diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..98a49a0ec68a24a2ec5bb6d0ab63d8f552335047 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,9 @@ 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/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..a469c196bd926f965f505d263634459e6b0e94a8 --- /dev/null +++ b/README.md @@ -0,0 +1,769 @@ +--- +language: kg +language_name: Kongo +language_family: bantu_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-bantu_central +license: mit +library_name: wikilangs +pipeline_tag: text-generation +datasets: + - omarkamali/wikipedia-monthly +dataset_info: + name: wikipedia-monthly + description: Monthly snapshots of Wikipedia articles across 300+ languages +metrics: + - name: best_compression_ratio + type: compression + value: 4.520 + - name: best_isotropy + type: isotropy + value: 0.1871 + - name: vocabulary_size + type: vocab + value: 0 +generated: 2026-01-10 +--- + +# Kongo - Wikilangs Models +## Comprehensive Research Report & Full Ablation Study + +This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Kongo** 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** | 3.971x | 3.98 | 0.2014% | 98,310 | +| **16k** | 4.333x | 4.34 | 0.2197% | 90,112 | +| **32k** | 4.520x 🏆 | 4.53 | 0.2292% | 86,376 | + +### Tokenization Examples + +Below are sample sentences tokenized with each vocabulary size: + +**Sample 1:** `TĂŒbingen kele kizunga ya Baden-WĂŒrttemberg, Alemanyi.` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁t ĂŒ b ing en ▁kele ▁kizunga ▁ya ▁baden - ... (+4 more)` | 14 | +| 16k | `▁t ĂŒbingen ▁kele ▁kizunga ▁ya ▁baden - wĂŒrttemberg , ▁alemanyi ... (+1 more)` | 11 | +| 32k | `▁tĂŒbingen ▁kele ▁kizunga ▁ya ▁baden - wĂŒrttemberg , ▁alemanyi .` | 10 | + +**Sample 2:** `Ubuntu kele mpila mosi ya Linux. Nkumbu ya Ubuntu (na kikongo: bumuntu to kimunt...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁ubuntu ▁kele ▁mpila ▁mosi ▁ya ▁l inux . ▁nkumbu ▁ya ... (+27 more)` | 37 | +| 16k | `▁ubuntu ▁kele ▁mpila ▁mosi ▁ya ▁linux . ▁nkumbu ▁ya ▁ubuntu ... (+24 more)` | 34 | +| 32k | `▁ubuntu ▁kele ▁mpila ▁mosi ▁ya ▁linux . ▁nkumbu ▁ya ▁ubuntu ... (+24 more)` | 34 | + +**Sample 3:** `kele suka ya kondi ya Repubilika ya KĂŽngo. ya kondi` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁kele ▁suka ▁ya ▁kondi ▁ya ▁repubilika ▁ya ▁kĂŽngo . ▁ya ... (+1 more)` | 11 | +| 16k | `▁kele ▁suka ▁ya ▁kondi ▁ya ▁repubilika ▁ya ▁kĂŽngo . ▁ya ... (+1 more)` | 11 | +| 32k | `▁kele ▁suka ▁ya ▁kondi ▁ya ▁repubilika ▁ya ▁kĂŽngo . ▁ya ... (+1 more)` | 11 | + + +### Key Findings + +- **Best Compression:** 32k achieves 4.520x compression +- **Lowest UNK Rate:** 8k with 0.2014% 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 | 1,534 | 10.58 | 3,731 | 31.2% | 75.1% | +| **2-gram** | Subword | 168 🏆 | 7.39 | 1,390 | 77.7% | 99.7% | +| **3-gram** | Word | 3,413 | 11.74 | 6,815 | 19.8% | 55.8% | +| **3-gram** | Subword | 948 | 9.89 | 8,160 | 43.7% | 83.9% | +| **4-gram** | Word | 6,222 | 12.60 | 11,303 | 15.0% | 42.1% | +| **4-gram** | Subword | 3,320 | 11.70 | 28,230 | 28.1% | 63.0% | +| **5-gram** | Word | 4,107 | 12.00 | 7,664 | 18.9% | 48.5% | +| **5-gram** | Subword | 7,120 | 12.80 | 46,201 | 19.4% | 50.9% | + +### Top 5 N-grams by Size + +**2-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `sambu na` | 862 | +| 2 | `ya kongo` | 704 | +| 3 | `ya bantu` | 652 | +| 4 | `kele na` | 649 | +| 5 | `na yandi` | 613 | + +**3-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `ya kongo ya` | 375 | +| 2 | `repubilika ya kongo` | 369 | +| 3 | `na kati ya` | 361 | +| 4 | `kongo ya dimokalasi` | 332 | +| 5 | `na nima ya` | 314 | + +**4-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `ya kongo ya dimokalasi` | 332 | +| 2 | `repubilika ya kongo ya` | 283 | +| 3 | `ya repubilika ya kongo` | 216 | +| 4 | `mbanza ya kimfumu ya` | 181 | +| 5 | `kimfumu ya kizunga ya` | 164 | + +**5-grams (Word):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `repubilika ya kongo ya dimokalasi` | 270 | +| 2 | `ya repubilika ya kongo ya` | 172 | +| 3 | `mbanza ya kimfumu ya kizunga` | 161 | +| 4 | `ya kimfumu ya kizunga ya` | 160 | +| 5 | `kele mbanza kimfumu ya yinsi` | 109 | + +**2-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `a _` | 66,512 | +| 2 | `_ y` | 30,798 | +| 3 | `y a` | 27,604 | +| 4 | `_ n` | 21,445 | +| 5 | `_ k` | 21,155 | + +**3-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ y a` | 26,479 | +| 2 | `y a _` | 23,101 | +| 3 | `n a _` | 15,424 | +| 4 | `_ n a` | 11,972 | +| 5 | `a _ k` | 11,963 | + +**4-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ y a _` | 22,832 | +| 2 | `_ n a _` | 11,648 | +| 3 | `a _ y a` | 8,946 | +| 4 | `u _ y a` | 6,395 | +| 5 | `a k a _` | 5,692 | + +**5-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `a _ y a _` | 7,419 | +| 2 | `u _ y a _` | 5,909 | +| 3 | `_ y a _ k` | 5,126 | +| 4 | `i _ y a _` | 4,012 | +| 5 | `a _ n a _` | 3,703 | + + +### Key Findings + +- **Best Perplexity:** 2-gram (subword) with 168 +- **Entropy Trend:** Decreases with larger n-grams (more predictable) +- **Coverage:** Top-1000 patterns cover ~51% 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 | 0.6753 | 1.597 | 3.82 | 13,889 | 32.5% | +| **1** | Subword | 1.1440 | 2.210 | 8.61 | 367 | 0.0% | +| **2** | Word | 0.2863 | 1.219 | 1.74 | 52,840 | 71.4% | +| **2** | Subword | 0.9575 | 1.942 | 5.20 | 3,158 | 4.3% | +| **3** | Word | 0.1457 | 1.106 | 1.27 | 91,186 | 85.4% | +| **3** | Subword | 0.7225 | 1.650 | 3.20 | 16,384 | 27.8% | +| **4** | Word | 0.0715 🏆 | 1.051 | 1.11 | 115,336 | 92.9% | +| **4** | Subword | 0.4661 | 1.381 | 2.03 | 52,366 | 53.4% | + +### Generated Text Samples (Word-based) + +Below are text samples generated from each word-based Markov chain model: + +**Context Size 1:** + +1. `ya 8 bantu ya kimpwanza mosi ya saint lucia saint marin serbie slovakia slovenia solomon islands` +2. `na provense ya kutadila ntalu ya kutwadisa mpi bo na mayi na ouganda bantu ya dibulu` +3. `kele mbanza goma mpi yo mutindu yina vandaka me tulaka yandi kele kaka na biro ya` + +**Context Size 2:** + +1. `sambu na kisalu mpi kutomisa bima ya nkaka ke sala nde bantu yina vandaka kumonisa nsoba ya` +2. `ya kongo rdc category sĂ©nateur ya kasai na baluba ne mvuta ya bantu ya nkaka ya me` +3. `ya bantu ya mubulu na mvu bo me binga sambu na kuzabisa luzayisu yayi kusalama na kutadila` + +**Context Size 3:** + +1. `ya kongo ya dimokalasi mbanza mfumu ya kizunga jiangsu ya sina ya sina` +2. `na kati ya bazulunalu yina salaka mambu ya yimbi mpe kimbeni yina ke vuandaka na mukidi ya nzadi` +3. `repubilika ya kongo ya dimokalasi sambu bo kezabaka nde nzo nkanda vandaka kufuta yves piron mpi sam...` + +**Context Size 4:** + +1. `repubilika ya kongo ya dimokalasi ya kati` +2. `ya kongo ya dimokalasi category guvernere ya tshopo category lubutuku na zaire category avocat congo...` +3. `ya repubilika ya kongo yandi vuandaka muene ya brazzaville ti kuna nĂĄ ntumua ya ntete ya repubilika ...` + + +### Generated Text Samples (Subword-based) + +Below are text samples generated from each subword-based Markov chain model: + +**Context Size 1:** + +1. `_nakama_mbo_mba_` +2. `alaba_a_yi_yikin` +3. `n_yo_yasa_yingar` + +**Context Size 2:** + +1. `a_keles)_ta._ba_y` +2. `_yan_john_luzwalb` +3. `ya_ntu_ya_yandimo` + +**Context Size 3:** + +1. `_ya_kizunga_na_ket` +2. `ya_kusalu_ya_los_k` +3. `na_nkandakaataka_k` + +**Context Size 4:** + +1. `_ya_kuponaka_kimban` +2. `_na_ntinu,_kinkundi` +3. `a_ya_repubilika_ya_` + + +### Key Findings + +- **Best Predictability:** Context-4 (word) with 92.9% predictability +- **Branching Factor:** Decreases with context size (more deterministic) +- **Memory Trade-off:** Larger contexts require more storage (52,366 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 | 6,048 | +| Total Tokens | 147,208 | +| Mean Frequency | 24.34 | +| Median Frequency | 3 | +| Frequency Std Dev | 343.74 | + +### Most Common Words + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | ya | 22,856 | +| 2 | na | 11,712 | +| 3 | kele | 3,421 | +| 4 | yandi | 2,406 | +| 5 | yina | 2,286 | +| 6 | mpi | 2,116 | +| 7 | ke | 1,925 | +| 8 | bantu | 1,619 | +| 9 | bo | 1,431 | +| 10 | ti | 1,160 | + +### Least Common Words (from vocabulary) + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | lukwikulu | 2 | +| 2 | kinama | 2 | +| 3 | bangolo | 2 | +| 4 | difuta | 2 | +| 5 | mbatukulu | 2 | +| 6 | kifumba | 2 | +| 7 | weto | 2 | +| 8 | metangama | 2 | +| 9 | dieumerci | 2 | +| 10 | xoon | 2 | + +### Zipf's Law Analysis + +| Metric | Value | +|--------|-------| +| Zipf Coefficient | 1.1604 | +| RÂČ (Goodness of Fit) | 0.989979 | +| Adherence Quality | **excellent** | + +### Coverage Analysis + +| Top N Words | Coverage | +|-------------|----------| +| Top 100 | 59.2% | +| Top 1,000 | 85.9% | +| Top 5,000 | 98.6% | +| Top 10,000 | 0.0% | + +### Key Findings + +- **Zipf Compliance:** RÂČ=0.9900 indicates excellent adherence to Zipf's law +- **High Frequency Dominance:** Top 100 words cover 59.2% of corpus +- **Long Tail:** -3,952 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 | 0.1871 🏆 | 0.4938 | N/A | N/A | +| **mono_64d** | 64 | 0.0298 | 0.4879 | N/A | N/A | +| **mono_128d** | 128 | 0.0037 | 0.5051 | N/A | N/A | +| **aligned_32d** | 32 | 0.1871 | 0.5017 | 0.0140 | 0.0900 | +| **aligned_64d** | 64 | 0.0298 | 0.4772 | 0.0120 | 0.1260 | +| **aligned_128d** | 128 | 0.0037 | 0.4863 | 0.0100 | 0.1280 | + +### Key Findings + +- **Best Isotropy:** mono_32d with 0.1871 (more uniform distribution) +- **Semantic Density:** Average pairwise similarity of 0.4920. Lower values indicate better semantic separation. +- **Alignment Quality:** Aligned models achieve up to 1.4% 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 | **-0.048** | 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` | mphotho, motĂĄngo, mapi | +| `-k` | kimenga, kontina, kubutukaka | +| `-ba` | balongoki, baviĂšre, baministre | +| `-b` | bzl, balongoki, baviĂšre | +| `-ku` | kubutukaka, kusadisa, kufua | +| `-n` | nima, nzundu, ndalama | +| `-ma` | mapi, maulalo, manimba | +| `-ki` | kimenga, kimama, kinkita | + +#### Productive Suffixes +| Suffix | Examples | +|--------|----------| +| `-a` | kimenga, tendula, nima | +| `-e` | laurĂšne, jenerale, baviĂšre | +| `-ka` | kubutukaka, twadisaka, vwandaka | +| `-i` | tournoi, balongoki, mapi | +| `-s` | chinois, awards, mois | +| `-o` | mphotho, motĂĄngo, mpozo | +| `-u` | nzundu, banduku, dibuku | +| `-n` | installation, radiodiffusion, american | + +### 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 | +|------|----------|------------------|----------| +| `anga` | 1.59x | 42 contexts | sanga, tanga, nanga | +| `angu` | 1.34x | 33 contexts | hangu, kangu, wangu | +| `kand` | 1.75x | 12 contexts | kanda, kandy, nkandu | +| `tion` | 1.77x | 11 contexts | option, action, motion | +| `unga` | 1.38x | 23 contexts | zunga, lunga, tunga | +| `ambu` | 1.31x | 26 contexts | sambu, mambu, wambu | +| `ndak` | 1.61x | 12 contexts | vandaka, bandaka, fundaka | +| `alak` | 1.60x | 12 contexts | palaki, talaka, salaka | +| `laka` | 1.65x | 11 contexts | kulaka, talaka, bulaka | +| `kisa` | 1.63x | 10 contexts | kisaka, vukisa, kisalu | +| `anza` | 1.52x | 12 contexts | sanza, kanza, banza | +| `bans` | 1.65x | 8 contexts | bansi, banswa, bansaka | + +### 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 | +|--------|--------|-----------|----------| +| `-k` | `-a` | 369 words | kimenga, kontina | +| `-k` | `-ka` | 114 words | kubutukaka, kusalaka | +| `-m` | `-a` | 102 words | mbĂąnza, manimba | +| `-ba` | `-a` | 90 words | bandĂźnga, bafwana | +| `-k` | `-la` | 55 words | kufokula, kubokila | +| `-n` | `-a` | 48 words | nima, ndalama | +| `-k` | `-i` | 48 words | kasi, kimosi | +| `-ba` | `-i` | 46 words | balongoki, bankengi | +| `-b` | `-a` | 46 words | bandĂźnga, beba | +| `-ba` | `-e` | 45 words | baviĂšre, baministre | + +### 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 | +|------|-----------------|------------|------| +| kudibingaka | **`ku-di-bingaka`** | 7.5 | `bingaka` | +| twadisama | **`twadis-a-ma`** | 7.5 | `a` | +| kesalamaka | **`kesalam-a-ka`** | 7.5 | `a` | +| tungamaka | **`tunga-ma-ka`** | 7.5 | `ma` | +| kendimaka | **`kendim-a-ka`** | 7.5 | `a` | +| commandant | **`command-a-nt`** | 7.5 | `a` | +| nwaninaka | **`nwanin-a-ka`** | 7.5 | `a` | +| kukutanaka | **`kukutan-a-ka`** | 7.5 | `a` | +| entrepreneuriat | **`entrepreneuri-a-t`** | 7.5 | `a` | +| azərbaycan | **`azərbayc-a-n`** | 7.5 | `a` | +| nsambukila | **`nsambu-ki-la`** | 7.5 | `ki` | +| kudibanza | **`ku-di-banza`** | 7.5 | `banza` | +| twadisaka | **`twadis-a-ka`** | 7.5 | `a` | +| acheulean | **`acheule-a-n`** | 7.5 | `a` | +| championnat | **`championn-a-t`** | 7.5 | `a` | + +### 6.6 Linguistic Interpretation + +> **Automated Insight:** +The language Kongo 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 (4.52x) | +| N-gram | **2-gram** | Lowest perplexity (168) | +| Markov | **Context-4** | Highest predictability (92.9%) | +| 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: 2026-01-10 07:31:40* diff --git a/kg_morph_tokenizer.json b/kg_morph_tokenizer.json new file mode 100644 index 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