fasttext-en-vectors
| Property | Value |
|---|---|
| License | CC-BY-SA 3.0 |
| Vector Dimension | 300 |
| Vocabulary Size | 145,940 words |
| Training Data | Wikipedia and Common Crawl |
What is fasttext-en-vectors?
fasttext-en-vectors is a lightweight, efficient word embedding model developed by Facebook that provides high-quality word representations for English text. The model was trained using the CBOW (Continuous Bag of Words) architecture with position-weights, incorporating character n-grams of length 5 and a context window of size 5.
Implementation Details
The model implements sophisticated word representation learning techniques, utilizing subword information to enhance vector quality. It operates on standard hardware and can process billions of words efficiently.
- Trained on massive datasets including Wikipedia and Common Crawl
- Uses character n-grams for robust representation of rare words
- Implements position-weighted CBOW with 10 negative samples
- Supports nearest neighbor queries and language identification
Core Capabilities
- Word vector representation in 300 dimensions
- Fast and efficient text classification
- Nearest neighbor word queries
- Handles out-of-vocabulary words through subword information
- Supports multilingual applications (part of a 157-language collection)
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its ability to generate high-quality word representations while maintaining computational efficiency. It can be trained on billion-word datasets in minutes on standard CPUs, making it highly accessible for various applications.
Q: What are the recommended use cases?
The model is ideal for text classification tasks, word similarity analysis, language identification, and as a feature extractor for downstream NLP tasks. It's particularly useful when working with limited computational resources or when quick model iteration is needed.
