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+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
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+---
+language: hi
+language_name: Hindi
+language_family: indoaryan_central
+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-indoaryan_central
+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: 4.250
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8141
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Hindi - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Hindi** 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** | 3.436x | 3.44 | 0.0604% | 2,277,747 |
+| **16k** | 3.796x | 3.80 | 0.0667% | 2,061,793 |
+| **32k** | 4.066x | 4.07 | 0.0715% | 1,924,898 |
+| **64k** | 4.250x 🏆 | 4.25 | 0.0747% | 1,841,478 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `जेरोम इसाक फ्रीडमन अमेरिका के प्रसिद्द वैज्ञानिक हैं। में इन्हें भौतिक विज्ञान म...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ज ेर ोम ▁इस ाक ▁फ्री ड मन ▁अमेरिका ▁के ... (+19 more)` | 29 |
+| 16k | `▁जेर ोम ▁इस ाक ▁फ्री ड मन ▁अमेरिका ▁के ▁प्रसि ... (+17 more)` | 27 |
+| 32k | `▁जेर ोम ▁इस ाक ▁फ्री ड मन ▁अमेरिका ▁के ▁प्रसिद्द ... (+16 more)` | 26 |
+| 64k | `▁जेरोम ▁इसाक ▁फ्री ड मन ▁अमेरिका ▁के ▁प्रसिद्द ▁वैज्ञानिक ▁हैं ... (+14 more)` | 24 |
+
+**Sample 2:** `मवैया हंडिया, इलाहाबाद, उत्तर प्रदेश स्थित एक गाँव है। भूगोल जनसांख्यिकी यातायात...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁म वै या ▁हंडिया , ▁इलाहाबाद , ▁उत्तर ▁प्रदेश ▁स्थित ... (+16 more)` | 26 |
+| 16k | `▁म वै या ▁हंडिया , ▁इलाहाबाद , ▁उत्तर ▁प्रदेश ▁स्थित ... (+16 more)` | 26 |
+| 32k | `▁म वैया ▁हंडिया , ▁इलाहाबाद , ▁उत्तर ▁प्रदेश ▁स्थित ▁एक ... (+15 more)` | 25 |
+| 64k | `▁म वैया ▁हंडिया , ▁इलाहाबाद , ▁उत्तर ▁प्रदेश ▁स्थित ▁एक ... (+15 more)` | 25 |
+
+**Sample 3:** `माधवी हिन्दी फिल्मों की एक प्रसिद्ध अभिनेत्री हैं। व्यक्तिगत जीवन फिल्मी सफर प्र...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁माध वी ▁हिन्दी ▁फिल्मों ▁की ▁एक ▁प्रसिद्ध ▁अभिनेत्री ▁हैं । ... (+14 more)` | 24 |
+| 16k | `▁माध वी ▁हिन्दी ▁फिल्मों ▁की ▁एक ▁प्रसिद्ध ▁अभिनेत्री ▁हैं । ... (+12 more)` | 22 |
+| 32k | `▁माधवी ▁हिन्दी ▁फिल्मों ▁की ▁एक ▁प्रसिद्ध ▁अभिनेत्री ▁हैं । ▁व्यक्तिगत ... (+11 more)` | 21 |
+| 64k | `▁माधवी ▁हिन्दी ▁फिल्मों ▁की ▁एक ▁प्रसिद्ध ▁अभिनेत्री ▁हैं । ▁व्यक्तिगत ... (+11 more)` | 21 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.250x compression
+- **Lowest UNK Rate:** 8k with 0.0604% 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 | 99,591 | 16.60 | 936,439 | 10.1% | 23.9% |
+| **2-gram** | Subword | 2,241 🏆 | 11.13 | 158,282 | 38.5% | 70.6% |
+| **3-gram** | Word | 399,267 | 18.61 | 1,969,797 | 5.9% | 14.2% |
+| **3-gram** | Subword | 22,500 | 14.46 | 933,655 | 15.0% | 35.5% |
+| **4-gram** | Word | 884,119 | 19.75 | 3,325,655 | 5.2% | 12.2% |
+| **4-gram** | Subword | 140,402 | 17.10 | 4,229,461 | 7.4% | 21.2% |
+| **5-gram** | Word | 517,438 | 18.98 | 2,208,715 | 8.0% | 17.2% |
+| **5-gram** | Subword | 516,632 | 18.98 | 8,451,936 | 4.4% | 13.2% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `के लिए` | 298,043 |
+| 2 | `जाता है` | 144,432 |
+| 3 | `रूप में` | 130,790 |
+| 4 | `के रूप` | 124,867 |
+| 5 | `के साथ` | 119,967 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `के रूप में` | 123,170 |
+| 2 | `इन्हें भी देखें` | 48,061 |
+| 3 | `करने के लिए` | 45,421 |
+| 4 | `किया जाता है` | 36,326 |
+| 5 | `किया गया था` | 35,930 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `का एक गाँव है` | 19,177 |
+| 2 | `है इन्हें भी देखें` | 16,636 |
+| 3 | `जिले का एक गाँव` | 14,515 |
+| 4 | `सरकार का आधिकारिक जालपृष्ठ` | 12,365 |
+| 5 | `भारत सरकार के आधिकारिक` | 12,363 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `जिले का एक गाँव है` | 14,430 |
+| 2 | `सरकार के आधिकारिक पोर्टल पर` | 12,360 |
+| 3 | `भारत सरकार के आधिकारिक पोर्टल` | 12,359 |
+| 4 | `उत्तराखण्ड सरकार का आधिकारिक जालपृष्ठ` | 10,606 |
+| 5 | `में विस्तृत एवं प्रामाणिक जानकारी` | 10,604 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `र _` | 3,514,012 |
+| 2 | `के _` | 2,568,356 |
+| 3 | `_ के` | 2,390,034 |
+| 4 | `, _` | 1,985,295 |
+| 5 | `न _` | 1,962,003 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ के _` | 2,311,181 |
+| 2 | `_ में _` | 1,613,203 |
+| 3 | `_ की _` | 1,000,357 |
+| 4 | `औ र _` | 977,524 |
+| 5 | `_ औ र` | 976,951 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ औ र _` | 973,900 |
+| 2 | `_ है । _` | 728,766 |
+| 3 | `_ ए क _` | 550,979 |
+| 4 | `_ प र _` | 374,271 |
+| 5 | `_ के _ लि` | 322,541 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ के _ लि ए` | 298,051 |
+| 2 | `के _ लि ए _` | 290,034 |
+| 3 | `ता _ है । _` | 229,566 |
+| 4 | `_ क र ने _` | 156,232 |
+| 5 | `_ जा ता _ है` | 144,571 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 2,241
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~13% 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 | 0.7477 | 1.679 | 9.12 | 1,271,248 | 25.2% |
+| **1** | Subword | 0.8713 | 1.829 | 13.88 | 36,229 | 12.9% |
+| **2** | Word | 0.3806 | 1.302 | 2.43 | 11,582,358 | 61.9% |
+| **2** | Subword | 0.6086 | 1.525 | 5.14 | 502,605 | 39.1% |
+| **3** | Word | 0.1696 | 1.125 | 1.40 | 28,143,624 | 83.0% |
+| **3** | Subword | 0.5073 | 1.421 | 3.65 | 2,584,793 | 49.3% |
+| **4** | Word | 0.0689 🏆 | 1.049 | 1.13 | 39,392,974 | 93.1% |
+| **4** | Subword | 0.4131 | 1.331 | 2.43 | 9,433,636 | 58.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `के प्रयासों के बाद गैन्डैल्फ़ के साथ स्थगित कर सभिको अचंभे में तर्क के नए स्थापित`
+2. `में जीप वैन ज़ांड्ट एंड फ़्रैंसिस न्यूयॉर्क टाइम्स 10 से समझना लोगों को पुलिस ने सक्रिय`
+3. `है लेनार्ट के साथ जारी की खेती श्रम बाजार में जेमस्टोन्स ब्रुन्सविक शामिल हो जाता है`
+
+**Context Size 2:**
+
+1. `के लिए वात्स्यायन ने कविता और नई विश्व व्यवस्था बनाने की सख्त जरूरत थी 24 फरवरी हिन्दी`
+2. `जाता है कारण यह था कि जिन तस्वीरों में मंदिर के बगल में देखे बिना इस घोषणा`
+3. `रूप में जिन में पाइथागोरस पहला व्यक्ति है जो वास्तविकता पर कम से कम कमाने वाले सदस्यों`
+
+**Context Size 3:**
+
+1. `के रूप में हॉलीवुड के पेशेवर लोगों के लिए उचित वक्त का इंतजार करने लगे उसे मारने के`
+2. `इन्हें भी देखें उत्तराखण्ड के जिले उत्तराखण्ड के नगर कुमाऊँ मण्डल गढ़वाल मण्डल बाहरी कड़ियाँ उत्तराख...`
+3. `करने के लिए सरकार द्वारा कोई वित्तीय सहायता प्राप्त हुई थी उन्होंने 14 फरवरी को विजय हजारे ट्रॉफी`
+
+**Context Size 4:**
+
+1. `का एक गाँव है बाहरी कड़ियाँ छत्तीसगढ़ सांस्कृतिक छत्तीसगढ जनजातियां कला खेल गोठ सतनाम पंथ छत्तीसगढ़ ...`
+2. `है इन्हें भी देखें उत्तराखण्ड के जिले उत्तराखण्ड के नगर कुमाऊँ मण्डल गढ़वाल मण्डल बाहरी कड़ियाँ उत्त...`
+3. `जिले का एक गाँव है बाहरी कड़ियाँ छत्तीसगढ़ सांस्कृतिक छत्तीसगढ जनजातियां कला खेल गोठ सतनाम पंथ छत्ती...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_के_का_इन्हें_की_दिरों_दमाने`
+2. `र_अवल_विधानी_वर्ष_विश्लेष`
+3. `क_आंकलालका_उगाव_नेगिस्तानी`
+
+**Context Size 2:**
+
+1. `र_ने_के_मंदिर_महाविद्यालय_`
+2. `के_लिखने_में_इसे_कि_फ़िल्म_है`
+3. `_के_पौड़ी_का_कुओमिक_रहने_`
+
+**Context Size 3:**
+
+1. `_के_बाद_वीकेंड_इंडोनेशिया_कोर`
+2. `_में_52_नवंबर_दिए_गए।_`
+3. `_की_मांग_की_कुछ_देशों_में_शामि`
+
+**Context Size 4:**
+
+1. `_और_युग_के_उत्तराखण्ड_राज्य_उ`
+2. `_है।_कायाको_का_परित्याग_करती_`
+3. `_एक_गाँव_है।_सूत्र_नहीं_देता_`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.1% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (9,433,636 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 | 503,387 |
+| Total Tokens | 51,225,358 |
+| Mean Frequency | 101.76 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 5660.20 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | के | 2,319,434 |
+| 2 | में | 1,706,170 |
+| 3 | है | 1,377,542 |
+| 4 | की | 1,046,592 |
+| 5 | और | 978,950 |
+| 6 | से | 789,677 |
+| 7 | का | 776,115 |
+| 8 | को | 650,931 |
+| 9 | एक | 563,314 |
+| 10 | हैं | 479,404 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | तरॉरी | 2 |
+| 2 | ग़ौरीद | 2 |
+| 3 | ओकहर्स्ट | 2 |
+| 4 | ओवरडेवलपमेंट | 2 |
+| 5 | मिस्कैवेज | 2 |
+| 6 | ज़ाल्स्की | 2 |
+| 7 | aita | 2 |
+| 8 | सूरजनसिंह | 2 |
+| 9 | दीवानबगी | 2 |
+| 10 | आशेक | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0969 |
+| R² (Goodness of Fit) | 0.991607 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 39.0% |
+| Top 1,000 | 63.2% |
+| Top 5,000 | 80.3% |
+| Top 10,000 | 86.1% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9916 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 39.0% of corpus
+- **Long Tail:** 493,387 words needed for remaining 13.9% 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 | 0.8141 | 0.3993 | N/A | N/A |
+| **mono_64d** | 64 | 0.7949 | 0.3123 | N/A | N/A |
+| **mono_128d** | 128 | 0.7461 | 0.2670 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8141 🏆 | 0.3944 | 0.0840 | 0.4400 |
+| **aligned_64d** | 64 | 0.7949 | 0.3145 | 0.2320 | 0.5660 |
+| **aligned_128d** | 128 | 0.7461 | 0.2559 | 0.2760 | 0.6860 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8141 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3239. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 27.6% 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 | **0.307** | 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 |
+|--------|----------|
+| `-स` | सुना, सिस्सु, स्नेहभरे |
+| `-क` | करतारी, कोबिता, क्लिफफोर्ड |
+| `-म` | माबैन, मखीजा, महाराना |
+| `-ब` | बाइटों, ब्रेवहार्ट्स, ब्लूहोल |
+| `-प` | पांडिकाट्ट, पर्यवेक्षणीय, प्रसन्नतापूवर्क |
+| `-अ` | अहमदगर, अर्गला, अंजुल |
+| `-र` | रगति, राठौरों, रतनगड |
+| `-ज` | जीप्स, जच्चन्धवग्गो, जवहार |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-न` | गेलसान, माबैन, गमभन |
+| `-र` | डाइबर, नयानगर, दखर |
+| `-स` | जीप्स, ब्रेवहार्ट्स, नॉमिनेशंस |
+| `-s` | siblings, sheriffs, hieroglyphics |
+| `-क` | नखचंद्रक, प्रसन्नतापूवर्क, बाहुबंधक |
+| `-ल` | नौटियाल, ब्लूहोल, आवर्तकाल |
+| `-त` | द्विपरत, कुत, ऑस्ट्रेलियाभारत |
+| `-ट` | पांडिकाट्ट, एग्रीमेंट, यूरोसेंट |
+
+### 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 |
+|------|----------|------------------|----------|
+| `nter` | 3.13x | 80 contexts | inter, enter, unter |
+| `atio` | 3.06x | 61 contexts | patio, ation, ratio |
+| `tion` | 2.97x | 67 contexts | tiong, ation, nation |
+| `ctio` | 3.12x | 40 contexts | action, actions, section |
+| `iona` | 3.07x | 26 contexts | ciona, fiona, acciona |
+| `ubli` | 2.96x | 23 contexts | hubli, publié, public |
+| `rpor` | 3.33x | 11 contexts | corpore, corpora, airport |
+| `onal` | 3.05x | 11 contexts | tonal, monal, zonal |
+| `guid` | 3.19x | 9 contexts | guide, guido, eguide |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-क` | `-र` | 33 words | कुक्कुटेश्वर, कंपोज़र |
+| `-स` | `-स` | 33 words | सोफ्रोनियस, सत्यदास |
+| `-प` | `-स` | 30 words | प्रोड्क्शन्स, पैवोनिस |
+| `-म` | `-र` | 28 words | माष्टर, मऊरानीपुर |
+| `-स` | `-न` | 27 words | सेक्‍शन, सीस्तान |
+| `-स` | `-र` | 24 words | साहेर, सूर्यवीर |
+| `-प` | `-न` | 24 words | पराधीन, पिकायून |
+| `-प` | `-त` | 23 words | पिएत, प्रोग्रामित |
+| `-स` | `-क` | 23 words | सौंदर्यबोधक, सफलतार्पूक |
+| `-व` | `-र` | 22 words | वुर, विज्कगवर्नर |
+
+### 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 |
+|------|-----------------|------------|------|
+| संगीतनाटक | **`संगीतना-ट-क`** | 7.5 | `ट` |
+| साहित्यकर | **`साहित्य-क-र`** | 7.5 | `क` |
+| sanderson | **`sanders-on`** | 4.5 | `sanders` |
+| hornbills | **`hornbill-s`** | 4.5 | `hornbill` |
+| मैगाट्रॉनस | **`मैगाट्रॉन-स`** | 4.5 | `मैगाट्रॉन` |
+| जनसांख्यकीय | **`जनसांख्यकी-य`** | 4.5 | `जनसांख्यकी` |
+| इन्फ्लुएंसस | **`इन्फ्लुएंस-स`** | 4.5 | `इन्फ्लुएंस` |
+| österreichs | **`österreich-s`** | 4.5 | `österreich` |
+| दक्षिणमध्य | **`द-क-्षिणमध्य`** | 4.5 | `्षिणमध्य` |
+| अर्धसूत्र | **`अ-र-्धसूत्र`** | 4.5 | `्धसूत्र` |
+| anatolian | **`anatoli-an`** | 4.5 | `anatoli` |
+| responded | **`respond-ed`** | 4.5 | `respond` |
+| paralympics | **`paralympic-s`** | 4.5 | `paralympic` |
+| उष्मागतिक | **`उष्मागति-क`** | 4.5 | `उष्मागति` |
+| एजेंसियाँ | **`ए-ज-ेंसियाँ`** | 4.5 | `ेंसियाँ` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Hindi 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 (4.25x) |
+| N-gram | **2-gram** | Lowest perplexity (2,241) |
+| Markov | **Context-4** | Highest predictability (93.1%) |
+| 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: 2026-01-10 08:17:37*
diff --git a/hi_morph_tokenizer.json b/hi_morph_tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..257fb0df88a3ef87bb5f8b58c6492d2309f623d0
--- /dev/null
+++ b/hi_morph_tokenizer.json
@@ -0,0 +1,50109 @@
+{
+  "language": "hi",
+  "prefixes": {
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+    "क": 62970.7,
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+    "ns": 9662.2,
+    "ic": 9252.1,
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+  },
+  "ngram_scores": {
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+    "के": 2568356,
+    ",": 1985295,
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+    "ज": 400683,
+    "व": 394691,
+    "भा": 383434,
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+    "लिए": 329871,
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+    "किया": 327397,
+    "ते": 325150,
+    "मा": 318949,
+    "केलिए": 306456,
+    ".": 304840,
+    "श": 302175,
+    "ले": 301747,
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+    "परपा": 1989,
+    "कनई": 1989,
+    "शेषकर": 1989,
+    "6%": 1988,
+    "शाके": 1988,
+    "त्रहैं": 1988,
+    "आए।": 1988,
+    "यगढ़": 1988,
+    "एकई": 1988,
+    "न्हेंसन्": 1988,
+    "बीआ": 1987,
+    "लखा": 1987,
+    "त्वका": 1987,
+    "सेंट्रल": 1987,
+    "दोबार": 1987,
+    "मैचखे": 1987,
+    "न्यूनतम": 1987,
+    "8%": 1986,
+    "v)": 1986,
+    "त्ररा": 1986,
+    "रामर्श": 1986,
+    "आदित्य": 1986,
+    "rra": 1986,
+    "सैणत": 1986,
+    "0वि": 1985,
+    "भीसा": 1985,
+    "अक्षांश": 1985,
+    "मानल": 1985,
+    "परामर्श": 1985,
+    "कयह": 1985,
+    "mall": 1985,
+    "ग्नि": 1984,
+    "निश्च": 1984,
+    ",गा": 1984,
+    "fma": 1984,
+    "बजट": 1984,
+    "अंतत": 1984,
+    "याँबि": 1984,
+    "अफ़्रीका": 1984,
+    ",de": 1984,
+    "केहिस्से": 1984,
+    "घम": 1983,
+    "उंसि": 1983,
+    "\"वि": 1983,
+    "मी.": 1983,
+    "r>": 1983,
+    "एजो": 1983,
+    "कफै": 1983,
+    "मासिक": 1983,
+    "देनेकी": 1983,
+    "रबढ़": 1983,
+    "प्रदू": 1982,
+    "यांग": 1982,
+    "हाँका": 1982,
+    "pon": 1982,
+    "ra,": 1982,
+    "स्वर्णप": 1982,
+    "नीतिके": 1982,
+    "या।क": 1982,
+    "हैं।के": 1982,
+    "कीमेज": 1982,
+    "रियोंकी": 1982,
+    "सेफ": 1981,
+    "योनि": 1981,
+    "जेप": 1981,
+    "जोंको": 1981,
+    "न्हेंआ": 1981,
+    "र्वए": 1981,
+    "लोंप": 1981,
+    "120": 1981,
+    "मेंकमी": 1981,
+    "डीसी": 1980,
+    "टॉम": 1980,
+    "मेंता": 1980,
+    "०कि": 1980,
+    "चोपड़ा": 1980,
+    "सनहीं": 1980,
+    "दोंका": 1979,
+    "कस्कू": 1979,
+    "भयार": 1979,
+    "वंप्र": 1979,
+    "केशीर्ष": 1979,
+    "arge": 1979,
+    "कौर": 1978,
+    "क्सी": 1978,
+    "सिलि": 1978,
+    "प्राणि": 1978,
+    "डीके": 1978,
+    "सम्प्रदा": 1978,
+    "र्यटक": 1978,
+    "भीथे": 1978,
+    "पावर": 1978,
+    "गाबाद": 1978,
+    "अंतरा": 1978,
+    "यसर": 1978,
+    "।विशे": 1978,
+    "नक्षेत्रों": 1978,
+    "कोजोड़": 1978,
+    "hich": 1978,
+    "पर्यटक": 1978,
+    "न्हेंएक": 1978,
+    "द्देश्यसे": 1978,
+    "ल्डन": 1977,
+    "ताइ": 1977,
+    "यामी": 1977,
+    "छोड़ने": 1977,
+    "र्भकी": 1977,
+    "वनाओं": 1977,
+    "हामारी": 1977,
+    ")कहा": 1977,
+    "परभा": 1977,
+    "किभार": 1977,
+    "कऐसी": 1977,
+    "अभयार": 1977,
+    "स्वाय": 1976,
+    "ब्रेक": 1976,
+    "सबू": 1976,
+    ",ये": 1976,
+    "lip": 1976,
+    "महामारी": 1976,
+    "मेंबता": 1976,
+    "साला": 1975,
+    "स्टीट्यूट": 1975,
+    "रीमें,": 1975,
+    "berg": 1975,
+    "लचि": 1974,
+    "तिस्था": 1974,
+    "र0": 1974,
+    "dus": 1974,
+    "मेंमो": 1974,
+    "सएस": 1974,
+    "गतीहै": 1974,
+    "एवंप्र": 1974,
+    "डातह": 1974,
+    "श्रा": 1973,
+    "निबं": 1973,
+    "रति": 1973,
+    "1दि": 1973,
+    "नेदा": 1973,
+    "।किं": 1973,
+    "osh": 1973,
+    "परस्प": 1973,
+    "फारसी": 1973,
+    "कम्युनिस्ट": 1973,
+    "ड़ागया": 1973,
+    "चारकि": 1973,
+    "पार्व": 1972,
+    "सवी": 1972,
+    "प्पा": 1972,
+    ",चे": 1972,
+    "घनत्व": 1972,
+    "lad": 1972,
+    "lma": 1972,
+    "हार्ट": 1971,
+    "ड्र": 1971,
+    "रव्यव": 1971,
+    "में6": 1971,
+    "काउंसि": 1971,
+    "रूपांतर": 1971,
+    "सेआम": 1971,
+    "डिश": 1970,
+    ",सै": 1970,
+    "lio": 1970,
+    "।यही": 1970,
+    "epu": 1970,
+    "खतरा": 1970,
+    "।आम": 1970,
+    "यलि": 1969,
+    "थको": 1969,
+    "वाउ": 1969,
+    "त्राअ": 1969,
+    "सनीय": 1969,
+    ",पूर्व": 1969,
+    "ames": 1969,
+    "भारत,": 1969,
+    "करण,": 1969,
+    "करेगा।": 1969,
+    "वलोकन": 1969,
+    "तिष": 1968,
+    "खुश": 1968,
+    "कारखा": 1968,
+    "esp": 1968,
+    "ion.": 1968,
+    "इंस्टीट्यूट": 1968,
+    "कीसुर": 1968,
+    "स्रोतों": 1967,
+    "पूर्वो": 1967,
+    "मेत": 1967,
+    "टतम": 1967,
+    "काप्रसा": 1967,
+    "जारमें": 1967,
+    "नरूप": 1967,
+    "वर्षका": 1967,
+    "थे,जो": 1967,
+    ",जैसा": 1967,
+    "मेंप्रयो": 1967,
+    "बउस": 1967,
+    "कटतम": 1967,
+    "एसएस": 1967,
+    "hysi": 1967,
+    "किपी": 1966,
+    "त्री,": 1966,
+    "सप्त": 1966,
+    "ठस": 1966,
+    "htr": 1966,
+    "पृष्ठके": 1966,
+    "ईपु": 1965,
+    "भोपाल": 1965,
+    "कनए": 1965,
+    "गिरावट": 1965,
+    "रजाघर": 1965,
+    "किमा": 1964,
+    "तभ": 1964,
+    "मीका": 1964,
+    "एहु": 1964,
+    "विकिपी": 1964,
+    ".in": 1964,
+    "y,i": 1964,
+    "रचनाएँ": 1964,
+    "मैचोंमें": 1964,
+    "सीडी": 1963,
+    "टी)": 1963,
+    "केछो": 1963,
+    "रिवारि": 1963,
+    "यात्राक": 1963,
+    "किकुछ": 1963,
+    "मिलहु": 1963,
+    "मलिक": 1962,
+    "।उसे": 1962,
+    "bn.": 1962,
+    "कीआवा": 1962,
+    "न्यूज़": 1961,
+    ":2": 1961,
+    "लरे": 1961,
+    "दमु": 1961,
+    "1]": 1961,
+    "।स्था": 1961,
+    "कचा": 1961,
+    "कर्षक": 1961,
+    "उंसिल": 1961,
+    "शरण": 1961,
+    "pow": 1961,
+    "रसमु": 1961,
+    "आलोचकों": 1961,
+    "amma": 1961,
+    "span": 1961,
+    ")नि": 1960,
+    "है।सि": 1960,
+    "रिवारिक": 1960,
+    "तां": 1959,
+    "रस्पर": 1959,
+    "स्थापन": 1959,
+    "संतुल": 1959,
+    "पानीकी": 1959,
+    "दुर्ल": 1958,
+    "वरो": 1958,
+    "set": 1958,
+    "र्मिनल": 1958,
+    "तबसे": 1958,
+    "एकव्या": 1958,
+    "ज़किया": 1958,
+    "ष्ट्रीयस्त": 1958,
+    "अनुमानि": 1958,
+    "नाहोगा": 1958,
+    "वानशि": 1958,
+    "गंध": 1957,
+    "त्पादक": 1957,
+    "लड़कि": 1957,
+    "uun": 1957,
+    "ourn": 1957,
+    "ब्धहै।": 1957,
+    "कस्वतं": 1957,
+    "सजी": 1956,
+    "पात्रों": 1956,
+    ")न": 1956,
+    "ख्यका": 1956,
+    "किपीडि": 1956,
+    "icl": 1956,
+    "जंगली": 1956,
+    "एंहैं": 1956,
+    "लियामें": 1956,
+    "\"\"": 1955,
+    "प्रपा": 1955,
+    "हक्षे": 1955,
+    "1म": 1955,
+    "गराज": 1955,
+    "रूप,": 1955,
+    "।फ़िल्म": 1955,
+    "समहा": 1955,
+    "विकिपीडि": 1955,
+    "कों,": 1954,
+    "ड्रोज": 1954,
+    "ह\"": 1954,
+    "नचित्र": 1954,
+    "प्रतिस्था": 1954,
+    "हारका": 1954,
+    "चौड़ा": 1953,
+    "ने'": 1953,
+    "टिकट": 1953,
+    "दिनके": 1953,
+    "न्होंनेस": 1953,
+    "pter": 1953,
+    "क्षियों": 1952,
+    "यच": 1952,
+    "बोलने": 1952,
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+    "सेउन्हों": 1952,
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+    "कीकई": 1952,
+    "टर्मिनल": 1952,
+    "र्गीकृ": 1951,
+    "मेंते": 1951,
+    "ry)": 1951,
+    "खरीदा": 1951,
+    "tea": 1951,
+    ",ch": 1951,
+    "स्कारके": 1951,
+    "सउप": 1951,
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+    "गढ़की": 1951,
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+    "केखे": 1950,
+    "दोंकी": 1950,
+    "परागत": 1950,
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+    "काउंसिल": 1950,
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+    "टरों": 1949,
+    "gp": 1949,
+    "।डॉ": 1949,
+    "फके": 1949,
+    "मन्वय": 1949,
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+    "प्रवर्त": 1949,
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+    "पाठक": 1949,
+    "था।बा": 1949,
+    "vent": 1949,
+    "स्टपार्टी": 1949,
+    "नेघोष": 1949,
+    "मितक": 1949,
+    "ननाहै": 1949,
+    "issa": 1949,
+    "न्म:": 1948,
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+    "र्लभ": 1948,
+    "डेड": 1948,
+    ".f": 1948,
+    ")जि": 1948,
+    "र्गीकृत": 1948,
+    "छभी": 1948,
+    "एजेंट": 1948,
+    "र्योंके": 1948,
+    "रसंगी": 1948,
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+    "यहांत": 1948,
+    "परस्पर": 1948,
+    "साँ": 1947,
+    "पब": 1947,
+    "यसि": 1947,
+    "डॉन": 1947,
+    "उल्लं": 1947,
+    "ल्लंघ": 1947,
+    "जन्म:": 1947,
+    "किपीडिया": 1947,
+    "जैसेअ": 1947,
+    "कोपरि": 1947,
+    "गड़": 1946,
+    "पक्षि": 1946,
+    "cm": 1946,
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+    "दुर्लभ": 1946,
+    ",अर्था": 1946,
+    "rope": 1946,
+    "सकाप": 1946,
+    "कानपु": 1945,
+    "(ba": 1945,
+    "ika": 1945,
+    "व्यापारी": 1945,
+    "lti": 1945,
+    "क्टोरिया": 1945,
+    "निकाय": 1945,
+    "तमह": 1945,
+    "आर्क": 1944,
+    "पग": 1944,
+    "भिग": 1944,
+    "चको": 1944,
+    "आवाज़": 1944,
+    "भीप्रा": 1944,
+    "-दूस": 1944,
+    "प्राधिक": 1944,
+    "bir": 1944,
+    "ल्लेबाज": 1944,
+    "तारह": 1944,
+    "तथाप": 1944,
+    "एअनु": 1944,
+    "phil": 1944,
+    "लजाता": 1944,
+    "खबा": 1943,
+    "ij": 1943,
+    "-\"": 1943,
+    ".हा": 1943,
+    "ई1": 1943,
+    "तिब्बत": 1943,
+    "डरका": 1943,
+    "सेजुड़ी": 1943,
+    "रंपराग": 1943,
+    "वियतना": 1943,
+    "बल्लेबाज": 1943,
+    "film": 1943,
+    "मलो": 1942,
+    "सूल": 1942,
+    "एबि": 1942,
+    "(10": 1942,
+    "रविज्ञा": 1942,
+    "मानचित्र": 1942,
+    "प्राधिकर": 1942,
+    "कादर्जा": 1942,
+    "ताप्रदा": 1942,
+    "क्रेडि": 1941,
+    "नेशा": 1941,
+    "आई.": 1941,
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+    "रयूरो": 1941,
+    "सीलको": 1941,
+    "||": 1940,
+    "र्गद": 1940,
+    "चरम": 1940,
+    "गेबढ़": 1940,
+    "लितहै": 1940,
+    "स्लामि": 1939,
+    "।धा": 1939,
+    "हुंचती": 1939,
+    "।प्राची": 1939,
+    "कारूप": 1939,
+    "रदेने": 1939,
+    "मेंप्रमु": 1939,
+    "पहुंचती": 1939,
+    "ष्टाचा": 1938,
+    "नांक": 1938,
+    "ehr": 1938,
+    "ए।अ": 1938,
+    "ofr": 1938,
+    "में,इ": 1938,
+    "-पश्चिम": 1938,
+    "ngui": 1938,
+    "औरओ": 1938,
+    "काअभि": 1938,
+    "खर्जी": 1937,
+    "न्तरि": 1937,
+    "iri": 1937,
+    "जकल": 1937,
+    "liv": 1937,
+    "मुखर्जी": 1937,
+    "इस्लामि": 1937,
+    "औरवै": 1937,
+    "ईमें,": 1937,
+    "madh": 1937,
+    "कीट": 1936,
+    "ब्रॉ": 1936,
+    "टाय": 1936,
+    "पंचां": 1936,
+    "वर्गीकृ": 1936,
+    "चाजा": 1936,
+    "जोप्र": 1936,
+    "नेत्र": 1935,
+    "सीसा": 1935,
+    "साहु": 1935,
+    "क्तकी": 1935,
+    "ol.": 1935,
+    "ety": 1935,
+    "tabl": 1935,
+    "रलोगों": 1935,
+    "औररि": 1935,
+    "bert": 1935,
+    "जाताहैं": 1935,
+    "दिरका": 1935,
+    "साफ": 1934,
+    "म्भव": 1934,
+    "ग3": 1934,
+    "-से": 1934,
+    "याफि": 1934,
+    "काचि": 1934,
+    "नुभूति": 1934,
+    "उल्लंघ": 1934,
+    "वर्गीकृत": 1934,
+    "alis": 1934,
+    "सेदूस": 1934,
+    "तरह,": 1934,
+    "ures": 1934,
+    "खेलके": 1934,
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+    "(pr": 1933,
+    "सेनानी": 1933,
+    "kal": 1933,
+    "ड़ेके": 1933,
+    "रियामें": 1933,
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+    "भट": 1932,
+    ",बं": 1932,
+    "एहो": 1932,
+    "नकाप": 1932,
+    "नजैसे": 1932,
+    "कुछभी": 1932,
+    "शितक": 1932,
+    "पोल": 1931,
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+    "।ची": 1931,
+    "रिसर्च": 1931,
+    "या।1": 1931,
+    "thro": 1931,
+    "ककंप": 1931,
+    "जोसे": 1930,
+    "वनि": 1930,
+    "म:": 1930,
+    "प्रदाय": 1930,
+    "क-दू": 1930,
+    "कारप": 1930,
+    "essi": 1930,
+    "बइस": 1930,
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+    "नोंद्वा": 1929,
+    "कबीर": 1929,
+    "मानका": 1929,
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+    "कानपुर": 1929,
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+    "ईलोगों": 1929,
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+    "दीना": 1928,
+    "पसमू": 1928,
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+    "मार्गद": 1928,
+    "।सन": 1928,
+    "परबा": 1928,
+    "कारोंमें": 1928,
+    "सेनामें": 1928,
+    "जाँच": 1927,
+    "स्वभा": 1927,
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+    "भाग्य": 1927,
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+    "डवर्ड": 1927,
+    "माधव": 1927,
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+    "नीदरलैं": 1927,
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+    "ता.": 1926,
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+    "राय,": 1926,
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+    "z.": 1925,
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+    "लिएना": 1925,
+    "एक-दू": 1925,
+    "कोबता": 1925,
+    "मानेजा": 1925,
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+    "स्कूलों": 1924,
+    "एदे": 1924,
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+    "तीयभा": 1924,
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+    "lde": 1923,
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+    "क-दूस": 1922,
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+    "cros": 1922,
+    "संतुलन": 1922,
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+    "पैट": 1921,
+    "हजी": 1921,
+    "कखा": 1921,
+    "मेंबां": 1921,
+    "रराम": 1921,
+    ".एक": 1921,
+    "वर्षहै": 1921,
+    "गोरीकै": 1921,
+    "कोनियं": 1921,
+    "कारोंको": 1921,
+    "होतेहै": 1921,
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+    "थऔ": 1920,
+    "eff": 1920,
+    "सम्भव": 1920,
+    "कारीस": 1920,
+    "नोंद्वारा": 1920,
+    "एआव": 1920,
+    "कसंर": 1920,
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