Upload all models and assets for kv (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: kv
+language_name: Komi
+language_family: uralic_permian
+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-uralic_permian
+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: 4.057
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7808
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Komi - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Komi** 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** | 3.121x | 3.13 | 0.1052% | 211,919 |
+| **16k** | 3.570x | 3.58 | 0.1204% | 185,286 |
+| **32k** | 3.866x | 3.87 | 0.1303% | 171,084 |
+| **64k** | 4.057x 🏆 | 4.06 | 0.1368% | 163,039 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Сизимсё кӧкъямысдасӧд вояс - 781 восянь 790 воӧдз. Медыджыд лоӧмторъяс`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁сизим сё ▁кӧкъямыс дасӧд ▁вояс ▁- ▁ 7 8 1 ... (+9 more)` | 19 |
+| 16k | `▁сизимсё ▁кӧкъямысдасӧд ▁вояс ▁- ▁ 7 8 1 ▁восянь ▁ ... (+7 more)` | 17 |
+| 32k | `▁сизимсё ▁кӧкъямысдасӧд ▁вояс ▁- ▁ 7 8 1 ▁восянь ▁ ... (+7 more)` | 17 |
+| 64k | `▁сизимсё ▁кӧкъямысдасӧд ▁вояс ▁- ▁ 7 8 1 ▁восянь ▁ ... (+7 more)` | 17 |
+
+**Sample 2:** `451 Патиентия — тайӧ Шонді ылдӧсын астероид. Сылӧн ыджда — 224 км. Патиентия вос...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ 4 5 1 ▁п ат и ент ия ▁— ... (+36 more)` | 46 |
+| 16k | `▁ 4 5 1 ▁пат и ент ия ▁— ▁тайӧ ... (+32 more)` | 42 |
+| 32k | `▁ 4 5 1 ▁пат и ентия ▁— ▁тайӧ ▁шонді ... (+26 more)` | 36 |
+| 64k | `▁ 4 5 1 ▁патиентия ▁— ▁тайӧ ▁шонді ▁ылдӧсын ▁астероид ... (+22 more)` | 32 |
+
+**Sample 3:** `Тюмень обласьт тайӧ регион Рочмуын. Видзӧдӧй тшӧтш Ханты-Вӧгул асвеськӧдлан кытш...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁т ю мен ь ▁обласьт ▁тайӧ ▁регион ▁рочмуын . ▁видзӧдӧй ... (+16 more)` | 26 |
+| 16k | `▁тю мен ь ▁обласьт ▁тайӧ ▁регион ▁рочмуын . ▁видзӧдӧй ▁тшӧтш ... (+11 more)` | 21 |
+| 32k | `▁тюмень ▁обласьт ▁тайӧ ▁регион ▁рочмуын . ▁видзӧдӧй ▁тшӧтш ▁ханты - ... (+9 more)` | 19 |
+| 64k | `▁тюмень ▁обласьт ▁тайӧ ▁регион ▁рочмуын . ▁видзӧдӧй ▁тшӧтш ▁ханты - ... (+9 more)` | 19 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.057x compression
+- **Lowest UNK Rate:** 8k with 0.1052% 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 | 4,415 | 12.11 | 14,094 | 22.1% | 55.9% |
+| **2-gram** | Subword | 681 🏆 | 9.41 | 6,463 | 44.2% | 94.6% |
+| **3-gram** | Word | 5,552 | 12.44 | 19,425 | 23.7% | 51.7% |
+| **3-gram** | Subword | 5,657 | 12.47 | 40,644 | 16.0% | 51.0% |
+| **4-gram** | Word | 8,996 | 13.14 | 34,620 | 23.6% | 45.0% |
+| **4-gram** | Subword | 24,300 | 14.57 | 169,451 | 9.1% | 29.8% |
+| **5-gram** | Word | 6,977 | 12.77 | 28,246 | 27.6% | 47.7% |
+| **5-gram** | Subword | 55,081 | 15.75 | 319,260 | 6.7% | 22.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ӧд лун` | 2,382 |
+| 2 | `республики коми` | 1,598 |
+| 3 | `республика коми` | 1,394 |
+| 4 | `сикт овмӧдчӧмин` | 1,392 |
+| 5 | `коми республикаса` | 1,281 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `сыктывкар республика коми` | 1,059 |
+| 2 | `республика коми энциклопедия` | 811 |
+| 3 | `августа г издание` | 797 |
+| 4 | `1 августа г` | 797 |
+| 5 | `на 1 августа` | 797 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `1 августа г издание` | 797 |
+| 2 | `на 1 августа г` | 797 |
+| 3 | `и л где ты` | 717 |
+| 4 | `жеребцов и л где` | 714 |
+| 5 | `коми историко демографический справочник` | 704 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `на 1 августа г издание` | 797 |
+| 2 | `жеребцов и л где ты` | 714 |
+| 3 | `республики коми историко демографический справочник` | 704 |
+| 4 | `пункты республики коми историко демографический` | 704 |
+| 5 | `населенные пункты республики коми историко` | 703 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `а _` | 76,965 |
+| 2 | `. _` | 76,956 |
+| 3 | `_ к` | 64,740 |
+| 4 | `_ в` | 54,790 |
+| 5 | `, _` | 52,769 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ к о` | 26,805 |
+| 2 | `ы с ь` | 25,301 |
+| 3 | `ъ я с` | 23,484 |
+| 4 | `_ — _` | 22,691 |
+| 5 | `_ в о` | 20,230 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ы с ь _` | 16,760 |
+| 2 | `к о м и` | 15,656 |
+| 3 | `_ к о м` | 15,118 |
+| 4 | `ъ я с _` | 13,192 |
+| 5 | `л ы с ь` | 12,862 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ к о м и` | 14,450 |
+| 2 | `к о м и _` | 10,888 |
+| 3 | `л ы с ь _` | 9,228 |
+| 4 | `с ы к т ы` | 6,769 |
+| 5 | `ы к т ы в` | 6,764 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 681
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~23% 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 | 0.6233 | 1.540 | 3.71 | 115,439 | 37.7% |
+| **1** | Subword | 0.4379 | 1.355 | 4.01 | 7,808 | 56.2% |
+| **2** | Word | 0.1549 | 1.113 | 1.31 | 426,513 | 84.5% |
+| **2** | Subword | 0.5508 | 1.465 | 3.55 | 31,340 | 44.9% |
+| **3** | Word | 0.0585 | 1.041 | 1.11 | 556,965 | 94.2% |
+| **3** | Subword | 0.5879 | 1.503 | 3.07 | 111,343 | 41.2% |
+| **4** | Word | 0.0316 🏆 | 1.022 | 1.06 | 612,330 | 96.8% |
+| **4** | Subword | 0.4947 | 1.409 | 2.22 | 341,894 | 50.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `коми пермяцко русский словарь грузинского языка ч ʧ сі чи ті vos сійӧ вӧлі лӧсьӧдавны каникуласигас`
+2. `да 535 morinda phyllireoides sert austro caledon 49 м изд во 45 80 4 ньыль 5`
+3. `сыктывкар коми республикаса почёта грамота коми литературы и муниципальное устройство республики ком...`
+
+**Context Size 2:**
+
+1. `ӧд лун коми кыв автономия панысьяс асшӧрлун шедӧдӧмын пайыс эм уналӧн кызьӧд воясӧ виз рочӧн княжпог...`
+2. `республики коми историко демографический справочник сыктывкар история коми с древнейших времен до ко...`
+3. `республика коми энциклопедия в 3 х тт емва да эжва юяс бокын но сикт грездъяс сикт сӧвет`
+
+**Context Size 3:**
+
+1. `сыктывкар республика коми административно территориальное деление на 1 августа г издание пятое сыкты...`
+2. `республика коми энциклопедия сыктывкар т 1 3 ыстӧдъяс республикалӧн сиктъяс сикт грезд сикт овмӧдчӧм...`
+3. `на 1 августа г издание шестое официальное гу тфи рк сыктывкар 278 с изьва мулӧн ин нимъяс топонимия`
+
+**Context Size 4:**
+
+1. `1 августа г издание шестое официальное гу тфи рк сыктывкар 278 с сикт грезд сикт овмӧдчӧмин грездъяс...`
+2. `на 1 августа г издание пятое сыктывкар республика коми энциклопедия в 3 тт сыктывкар ыстӧдъяс вылыс ...`
+3. `и л где ты живешь населенные пункты республики коми историко демографический справочник сыктывкар ис...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_ктыджӧйисечмико`
+2. `алломахъя_млазат`
+3. `сопаын_кило_жез_`
+
+**Context Size 2:**
+
+1. `а_талӧны_jʊ_23932`
+2. `._спублавканбур_о`
+3. `_кмын_шойдъяслаын`
+
+**Context Size 3:**
+
+1. `_кокнижнӧй_уджаліс`
+2. `ысьясӧ_сьыс_—_пемӧ`
+3. `_—_коми_сарина_тэ_`
+
+**Context Size 4:**
+
+1. `ысь_18-ӧд_лун_лои_п`
+2. `коми_музейӧн»,_арав`
+3. `_комияса_кыв_(tod._`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.8% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (341,894 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 | 41,073 |
+| Total Tokens | 725,042 |
+| Mean Frequency | 17.65 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 140.79 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | коми | 13,968 |
+| 2 | да | 11,866 |
+| 3 | сыктывкар | 5,358 |
+| 4 | и | 5,043 |
+| 5 | а | 4,697 |
+| 6 | ӧд | 4,292 |
+| 7 | тӧлысь | 4,290 |
+| 8 | в | 4,031 |
+| 9 | лун | 4,030 |
+| 10 | сикт | 3,821 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | былины | 2 |
+| 2 | символыс | 2 |
+| 3 | палуоя | 2 |
+| 4 | байтэрек | 2 |
+| 5 | кухмо | 2 |
+| 6 | хэнкок | 2 |
+| 7 | тычы | 2 |
+| 8 | идеал | 2 |
+| 9 | университетъясысь | 2 |
+| 10 | елюенэ | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0595 |
+| R² (Goodness of Fit) | 0.993095 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 26.6% |
+| Top 1,000 | 59.7% |
+| Top 5,000 | 79.2% |
+| Top 10,000 | 86.7% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9931 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 26.6% of corpus
+- **Long Tail:** 31,073 words needed for remaining 13.3% 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 | 0.7808 | 0.3587 | N/A | N/A |
+| **mono_64d** | 64 | 0.5590 | 0.3120 | N/A | N/A |
+| **mono_128d** | 128 | 0.1539 | 0.3129 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7808 🏆 | 0.3525 | 0.0260 | 0.1300 |
+| **aligned_64d** | 64 | 0.5590 | 0.3133 | 0.0460 | 0.1960 |
+| **aligned_128d** | 128 | 0.1539 | 0.3018 | 0.0580 | 0.2120 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7808 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3252. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 5.8% 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 | **1.101** | 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 |
+|--------|----------|
+| `-к` | калипсо, как, куратовалысь |
+| `-с` | сартас, связь, сапёрнӧй |
+| `-п` | пещераа, пансигӧн, партияӧн |
+| `-в` | волывлӧма, вичӧн, выльног |
+| `-м` | мыжы, медасьлісны, мирон |
+| `-т` | турьев, тышкасьӧмлӧн, таын |
+| `-ко` | комсь, колльӧдӧны, корӧмаӧсь |
+| `-s` | semperflorens, scabrifolia, sz |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-н` | айясыслӧн, вичӧн, пансигӧн |
+| `-а` | волывлӧма, жанетта, пещераа |
+| `-с` | сартас, бӧраныс, гӧгӧрсьыс |
+| `-a` | trullifolia, dresslerara, carinilabia |
+| `-ӧн` | айясыслӧн, вичӧн, пансигӧн |
+| `-ь` | куратовалысь, связь, ль |
+| `-яс` | квенъяс, войтыръяс, геологъяс |
+| `-сь` | куратовалысь, комсь, лӧсьӧдӧмлысь |
+
+### 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 |
+|------|----------|------------------|----------|
+| `лӧма` | 2.03x | 47 contexts | олӧма, вӧлӧма, кылӧма |
+| `ӧдӧм` | 1.80x | 62 contexts | тӧдӧм, ӧлӧдӧм, ӧшӧдӧм |
+| `ръяс` | 1.67x | 76 contexts | уръяс, юръяс, юӧръяс |
+| `існы` | 2.08x | 23 contexts | юлісны, олісны, кулісны |
+| `ӧлыс` | 2.30x | 15 contexts | тӧлыс, пӧлыс, йӧлыс |
+| `ӧдъя` | 1.98x | 23 contexts | мӧдъяс, юкӧдъяс, инӧдъяс |
+| `дъяс` | 1.62x | 39 contexts | садъяс, андъяс, видъяс |
+| `въяс` | 1.62x | 38 contexts | увъяс, овъяс, левъяс |
+| `отыр` | 1.91x | 21 contexts | котыр, котыра, котырӧ |
+| `исто` | 2.02x | 15 contexts | истор, исток, истоки |
+| `стор` | 1.89x | 16 contexts | истор, пастор, простор |
+| `коты` | 1.93x | 15 contexts | котыр, котыра, котырӧ |
+
+### 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` | `-н` | 88 words | кн, кипрушевлӧн |
+| `-п` | `-н` | 70 words | предприятиеяслӧн, поэмаясын |
+| `-к` | `-а` | 68 words | кипасалӧма, косьювомса |
+| `-с` | `-н` | 64 words | семуковын, сборникъясын |
+| `-к` | `-с` | 64 words | коммунистъяс, кыръясыс |
+| `-с` | `-а` | 61 words | ставмирса, сорта |
+| `-п` | `-а` | 61 words | пырӧма, пылаева |
+| `-в` | `-н` | 60 words | вӧркутаын, войын |
+| `-п` | `-с` | 58 words | примитіс, поэтъясӧс |
+| `-в` | `-ы` | 58 words | восьтны, вуджны |
+
+### 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 |
+|------|-----------------|------------|------|
+| странанас | **`страна-н-ас`** | 7.5 | `н` |
+| парткомса | **`партко-м-са`** | 7.5 | `м` |
+| скрипкаӧн | **`скрипк-а-ӧн`** | 7.5 | `а` |
+| институтлысь | **`институт-лы-сь`** | 6.0 | `институт` |
+| висьтавсьӧ | **`висьтав-сь-ӧ`** | 6.0 | `висьтав` |
+| кальӧлысь | **`кальӧ-лы-сь`** | 6.0 | `кальӧ` |
+| авторлысь | **`автор-лы-сь`** | 6.0 | `автор` |
+| ветлысьяслы | **`ветлысь-яс-лы`** | 6.0 | `ветлысь` |
+| войскаясӧн | **`войска-яс-ӧн`** | 6.0 | `войска` |
+| абхазияын | **`абхаз-ия-ын`** | 6.0 | `абхаз` |
+| национальносьт | **`национально-сь-т`** | 6.0 | `национально` |
+| пемӧслысь | **`пемӧс-лы-сь`** | 6.0 | `пемӧс` |
+| ӧтувтчӧмӧн | **`ӧтувтчӧм-ӧн`** | 4.5 | `ӧтувтчӧм` |
+| пемӧсъясӧс | **`пе-мӧсъясӧс`** | 4.5 | `мӧсъясӧс` |
+| балтикаса | **`балтика-са`** | 4.5 | `балтика` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Komi 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 (4.06x) |
+| N-gram | **2-gram** | Lowest perplexity (681) |
+| Markov | **Context-4** | Highest predictability (96.8%) |
+| 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: 2026-01-10 08:51:50*

models/embeddings/aligned/kv_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ab39113c67dc5c0bf25d84ea478bbb5d39cc17c9cfc0398727aece749867a3c
+size 1041345064

models/embeddings/aligned/kv_128d.meta.json

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

models/embeddings/aligned/kv_128d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28739fe5f236595d2dd668fc28313534ed41bfea2774208c6d61783b1ffd7769
+size 65664

models/embeddings/aligned/kv_128d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "kv",
+  "dimension": 128,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 3136,
+  "vocab_size": 16591
+}

models/embeddings/aligned/kv_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:524c1668b03b3fd0c1c47e28c22c69aaa153deff1b5af998cea87ff07ccea5ca
+size 260603176

models/embeddings/aligned/kv_32d.meta.json

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

models/embeddings/aligned/kv_32d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ddecebf5092eabc43a5c6d26e912443e32d5a58e735ba74abdf2fbb5ece1c66b
+size 4224

models/embeddings/aligned/kv_32d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "kv",
+  "dimension": 32,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 3136,
+  "vocab_size": 16591
+}

models/embeddings/aligned/kv_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b99811817086baec0190abf01600f3f9eedeeb5df1bcc60244f785dfa687fb13
+size 520850472

models/embeddings/aligned/kv_64d.meta.json

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

models/embeddings/aligned/kv_64d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a470430227f03acf9d1e0b380ab771aedd5e689412ec960fdfcb2b32ba788c88
+size 16512

models/embeddings/aligned/kv_64d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "kv",
+  "dimension": 64,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 3136,
+  "vocab_size": 16591
+}

models/embeddings/monolingual/kv_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ab39113c67dc5c0bf25d84ea478bbb5d39cc17c9cfc0398727aece749867a3c
+size 1041345064

models/embeddings/monolingual/kv_128d.meta.json

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

models/embeddings/monolingual/kv_128d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "kv",
+  "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": 16591
+}

models/embeddings/monolingual/kv_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:524c1668b03b3fd0c1c47e28c22c69aaa153deff1b5af998cea87ff07ccea5ca
+size 260603176

models/embeddings/monolingual/kv_32d.meta.json

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

models/embeddings/monolingual/kv_32d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "kv",
+  "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": 16591
+}

models/embeddings/monolingual/kv_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b99811817086baec0190abf01600f3f9eedeeb5df1bcc60244f785dfa687fb13
+size 520850472

models/embeddings/monolingual/kv_64d.meta.json

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

models/embeddings/monolingual/kv_64d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "kv",
+  "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": 16591
+}

models/subword_markov/kv_markov_ctx1_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1718059ba3fdd60e13fc853703b14a55fbcc47478c4e142f07d3295e5ad9ff15
+size 239814

models/subword_markov/kv_markov_ctx1_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "subword",
+  "language": "kv",
+  "unique_contexts": 7808,
+  "total_transitions": 5564131
+}

models/subword_markov/kv_markov_ctx2_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fdbfabde1e2f1d5978ea93f649afed0f4952ee9e5cb7db24cabeba26c043cdce
+size 915923

models/subword_markov/kv_markov_ctx2_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "subword",
+  "language": "kv",
+  "unique_contexts": 31340,
+  "total_transitions": 5558350
+}

models/subword_markov/kv_markov_ctx3_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c42c5d5b3e5f2d36d7276e35938e30273b16dfda782b9e505bbfdd3f03fd7766
+size 2608548

models/subword_markov/kv_markov_ctx3_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 3,
+  "variant": "subword",
+  "language": "kv",
+  "unique_contexts": 111343,
+  "total_transitions": 5552569
+}

models/subword_markov/kv_markov_ctx4_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13ed92dfcbf5e220288490e718cfc6c7bc256f0319ad639254ead61c4d0365c8
+size 6962850

models/subword_markov/kv_markov_ctx4_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 4,
+  "variant": "subword",
+  "language": "kv",
+  "unique_contexts": 341894,
+  "total_transitions": 5546788
+}

models/subword_ngram/kv_2gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca89785b611084969c4efd2b19c46ba4716d0cda76c7d55c4702d6443ed3e92f
+size 83397

models/subword_ngram/kv_2gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 2,
+  "variant": "subword",
+  "language": "kv",
+  "unique_ngrams": 6463,
+  "total_ngrams": 5564131
+}

models/subword_ngram/kv_3gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:460c41f2e08ba55401fe4c5c36d382c0e948397b93bbda234e11006e6909f03d
+size 528543

models/subword_ngram/kv_3gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 3,
+  "variant": "subword",
+  "language": "kv",
+  "unique_ngrams": 40644,
+  "total_ngrams": 5558350
+}

models/subword_ngram/kv_4gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49fcaa1c55f8e34ba2c18a045d9e2e6dcf9d35d33afb23ec4fa206004a209673
+size 2029584

models/subword_ngram/kv_4gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 4,
+  "variant": "subword",
+  "language": "kv",
+  "unique_ngrams": 169451,
+  "total_ngrams": 5552569
+}

models/subword_ngram/kv_5gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d12bf7b2bef36c1979f66f12ca24c9fab18b679304af079e244b32ee53320719
+size 4077513

models/subword_ngram/kv_5gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "kv",
+  "unique_ngrams": 319260,
+  "total_ngrams": 5546788
+}

models/tokenizer/kv_tokenizer_16k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e7cef5935888761751e442da95456031c75ffdfeea9d784c6a745d405baa945d
+size 522776

models/tokenizer/kv_tokenizer_32k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd6dbf32e99e4208cc7e66c2d680dc73c1d0546e7cce353f6acea12188d189f1
+size 829220

models/tokenizer/kv_tokenizer_64k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5111a75acaf4a4aaa09c230bd567e6e45ece656a4700ab6f7a59ddd0848568c1
+size 1489027

models/tokenizer/kv_tokenizer_8k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:257b1c5117bb155d3def2fc557bbbf39dabc34953612cf68b32ea4af83070e91
+size 369336

models/vocabulary/kv_vocabulary.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7fc75332fed825c9e1a2cebb19cf708b5d3a6e590e41762544eb6c03ef3ea93b
+size 759706

models/vocabulary/kv_vocabulary_metadata.json

@@ -0,0 +1,17 @@
+{
+  "language": "kv",
+  "vocabulary_size": 41073,
+  "variant": "full",
+  "statistics": {
+    "type_token_ratio": 0.1445372349539918,
+    "coverage": {
+      "top_100": 0.24137150404424593,
+      "top_1000": 0.5418176405917969,
+      "top_5000": 0.7185587550701599,
+      "top_10000": 0.7863860188035235
+    },
+    "hapax_count": 74489,
+    "hapax_ratio": 0.6445803984008585,
+    "total_documents": 5781
+  }
+}