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..8246b20f8ad807808609f8def99af42e97556932 --- /dev/null +++ b/README.md @@ -0,0 +1,773 @@ +--- +language: war +language_name: Waray +language_family: austronesian_philippine_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-austronesian_philippine_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: 3.934 + - name: best_isotropy + type: isotropy + value: 0.8470 + - name: vocabulary_size + type: vocab + value: 0 +generated: 2026-01-11 +--- + +# Waray - Wikilangs Models +## Comprehensive Research Report & Full Ablation Study + +This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Waray** 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** | 3.050x | 3.05 | 0.5424% | 356,557 | +| **16k** | 3.360x | 3.36 | 0.5975% | 323,705 | +| **32k** | 3.653x | 3.66 | 0.6496% | 297,739 | +| **64k** | 3.934x 🏆 | 3.94 | 0.6996% | 276,458 | + +### Tokenization Examples + +Below are sample sentences tokenized with each vocabulary size: + +**Sample 1:** `An Condado han Philadelphia in uska condado ha estado han Estados Unidos nga Pen...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁an ▁condado ▁han ▁philadelphia ▁in ▁uska ▁condado ▁ha ▁estado ▁han ... (+9 more)` | 19 | +| 16k | `▁an ▁condado ▁han ▁philadelphia ▁in ▁uska ▁condado ▁ha ▁estado ▁han ... (+6 more)` | 16 | +| 32k | `▁an ▁condado ▁han ▁philadelphia ▁in ▁uska ▁condado ▁ha ▁estado ▁han ... (+5 more)` | 15 | +| 64k | `▁an ▁condado ▁han ▁philadelphia ▁in ▁uska ▁condado ▁ha ▁estado ▁han ... (+5 more)` | 15 | + +**Sample 2:** `An Condado han Atchison in uska condado ha estado han Estados Unidos nga Missour...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁an ▁condado ▁han ▁at chis on ▁in ▁uska ▁condado ▁ha ... (+7 more)` | 17 | +| 16k | `▁an ▁condado ▁han ▁at chis on ▁in ▁uska ▁condado ▁ha ... (+7 more)` | 17 | +| 32k | `▁an ▁condado ▁han ▁at chis on ▁in ▁uska ▁condado ▁ha ... (+7 more)` | 17 | +| 64k | `▁an ▁condado ▁han ▁at chis on ▁in ▁uska ▁condado ▁ha ... (+7 more)` | 17 | + +**Sample 3:** `An Briatexte amo in usa ka komyun ha departamento han Tarn ngan ha rehiyon han M...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁an ▁b ria tex te ▁amo ▁in ▁usa ▁ka ▁komyun ... (+21 more)` | 31 | +| 16k | `▁an ▁b ria tex te ▁amo ▁in ▁usa ▁ka ▁komyun ... (+19 more)` | 29 | +| 32k | `▁an ▁b ria tex te ▁amo ▁in ▁usa ▁ka ▁komyun ... (+19 more)` | 29 | +| 64k | `▁an ▁b ria tex te ▁amo ▁in ▁usa ▁ka ▁komyun ... (+19 more)` | 29 | + + +### Key Findings + +- **Best Compression:** 64k achieves 3.934x compression +- **Lowest UNK Rate:** 8k with 0.5424% 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 | 6,771 | 12.73 | 587,286 | 39.6% | 56.6% | +| **2-gram** | Subword | 247 🏆 | 7.95 | 5,747 | 68.3% | 99.7% | +| **3-gram** | Word | 17,032 | 14.06 | 1,208,224 | 31.7% | 47.8% | +| **3-gram** | Subword | 1,598 | 10.64 | 47,440 | 34.4% | 76.9% | +| **4-gram** | Word | 50,245 | 15.62 | 3,168,479 | 23.5% | 38.5% | +| **4-gram** | Subword | 5,867 | 12.52 | 301,900 | 26.2% | 55.3% | +| **5-gram** | Word | 68,533 | 16.06 | 2,392,175 | 19.2% | 34.2% | +| **5-gram** | Subword | 14,203 | 13.79 | 1,156,846 | 23.4% | 47.8% | + +### Top 5 N-grams by Size + +**2-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `nahilalakip ha` | 1,196,502 | +| 2 | `in nahilalakip` | 1,196,444 | +| 3 | `in uska` | 1,161,092 | +| 4 | `mga kasarigan` | 1,140,811 | +| 5 | `familia nga` | 1,136,619 | + +**3-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `in nahilalakip ha` | 1,196,442 | +| 2 | `ha genus nga` | 1,059,395 | +| 3 | `nahilalakip ha genus` | 1,059,391 | +| 4 | `uska species han` | 1,059,159 | +| 5 | `in uska species` | 1,059,158 | + +**4-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `nahilalakip ha genus nga` | 1,059,391 | +| 2 | `in nahilalakip ha genus` | 1,059,349 | +| 3 | `in uska species han` | 1,059,153 | +| 4 | `hini subspecies nga nakalista` | 1,022,924 | +| 5 | `waray hini subspecies nga` | 1,022,924 | + +**5-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `in nahilalakip ha genus nga` | 1,059,349 | +| 2 | `waray hini subspecies nga nakalista` | 1,022,924 | +| 3 | `hini subspecies nga nakalista mga` | 1,022,834 | +| 4 | `subspecies nga nakalista mga kasarigan` | 1,022,831 | +| 5 | `in uska species han magnoliopsida` | 202,211 | + +**2-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `a _` | 15,944,152 | +| 2 | `n _` | 13,013,735 | +| 3 | `a n` | 11,288,391 | +| 4 | `_ n` | 11,062,347 | +| 5 | `g a` | 9,641,404 | + +**3-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `a n _` | 7,717,416 | +| 2 | `n g a` | 6,387,666 | +| 3 | `_ n g` | 6,209,737 | +| 4 | `g a _` | 6,146,693 | +| 5 | `_ h a` | 4,483,798 | + +**4-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ n g a` | 6,206,771 | +| 2 | `n g a _` | 4,713,126 | +| 3 | `g a n _` | 2,648,745 | +| 4 | `_ i n _` | 2,593,266 | +| 5 | `s p e c` | 2,524,428 | + +**5-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ n g a _` | 4,702,772 | +| 2 | `s p e c i` | 2,510,965 | +| 3 | `p e c i e` | 2,471,786 | +| 4 | `e c i e s` | 2,467,835 | +| 5 | `c i e s _` | 2,396,660 | + + +### Key Findings + +- **Best Perplexity:** 2-gram (subword) with 247 +- **Entropy Trend:** Decreases with larger n-grams (more predictable) +- **Coverage:** Top-1000 patterns cover ~48% 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 | 0.9524 | 1.935 | 6.58 | 763,014 | 4.8% | +| **1** | Subword | 0.8828 | 1.844 | 5.77 | 3,095 | 11.7% | +| **2** | Word | 0.3392 | 1.265 | 1.87 | 4,989,422 | 66.1% | +| **2** | Subword | 0.6967 | 1.621 | 4.99 | 17,724 | 30.3% | +| **3** | Word | 0.2062 | 1.154 | 1.43 | 9,222,656 | 79.4% | +| **3** | Subword | 0.7365 | 1.666 | 4.85 | 88,254 | 26.3% | +| **4** | Word | 0.1336 🏆 | 1.097 | 1.25 | 13,040,247 | 86.6% | +| **4** | Subword | 0.7108 | 1.637 | 3.87 | 427,478 | 28.9% | + +### Generated Text Samples (Word-based) + +Below are text samples generated from each word-based Markov chain model: + +**Context Size 1:** + +1. `nga nakalista mga sumpay ha familia nga ginhulagway ni lĂ©on dufour hadton an notocelis ngan familia` +2. `in nahilalakip ha genus nga nakalista mga kasarigan website zoonomen zoological nomenclature and ter...` +3. `an leptomastix tanasijtshuki in uska species han bryozoa world checklist of the global lepidoptera n...` + +**Context Size 2:** + +1. `nahilalakip ha genus nga tonnoiriella ngan familia nga terebellidae waray hini subspecies nga nakali...` +2. `in nahilalakip ha genus nga clitoria ngan familia nga rubiaceae waray hini subspecies nga nakalista ...` +3. `in uska species han diptera nga ginhulagway ni henri ernest baillon ngan ginhatag han pagkayana nga ...` + +**Context Size 3:** + +1. `in nahilalakip ha genus nga buxus ngan familia nga cheliferidae waray hini subspecies nga nakalista ...` +2. `ha genus nga exorista ngan familia nga tetranychidae waray hini subspecies nga nakalista mga kasarig...` +3. `nahilalakip ha genus nga miostauropus ngan familia nga rutelidae waray hini subspecies nga nakalista...` + +**Context Size 4:** + +1. `nahilalakip ha genus nga euprosopia ngan familia nga platystomatidae mabibilngan ini ha sri lanka wa...` +2. `in nahilalakip ha genus nga haploops ngan familia nga ampeliscidae waray hini subspecies nga nakalis...` +3. `in uska species han liliopsida nga ginhulagway ni john hutchinson ngan john mcewan dalziel an bertie...` + + +### Generated Text Samples (Subword-based) + +Below are text samples generated from each subword-based Markov chain model: + +**Context Size 1:** + +1. `_doni_n_his_wano` +2. `abs_ithonce_ndul` +3. `ngspysogakid_and` + +**Context Size 2:** + +1. `a_ini_wcspectic_t` +2. `n_in_390._ara).,_` +3. `anterk_nganthopte` + +**Context Size 3:** + +1. `an_pindros_the_e._` +2. `nga_cera_nga_syste` +3. `_nga_sipalmeestrit` + +**Context Size 4:** + +1. `_nga_syahan_diaphen` +2. `nga_ginhulagway_ni_` +3. `gan_familia_nga_nak` + + +### Key Findings + +- **Best Predictability:** Context-4 (word) with 86.6% predictability +- **Branching Factor:** Decreases with context size (more deterministic) +- **Memory Trade-off:** Larger contexts require more storage (427,478 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 | 548,741 | +| Total Tokens | 79,294,042 | +| Mean Frequency | 144.50 | +| Median Frequency | 4 | +| Frequency Std Dev | 11269.34 | + +### Most Common Words + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | nga | 4,703,546 | +| 2 | in | 2,921,835 | +| 3 | an | 2,589,624 | +| 4 | ha | 1,893,436 | +| 5 | han | 1,625,103 | +| 6 | ngan | 1,415,641 | +| 7 | mga | 1,401,419 | +| 8 | species | 1,374,922 | +| 9 | of | 1,347,178 | +| 10 | nahilalakip | 1,196,509 | + +### Least Common Words (from vocabulary) + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | sermersooq | 2 | +| 2 | kulusuk | 2 | +| 3 | sporcle | 2 | +| 4 | loocnon | 2 | +| 5 | estadyum | 2 | +| 6 | katalyst | 2 | +| 7 | haluk | 2 | +| 8 | bilginer | 2 | +| 9 | squibb | 2 | +| 10 | paca | 2 | + +### Zipf's Law Analysis + +| Metric | Value | +|--------|-------| +| Zipf Coefficient | 1.1284 | +| RÂČ (Goodness of Fit) | 0.998865 | +| Adherence Quality | **excellent** | + +### Coverage Analysis + +| Top N Words | Coverage | +|-------------|----------| +| Top 100 | 58.7% | +| Top 1,000 | 76.4% | +| Top 5,000 | 85.5% | +| Top 10,000 | 89.1% | + +### Key Findings + +- **Zipf Compliance:** RÂČ=0.9989 indicates excellent adherence to Zipf's law +- **High Frequency Dominance:** Top 100 words cover 58.7% of corpus +- **Long Tail:** 538,741 words needed for remaining 10.9% 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 | 0.8470 | 0.3349 | N/A | N/A | +| **mono_64d** | 64 | 0.8029 | 0.3103 | N/A | N/A | +| **mono_128d** | 128 | 0.7779 | 0.2306 | N/A | N/A | +| **aligned_32d** | 32 | 0.8470 🏆 | 0.3453 | 0.0380 | 0.2940 | +| **aligned_64d** | 64 | 0.8029 | 0.3113 | 0.1440 | 0.4780 | +| **aligned_128d** | 128 | 0.7779 | 0.2362 | 0.2780 | 0.6400 | + +### Key Findings + +- **Best Isotropy:** aligned_32d with 0.8470 (more uniform distribution) +- **Semantic Density:** Average pairwise similarity of 0.2947. Lower values indicate better semantic separation. +- **Alignment Quality:** Aligned models achieve up to 27.8% 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 | **-0.284** | Low formulaic content | - | + +### 6.2 Affix Inventory (Productive Units) + +These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts. + +#### Productive Prefixes +| Prefix | Examples | +|--------|----------| +| `-a` | ameziane, alucao, astroma | +| `-ma` | majerorum, maracasi, marsa | +| `-s` | sustansiya, szijji, subtriflora | +| `-pa` | parle, pasiphimus, paradontophora | +| `-ca` | callites, callochiton, cabanisii | +| `-p` | parle, psdamyc, polystylata | +| `-ba` | basilewskyanus, bathyprion, balangigan | +| `-b` | biselinifera, basilewskyanus, bicolora | + +#### Productive Suffixes +| Suffix | Examples | +|--------|----------| +| `-s` | basilewskyanus, bomareoides, pasiphimus | +| `-a` | sustansiya, wolynia, biselinifera | +| `-us` | basilewskyanus, pasiphimus, hesperotychus | +| `-is` | niasensis, moniliventris, admotalis | +| `-e` | ocellate, ameziane, parle | +| `-um` | majerorum, curvinervium, tapinelytrum | +| `-i` | kuehhasii, minjujuĆ­i, maracasi | +| `-es` | bomareoides, callites, pedes | + +### 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 | +|------|----------|------------------|----------| +| `kasa` | 2.52x | 45 contexts | akasa, kasaĂŻ, kasal | +| `ilal` | 2.61x | 36 contexts | silal, bilal, kilal | +| `asar` | 2.10x | 62 contexts | casar, asara, pasar | +| `opte` | 1.62x | 202 contexts | opter, scoptes, eoptera | +| `ulag` | 2.87x | 14 contexts | bulag, dulag, nabulag | +| `ahil` | 2.32x | 27 contexts | dahil, tahil, kahili | +| `akal` | 2.15x | 34 contexts | zakal, yakal, bakal | +| `akip` | 3.06x | 11 contexts | lakip, nakipa, kalakip | +| `agwa` | 2.64x | 16 contexts | dagway, magway, nagwara | +| `fami` | 2.07x | 24 contexts | famiy, famil, famiie | +| `peci` | 1.77x | 36 contexts | specis, specia, pecina | +| `subs` | 2.90x | 8 contexts | subst, subsp, subsaga | + +### 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 | +|--------|--------|-----------|----------| +| `-a` | `-s` | 303 words | antoninus, albocingulatus | +| `-p` | `-s` | 258 words | plethobasus, platypygus | +| `-a` | `-a` | 251 words | artopenna, anahita | +| `-p` | `-a` | 250 words | pindica, paradoxophyla | +| `-s` | `-a` | 225 words | spodopa, synpsylla | +| `-s` | `-s` | 220 words | simaethis, spartiformis | +| `-m` | `-s` | 147 words | machetis, mesozonalis | +| `-a` | `-us` | 135 words | antoninus, albocingulatus | +| `-b` | `-s` | 111 words | beombawigulensis, boridiensis | +| `-m` | `-a` | 109 words | missiona, mauritacantha | + +### 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 | +|------|-----------------|------------|------| +| yungchangense | **`yungchangen-s-e`** | 7.5 | `s` | +| betinensis | **`betinen-s-is`** | 7.5 | `s` | +| hundungensis | **`hundungen-s-is`** | 7.5 | `s` | +| yoneyamai | **`yoneyam-a-i`** | 7.5 | `a` | +| austrocolumbiana | **`austrocolumbi-a-na`** | 7.5 | `a` | +| glycereen | **`glycer-e-en`** | 7.5 | `e` | +| agesilaus | **`agesi-la-us`** | 7.5 | `la` | +| discoelongata | **`discoelong-a-ta`** | 7.5 | `a` | +| desordenata | **`desorden-a-ta`** | 7.5 | `a` | +| sexpectinata | **`sexpectin-a-ta`** | 7.5 | `a` | +| pluvigena | **`pluvig-e-na`** | 7.5 | `e` | +| chondrichthyans | **`chondrichthy-a-ns`** | 7.5 | `a` | +| ecorticata | **`ecortic-a-ta`** | 7.5 | `a` | +| binuangan | **`binuang-a-n`** | 7.5 | `a` | +| dallasiana | **`dallasi-a-na`** | 7.5 | `a` | + +### 6.6 Linguistic Interpretation + +> **Automated Insight:** +The language Waray shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding. + +--- +## 7. Summary & Recommendations + +![Performance Dashboard](visualizations/performance_dashboard.png) + +### Production Recommendations + +| Component | Recommended | Rationale | +|-----------|-------------|-----------| +| Tokenizer | **64k BPE** | Best compression (3.93x) | +| N-gram | **2-gram** | Lowest perplexity (247) | +| Markov | **Context-4** | Highest predictability (86.6%) | +| 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 05:52:21* diff --git a/models/embeddings/aligned/war_128d.bin b/models/embeddings/aligned/war_128d.bin new file mode 100644 index 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