Upload all models and assets for tr (latest)

.gitattributes

@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+visualizations/embedding_similarity.png filter=lfs diff=lfs merge=lfs -text
+visualizations/embedding_tsne_multilingual.png filter=lfs diff=lfs merge=lfs -text
+visualizations/performance_dashboard.png filter=lfs diff=lfs merge=lfs -text
+visualizations/position_encoding_comparison.png filter=lfs diff=lfs merge=lfs -text
+visualizations/tsne_sentences.png filter=lfs diff=lfs merge=lfs -text
+visualizations/tsne_words.png filter=lfs diff=lfs merge=lfs -text
+visualizations/zipf_law.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,773 @@
+---
+language: tr
+language_name: Turkish
+language_family: turkic_oghuz
+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-turkic_oghuz
+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: 4.777
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7797
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-18
+---
+
+# Turkish - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Turkish** 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** | 3.702x | 3.70 | 0.0636% | 2,131,433 |
+| **16k** | 4.112x | 4.11 | 0.0706% | 1,918,952 |
+| **32k** | 4.477x | 4.48 | 0.0769% | 1,762,504 |
+| **64k** | 4.777x 🏆 | 4.78 | 0.0820% | 1,651,752 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Bubiacoris Harpactorini oymağına bağlı bir böcek cinsidir. Kaynakça Dış bağlantı...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁bu bi ac or is ▁harp ac tor ini ▁oy ... (+13 more)` | 23 |
+| 16k | `▁bu bi ac or is ▁harp ac tor ini ▁oy ... (+12 more)` | 22 |
+| 32k | `▁bu bi ac or is ▁harp ac tor ini ▁oy ... (+12 more)` | 22 |
+| 64k | `▁bu bi ac oris ▁harp actor ini ▁oym ağına ▁bağlı ... (+9 more)` | 19 |
+
+**Sample 2:** `Monobothrium, Caryophyllaeidae familyasına bağlı bir hayvan cinsidir. Kaynakça D...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁mon ob oth ri um , ▁car y op hy ... (+16 more)` | 26 |
+| 16k | `▁mon ob oth ri um , ▁car y ophy l ... (+14 more)` | 24 |
+| 32k | `▁mon ob oth rium , ▁car y ophy l la ... (+13 more)` | 23 |
+| 64k | `▁mon ob oth rium , ▁cary ophyl la e idae ... (+11 more)` | 21 |
+
+**Sample 3:** `Spilophora, Spilophorini oymağına bağlı bir hayvan cinsidir. Kaynakça Dış bağlan...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁sp il oph ora , ▁sp il oph or ini ... (+14 more)` | 24 |
+| 16k | `▁sp il oph ora , ▁sp il oph or ini ... (+13 more)` | 23 |
+| 32k | `▁sp il oph ora , ▁sp il oph or ini ... (+13 more)` | 23 |
+| 64k | `▁sp il ophora , ▁sp il oph or ini ▁oym ... (+11 more)` | 21 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.777x compression
+- **Lowest UNK Rate:** 8k with 0.0636% 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 | 517,762 | 18.98 | 3,151,475 | 4.5% | 12.1% |
+| **2-gram** | Subword | 369 🏆 | 8.53 | 40,485 | 60.0% | 98.4% |
+| **3-gram** | Word | 1,234,358 | 20.24 | 4,813,201 | 4.1% | 9.2% |
+| **3-gram** | Subword | 3,553 | 11.79 | 274,497 | 20.2% | 62.5% |
+| **4-gram** | Word | 2,217,879 | 21.08 | 7,386,404 | 3.8% | 8.3% |
+| **4-gram** | Subword | 22,183 | 14.44 | 1,542,206 | 9.2% | 31.9% |
+| **5-gram** | Word | 1,582,796 | 20.59 | 5,151,376 | 4.3% | 9.3% |
+| **5-gram** | Subword | 97,109 | 16.57 | 5,359,470 | 5.4% | 19.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `dış bağlantılar` | 350,729 |
+| 2 | `kaynakça dış` | 266,247 |
+| 3 | `bağlı bir` | 174,377 |
+| 4 | `daha sonra` | 94,043 |
+| 5 | `ya da` | 87,702 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kaynakça dış bağlantılar` | 265,490 |
+| 2 | `cinsine bağlı bir` | 66,200 |
+| 3 | `türüdür kaynakça dış` | 54,307 |
+| 4 | `bağlı bir hayvan` | 46,777 |
+| 5 | `amerika birleşik devletleri` | 46,424 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `türüdür kaynakça dış bağlantılar` | 54,307 |
+| 2 | `kaynakça dış bağlantılar tanımlanan` | 39,619 |
+| 3 | `dış bağlantılar tanımlanan taksonlar` | 38,187 |
+| 4 | `bağlı bir bitki türüdür` | 34,768 |
+| 5 | `cinsine bağlı bir bitki` | 34,767 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kaynakça dış bağlantılar tanımlanan taksonlar` | 38,177 |
+| 2 | `cinsine bağlı bir bitki türüdür` | 34,766 |
+| 3 | `bitki türüdür kaynakça dış bağlantılar` | 33,470 |
+| 4 | `bir bitki türüdür kaynakça dış` | 33,468 |
+| 5 | `bağlı bir bitki türüdür kaynakça` | 33,157 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n _` | 17,593,330 |
+| 2 | `a r` | 17,219,363 |
+| 3 | `a n` | 15,720,144 |
+| 4 | `e _` | 15,698,088 |
+| 5 | `l a` | 14,911,706 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l a r` | 7,165,653 |
+| 2 | `l e r` | 5,383,393 |
+| 3 | `a n _` | 5,370,904 |
+| 4 | `e r i` | 4,812,356 |
+| 5 | `_ v e` | 4,389,139 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ v e _` | 3,553,259 |
+| 2 | `_ b i r` | 3,136,915 |
+| 3 | `l a r ı` | 3,011,298 |
+| 4 | `l e r i` | 2,857,164 |
+| 5 | `ı n d a` | 2,744,063 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ b i r _` | 2,224,652 |
+| 2 | `ı n d a _` | 1,537,643 |
+| 3 | `l a r ı _` | 1,490,277 |
+| 4 | `l e r i _` | 1,341,301 |
+| 5 | `l a r ı n` | 1,195,116 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 369
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~20% 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 | 0.9720 | 1.962 | 14.51 | 3,106,535 | 2.8% |
+| **1** | Subword | 1.4308 | 2.696 | 11.15 | 15,908 | 0.0% |
+| **2** | Word | 0.3547 | 1.279 | 2.15 | 45,020,523 | 64.5% |
+| **2** | Subword | 0.6247 | 1.542 | 4.03 | 177,402 | 37.5% |
+| **3** | Word | 0.1200 | 1.087 | 1.24 | 96,715,794 | 88.0% |
+| **3** | Subword | 0.6614 | 1.582 | 3.95 | 714,448 | 33.9% |
+| **4** | Word | 0.0421 🏆 | 1.030 | 1.07 | 119,918,003 | 95.8% |
+| **4** | Subword | 0.6640 | 1.584 | 3.51 | 2,821,176 | 33.6% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ve ağır bir şehirdir visayas bikol vikipedi maddeleri toplam 4 sumon olarak bilinmekteydi ve mevcut ...`
+2. `bir puan durumu the protein kodlamayan genlerin etkisinin bu yolla temin etmek zorunda kalan ve yazı...`
+3. `olarak başladı bir şekilde sona eren i hakîkat mecmuasında yayınlamıştır aktör adaylığı da kariyerin...`
+
+**Context Size 2:**
+
+1. `dış bağlantılar tanımlanan taksonlar j currie tarafından adlandırılmış taksonlar tanımlanan bitkiler...`
+2. `kaynakça dış bağlantılar dünya su forumu bakanlar arası toplantılarına komitelerine konseylerine ve ...`
+3. `bağlı bir beldeye dönüştü coğrafya köy adıyaman il merkezine 17 km uzaklıktadır nüfus yıllara göre m...`
+
+**Context Size 3:**
+
+1. `kaynakça dış bağlantılar tbmm internet sitesinde nilhan ayan doğumlular kadın milletvekilleri üniver...`
+2. `cinsine bağlı bir bitki türüdür kaynakça dış bağlantılar tanımlanan taksonlar weed fowler tarafından...`
+3. `türüdür kaynakça dış bağlantılar tanımlanan taksonlar jakob kaup tarafından adlandırılmış taksonlar ...`
+
+**Context Size 4:**
+
+1. `kaynakça dış bağlantılar tanımlanan taksonlar jakob kaup tarafından adlandırılmış taksonlar tanımlan...`
+2. `bağlı bir bitki türüdür kaynakça dış bağlantılar florası florası florası florası florası florası flo...`
+3. `cinsine bağlı bir bitki türüdür kaynakça dış bağlantılar florası tanımlanan bitkiler`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_dharirışman_ver`
+2. `aoktasi_k_onanl_`
+3. `er,_olurdık,_ve_`
+
+**Context Size 2:**
+
+1. `n_başmarımca:_"he`
+2. `ar_sörlenişiklar_`
+3. `an_pkkande_i̇ste_d`
+
+**Context Size 3:**
+
+1. `ları_şarkan_van_jo`
+2. `an_aman_kada,_eyal`
+3. `ler_için_iler_açta`
+
+**Context Size 4:**
+
+1. `_ve_kendi_hükûmet-e`
+2. `_biridir._dışı_örne`
+3. `larına_ve_inşasında`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 95.8% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (2,821,176 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 | 1,401,382 |
+| Total Tokens | 145,506,395 |
+| Mean Frequency | 103.83 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 4551.48 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ve | 3,563,467 |
+| 2 | bir | 2,239,393 |
+| 3 | olarak | 863,214 |
+| 4 | da | 854,555 |
+| 5 | bu | 838,172 |
+| 6 | ile | 733,358 |
+| 7 | de | 723,655 |
+| 8 | 1 | 660,785 |
+| 9 | için | 614,599 |
+| 10 | the | 514,508 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | rawvixen | 2 |
+| 2 | groupweb | 2 |
+| 3 | castorum | 2 |
+| 4 | othonlular | 2 |
+| 5 | pnujsciewarty | 2 |
+| 6 | mensuris | 2 |
+| 7 | ponderibus | 2 |
+| 8 | hexaplaric | 2 |
+| 9 | titanozorların | 2 |
+| 10 | noëp | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9525 |
+| R² (Goodness of Fit) | 0.994421 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 21.0% |
+| Top 1,000 | 44.4% |
+| Top 5,000 | 63.7% |
+| Top 10,000 | 71.8% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9944 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 21.0% of corpus
+- **Long Tail:** 1,391,382 words needed for remaining 28.2% 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 | 0.7797 🏆 | 0.3582 | N/A | N/A |
+| **mono_64d** | 64 | 0.7747 | 0.2849 | N/A | N/A |
+| **mono_128d** | 128 | 0.7155 | 0.2346 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7797 | 0.3781 | 0.4900 | 0.8140 |
+| **aligned_64d** | 64 | 0.7747 | 0.2854 | 0.6780 | 0.9160 |
+| **aligned_128d** | 128 | 0.7155 | 0.2378 | 0.7720 | 0.9880 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7797 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2965. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 77.2% 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 | **-0.527** | 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 |
+|--------|----------|
+| `-a` | alexandrovna, almazdan, alayoğlu |
+| `-s` | saldırganlara, strimonas, stefańska |
+| `-m` | marmarás, maiori, makura |
+| `-k` | kentinte, kolonisiydiler, khmaer |
+| `-ma` | marmarás, maiori, makura |
+| `-t` | trioedd, teräsbetoni, tükettiklerini |
+| `-b` | brakana, burgard, blaxland |
+| `-ka` | karahasanuşağı, kabrinin, kapilvastu |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | eklentilerinden, i̇smailîliğin, almazdan |
+| `-a` | brakana, saldırganlara, göremiyorsa |
+| `-r` | kolonisiydiler, dieringer, khmaer |
+| `-e` | økonomiske, kentinte, dokuzsele |
+| `-i` | zemberekli, eğilimlileri, yükselenleri |
+| `-s` | marmarás, cortos, strimonas |
+| `-en` | eklentilerinden, eyatlerinden, bezden |
+| `-an` | almazdan, zayıfladıktan, sawaiyan |
+
+### 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 |
+|------|----------|------------------|----------|
+| `utbo` | 2.90x | 41 contexts | futbol, futboll, futbola |
+| `tbol` | 2.46x | 62 contexts | tboli, fotbol, futbol |
+| `futb` | 2.98x | 27 contexts | futbol, futboll, futbola |
+| `mışt` | 1.90x | 153 contexts | mıştı, mıştır, aşmıştı |
+| `mler` | 1.60x | 310 contexts | imler, emler, dumler |
+| `ılar` | 1.44x | 549 contexts | yılar, kılar, cılar |
+| `bolc` | 2.73x | 24 contexts | bolca, bolcom, bolcan |
+| `nakç` | 2.57x | 29 contexts | inakçı, oynakçı, konakçı |
+| `sınd` | 1.75x | 125 contexts | sında, sınde, sındı |
+| `ıştı` | 1.59x | 201 contexts | kıştı, mıştı, kıştım |
+| `bağl` | 2.21x | 44 contexts | bağli, bağlı, bağla |
+| `ılın` | 1.54x | 139 contexts | yılın, kılın, ılında |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-k` | `-n` | 119 words | kelamcıların, kressentein |
+| `-s` | `-n` | 96 words | swahn, scgn |
+| `-s` | `-a` | 93 words | shibahara, stictigastra |
+| `-a` | `-a` | 92 words | alaa, ayırdığında |
+| `-s` | `-r` | 84 words | sanatsaldır, sodomiler |
+| `-a` | `-n` | 84 words | aowin, avron |
+| `-k` | `-a` | 78 words | kasnaklara, konaklamaya |
+| `-k` | `-r` | 75 words | kürsüler, kuşatılmasıdır |
+| `-s` | `-e` | 72 words | salomonmadeleine, sozialtechnologie |
+| `-d` | `-n` | 71 words | destanıhaldun, digitalisation |
+
+### 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 |
+|------|-----------------|------------|------|
+| makinalarına | **`makinaları-n-a`** | 7.5 | `n` |
+| uzaklaşırken | **`uzaklaşır-k-en`** | 7.5 | `k` |
+| kavuşacağına | **`kavuşacağı-n-a`** | 7.5 | `n` |
+| kurslarına | **`kursları-n-a`** | 7.5 | `n` |
+| willdenowia | **`willdenow-i-a`** | 7.5 | `i` |
+| aktarımdan | **`aktarım-da-n`** | 7.5 | `da` |
+| nicaeensis | **`nicaeen-s-is`** | 7.5 | `s` |
+| falankstaki | **`falankst-a-ki`** | 7.5 | `a` |
+| çekilirken | **`çekilir-k-en`** | 7.5 | `k` |
+| toprakküre | **`toprakkü-r-e`** | 7.5 | `r` |
+| luvicedeki | **`luvice-de-ki`** | 7.5 | `de` |
+| irtifadaki | **`irtifad-a-ki`** | 7.5 | `a` |
+| öschelbronn | **`öschelbro-n-n`** | 7.5 | `n` |
+| ticketları | **`ticketl-a-rı`** | 7.5 | `a` |
+| çalışarak | **`çalışa-ra-k`** | 7.5 | `ra` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Turkish 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 (4.78x) |
+| N-gram | **2-gram** | Lowest perplexity (369) |
+| Markov | **Context-4** | Highest predictability (95.8%) |
+| 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: 2026-01-18 06:49:15*

models/embeddings/aligned/tr_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc6292266e08d72d7337e8e9befc86abda9d56acc911bfb801dba158f0124bea
+size 2120798920

models/embeddings/aligned/tr_128d.meta.json

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

models/embeddings/aligned/tr_128d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d91b0724cfbce2e557606227e3651fa130fa84f0dcc7ddad1b6f2ca4eff142f
+size 65664

models/embeddings/aligned/tr_128d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "tr",
+  "dimension": 128,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 249084,
+  "vocab_size": 1051495
+}

models/embeddings/aligned/tr_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a92b39e53bdfab37f20b2d9e5b5e7510084783d62286c2b3ad2306ee0a4acbd
+size 545250760

models/embeddings/aligned/tr_32d.meta.json

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

models/embeddings/aligned/tr_32d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f44cc6b43dfabf965107ff67beb72c7ee3bbe7c538b254f403e5bd9701028f3
+size 4224

models/embeddings/aligned/tr_32d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "tr",
+  "dimension": 32,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 249084,
+  "vocab_size": 1051495
+}

models/embeddings/aligned/tr_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:578503c5eed70f56deb69cd072297841a1f1b8aac413d7dcff44095d6f3021ce
+size 1070433480

models/embeddings/aligned/tr_64d.meta.json

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

models/embeddings/aligned/tr_64d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2b861194f719b5419973c524953da0fba82e7e4e059b895622dd1a2ef1d0ad9
+size 16512

models/embeddings/aligned/tr_64d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "tr",
+  "dimension": 64,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 249084,
+  "vocab_size": 1051495
+}

models/embeddings/monolingual/tr_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc6292266e08d72d7337e8e9befc86abda9d56acc911bfb801dba158f0124bea
+size 2120798920

models/embeddings/monolingual/tr_128d.meta.json

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

models/embeddings/monolingual/tr_128d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "tr",
+  "dimension": 128,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 128,
+    "threads": 32
+  },
+  "vocab_size": 1051495
+}

models/embeddings/monolingual/tr_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a92b39e53bdfab37f20b2d9e5b5e7510084783d62286c2b3ad2306ee0a4acbd
+size 545250760

models/embeddings/monolingual/tr_32d.meta.json

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

models/embeddings/monolingual/tr_32d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "tr",
+  "dimension": 32,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 32,
+    "threads": 32
+  },
+  "vocab_size": 1051495
+}

models/embeddings/monolingual/tr_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:578503c5eed70f56deb69cd072297841a1f1b8aac413d7dcff44095d6f3021ce
+size 1070433480

models/embeddings/monolingual/tr_64d.meta.json

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

models/embeddings/monolingual/tr_64d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "tr",
+  "dimension": 64,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 64,
+    "threads": 32
+  },
+  "vocab_size": 1051495
+}

models/subword_markov/tr_markov_ctx1_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:611dbc8942a8874e34dc1d1f4fd4c9d1fb90b6fad4952264f27f0493e0070dfc
+size 1077396

models/subword_markov/tr_markov_ctx1_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "subword",
+  "language": "tr",
+  "unique_contexts": 15908,
+  "total_transitions": 1062642919
+}

models/subword_markov/tr_markov_ctx2_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6869246e53c4e12d91ada77b00216dc115f5fea7bd22cc23937b11196d965385
+size 5742727

models/subword_markov/tr_markov_ctx2_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "subword",
+  "language": "tr",
+  "unique_contexts": 177402,
+  "total_transitions": 1061990215
+}

models/subword_markov/tr_markov_ctx3_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d26ac521b40c3dd4855b4a091abd4100778f66fe33159bd65721b7a218a8c0dc
+size 22498555

models/subword_markov/tr_markov_ctx3_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 3,
+  "variant": "subword",
+  "language": "tr",
+  "unique_contexts": 714448,
+  "total_transitions": 1061337511
+}

models/subword_markov/tr_markov_ctx4_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e6343a2ebdb920e909acbbc51d61020695efd261ed1741ca10a5bd24ba034ec
+size 81401229

models/subword_markov/tr_markov_ctx4_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 4,
+  "variant": "subword",
+  "language": "tr",
+  "unique_contexts": 2821176,
+  "total_transitions": 1060684807
+}

models/subword_ngram/tr_2gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:200e9eb2535746ffa276f1724401206a8af27325fec7e9da1ca1866fc5e7e4e6
+size 564124

models/subword_ngram/tr_2gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 2,
+  "variant": "subword",
+  "language": "tr",
+  "unique_ngrams": 40485,
+  "total_ngrams": 1062642919
+}

models/subword_ngram/tr_3gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be1a27a151d7a8e1bf3875bae68b5a895b1bf354d4393e23e374772be8b385b5
+size 3465447

models/subword_ngram/tr_3gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 3,
+  "variant": "subword",
+  "language": "tr",
+  "unique_ngrams": 274497,
+  "total_ngrams": 1061990215
+}

models/subword_ngram/tr_4gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:04e79e0c515de676b8c43d1dfcabd7907a60311f229a1fa23a51d1a41cd3d8bd
+size 19159978

models/subword_ngram/tr_4gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 4,
+  "variant": "subword",
+  "language": "tr",
+  "unique_ngrams": 1542206,
+  "total_ngrams": 1061337511
+}

models/subword_ngram/tr_5gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8900b80047ef646ecbc4744ba206c3a57bbd626bb2eec08131a5e073e4682de
+size 66499260

models/subword_ngram/tr_5gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "tr",
+  "unique_ngrams": 5359470,
+  "total_ngrams": 1060684807
+}

models/tokenizer/tr_tokenizer_16k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:861bc6f215952fcfe3d94434cdaa427b313d383c302a30e5386d83b11882d9f3
+size 517262

models/tokenizer/tr_tokenizer_32k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c58d8f946966804a80e289a05350efa163ab24553b39944c0ba035646b864c67
+size 807988

models/tokenizer/tr_tokenizer_64k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98bc96be94d29cc943f7b9eb77cf65c2a2b7a982e4d4aebbc890d78558110348
+size 1404276

models/tokenizer/tr_tokenizer_8k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:adaf6ff7409114e9171b28808d79ce7a50e4147d5bb5a0d0a9c684f930970b1d
+size 376819

models/vocabulary/tr_vocabulary.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f58cb5069bc96b4109555583804e017393890ecabe3cbde3b1c8cd24b03af0ad
+size 21750175

models/vocabulary/tr_vocabulary_metadata.json

@@ -0,0 +1,17 @@
+{
+  "language": "tr",
+  "vocabulary_size": 1401382,
+  "variant": "full",
+  "statistics": {
+    "type_token_ratio": 0.021105089211041816,
+    "coverage": {
+      "top_100": 0.20771422648383928,
+      "top_1000": 0.43925667738323443,
+      "top_5000": 0.6300293833514884,
+      "top_10000": 0.7100298607584355
+    },
+    "hapax_count": 1705539,
+    "hapax_ratio": 0.5489482996188187,
+    "total_documents": 652704
+  }
+}

models/vocabulary/tr_vocabulary_top.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36bc1bd029ce6ea07765cb107f882a17181c93fecdb6ed5c74464d98617df582
+size 15467216