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..437d6b2933be1efb2511f47cce53e9955a704975 --- /dev/null +++ b/README.md @@ -0,0 +1,771 @@ +--- +language: za +language_name: Zhuang +language_family: taikadai_other +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-taikadai_other +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: 3.419 + - name: best_isotropy + type: isotropy + value: 0.1745 + - name: vocabulary_size + type: vocab + value: 0 +generated: 2026-01-11 +--- + +# Zhuang - Wikilangs Models +## Comprehensive Research Report & Full Ablation Study + +This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Zhuang** Wikipedia data. +We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings. + +## 📋 Repository Contents + +### Models & Assets + +- Tokenizers (8k, 16k, 32k, 64k) +- N-gram models (2, 3, 4, 5-gram) +- Markov chains (context of 1, 2, 3, 4 and 5) +- Subword N-gram and Markov chains +- Embeddings in various sizes and dimensions (aligned and unaligned) +- Language Vocabulary +- Language Statistics + +![Performance Dashboard](visualizations/performance_dashboard.png) + +### Analysis and Evaluation + +- [1. Tokenizer Evaluation](#1-tokenizer-evaluation) +- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation) +- [3. Markov Chain Evaluation](#3-markov-chain-evaluation) +- [4. Vocabulary Analysis](#4-vocabulary-analysis) +- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation) +- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental) +- [7. Summary & Recommendations](#7-summary--recommendations) +- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide) +- [Visualizations Index](#visualizations-index) + +--- +## 1. Tokenizer Evaluation + +![Tokenizer Compression](visualizations/tokenizer_compression.png) + +![Tokenizer Fertility](visualizations/tokenizer_fertility.png) + +![Tokenizer OOV](visualizations/tokenizer_oov.png) + +![Total Tokens](visualizations/tokenizer_total_tokens.png) + +### Results + +| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens | +|------------|-------------|---------------|----------|--------------| +| **8k** | 2.806x | 2.81 | 0.4774% | 128,613 | +| **16k** | 3.128x | 3.14 | 0.5321% | 115,393 | +| **32k** | 3.419x 🏆 | 3.43 | 0.5815% | 105,580 | + +### Tokenization Examples + +Below are sample sentences tokenized with each vocabulary size: + +**Sample 1:** `Bingh Conghhozhau（Vahgun：白喉）《常见病证壮医诊疗规范》, dwg cungj bingh ndeu. Doeg Wnq bingh W...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁bingh ▁congh hozhau ( vahgun : 白 喉 )《 常见病证壮医诊疗规范 ... (+18 more)` | 28 | +| 16k | `▁bingh ▁congh hozhau ( vahgun : 白 喉 )《 常见病证壮医诊疗规范 ... (+18 more)` | 28 | +| 32k | `▁bingh ▁conghhozhau ( vahgun : 白喉 )《 常见病证壮医诊疗规范 》, ▁dwg ... (+16 more)` | 26 | + +**Sample 2:** `Mali dwg aen guekgya youq Feihcouh, soujduh dwg Bamako. Feihcouh` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁mal i ▁dwg ▁aen ▁guekgya ▁youq ▁feihcouh , ▁soujduh ▁dwg ... (+6 more)` | 16 | +| 16k | `▁mali ▁dwg ▁aen ▁guekgya ▁youq ▁feihcouh , ▁soujduh ▁dwg ▁bamak ... (+3 more)` | 13 | +| 32k | `▁mali ▁dwg ▁aen ▁guekgya ▁youq ▁feihcouh , ▁soujduh ▁dwg ▁bamako ... (+2 more)` | 12 | + +**Sample 3:** `Niger dwg aen guekgya youq Feihcouh, soujduh dwg Niamey. Feihcouh` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁niger ▁dwg ▁aen ▁guekgya ▁youq ▁feihcouh , ▁soujduh ▁dwg ▁ni ... (+4 more)` | 14 | +| 16k | `▁niger ▁dwg ▁aen ▁guekgya ▁youq ▁feihcouh , ▁soujduh ▁dwg ▁niamey ... (+2 more)` | 12 | +| 32k | `▁niger ▁dwg ▁aen ▁guekgya ▁youq ▁feihcouh , ▁soujduh ▁dwg ▁niamey ... (+2 more)` | 12 | + + +### Key Findings + +- **Best Compression:** 32k achieves 3.419x compression +- **Lowest UNK Rate:** 8k with 0.4774% 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 | 1,324 | 10.37 | 2,862 | 33.4% | 75.3% | +| **2-gram** | Subword | 292 🏆 | 8.19 | 2,421 | 66.8% | 98.4% | +| **3-gram** | Word | 1,603 | 10.65 | 3,591 | 31.8% | 70.2% | +| **3-gram** | Subword | 1,849 | 10.85 | 11,233 | 31.0% | 74.9% | +| **4-gram** | Word | 3,210 | 11.65 | 7,510 | 26.0% | 54.9% | +| **4-gram** | Subword | 7,224 | 12.82 | 38,214 | 16.4% | 49.0% | +| **5-gram** | Word | 2,596 | 11.34 | 5,995 | 28.0% | 58.6% | +| **5-gram** | Subword | 16,250 | 13.99 | 64,425 | 10.8% | 35.3% | + +### Top 5 N-grams by Size + +**2-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `dwg aen` | 951 | +| 2 | `doeg wnq` | 505 | +| 3 | `yinzminz gunghozgoz` | 489 | +| 4 | `cunghvaz yinzminz` | 409 | +| 5 | `dwg cungj` | 393 | + +**3-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `cunghvaz yinzminz gunghozgoz` | 409 | +| 2 | `vwnzyen doiqciuq baihrog` | 260 | +| 3 | `doiqciuq baihrog lienzcanh` | 259 | +| 4 | `saehgienh doekfag dai` | 204 | +| 5 | `doekfag dai nyied` | 203 | + +**4-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `vwnzyen doiqciuq baihrog lienzcanh` | 259 | +| 2 | `dwg aen swhyienzsoq beij` | 198 | +| 3 | `youq ligmoq ndeu bi` | 192 | +| 4 | `ligmoq ndeu bi neix` | 192 | +| 5 | `𬆗 ngoenzciet 節日 𭥓節` | 192 | + +**5-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `dai 去世 𬆗 ngoenzciet 節日` | 192 | +| 2 | `去世 𬆗 ngoenzciet 節日 𭥓節` | 192 | +| 3 | `youq ligmoq ndeu bi neix` | 192 | +| 4 | `ligmoq ndeu bi neix daj` | 192 | +| 5 | `doekfag 出生 𬻨𰅞 dai 去世` | 191 | + +**2-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `n g` | 30,692 | +| 2 | `e n` | 25,550 | +| 3 | `a e` | 19,708 | +| 4 | `_ d` | 17,651 | +| 5 | `z _` | 17,038 | + +**3-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `e n g` | 8,544 | +| 2 | `n g h` | 8,287 | +| 3 | `a e n` | 7,258 | +| 4 | `_ d a` | 6,381 | +| 5 | `i n g` | 6,244 | + +**4-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `n g h _` | 3,421 | +| 2 | `a e n _` | 3,094 | +| 3 | `d w g _` | 3,059 | +| 4 | `n g j _` | 2,964 | +| 5 | `_ d w g` | 2,805 | + +**5-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ d w g _` | 2,772 | +| 2 | `_ a e n _` | 2,723 | +| 3 | `_ c u n g` | 2,337 | +| 4 | `_ y o u q` | 1,886 | +| 5 | `y o u q _` | 1,809 | + + +### Key Findings + +- **Best Perplexity:** 2-gram (subword) with 292 +- **Entropy Trend:** Decreases with larger n-grams (more predictable) +- **Coverage:** Top-1000 patterns cover ~35% 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 | 0.6137 | 1.530 | 3.36 | 23,267 | 38.6% | +| **1** | Subword | 1.1528 | 2.223 | 5.67 | 3,715 | 0.0% | +| **2** | Word | 0.1628 | 1.119 | 1.30 | 77,229 | 83.7% | +| **2** | Subword | 0.3332 | 1.260 | 2.13 | 21,044 | 66.7% | +| **3** | Word | 0.0535 | 1.038 | 1.09 | 99,423 | 94.6% | +| **3** | Subword | 0.3942 | 1.314 | 2.14 | 44,681 | 60.6% | +| **4** | Word | 0.0282 🏆 | 1.020 | 1.04 | 106,492 | 97.2% | +| **4** | Subword | 0.3653 | 1.288 | 1.80 | 95,273 | 63.5% | + +### Generated Text Samples (Word-based) + +Below are text samples generated from each word-based Markov chain model: + +**Context Size 1:** + +1. `dwg aen yenzgiucunghsinh ciusou caeuq cwngfuj doeg wnq gij nei swenj hwnjdaeuj lo hoeng dingzlai seu...` +2. `aen fap hingzcwng bumwnz caeuq dajciengj gij guenjleix gaicawx daehyinh gij swhliu nangqdaengz cwzyi...` +3. `youq imdb ngaeuzgyae daigoz caivi nienz caeuq baugau bonjdieg caeuq gwzming dihgaeuq miz 5 aen fap` + +**Context Size 2:** + +1. `dwg aen hawsingz youq baihnamz yacouh soujduh de dwg youq yiengh lizsij cingzgvang lawz cungj mbouj ...` +2. `doeg wnq haijnanz vwnzyen doxgven lienhciep baihrog meijgoz dakota` +3. `cunghvaz yinzminz gunghozgoz de hix dwg aen vuengzciuz cunghgoz dungjci daj 960 nienz ciq nienz` + +**Context Size 3:** + +1. `cunghvaz yinzminz gunghozgoz 115 中华人民共和国公共图书馆法 aen fap duzsuhgvanj caezyungh cunghvaz yinzminz gungh...` +2. `vwnzyen doiqciuq baihrog lienzcanh 港珠澳大桥管理局官網 香港政府 港珠澳大橋香港段網頁 澳門政府 港珠澳大橋交通資訊` +3. `saehgienh doekfag dai nyied` + +**Context Size 4:** + +1. `dwg aen swhyienzsoq beij gouj cib gouj nyaeq` +2. `ligmoq ndeu bi neix daj singhgiz roek codaeuz saehgienh 事件 doekfag 出生 𬻨𰅞 dai 去世 𬆗 ngoenzciet 節日 𭥓節` +3. `去世 𬆗 ngoenzciet 節日 𭥓節 1 roxnaeuz 2 nyied cieng lienhciep baihrog` + + +### Generated Text Samples (Subword-based) + +Below are text samples generated from each subword-based Markov chain model: + +**Context Size 1:** + +1. `_daeramionq_gmbi` +2. `ngveu_caenggh_do` +3. `elou_cinyizhadwg` + +**Context Size 2:** + +1. `ngzsoux_geiz_cuz_` +2. `enh_hatitzahgingz` +3. `aenz_dengh_sawz_d` + +**Context Size 3:** + +1. `eng_cei._noemhyung` +2. `ngh/www.gxfs.gover` +3. `aengniengz_dawz_it` + +**Context Size 4:** + +1. `ngh_baenz。de_mbouj_` +2. `aen_ngawh_gvidinghc` +3. `dwg_boux_cung_hawj_` + + +### Key Findings + +- **Best Predictability:** Context-4 (word) with 97.2% predictability +- **Branching Factor:** Decreases with context size (more deterministic) +- **Memory Trade-off:** Larger contexts require more storage (95,273 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 | 8,300 | +| Total Tokens | 126,265 | +| Mean Frequency | 15.21 | +| Median Frequency | 3 | +| Frequency Std Dev | 76.56 | + +### Most Common Words + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | dwg | 3,064 | +| 2 | aen | 2,774 | +| 3 | youq | 1,787 | +| 4 | gij | 1,716 | +| 5 | caeuq | 1,707 | +| 6 | de | 1,173 | +| 7 | dangj | 1,155 | +| 8 | ndeu | 1,074 | +| 9 | miz | 1,071 | +| 10 | nienz | 955 | + +### Least Common Words (from vocabulary) + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | eb | 2 | +| 2 | domq | 2 | +| 3 | roxcaek | 2 | +| 4 | cazlix | 2 | +| 5 | yienzyaigyaj | 2 | +| 6 | ciglouz | 2 | +| 7 | gaiconh | 2 | +| 8 | siujse | 2 | +| 9 | daihdaeuz | 2 | +| 10 | ndawdeih | 2 | + +### Zipf's Law Analysis + +| Metric | Value | +|--------|-------| +| Zipf Coefficient | 1.0806 | +| R² (Goodness of Fit) | 0.988587 | +| Adherence Quality | **excellent** | + +### Coverage Analysis + +| Top N Words | Coverage | +|-------------|----------| +| Top 100 | 39.4% | +| Top 1,000 | 74.5% | +| Top 5,000 | 94.4% | +| Top 10,000 | 0.0% | + +### Key Findings + +- **Zipf Compliance:** R²=0.9886 indicates excellent adherence to Zipf's law +- **High Frequency Dominance:** Top 100 words cover 39.4% of corpus +- **Long Tail:** -1,700 words needed for remaining 100.0% 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 | 0.1745 🏆 | 0.4909 | N/A | N/A | +| **mono_64d** | 64 | 0.0267 | 0.4790 | N/A | N/A | +| **mono_128d** | 128 | 0.0037 | 0.5068 | N/A | N/A | +| **aligned_32d** | 32 | 0.1745 | 0.4940 | 0.0060 | 0.0520 | +| **aligned_64d** | 64 | 0.0267 | 0.4851 | 0.0060 | 0.0760 | +| **aligned_128d** | 128 | 0.0037 | 0.4813 | 0.0080 | 0.0580 | + +### Key Findings + +- **Best Isotropy:** mono_32d with 0.1745 (more uniform distribution) +- **Semantic Density:** Average pairwise similarity of 0.4895. Lower values indicate better semantic separation. +- **Alignment Quality:** Aligned models achieve up to 0.8% 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 | **0.660** | 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 | +|--------|----------| +| `-s` | seen, soengqfangz, seite | +| `-g` | gaemmaenh, gaenj, gisuz | +| `-c` | cawqfad, cwnggen, cingsuj | +| `-d` | duzguk, daengx, dawznduj | +| `-b` | bouxciengqfwen, bihbingz, besatzungen | +| `-da` | daengx, dawznduj, daiseiq | +| `-m` | mostly, mboengq, mittig | +| `-h` | hermann, hwng, houz | + +#### Productive Suffixes +| Suffix | Examples | +|--------|----------| +| `-z` | soengqfangz, vangz, bihbingz | +| `-h` | gaemmaenh, veih, haemh | +| `-j` | gaenj, cingsuj, dawznduj | +| `-n` | hermann, seen, bouxciengqfwen | +| `-g` | hwng, öffnung, mittig | +| `-ng` | hwng, öffnung, doxceng | +| `-gh` | sihgingh, doucwngh, swhcungh | +| `-gz` | soengqfangz, vangz, bihbingz | + +### 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 | +|------|----------|------------------|----------| +| `engz` | 1.59x | 48 contexts | mengz, rengz, nengz | +| `ungh` | 1.57x | 37 contexts | yungh, cungh, gungh | +| `engh` | 1.66x | 28 contexts | naengh, nyengh, yiengh | +| `oeng` | 1.53x | 37 contexts | coeng, doeng, soeng | +| `ieng` | 1.57x | 33 contexts | sieng, cieng, rieng | +| `angj` | 1.63x | 24 contexts | dangj, gangj, yangj | +| `ingz` | 1.52x | 27 contexts | lingz, cingz, hingz | +| `aeng` | 1.54x | 25 contexts | naeng, daeng, laeng | +| `angh` | 1.48x | 26 contexts | gangh, yangh, vangh | +| `ungj` | 1.68x | 15 contexts | cungj, dungj, dungjci | +| `ingh` | 1.49x | 20 contexts | cingh, lingh, dingh | +| `daen` | 1.56x | 17 contexts | daeng, ndaen, daenj | + +### 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 | +|--------|--------|-----------|----------| +| `-g` | `-z` | 135 words | gisuz, gungyungz | +| `-g` | `-h` | 104 words | gaemmaenh, gveicouh | +| `-c` | `-h` | 94 words | ciemqfamh, cugciemh | +| `-c` | `-z` | 92 words | congz, cauhbaenz | +| `-d` | `-z` | 88 words | deuz, denhgoz | +| `-d` | `-h` | 87 words | doengjnyouh, diengzcah | +| `-s` | `-z` | 84 words | soengqfangz, swyenz | +| `-b` | `-z` | 81 words | bihbingz, bienliz | +| `-s` | `-h` | 81 words | saeh, sihgingh | +| `-d` | `-j` | 62 words | dawznduj, doxbeij | + +### 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 | +|------|-----------------|------------|------| +| habdoengz | **`ha-b-doengz`** | 6.0 | `doengz` | +| doengzeiq | **`doengz-e-iq`** | 6.0 | `doengz` | +| kampfraumes | **`kampfraum-es`** | 4.5 | `kampfraum` | +| ausführungen | **`ausführung-en`** | 4.5 | `ausführung` | +| individuals | **`individual-s`** | 4.5 | `individual` | +| totalverluste | **`totalverlust-e`** | 4.5 | `totalverlust` | +| hergestellten | **`hergestellt-en`** | 4.5 | `hergestellt` | +| ausgerüsteten | **`ausgerüstet-en`** | 4.5 | `ausgerüstet` | +| misuhcangj | **`mi-s-uhcangj`** | 4.5 | `uhcangj` | +| cwngcigyah | **`cwngcigya-h`** | 4.5 | `cwngcigya` | +| interviews | **`interview-s`** | 4.5 | `interview` | +| eingebaute | **`eingebaut-e`** | 4.5 | `eingebaut` | +| diengingh | **`diengi-ng-h`** | 3.0 | `diengi` | +| mingzleih | **`mingzl-e-ih`** | 3.0 | `mingzl` | +| cangqmaenh | **`cangqma-en-h`** | 3.0 | `cangqma` | + +### 6.6 Linguistic Interpretation + +> **Automated Insight:** +The language Zhuang 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 | **32k BPE** | Best compression (3.42x) | +| N-gram | **2-gram** | Lowest perplexity (292) | +| Markov | **Context-4** | Highest predictability (97.2%) | +| Embeddings | **100d** | Balanced semantic capture and isotropy | + + +--- +## Appendix: Metrics Glossary & Interpretation Guide + +This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report. + +### Tokenizer Metrics + +**Compression Ratio** +> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text. +> +> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average. +> +> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information. + +**Average Token Length (Fertility)** +> *Definition:* Mean number of characters per token produced by the tokenizer. +> +> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length. +> +> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens. + +**Unknown Token Rate (OOV Rate)** +> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent. +> +> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences. +> +> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback. + +### N-gram Model Metrics + +**Perplexity** +> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction. +> +> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options. +> +> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size. + +**Entropy** +> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy. +> +> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character. +> +> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases. + +**Coverage (Top-K)** +> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams. +> +> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage. +> +> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text. + +### Markov Chain Metrics + +**Average Entropy** +> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction. +> +> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations). +> +> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions. + +**Branching Factor** +> *Definition:* Average number of unique next tokens observed for each context. +> +> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive). +> +> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains. + +**Predictability** +> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are. +> +> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes. +> +> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output. + +### Vocabulary & Zipf's Law Metrics + +**Zipf's Coefficient** +> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1. +> +> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare. +> +> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text. + +**R² (Coefficient of Determination)** +> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1. +> +> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns. +> +> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora. + +**Vocabulary Coverage** +> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words. +> +> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words. +> +> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary. + +### Word Embedding Metrics + +**Isotropy** +> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values. +> +> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness. +> +> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy. + +**Average Norm** +> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space. +> +> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained. +> +> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation). + +**Cosine Similarity** +> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction). +> +> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings. +> +> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7. + +**t-SNE Visualization** +> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization. +> +> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence. +> +> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure. + +### General Interpretation Guidelines + +1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer). +2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate). +3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification. +4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature. +5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages. + + +### Visualizations Index + +| Visualization | Description | +|---------------|-------------| +| Tokenizer Compression | Compression ratios by vocabulary size | +| Tokenizer Fertility | Average token length by vocabulary | +| Tokenizer OOV | Unknown token rates | +| Tokenizer Total Tokens | Total tokens by vocabulary | +| N-gram Perplexity | Perplexity by n-gram size | +| N-gram Entropy | Entropy by n-gram size | +| N-gram Coverage | Top pattern coverage | +| N-gram Unique | Unique n-gram counts | +| Markov Entropy | Entropy by context size | +| Markov Branching | Branching factor by context | +| Markov Contexts | Unique context counts | +| Zipf's Law | Frequency-rank distribution with fit | +| Vocab Frequency | Word frequency distribution | +| Top 20 Words | Most frequent words | +| Vocab Coverage | Cumulative coverage curve | +| Embedding Isotropy | Vector space uniformity | +| Embedding Norms | Vector magnitude distribution | +| Embedding Similarity | Word similarity heatmap | +| Nearest Neighbors | Similar words for key terms | +| t-SNE Words | 2D word embedding visualization | +| t-SNE Sentences | 2D sentence embedding visualization | +| Position Encoding | Encoding method comparison | +| Model Sizes | Storage requirements | +| Performance Dashboard | Comprehensive performance overview | + +--- +## About This Project + +### Data Source + +Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages. + +### Project + +A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language. + +### Maintainer + +[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com) + +### Citation + +If you use these models in your research, please cite: + +```bibtex +@misc{wikilangs2025, + author = {Kamali, Omar}, + title = {Wikilangs: Open NLP Models for Wikipedia Languages}, + year = {2025}, + doi = {10.5281/zenodo.18073153}, + publisher = {Zenodo}, + url = {https://huggingface.co/wikilangs} + institution = {Omneity Labs} +} +``` + +### License + +MIT License - Free for academic and commercial use. + +### Links + +- 🌐 Website: [wikilangs.org](https://wikilangs.org) +- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs) +- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) +- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali) +- 🤝 Sponsor: [Featherless AI](https://featherless.ai) +--- +*Generated by Wikilangs Models Pipeline* + +*Report Date: 2026-01-11 05:47:48* diff --git a/models/embeddings/aligned/za_128d.bin b/models/embeddings/aligned/za_128d.bin new file mode 100644 index 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