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README.md

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+---
+language: tl
+language_name: Filipino
+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: 4.787
+  - name: best_isotropy
+    type: isotropy
+    value: 0.8025
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Filipino - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Filipino** 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** | 3.870x | 3.87 | 0.0846% | 1,144,874 |
+| **16k** | 4.258x | 4.26 | 0.0930% | 1,040,653 |
+| **32k** | 4.570x | 4.57 | 0.0998% | 969,681 |
+| **64k** | 4.787x 🏆 | 4.79 | 0.1046% | 925,672 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Ang Anastasius I o Anastasio I ay maaaring tumukoy kina: Anastasius I (emperador...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ang ▁ana sta si us ▁i ▁o ▁ana sta sio ... (+25 more)` | 35 |
+| 16k | `▁ang ▁anasta sius ▁i ▁o ▁anasta sio ▁i ▁ay ▁maaaring ... (+17 more)` | 27 |
+| 32k | `▁ang ▁anasta sius ▁i ▁o ▁anasta sio ▁i ▁ay ▁maaaring ... (+15 more)` | 25 |
+| 64k | `▁ang ▁anastasius ▁i ▁o ▁anastasio ▁i ▁ay ▁maaaring ▁tumukoy ▁kina ... (+11 more)` | 21 |
+
+**Sample 2:** `Ang alupihan ay tumutukoy sa mga sumusunod: alupihan, hayop na maraming mga paa ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ang ▁a lu pi han ▁ay ▁tumutukoy ▁sa ▁mga ▁sumusunod ... (+23 more)` | 33 |
+| 16k | `▁ang ▁alu pi han ▁ay ▁tumutukoy ▁sa ▁mga ▁sumusunod : ... (+19 more)` | 29 |
+| 32k | `▁ang ▁alu pi han ▁ay ▁tumutukoy ▁sa ▁mga ▁sumusunod : ... (+19 more)` | 29 |
+| 64k | `▁ang ▁alupihan ▁ay ▁tumutukoy ▁sa ▁mga ▁sumusunod : ▁alupihan , ... (+15 more)` | 25 |
+
+**Sample 3:** `Tumutukoy ang Getafe sa: Getafe, Bohol, Pilipinas Getafe, Espanya`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁tumutukoy ▁ang ▁ge ta fe ▁sa : ▁ge ta fe ... (+9 more)` | 19 |
+| 16k | `▁tumutukoy ▁ang ▁ge ta fe ▁sa : ▁ge ta fe ... (+9 more)` | 19 |
+| 32k | `▁tumutukoy ▁ang ▁ge ta fe ▁sa : ▁ge ta fe ... (+9 more)` | 19 |
+| 64k | `▁tumutukoy ▁ang ▁geta fe ▁sa : ▁geta fe , ▁bohol ... (+6 more)` | 16 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.787x compression
+- **Lowest UNK Rate:** 8k with 0.0846% 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 | 47,186 | 15.53 | 318,514 | 13.3% | 28.2% |
+| **2-gram** | Subword | 197 🏆 | 7.62 | 12,564 | 75.1% | 99.3% |
+| **3-gram** | Word | 194,690 | 17.57 | 626,197 | 5.1% | 14.4% |
+| **3-gram** | Subword | 1,562 | 10.61 | 73,993 | 36.4% | 76.3% |
+| **4-gram** | Word | 444,151 | 18.76 | 1,007,564 | 4.2% | 10.1% |
+| **4-gram** | Subword | 8,805 | 13.10 | 386,404 | 20.7% | 48.0% |
+| **5-gram** | Word | 288,906 | 18.14 | 622,946 | 5.8% | 12.4% |
+| **5-gram** | Subword | 34,036 | 15.05 | 1,176,700 | 12.2% | 33.2% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ng mga` | 122,547 |
+| 2 | `sa mga` | 92,284 |
+| 3 | `ang mga` | 86,243 |
+| 4 | `ay isang` | 47,028 |
+| 5 | `mula sa` | 45,918 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `sa pamamagitan ng` | 15,624 |
+| 2 | `sa lalawigan ng` | 8,276 |
+| 3 | `sa pagitan ng` | 8,017 |
+| 4 | `mga sanggunian mga` | 7,752 |
+| 5 | `iba t ibang` | 7,698 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mga panlabas na link` | 5,294 |
+| 2 | `sanggunian mga panlabas na` | 4,753 |
+| 3 | `mga sanggunian mga panlabas` | 4,623 |
+| 4 | `munisipalidad sa lalawigan ng` | 3,555 |
+| 5 | `comune komuna o munisipalidad` | 3,547 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mga sanggunian mga panlabas na` | 4,621 |
+| 2 | `sanggunian mga panlabas na link` | 4,299 |
+| 3 | `comune komuna o munisipalidad sa` | 3,419 |
+| 4 | `sa mga sumusunod na munisipalidad` | 3,189 |
+| 5 | `ay isang comune komuna o` | 3,156 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g` | 3,917,952 |
+| 2 | `a n` | 3,737,257 |
+| 3 | `g _` | 3,418,646 |
+| 4 | `a _` | 3,186,790 |
+| 5 | `_ n` | 2,406,716 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n g _` | 3,291,039 |
+| 2 | `a n g` | 2,010,994 |
+| 3 | `_ s a` | 1,072,670 |
+| 4 | `_ n a` | 1,030,586 |
+| 5 | `_ n g` | 987,165 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n g _` | 1,606,671 |
+| 2 | `_ n g _` | 960,600 |
+| 3 | `_ s a _` | 872,495 |
+| 4 | `_ n a _` | 613,902 |
+| 5 | `_ a n g` | 594,113 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ a n g _` | 585,381 |
+| 2 | `_ m g a _` | 498,790 |
+| 3 | `n g _ p a` | 315,071 |
+| 4 | `g _ m g a` | 277,715 |
+| 5 | `n g _ m g` | 277,460 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 197
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~33% 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 | 0.8300 | 1.778 | 7.53 | 527,629 | 17.0% |
+| **1** | Subword | 0.9447 | 1.925 | 6.25 | 10,325 | 5.5% |
+| **2** | Word | 0.3582 | 1.282 | 2.25 | 3,967,765 | 64.2% |
+| **2** | Subword | 0.5676 | 1.482 | 3.43 | 64,498 | 43.2% |
+| **3** | Word | 0.1673 | 1.123 | 1.38 | 8,894,925 | 83.3% |
+| **3** | Subword | 0.5929 | 1.508 | 3.39 | 221,050 | 40.7% |
+| **4** | Word | 0.0699 🏆 | 1.050 | 1.12 | 12,295,618 | 93.0% |
+| **4** | Subword | 0.6330 | 1.551 | 3.10 | 748,630 | 36.7% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ng magaang mga nakamit kasunod ng mga tren kiha 20 second movement noong heograpiya ang timog`
+2. `sa kasaysayan ng diyos at idinagdag ang pagkakasakit namatay ang estado sa hilaga lungsod sa benta`
+3. `ang bayan sa silangang eslabong kaharian maaring magbayad ng pagkakaroon o mala pabilog harapang nak...`
+
+**Context Size 2:**
+
+1. `ng mga tao sinasabi na parang gunting pagguguntingan kalish nancy the nice guys holly march sa isang`
+2. `sa mga katangian ng larangang ito bagaman ang christ ang pananampalataya sa diyos sapagkat nawalan n...`
+3. `ang mga teoretikal na edukasyon na si tenzin gyatso ang ikawalong baitang 13 taon chronology of afri...`
+
+**Context Size 3:**
+
+1. `sa pamamagitan ng plots and distribusyon ng isang natutunghayan ang eigen ay sarili sa aleman mainam...`
+2. `sa lalawigan ng cuneo sa rehiyon ng lazio na matatagpuan mga timog ng mantua matatagpuan sa isang bu...`
+3. `sa pagitan ng dalawang organismo sa kaso ng isang kurtinang pang shower ang kurtina ay iyon ding nag...`
+
+**Context Size 4:**
+
+1. `mga panlabas na link opisyal na website thayers gazetteer international school of painting drawing a...`
+2. `sanggunian mga panlabas na link opisyal na website bayan at lungsod sa pilipinas subalit bilang kara...`
+3. `mga sanggunian mga panlabas na link plundering desire articles interviews release reviews live revie...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `anikuw_ng_shinit`
+2. `_likataltung_nga`
+3. `nfedinasonyahepa`
+
+**Context Size 2:**
+
+1. `ng_noong_ga_sang_`
+2. `ana_mga_markilang`
+3. `g_magpumish._puna`
+
+**Context Size 3:**
+
+1. `ng_malawan_nasakup`
+2. `ang_tagpuanibersiy`
+3. `_sa_ay_mayroon_tum`
+
+**Context Size 4:**
+
+1. `ang_pagtuunawaganap`
+2. `_ng_telepono,_dahil`
+3. `_sa_mga_panahong_om`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 93.0% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (748,630 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 | 223,605 |
+| Total Tokens | 15,229,985 |
+| Mean Frequency | 68.11 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 3743.03 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ng | 962,341 |
+| 2 | sa | 881,526 |
+| 3 | ang | 628,027 |
+| 4 | na | 621,434 |
+| 5 | mga | 506,055 |
+| 6 | ay | 352,169 |
+| 7 | at | 351,974 |
+| 8 | isang | 180,575 |
+| 9 | noong | 112,415 |
+| 10 | ito | 97,397 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | madiclum | 2 |
+| 2 | festivalpinakamahusay | 2 |
+| 3 | siboryo | 2 |
+| 4 | slazenger | 2 |
+| 5 | yuwji | 2 |
+| 6 | mandoriao | 2 |
+| 7 | buzinkai | 2 |
+| 8 | hiveswap | 2 |
+| 9 | writerin | 2 |
+| 10 | sskp | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0072 |
+| R² (Goodness of Fit) | 0.995022 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 44.9% |
+| Top 1,000 | 64.0% |
+| Top 5,000 | 79.3% |
+| Top 10,000 | 85.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9950 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 44.9% of corpus
+- **Long Tail:** 213,605 words needed for remaining 14.7% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.8025 | 0.3575 | N/A | N/A |
+| **mono_64d** | 64 | 0.7423 | 0.3056 | N/A | N/A |
+| **mono_128d** | 128 | 0.6846 | 0.2378 | N/A | N/A |
+| **aligned_32d** | 32 | 0.8025 🏆 | 0.3655 | 0.3000 | 0.7020 |
+| **aligned_64d** | 64 | 0.7423 | 0.2994 | 0.4300 | 0.8300 |
+| **aligned_128d** | 128 | 0.6846 | 0.2419 | 0.5400 | 0.8680 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.8025 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3013. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 54.0% 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 | **-0.628** | 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 |
+|--------|----------|
+| `-ma` | mangangasiwa, maruja, masangkot |
+| `-a` | aggie, arkimedes, antoni |
+| `-s` | suleiman, sutan, steri |
+| `-d` | democratikong, dugong, dlä |
+| `-pa` | paranorman, parañaquelungsod, panti |
+| `-m` | mánudagur, mundhum, moluccan |
+| `-na` | nakalilitong, nangangagat, nagpupunyagi |
+| `-ka` | kabuwanan, kalokohang, kalbaryo |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ng` | improvising, democratikong, sikiyatriyang |
+| `-n` | buogn, suleiman, sutan |
+| `-a` | echeverría, periyodontista, tasya |
+| `-g` | improvising, democratikong, sikiyatriyang |
+| `-s` | rudolfensis, gulbis, arkimedes |
+| `-o` | campochiaro, villonco, incognito |
+| `-e` | aggie, batake, zakopane |
+| `-an` | suleiman, sutan, paranorman |
+
+### 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 |
+|------|----------|------------------|----------|
+| `inak` | 2.61x | 78 contexts | inako, pinak, inakma |
+| `angg` | 2.17x | 161 contexts | sangg, angge, anggi |
+| `inag` | 2.25x | 112 contexts | sinag, tinag, inagi |
+| `agka` | 2.24x | 106 contexts | nagka, magka, sagka |
+| `ngga` | 2.16x | 122 contexts | ungga, angga, tingga |
+| `atag` | 2.19x | 110 contexts | patag, latag, datag |
+| `agpa` | 2.21x | 92 contexts | pagpa, magpa, agpay |
+| `angk` | 1.90x | 168 contexts | angka, sangka, sangko |
+| `tion` | 2.15x | 82 contexts | tiong, ation, tione |
+| `alaw` | 2.01x | 105 contexts | galaw, kalaw, alaws |
+| `asyo` | 2.07x | 90 contexts | basyo, rasyo, tasyo |
+| `inas` | 1.84x | 127 contexts | sinas, rinas, linas |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-pa` | `-g` | 84 words | pankalakalang, pangangatawang |
+| `-s` | `-n` | 76 words | saksakyan, sulangan |
+| `-s` | `-a` | 73 words | sharmiela, semigallia |
+| `-pa` | `-ng` | 71 words | pankalakalang, pangangatawang |
+| `-pa` | `-n` | 71 words | pamain, paparusahan |
+| `-na` | `-g` | 68 words | nagnangalang, napakabantog |
+| `-pa` | `-a` | 66 words | pagkokomplementa, pamina |
+| `-a` | `-a` | 66 words | alionushka, atienza |
+| `-ka` | `-n` | 66 words | kasalukyan, karangyaan |
+| `-ma` | `-g` | 66 words | masong, mabubuwag |
+
+### 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 |
+|------|-----------------|------------|------|
+| napakalapot | **`napakalap-o-t`** | 7.5 | `o` |
+| makapagpapatisod | **`makapagpapatis-o-d`** | 7.5 | `o` |
+| montmirail | **`montmira-i-l`** | 7.5 | `i` |
+| magtutuos | **`magtutu-o-s`** | 7.5 | `o` |
+| kinaroroonang | **`kinaroroon-a-ng`** | 7.5 | `a` |
+| sampaybakod | **`sampaybak-o-d`** | 7.5 | `o` |
+| obergefell | **`obergefe-l-l`** | 7.5 | `l` |
+| tinablang | **`tinab-la-ng`** | 7.5 | `la` |
+| nababayarang | **`nababayar-a-ng`** | 7.5 | `a` |
+| masmataas | **`ma-s-mataas`** | 7.5 | `mataas` |
+| maghuhugas | **`maghuhu-g-as`** | 7.5 | `g` |
+| napakakipot | **`napakakip-o-t`** | 7.5 | `o` |
+| inglewood | **`inglewo-o-d`** | 7.5 | `o` |
+| concerned | **`concer-n-ed`** | 7.5 | `n` |
+| internationally | **`international-l-y`** | 7.5 | `l` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Filipino 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 (4.79x) |
+| N-gram | **2-gram** | Lowest perplexity (197) |
+| Markov | **Context-4** | Highest predictability (93.0%) |
+| 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 02:21:03*

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