{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1","slug":"sentence-transformers--multi-qa-mpnet-base-dot-v1","name":"multi-qa-mpnet-base-dot-v1","type":"model","url":"https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1","page_url":"https://unfragile.ai/sentence-transformers--multi-qa-mpnet-base-dot-v1","categories":["research-search"],"tags":["sentence-transformers","pytorch","onnx","safetensors","openvino","mpnet","fill-mask","feature-extraction","sentence-similarity","transformers","text-embeddings-inference","en","dataset:flax-sentence-embeddings/stackexchange_xml","dataset:ms_marco","dataset:gooaq","dataset:yahoo_answers_topics","dataset:search_qa","dataset:eli5","dataset:natural_questions","dataset:trivia_qa"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1__cap_0","uri":"capability://search.retrieval.dense.passage.retrieval.with.dot.product.similarity","name":"dense-passage-retrieval-with-dot-product-similarity","description":"Encodes text passages and queries into 768-dimensional dense vectors using MPNet architecture, enabling fast retrieval via dot-product similarity scoring. Trained on MS MARCO, StackExchange, and QA datasets to optimize for ranking relevance in information retrieval scenarios. Uses contrastive learning with in-batch negatives to align query and passage embeddings in the same vector space, allowing efficient approximate nearest neighbor search via FAISS or similar indexing.","intents":["I need to build a semantic search engine that ranks documents by relevance to a user query","I want to retrieve the most relevant passages from a large corpus without full-text search","I need embeddings optimized for question-answering tasks where dot-product similarity matters","I'm building a RAG pipeline and need a retriever that understands semantic meaning across domains"],"best_for":["teams building production search systems with millions of documents","developers implementing retrieval-augmented generation (RAG) pipelines","researchers benchmarking dense retrieval methods on MS MARCO-style datasets","engineers optimizing for inference speed with dot-product similarity (vs cosine)"],"limitations":["Fixed 768-dimensional output — cannot reduce dimensionality without retraining or post-hoc projection","Optimized for English text only — cross-lingual performance degrades significantly on non-English queries","Dot-product similarity requires L2-normalized vectors for fair comparison; unnormalized vectors may produce unexpected ranking","Training data biased toward StackExchange/QA domains — may underperform on specialized technical or domain-specific corpora","No built-in handling of long documents >512 tokens — requires chunking strategy external to the model"],"requires":["PyTorch 1.11+ or ONNX Runtime 1.13+ for inference","sentence-transformers library 2.2.0+ for easy integration","GPU with 2GB+ VRAM for batch encoding (CPU inference ~10x slower)","Vector database or FAISS index for efficient similarity search at scale (>10k documents)"],"input_types":["text (raw strings, queries, passages)","batched text (lists of strings for efficient encoding)"],"output_types":["dense vectors (float32, 768-dimensional)","similarity scores (float, dot-product between query and passage vectors)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1__cap_1","uri":"capability://search.retrieval.multi.lingual.query.passage.alignment","name":"multi-lingual-query-passage-alignment","description":"Encodes queries and passages from multiple languages into a shared 768-dimensional embedding space trained on diverse QA datasets (Yahoo Answers, Natural Questions, TriviaQA, ELI5). The model learns language-agnostic semantic representations through contrastive learning across parallel and non-parallel QA pairs, enabling cross-language retrieval where a query in one language can retrieve passages in another. Architecture uses MPNet encoder with shared vocabulary across languages.","intents":["I need to retrieve documents in multiple languages using a single query","I want to build a multilingual search engine without training separate models per language","I need to align questions and answers across different languages for QA systems","I'm indexing a multilingual corpus and need a single embedding space for all languages"],"best_for":["teams building multilingual search products (e.g., international e-commerce, global support systems)","researchers working on cross-lingual information retrieval benchmarks","developers implementing multilingual RAG systems with mixed-language corpora"],"limitations":["Performance degrades for low-resource languages not well-represented in training data (e.g., Swahili, Tagalog)","No explicit language identification — model assumes input is valid text in supported languages","Alignment quality varies by language pair — English-Spanish better than English-Urdu due to training data distribution","512-token limit applies per language — some languages require more tokens for same semantic content"],"requires":["sentence-transformers 2.2.0+","PyTorch 1.11+ or ONNX Runtime","Text in supported languages (primarily European, Asian, and major world languages)"],"input_types":["text in multiple languages (English, German, French, Spanish, Italian, Dutch, Portuguese, Russian, Chinese, Japanese, Korean, etc.)","mixed-language batches (queries in one language, passages in another)"],"output_types":["dense vectors (float32, 768-dimensional, language-agnostic)","cross-lingual similarity scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1__cap_2","uri":"capability://data.processing.analysis.efficient.batch.encoding.with.pooling.strategies","name":"efficient-batch-encoding-with-pooling-strategies","description":"Encodes variable-length text sequences into fixed 768-dimensional vectors using mean pooling over token embeddings from MPNet's final layer. Supports efficient batching with dynamic padding to minimize computation on padding tokens, and includes optional attention-weighted pooling to emphasize semantically important tokens. Inference optimized for both CPU and GPU with ONNX export support for production deployment.","intents":["I need to encode thousands of documents efficiently for indexing into a vector database","I want to batch-encode queries and passages with minimal memory overhead","I need to deploy embeddings in production with sub-100ms latency per batch","I'm building a real-time search system and need fast encoding without GPU"],"best_for":["engineers optimizing embedding pipelines for production latency (batch size 32-128)","teams deploying embeddings on edge devices or CPU-only infrastructure","developers building indexing pipelines that process millions of documents"],"limitations":["Mean pooling loses positional information — may underperform on tasks requiring word-order sensitivity","Batch size affects embedding quality slightly — very small batches (<8) may have higher variance","ONNX export requires manual conversion; no automatic quantization to int8 (requires external tools like ONNX Runtime)","Dynamic padding adds ~5-10% overhead vs static padding for variable-length inputs"],"requires":["sentence-transformers 2.2.0+","PyTorch 1.11+ or ONNX Runtime 1.13+","Batch size typically 8-128 for optimal throughput (larger batches = better GPU utilization)"],"input_types":["text sequences (variable length, up to 512 tokens)","batched text (lists of 1-1000+ strings)"],"output_types":["dense vectors (float32, 768-dimensional)","batch embeddings (2D array: batch_size x 768)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1__cap_3","uri":"capability://search.retrieval.vector.database.integration.with.approximate.nearest.neighbor.search","name":"vector-database-integration-with-approximate-nearest-neighbor-search","description":"Produces embeddings compatible with FAISS, Pinecone, Weaviate, and other vector databases via standard float32 768-dimensional vectors. Embeddings are optimized for dot-product similarity (not cosine), enabling efficient approximate nearest neighbor (ANN) search using HNSW, IVF, or other indexing structures. Model outputs unnormalized vectors by default, which is critical for dot-product indexing performance.","intents":["I need to index millions of embeddings and retrieve top-k similar items in <100ms","I want to use FAISS or Pinecone to scale semantic search beyond in-memory limits","I'm building a vector database application and need embeddings that work with standard ANN indexes","I need to store and query embeddings with minimal infrastructure overhead"],"best_for":["teams deploying large-scale search systems (>1M documents)","developers using managed vector databases (Pinecone, Weaviate, Milvus)","engineers building FAISS-based retrieval systems with custom indexing strategies"],"limitations":["Dot-product similarity requires unnormalized vectors — normalizing vectors changes ranking order and breaks index assumptions","FAISS indexing requires careful hyperparameter tuning (nlist, nprobe) — suboptimal settings reduce recall by 10-30%","No built-in deduplication — duplicate documents produce identical embeddings and may inflate retrieval results","Vector database costs scale with embedding dimension (768) — cannot reduce without retraining"],"requires":["FAISS 1.7.0+ (for local indexing) or managed vector DB API key","Storage for 768-dimensional float32 vectors (~3KB per embedding)","Indexing infrastructure (FAISS, Pinecone, Weaviate, Milvus, etc.)"],"input_types":["text (queries and passages to embed)"],"output_types":["dense vectors (float32, 768-dimensional, unnormalized)","similarity scores (dot-product, typically 0-100+ range)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1__cap_4","uri":"capability://search.retrieval.question.answering.passage.ranking","name":"question-answering-passage-ranking","description":"Ranks candidate passages by relevance to a question using dot-product similarity between question and passage embeddings. Trained on MS MARCO, Natural Questions, TriviaQA, and ELI5 datasets where the model learned to align semantically relevant question-passage pairs in embedding space. Enables re-ranking of BM25 results or standalone ranking of pre-retrieved candidates without explicit relevance labels.","intents":["I need to rank search results by relevance to a user question","I want to re-rank BM25 results using semantic similarity for better QA performance","I'm building a question-answering system and need to identify the most relevant passages","I need to score passage relevance without training a supervised ranking model"],"best_for":["teams building QA systems that need semantic passage ranking","developers implementing two-stage retrieval (BM25 + semantic re-ranking)","researchers benchmarking passage ranking on MS MARCO or similar datasets"],"limitations":["Ranking quality depends on question clarity — ambiguous or vague questions may produce poor rankings","No explicit handling of multi-hop reasoning — cannot rank passages requiring information fusion across multiple documents","Trained on English QA datasets — performance degrades on non-English questions or domain-specific terminology","Ranking is purely semantic — cannot incorporate metadata, recency, or other non-textual signals"],"requires":["sentence-transformers 2.2.0+","Question and candidate passages as text input","Batch encoding capability for efficient ranking of 10-1000+ candidates"],"input_types":["question text (string)","candidate passages (list of strings)"],"output_types":["similarity scores (float, dot-product between question and passage embeddings)","ranked passage indices (sorted by score, descending)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1__cap_5","uri":"capability://data.processing.analysis.feature.extraction.for.downstream.tasks","name":"feature-extraction-for-downstream-tasks","description":"Extracts 768-dimensional contextual embeddings from text that can be used as features for downstream machine learning tasks (classification, clustering, similarity prediction). Embeddings capture semantic meaning learned from QA and retrieval training, enabling transfer learning without task-specific fine-tuning. Compatible with scikit-learn, XGBoost, and other ML frameworks via standard numpy/PyTorch tensor output.","intents":["I need semantic features for text classification without training a custom model","I want to cluster documents by semantic similarity using embeddings as features","I'm building a recommendation system and need text embeddings as input features","I need to extract features from text for a downstream ML pipeline"],"best_for":["data scientists building ML pipelines with text features","teams using embeddings as input to classical ML models (logistic regression, SVM, random forest)","developers implementing document clustering or similarity-based recommendations"],"limitations":["Fixed 768-dimensional output — may be too high-dimensional for some classical ML models without dimensionality reduction","Embeddings are task-agnostic — may not capture domain-specific semantic nuances without fine-tuning","No built-in feature normalization — downstream models may require standardization (mean=0, std=1)","Transfer learning quality depends on domain similarity to training data (QA/retrieval tasks)"],"requires":["sentence-transformers 2.2.0+","ML framework (scikit-learn, XGBoost, PyTorch, TensorFlow) for downstream tasks","Optional: dimensionality reduction (PCA, UMAP) for high-dimensional feature spaces"],"input_types":["text (strings or batches of strings)"],"output_types":["dense vectors (float32, 768-dimensional)","feature matrices (2D numpy arrays or PyTorch tensors)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1__cap_6","uri":"capability://search.retrieval.semantic.similarity.scoring.for.text.pairs","name":"semantic-similarity-scoring-for-text-pairs","description":"Computes semantic similarity between arbitrary text pairs (sentences, paragraphs, documents) by encoding both texts and computing dot-product similarity between their embeddings. Similarity scores range from 0 to ~100+ (unnormalized dot-product) and indicate semantic relatedness regardless of lexical overlap. Useful for detecting paraphrases, duplicate content, or semantic equivalence without explicit training on similarity labels.","intents":["I need to detect duplicate or near-duplicate documents in a corpus","I want to find paraphrases or semantically equivalent text without keyword matching","I need to compute similarity between user queries and document titles for ranking","I'm building a content deduplication system and need semantic similarity scores"],"best_for":["teams building content deduplication systems","developers implementing plagiarism detection or paraphrase identification","engineers building semantic similarity-based filtering or matching systems"],"limitations":["Dot-product similarity is unbounded — scores depend on vector magnitude and are not directly comparable across different text lengths","No threshold provided for 'similar' vs 'dissimilar' — requires empirical calibration on domain-specific data","Similarity is symmetric — similarity(A, B) = similarity(B, A), which may not match human judgment for asymmetric relationships","No handling of negation or semantic inversion — 'good' and 'bad' may have high similarity if they appear in similar contexts"],"requires":["sentence-transformers 2.2.0+","Two text inputs (strings or batches)"],"input_types":["text pair (two strings)","batched text pairs (lists of tuples)"],"output_types":["similarity score (float, dot-product, typically 0-100+ range)","similarity matrix (2D array for all-pairs comparison)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1__cap_7","uri":"capability://automation.workflow.onnx.and.openvino.export.for.edge.deployment","name":"onnx-and-openvino-export-for-edge-deployment","description":"Exports model to ONNX and OpenVINO formats for deployment on edge devices, mobile platforms, and CPU-only infrastructure without PyTorch dependency. ONNX export includes optimizations for inference engines like ONNX Runtime, TensorRT, and CoreML. OpenVINO export enables deployment on Intel hardware with quantization support (int8) for reduced model size and latency.","intents":["I need to deploy embeddings on edge devices or mobile without PyTorch","I want to reduce model size and latency using ONNX quantization","I'm building a CPU-only inference pipeline and need optimized model formats","I need to deploy on Intel hardware with OpenVINO optimization"],"best_for":["teams deploying embeddings on edge devices (mobile, IoT, embedded systems)","engineers optimizing for CPU-only inference with minimal latency","developers building on-device search or recommendation systems","teams requiring model portability across inference engines"],"limitations":["ONNX export requires manual conversion — no automatic quantization to int8 (requires external tools)","OpenVINO export is Intel-specific — limited portability to other hardware platforms","Quantization (int8) may reduce accuracy by 1-3% depending on calibration data","ONNX Runtime performance varies by hardware — CPU inference still 5-10x slower than GPU"],"requires":["ONNX Runtime 1.13+ (for ONNX inference)","OpenVINO toolkit 2022.1+ (for OpenVINO deployment)","Optional: quantization tools (ONNX Runtime QAT, OpenVINO POT) for int8 conversion"],"input_types":["text (strings or batches)"],"output_types":["dense vectors (float32 or int8 quantized, 768-dimensional)","ONNX model files (.onnx)","OpenVINO model files (.xml, .bin)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sentence-transformers--multi-qa-mpnet-base-dot-v1__cap_8","uri":"capability://safety.moderation.safetensors.format.support.for.secure.model.loading","name":"safetensors-format-support-for-secure-model-loading","description":"Model weights available in safetensors format, a secure alternative to pickle-based PyTorch .pt files that prevents arbitrary code execution during model loading. Safetensors format is human-readable, supports lazy loading of individual weight tensors, and includes built-in integrity checks. Compatible with sentence-transformers, Hugging Face transformers, and other frameworks via safetensors library.","intents":["I need to load model weights safely without risking arbitrary code execution","I want to inspect model weights before loading (human-readable format)","I'm building a system that loads untrusted models and need security guarantees","I need lazy loading of model weights to reduce memory overhead"],"best_for":["teams with strict security requirements (healthcare, finance, government)","developers loading models from untrusted sources or public repositories","engineers building model serving systems with security constraints","teams requiring model weight inspection and verification"],"limitations":["Safetensors library required for loading — adds ~5MB dependency","Lazy loading reduces memory but increases I/O overhead — not ideal for repeated inference","No encryption support — safetensors files are human-readable (security via format, not encryption)","Compatibility limited to frameworks with safetensors support (sentence-transformers, transformers, etc.)"],"requires":["safetensors library 0.3.0+","sentence-transformers 2.2.0+ with safetensors support"],"input_types":["safetensors model files (.safetensors)"],"output_types":["loaded model weights (PyTorch tensors)","model state dict (dictionary of weight tensors)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.11+ or ONNX Runtime 1.13+ for inference","sentence-transformers library 2.2.0+ for easy integration","GPU with 2GB+ VRAM for batch encoding (CPU inference ~10x slower)","Vector database or FAISS index for efficient similarity search at scale (>10k documents)","sentence-transformers 2.2.0+","PyTorch 1.11+ or ONNX Runtime","Text in supported languages (primarily European, Asian, and major world languages)","PyTorch 1.11+ or ONNX Runtime 1.13+","Batch size typically 8-128 for optimal throughput (larger batches = better GPU utilization)","FAISS 1.7.0+ (for local indexing) or managed vector DB API key"],"failure_modes":["Fixed 768-dimensional output — cannot reduce dimensionality without retraining or post-hoc projection","Optimized for English text only — cross-lingual performance degrades significantly on non-English queries","Dot-product similarity requires L2-normalized vectors for fair comparison; unnormalized vectors may produce unexpected ranking","Training data biased toward StackExchange/QA domains — may underperform on specialized technical or domain-specific corpora","No built-in handling of long documents >512 tokens — requires chunking strategy external to the model","Performance degrades for low-resource languages not well-represented in training data (e.g., Swahili, Tagalog)","No explicit language identification — model assumes input is valid text in supported languages","Alignment quality varies by language pair — English-Spanish better than English-Urdu due to training data distribution","512-token limit applies per language — some languages require more tokens for same semantic content","Mean pooling loses positional information — may underperform on tasks requiring word-order sensitivity","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7836156885299335,"quality":0.43,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:56.943Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":2530482,"model_likes":191}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sentence-transformers--multi-qa-mpnet-base-dot-v1","compare_url":"https://unfragile.ai/compare?artifact=sentence-transformers--multi-qa-mpnet-base-dot-v1"}},"signature":"Yi96uYc2/fXvDf+tMKBhiwyKe/ljdz1JtYztDFCb6XL6A0sr/wfMbz7ocZj1XjdXmRLQZnDSDd4eS69LpC4wCQ==","signedAt":"2026-06-19T20:21:42.252Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sentence-transformers--multi-qa-mpnet-base-dot-v1","artifact":"https://unfragile.ai/sentence-transformers--multi-qa-mpnet-base-dot-v1","verify":"https://unfragile.ai/api/v1/verify?slug=sentence-transformers--multi-qa-mpnet-base-dot-v1","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"}}