README.md

metadata

language:
  - en
  - ar
  - ru
  - es
  - fr
  - de
  - it
  - pt
  - ko
  - zh
  - ja
  - hi
  - bn
  - tr
  - id
  - sv
  - nl
  - pl
  - vi
license: cc-by-4.0
task_categories:
  - translation
  - text-classification
tags:
  - frequency
  - dictionary
  - transliteration
  - romanization
  - pos-tagging

Multilingual Core Vocabulary 🌍

This dataset contains millions of frequency-sorted, highly accurate words across 19 languages. It is designed to be the ultimate resource for building cross-lingual applications, AI similarity agents, and translation models.

Dataset Structure

This repository contains two variations of the dataset:

• Massive_Dataset: Contains over 6 million words. The words were extracted and frequency-sorted from FastText, cleaned from internet noise using official Hunspell dictionaries, translated using Meta's NLLB-600M AI, and Romanized using specialized linguistic libraries.
• Balanced_Dataset: Contains highly accurate core vocabulary cross-referenced against Facebook MUSE bilingual dictionaries for perfect 1-to-1 translations.

Features (Columns)

• Word: The original word in its native script.
• Pronunciation: Romanized English representation of the phonetic pronunciation (e.g., Pinyin for Chinese, Romaji for Japanese).
• POS_Tag: The Universal Part-of-Speech tag (e.g., NOUN, VERB, ADJ) generated using the spaCy English NLP model.
• Translation: English meaning of the word.

Languages Included

Arabic, Russian, Spanish, French, German, Italian, Portuguese, Korean, Chinese, Japanese, Hindi, Bengali, Turkish, Indonesian, Swedish, Dutch, Polish, Vietnamese, and English.

Pipeline Overview

1. Extraction: Top words scraped from Common Crawl / Wikipedia via FastText.
2. Filtration: Strict dictionary validation using Linux Hunspell (removed 15+ million invalid tokens/emojis).
3. Translation: GPU-accelerated translation via Meta's NLLB-200-distilled-600M.
4. Romanization: Custom phonetic transliteration using pypinyin, pykakasi, transliterate, and indic-transliteration.
5. POS Tagging: English translations were tagged using en_core_web_sm (spaCy) to provide a unified grammatical classification across all languages.