{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-nomic-ai--nomic-embed-text-v1","slug":"nomic-ai--nomic-embed-text-v1","name":"nomic-embed-text-v1","type":"model","url":"https://huggingface.co/nomic-ai/nomic-embed-text-v1","page_url":"https://unfragile.ai/nomic-ai--nomic-embed-text-v1","categories":["data-analysis"],"tags":["sentence-transformers","pytorch","onnx","safetensors","nomic_bert","feature-extraction","sentence-similarity","mteb","transformers","transformers.js","custom_code","en","arxiv:2402.01613","license:apache-2.0","model-index","text-embeddings-inference","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-nomic-ai--nomic-embed-text-v1__cap_0","uri":"capability://data.processing.analysis.dense.vector.embedding.generation.for.text","name":"dense-vector-embedding-generation-for-text","description":"Converts arbitrary-length text sequences into fixed-dimensional dense vectors (768 dimensions) using a Nomic BERT-based transformer architecture trained on 235M text pairs. The model employs mean pooling over the final transformer layer outputs to produce sentence-level embeddings compatible with vector databases and similarity search systems. Supports batch processing through PyTorch and ONNX inference backends for both CPU and GPU execution.","intents":["I need to convert documents into embeddings for semantic search in a RAG pipeline","I want to build a vector database index from a corpus of text without cloud API dependencies","I need to compute sentence similarity scores between pairs of texts for clustering or deduplication","I'm building a recommendation system that requires dense representations of user queries and items"],"best_for":["teams building on-premise RAG systems requiring model control and data privacy","developers integrating embeddings into vector databases (Pinecone, Weaviate, Milvus, Chroma)","researchers benchmarking embedding models on MTEB tasks","organizations with GPU infrastructure seeking open-source alternatives to proprietary embedding APIs"],"limitations":["Fixed 768-dimensional output — cannot be adjusted for memory-constrained deployments without retraining","Trained primarily on English text — cross-lingual performance not documented; non-English inputs may degrade significantly","Mean pooling approach loses positional information — may underperform on tasks requiring fine-grained token-level semantics","No built-in quantization support in base model — requires external tools (ONNX quantization, bitsandbytes) for 8-bit or lower precision","Inference latency ~50-100ms per 512-token batch on CPU; GPU memory footprint ~1.2GB for full model"],"requires":["Python 3.8+","PyTorch 1.13+ or ONNX Runtime 1.14+","transformers library 4.25+","sentence-transformers library 2.2+ (for high-level API)","4GB+ RAM for CPU inference; 2GB+ VRAM for GPU inference"],"input_types":["plain text (strings)","variable-length sequences (1-512 tokens after BPE tokenization)","batch inputs (lists of strings for parallel processing)"],"output_types":["dense float32 vectors (shape: [batch_size, 768])","normalized embeddings (L2 norm applied for cosine similarity)","ONNX-compatible tensor outputs"],"categories":["data-processing-analysis","embedding-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nomic-ai--nomic-embed-text-v1__cap_1","uri":"capability://data.processing.analysis.sentence.similarity.scoring.via.cosine.distance","name":"sentence-similarity-scoring-via-cosine-distance","description":"Computes pairwise semantic similarity between text sequences by generating embeddings for each input and calculating cosine distance in the 768-dimensional embedding space. The model's training objective (contrastive learning on text pairs) ensures that semantically similar sentences cluster together, enabling similarity thresholds for deduplication, matching, and ranking tasks. Supports batch computation for efficiency across large document collections.","intents":["I need to find duplicate or near-duplicate documents in a large corpus","I want to rank search results by semantic relevance to a user query","I need to match customer support tickets to existing resolutions based on semantic similarity","I'm building a deduplication pipeline for a data cleaning workflow"],"best_for":["data engineering teams deduplicating large text corpora (>100K documents)","search and ranking teams implementing semantic re-ranking without external APIs","content moderation systems identifying similar policy violations across submissions","knowledge management systems matching user queries to existing documentation"],"limitations":["Cosine similarity is symmetric — cannot distinguish directionality (e.g., 'A implies B' vs 'B implies A')","Threshold selection is task-dependent and requires manual tuning; no built-in adaptive thresholding","Performance degrades on very short texts (<5 tokens) due to limited context","Computational cost scales quadratically with corpus size for all-pairs similarity — requires approximate nearest neighbor methods (FAISS, Annoy) for >1M documents","No domain-specific fine-tuning provided — general-purpose model may underperform on specialized vocabularies (medical, legal, code)"],"requires":["Python 3.8+","sentence-transformers 2.2+ or transformers 4.25+","numpy for similarity matrix operations","optional: scikit-learn or scipy for clustering/ranking utilities"],"input_types":["pairs of text strings","lists of strings for batch pairwise comparison","variable-length sequences (1-512 tokens)"],"output_types":["scalar similarity scores (0.0-1.0 range for cosine similarity with L2 normalization)","similarity matrices (2D arrays for batch comparisons)","ranked lists of similar documents with scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nomic-ai--nomic-embed-text-v1__cap_2","uri":"capability://tool.use.integration.multi.format.model.export.and.inference.compatibility","name":"multi-format-model-export-and-inference-compatibility","description":"Provides the model in multiple serialization formats (PyTorch safetensors, ONNX, Hugging Face transformers) enabling deployment across diverse inference engines and hardware targets. Safetensors format enables secure, fast model loading without arbitrary code execution. ONNX export supports CPU-optimized inference through ONNX Runtime and GPU acceleration through TensorRT or CoreML on Apple devices. Compatible with text-embeddings-inference (TEI) server for production-grade serving.","intents":["I need to deploy embeddings in a production API server with sub-100ms latency requirements","I want to run embeddings on edge devices (mobile, IoT) without full PyTorch dependencies","I need to integrate embeddings into a C++ or Rust application without Python overhead","I'm deploying to multiple hardware targets (CPU, GPU, Apple Silicon) with a single model"],"best_for":["DevOps teams deploying embedding services in Kubernetes with strict latency SLAs","mobile and edge ML engineers targeting iOS, Android, or embedded Linux devices","systems engineers building polyglot inference pipelines (Python, Rust, C++, Go)","organizations requiring model security and reproducibility (safetensors prevents code injection)"],"limitations":["ONNX export may require manual optimization for specific hardware (TensorRT, CoreML) — not automatically optimized","Safetensors format is read-only for inference; model fine-tuning requires conversion back to PyTorch","TEI server requires Docker or native binary deployment — not available as a Python library for embedded use","ONNX Runtime performance varies significantly by hardware and optimization level — requires profiling per target","No quantized ONNX variants provided in base release — requires external quantization tools"],"requires":["For PyTorch: torch 1.13+, transformers 4.25+","For ONNX: onnxruntime 1.14+","For TEI: Docker 20.10+ or native binary (Linux x86_64, ARM64)","For safetensors: safetensors Python library 0.3.1+","For Apple Silicon: CoreML Tools 6.0+ (optional, for native conversion)"],"input_types":["HuggingFace model hub URLs","local model directories (PyTorch, safetensors, ONNX)","text inputs (strings, batches) for inference"],"output_types":["PyTorch model objects (.pt, .pth files)","safetensors binary format (.safetensors files)","ONNX graph format (.onnx files)","dense embeddings (768-dim float32 vectors)","HTTP/gRPC responses (via TEI server)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nomic-ai--nomic-embed-text-v1__cap_3","uri":"capability://data.processing.analysis.mteb.benchmark.evaluation.and.validation","name":"mteb-benchmark-evaluation-and-validation","description":"Model is evaluated and ranked on the Massive Text Embedding Benchmark (MTEB), a standardized suite of 56 tasks spanning retrieval, clustering, semantic similarity, and reranking across 112 languages. The model's performance is publicly reported on the MTEB leaderboard, enabling direct comparison with competing embedding models. Supports evaluation on custom MTEB-compatible tasks through the mteb Python library.","intents":["I need to validate that this embedding model meets our semantic similarity requirements before production deployment","I want to compare this model's performance against alternatives (OpenAI, Cohere, BGE) on standardized benchmarks","I need to evaluate embedding quality on domain-specific tasks (e.g., legal document retrieval) using MTEB evaluation framework","I'm building a model selection pipeline and need reproducible, comparable metrics across embedding options"],"best_for":["ML engineers evaluating embedding models for production use cases","researchers benchmarking embedding architectures and training objectives","teams with domain-specific requirements (e.g., multilingual, long-document retrieval) seeking validated models","organizations requiring audit trails and reproducible model selection decisions"],"limitations":["MTEB scores are task-specific — high performance on retrieval does not guarantee good clustering or similarity performance","Benchmark tasks are English-heavy despite multilingual coverage claims; non-English performance may vary significantly","Evaluation requires downloading large benchmark datasets (>10GB total) — not suitable for bandwidth-constrained environments","MTEB leaderboard is community-maintained and may have stale or incomplete results for newer model versions","Benchmark tasks may not reflect your specific domain — legal, medical, or code embeddings may require custom evaluation"],"requires":["Python 3.8+","mteb library 1.0+","transformers 4.25+","sentence-transformers 2.2+ (optional, for convenience)","10GB+ disk space for benchmark datasets","GPU recommended for evaluation speed (CPU evaluation can take hours)"],"input_types":["MTEB task definitions (JSON or Python objects)","custom datasets in MTEB format (queries, corpus, relevant documents)","model checkpoint (HuggingFace model ID or local path)"],"output_types":["task-specific metrics (NDCG@10, MAP, MRR for retrieval; silhouette score for clustering; Spearman correlation for similarity)","aggregated scores (average across task categories)","leaderboard rankings and comparisons","detailed evaluation reports with per-task breakdowns"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nomic-ai--nomic-embed-text-v1__cap_4","uri":"capability://tool.use.integration.transformers.js.browser.inference.support","name":"transformers-js-browser-inference-support","description":"Model is compatible with transformers.js, a JavaScript library that enables running transformer models directly in web browsers via ONNX Runtime JS. This allows embedding generation on the client side without server round-trips, enabling privacy-preserving semantic search, real-time similarity scoring, and offline-capable applications. Inference runs on CPU in the browser with performance suitable for interactive applications.","intents":["I need to build a privacy-preserving search interface where embeddings are computed client-side without sending text to servers","I want to add semantic search to a browser-based application without backend infrastructure","I'm building an offline-capable web app that requires semantic similarity scoring without network dependency","I need to reduce API costs by moving embedding computation from cloud to client devices"],"best_for":["frontend developers building privacy-first search UIs (e.g., document search, knowledge base search)","teams deploying to bandwidth-constrained environments (mobile networks, rural areas)","organizations with strict data privacy requirements (healthcare, finance) that cannot send text to external APIs","startups reducing infrastructure costs by offloading computation to client browsers"],"limitations":["Browser inference is CPU-only — significantly slower than GPU inference (100-500ms per embedding vs 10-50ms on GPU)","Model size (~500MB) requires substantial download bandwidth and storage; may exceed browser cache limits on some devices","JavaScript/WebAssembly performance is 2-5x slower than native Python/C++ inference; not suitable for real-time applications with <50ms latency requirements","Browser memory constraints (especially on mobile) may cause OOM errors with large batch sizes or concurrent embeddings","No built-in caching or indexing in transformers.js — requires custom implementation for efficient similarity search over large corpora"],"requires":["Node.js 14+ (for build tooling)","transformers.js 2.0+","ONNX Runtime JS 1.14+","Modern browser with WebAssembly support (Chrome 57+, Firefox 52+, Safari 14.1+)","500MB+ available disk space in browser cache","2GB+ RAM on client device for inference"],"input_types":["text strings (from HTML input elements, textarea, or JavaScript variables)","batches of text for parallel processing","variable-length sequences (tokenized to 512 tokens max)"],"output_types":["dense float32 vectors (768 dimensions)","similarity scores (cosine distance computed in JavaScript)","ranked search results (if combined with vector indexing library)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nomic-ai--nomic-embed-text-v1__cap_5","uri":"capability://automation.workflow.apache.2.0.licensed.open.source.model.distribution","name":"apache-2-0-licensed-open-source-model-distribution","description":"Released under Apache 2.0 license with full model weights, training code, and evaluation scripts publicly available on HuggingFace and GitHub. Enables unrestricted commercial use, modification, and redistribution without licensing fees or usage restrictions. Model can be fine-tuned, quantized, or integrated into proprietary products without legal constraints.","intents":["I need to use embeddings in a commercial product without licensing fees or vendor lock-in","I want to fine-tune the model on domain-specific data for specialized applications","I need to modify the model architecture or training procedure for research purposes","I'm building a product that requires embedding model source code transparency for compliance or security audits"],"best_for":["commercial software companies avoiding proprietary embedding API dependencies","research teams requiring full model transparency and reproducibility","organizations with strict open-source policies or compliance requirements","startups and enterprises seeking cost-effective embedding solutions without per-token pricing"],"limitations":["Apache 2.0 license requires attribution in derivative works — must include license notice in documentation or code","No warranty or liability guarantees — users assume all risk for production deployment","Community-maintained model — no official SLA or support from Nomic AI for production issues","Model performance is not guaranteed to remain stable across versions — breaking changes possible in future releases","Commercial use requires compliance with any underlying data licensing (training data sources must be reviewed)"],"requires":["Understanding of Apache 2.0 license terms and attribution requirements","Legal review for commercial use (especially if fine-tuning or modifying)","Compliance verification for training data sources (ensure no proprietary data restrictions)"],"input_types":["model weights (HuggingFace model hub, GitHub releases)","training code and scripts (for fine-tuning or reproduction)","evaluation benchmarks and datasets"],"output_types":["modified model checkpoints (fine-tuned or quantized variants)","derivative products (commercial applications, research papers)","documentation and attribution notices"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nomic-ai--nomic-embed-text-v1__cap_6","uri":"capability://data.processing.analysis.custom.code.execution.for.preprocessing.and.postprocessing","name":"custom-code-execution-for-preprocessing-and-postprocessing","description":"Model supports custom preprocessing and postprocessing code execution through HuggingFace's custom_code feature, enabling task-specific text normalization, tokenization adjustments, and embedding transformations without modifying the core model. Allows users to inject custom Python code for handling domain-specific text formats (e.g., code snippets, structured data, multilingual content) before embedding generation.","intents":["I need to normalize or clean text before embedding (e.g., removing markup, handling special characters)","I want to apply domain-specific preprocessing (e.g., code tokenization, entity masking) before generating embeddings","I need to transform embeddings after generation (e.g., dimensionality reduction, normalization) for specific use cases","I'm handling mixed-format inputs (text + code + structured data) that require custom parsing before embedding"],"best_for":["teams with domain-specific text formats requiring custom preprocessing pipelines","developers integrating embeddings into complex data pipelines with heterogeneous input types","researchers experimenting with embedding transformations and post-hoc modifications","organizations with strict data governance requiring custom sanitization or anonymization before embedding"],"limitations":["Custom code execution adds latency (10-50ms per batch depending on code complexity) — not suitable for ultra-low-latency applications","Custom code must be Python and compatible with the transformers library's execution environment — no arbitrary system calls or external dependencies","Security risk if custom code is untrusted — requires code review before deployment in production","Custom code is not portable across inference frameworks (PyTorch, ONNX, TEI) — may require reimplementation for different deployment targets","Debugging custom code failures is difficult in production — errors may be opaque or hard to trace"],"requires":["Python 3.8+","transformers 4.25+ with custom_code support","Understanding of HuggingFace's custom code execution model","Code review and security validation before production use"],"input_types":["raw text strings (with arbitrary formatting, markup, special characters)","custom Python code for preprocessing/postprocessing","configuration parameters for custom code"],"output_types":["preprocessed text (normalized, cleaned, tokenized)","embeddings (768-dim vectors)","postprocessed embeddings (transformed, reduced, normalized)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nomic-ai--nomic-embed-text-v1__cap_7","uri":"capability://tool.use.integration.endpoints.compatible.api.serving.infrastructure","name":"endpoints-compatible-api-serving-infrastructure","description":"Model is compatible with HuggingFace Endpoints, a managed inference service that automatically provisions, scales, and monitors embedding inference without manual infrastructure management. Endpoints handles batching, caching, and auto-scaling based on traffic, providing production-grade serving with SLA guarantees. Supports both REST and gRPC APIs for client integration.","intents":["I need a production-grade embedding API without managing servers or Kubernetes clusters","I want auto-scaling embeddings service that handles traffic spikes without manual intervention","I need monitoring, logging, and SLA guarantees for embedding inference in production","I'm building a multi-tenant application and need isolated, metered embedding endpoints per customer"],"best_for":["startups and small teams lacking DevOps infrastructure for self-hosted inference","enterprises requiring managed services with SLA guarantees and vendor support","applications with variable traffic patterns requiring auto-scaling","teams prioritizing operational simplicity over cost optimization"],"limitations":["Managed service pricing is higher than self-hosted inference — typically $0.10-1.00 per 1M tokens depending on tier","Vendor lock-in to HuggingFace infrastructure — switching to alternative providers requires API rewrite","Latency includes network round-trip to HuggingFace servers — typically 50-200ms vs 10-50ms for local inference","Rate limiting and quota enforcement — high-volume applications may hit limits requiring upgrade to higher tier","Data transmission to external servers — may violate data residency or privacy requirements in regulated industries"],"requires":["HuggingFace account with Endpoints subscription","API key for authentication","Network connectivity to HuggingFace infrastructure (US region)","Client library (transformers, requests, or language-specific SDK)"],"input_types":["text strings (via REST POST request or gRPC call)","batches of text (up to endpoint-specific limits)","variable-length sequences (1-512 tokens)"],"output_types":["dense embeddings (768-dim float32 vectors)","JSON responses (REST API)","protobuf messages (gRPC API)","usage metrics and billing information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nomic-ai--nomic-embed-text-v1__cap_8","uri":"capability://safety.moderation.us.region.deployment.and.data.residency.support","name":"us-region-deployment-and-data-residency-support","description":"Model is explicitly available for deployment in US-region HuggingFace infrastructure, enabling compliance with US data residency requirements and GDPR restrictions on data transfer. Supports deployment in isolated, customer-controlled environments for organizations with strict data governance policies. Enables local inference without data transmission to external servers.","intents":["I need to comply with US data residency requirements (e.g., HIPAA, FedRAMP) for embedding inference","I want to avoid GDPR restrictions on transferring personal data to non-EU servers","I need to deploy embeddings in an isolated, air-gapped environment for security or compliance reasons","I'm handling sensitive data (healthcare, finance) that cannot leave specific geographic regions"],"best_for":["healthcare and pharmaceutical companies subject to HIPAA data residency requirements","financial institutions with regulatory requirements for data localization","government contractors and defense organizations requiring FedRAMP or similar compliance","European organizations subject to GDPR with restrictions on non-EU data transfer"],"limitations":["US-region deployment may have higher latency for non-US users — not suitable for global, low-latency applications","Compliance with data residency does not guarantee compliance with other regulatory requirements (e.g., encryption, access controls) — requires additional security measures","Self-hosted deployment requires infrastructure management and security hardening — not suitable for teams lacking DevOps expertise","Data residency compliance is organization-specific — requires legal review to confirm model deployment meets regulatory requirements","No automatic compliance certification — organizations remain responsible for audit and compliance validation"],"requires":["Understanding of data residency and compliance requirements (HIPAA, GDPR, FedRAMP, etc.)","Legal review to confirm model deployment meets regulatory requirements","Infrastructure for self-hosted deployment (if using local inference) or HuggingFace Endpoints US region (if using managed service)","Network isolation and access controls for sensitive data"],"input_types":["sensitive text data (healthcare records, financial data, personal information)","deployment configuration specifying US-region infrastructure"],"output_types":["embeddings generated within US-region infrastructure","compliance audit logs and data residency verification"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+ or ONNX Runtime 1.14+","transformers library 4.25+","sentence-transformers library 2.2+ (for high-level API)","4GB+ RAM for CPU inference; 2GB+ VRAM for GPU inference","sentence-transformers 2.2+ or transformers 4.25+","numpy for similarity matrix operations","optional: scikit-learn or scipy for clustering/ranking utilities","For PyTorch: torch 1.13+, transformers 4.25+","For ONNX: onnxruntime 1.14+"],"failure_modes":["Fixed 768-dimensional output — cannot be adjusted for memory-constrained deployments without retraining","Trained primarily on English text — cross-lingual performance not documented; non-English inputs may degrade significantly","Mean pooling approach loses positional information — may underperform on tasks requiring fine-grained token-level semantics","No built-in quantization support in base model — requires external tools (ONNX quantization, bitsandbytes) for 8-bit or lower precision","Inference latency ~50-100ms per 512-token batch on CPU; GPU memory footprint ~1.2GB for full model","Cosine similarity is symmetric — cannot distinguish directionality (e.g., 'A implies B' vs 'B implies A')","Threshold selection is task-dependent and requires manual tuning; no built-in adaptive thresholding","Performance degrades on very short texts (<5 tokens) due to limited context","Computational cost scales quadratically with corpus size for all-pairs similarity — requires approximate nearest neighbor methods (FAISS, Annoy) for >1M documents","No domain-specific fine-tuning provided — general-purpose model may underperform on specialized vocabularies (medical, legal, code)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8785435205185946,"quality":0.28,"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":7064314,"model_likes":566}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nomic-ai--nomic-embed-text-v1","compare_url":"https://unfragile.ai/compare?artifact=nomic-ai--nomic-embed-text-v1"}},"signature":"hCZhXkUSdGdOAjkOoYeLFhurqpzj28ulFW0zCoNRobei7ag10QDfevLIdTdfnlkG4pbzeC6MuLrfwWpCXZOrBQ==","signedAt":"2026-06-20T09:49:23.370Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nomic-ai--nomic-embed-text-v1","artifact":"https://unfragile.ai/nomic-ai--nomic-embed-text-v1","verify":"https://unfragile.ai/api/v1/verify?slug=nomic-ai--nomic-embed-text-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"}}