Upload all models and assets for es (latest)

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README.md

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+---
+language: es
+language_name: Spanish
+language_family: romance_iberian
+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-romance_iberian
+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: 4.831
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7898
+  - name: best_alignment_r10
+    type: alignment
+    value: 0.9680
+  - name: vocabulary_size
+    type: vocab
+    value: 1128398
+generated: 2026-03-04
+---
+
+# Spanish — Wikilangs Models
+
+Open-source tokenizers, n-gram & Markov language models, vocabulary stats, and word embeddings trained on **Spanish** Wikipedia by [Wikilangs](https://wikilangs.org).
+
+🌐 [Language Page](https://wikilangs.org/languages/es/) · 🎮 [Playground](https://wikilangs.org/playground/?lang=es) · 📊 [Full Research Report](RESEARCH_REPORT.md)
+
+## Language Samples
+
+Example sentences drawn from the Spanish Wikipedia corpus:
+
+> Apogonia es un género de escarabajos. Algunos son plagas de los árboles de durio. Referencias
+
+> Elymordeum es un género monotípico de plantas herbáceas perteneciente a la familia de las poáceas. Su única especie es Elymordeum montanense (Scribn.) Bowden. Referencias
+
+> Graphis es un género de hongos liquenizados de la familia Graphidaceae. Fue descrito por el naturalista francés Michel Adanson en Referencias de Graphidales
+
+> Modem puede hacer referencia: el módem, dispositivo electrónico de comunicación; o el partido político francés MoDem.
+
+> Opegrapha es un género de hongos liquenizados de la familia Opegraphaceae. Especies Referencias de Arthoniales
+
+## Quick Start
+
+### Load the Tokenizer
+
+```python
+import sentencepiece as spm
+
+sp = spm.SentencePieceProcessor()
+sp.Load("es_tokenizer_32k.model")
+
+text = "Opegrapha es un género de hongos liquenizados de la familia Opegraphaceae. Espec"
+tokens = sp.EncodeAsPieces(text)
+ids    = sp.EncodeAsIds(text)
+
+print(tokens)  # subword pieces
+print(ids)     # integer ids
+
+# Decode back
+print(sp.DecodeIds(ids))
+```
+
+<details>
+<summary><b>Tokenization examples (click to expand)</b></summary>
+
+**Sample 1:** `Opegrapha es un género de hongos liquenizados de la familia Opegraphaceae. Espec…`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁o pe gra p ha ▁es ▁un ▁género ▁de ▁hon … (+22 more)` | 32 |
+| 16k | `▁o pe gra pha ▁es ▁un ▁género ▁de ▁hongos ▁li … (+18 more)` | 28 |
+| 32k | `▁o pe gra pha ▁es ▁un ▁género ▁de ▁hongos ▁li … (+17 more)` | 27 |
+| 64k | `▁o pe gra pha ▁es ▁un ▁género ▁de ▁hongos ▁li … (+17 more)` | 27 |
+
+**Sample 2:** `Una única familia: Salicaceae. Árboles, arbustos y matas. Numerosos óvulos; 2 ca…`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁una ▁única ▁familia : ▁sal ica ceae . ▁árboles , … (+29 more)` | 39 |
+| 16k | `▁una ▁única ▁familia : ▁sal ica ceae . ▁árboles , … (+24 more)` | 34 |
+| 32k | `▁una ▁única ▁familia : ▁sal icaceae . ▁árboles , ▁arbustos … (+17 more)` | 27 |
+| 64k | `▁una ▁única ▁familia : ▁sal icaceae . ▁árboles , ▁arbustos … (+17 more)` | 27 |
+
+**Sample 3:** `Apogonia es un género de escarabajos. Algunos son plagas de los árboles de durio…`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁apo gon ia ▁es ▁un ▁género ▁de ▁esca ra ba … (+14 more)` | 24 |
+| 16k | `▁apo gon ia ▁es ▁un ▁género ▁de ▁esca raba jos … (+13 more)` | 23 |
+| 32k | `▁apo gonia ▁es ▁un ▁género ▁de ▁esca raba jos . … (+12 more)` | 22 |
+| 64k | `▁apo gonia ▁es ▁un ▁género ▁de ▁escarabajos . ▁algunos ▁son … (+9 more)` | 19 |
+
+</details>
+
+### Load Word Embeddings
+
+```python
+from gensim.models import KeyedVectors
+
+# Aligned embeddings (cross-lingual, mapped to English vector space)
+wv = KeyedVectors.load("es_embeddings_128d_aligned.kv")
+
+similar = wv.most_similar("word", topn=5)
+for word, score in similar:
+    print(f"  {word}: {score:.3f}")
+```
+
+### Load N-gram Model
+
+```python
+import pyarrow.parquet as pq
+
+df = pq.read_table("es_3gram_word.parquet").to_pandas()
+print(df.head())
+```
+
+## Models Overview
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+| Category | Assets |
+|----------|--------|
+| Tokenizers | BPE at 8k, 16k, 32k, 64k vocab sizes |
+| N-gram models | 2 / 3 / 4 / 5-gram (word & subword) |
+| Markov chains | Context 1–5 (word & subword) |
+| Embeddings | 32d, 64d, 128d — mono & aligned |
+| Vocabulary | Full frequency list + Zipf analysis |
+| Statistics | Corpus & model statistics JSON |
+
+## Metrics Summary
+
+| Component | Model | Key Metric | Value |
+|-----------|-------|------------|-------|
+| Tokenizer | 8k BPE | Compression | 3.89x |
+| Tokenizer | 16k BPE | Compression | 4.28x |
+| Tokenizer | 32k BPE | Compression | 4.60x |
+| Tokenizer | 64k BPE | Compression | 4.83x 🏆 |
+| N-gram | 2-gram (subword) | Perplexity | 225 🏆 |
+| N-gram | 2-gram (word) | Perplexity | 183,447 |
+| N-gram | 3-gram (subword) | Perplexity | 1,802 |
+| N-gram | 3-gram (word) | Perplexity | 1,817,727 |
+| N-gram | 4-gram (subword) | Perplexity | 10,272 |
+| N-gram | 4-gram (word) | Perplexity | 7,309,961 |
+| N-gram | 5-gram (subword) | Perplexity | 43,696 |
+| N-gram | 5-gram (word) | Perplexity | 8,151,138 |
+| Markov | ctx-1 (subword) | Predictability | 0.0% |
+| Markov | ctx-1 (word) | Predictability | 0.0% |
+| Markov | ctx-2 (subword) | Predictability | 37.1% |
+| Markov | ctx-2 (word) | Predictability | 53.8% |
+| Markov | ctx-3 (subword) | Predictability | 32.1% |
+| Markov | ctx-3 (word) | Predictability | 76.0% |
+| Markov | ctx-4 (subword) | Predictability | 32.2% |
+| Markov | ctx-4 (word) | Predictability | 88.3% 🏆 |
+| Vocabulary | full | Size | 1,128,398 |
+| Vocabulary | full | Zipf R² | 0.9938 |
+| Embeddings | mono_32d | Isotropy | 0.7898 |
+| Embeddings | mono_64d | Isotropy | 0.7625 |
+| Embeddings | mono_128d | Isotropy | 0.6860 |
+| Embeddings | aligned_32d | Isotropy | 0.7898 🏆 |
+| Embeddings | aligned_64d | Isotropy | 0.7625 |
+| Embeddings | aligned_128d | Isotropy | 0.6860 |
+| Alignment | aligned_32d | R@1 / R@5 / R@10 | 56.6% / 81.2% / 86.8% |
+| Alignment | aligned_64d | R@1 / R@5 / R@10 | 75.2% / 88.6% / 92.6% |
+| Alignment | aligned_128d | R@1 / R@5 / R@10 | 79.6% / 94.4% / 96.8% 🏆 |
+
+📊 **[Full ablation study, per-model breakdowns, and interpretation guide →](RESEARCH_REPORT.md)**
+
+---
+
+## About
+
+Trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) — monthly snapshots of 300+ Wikipedia languages.
+
+A project by **[Wikilangs](https://wikilangs.org)** · Maintainer: [Omar Kamali](https://omarkamali.com) · [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+```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}
+}
+```
+
+### Links
+
+- 🌐 [wikilangs.org](https://wikilangs.org)
+- 🌍 [Language page](https://wikilangs.org/languages/es/)
+- 🎮 [Playground](https://wikilangs.org/playground/?lang=es)
+- 🤗 [HuggingFace models](https://huggingface.co/wikilangs)
+- 📊 [wikipedia-monthly dataset](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+
+**License:** MIT — free for academic and commercial use.
+
+---
+*Generated by Wikilangs Pipeline · 2026-03-04 04:26:07*

RESEARCH_REPORT.md

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+# Spanish — Full Ablation Study & Research Report
+
+Detailed evaluation of all model variants trained on **Spanish** Wikipedia data by [Wikilangs](https://wikilangs.org).
+
+👈 [Back to README](README.md)
+
+## 📋 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** | 3.885x | 3.89 | 0.0687% | 4,882,549 |
+| **16k** | 4.280x | 4.28 | 0.0756% | 4,432,264 |
+| **32k** | 4.603x | 4.60 | 0.0813% | 4,121,359 |
+| **64k** | 4.831x 🏆 | 4.83 | 0.0854% | 3,926,906 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Opegrapha es un género de hongos liquenizados de la familia Opegraphaceae. Espec...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁o pe gra p ha ▁es ▁un ▁género ▁de ▁hon ... (+22 more)` | 32 |
+| 16k | `▁o pe gra pha ▁es ▁un ▁género ▁de ▁hongos ▁li ... (+18 more)` | 28 |
+| 32k | `▁o pe gra pha ▁es ▁un ▁género ▁de ▁hongos ▁li ... (+17 more)` | 27 |
+| 64k | `▁o pe gra pha ▁es ▁un ▁género ▁de ▁hongos ▁li ... (+17 more)` | 27 |
+
+**Sample 2:** `Una única familia: Salicaceae. Árboles, arbustos y matas. Numerosos óvulos; 2 ca...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁una ▁única ▁familia : ▁sal ica ceae . ▁árboles , ... (+29 more)` | 39 |
+| 16k | `▁una ▁única ▁familia : ▁sal ica ceae . ▁árboles , ... (+24 more)` | 34 |
+| 32k | `▁una ▁única ▁familia : ▁sal icaceae . ▁árboles , ▁arbustos ... (+17 more)` | 27 |
+| 64k | `▁una ▁única ▁familia : ▁sal icaceae . ▁árboles , ▁arbustos ... (+17 more)` | 27 |
+
+**Sample 3:** `Apogonia es un género de escarabajos. Algunos son plagas de los árboles de durio...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁apo gon ia ▁es ▁un ▁género ▁de ▁esca ra ba ... (+14 more)` | 24 |
+| 16k | `▁apo gon ia ▁es ▁un ▁género ▁de ▁esca raba jos ... (+13 more)` | 23 |
+| 32k | `▁apo gonia ▁es ▁un ▁género ▁de ▁esca raba jos . ... (+12 more)` | 22 |
+| 64k | `▁apo gonia ▁es ▁un ▁género ▁de ▁escarabajos . ▁algunos ▁son ... (+9 more)` | 19 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.831x compression
+- **Lowest UNK Rate:** 8k with 0.0687% 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 | 183,447 | 17.49 | 4,181,700 | 10.2% | 22.2% |
+| **2-gram** | Subword | 225 🏆 | 7.82 | 32,676 | 73.3% | 99.3% |
+| **3-gram** | Word | 1,817,727 | 20.79 | 12,295,310 | 2.4% | 7.7% |
+| **3-gram** | Subword | 1,802 | 10.82 | 237,444 | 31.5% | 76.4% |
+| **4-gram** | Word | 7,309,961 | 22.80 | 24,272,836 | 1.0% | 3.5% |
+| **4-gram** | Subword | 10,272 | 13.33 | 1,392,210 | 16.3% | 43.2% |
+| **5-gram** | Word | 8,151,138 | 22.96 | 17,610,926 | 0.6% | 2.4% |
+| **5-gram** | Subword | 43,696 | 15.42 | 4,988,047 | 9.3% | 26.6% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de la` | 3,764,844 |
+| 2 | `en el` | 1,831,679 |
+| 3 | `en la` | 1,685,738 |
+| 4 | `de los` | 1,321,114 |
+| 5 | `a la` | 938,285 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `uno de los` | 141,403 |
+| 2 | `de la ciudad` | 115,570 |
+| 3 | `la ciudad de` | 108,727 |
+| 4 | `referencias enlaces externos` | 100,698 |
+| 5 | `la provincia de` | 97,604 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de la provincia de` | 59,022 |
+| 2 | `de la ciudad de` | 41,783 |
+| 3 | `a lo largo de` | 38,783 |
+| 4 | `de la universidad de` | 33,450 |
+| 5 | `en la ciudad de` | 31,628 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a lo largo de la` | 12,052 |
+| 2 | `cuenta con una población de` | 11,005 |
+| 3 | `0 0 0 0 0` | 10,612 |
+| 4 | `en los juegos olímpicos de` | 8,927 |
+| 5 | `de la segunda guerra mundial` | 8,768 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 52,737,608 |
+| 2 | `a _` | 52,540,713 |
+| 3 | `_ d` | 41,585,544 |
+| 4 | `d e` | 41,490,874 |
+| 5 | `s _` | 40,981,306 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 34,910,171 |
+| 2 | `d e _` | 27,000,114 |
+| 3 | `_ l a` | 16,444,469 |
+| 4 | `o s _` | 15,082,263 |
+| 5 | `e l _` | 14,921,174 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 25,355,198 |
+| 2 | `_ l a _` | 12,685,143 |
+| 3 | `_ e n _` | 10,248,634 |
+| 4 | `_ e l _` | 9,367,910 |
+| 5 | `o _ d e` | 6,941,874 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _ l` | 6,404,305 |
+| 2 | `o _ d e _` | 5,587,851 |
+| 3 | `s _ d e _` | 5,212,347 |
+| 4 | `_ q u e _` | 5,016,845 |
+| 5 | `d e _ l a` | 4,732,721 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 225
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~27% 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 | 1.0184 | 2.026 | 16.60 | 2,511,755 | 0.0% |
+| **1** | Subword | 1.1686 | 2.248 | 8.74 | 17,433 | 0.0% |
+| **2** | Word | 0.4618 | 1.377 | 3.10 | 41,654,830 | 53.8% |
+| **2** | Subword | 0.6288 | 1.546 | 4.11 | 152,257 | 37.1% |
+| **3** | Word | 0.2403 | 1.181 | 1.67 | 128,974,391 | 76.0% |
+| **3** | Subword | 0.6792 | 1.601 | 4.08 | 625,267 | 32.1% |
+| **4** | Word | 0.1170 🏆 | 1.084 | 1.24 | 214,851,229 | 88.3% |
+| **4** | Subword | 0.6781 | 1.600 | 3.60 | 2,547,890 | 32.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de boca juniors al alcanzar sus danzas en el nk con ellos el español y el`
+2. `la ribera de jabez aúl en el primer álbum considerada una variante guacara data del encéfalo`
+3. `en la pequeña localidad recibió una especie musa valí de megaron del origen suizo enfrentar demandas`
+
+**Context Size 2:**
+
+1. `de la campana de huesca por el proyecto de igual manera considera a los que la rebelión`
+2. `en el reino humano ahí habitaban las estribaciones de la flota de la presidencia de manuel fernández`
+3. `en la victoria del ejército mexicano las investigaciones arqueológicas fue también del talmud en el ...`
+
+**Context Size 3:**
+
+1. `uno de los testimonios más antiguos independientes de eugène canseliet y tomados exclusivamente de f...`
+2. `de la ciudad donde el cadáver yacía aún en el aeropuerto recibió a 4 120 000 de los`
+3. `la ciudad de bogotá ya que también fue considerado para ser desarrollado como una expresión profunda...`
+
+**Context Size 4:**
+
+1. `de la provincia de buenos aires de argentina de bienestar social de mallorca cirer toma posesión del...`
+2. `de la ciudad de méxico y dentro de la esfera de las tradiciones judías con elementos de culto judío`
+3. `a lo largo de la jornada feria barroca a primeros de octubre embarcaron rumbo a la desierta isla de`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_uien_y_sene_fuá`
+2. `e_locipa_y_tatr_`
+3. `atrs_a_playblay_`
+
+**Context Size 2:**
+
+1. `e_con_utien_dity,`
+2. `a_a_ta_y_carro_el`
+3. `_de_tes_perona_pr`
+
+**Context Size 3:**
+
+1. `_de_un_mar_más_all`
+2. `de_la_bra_con_el_,`
+3. `_la_la_conte,_g._c`
+
+**Context Size 4:**
+
+1. `_de_la_justaventas_`
+2. `_la_interés_pequeta`
+3. `_en_varie_daño_a_la`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 88.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (2,547,890 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 | 1,128,398 |
+| Total Tokens | 317,857,480 |
+| Mean Frequency | 281.69 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 33492.75 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 25,424,319 |
+| 2 | la | 12,852,916 |
+| 3 | en | 10,451,863 |
+| 4 | el | 9,561,089 |
+| 5 | y | 8,147,125 |
+| 6 | a | 5,543,222 |
+| 7 | que | 5,130,281 |
+| 8 | del | 4,632,587 |
+| 9 | los | 4,528,979 |
+| 10 | se | 3,615,320 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | drammenselva | 2 |
+| 2 | bidagos | 2 |
+| 3 | guillenpbro | 2 |
+| 4 | peytrequincomisión | 2 |
+| 5 | méndezpbro | 2 |
+| 6 | ollerhno | 2 |
+| 7 | ricamonseñor | 2 |
+| 8 | grezillé | 2 |
+| 9 | leguedeniau | 2 |
+| 10 | lajubaudiere | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9940 |
+| R² (Goodness of Fit) | 0.993771 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 44.4% |
+| Top 1,000 | 62.8% |
+| Top 5,000 | 78.2% |
+| Top 10,000 | 84.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9938 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 44.4% of corpus
+- **Long Tail:** 1,118,398 words needed for remaining 15.7% 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 | 0.7898 | 0.3869 | N/A | N/A |
+| **mono_64d** | 64 | 0.7625 | 0.3145 | N/A | N/A |
+| **mono_128d** | 128 | 0.6860 | 0.2555 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7898 🏆 | 0.3861 | 0.5660 | 0.8680 |
+| **aligned_64d** | 64 | 0.7625 | 0.3206 | 0.7520 | 0.9260 |
+| **aligned_128d** | 128 | 0.6860 | 0.2619 | 0.7960 | 0.9680 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7898 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3209. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 79.6% 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 | **-0.909** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-a` | aprile, akiya, argumenta |
+| `-s` | seifer, seninho, stobar |
+| `-ma` | maremmae, maozim, manks |
+| `-m` | mizrajíes, moguereños, morganáticas |
+| `-c` | captivos, coevolucionarias, clips |
+| `-p` | pk3, polistinae, polypetalæ |
+| `-t` | tangamanga, tedros, tubariales |
+| `-b` | bundesagentur, bitschnau, botrioides |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-s` | lebbeus, tedros, captivos |
+| `-a` | tangamanga, akiya, luvana |
+| `-o` | kajanto, seninho, ducetio |
+| `-e` | aprile, trimble, dumonde |
+| `-n` | hazzan, ameln, bebieron |
+| `-os` | tedros, captivos, moguereños |
+| `-es` | tubariales, emboques, mizrajíes |
+| `-as` | coevolucionarias, morganáticas, turillas |
+
+### 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 |
+|------|----------|------------------|----------|
+| `iend` | 1.89x | 259 contexts | iendo, fiend, liendo |
+| `ient` | 1.55x | 383 contexts | aient, iente, cient |
+| `spañ` | 2.35x | 44 contexts | españ, spaña, españa |
+| `ació` | 1.84x | 114 contexts | ación, vació, yació |
+| `lmen` | 1.79x | 97 contexts | ülmen, olmen, ilmen |
+| `aliz` | 1.40x | 288 contexts | aliza, valiz, alizé |
+| `ombr` | 1.52x | 179 contexts | ombri, sombr, ombre |
+| `resi` | 1.36x | 299 contexts | resis, resid, resit |
+| `stru` | 1.34x | 259 contexts | strub, strul, struk |
+| `ontr` | 1.45x | 156 contexts | contr, pontro, lontra |
+| `renc` | 1.40x | 185 contexts | prenc, renck, frenc |
+| `ntre` | 1.41x | 176 contexts | antre, intre, entre |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-a` | `-s` | 162 words | arrianas, alumbrados |
+| `-c` | `-s` | 149 words | certhiaxis, corpasinos |
+| `-c` | `-a` | 139 words | contrarreforma, cusítica |
+| `-p` | `-s` | 132 words | phitos, preformados |
+| `-a` | `-a` | 118 words | azaña, artemisina |
+| `-s` | `-s` | 116 words | subtropicalis, senderistas |
+| `-p` | `-a` | 114 words | proteobacteria, prevalescencia |
+| `-e` | `-s` | 111 words | estamos, escarpes |
+| `-t` | `-s` | 94 words | tragaluces, thenailles |
+| `-c` | `-o` | 88 words | cristofano, calpetano |
+
+### 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 |
+|------|-----------------|------------|------|
+| desarrollarlas | **`desarrollar-la-s`** | 7.5 | `la` |
+| peptoides | **`peptoi-d-es`** | 7.5 | `d` |
+| şemsiruhsar | **`şemsiruh-s-ar`** | 7.5 | `s` |
+| zakrisson | **`zakris-s-on`** | 7.5 | `s` |
+| caesarobrigenses | **`caesarobrigen-s-es`** | 7.5 | `s` |
+| kushiyara | **`kushiy-a-ra`** | 7.5 | `a` |
+| ngwempisi | **`ngwempi-s-i`** | 7.5 | `s` |
+| hēmitheos | **`hēmith-e-os`** | 7.5 | `e` |
+| tsimliansk | **`tsimlian-s-k`** | 7.5 | `s` |
+| inculcado | **`inculc-a-do`** | 7.5 | `a` |
+| cbgranada | **`cbgran-a-da`** | 7.5 | `a` |
+| trespasser | **`trespas-s-er`** | 7.5 | `s` |
+| megasares | **`megas-ar-es`** | 7.5 | `ar` |
+| programarlas | **`programar-la-s`** | 7.5 | `la` |
+| galactano | **`galac-ta-no`** | 7.5 | `ta` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Spanish 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 (4.83x) |
+| N-gram | **2-gram** | Lowest perplexity (225) |
+| Markov | **Context-4** | Highest predictability (88.3%) |
+| 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 |
+
+---
+👈 [Back to README](README.md)
+
+*Generated by Wikilangs Pipeline · 2026-03-04 06:09:07*

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