diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..291f62bc2f4728f8016ad17ad14264621981f304 100644 --- a/.gitattributes +++ b/.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 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..329d04102f0e576f33168bbaeb9d5a6b32aac18d --- /dev/null +++ b/README.md @@ -0,0 +1,769 @@ +--- +language: ts +language_name: Tsonga +language_family: bantu_southern +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-bantu_southern +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: 4.757 + - name: best_isotropy + type: isotropy + value: 0.4521 + - name: vocabulary_size + type: vocab + value: 0 +generated: 2026-01-11 +--- + +# Tsonga - Wikilangs Models +## Comprehensive Research Report & Full Ablation Study + +This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Tsonga** 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** | 4.083x | 4.09 | 0.1543% | 377,103 | +| **16k** | 4.448x | 4.45 | 0.1681% | 346,189 | +| **32k** | 4.757x 🏆 | 4.76 | 0.1798% | 323,678 | + +### Tokenization Examples + +Below are sample sentences tokenized with each vocabulary size: + +**Sample 1:** `Ostraliya (Xinghezi: Australia) i tiko ra Oxiyeniya.` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁ostraliya ▁( xing hezi : ▁australia ) ▁i ▁tiko ▁ra ... (+2 more)` | 12 | +| 16k | `▁ostraliya ▁( xinghezi : ▁australia ) ▁i ▁tiko ▁ra ▁oxiyeniya ... (+1 more)` | 11 | +| 32k | `▁ostraliya ▁( xinghezi : ▁australia ) ▁i ▁tiko ▁ra ▁oxiyeniya ... (+1 more)` | 11 | + +**Sample 2:** `E ndhau leyinga le Soweto la ku ngava ni Soweto uprising.` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁e ▁ndha u ▁leyin ga ▁le ▁soweto ▁la ▁ku ▁ngava ... (+6 more)` | 16 | +| 16k | `▁e ▁ndha u ▁leyinga ▁le ▁soweto ▁la ▁ku ▁ngava ▁ni ... (+5 more)` | 15 | +| 32k | `▁e ▁ndhau ▁leyinga ▁le ▁soweto ▁la ▁ku ▁ngava ▁ni ▁soweto ... (+2 more)` | 12 | + +**Sample 3:** `+Jamhuri ya Kenya 125px 125px (Flag) (Coat of Arms) 300px Kenya i ...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁+ ja m huri ▁ya ▁kenya ▁ 1 2 5 ... (+30 more)` | 40 | +| 16k | `▁+ jamhuri ▁ya ▁kenya ▁ 1 2 5 px ▁ ... (+28 more)` | 38 | +| 32k | `▁+ jamhuri ▁ya ▁kenya ▁ 1 2 5 px ▁ ... (+28 more)` | 38 | + + +### Key Findings + +- **Best Compression:** 32k achieves 4.757x compression +- **Lowest UNK Rate:** 8k with 0.1543% unknown tokens +- **Trade-off:** Larger vocabularies improve compression but increase model size +- **Recommendation:** 32k vocabulary provides optimal balance for production use + +--- +## 2. N-gram Model Evaluation + +![N-gram Perplexity](visualizations/ngram_perplexity.png) + +![N-gram Unique](visualizations/ngram_unique.png) + +![N-gram Coverage](visualizations/ngram_coverage.png) + +### Results + +| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage | +|--------|---------|------------|---------|----------------|------------------|-------------------| +| **2-gram** | Word | 3,674 | 11.84 | 6,696 | 17.5% | 52.9% | +| **2-gram** | Subword | 203 🏆 | 7.66 | 1,426 | 73.8% | 99.8% | +| **3-gram** | Word | 5,300 | 12.37 | 7,866 | 12.8% | 41.5% | +| **3-gram** | Subword | 1,457 | 10.51 | 10,147 | 33.7% | 80.2% | +| **4-gram** | Word | 9,675 | 13.24 | 12,992 | 9.7% | 28.5% | +| **4-gram** | Subword | 6,717 | 12.71 | 42,330 | 16.4% | 49.9% | +| **5-gram** | Word | 6,251 | 12.61 | 8,596 | 13.3% | 34.1% | +| **5-gram** | Subword | 18,231 | 14.15 | 82,731 | 8.9% | 32.4% | + +### Top 5 N-grams by Size + +**2-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `hi ku` | 816 | +| 2 | `na ku` | 518 | +| 3 | `tani hi` | 433 | +| 4 | `lembe ra` | 416 | +| 5 | `hi lembe` | 403 | + +**3-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `hi lembe ra` | 342 | +| 2 | `hi siku leri` | 165 | +| 3 | `a ku ri` | 136 | +| 4 | `member of the` | 119 | +| 5 | `ku sukela hi` | 105 | + +**4-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `add text add text` | 103 | +| 2 | `text add text add` | 81 | +| 3 | `flag coat of arms` | 70 | +| 4 | `coat of arms small` | 67 | +| 5 | `hi ku ya hi` | 66 | + +**5-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `add text add text add` | 81 | +| 2 | `text add text add text` | 81 | +| 3 | `flag coat of arms small` | 67 | +| 4 | `life of a south african` | 65 | +| 5 | `of a south african tribe` | 65 | + +**2-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `a _` | 92,010 | +| 2 | `i _` | 42,197 | +| 3 | `a n` | 25,791 | +| 4 | `_ n` | 22,549 | +| 5 | `u _` | 22,416 | + +**3-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `n a _` | 15,155 | +| 2 | `k a _` | 14,105 | +| 3 | `_ k u` | 11,845 | +| 4 | `w a _` | 11,809 | +| 5 | `y a _` | 10,410 | + +**4-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ k u _` | 7,542 | +| 2 | `_ y a _` | 7,064 | +| 3 | `_ h i _` | 6,736 | +| 4 | `_ n a _` | 6,398 | +| 5 | `_ w a _` | 5,407 | + +**5-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ e k a _` | 3,747 | +| 2 | `a _ k u _` | 3,382 | +| 3 | `a _ s w i` | 2,884 | +| 4 | `a _ h i _` | 2,861 | +| 5 | `a _ n a _` | 2,635 | + + +### Key Findings + +- **Best Perplexity:** 2-gram (subword) with 203 +- **Entropy Trend:** Decreases with larger n-grams (more predictable) +- **Coverage:** Top-1000 patterns cover ~32% 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 | 0.7323 | 1.661 | 4.40 | 27,675 | 26.8% | +| **1** | Subword | 1.1293 | 2.187 | 8.40 | 380 | 0.0% | +| **2** | Word | 0.2742 | 1.209 | 1.61 | 121,281 | 72.6% | +| **2** | Subword | 1.0411 | 2.058 | 5.99 | 3,190 | 0.0% | +| **3** | Word | 0.0957 | 1.069 | 1.15 | 195,065 | 90.4% | +| **3** | Subword | 0.8570 | 1.811 | 3.83 | 19,088 | 14.3% | +| **4** | Word | 0.0308 🏆 | 1.022 | 1.04 | 223,778 | 96.9% | +| **4** | Subword | 0.5850 | 1.500 | 2.39 | 72,986 | 41.5% | + +### Generated Text Samples (Word-based) + +Below are text samples generated from each word-based Markov chain model: + +**Context Size 1:** + +1. `ku tiyisisa leswaku mati eka buccaneers loko ku betsa timbalelo u tirhile tanihi ntiro wavutshila mi...` +2. `ya siku relero e nkaveni kenya i xirho xa mozambhiki ni moya wun we people amp` +3. `hi maribye ya 70 sangiovese na vasuvuki va a xihlanganisi xa vatsari va minhlangano leyi humesiweke` + +**Context Size 2:** + +1. `hi ku angarhela va vuriwa vatatana ntirho wa vukorhokeri mati na chukele leswi bakiweke hi mahiselo ...` +2. `na ku thyakisiwa vito hi nandzu wa ku tihlanganisa na swilo kumbe swiendlakalo swin wana leswi a` +3. `tani hi psitjemba kambe a va swi dyaka swinene mapa lawa ya nyiketeriweke eka hosi kheto uve` + +**Context Size 3:** + +1. `hi lembe ra huvo leyi ku hlanganisa na swihlawulekisi swa yona xivutiso lexi saleke hi ta mihleketo ...` +2. `hi siku leri lava tswariweke hi siku leri nelson mandela khale ka phresidenti ya afrika dzonga hinkw...` +3. `a ku ri xihaha mpfhuka lexi na xona kutani va famba va cela makhele ehenhla ka xona lomu` + +**Context Size 4:** + +1. `add text add text add text add text add text add text jkl add text add text add text` +2. `text add text add text add text add text m add text add text add text xyz add text` +3. `flag coat of arms small big 300px mauritius i tiko ra afrika leri kumekaka exikhari ka afrika dzonga...` + + +### Generated Text Samples (Subword-based) + +Below are text samples generated from each subword-based Markov chain model: + +**Context Size 1:** + +1. `_keka_yowi._nya_` +2. `awambo_lo_yana_h` +3. `i_ncavu_mananatl` + +**Context Size 2:** + +1. `a_mpito_yi_ntso_h` +2. `i_tioatimisi._mu_` +3. `anitsof_tiwa_hezi` + +**Context Size 3:** + +1. `na_le_ealt=blackso` +2. `ka_mbana_e_tala_nh` +3. `_ku_andla_kufiketo` + +**Context Size 4:** + +1. `_ku_tirho_leyintirh` +2. `_ya_le_makarta)_abu` +3. `_hi_fambia_nelsprud` + + +### Key Findings + +- **Best Predictability:** Context-4 (word) with 96.9% predictability +- **Branching Factor:** Decreases with context size (more deterministic) +- **Memory Trade-off:** Larger contexts require more storage (72,986 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 | 11,975 | +| Total Tokens | 236,184 | +| Mean Frequency | 19.72 | +| Median Frequency | 4 | +| Frequency Std Dev | 174.31 | + +### Most Common Words + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | ku | 7,660 | +| 2 | ya | 7,078 | +| 3 | hi | 6,805 | +| 4 | na | 6,438 | +| 5 | wa | 5,468 | +| 6 | a | 4,111 | +| 7 | eka | 3,768 | +| 8 | va | 3,734 | +| 9 | ka | 3,663 | +| 10 | ra | 2,819 | + +### Least Common Words (from vocabulary) + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | ĂŒbersetzerinnen | 2 | +| 2 | ĂŒbersetzern | 2 | +| 3 | allgemeinen | 2 | +| 4 | digitalisierung | 2 | +| 5 | linguistische | 2 | +| 6 | ĂŒbersetzungsdienstleistungen | 2 | +| 7 | elektronischer | 2 | +| 8 | literarischen | 2 | +| 9 | ĂŒbersetzungssektor | 2 | +| 10 | erfolgt | 2 | + +### Zipf's Law Analysis + +| Metric | Value | +|--------|-------| +| Zipf Coefficient | 1.1100 | +| RÂČ (Goodness of Fit) | 0.990922 | +| Adherence Quality | **excellent** | + +### Coverage Analysis + +| Top N Words | Coverage | +|-------------|----------| +| Top 100 | 49.2% | +| Top 1,000 | 75.0% | +| Top 5,000 | 92.4% | +| Top 10,000 | 98.3% | + +### Key Findings + +- **Zipf Compliance:** RÂČ=0.9909 indicates excellent adherence to Zipf's law +- **High Frequency Dominance:** Top 100 words cover 49.2% of corpus +- **Long Tail:** 1,975 words needed for remaining 1.7% 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 | 0.4521 | 0.4099 | N/A | N/A | +| **mono_64d** | 64 | 0.0928 | 0.3968 | N/A | N/A | +| **mono_128d** | 128 | 0.0116 | 0.4051 | N/A | N/A | +| **aligned_32d** | 32 | 0.4521 🏆 | 0.3983 | 0.0120 | 0.0800 | +| **aligned_64d** | 64 | 0.0928 | 0.4034 | 0.0220 | 0.1360 | +| **aligned_128d** | 128 | 0.0116 | 0.4050 | 0.0220 | 0.1020 | + +### Key Findings + +- **Best Isotropy:** aligned_32d with 0.4521 (more uniform distribution) +- **Semantic Density:** Average pairwise similarity of 0.4031. Lower values indicate better semantic separation. +- **Alignment Quality:** Aligned models achieve up to 2.2% R@1 in cross-lingual retrieval. +- **Recommendation:** 128d aligned for best cross-lingual performance + +--- +## 6. Morphological Analysis (Experimental) + +This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data. + +### 6.1 Productivity & Complexity + +| Metric | Value | Interpretation | Recommendation | +|--------|-------|----------------|----------------| +| Productivity Index | **5.000** | High morphological productivity | Reliable analysis | +| Idiomaticity Gap | **0.051** | 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 | +|--------|----------| +| `-m` | mavulavulelo, mankweng, mindzhuti | +| `-ma` | mavulavulelo, mankweng, makumekaka | +| `-xi` | xitanga, xiendla, xiximiwa | +| `-s` | swisaka, switereka, siku | +| `-t` | thoveriwa, tiviwaka, tise | +| `-ti` | tiviwaka, tise, tikonkulu | +| `-n` | ntlambi, nwaka, ngoni | +| `-e` | endliwa, evuhlongeni, endyangwini | + +#### Productive Suffixes +| Suffix | Examples | +|--------|----------| +| `-a` | thoveriwa, rhandziwa, vitana | +| `-i` | ntlambi, wansati, imini | +| `-e` | gewĂŒnschte, have, compare | +| `-o` | hikwalaho, mavulavulelo, ko | +| `-ni` | imini, koroni, evuhlongeni | +| `-le` | fuwile, uyile, chukele | +| `-wa` | thoveriwa, rhandziwa, endliwa | +| `-ka` | nwaka, swisaka, switereka | + +### 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 | +|------|----------|------------------|----------| +| `ungu` | 1.62x | 54 contexts | hungu, kungu, lungu | +| `andz` | 1.52x | 54 contexts | andza, pandza, kandza | +| `hamb` | 1.74x | 27 contexts | hamba, hambi, rhambu | +| `isiw` | 1.56x | 36 contexts | yisiwa, hisiwa, nwisiwa | +| `tirh` | 1.54x | 35 contexts | tirha, tirhe, tirhi | +| `karh` | 1.70x | 24 contexts | karhi, nkarhi, mikarhi | +| `ngan` | 1.52x | 34 contexts | ngana, ngani, angana | +| `lela` | 1.62x | 26 contexts | hlela, fulela, leland | +| `riwa` | 1.61x | 25 contexts | siriwa, mariwa, soriwa | +| `tson` | 1.68x | 21 contexts | watson, tsongo, tsonga | +| `arhi` | 1.66x | 16 contexts | harhi, karhi, marhi | +| `ngul` | 1.66x | 15 contexts | angula, nguluve, sungule | + +### 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 | +|--------|--------|-----------|----------| +| `-t` | `-a` | 253 words | titimela, tivekaka | +| `-m` | `-a` | 207 words | mfukuzana, mintlwa | +| `-v` | `-i` | 187 words | vulavisisi, vukarhi | +| `-m` | `-i` | 177 words | mthombheni, mimiti | +| `-v` | `-a` | 141 words | vona, vatshila | +| `-e` | `-i` | 138 words | ematini, enyameni | +| `-m` | `-o` | 133 words | mikomiso, minxaxamelo | +| `-t` | `-i` | 129 words | tshuri, tihanyi | +| `-s` | `-a` | 128 words | swekeriwa, swokoma | +| `-t` | `-e` | 125 words | tumbuluxe, tshahiwile | + +### 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 | +|------|-----------------|------------|------| +| tumbuluxiweke | **`tumbuluxiw-e-ke`** | 7.5 | `e` | +| chavisaka | **`chavis-a-ka`** | 7.5 | `a` | +| mudyandhzaka | **`mudyandhz-a-ka`** | 7.5 | `a` | +| swivulavulelo | **`swivulavu-le-lo`** | 7.5 | `le` | +| xirimbyati | **`xirimby-a-ti`** | 7.5 | `a` | +| wusunguleke | **`wusungu-le-ke`** | 7.5 | `le` | +| okmalumkoolkat | **`okmalumkoolk-a-t`** | 7.5 | `a` | +| nyangweni | **`nyangw-e-ni`** | 7.5 | `e` | +| fikeleleke | **`fikele-le-ke`** | 7.5 | `le` | +| xikalanga | **`xi-ka-langa`** | 7.5 | `langa` | +| nhlengani | **`nhleng-a-ni`** | 7.5 | `a` | +| leswivulaka | **`leswivu-la-ka`** | 7.5 | `la` | +| robertson | **`robert-s-on`** | 7.5 | `s` | +| exihlaleni | **`exihla-le-ni`** | 7.5 | `le` | +| hlanganani | **`hlangan-a-ni`** | 7.5 | `a` | + +### 6.6 Linguistic Interpretation + +> **Automated Insight:** +The language Tsonga 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 | **32k BPE** | Best compression (4.76x) | +| N-gram | **2-gram** | Lowest perplexity (203) | +| Markov | **Context-4** | Highest predictability (96.9%) | +| 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. + +**RÂČ (Coefficient of Determination)** +> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1. +> +> *Intuition:* RÂČ near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns. +> +> *What to seek:* RÂČ > 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) +- đŸ€— Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs) +- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) +- đŸ‘€ Author: [Omar Kamali](https://huggingface.co/omarkamali) +- đŸ€ Sponsor: [Featherless AI](https://featherless.ai) +--- +*Generated by Wikilangs Models Pipeline* + +*Report Date: 2026-01-11 01:42:25* diff --git a/models/embeddings/aligned/ts_128d.bin b/models/embeddings/aligned/ts_128d.bin new file mode 100644 index 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