fasttext-bg-vectors
| Property | Value |
|---|---|
| License | Creative Commons Attribution-Share-Alike 3.0 |
| Language | Bulgarian |
| Vector Dimension | 300 |
| Training Data | Common Crawl and Wikipedia |
What is fasttext-bg-vectors?
fasttext-bg-vectors is a specialized word embedding model for the Bulgarian language, developed by Facebook's AI research team. It's part of FastText's extensive collection of pre-trained word vectors covering 157 languages. The model generates 300-dimensional vector representations of words, incorporating subword information through character n-grams.
Implementation Details
The model was trained using the CBOW (Continuous Bag of Words) architecture with position-weights, utilizing character n-grams of length 5, a context window of size 5, and 10 negative samples. The training process incorporated both Wikipedia and Common Crawl data to ensure comprehensive coverage of the Bulgarian language.
- Efficient word representation learning with subword information
- Supports fast text classification and nearest neighbor semantic queries
- Handles out-of-vocabulary words through subword modeling
Core Capabilities
- Word vector generation for Bulgarian text
- Semantic similarity computation between words
- Text classification tasks
- Language identification
- Nearest neighbor word queries
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines subword information with traditional word embeddings, making it especially effective for morphologically rich languages like Bulgarian. It can handle out-of-vocabulary words and maintains small model size while providing robust performance.
Q: What are the recommended use cases?
The model is ideal for text classification, language identification, semantic similarity analysis, and information retrieval tasks in Bulgarian. It's particularly useful in applications requiring understanding of word relationships and text categorization.
