{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-intfloat--multilingual-e5-base","slug":"intfloat--multilingual-e5-base","name":"multilingual-e5-base","type":"model","url":"https://huggingface.co/intfloat/multilingual-e5-base","page_url":"https://unfragile.ai/intfloat--multilingual-e5-base","categories":["research-search"],"tags":["sentence-transformers","pytorch","onnx","safetensors","openvino","xlm-roberta","mteb","Sentence Transformers","sentence-similarity","multilingual","af","am","ar","as","az","be","bg","bn","br","bs"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-intfloat--multilingual-e5-base__cap_0","uri":"capability://data.processing.analysis.multilingual.sentence.embedding.generation","name":"multilingual sentence embedding generation","description":"Generates dense vector embeddings (768-dimensional) for input text across 100+ languages using XLM-RoBERTa architecture fine-tuned on multilingual contrastive learning objectives. The model encodes sentences into a shared semantic space where similarity in embedding distance reflects semantic similarity, enabling language-agnostic comparison of text meaning without translation.","intents":["I need to embed sentences in multiple languages into a single vector space for cross-lingual semantic search","I want to find semantically similar documents regardless of their language","I need to build a multilingual FAQ retrieval system that matches user queries to answers in different languages","I'm building a recommendation engine that needs to compare content similarity across language boundaries"],"best_for":["teams building multilingual search and retrieval systems","developers creating cross-lingual semantic similarity applications","organizations with content in 50+ languages needing unified embeddings","researchers working on multilingual NLP tasks requiring standardized representations"],"limitations":["Fixed 768-dimensional output — cannot be customized for memory-constrained deployments without retraining","Performance degrades on code, mathematical notation, and highly technical domain-specific terminology","Requires batch processing for optimal throughput; single-sentence inference adds per-request overhead","No built-in handling of very long documents (>512 tokens) — requires external truncation or chunking strategy","Trained on general web text; may underperform on specialized domains (medical, legal, scientific) without fine-tuning"],"requires":["Python 3.8+","PyTorch 1.11+ or ONNX Runtime 1.13+","sentence-transformers library 2.2.0+","4GB+ RAM for model loading (base variant)","GPU optional but recommended for batch inference (CUDA 11.8+ or compatible)"],"input_types":["plain text (strings)","UTF-8 encoded text in 100+ languages","variable-length sequences up to 512 tokens"],"output_types":["dense float32 vectors (768 dimensions)","normalized embeddings (L2 norm)","batch embeddings as numpy arrays or torch tensors"],"categories":["data-processing-analysis","embedding-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--multilingual-e5-base__cap_1","uri":"capability://data.processing.analysis.semantic.similarity.scoring.between.text.pairs","name":"semantic similarity scoring between text pairs","description":"Computes cosine similarity between pairs of sentence embeddings to quantify semantic relatedness on a 0-1 scale. Leverages the shared embedding space created by the model to directly measure how closely two texts align in meaning, enabling ranking, deduplication, and threshold-based matching without additional models.","intents":["I need to score how similar two sentences are to detect duplicate content","I want to rank search results by relevance to a user query","I need to find the best matching FAQ answer for a user question","I'm building a content deduplication pipeline that needs to identify near-duplicate documents"],"best_for":["search and information retrieval teams","content moderation and deduplication workflows","question-answering systems requiring relevance ranking","developers building similarity-based filtering or clustering"],"limitations":["Cosine similarity is symmetric — cannot distinguish directionality (e.g., 'A implies B' vs 'B implies A')","Threshold selection is task-dependent and requires empirical tuning; no universal cutoff for 'similar enough'","Similarity scores reflect surface-level semantic overlap, not factual correctness or logical entailment","Computational cost scales quadratically with corpus size for all-pairs similarity (requires approximate nearest neighbor for large-scale use)"],"requires":["Python 3.8+","sentence-transformers 2.2.0+","numpy or torch for similarity computation","pre-computed embeddings or ability to generate them in-memory"],"input_types":["two or more pre-computed embedding vectors (768-dimensional float32)","raw text strings (will be embedded on-the-fly)"],"output_types":["scalar similarity score (float, range 0.0-1.0)","similarity matrices (2D arrays for batch comparisons)","ranked lists with scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--multilingual-e5-base__cap_2","uri":"capability://data.processing.analysis.batch.embedding.inference.with.hardware.acceleration","name":"batch embedding inference with hardware acceleration","description":"Processes multiple sentences simultaneously through the transformer model with automatic batching, supporting GPU acceleration via CUDA/ROCm and CPU inference with optional ONNX Runtime optimization. Implements dynamic padding and attention masking to minimize computation on variable-length inputs while maintaining numerical stability across batch dimensions.","intents":["I need to embed 100k documents efficiently without hitting memory limits","I want to accelerate embedding generation using GPU for real-time inference","I need to deploy embeddings on edge devices or CPU-only servers","I'm building a data pipeline that processes embeddings in batches for cost efficiency"],"best_for":["teams processing large document corpora (10k+ documents)","production systems requiring sub-100ms latency per batch","resource-constrained environments (edge devices, serverless functions)","data engineering teams building ETL pipelines with embedding stages"],"limitations":["Batch size is memory-constrained; typical GPU (8GB VRAM) supports ~256-512 batch size at 512 token length","ONNX Runtime optimization requires model conversion and may have minor numerical differences vs PyTorch (typically <0.01 cosine distance)","Dynamic padding adds ~5-10% overhead vs fixed-size batches; optimal batch size varies by hardware","No built-in distributed inference — scaling to multi-GPU requires external orchestration (Ray, Spark, etc.)"],"requires":["Python 3.8+","PyTorch 1.11+ OR ONNX Runtime 1.13+","sentence-transformers 2.2.0+","GPU optional: CUDA 11.8+ or ROCm 5.0+ for acceleration","For ONNX: onnx 1.12+ and onnxruntime 1.13+"],"input_types":["list of text strings (variable length)","numpy arrays or torch tensors of token IDs","batch sizes from 1 to 512+ (hardware-dependent)"],"output_types":["numpy arrays (batch_size, 768) of embeddings","torch tensors with gradient tracking (if needed for fine-tuning)","ONNX-compatible float32 arrays"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--multilingual-e5-base__cap_3","uri":"capability://search.retrieval.cross.lingual.semantic.search.with.retrieval","name":"cross-lingual semantic search with retrieval","description":"Enables searching a corpus of documents in one language using queries in another language by embedding both into the shared multilingual space and ranking by cosine similarity. The model's contrastive training ensures that semantically equivalent phrases in different languages have similar embeddings, enabling zero-shot cross-lingual retrieval without translation or language-specific indices.","intents":["I need to search an English knowledge base using queries in Spanish, Arabic, or Chinese","I want to build a multilingual customer support system that matches queries to FAQs regardless of language","I'm creating a global product search that works across 50+ languages without separate indices","I need to find relevant documents in a mixed-language corpus using a single query"],"best_for":["global companies with multilingual content and user bases","international customer support and knowledge management systems","research platforms aggregating content across languages","teams building language-agnostic search without translation infrastructure"],"limitations":["Cross-lingual performance varies by language pair; high-resource languages (English, Chinese, Spanish) perform better than low-resource languages (Amharic, Assamese)","Requires pre-computed embeddings for the entire corpus; adding new documents requires re-embedding","No built-in approximate nearest neighbor (ANN) index — requires external vector database (Pinecone, Weaviate, Milvus) for large-scale retrieval","Semantic search may return culturally or contextually irrelevant results if training data lacks diversity for specific domains"],"requires":["Python 3.8+","sentence-transformers 2.2.0+","vector database or ANN library (faiss, annoy, or managed service)","pre-computed embeddings for corpus (768 dimensions per document)","indexing infrastructure for 10k+ documents"],"input_types":["query text in any of 100+ supported languages","corpus of documents (pre-embedded or embedded on-the-fly)","optional metadata for filtering (language, category, date)"],"output_types":["ranked list of documents with similarity scores","top-k results (typically 5-50 documents)","metadata-enriched results with language tags"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--multilingual-e5-base__cap_4","uri":"capability://data.processing.analysis.document.clustering.and.deduplication","name":"document clustering and deduplication","description":"Groups semantically similar documents by computing pairwise embeddings and applying clustering algorithms (k-means, DBSCAN, hierarchical) on the embedding space. Leverages the model's ability to map semantically equivalent content to nearby regions in the 768-dimensional space, enabling unsupervised discovery of duplicate or near-duplicate documents across languages.","intents":["I need to identify and remove duplicate documents from a large corpus","I want to group similar customer support tickets for analysis","I'm organizing a document collection into semantic topics without manual labeling","I need to detect near-duplicate content across multiple languages in my dataset"],"best_for":["data quality and deduplication teams","content management and organization workflows","unsupervised document discovery and exploration","teams preparing datasets for training or analysis"],"limitations":["Clustering quality depends heavily on hyperparameter tuning (number of clusters, distance threshold); no automatic optimal selection","Computational cost for clustering scales as O(n²) for distance matrix computation on large corpora; requires approximate methods for 100k+ documents","Multilingual clustering may create language-specific clusters rather than semantic clusters if language signal dominates content signal","No built-in handling of hierarchical or temporal relationships — treats all documents as independent points"],"requires":["Python 3.8+","sentence-transformers 2.2.0+","scikit-learn 1.0+ for clustering algorithms","numpy for matrix operations","optional: faiss for approximate nearest neighbor clustering on large datasets"],"input_types":["list of documents (text strings)","pre-computed embedding matrix (n_docs, 768)","optional: distance threshold or target cluster count"],"output_types":["cluster assignments (array of cluster IDs per document)","cluster centroids (768-dimensional vectors)","distance matrices or similarity graphs","duplicate pairs with similarity scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--multilingual-e5-base__cap_5","uri":"capability://data.processing.analysis.fine.tuning.on.domain.specific.data","name":"fine-tuning on domain-specific data","description":"Allows adaptation of the pre-trained multilingual embeddings to specialized domains by continuing training on domain-specific sentence pairs with contrastive loss. Uses the sentence-transformers framework to update model weights while preserving multilingual capabilities, enabling improved performance on technical, medical, legal, or other specialized vocabularies without retraining from scratch.","intents":["I need to improve embedding quality for medical or legal documents in my domain","I want to adapt the model to my company's specific terminology and content style","I'm building a specialized search system that needs better relevance for technical queries","I need to fine-tune embeddings on domain-specific parallel sentences to improve cross-lingual matching"],"best_for":["teams with domain-specific corpora (medical, legal, scientific, financial)","organizations with proprietary training data and custom similarity requirements","developers building specialized search or recommendation systems","researchers adapting models for low-resource languages or niche domains"],"limitations":["Requires labeled training data (sentence pairs with similarity labels); typically 1k-10k pairs needed for meaningful improvement","Fine-tuning on small datasets (<1k pairs) risks overfitting and degrading performance on out-of-domain data","No automatic curriculum learning or hard negative mining — requires manual data curation for optimal results","Fine-tuned models are not compatible with the original model's embeddings; requires re-embedding entire corpus","Multilingual fine-tuning requires balanced data across languages to avoid language-specific drift"],"requires":["Python 3.8+","PyTorch 1.11+","sentence-transformers 2.2.0+","GPU with 8GB+ VRAM for efficient fine-tuning","labeled training data (sentence pairs with similarity scores or binary labels)","optional: wandb or tensorboard for training monitoring"],"input_types":["CSV or JSON files with sentence pairs and similarity labels (0-1 scale or binary)","triplet data (anchor, positive, negative examples)","optional: validation set for hyperparameter tuning"],"output_types":["fine-tuned model checkpoint (PyTorch or ONNX format)","training metrics (loss curves, validation accuracy)","updated embeddings for the fine-tuned model"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--multilingual-e5-base__cap_6","uri":"capability://automation.workflow.onnx.and.openvino.model.export.for.edge.deployment","name":"onnx and openvino model export for edge deployment","description":"Exports the multilingual-e5-base model to ONNX and OpenVINO formats, enabling inference on edge devices, mobile platforms, and CPU-only servers without PyTorch dependencies. The export process quantizes weights and optimizes graph structure for inference, reducing model size by 50-75% and latency by 2-4x compared to PyTorch while maintaining embedding quality within 0.01 cosine distance.","intents":["I need to deploy embeddings on edge devices or mobile apps without PyTorch overhead","I want to reduce inference latency and memory footprint for real-time applications","I'm building a CPU-only inference service that needs to handle 1000+ requests per second","I need to deploy the model on Intel hardware with OpenVINO optimization"],"best_for":["edge computing and IoT teams","mobile app developers building on-device search or recommendation","teams deploying to serverless functions or resource-constrained environments","organizations optimizing inference cost and latency for high-volume APIs"],"limitations":["ONNX export requires manual conversion; no automated export in sentence-transformers (requires custom scripts)","Quantization (int8) may introduce 0.5-2% performance degradation on some tasks; requires validation","OpenVINO optimization is Intel-specific; performance gains vary by CPU architecture","Exported models are not easily updatable; re-export and re-deployment required for model updates","ONNX Runtime compatibility varies by version; older runtimes may not support all optimizations"],"requires":["Python 3.8+","PyTorch 1.11+ (for export)","onnx 1.12+ and onnxruntime 1.13+ (for ONNX inference)","openvino-dev 2022.1+ (for OpenVINO export and inference)","optional: onnx-simplifier for graph optimization"],"input_types":["pre-trained multilingual-e5-base model (PyTorch format)","export configuration (quantization level, optimization flags)"],"output_types":["ONNX model file (.onnx, typically 200-300MB)","OpenVINO IR files (.xml + .bin, typically 100-150MB after quantization)","quantized weights (int8 or float16 options)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--multilingual-e5-base__cap_7","uri":"capability://data.processing.analysis.multilingual.text.representation.in.unified.embedding.space","name":"multilingual text representation in unified embedding space","description":"Maps text from 100+ languages into a single 768-dimensional vector space where semantic relationships are preserved across language boundaries. The model uses XLM-RoBERTa's multilingual tokenizer and transformer backbone trained with contrastive objectives on parallel and monolingual data, ensuring that semantically equivalent phrases in different languages occupy nearby regions regardless of linguistic structure.","intents":["I need a single embedding model that works for all my languages without language detection or routing","I want to compare semantic similarity between documents in different languages","I'm building a recommendation system that needs to work across my entire multilingual user base","I need to create a unified knowledge base that treats all languages equally"],"best_for":["global platforms with diverse language support","multilingual NLP teams avoiding language-specific model management","organizations building language-agnostic AI features","researchers studying cross-lingual semantic representations"],"limitations":["Performance is not uniform across languages; high-resource languages (English, Chinese, Spanish) have better embeddings than low-resource languages (Amharic, Assamese, Breton)","Language-specific nuances and idioms may not be fully captured in the shared space","The 768-dimensional space may not be optimal for all languages; some languages might benefit from higher dimensionality","No language identification — model assumes input is valid text in a supported language; garbage input produces meaningless embeddings"],"requires":["Python 3.8+","sentence-transformers 2.2.0+","PyTorch 1.11+ or ONNX Runtime 1.13+","UTF-8 text encoding support","4GB+ RAM for model loading"],"input_types":["text strings in any of 100+ supported languages","UTF-8 encoded input","variable-length sequences (up to 512 tokens)"],"output_types":["768-dimensional float32 vectors","normalized embeddings (L2 norm)","batch embeddings as numpy arrays or torch tensors"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-intfloat--multilingual-e5-base__cap_8","uri":"capability://data.processing.analysis.semantic.textual.similarity.benchmarking.and.evaluation","name":"semantic textual similarity benchmarking and evaluation","description":"Provides standardized evaluation on MTEB (Massive Text Embedding Benchmark) multilingual tasks, enabling comparison against other embedding models on 56+ datasets across 100+ languages. The model's performance is publicly reported on MTEB leaderboards, allowing developers to assess suitability for specific use cases (semantic similarity, retrieval, clustering, reranking) before deployment.","intents":["I need to evaluate if this model is suitable for my semantic similarity task","I want to compare this model's performance against alternatives on standard benchmarks","I'm choosing between embedding models and need objective performance metrics","I need to understand the model's strengths and weaknesses on different languages and tasks"],"best_for":["teams evaluating embedding models for production use","researchers comparing multilingual embedding approaches","developers making model selection decisions","organizations assessing model performance on their language distribution"],"limitations":["MTEB benchmarks may not reflect performance on proprietary or domain-specific data","Benchmark scores are aggregate metrics; performance varies significantly by language and task","Evaluation is static; model performance on new data or emerging languages is not captured","No task-specific fine-tuning results on MTEB; reported scores are for the base pre-trained model"],"requires":["access to MTEB leaderboard (https://huggingface.co/spaces/mteb/leaderboard)","optional: mteb library (pip install mteb) to run custom evaluations","Python 3.8+ for custom evaluation"],"input_types":["MTEB benchmark datasets (automatically downloaded)","custom evaluation datasets in MTEB format"],"output_types":["performance metrics (Spearman correlation, NDCG, MAP, etc.)","per-language and per-task breakdowns","comparison tables vs other models"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.11+ or ONNX Runtime 1.13+","sentence-transformers library 2.2.0+","4GB+ RAM for model loading (base variant)","GPU optional but recommended for batch inference (CUDA 11.8+ or compatible)","sentence-transformers 2.2.0+","numpy or torch for similarity computation","pre-computed embeddings or ability to generate them in-memory","GPU optional: CUDA 11.8+ or ROCm 5.0+ for acceleration","For ONNX: onnx 1.12+ and onnxruntime 1.13+"],"failure_modes":["Fixed 768-dimensional output — cannot be customized for memory-constrained deployments without retraining","Performance degrades on code, mathematical notation, and highly technical domain-specific terminology","Requires batch processing for optimal throughput; single-sentence inference adds per-request overhead","No built-in handling of very long documents (>512 tokens) — requires external truncation or chunking strategy","Trained on general web text; may underperform on specialized domains (medical, legal, scientific) without fine-tuning","Cosine similarity is symmetric — cannot distinguish directionality (e.g., 'A implies B' vs 'B implies A')","Threshold selection is task-dependent and requires empirical tuning; no universal cutoff for 'similar enough'","Similarity scores reflect surface-level semantic overlap, not factual correctness or logical entailment","Computational cost scales quadratically with corpus size for all-pairs similarity (requires approximate nearest neighbor for large-scale use)","Batch size is memory-constrained; typical GPU (8GB VRAM) supports ~256-512 batch size at 512 token length","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8225987655134483,"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":3660082,"model_likes":353}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=intfloat--multilingual-e5-base","compare_url":"https://unfragile.ai/compare?artifact=intfloat--multilingual-e5-base"}},"signature":"/3PSy1zjJd/WC298YfgmH0UbaW7U1OPlhkdHPii4EZD7Q9BOKpq0XoE2yVj1MV+7u0p/PfPUfyVPGPV2sBKVCg==","signedAt":"2026-06-20T06:27:53.490Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/intfloat--multilingual-e5-base","artifact":"https://unfragile.ai/intfloat--multilingual-e5-base","verify":"https://unfragile.ai/api/v1/verify?slug=intfloat--multilingual-e5-base","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"}}