{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-intfloat--e5-base-v2","slug":"intfloat--e5-base-v2","name":"e5-base-v2","type":"model","url":"https://huggingface.co/intfloat/e5-base-v2","page_url":"https://unfragile.ai/intfloat--e5-base-v2","categories":["research-search"],"tags":["sentence-transformers","pytorch","onnx","safetensors","openvino","bert","mteb","Sentence Transformers","sentence-similarity","en","arxiv:2212.03533","arxiv:2104.08663","arxiv:2210.07316","license:mit","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-intfloat--e5-base-v2__cap_0","uri":"capability://data.processing.analysis.multilingual.sentence.embedding.generation.with.contrastive.learning","name":"multilingual sentence embedding generation with contrastive learning","description":"Generates dense vector embeddings (768-dimensional) for sentences and documents using a BERT-based architecture trained with contrastive learning on 1B+ sentence pairs. The model uses a masked language modeling objective combined with in-batch negatives and hard negative mining to learn representations where semantically similar sentences cluster together in embedding space. Supports 100+ languages through multilingual BERT pretraining, enabling cross-lingual semantic search without language-specific fine-tuning.","intents":["I need to embed sentences for semantic similarity search across a large corpus","I want to find semantically related documents without keyword matching","I need to build a multilingual search system that works across languages","I want to cluster similar customer queries or support tickets automatically","I need to implement semantic deduplication across text documents"],"best_for":["teams building semantic search engines or RAG systems","developers implementing similarity-based recommendation systems","organizations needing multilingual document retrieval without language-specific models","researchers benchmarking embedding quality on MTEB tasks"],"limitations":["Fixed 512-token context window — longer documents must be chunked or truncated","768-dimensional embeddings require ~3KB storage per sentence, scaling linearly with corpus size","Inference latency ~50-100ms per sentence on CPU, requiring batching for production throughput","No domain-specific fine-tuning included — performance may degrade on highly specialized vocabulary (medical, legal, code)","Trained primarily on English text with multilingual support as secondary objective — English semantic understanding is stronger than other languages"],"requires":["Python 3.7+","PyTorch 1.11+ or ONNX Runtime 1.13+","sentence-transformers library 2.2.0+","4GB+ RAM for model weights and inference","GPU optional but recommended for batch inference (CUDA 11.8+ or compatible)"],"input_types":["raw text strings","sentences (optimal: 10-500 tokens)","documents (up to 512 tokens, longer texts require chunking)","multilingual text (100+ languages supported)"],"output_types":["dense float32 vectors (768 dimensions)","cosine similarity scores (0-1 range)","structured embeddings compatible with vector databases (Pinecone, Weaviate, Milvus)"],"categories":["data-processing-analysis","search-retrieval","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_1","uri":"capability://search.retrieval.cross.lingual.semantic.similarity.scoring.with.zero.shot.transfer","name":"cross-lingual semantic similarity scoring with zero-shot transfer","description":"Computes cosine similarity between embeddings of sentences in different languages by leveraging multilingual BERT's shared embedding space, enabling cross-lingual retrieval without language-specific alignment or translation. The model transfers semantic understanding across languages through shared subword tokenization and joint pretraining, allowing queries in one language to retrieve relevant documents in another language with minimal performance degradation.","intents":["I need to find Spanish documents relevant to an English query","I want to build a search system that works across multiple languages simultaneously","I need to identify duplicate content across language versions of a website","I want to cluster customer feedback from multiple countries without translating first"],"best_for":["multinational companies with multilingual content repositories","international SaaS platforms needing unified search across languages","researchers studying cross-lingual information retrieval","organizations avoiding translation costs for similarity tasks"],"limitations":["Cross-lingual performance degrades 5-15% compared to same-language similarity due to representation space misalignment","Language pairs with low pretraining data (e.g., low-resource languages) show weaker transfer than high-resource pairs","No explicit alignment training — similarity scores may be less calibrated across language pairs than within-language","Requires both query and document embeddings to be computed separately, doubling inference cost vs. monolingual search"],"requires":["Python 3.7+","sentence-transformers 2.2.0+","PyTorch 1.11+ or ONNX Runtime","Embeddings for both source and target language documents pre-computed"],"input_types":["sentence pairs in different languages","query text in one language","document corpus in one or more languages"],"output_types":["similarity scores (0-1 range)","ranked lists of cross-lingual matches","language-agnostic relevance rankings"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_10","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.embedding.support.with.vector.database.integration","name":"retrieval-augmented generation (rag) embedding support with vector database integration","description":"Provides embeddings optimized for retrieval-augmented generation pipelines, where embeddings are used to retrieve relevant documents from a knowledge base to augment LLM prompts. The model's embeddings are designed for high recall on semantic search (retrieving all relevant documents) while maintaining precision for ranking. Integration with vector databases enables efficient retrieval at scale, and the embeddings are compatible with popular RAG frameworks (LangChain, LlamaIndex, Haystack).","intents":["I need to build a RAG system that retrieves relevant documents to augment LLM responses","I want to improve LLM answer quality by providing relevant context from a knowledge base","I need to implement semantic search over company documents for a chatbot","I want to reduce hallucinations in LLM outputs by grounding responses in retrieved documents"],"best_for":["teams building LLM-powered chatbots or Q&A systems","organizations implementing enterprise search with LLM augmentation","developers creating knowledge-base-grounded AI assistants","companies reducing LLM hallucinations through retrieval-based grounding"],"limitations":["Retrieval quality depends on embedding quality and knowledge base coverage — missing documents cannot be retrieved","No built-in reranking — top-K retrieval may include irrelevant documents that confuse the LLM","Requires pre-indexing knowledge base embeddings — adding new documents requires re-embedding and re-indexing","No automatic query expansion or reformulation — user queries must be well-formed for effective retrieval","Embedding-based retrieval may miss documents with different vocabulary but same meaning (requires semantic understanding)"],"requires":["Python 3.7+","sentence-transformers 2.2.0+","Vector database (Pinecone, Weaviate, Milvus, Qdrant, Chroma)","LLM framework (LangChain, LlamaIndex, Haystack) optional but recommended","Pre-indexed knowledge base with embeddings"],"input_types":["user queries (text)","knowledge base documents (text)","optional: query metadata (source, date, etc.)"],"output_types":["retrieved documents ranked by relevance","similarity scores for retrieved documents","augmented prompts with retrieved context for LLM"],"categories":["memory-knowledge","search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_2","uri":"capability://data.processing.analysis.batch.embedding.inference.with.automatic.batching.and.format.conversion","name":"batch embedding inference with automatic batching and format conversion","description":"Processes multiple sentences or documents in parallel through the model, automatically batching inputs to maximize GPU/CPU utilization and converting outputs to multiple formats (PyTorch tensors, NumPy arrays, ONNX, OpenVINO). The implementation handles variable-length sequences through dynamic padding, manages memory efficiently for large batches, and supports multiple serialization formats for downstream integration with vector databases or ML pipelines.","intents":["I need to embed 1M documents efficiently without running out of memory","I want to convert embeddings to ONNX format for edge deployment","I need to batch-process embeddings and save them to a vector database","I want to use the model in a production inference service with automatic batching"],"best_for":["data engineers building ETL pipelines for embedding large corpora","ML engineers deploying embeddings to edge devices or mobile apps","teams using vector databases (Pinecone, Weaviate, Milvus) requiring bulk indexing","organizations optimizing inference cost through batch processing"],"limitations":["Batch size is memory-constrained — typical GPU (24GB) handles ~500-1000 sentences per batch depending on length","Dynamic padding adds 5-10% overhead for variable-length sequences vs. fixed-length batches","ONNX conversion requires separate quantization step for int8 optimization — not automatic","No built-in streaming or online batching — requires buffering inputs before processing","OpenVINO conversion requires Intel OpenVINO toolkit installation, adding dependency complexity"],"requires":["Python 3.7+","sentence-transformers 2.2.0+","PyTorch 1.11+ or ONNX Runtime 1.13+","For ONNX export: onnx 1.12+, onnxruntime 1.13+","For OpenVINO: openvino 2022.1+","GPU memory: 8GB+ for batch inference, 4GB+ for CPU-only"],"input_types":["list of text strings","CSV/JSON files with text columns","streaming text data (requires buffering)","variable-length documents (auto-padded)"],"output_types":["PyTorch tensors","NumPy arrays","ONNX model files","OpenVINO IR format","JSON/CSV with embedding vectors","vector database import formats"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_3","uri":"capability://search.retrieval.semantic.similarity.ranking.with.configurable.similarity.metrics","name":"semantic similarity ranking with configurable similarity metrics","description":"Ranks documents or sentences by semantic similarity to a query using multiple distance metrics (cosine, euclidean, dot product) computed directly on embedding vectors. The implementation supports both dense-only ranking and hybrid ranking (combining semantic similarity with BM25 keyword scores), enabling flexible relevance tuning for different use cases through metric selection and score normalization.","intents":["I need to rank search results by semantic relevance, not keyword matching","I want to implement a hybrid search combining semantic and keyword similarity","I need to tune ranking behavior for domain-specific relevance (e.g., prioritize exact matches)","I want to find the top-K most similar documents from a large corpus efficiently"],"best_for":["search engineers building semantic ranking pipelines","teams implementing hybrid search systems combining keyword and semantic signals","developers tuning relevance for domain-specific applications (e-commerce, support, research)","organizations A/B testing different similarity metrics for ranking quality"],"limitations":["Cosine similarity requires normalized embeddings — non-normalized vectors produce incorrect scores","Dot product similarity is scale-sensitive and requires careful normalization for fair comparison with other metrics","Ranking quality depends heavily on embedding quality — poor embeddings produce poor rankings regardless of metric choice","No built-in relevance feedback or learning-to-rank — ranking is static based on embedding similarity alone","Euclidean distance is computationally expensive for high-dimensional vectors (768D) compared to cosine similarity"],"requires":["Python 3.7+","sentence-transformers 2.2.0+","NumPy 1.19+ for efficient vector operations","Pre-computed embeddings for query and document corpus","Optional: scikit-learn for hybrid ranking with BM25 scores"],"input_types":["query embedding (768-dimensional vector)","document embeddings (768-dimensional vectors)","similarity metric specification (cosine, euclidean, dot-product)","optional: BM25 scores for hybrid ranking"],"output_types":["ranked list of documents with similarity scores","top-K results with relevance scores","similarity score matrices for batch ranking"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_4","uri":"capability://memory.knowledge.vector.database.integration.with.standardized.embedding.export","name":"vector database integration with standardized embedding export","description":"Exports embeddings in formats compatible with major vector databases (Pinecone, Weaviate, Milvus, Qdrant, Chroma) through standardized serialization and metadata handling. The model outputs embeddings with optional metadata (document IDs, text, timestamps) that can be directly ingested into vector stores, supporting both batch indexing and streaming updates with automatic schema mapping.","intents":["I need to index 10M documents into a vector database for semantic search","I want to update embeddings in Pinecone/Weaviate without manual format conversion","I need to export embeddings with metadata for retrieval-augmented generation","I want to build a production RAG system with persistent vector storage"],"best_for":["teams building RAG systems with persistent vector storage","data engineers managing large-scale embedding indexing pipelines","developers integrating semantic search into production applications","organizations migrating between vector database providers"],"limitations":["No built-in vector database client — requires separate SDK for each database (pinecone, weaviate, etc.)","Metadata handling varies by database — requires custom mapping logic for non-standard fields","Batch indexing throughput depends on vector database rate limits, not model performance","No automatic schema inference — requires pre-defining vector dimensions (768) and metadata fields","Streaming updates require managing connection state and retry logic externally"],"requires":["Python 3.7+","sentence-transformers 2.2.0+","Vector database SDK (pinecone-client, weaviate-client, pymilvus, etc.)","Vector database instance (cloud or self-hosted)","API credentials for target vector database"],"input_types":["text documents with optional metadata","pre-computed embeddings (768-dimensional)","document IDs and metadata fields","batch or streaming data sources"],"output_types":["vector database records with embeddings and metadata","indexed collections ready for semantic search","batch import files (JSON, CSV) for vector databases"],"categories":["memory-knowledge","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_5","uri":"capability://data.processing.analysis.fine.tuning.on.domain.specific.sentence.pairs.with.contrastive.loss","name":"fine-tuning on domain-specific sentence pairs with contrastive loss","description":"Enables domain-specific adaptation by fine-tuning the base model on custom sentence pairs using contrastive learning (triplet loss, in-batch negatives). The fine-tuning process preserves the pretrained multilingual knowledge while optimizing embeddings for domain-specific similarity patterns, supporting both supervised pairs (positive/negative examples) and weak supervision from domain data. Training uses the sentence-transformers library's built-in loss functions and data loaders, enabling efficient adaptation with minimal code.","intents":["I need to improve embedding quality for domain-specific text (medical, legal, code)","I want to fine-tune the model on my company's proprietary similarity judgments","I need to adapt embeddings for a specific task like duplicate detection or paraphrase identification","I want to improve cross-lingual performance for specific language pairs in my domain"],"best_for":["teams with labeled domain-specific similarity data (100+ pairs minimum)","organizations optimizing embeddings for specialized vocabularies or tasks","researchers adapting pretrained models to new domains or languages","companies with proprietary relevance judgments wanting to encode them into embeddings"],"limitations":["Requires labeled training data — minimum 100-1000 sentence pairs for meaningful improvement, 10K+ for strong adaptation","Fine-tuning on small datasets (< 1K pairs) risks overfitting and degrading general-purpose performance","Training time scales with dataset size — 10K pairs requires 1-4 hours on single GPU","No automatic hyperparameter tuning — requires manual tuning of learning rate, batch size, and loss function","Fine-tuned models lose some zero-shot generalization — performance on out-of-domain tasks may degrade"],"requires":["Python 3.7+","PyTorch 1.11+","sentence-transformers 2.2.0+","GPU with 8GB+ VRAM for efficient training","Labeled training data: sentence pairs with similarity labels or positive/negative examples","Validation data to monitor overfitting (10-20% of training data)"],"input_types":["CSV/JSON with sentence pairs and similarity scores (0-1)","triplet data (anchor, positive, negative examples)","weak supervision from domain-specific signals (e.g., click-through data)"],"output_types":["fine-tuned model checkpoint","updated embeddings reflecting domain-specific similarity","training metrics (loss curves, validation performance)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_6","uri":"capability://automation.workflow.onnx.and.openvino.model.export.for.edge.and.on.premise.deployment","name":"onnx and openvino model export for edge and on-premise deployment","description":"Exports the model to ONNX (Open Neural Network Exchange) and OpenVINO intermediate representation formats, enabling deployment on edge devices, mobile platforms, and on-premise servers without PyTorch dependencies. The export process converts the model graph and weights to standardized formats, supporting quantization (int8, fp16) for reduced model size and inference latency. Exported models run on CPUs, GPUs, and specialized accelerators (Intel VPU, ARM processors) with minimal performance degradation.","intents":["I need to deploy embeddings on edge devices (phones, IoT) with minimal latency","I want to run inference on-premise without cloud API calls for privacy compliance","I need to reduce model size from 440MB to <100MB for mobile deployment","I want to use Intel hardware accelerators (VPU, CPU) for inference optimization"],"best_for":["mobile and edge AI teams deploying embeddings on resource-constrained devices","organizations with strict data privacy requirements avoiding cloud inference","teams optimizing inference cost through on-premise deployment","developers targeting Intel hardware or ARM processors for inference"],"limitations":["ONNX export requires manual quantization step — int8 quantization reduces accuracy 1-3% depending on calibration data","OpenVINO conversion requires Intel OpenVINO toolkit (additional dependency), limiting portability","Exported models lose dynamic shape support — require fixed input dimensions or separate models per sequence length","Inference latency on CPU is 5-10x slower than GPU (50-100ms per sentence on CPU vs. 5-10ms on GPU)","No built-in batching in exported models — requires external batching logic for throughput optimization"],"requires":["Python 3.7+","PyTorch 1.11+ (for export only, not required for inference)","onnx 1.12+, onnxruntime 1.13+ (for ONNX export and inference)","openvino 2022.1+ (for OpenVINO export)","Quantization data (optional, 100-1000 samples for int8 calibration)","Target device with ONNX Runtime or OpenVINO Runtime installed"],"input_types":["PyTorch model checkpoint","sample input data for shape inference","quantization calibration data (optional)"],"output_types":["ONNX model file (.onnx)","OpenVINO IR files (.xml, .bin)","quantized models (int8, fp16)","deployment-ready model bundles"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_7","uri":"capability://data.processing.analysis.mteb.benchmark.evaluation.and.task.specific.performance.assessment","name":"mteb benchmark evaluation and task-specific performance assessment","description":"Provides standardized evaluation against the Massive Text Embedding Benchmark (MTEB) covering 56+ tasks across 8 categories (clustering, reranking, retrieval, semantic similarity, STS, summarization, classification, paraphrase detection). The model's performance is pre-computed and published on the MTEB leaderboard, enabling comparison against 100+ other embedding models. Users can run local MTEB evaluation to measure performance on custom datasets using the same standardized metrics (NDCG@10 for retrieval, Spearman correlation for STS, etc.).","intents":["I need to compare this model's performance against other embedding models on standard benchmarks","I want to evaluate embedding quality on specific tasks (retrieval, clustering, classification)","I need to measure performance degradation when fine-tuning on domain data","I want to understand which tasks this model excels at vs. alternatives"],"best_for":["researchers selecting embedding models for specific tasks","teams evaluating model quality before production deployment","organizations benchmarking fine-tuning impact on task-specific performance","developers understanding model strengths and weaknesses across use cases"],"limitations":["MTEB evaluation is compute-intensive — full benchmark requires 2-8 hours on single GPU","Benchmark tasks may not reflect domain-specific performance — MTEB is general-purpose, not specialized","Published leaderboard scores are static snapshots — don't reflect model updates or fine-tuning","Some MTEB tasks (e.g., summarization) are weakly correlated with embedding quality, making interpretation difficult","No built-in statistical significance testing — cannot determine if performance differences are meaningful"],"requires":["Python 3.7+","mteb library 1.0+","sentence-transformers 2.2.0+","GPU recommended (8GB+ VRAM) for reasonable evaluation time","Internet connection to download benchmark datasets (10-50GB total)"],"input_types":["model checkpoint or HuggingFace model ID","MTEB task specifications (optional custom tasks)","custom evaluation datasets (optional)"],"output_types":["task-specific scores (NDCG@10, Spearman correlation, etc.)","aggregated benchmark scores","per-task performance breakdowns","comparison against leaderboard models"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_8","uri":"capability://data.processing.analysis.multilingual.text.preprocessing.with.automatic.language.detection","name":"multilingual text preprocessing with automatic language detection","description":"Handles text preprocessing for 100+ languages through multilingual BERT's tokenizer, automatically detecting language and applying appropriate tokenization, lowercasing, and special token handling. The preprocessing pipeline normalizes text (whitespace, punctuation), handles out-of-vocabulary words through subword tokenization, and manages sequence length constraints (512 tokens max) through truncation or chunking. Language detection is implicit through the tokenizer's multilingual vocabulary, requiring no explicit language specification.","intents":["I need to preprocess text in multiple languages without language-specific pipelines","I want to handle variable-length documents automatically with truncation or chunking","I need to normalize text (whitespace, punctuation) before embedding","I want to process mixed-language documents without separate preprocessing steps"],"best_for":["teams processing multilingual corpora without language-specific preprocessing","developers building language-agnostic text processing pipelines","organizations handling user-generated content in multiple languages","researchers studying multilingual NLP without language detection overhead"],"limitations":["512-token context window requires chunking long documents — no automatic optimal chunking strategy provided","Subword tokenization may split domain-specific terms (e.g., medical terms, brand names) incorrectly, degrading embedding quality","No language-specific normalization (e.g., accent removal for French, diacritics for Arabic) — uses generic lowercasing","Mixed-language documents may have suboptimal tokenization due to vocabulary mismatch between languages","Implicit language detection provides no confidence scores or language identification output"],"requires":["Python 3.7+","sentence-transformers 2.2.0+","transformers 4.10+ (for tokenizer)","No external language detection library required"],"input_types":["raw text strings in any of 100+ languages","mixed-language documents","variable-length text (auto-truncated to 512 tokens)"],"output_types":["tokenized sequences with token IDs","attention masks for variable-length sequences","preprocessed text ready for embedding"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--e5-base-v2__cap_9","uri":"capability://data.processing.analysis.semantic.clustering.with.embedding.based.grouping","name":"semantic clustering with embedding-based grouping","description":"Groups similar documents or sentences into clusters using embeddings and clustering algorithms (K-means, hierarchical clustering, DBSCAN) applied to the 768-dimensional embedding space. The clustering leverages the semantic structure learned by the model, where similar texts naturally cluster together. Users can specify the number of clusters or use automatic cluster detection, and retrieve cluster assignments and centroids for downstream analysis or organization.","intents":["I need to automatically group customer feedback into topics without manual labeling","I want to organize a large document corpus into semantic categories","I need to detect duplicate or near-duplicate documents in a corpus","I want to discover natural groupings in unlabeled text data"],"best_for":["data analysts organizing unstructured text data","teams performing exploratory data analysis on document corpora","organizations automating content categorization without manual labeling","researchers studying semantic structure in text collections"],"limitations":["Clustering quality depends on embedding quality — poor embeddings produce poor clusters","No automatic optimal cluster number selection — requires manual tuning or silhouette analysis","K-means assumes spherical clusters — may fail on non-convex semantic structures","Clustering is unsupervised — no guarantee clusters align with human-defined categories","Computational cost scales with corpus size — clustering 1M documents requires significant memory and compute"],"requires":["Python 3.7+","sentence-transformers 2.2.0+","scikit-learn 0.24+ (for clustering algorithms)","Pre-computed embeddings for all documents","Memory for storing embeddings: ~3KB per document (768 dimensions × 4 bytes)"],"input_types":["pre-computed embeddings (768-dimensional vectors)","cluster count or clustering parameters","optional: distance metric (cosine, euclidean)"],"output_types":["cluster assignments for each document","cluster centroids (representative embeddings)","cluster sizes and statistics","silhouette scores for cluster quality assessment"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 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stronger than other languages","Cross-lingual performance degrades 5-15% compared to same-language similarity due to representation space misalignment","Language pairs with low pretraining data (e.g., low-resource languages) show weaker transfer than high-resource pairs","No explicit alignment training — similarity scores may be less calibrated across language pairs than within-language","Requires both query and document embeddings to be computed separately, doubling inference cost vs. monolingual search","Retrieval quality depends on embedding quality and knowledge base coverage — missing documents cannot be retrieved","builder identity is not verified yet","no observed match outcomes 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