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README.md

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+---
+language: fi
+language_name: Finnish
+language_family: uralic_finnic
+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-uralic_finnic
+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: 5.221
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7459
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-12
+---
+
+# Finnish - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Finnish** 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** | 3.799x | 3.80 | 0.1369% | 3,461,300 |
+| **16k** | 4.273x | 4.27 | 0.1539% | 3,077,483 |
+| **32k** | 4.760x | 4.76 | 0.1714% | 2,763,001 |
+| **64k** | 5.221x 🏆 | 5.22 | 0.1881% | 2,518,757 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Lähteet judokat olympiamitalistit syntyneet henkilöt`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lähteet ▁jud ok at ▁olympiamital istit ▁syntyneet ▁henkilöt` | 8 |
+| 16k | `▁lähteet ▁jud ok at ▁olympiamital istit ▁syntyneet ▁henkilöt` | 8 |
+| 32k | `▁lähteet ▁jud ok at ▁olympiamital istit ▁syntyneet ▁henkilöt` | 8 |
+| 64k | `▁lähteet ▁jud okat ▁olympiamital istit ▁syntyneet ▁henkilöt` | 7 |
+
+**Sample 2:** `Tapahtumia Anicetus vastaanotti paavin viran. Syntyneitä Chang Tao Ling, taolain...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁tapahtumia ▁an ic et us ▁vastaan otti ▁paa vin ▁viran ... (+16 more)` | 26 |
+| 16k | `▁tapahtumia ▁an ic et us ▁vastaan otti ▁paavin ▁viran . ... (+14 more)` | 24 |
+| 32k | `▁tapahtumia ▁an ic etus ▁vastaanotti ▁paavin ▁viran . ▁syntyneitä ▁chang ... (+11 more)` | 21 |
+| 64k | `▁tapahtumia ▁an ic etus ▁vastaanotti ▁paavin ▁viran . ▁syntyneitä ▁chang ... (+9 more)` | 19 |
+
+**Sample 3:** `Los Ríos on yksi Ecuadorin 24 maakunnasta. Sen pääkaupunki on Babahoyo, pinta-al...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁los ▁r í os ▁on ▁yksi ▁ec ua dor in ... (+43 more)` | 53 |
+| 16k | `▁los ▁r í os ▁on ▁yksi ▁ec ua dorin ▁ ... (+41 more)` | 51 |
+| 32k | `▁los ▁r í os ▁on ▁yksi ▁ecua dorin ▁ 2 ... (+38 more)` | 48 |
+| 64k | `▁los ▁r í os ▁on ▁yksi ▁ecuadorin ▁ 2 4 ... (+37 more)` | 47 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.221x compression
+- **Lowest UNK Rate:** 8k with 0.1369% 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 | 468,139 | 18.84 | 3,042,410 | 6.0% | 13.9% |
+| **2-gram** | Subword | 278 🏆 | 8.12 | 22,535 | 67.1% | 99.2% |
+| **3-gram** | Word | 1,065,692 | 20.02 | 4,275,337 | 4.6% | 9.6% |
+| **3-gram** | Subword | 2,642 | 11.37 | 185,096 | 22.8% | 69.4% |
+| **4-gram** | Word | 2,274,790 | 21.12 | 6,954,562 | 3.3% | 7.6% |
+| **4-gram** | Subword | 17,026 | 14.06 | 1,194,419 | 9.7% | 35.2% |
+| **5-gram** | Word | 1,753,957 | 20.74 | 4,818,809 | 2.9% | 7.7% |
+| **5-gram** | Subword | 77,677 | 16.25 | 4,549,709 | 5.0% | 20.0% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `aiheesta muualla` | 249,855 |
+| 2 | `kitt peak` | 206,017 |
+| 3 | `peak spacewatch` | 204,244 |
+| 4 | `lähteet aiheesta` | 179,493 |
+| 5 | `mount lemmon` | 164,266 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kitt peak spacewatch` | 204,244 |
+| 2 | `lähteet aiheesta muualla` | 179,390 |
+| 3 | `mt lemmon survey` | 67,208 |
+| 4 | `lemmon mt lemmon` | 67,205 |
+| 5 | `mount lemmon mt` | 67,205 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mount lemmon mt lemmon` | 67,205 |
+| 2 | `lemmon mt lemmon survey` | 67,205 |
+| 3 | `lemmon mount lemmon survey` | 48,518 |
+| 4 | `mount lemmon mount lemmon` | 48,517 |
+| 5 | `haleakala pan starrs 1` | 41,305 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `mount lemmon mt lemmon survey` | 67,205 |
+| 2 | `mount lemmon mount lemmon survey` | 48,517 |
+| 3 | `lokakuuta mount lemmon mt lemmon` | 12,734 |
+| 4 | `syyskuuta mount lemmon mt lemmon` | 9,683 |
+| 5 | `0 0 0 0 0` | 9,576 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `n _` | 35,431,794 |
+| 2 | `a _` | 28,224,764 |
+| 3 | `e n` | 20,320,601 |
+| 4 | `i n` | 18,392,995 |
+| 5 | `t a` | 18,015,565 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e n _` | 11,913,920 |
+| 2 | `i n _` | 7,559,259 |
+| 3 | `a n _` | 6,328,547 |
+| 4 | `t a _` | 6,095,039 |
+| 5 | `j a _` | 5,873,170 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ j a _` | 4,688,853 |
+| 2 | `s s a _` | 3,594,453 |
+| 3 | `n e n _` | 2,793,972 |
+| 4 | `_ o n _` | 2,528,919 |
+| 5 | `s t a _` | 2,335,812 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `i n e n _` | 2,066,240 |
+| 2 | `k u u t a` | 1,605,934 |
+| 3 | `u u t a _` | 1,591,336 |
+| 4 | `a _ j a _` | 1,344,019 |
+| 5 | `_ o l i _` | 1,224,801 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 278
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~20% 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 | 0.9729 | 1.963 | 11.25 | 5,006,345 | 2.7% |
+| **1** | Subword | 1.1405 | 2.205 | 8.17 | 12,030 | 0.0% |
+| **2** | Word | 0.2871 | 1.220 | 1.85 | 56,234,784 | 71.3% |
+| **2** | Subword | 0.6527 | 1.572 | 4.40 | 98,107 | 34.7% |
+| **3** | Word | 0.0982 | 1.070 | 1.20 | 104,064,802 | 90.2% |
+| **3** | Subword | 0.7699 | 1.705 | 4.59 | 431,251 | 23.0% |
+| **4** | Word | 0.0383 🏆 | 1.027 | 1.07 | 124,192,112 | 96.2% |
+| **4** | Subword | 0.7445 | 1.675 | 3.90 | 1,979,645 | 25.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ja qing dynastioiden 11 6 h f girolamo savonarolalta hellinckin poika golden age animaatioelokuvissa...`
+2. `on yhdysvaltalainen rattikelkkailija william diller yhdysvaltalainen ooppera tampereen klassillisest...`
+3. `oli sitten valmistui vuonna kuningas arthuriin venäjän tiedeakatemia isännöi toisen sijan koko heimo...`
+
+**Context Size 2:**
+
+1. `aiheesta muualla albumit albumit crissin albumit`
+2. `kitt peak spacewatch dy6 16 maaliskuuta socorro linear fs36 18 maaliskuuta oslossa miesten kalenteri...`
+3. `peak spacewatch tl36 12 lokakuuta charles nunzio joka aloitti lähetyksensä 18 huhtikuuta kapkaupunki...`
+
+**Context Size 3:**
+
+1. `kitt peak spacewatch tym xa58 4 tammikuuta tincana m kusiak m żołnowsk aq12 5 lokakuuta kitt peak sp...`
+2. `lähteet aiheesta muualla piirikunnat kartli pl chaszuri`
+3. `mt lemmon survey yz11 17 tammikuuta haleakala pan starrs 1 17 lokakuuta mount lemmon mount lemmon su...`
+
+**Context Size 4:**
+
+1. `lemmon mt lemmon survey sv65 21 syyskuuta mount lemmon mount lemmon survey 22 toukokuuta wise wise k...`
+2. `mount lemmon mt lemmon survey vv 8 marraskuuta mayhill mayhill vd8 8 marraskuuta catalina css 14 tou...`
+3. `lemmon mount lemmon survey 8 tammikuuta mount lemmon mt lemmon survey fk38 28 maaliskuuta kitt peak ...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_hätionewone_–_r`
+2. `apuuikikakeniipo`
+3. `in_sentalisaline`
+
+**Context Size 2:**
+
+1. `n_outehiaan_taan,`
+2. `a_1_kuusopirthred`
+3. `en_pilöys._kerumi`
+
+**Context Size 3:**
+
+1. `en_eze._brit_dimik`
+2. `in_sureisi_lan_”tj`
+3. `an_koin_(s._29._ta`
+
+**Context Size 4:**
+
+1. `_ja_myös_aren_regio`
+2. `ssa_101,56_metriä_l`
+3. `nen_tuottana._vuott`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 96.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,979,645 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 | 2,250,455 |
+| Total Tokens | 145,574,709 |
+| Mean Frequency | 64.69 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 4199.88 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ja | 4,696,959 |
+| 2 | on | 2,545,540 |
+| 3 | oli | 1,230,343 |
+| 4 | hän | 1,028,773 |
+| 5 | vuonna | 905,604 |
+| 6 | 1 | 689,784 |
+| 7 | myös | 653,305 |
+| 8 | s | 616,597 |
+| 9 | 2 | 541,496 |
+| 10 | lähteet | 519,252 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | navkatin | 2 |
+| 2 | xovosista | 2 |
+| 3 | sauvagetin | 2 |
+| 4 | bundžikatin | 2 |
+| 5 | keltaeväkuukala | 2 |
+| 6 | glädjekällan | 2 |
+| 7 | wydlerin | 2 |
+| 8 | joshualla | 2 |
+| 9 | charmatzn | 2 |
+| 10 | kidugala | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9214 |
+| R² (Goodness of Fit) | 0.998159 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 21.8% |
+| Top 1,000 | 41.3% |
+| Top 5,000 | 57.6% |
+| Top 10,000 | 64.9% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9982 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 21.8% of corpus
+- **Long Tail:** 2,240,455 words needed for remaining 35.1% 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 | 0.7459 | 0.3486 | N/A | N/A |
+| **mono_64d** | 64 | 0.7204 | 0.2821 | N/A | N/A |
+| **mono_128d** | 128 | 0.6228 | 0.2311 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7459 🏆 | 0.3499 | 0.3560 | 0.7800 |
+| **aligned_64d** | 64 | 0.7204 | 0.2899 | 0.5740 | 0.8760 |
+| **aligned_128d** | 128 | 0.6228 | 0.2356 | 0.7020 | 0.9140 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7459 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.2895. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 70.2% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.615** | 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 |
+|--------|----------|
+| `-s` | sorapohjille, suus, suolamminpuro |
+| `-a` | asiakkuuksien, anregungen, anglosaksissa |
+| `-k` | kanadansuomalaiset, kotitaloustyöntekijöiden, kampanjoimalla |
+| `-t` | taskilassa, tehostuu, tujh |
+| `-p` | puhalluksen, pantaisiin, poismeno |
+| `-m` | mq, männistönpolun, miehittäjävaltioiden |
+| `-e` | eddarunoutta, everst, edsevö |
+| `-b` | boeingillä, bratslavista, bundille |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | puhalluksen, asiakkuuksien, anregungen |
+| `-a` | anglosaksissa, taskilassa, unimatka |
+| `-en` | puhalluksen, asiakkuuksien, anregungen |
+| `-in` | näyttelyihin, pantaisiin, tulviviin |
+| `-ta` | bratslavista, todetuista, karstulasta |
+| `-i` | darski, suolaiseksi, kuvernööreiksi |
+| `-sa` | anglosaksissa, taskilassa, nerjassa |
+| `-an` | ulosteitaan, vallankumoustaan, apsaran |
+
+### 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 |
+|------|----------|------------------|----------|
+| `ivat` | 1.84x | 221 contexts | nivat, ivata, livat |
+| `ttii` | 1.81x | 221 contexts | ottii, uttiin, fättii |
+| `ises` | 1.76x | 230 contexts | sises, isesi, rises |
+| `tett` | 1.36x | 562 contexts | tette, tetto, tettu |
+| `staa` | 1.45x | 361 contexts | staav, staar, staab |
+| `ukse` | 1.35x | 445 contexts | uksen, ukset, suksea |
+| `sess` | 1.58x | 144 contexts | sessa, sessi, sesso |
+| `uome` | 1.73x | 78 contexts | suome, luomen, luomea |
+| `isuu` | 1.65x | 85 contexts | fisuu, fisuun, paisuu |
+| `äytt` | 1.56x | 109 contexts | käyttä, käytto, näyttä |
+| `tuks` | 1.32x | 244 contexts | tuksu, tuksa, tuksi |
+| `htee` | 1.43x | 137 contexts | ahtee, yhteet, ähteet |
+
+### 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` | `-n` | 338 words | kaksoisruokolehdykkäsoittimien, kyanzitthan |
+| `-k` | `-a` | 304 words | käyttäytymisongelmia, karjalohja |
+| `-s` | `-n` | 259 words | sisustusarkkitehtuurin, sallyyn |
+| `-p` | `-a` | 236 words | paviaanista, polyamorisia |
+| `-s` | `-a` | 228 words | sairausjaksoista, sponsoroinnista |
+| `-m` | `-n` | 195 words | mamemon, mustionselän |
+| `-p` | `-n` | 194 words | poweraden, puolueettomuuspolitiikkaan |
+| `-t` | `-n` | 189 words | täyttämiin, tieoikeuteen |
+| `-t` | `-a` | 180 words | tutkalaitteella, tappioissa |
+| `-m` | `-a` | 160 words | maeba, minisarjassa |
+
+### 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 |
+|------|-----------------|------------|------|
+| päätoimessaan | **`päätoimes-sa-an`** | 7.5 | `sa` |
+| nicolasia | **`nicola-si-a`** | 7.5 | `si` |
+| seksiaiheisia | **`seksiaihei-si-a`** | 7.5 | `si` |
+| elämänlangat | **`elämänlang-a-t`** | 7.5 | `a` |
+| vauvanruokaa | **`vauvanruok-a-a`** | 7.5 | `a` |
+| puuttunutkaan | **`puuttunutk-a-an`** | 7.5 | `a` |
+| antenniverkkonsa | **`antenniverkko-n-sa`** | 7.5 | `n` |
+| kirjoittamistaan | **`kirjoittamis-ta-an`** | 7.5 | `ta` |
+| biogeenisiin | **`biogeeni-si-in`** | 7.5 | `si` |
+| torppasivat | **`torppasiv-a-t`** | 7.5 | `a` |
+| mediatoimijat | **`mediatoimij-a-t`** | 7.5 | `a` |
+| artemísio | **`artemí-si-o`** | 7.5 | `si` |
+| havaintoasemaa | **`havaintoasem-a-a`** | 7.5 | `a` |
+| christóforos | **`christófor-o-s`** | 7.5 | `o` |
+| porontiman | **`porontim-a-n`** | 7.5 | `a` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Finnish 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 (5.22x) |
+| N-gram | **2-gram** | Lowest perplexity (278) |
+| Markov | **Context-4** | Highest predictability (96.2%) |
+| 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: 2026-01-13 06:45:42*

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