,
+  "total_ngrams": 4000207
+}

models/subword_ngram/os_4gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:281a7e42dd1005d13312b3ab8c454310fad95753c56a01ea5ec2ff3e566aea72
+size 1483102

models/subword_ngram/os_4gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 4,
+  "variant": "subword",
+  "language": "os",
+  "unique_ngrams": 117148,
+  "total_ngrams": 3984646
+}

models/subword_ngram/os_5gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1298c5b1138d6f07309700d44402a77517f3718dffe5aecf29634968b90ef386
+size 2970475

models/subword_ngram/os_5gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "os",
+  "unique_ngrams": 219588,
+  "total_ngrams": 3969085
+}

models/tokenizer/os_tokenizer_16k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c796070e69f577eb318e6d81b4b66ba0e216321b39ff0c90a9a8d5ce5e2117ee
+size 574645

models/tokenizer/os_tokenizer_32k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1331429364e96e8f63c1efb9e466cfd5f99c85b6270da406dae6dfbf80f45c20
+size 912099

models/tokenizer/os_tokenizer_64k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4cc925bb2db729958b0657b1f2467c1310ca60e30b7602c70a06eb708247c805
+size 1655607

models/tokenizer/os_tokenizer_8k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00226e3bbf42238f3aa52879518c4d26949309d94368b4f77a240a99872c941c
+size 404144

models/vocabulary/os_vocabulary.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67a72c9488355c67ee017b86087df510c0de97c06bfa2377b4d748ce42172913
+size 496207

models/vocabulary/os_vocabulary_metadata.json

@@ -0,0 +1,17 @@
+{
+  "language": "os",
+  "vocabulary_size": 26680,
+  "variant": "full",
+  "statistics": {
+    "type_token_ratio": 0.11553743081852608,
+    "coverage": {
+      "top_100": 0.38282052349049145,
+      "top_1000": 0.6537688818609296,
+      "top_5000": 0.804019807748325,
+      "top_10000": 0.8580466896924805
+    },
+    "hapax_count": 42732,
+    "hapax_ratio": 0.6156284215985709,
+    "total_documents": 15561
+  }
+}

models/word_markov/os_markov_ctx1_word.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5be5c64f4ce151965532d4bf3e263fd170153261779777eb6a91262927c60be5
+size 2868024