Upload all models and assets for tig (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 +1,758 @@
+---
+language: tig
+language_name: Tigre
+language_family: semitic_ethiopic
+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-semitic_ethiopic
+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: 2.463
+  - name: best_isotropy
+    type: isotropy
+    value: 0.6615
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Tigre - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Tigre** 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** | 2.305x | 2.31 | 0.2982% | 879,983 |
+| **16k** | 2.463x 🏆 | 2.46 | 0.3185% | 823,793 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `አልአሚን ዐብደለጢፍ - ሰር-ዘመ ን እት ፈን እድሪስ መሐመድ ዐሊ ሐጂ ሕላይ - ወድ ባሸቂር፡ ሕላይ ሻም ሕላይ - ወድ ባሸቂር...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁አልአሚን ▁ዐብደለጢፍ ▁- ▁ሰር - ዘ መ ▁ን ▁እት ▁ፈን ... (+23 more)` | 33 |
+| 16k | `▁አልአሚን ▁ዐብደለጢፍ ▁- ▁ሰር - ዘመ ▁ን ▁እት ▁ፈን ▁እድሪስ ... (+17 more)` | 27 |
+
+**Sample 2:** `ብለዕ ወስታይ መንፈዐት ሐበት-አሰውዳ ምን ቡን አክል አዪ እግል ትስቴ ብከ ሐሊብ እንሰ ቀርፈ እከለት`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ብ ለዕ ▁ወ ስታ ይ ▁መንፈዐት ▁ሐበት - አሰውዳ ▁ምን ... (+10 more)` | 20 |
+| 16k | `▁ብለዕ ▁ወስታይ ▁መንፈዐት ▁ሐበት - አሰውዳ ▁ምን ▁ቡን ▁አክል ▁አዪ ... (+7 more)` | 17 |
+
+**Sample 3:** `ኣሜሪካ (እብ ኢንግሊዝ፥ United States of America) እት ቅብለት ኣሜሪካ ለትትረከብ ዐድ ተ። እብ ቅብለት ምስል ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ኣሜሪካ ▁( እብ ▁ኢ ንግሊዝ፥ ▁un ited ▁s t at ... (+42 more)` | 52 |
+| 16k | `▁ኣሜሪካ ▁( እብ ▁ኢንግሊዝ፥ ▁united ▁states ▁of ▁america ) ▁እት ... (+27 more)` | 37 |
+
+
+### Key Findings
+
+- **Best Compression:** 16k achieves 2.463x compression
+- **Lowest UNK Rate:** 8k with 0.2982% unknown tokens
+- **Trade-off:** Larger vocabularies improve compression but increase model size
+- **Recommendation:** 32k vocabulary provides optimal balance for production use
+
+---
+## 2. N-gram Model Evaluation
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 5,051 | 12.30 | 7,801 | 13.2% | 43.4% |
+| **2-gram** | Subword | 1,101 🏆 | 10.10 | 11,050 | 45.6% | 78.3% |
+| **3-gram** | Word | 5,036 | 12.30 | 6,311 | 11.0% | 37.6% |
+| **3-gram** | Subword | 8,481 | 13.05 | 53,840 | 19.1% | 46.6% |
+| **4-gram** | Word | 23,464 | 14.52 | 25,105 | 3.3% | 9.9% |
+| **4-gram** | Subword | 38,109 | 15.22 | 169,447 | 10.8% | 26.2% |
+| **5-gram** | Word | 21,344 | 14.38 | 22,370 | 3.0% | 9.1% |
+| **5-gram** | Subword | 76,266 | 16.22 | 232,751 | 6.8% | 19.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ምን ገብእ` | 530 |
+| 2 | `እት ልብል` | 428 |
+| 3 | `ሰበት ዐለ` | 355 |
+| 4 | `እንዴ ቤለ` | 325 |
+| 5 | `እሊ ህዬ` | 233 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ሓምድ እድሪስ ዓዋተ` | 108 |
+| 2 | `መነዘመት ምጅልስ ቅራን` | 88 |
+| 3 | `ሌጠ እንዴ ኢገብእ` | 87 |
+| 4 | `መቃበለት ምሰል ኬትባይ` | 72 |
+| 5 | `ቅብለት ምፍጋር ጸሓይ` | 70 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ቅብለት ምፍጋር ጸሓይ ሳሕል` | 63 |
+| 2 | `ሜራስ አድጋማት ትግሬ ክምኩም` | 49 |
+| 3 | `ክታብ ሜራስ አድጋማት ትግሬ` | 49 |
+| 4 | `አድጋማት ትግሬ ክምኩም ድግም` | 42 |
+| 5 | `እብ ዶ ር አሕመድ` | 41 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ክታብ ሜራስ አድጋማት ትግሬ ክምኩም` | 49 |
+| 2 | `ሜራስ አድጋማት ትግሬ ክምኩም ድግም` | 42 |
+| 3 | `እብ ዶ ር አሕመድ ሐሰን` | 41 |
+| 4 | `ዶ ር አሕመድ ሐሰን ድሕሊ` | 41 |
+| 5 | `እት ደንጎበ ናይ እሊ ምህሮ` | 31 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ እ` | 66,028 |
+| 2 | `ት _` | 57,371 |
+| 3 | `ል _` | 32,446 |
+| 4 | `_ ለ` | 31,481 |
+| 5 | `_ አ` | 28,736 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ እ ግ` | 14,781 |
+| 2 | `እ ግ ል` | 12,703 |
+| 3 | `ግ ል _` | 12,617 |
+| 4 | `_ እ ን` | 12,149 |
+| 5 | `_ እ ት` | 10,195 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `እ ግ ል _` | 12,107 |
+| 2 | `_ እ ግ ል` | 12,029 |
+| 3 | `እ ን ዴ _` | 9,201 |
+| 4 | `_ እ ን ዴ` | 9,099 |
+| 5 | `_ እ ት _` | 8,997 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ እ ግ ል _` | 11,475 |
+| 2 | `_ እ ን ዴ _` | 9,019 |
+| 3 | `_ ክ ም ሰ ል` | 3,323 |
+| 4 | `እ ግ ል _ ል` | 3,125 |
+| 5 | `ክ ም ሰ ል _` | 3,063 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 1,101
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~19% 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 | 0.7017 | 1.626 | 4.17 | 72,666 | 29.8% |
+| **1** | Subword | 2.7582 | 6.766 | 44.54 | 494 | 0.0% |
+| **2** | Word | 0.1717 | 1.126 | 1.32 | 302,688 | 82.8% |
+| **2** | Subword | 1.0638 | 2.090 | 6.10 | 21,999 | 0.0% |
+| **3** | Word | 0.0349 | 1.024 | 1.05 | 399,907 | 96.5% |
+| **3** | Subword | 0.6056 | 1.522 | 2.94 | 134,244 | 39.4% |
+| **4** | Word | 0.0091 🏆 | 1.006 | 1.01 | 418,313 | 99.1% |
+| **4** | Subword | 0.4078 | 1.327 | 1.90 | 395,253 | 59.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `እግል ሓበሮት ወምስል ገሮቡ እንዴ አግንዐ እሉ ሐንስ ተምነዎ ምሰል ሰብ ዐድ ከአፎ ለአምሩ አማኖም ቱ`
+2. `እት ሐበት አሰውደ ዲብ ኤስያት ወፓስፊክ 138 ብድሆ ናይ መትከባት ክም ትበጥር ገብአት አተላሌት ለሸሪጥ እሊ`
+3. `እንዴ ከዐ እቶም አውመ እተ ጽንሖ እብል ትሰአልኩዉ አይወ ገሌ መደት ሰህ ጀነራል ተድለ ዑቅቢት ዐለ`
+
+**Context Size 2:**
+
+1. `ምን ገብእ አባይካ እለ ሊበል እላ ሐሊብ ጅሉጥ ኢቲበለ ተ ለትብለከ እሊ ላኪን እተ ለደረርኩም ዲቡ ዐድ`
+2. `እት ልብል በሊስ ለገብእ እግሉ ሐዲስ አፍካር ምን ከምከሞት ላተ ይዓረፈ እት ደንጎበ ናይ እሊ ክታብ ለወሰከዩ`
+3. `ሰበት ዐለ መዓርክ እንዴ ��ዕለው ጎይላታት ድራሮም እት ልትበህል ልትህደግ እቡ እብ ምልሃዮም ልትጫፈሮ ወለአጎብሎ ዐለው ሰውረት`
+
+**Context Size 3:**
+
+1. `ሓምድ እድሪስ ዓዋተ ዩልዮ 196 ሓምድ እብራሂም መሐመድ ዐሊ ወዑመር ከራይ አብ ሓምድ ለትህየበ ተሕዚር አእንዴ ትቃወመው ሕነ`
+2. `መነዘመት ምጅልስ ቅራን እተሓድ አፍሪቀ አልጃምዐ አልዐረብየ ወሐምሲተን ዳይማት አንፋር ምጅልስ አምን እግል ልቀስብ ለዐለት ሰእየት ክምሰል ፈሽለት`
+3. `ሌጠ እንዴ ኢገብእ ዲብለ ዲብ እም ኩሉ ለዐለት መጥበዐት ልትጠበዕ ለዐለ ቱ ነፈዕ ወድ ዕትማን መን ቱ ነፈዕ`
+
+**Context Size 4:**
+
+1. `ቅብለት ምፍጋር ጸሓይ ሳሕል እግል ትደውሸሽ ክምቱ በሸረው ክልኢቶም ሜርሐት ወሕዳቶም እንዴ መርሐው ስስ ስዖታት እብ ድማናይ ደንበር እንዴ`
+2. `ሜራስ አድጋማት ትግሬ ክምኩም ድግም ለትነፈ ሐት ቆሬዕ ክታብ ሜራስ አድጋማት ትግሬ ክምኩም ድግም ባርህ ወጻልም ክታብ ሜራስ አድጋማት`
+3. `ክታብ ሜራስ አድጋማት ትግሬ ክምኩም ፋል እብ ነሃቅ አድግ ፋልመ ፋላት ለገ ብእ ምን ብዞሕ ሞላድ ለዐቀሙ ቶ ወእቡ`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_መናታክሉሉ_ኣሳደረአግየ_`
+2. `ት_ካር።_ግለ_ማን_ህቶምል`
+3. `እ_ሶ_አክ_ብ_ብልብ_ወሐቆ`
+
+**Context Size 2:**
+
+1. `_እት_አግማን_ቀርደመ።_ወራ`
+2. `ት_ዐለት_ልትበሀልየት_እብ_`
+3. `ል_እቱ_እግለ_አዜመ_እግል_`
+
+**Context Size 3:**
+
+1. `_እግል_ትርእዩ፡'_እግል_እን`
+2. `እግል_ልርእዩ_ከልብ_።_(ለሔ`
+3. `ግል_“ገለድ_ፈናኔን_ወእብ_በ`
+
+**Context Size 4:**
+
+1. `እግል_ልፍገሮ_ልትጸዐነው_ሲኪን`
+2. `_እግል_ወጠነ።_._._.._ወለ`
+3. `እንዴ_ትየመመ_ለለአበጽሑ_ለነሐ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 99.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (395,253 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 | 28,756 |
+| Total Tokens | 406,203 |
+| Mean Frequency | 14.13 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 143.43 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | እግል | 11,614 |
+| 2 | እት | 9,133 |
+| 3 | እንዴ | 9,068 |
+| 4 | እብ | 7,587 |
+| 5 | ዲብ | 7,025 |
+| 6 | ምን | 6,293 |
+| 7 | ህዬ | 3,645 |
+| 8 | እሊ | 3,461 |
+| 9 | ቱ | 3,197 |
+| 10 | ክምሰል | 3,001 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | prayer | 2 |
+| 2 | ነዊሕ | 2 |
+| 3 | ሓውሳ | 2 |
+| 4 | ለይቲ | 2 |
+| 5 | ሩግ | 2 |
+| 6 | ስኩር | 2 |
+| 7 | ይቤ | 2 |
+| 8 | ዋናጋሽ | 2 |
+| 9 | ዘርሲ | 2 |
+| 10 | ትእምርታት | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9964 |
+| R² (Goodness of Fit) | 0.996594 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 34.4% |
+| Top 1,000 | 60.7% |
+| Top 5,000 | 80.2% |
+| Top 10,000 | 88.2% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9966 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 34.4% of corpus
+- **Long Tail:** 18,756 words needed for remaining 11.8% 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 | 0.6615 🏆 | 0.4348 | N/A | N/A |
+| **mono_64d** | 64 | 0.2662 | 0.3804 | N/A | N/A |
+| **mono_128d** | 128 | 0.0675 | 0.3801 | N/A | N/A |
+| **aligned_32d** | 32 | 0.6615 | 0.4156 | 0.0233 | 0.1808 |
+| **aligned_64d** | 64 | 0.2662 | 0.3694 | 0.0379 | 0.2857 |
+| **aligned_128d** | 128 | 0.0675 | 0.3732 | 0.0787 | 0.3294 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.6615 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3922. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 7.9% 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 | **-0.518** | 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 |
+|--------|----------|
+| `-ለ` | ለምህነት, ለዐቀብለ, ለደላ |
+| `-ወ` | ወአከይ, ወእግሎም, ወደገልል |
+| `-አ` | አስደረ, አእጅትመዕ, አተጀሀ |
+| `-ት` | ትለብስ, ትደቀበ, ትዋጅህነ |
+| `-መ` | መጃልስ, መልክ, መልህይ |
+| `-ል` | ልትሃጌኒ, ልትሰአሎም, ልትካሬ |
+| `-ልት` | ልትሃጌኒ, ልትሰአሎም, ልትካሬ |
+| `-ኢ` | ኢመጽአው, ኢትረይሐው, ኢረአው |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ት` | ለምህነት, ጂፒት, ሸሃደት |
+| `-ም` | ወእግሎም, ሸካም, ለነሰም |
+| `-ን` | ማይረፎን, ሓለፈየን, ድመን |
+| `-ር` | ተዐይር, ሰምሀር, ታእሲር |
+| `-ይ` | ወአከይ, ኔብራይ, መልህይ |
+| `-የት` | ፍሌንየት, ውላየት, ወማህየት |
+| `-ቶም` | ልትከቦቶም, በጽሐቶም, ጅማዐቶም |
+| `-ይት` | ሓምሳይት, ጥይት, ምግበይት |
+
+### 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 |
+|------|----------|------------------|----------|
+| `መልህያ` | 1.72x | 11 contexts | መልህያም, መልህያመ, መልህያሙ |
+| `ልትአመ` | 1.54x | 11 contexts | ልትአመር, ልትአመን, ልትአመሮ |
+| `እርትር` | 1.65x | 9 contexts | እርትርያ, እርትርየ, እርትርያይ |
+| `አርወሐ` | 1.57x | 10 contexts | አርወሐት, አርወሐቱ, አርወሐቼ |
+| `ለትፈና` | 1.67x | 8 contexts | ለትፈናተ, ለትፈናታ, ወለትፈናተ |
+| `ልትበህ` | 1.64x | 8 contexts | ልትበህሉ, ልትበህሎ, ልትበህል |
+| `ለልትበ` | 1.45x | 11 contexts | ለልትበህለ, ለልትበሀለ, ለልትበሀሎ |
+| `ኤረትር` | 1.53x | 9 contexts | ኤረትርያ, ኤረትርየ, ኤረትርዪን |
+| `ትረከብ` | 1.52x | 8 contexts | ልትረከብ, ትትረከብ, ኢልትረከብ |
+| `ትአመር` | 1.39x | 10 contexts | ትትአመር, ልትአመር, ኢትትአመር |
+| `ብራሂም` | 1.70x | 6 contexts | አብራሂም, እብራሂም, ኢብራሂም |
+| `ልትበሀ` | 1.49x | 8 contexts | ልትበሀል, ልትበሀለ, ልትበሀሎ |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-ለ` | `-ም` | 12 words | ለአገርም, ለአልቃም |
+| `-ወ` | `-ት` | 10 words | ወአእት, ወዝብጠት |
+| `-ለ` | `-ት` | 5 words | ለምዴርየት, ለሔልየት |
+| `-ለ` | `-ዮም` | 5 words | ለትሰመዐዮም, ለሐረዮም |
+| `-ለ` | `-ር` | 5 words | ለሄራር, ለትቀድር |
+| `-ወ` | `-ም` | 5 words | ወጸገም, ወፈሀም |
+| `-ለ` | `-ን` | 4 words | ለአቅርን, ለኢልተመን |
+| `-እ` | `-ት` | 4 words | እቅቡላት, እስባታት |
+| `-እ` | `-የት` | 4 words | እሕሳእየት, እስብዳልየት |
+| `-አ` | `-ት` | 3 words | አውለውያት, አፍዐበት |
+
+### 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 |
+|------|-----------------|------------|------|
+| ወእተክምሰልሁመ | **`ወ-እተክምሰልሁመ`** | 4.5 | `እተክምሰልሁመ` |
+| ወለልአስተሽህድ | **`ወ-ለ-ልአስተሽህድ`** | 3.0 | `ልአስተሽህድ` |
+| ወለምትከብታይመ | **`ወ-ለ-ምትከብታይመ`** | 3.0 | `ምትከብታይመ` |
+| ተወልዳዴመድህን | **`ተ-ወ-ልዳዴመድህን`** | 3.0 | `ልዳዴመድህን` |
+| ኤለክትሮኒካይት | **`ኤለክትሮኒካይ-ት`** | 1.5 | `ኤለክትሮኒካይ` |
+| ለሐቡሸትወአርዌተኒ | **`ለ-ሐቡሸትወአርዌተኒ`** | 1.5 | `ሐቡሸትወአርዌተኒ` |
+| መሐመድአልአሚን | **`መ-ሐመድአልአሚን`** | 1.5 | `ሐመድአልአሚን` |
+| ብዕራይኢረክበት | **`ብዕራይኢረክበ-ት`** | 1.5 | `ብዕራይኢረክበ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Tigre 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 | **16k BPE** | Best compression (2.46x) |
+| N-gram | **2-gram** | Lowest perplexity (1,101) |
+| Markov | **Context-4** | Highest predictability (99.1%) |
+| 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: 2026-01-11 00:55:27*

models/embeddings/aligned/tig_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c85e74dc99926b9ab01946a97fe094143f4aae7170e429b14b3759f6ec3acbd
+size 1034284977

models/embeddings/aligned/tig_128d.meta.json

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

models/embeddings/aligned/tig_128d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26feadb32cf2ea6346462acf96259f389b499017b2be17473909700936a5e92e
+size 65664

models/embeddings/aligned/tig_128d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "tig",
+  "dimension": 128,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 343,
+  "vocab_size": 9841
+}

models/embeddings/aligned/tig_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80fff0ed468992e944a62da863e8b18904a912a145b57ab58b8f9cb6dd00d72e
+size 258727089

models/embeddings/aligned/tig_32d.meta.json

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

models/embeddings/aligned/tig_32d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f403e7606466e8c741384e8e93615fe593689250ee57afc6ebed699665781771
+size 4224

models/embeddings/aligned/tig_32d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "tig",
+  "dimension": 32,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 343,
+  "vocab_size": 9841
+}

models/embeddings/aligned/tig_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:603abd3f0b77fd64eb9ebfd5a63e24011fbbfc1f599bcaa198688918a3946093
+size 517246385

models/embeddings/aligned/tig_64d.meta.json

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

models/embeddings/aligned/tig_64d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:78d6047ae7b2c4e7008121c2667f875e93b3912831f9ccebbb923fc4e80d4044
+size 16512

models/embeddings/aligned/tig_64d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "tig",
+  "dimension": 64,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 343,
+  "vocab_size": 9841
+}

models/embeddings/monolingual/tig_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c85e74dc99926b9ab01946a97fe094143f4aae7170e429b14b3759f6ec3acbd
+size 1034284977

models/embeddings/monolingual/tig_128d.meta.json

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

models/embeddings/monolingual/tig_128d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "tig",
+  "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": 9841
+}

models/embeddings/monolingual/tig_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80fff0ed468992e944a62da863e8b18904a912a145b57ab58b8f9cb6dd00d72e
+size 258727089

models/embeddings/monolingual/tig_32d.meta.json

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

models/embeddings/monolingual/tig_32d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "tig",
+  "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": 9841
+}

models/embeddings/monolingual/tig_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:603abd3f0b77fd64eb9ebfd5a63e24011fbbfc1f599bcaa198688918a3946093
+size 517246385

models/embeddings/monolingual/tig_64d.meta.json

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

models/embeddings/monolingual/tig_64d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "tig",
+  "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": 9841
+}

models/subword_markov/tig_markov_ctx1_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8d115dc4df23672cb65cb6a8f33c306541bba6088d5d71728dfe8374dc3a384
+size 138774

models/subword_markov/tig_markov_ctx1_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "subword",
+  "language": "tig",
+  "unique_contexts": 494,
+  "total_transitions": 2028264
+}

models/subword_markov/tig_markov_ctx2_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d607d43bb0f0eb20ebe131756ed948bf6b155c05a97d082113a0aa783894d9ae
+size 825091

models/subword_markov/tig_markov_ctx2_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "subword",
+  "language": "tig",
+  "unique_contexts": 21999,
+  "total_transitions": 2027903
+}

models/subword_markov/tig_markov_ctx3_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5fb40d9a5c0bee683b6f22c9e175474e58d2aef074326328815928735eb3efe
+size 2777903

models/subword_markov/tig_markov_ctx3_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 3,
+  "variant": "subword",
+  "language": "tig",
+  "unique_contexts": 134244,
+  "total_transitions": 2027542
+}

models/subword_markov/tig_markov_ctx4_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b0c0e0d8a5acddbfa15f6d1e86dc15c64d8282a3be59a5f6bdd2ce968250a18
+size 7056876

models/subword_markov/tig_markov_ctx4_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 4,
+  "variant": "subword",
+  "language": "tig",
+  "unique_contexts": 395253,
+  "total_transitions": 2027181
+}

models/subword_ngram/tig_2gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f93116a9fe6fc115e9907ae9ed91db98363b736b5d08ba92ecc24a84a88642e8
+size 125533

models/subword_ngram/tig_2gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 2,
+  "variant": "subword",
+  "language": "tig",
+  "unique_ngrams": 11050,
+  "total_ngrams": 2028264
+}

models/subword_ngram/tig_3gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:274ae5357caa02bd1c5b3d984087b11ba0eef5237c02044cfdf27af23e77bde6
+size 685486

models/subword_ngram/tig_3gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 3,
+  "variant": "subword",
+  "language": "tig",
+  "unique_ngrams": 53840,
+  "total_ngrams": 2027903
+}

models/subword_ngram/tig_4gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e05c00c7e209ff7af76bb609851720209874c42097d6b9a853e06f2d610e4afb
+size 2130527

models/subword_ngram/tig_4gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 4,
+  "variant": "subword",
+  "language": "tig",
+  "unique_ngrams": 169447,
+  "total_ngrams": 2027542
+}

models/subword_ngram/tig_5gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9cfe516a56e3bef358361f3f9819ce82a8428376d0e9e3c3a9ee8cff41b3c29d
+size 3129240

models/subword_ngram/tig_5gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "tig",
+  "unique_ngrams": 232751,
+  "total_ngrams": 2027181
+}

models/tokenizer/tig_tokenizer_16k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76debfb581a071f7ebbf69ece3b6397705653ed6cc92c53490117c4da08f257e
+size 577701

models/tokenizer/tig_tokenizer_8k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47dd1d363e5127b704cf5f2e5fb3da208dce9dabd45e51ec9a3cf303ee4d5815
+size 398079

models/vocabulary/tig_vocabulary.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d895ebbe9ba1b8ed2326077f3a200da91fdd2d2fe4a6312b5d205cee1d37b622
+size 485294

models/vocabulary/tig_vocabulary_metadata.json

@@ -0,0 +1,17 @@
+{
+  "language": "tig",
+  "vocabulary_size": 28756,
+  "variant": "full",
+  "statistics": {
+    "type_token_ratio": 0.1615083860935244,
+    "coverage": {
+      "top_100": 0.3100477618571587,
+      "top_1000": 0.5477218704876152,
+      "top_5000": 0.7236654448517161,
+      "top_10000": 0.7956570032211485
+    },
+    "hapax_count": 43947,
+    "hapax_ratio": 0.6044729928613675,
+    "total_documents": 361
+  }
+}

models/word_markov/tig_markov_ctx1_word.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eff9b1b4d496487cf3a7d03cdb6ca5451300076d4a1b7de3afd6208dd6fe1a1d
+size 2686523

models/word_markov/tig_markov_ctx1_word_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "word",
+  "language": "tig",
+  "unique_contexts": 72666,
+  "total_transitions": 449789
+}

models/word_markov/tig_markov_ctx2_word.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a5e6fb3e322c2f59f08ff9dc415daa54a0ab6eb25832ce38397e77f35693df8
+size 6599977

models/word_markov/tig_markov_ctx2_word_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "word",
+  "language": "tig",
+  "unique_contexts": 302688,
+  "total_transitions": 449428
+}

models/word_markov/tig_markov_ctx3_word.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5339a0627f56aa38f6a90ce1f41e5887e1d2b43892231c55cfc8c61826ca461a
+size 8811894