---
language: en
language_name: English
language_family: germanic_west_anglofrisian
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-germanic_west_anglofrisian
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: 4.699
  - name: best_isotropy
    type: isotropy
    value: 0.7693
  - name: vocabulary_size
    type: vocab
    value: 1867537
generated: 2026-03-03
---

# English — Wikilangs Models

Open-source tokenizers, n-gram & Markov language models, vocabulary stats, and word embeddings trained on **English** Wikipedia by [Wikilangs](https://wikilangs.org).

🌐 [Language Page](https://wikilangs.org/languages/en/) · 🎮 [Playground](https://wikilangs.org/playground/?lang=en) · 📊 [Full Research Report](RESEARCH_REPORT.md)

## Language Samples

Example sentences drawn from the English Wikipedia corpus:

> Alexander V may refer to: Alexander V of Macedon (died 294 BCE) Antipope Alexander V Alexander V of Imereti

> Alfonso IV may refer to: Alfonso IV of León (924–931) Afonso IV of Portugal Alfonso IV of Aragon Alfonso IV of Ribagorza Alfonso IV d'Este Duke of Modena and Regg

> Anastasius I or Anastasios I may refer to: Anastasius I Dicorus (–518), Roman emperor Anastasius I of Antioch (died 599), Patriarch of Antioch Pope Anastasius I (died 401), pope

> Angula may refer to: Aṅgula, a measure equal to a finger's breadth Eel, a biological order of fish Nahas Angula, former Prime Minister of Namibia Helmut Angula See also Angul (disambiguation)

> Two antipopes used the regnal name Victor IV: Antipope Victor IV Antipope Victor IV

## Quick Start

### Load the Tokenizer

```python
import sentencepiece as spm

sp = spm.SentencePieceProcessor()
sp.Load("en_tokenizer_32k.model")

text = "Albrecht Achilles may refer to: Albrecht III Achilles, Elector of Brandenburg Al"
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:** `Albrecht Achilles may refer to: Albrecht III Achilles, Elector of Brandenburg Al…`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁alb recht ▁ach illes ▁may ▁refer ▁to : ▁alb recht … (+27 more)` | 37 |
| 16k | `▁alb recht ▁ach illes ▁may ▁refer ▁to : ▁alb recht … (+26 more)` | 36 |
| 32k | `▁albrecht ▁achilles ▁may ▁refer ▁to : ▁albrecht ▁iii ▁achilles , … (+17 more)` | 27 |
| 64k | `▁albrecht ▁achilles ▁may ▁refer ▁to : ▁albrecht ▁iii ▁achilles , … (+16 more)` | 26 |

**Sample 2:** `Alexander V may refer to: Alexander V of Macedon (died 294 BCE) Antipope Alexand…`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁alexander ▁v ▁may ▁refer ▁to : ▁alexander ▁v ▁of ▁maced … (+20 more)` | 30 |
| 16k | `▁alexander ▁v ▁may ▁refer ▁to : ▁alexander ▁v ▁of ▁macedon … (+18 more)` | 28 |
| 32k | `▁alexander ▁v ▁may ▁refer ▁to : ▁alexander ▁v ▁of ▁macedon … (+15 more)` | 25 |
| 64k | `▁alexander ▁v ▁may ▁refer ▁to : ▁alexander ▁v ▁of ▁macedon … (+15 more)` | 25 |

**Sample 3:** `Two antipopes used the regnal name Victor IV: Antipope Victor IV Antipope Victor…`

| Vocab | Tokens | Count |
|-------|--------|-------|
| 8k | `▁two ▁antip op es ▁used ▁the ▁reg nal ▁name ▁victor … (+8 more)` | 18 |
| 16k | `▁two ▁antip opes ▁used ▁the ▁reg nal ▁name ▁victor ▁iv … (+7 more)` | 17 |
| 32k | `▁two ▁antip opes ▁used ▁the ▁regnal ▁name ▁victor ▁iv : … (+6 more)` | 16 |
| 64k | `▁two ▁antipopes ▁used ▁the ▁regnal ▁name ▁victor ▁iv : ▁antipope … (+5 more)` | 15 |

</details>

### Load Word Embeddings

```python
from gensim.models import KeyedVectors

# Aligned embeddings (cross-lingual, mapped to English vector space)
wv = KeyedVectors.load("en_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("en_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 | 3.84x |
| Tokenizer | 16k BPE | Compression | 4.22x |
| Tokenizer | 32k BPE | Compression | 4.51x |
| Tokenizer | 64k BPE | Compression | 4.70x 🏆 |
| N-gram | 2-gram (subword) | Perplexity | 257 🏆 |
| N-gram | 2-gram (word) | Perplexity | 386,225 |
| N-gram | 3-gram (subword) | Perplexity | 2,180 |
| N-gram | 3-gram (word) | Perplexity | 4,093,782 |
| N-gram | 4-gram (subword) | Perplexity | 12,758 |
| N-gram | 4-gram (word) | Perplexity | 14,465,722 |
| N-gram | 5-gram (subword) | Perplexity | 55,700 |
| N-gram | 5-gram (word) | Perplexity | 12,820,936 |
| Markov | ctx-1 (subword) | Predictability | 0.0% |
| Markov | ctx-1 (word) | Predictability | 6.2% |
| Markov | ctx-2 (subword) | Predictability | 46.4% |
| Markov | ctx-2 (word) | Predictability | 48.3% |
| Markov | ctx-3 (subword) | Predictability | 45.8% |
| Markov | ctx-3 (word) | Predictability | 75.9% |
| Markov | ctx-4 (subword) | Predictability | 36.8% |
| Markov | ctx-4 (word) | Predictability | 89.2% 🏆 |
| Vocabulary | full | Size | 1,867,537 |
| Vocabulary | full | Zipf R² | 0.9862 |
| Embeddings | mono_32d | Isotropy | 0.7693 🏆 |
| Embeddings | mono_64d | Isotropy | 0.7388 |
| Embeddings | mono_128d | Isotropy | 0.6687 |

📊 **[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/en/)
- 🎮 [Playground](https://wikilangs.org/playground/?lang=en)
- 🤗 [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 · 2026-03-03 22:59:51*