sentence-transformers

Encodes text inputs (sentences, paragraphs, documents) into fixed-dimensional dense vectors using pretrained transformer models loaded from Hugging Face Hub. The framework wraps transformer encoder outputs, applies mean pooling over token sequences, and returns numpy arrays or PyTorch tensors with configurable batch processing. Supports 100+ pretrained models optimized for semantic similarity tasks, enabling downstream vector-based operations without requiring model training.

Encodes text, images, audio, and video into a shared embedding space (v5.4+) using multimodal transformer models, enabling semantic search across modalities (e.g., finding images matching text queries). The framework aligns different input types through a unified embedding dimension, allowing direct similarity computation between text and image embeddings without separate models or alignment layers. Supports URLs and file paths as inputs, with automatic loading and preprocessing handled internally.

Evaluates embedding models on standardized benchmarks from the MTEB (Massive Text Embedding Benchmark) leaderboard, measuring performance on tasks like semantic similarity, retrieval, clustering, and reranking. The framework provides evaluation utilities and integration with MTEB datasets, enabling comparison against state-of-the-art models without manual benchmark implementation. Supports custom evaluation metrics and dataset-specific evaluation protocols.

Loads pretrained embedding models from Hugging Face Hub with automatic caching and version management. The framework handles model downloading, caching to local disk, and loading into memory with minimal user code. Supports model selection from 100+ pretrained models optimized for different tasks, with automatic device placement (GPU/CPU) and configuration loading from model cards.

Automatically tokenizes input text using transformer-specific tokenizers and applies padding/truncation to fixed sequence lengths. The framework handles tokenization internally during encoding, supporting variable-length inputs and automatic batching with proper padding. Provides configurable maximum sequence length and truncation strategies for handling long documents without exposing low-level tokenization details.

Optimizes embedding models for faster inference through quantization, distillation, and other optimization techniques. The framework supports loading quantized models and provides utilities for reducing model size and latency without significant quality loss. Enables deployment on resource-constrained devices (mobile, edge) and faster inference on CPU without GPU.

Computes pairwise similarity scores between embeddings using cosine similarity, dot product, or Euclidean distance metrics. The framework provides vectorized similarity computation across large embedding matrices, returning similarity matrices or ranked lists of most-similar items. Supports both dense embeddings and cross-encoder models for reranking search results, enabling efficient ranking without recomputing embeddings for each comparison.

Identifies semantically similar or duplicate text within large corpora by computing embeddings and finding pairs exceeding a similarity threshold. The framework provides efficient batch processing for mining paraphrases across millions of sentences, using vectorized similarity computation to avoid quadratic comparisons. Supports configurable similarity thresholds and filtering strategies to extract meaningful paraphrase pairs without manual annotation.

Groups similar texts into clusters based on embedding similarity using algorithms like k-means or agglomerative clustering. The framework computes embeddings, applies clustering algorithms, and returns cluster assignments and centroids. Supports hierarchical clustering for dendrogram visualization and flexible cluster count specification, enabling unsupervised organization of large text corpora without labeled training data.

Enables training or fine-tuning embedding models on custom datasets using various loss functions (contrastive, triplet, multiple negatives ranking). The framework provides training loops, data loading utilities, and loss function implementations for optimizing models on domain-specific data. Supports both supervised fine-tuning (with labeled pairs) and unsupervised training (with unlabeled corpora), allowing adaptation to specialized vocabularies or domains without starting from scratch.

Processes large text corpora into embeddings using batched inference with configurable batch sizes and automatic memory management. The framework handles tokenization, padding, and batching internally, allowing efficient processing of millions of documents without loading entire corpus into memory simultaneously. Supports GPU acceleration with automatic device management and fallback to CPU, enabling scalable embedding generation for production systems.

Implements efficient semantic search by encoding queries and documents into embeddings, then computing similarity to retrieve top-K most relevant documents. The framework supports both in-memory search (for small corpora) and integration with external vector databases for large-scale retrieval. Provides ranking utilities and result formatting for production search systems, enabling semantic search without building custom retrieval pipelines.

Uses cross-encoder models to score query-document pairs directly (rather than comparing embeddings), providing more accurate relevance judgments than dense retrieval alone. The framework loads cross-encoder models and computes scores for candidate documents, enabling two-stage retrieval pipelines (fast dense retrieval + accurate cross-encoder reranking). Supports batch scoring and flexible input formats for integration with existing search systems.

Generates sparse embeddings (high-dimensional vectors with mostly zeros) for hybrid search combining dense and sparse retrieval. The framework supports sparse encoder models that produce interpretable, keyword-aware embeddings complementing dense embeddings. Enables hybrid search systems leveraging both semantic understanding (dense) and keyword matching (sparse) without separate models or complex integration.