FastText Language Identification
| Property | Value |
|---|---|
| License | CC-BY-NC-4.0 |
| Languages Supported | 217 |
| Training Data | Common Crawl and Wikipedia |
| Downloads | 4,420,360 |
What is fasttext-language-identification?
FastText language identification is a lightweight, open-source library developed by Facebook for efficient text classification and language detection. This particular model (lid218e) can identify 217 different languages and was released as part of the NLLB project. It's designed to run efficiently on standard hardware, making it accessible for both development and production environments.
Implementation Details
The model utilizes CBOW (Continuous Bag of Words) with position-weights, featuring 300-dimensional vectors and character n-grams of length 5. It employs a window size of 5 and 10 negatives during training. The implementation includes specialized tokenization approaches for different languages, using Stanford word segmenter for Chinese, Mecab for Japanese, and UETsegmenter for Vietnamese.
- Efficient CPU-based processing
- Supports quick model iteration without specialized hardware
- Can process billions of words in minutes
- Mobile-device compatible through model reduction
Core Capabilities
- Language identification across 217 languages
- Word representation learning
- Text classification
- Cosine similarity calculations for word relationships
- Multiple language prediction with confidence scores
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to process text efficiently on standard hardware while maintaining high accuracy across 217 languages. It combines speed with versatility, making it suitable for both experimental and production environments.
Q: What are the recommended use cases?
The model is ideal for language identification in multilingual text processing, content filtering, routing messages to appropriate translators, and analyzing large text datasets. It's particularly useful in scenarios requiring quick language detection without heavy computational resources.
