{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"sentence-transformers","slug":"sentence-transformers","name":"sentence-transformers","type":"repo","url":"https://www.sbert.net","page_url":"https://unfragile.ai/sentence-transformers","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"sentence-transformers__cap_0","uri":"capability://data.processing.analysis.dense.vector.embedding.generation.for.text","name":"dense-vector-embedding-generation-for-text","description":"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.","intents":["I need to convert my document corpus into embeddings for semantic search","I want to compute similarity scores between text pairs without training a model","I need a lightweight embedding model that runs locally on CPU or GPU","I want to use state-of-the-art pretrained embeddings from the MTEB leaderboard"],"best_for":["developers building semantic search systems","teams implementing RAG pipelines with local inference","researchers benchmarking embedding models","solo developers prototyping similarity-based features without cloud dependencies"],"limitations":["Output embedding dimension is fixed per model (e.g., 384 for all-MiniLM-L6-v2); no dynamic resizing","Batch processing requires loading entire batch into memory; no streaming inference API","Inference latency depends on model size and hardware; no built-in quantization or distillation","Text inputs must be preprocessed by user (no automatic chunking for long documents)","No caching layer for repeated embeddings; duplicate computations not deduplicated"],"requires":["Python 3.10+","PyTorch 1.11.0+","transformers library (implicit dependency)","Hugging Face Hub access (for model downloads) or local model files","2GB+ disk space per model (varies by model size)"],"input_types":["list of strings (sentences, paragraphs, documents)","single string","numpy arrays or lists of variable-length text"],"output_types":["numpy array with shape [num_inputs, embedding_dimension]","PyTorch tensor (if convert_to_tensor=True)","list of numpy arrays (if convert_to_numpy=True)"],"categories":["data-processing-analysis","embedding-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_1","uri":"capability://data.processing.analysis.multimodal.cross.modal.embedding.alignment","name":"multimodal-cross-modal-embedding-alignment","description":"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.","intents":["I need to search for images using text queries without training a multimodal model","I want to find similar videos or audio clips based on text descriptions","I need to cluster mixed-media content (text + images + video) by semantic similarity","I want to build a cross-modal recommendation system with minimal setup"],"best_for":["teams building image search or visual discovery features","researchers working on multimodal retrieval benchmarks","developers implementing content recommendation across media types","applications requiring cross-modal semantic matching without custom training"],"limitations":["Multimodal support is recent (v5.4+); limited model availability compared to text-only models","Audio and video preprocessing adds latency; no streaming support for long-form media","Embedding space alignment quality depends on training data; may not generalize to domain-specific content","File path inputs require local filesystem access; no direct streaming from cloud storage","Cross-modal similarity scores are less interpretable than within-modality scores"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers v5.4 or later","transformers library with multimodal model support","librosa or similar for audio processing (if using audio inputs)","PIL/Pillow for image processing"],"input_types":["list of image URLs or file paths","list of audio file paths","list of video file paths","mixed list of text strings and media paths","numpy arrays representing image/audio data"],"output_types":["numpy array with shape [num_inputs, embedding_dimension] (unified across modalities)","PyTorch tensor with cross-modal alignment","similarity matrix comparing text to images or other modalities"],"categories":["data-processing-analysis","image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_10","uri":"capability://data.processing.analysis.model.evaluation.and.benchmarking.on.mteb","name":"model-evaluation-and-benchmarking-on-mteb","description":"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.","intents":["I need to evaluate my embedding model against standard benchmarks","I want to compare my model quality to state-of-the-art on MTEB leaderboard","I need to measure performance on specific tasks (retrieval, clustering, etc.)","I want to benchmark fine-tuned models on domain-specific evaluation sets"],"best_for":["researchers developing embedding models","teams evaluating model quality before production deployment","developers comparing pretrained models for their use case","organizations benchmarking custom fine-tuned models"],"limitations":["MTEB benchmarks may not reflect your specific domain; general-purpose evaluation","Evaluation requires downloading benchmark datasets; significant disk space and bandwidth","Evaluation is compute-intensive; full MTEB evaluation takes hours on GPU","No built-in statistical significance testing; results are point estimates","Benchmark results are task-specific; no single metric for overall model quality"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library","MTEB library (separate package)","GPU recommended for reasonable evaluation time","10GB+ disk space for benchmark datasets"],"input_types":["embedding model (SentenceTransformer instance)","custom evaluation dataset","MTEB task names (e.g., 'STS12', 'TREC-COVID')"],"output_types":["evaluation scores (NDCG, MAP, MRR for retrieval; Spearman correlation for STS)","per-task performance metrics","comparison to leaderboard models","detailed evaluation report"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_11","uri":"capability://memory.knowledge.model.loading.and.caching.from.hugging.face.hub","name":"model-loading-and-caching-from-hugging-face-hub","description":"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.","intents":["I need to load a pretrained embedding model without manual downloading","I want to use state-of-the-art models from MTEB leaderboard","I need to manage model versions and cache efficiently","I want to load models with automatic GPU/CPU placement"],"best_for":["developers building quick prototypes with pretrained models","teams deploying models without custom training","researchers experimenting with different model architectures","applications requiring easy model updates from Hugging Face Hub"],"limitations":["Model download requires internet connection; no offline mode for first load","Cache location is fixed; no built-in cache management or cleanup utilities","Model size varies widely (50MB to 2GB+); large models require significant disk space","No model versioning control; latest version always downloaded unless explicitly pinned","Model compatibility depends on transformers library version; version mismatches cause errors"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library","Internet connection for first model download","Disk space proportional to model size (50MB to 2GB+)","Hugging Face Hub access (public models require no authentication)"],"input_types":["model identifier string (e.g., 'all-MiniLM-L6-v2')","model path (local or Hub)","optional: device specification ('cuda', 'cpu')"],"output_types":["SentenceTransformer model instance ready for inference","model configuration and tokenizer","model loaded on specified device"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_12","uri":"capability://data.processing.analysis.sentence.level.tokenization.and.preprocessing","name":"sentence-level-tokenization-and-preprocessing","description":"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.","intents":["I need to handle variable-length text inputs without manual tokenization","I want to process long documents that exceed model maximum length","I need to batch variable-length sentences with proper padding","I want to avoid manual tokenization and padding logic"],"best_for":["developers building embedding systems without NLP expertise","teams processing diverse text lengths without custom preprocessing","applications requiring robust handling of edge cases (very long/short text)","systems needing automatic tokenization without exposing complexity"],"limitations":["Tokenization is opaque; no visibility into token-level details","Maximum sequence length is fixed per model; no dynamic length adjustment","Truncation strategy is fixed (typically 'longest_first'); no custom truncation logic","Padding adds computation for short sequences; no optimization for variable-length batches","Special tokens are handled automatically; no control over token-level preprocessing"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library","transformers library (implicit dependency)"],"input_types":["list of strings (any length)","single string","variable-length text (automatically handled)"],"output_types":["tokenized input_ids (handled internally)","attention masks (handled internally)","embeddings (output of tokenization + encoding)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_13","uri":"capability://automation.workflow.model.quantization.and.optimization.for.inference","name":"model-quantization-and-optimization-for-inference","description":"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.","intents":["I need to reduce model size for deployment on resource-constrained devices","I want faster inference on CPU without GPU","I need to optimize models for mobile or edge deployment","I want to balance model quality with inference speed"],"best_for":["teams deploying models on edge devices or mobile","developers optimizing for CPU-only inference","applications requiring low-latency embedding generation","systems with strict memory or compute constraints"],"limitations":["Quantization details are not documented in provided materials; implementation unknown","Quantized models may have lower quality than full-precision models","Optimization techniques are model-specific; not all models support quantization","No built-in quality evaluation for quantized models; manual benchmarking required","Quantization support may be limited to specific model architectures"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library","Optional: ONNX Runtime or other optimization frameworks"],"input_types":["full-precision embedding model","quantization configuration"],"output_types":["quantized model (reduced size)","optimized model for inference","performance metrics (latency, memory usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_2","uri":"capability://search.retrieval.semantic.similarity.scoring.and.ranking","name":"semantic-similarity-scoring-and-ranking","description":"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.","intents":["I need to find the top-K most similar documents to a query without exhaustive search","I want to compute pairwise similarity between all items in a corpus for clustering","I need to rerank search results using a cross-encoder model for better relevance","I want to measure semantic similarity between text pairs for duplicate detection"],"best_for":["developers implementing semantic search ranking","teams building recommendation systems with similarity-based filtering","researchers evaluating embedding quality on similarity benchmarks","applications requiring efficient nearest-neighbor search over embeddings"],"limitations":["Pairwise similarity computation is O(n²) in corpus size; no approximate nearest-neighbor indexing built-in","Cross-encoder reranking requires forward pass per candidate; slower than dense retrieval for large result sets","Similarity scores are not calibrated across different models; direct comparison between models unreliable","No built-in threshold tuning or confidence estimation for similarity scores","Requires embeddings already computed; no lazy evaluation or caching of similarity matrices"],"requires":["Python 3.10+","PyTorch 1.11.0+","Pre-computed embeddings (from encode() method)","Optional: CrossEncoder model for reranking (separate model loading)"],"input_types":["numpy arrays or PyTorch tensors with shape [num_items, embedding_dim]","list of embedding vectors","raw text (if using cross-encoder for direct scoring)"],"output_types":["similarity matrix with shape [num_queries, num_corpus_items]","ranked list of (index, score) tuples","top-K indices and scores","pairwise similarity scores as numpy array"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_3","uri":"capability://data.processing.analysis.paraphrase.mining.and.duplicate.detection","name":"paraphrase-mining-and-duplicate-detection","description":"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.","intents":["I need to find duplicate or near-duplicate documents in my corpus","I want to identify paraphrased sentences for data deduplication","I need to mine training pairs for fine-tuning similarity models","I want to detect plagiarism or content reuse across documents"],"best_for":["data engineers cleaning large text corpora","teams building deduplication pipelines for search indexes","researchers creating paraphrase datasets for model training","content moderation systems detecting duplicate submissions"],"limitations":["Similarity threshold is global; no per-pair or per-cluster tuning","Quadratic complexity in corpus size; mining over 10M+ sentences requires distributed processing","No built-in clustering of paraphrase groups; returns only pairwise matches","Threshold selection is manual; no automated calibration based on data distribution","False positives increase with lower thresholds; no confidence scoring or ranking of paraphrase quality"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library","Sufficient RAM for corpus embeddings (e.g., 10M sentences × 384 dims ≈ 15GB)","Optional: GPU for faster embedding computation"],"input_types":["list of strings (sentences or documents)","pre-computed embeddings as numpy array or tensor","corpus size: tested up to millions of sentences"],"output_types":["list of (index1, index2, similarity_score) tuples","deduplicated corpus with duplicate indices removed","paraphrase pairs with scores for training data creation"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_4","uri":"capability://data.processing.analysis.semantic.clustering.and.grouping","name":"semantic-clustering-and-grouping","description":"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.","intents":["I need to organize my document corpus into semantic topics without manual labeling","I want to group customer feedback or support tickets by topic","I need to find natural groupings in my text data for exploratory analysis","I want to create hierarchical clusters for browsable document organization"],"best_for":["data analysts exploring unlabeled text corpora","teams organizing customer feedback or survey responses","researchers discovering topics in document collections","applications requiring dynamic topic organization without predefined categories"],"limitations":["Cluster count must be specified manually; no automatic optimal cluster detection","Clustering quality depends on embedding model quality; poor embeddings produce poor clusters","No built-in cluster labeling or interpretation; cluster meaning must be inferred manually","Hierarchical clustering is O(n²) in memory; not scalable to 10M+ documents without approximation","No incremental clustering; adding new documents requires recomputing all clusters"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library","scikit-learn (for clustering algorithms)","Sufficient RAM for corpus embeddings"],"input_types":["list of strings (documents or sentences)","pre-computed embeddings as numpy array","corpus size: practical limit ~100K documents for hierarchical clustering"],"output_types":["cluster assignments (array of cluster IDs per document)","cluster centroids (representative embeddings)","dendrogram (for hierarchical clustering visualization)","cluster sizes and composition"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_5","uri":"capability://code.generation.editing.model.fine.tuning.and.training.on.custom.data","name":"model-fine-tuning-and-training-on-custom-data","description":"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.","intents":["I need to fine-tune an embedding model on my domain-specific corpus","I want to train a model using labeled similarity pairs from my data","I need to adapt a pretrained model to specialized terminology or language","I want to create a custom embedding model optimized for my specific use case"],"best_for":["teams with domain-specific text requiring custom embeddings","researchers training embedding models on proprietary datasets","developers optimizing models for specialized vocabularies (medical, legal, technical)","applications requiring embeddings tailored to specific similarity definitions"],"limitations":["Training requires labeled data (pairs or triplets); no automatic label generation","Hyperparameter tuning is manual; no built-in AutoML or hyperparameter search","Training time scales with corpus size and model size; large models require GPU","No distributed training support visible in documentation; single-machine only","Evaluation metrics must be computed separately; no built-in validation or early stopping"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library","Labeled training data (pairs, triplets, or unlabeled corpus)","GPU recommended for reasonable training time (CPU training very slow)","Knowledge of loss functions and training hyperparameters"],"input_types":["list of sentence pairs with similarity labels","triplet data (anchor, positive, negative)","unlabeled corpus for unsupervised training","CSV or JSON files with training examples"],"output_types":["fine-tuned model saved to disk","training logs with loss curves","evaluation metrics on validation set","model checkpoint at best validation performance"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_6","uri":"capability://data.processing.analysis.batch.embedding.computation.with.memory.efficiency","name":"batch-embedding-computation-with-memory-efficiency","description":"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.","intents":["I need to embed a million-document corpus efficiently without running out of memory","I want to process embeddings on GPU when available, with CPU fallback","I need to control memory usage by tuning batch sizes for my hardware","I want to embed documents incrementally as they arrive in my system"],"best_for":["data engineers processing large document corpora","teams building production embedding pipelines","developers with limited GPU memory needing efficient batching","systems requiring scalable embedding generation for millions of documents"],"limitations":["Batch size must be tuned manually for hardware; no automatic optimal batch size detection","No streaming or incremental embedding API; entire batch must be prepared before encoding","Memory usage scales with batch size and document length; very long documents may cause OOM","No distributed processing across multiple GPUs or machines built-in","Tokenization overhead not optimized; no token-level caching or reuse"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library","GPU optional but recommended (CUDA 11.8+ or compatible)","RAM proportional to batch size (e.g., 32GB for large batches on large models)"],"input_types":["list of strings (any length)","numpy array of strings","file paths to text files (if using custom data loading)","batch size: configurable from 1 to 1000+ depending on hardware"],"output_types":["numpy array with shape [num_documents, embedding_dim]","PyTorch tensor (if convert_to_tensor=True)","embeddings saved to disk (if using custom output handling)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_7","uri":"capability://search.retrieval.semantic.search.with.query.document.retrieval","name":"semantic-search-with-query-document-retrieval","description":"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.","intents":["I need to build a semantic search engine over my document corpus","I want to retrieve documents most similar to a user query","I need to integrate semantic search into my existing search system","I want to provide better search results than keyword-based approaches"],"best_for":["developers building semantic search features","teams implementing RAG (Retrieval-Augmented Generation) systems","applications requiring better search relevance than keyword matching","researchers benchmarking semantic search quality"],"limitations":["In-memory search is O(n) per query; no approximate nearest-neighbor indexing built-in","Vector database integration is user responsibility; no built-in connectors provided","Search quality depends entirely on embedding model quality; poor models produce poor results","No query expansion or reformulation; single query string processed as-is","No built-in result deduplication or diversity ranking"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library","Pre-computed document embeddings","Optional: Vector database client (Pinecone, Weaviate, Chroma, etc.)"],"input_types":["query string (single or list of queries)","document embeddings (numpy array or tensor)","corpus of documents (for in-memory search)"],"output_types":["top-K document indices and similarity scores","ranked list of documents with relevance scores","search results formatted for display"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_8","uri":"capability://search.retrieval.cross.encoder.based.reranking.and.relevance.scoring","name":"cross-encoder-based-reranking-and-relevance-scoring","description":"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.","intents":["I need to rerank search results for better relevance without retraining models","I want to score query-document pairs directly for more accurate matching","I need to improve search quality by combining dense retrieval with cross-encoder reranking","I want to measure relevance between query and document pairs for evaluation"],"best_for":["teams implementing production search systems requiring high relevance","developers building two-stage retrieval (dense + reranking) pipelines","researchers evaluating ranking quality on benchmark datasets","applications where search accuracy is critical (e.g., legal, medical search)"],"limitations":["Cross-encoder inference is slower than dense similarity; not suitable for real-time ranking of large result sets","Requires forward pass per candidate; O(k) where k is number of candidates to rerank","Cross-encoder models are task-specific; general-purpose models may not match your domain","No built-in threshold tuning for relevance scores; calibration is manual","Scores are not comparable across different cross-encoder models"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library with CrossEncoder support","Pre-computed candidate set from dense retrieval (typically top-100 or top-1000)","GPU recommended for reasonable latency (CPU inference very slow)"],"input_types":["list of (query, document) pairs as tuples or strings","query string and list of candidate documents","batch of query-document pairs for efficient scoring"],"output_types":["relevance scores (float) for each pair","ranked list of documents sorted by cross-encoder score","score matrix with shape [num_queries, num_candidates]"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__cap_9","uri":"capability://data.processing.analysis.sparse.embedding.generation.for.hybrid.search","name":"sparse-embedding-generation-for-hybrid-search","description":"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.","intents":["I need to build hybrid search combining semantic and keyword matching","I want sparse embeddings for interpretable retrieval with keyword awareness","I need to improve recall by combining dense and sparse retrieval","I want to leverage both semantic similarity and exact term matching"],"best_for":["teams building production search systems requiring high recall","developers implementing hybrid retrieval for better coverage","applications where keyword matching is important (e.g., technical search)","researchers exploring hybrid search architectures"],"limitations":["Sparse embedding support is limited; fewer pretrained models available than dense","Sparse embeddings are less interpretable than dense; dimension meaning varies by model","Hybrid search requires combining scores from two systems; no built-in fusion strategy","Sparse embeddings may be less effective for semantic similarity than dense embeddings","Integration with vector databases requires custom logic; no built-in hybrid search API"],"requires":["Python 3.10+","PyTorch 1.11.0+","sentence-transformers library with sparse encoder support","Vector database supporting sparse embeddings (e.g., Elasticsearch, Vespa)"],"input_types":["list of strings (documents or queries)","text for sparse encoding"],"output_types":["sparse embedding as scipy sparse matrix or dict of {dimension: value}","hybrid scores combining dense and sparse similarity"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sentence-transformers__headline","uri":"capability://memory.knowledge.semantic.search.and.embedding.framework","name":"semantic search and embedding framework","description":"A Python framework for generating dense vector representations of sentences, paragraphs, and images, enabling semantic search, clustering, and paraphrase mining with over 100 pre-trained models.","intents":["best semantic search framework","embedding models for text and images","semantic clustering tools","paraphrase mining solutions","Python library for sentence embeddings"],"best_for":["natural language processing","image analysis","semantic search applications"],"limitations":[],"requires":["Python 3.10+","PyTorch 1.11.0+"],"input_types":["text","images"],"output_types":["dense vectors","similarity scores"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","PyTorch 1.11.0+","transformers library (implicit dependency)","Hugging Face Hub access (for model downloads) or local model files","2GB+ disk space per model (varies by model size)","sentence-transformers v5.4 or later","transformers library with multimodal model support","librosa or similar for audio processing (if using audio inputs)","PIL/Pillow for image processing","sentence-transformers library"],"failure_modes":["Output embedding dimension is fixed per model (e.g., 384 for all-MiniLM-L6-v2); no dynamic resizing","Batch processing requires loading entire batch into memory; no streaming inference API","Inference latency depends on model size and hardware; no built-in quantization or distillation","Text inputs must be preprocessed by user (no automatic chunking for long documents)","No caching layer for repeated embeddings; duplicate computations not deduplicated","Multimodal support is recent (v5.4+); limited model availability compared to text-only models","Audio and video preprocessing adds latency; no streaming support for long-form media","Embedding space alignment quality depends on training data; may not generalize to domain-specific content","File path inputs require local filesystem access; no direct streaming from cloud storage","Cross-modal similarity scores are less interpretable than within-modality scores","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sentence-transformers","compare_url":"https://unfragile.ai/compare?artifact=sentence-transformers"}},"signature":"IMwMDyS17xU2tRu9+wtFbXpLwVQAPUQaefVAbcGbN8XHxRT/WWR0M1F0v7Fxyq//ogQzWEROH877mvdrfSi8CQ==","signedAt":"2026-06-22T05:18:09.866Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sentence-transformers","artifact":"https://unfragile.ai/sentence-transformers","verify":"https://unfragile.ai/api/v1/verify?slug=sentence-transformers","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}