Principle:Norrrrrrr lyn WAInjectBench Text Embedding Initialization
| Knowledge Sources | |
|---|---|
| Domains | NLP, Representation_Learning |
| Last Updated | 2026-02-14 16:00 GMT |
Overview
A sentence embedding model initialization step that loads a pre-trained Sentence Transformer for encoding text into fixed-dimensional dense vectors.
Description
Sentence embedding models map variable-length text inputs to fixed-dimensional vectors in a shared semantic space. The WAInjectBench project uses all-MiniLM-L6-v2, a lightweight 6-layer model producing 384-dimensional embeddings. These embeddings capture semantic similarity and are used as features for a downstream LogisticRegression classifier.
The model is loaded once and reused across all training files, amortizing the initialization cost.
Usage
Use this when you need to convert text into dense vector representations for classification. This is the prerequisite for text feature extraction in the embedding-based text detector training pipeline.
Theoretical Basis
Sentence Transformers use a Siamese network architecture with mean pooling over token embeddings to produce a single sentence vector:
The all-MiniLM-L6-v2 model distills knowledge from a larger model into a 6-layer architecture, producing 384-dim vectors optimized for semantic similarity.