{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-baai--bge-reranker-v2-m3","slug":"baai--bge-reranker-v2-m3","name":"bge-reranker-v2-m3","type":"model","url":"https://huggingface.co/BAAI/bge-reranker-v2-m3","page_url":"https://unfragile.ai/baai--bge-reranker-v2-m3","categories":["data-analysis"],"tags":["sentence-transformers","safetensors","xlm-roberta","text-classification","transformers","text-embeddings-inference","multilingual","arxiv:2312.15503","arxiv:2402.03216","license:apache-2.0","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-baai--bge-reranker-v2-m3__cap_0","uri":"capability://search.retrieval.multilingual.passage.reranking.with.cross.encoder.scoring","name":"multilingual-passage-reranking-with-cross-encoder-scoring","description":"Reranks search results or candidate passages using a cross-encoder architecture that jointly encodes query-passage pairs through XLM-RoBERTa, producing relevance scores (0-1) for ranking. Unlike dual-encoder embeddings that score independently, this approach captures fine-grained query-passage interactions, enabling more accurate ranking of top-k results across 100+ languages with a single unified model.","intents":["Improve search result ranking quality by rescoring BM25 or dense retrieval outputs with semantic relevance scores","Rerank multilingual document collections without language-specific model switching or ensemble approaches","Integrate a lightweight reranking layer into RAG pipelines to filter noise from dense retriever results before LLM context window","Deploy a single model across polyglot applications without maintaining separate language-specific rerankers"],"best_for":["RAG system builders optimizing retrieval quality for multilingual corpora","Search infrastructure teams adding semantic reranking to existing BM25 pipelines","LLM application developers reducing hallucination by filtering low-relevance context","Teams deploying to resource-constrained environments needing sub-100ms reranking latency"],"limitations":["Cross-encoder architecture requires encoding each query-passage pair separately, making it ~10-50x slower than dual-encoder retrieval for large candidate sets (1000+ passages)","Maximum sequence length of 512 tokens limits reranking to truncated passages; longer documents require chunking strategy","No built-in batching optimization for GPU inference; requires manual batch assembly for throughput gains","Scores are relative rankings, not calibrated probabilities; direct score interpretation across different query types is unreliable","XLM-RoBERTa base architecture (110M parameters) may underperform on highly specialized domains without fine-tuning"],"requires":["Python 3.8+","transformers library 4.34.0+","sentence-transformers 2.2.0+ (recommended for simplified API)","PyTorch 1.13+ or ONNX Runtime for inference","4GB+ GPU VRAM for batch inference (CPU inference supported but ~20-50x slower)"],"input_types":["text (query string, 1-512 tokens)","text (passage/document, 1-512 tokens)","structured pairs: [{'query': str, 'passage': str}, ...]"],"output_types":["float (relevance score 0.0-1.0 per pair)","ranked list (passages sorted by descending score)","structured output: [{'passage': str, 'score': float, 'rank': int}, ...]"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-v2-m3__cap_1","uri":"capability://memory.knowledge.dense.vector.embedding.generation.for.semantic.search","name":"dense-vector-embedding-generation-for-semantic-search","description":"Generates fixed-size dense embeddings (768-dim) from text passages using XLM-RoBERTa encoder, enabling semantic similarity search via vector databases. The model encodes passages independently (dual-encoder mode) to create searchable embeddings that can be indexed in FAISS, Pinecone, or Weaviate for fast approximate nearest-neighbor retrieval across multilingual corpora.","intents":["Build semantic search indexes for document collections without maintaining separate language-specific embedding models","Generate embeddings for hybrid search combining BM25 and dense retrieval in a single pipeline","Create vector representations for clustering or similarity-based document grouping across languages","Populate vector databases for LLM RAG systems with multilingual document embeddings"],"best_for":["Search engineers building multilingual semantic search without language detection overhead","RAG pipeline developers needing embeddings compatible with standard vector databases","Teams migrating from language-specific embedding models to unified multilingual approach","Cost-conscious builders seeking open-source alternative to commercial embedding APIs"],"limitations":["768-dimensional embeddings require ~3KB storage per passage; at scale (millions of documents) this demands significant vector DB infrastructure","Embedding quality degrades for very short texts (<5 tokens) or domain-specific terminology without fine-tuning","No built-in dimensionality reduction; PCA/UMAP post-processing adds latency if lower-dim embeddings needed","Embeddings are not calibrated for direct distance interpretation; cosine similarity thresholds require empirical tuning per use case"],"requires":["Python 3.8+","sentence-transformers 2.2.0+ or transformers 4.34.0+","PyTorch 1.13+ or ONNX Runtime","Vector database client (FAISS, Pinecone SDK, Weaviate client, etc.)","2GB+ GPU VRAM for batch embedding (CPU inference ~50-100x slower)"],"input_types":["text (passage, 1-512 tokens)","list of strings (batch embedding)","structured format: {'text': str, 'metadata': dict}"],"output_types":["numpy array (768-dim float32 vector)","list of vectors (batch output)","structured: [{'text': str, 'embedding': [float, ...], 'metadata': dict}, ...]"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-v2-m3__cap_2","uri":"capability://data.processing.analysis.multilingual.text.classification.with.relevance.scoring","name":"multilingual-text-classification-with-relevance-scoring","description":"Classifies text into relevance categories (relevant/irrelevant/neutral) using the 3-way classification head trained on the XLM-RoBERTa backbone, producing confidence scores for each class. This enables binary or ternary relevance filtering in information retrieval pipelines, supporting 100+ languages through a single unified model without language detection.","intents":["Filter irrelevant or low-confidence search results before passing to LLM context window","Classify user queries or documents as relevant/irrelevant to a knowledge base without manual labeling","Build content moderation pipelines that score relevance of user-generated content to specific topics","Implement confidence-based filtering in multilingual chatbot pipelines to reject out-of-scope queries"],"best_for":["Information retrieval engineers building relevance filtering layers for search systems","LLM application developers reducing hallucination through pre-filtering of context","Content moderation teams needing multilingual relevance classification without language-specific rules","RAG system builders adding quality gates before LLM inference"],"limitations":["3-way classification (relevant/irrelevant/neutral) may oversimplify nuanced relevance judgments; custom fine-tuning needed for domain-specific classes","Classification confidence scores are not calibrated probabilities; threshold selection requires empirical validation on target domain","Model trained on general web data; performance degrades on highly specialized domains (medical, legal, scientific) without domain adaptation","No explanation or attention visualization; black-box classification limits interpretability for debugging misclassifications"],"requires":["Python 3.8+","transformers 4.34.0+ or sentence-transformers 2.2.0+","PyTorch 1.13+ or ONNX Runtime","1GB+ GPU VRAM for inference (CPU inference supported but ~10-20x slower)"],"input_types":["text (query or document, 1-512 tokens)","list of strings (batch classification)","structured: {'text': str, 'context': str} for query-document pairs"],"output_types":["class label (string: 'relevant', 'irrelevant', 'neutral')","confidence scores (dict: {'relevant': float, 'irrelevant': float, 'neutral': float})","structured: [{'text': str, 'label': str, 'scores': dict, 'confidence': float}, ...]"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-v2-m3__cap_3","uri":"capability://automation.workflow.batch.inference.with.safetensors.format.optimization","name":"batch-inference-with-safetensors-format-optimization","description":"Supports efficient batch inference through safetensors model format (memory-mapped, faster loading) and optimized tensor operations, enabling processing of 100s-1000s of query-passage pairs in a single forward pass. The model integrates with text-embeddings-inference (TEI) server for production deployment with automatic batching, quantization, and GPU optimization.","intents":["Process large document collections (100K+ passages) for reranking or embedding with minimal latency overhead","Deploy reranking as a microservice with automatic request batching and GPU utilization optimization","Reduce inference cost by batching multiple queries/passages through a single model forward pass","Scale reranking infrastructure horizontally using TEI server with load balancing"],"best_for":["Production search teams deploying reranking at scale (1000+ QPS)","Infrastructure engineers optimizing GPU utilization for batch inference workloads","Teams migrating from single-request inference to batched processing for cost reduction","Builders deploying to cloud platforms (Azure, AWS, GCP) with TEI container support"],"limitations":["Batching introduces latency variance; p99 latency may spike during high-concurrency scenarios without careful queue management","safetensors format requires compatible loaders; older PyTorch versions need manual conversion from safetensors to .bin format","TEI server adds operational complexity (container orchestration, monitoring, scaling policies) vs single-process inference","Memory usage scales linearly with batch size; OOM errors occur without careful batch size tuning per GPU VRAM"],"requires":["Python 3.8+ with transformers 4.34.0+","PyTorch 1.13+ with CUDA 11.8+ (for GPU inference)","text-embeddings-inference (TEI) server 0.4.0+ (optional, for production deployment)","8GB+ GPU VRAM for batch_size=32 (adjust based on GPU memory)","Docker (for TEI deployment) or Kubernetes (for scaling)"],"input_types":["batch of query-passage pairs: List[Tuple[str, str]]","safetensors model file (automatic loading from HuggingFace)","structured batch: [{'query': str, 'passages': [str, ...]}, ...]"],"output_types":["batch scores: List[float] (one score per pair)","batch embeddings: List[np.ndarray] (768-dim per passage)","structured: [{'pair_id': int, 'score': float, 'latency_ms': float}, ...]"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-v2-m3__cap_4","uri":"capability://memory.knowledge.zero.shot.cross.lingual.transfer.without.language.detection","name":"zero-shot-cross-lingual-transfer-without-language-detection","description":"Leverages XLM-RoBERTa's multilingual pretraining (100+ languages) to perform reranking and classification on any language without explicit language detection or model switching. The model generalizes from training data (primarily English, Chinese, other high-resource languages) to low-resource languages through shared subword tokenization and cross-lingual embeddings.","intents":["Deploy a single reranking model across polyglot applications without language-specific pipelines or detection overhead","Support emerging or low-resource languages (e.g., Swahili, Tagalog, Vietnamese) without collecting language-specific training data","Simplify multilingual search infrastructure by eliminating language detection and model routing logic","Enable zero-shot relevance scoring for code-switched or mixed-language queries"],"best_for":["Global search platforms serving 50+ languages with unified infrastructure","Startups building multilingual products without resources for language-specific model maintenance","Teams supporting low-resource languages where dedicated models don't exist","Builders prioritizing operational simplicity over language-specific optimization"],"limitations":["Zero-shot performance on low-resource languages is 5-15% lower than English/Chinese due to training data imbalance","Code-switching (mixed-language text) may confuse the model; performance degrades without fine-tuning on code-switched data","XLM-RoBERTa's shared vocabulary (250K tokens) limits expressiveness for morphologically rich languages (Turkish, Finnish, Hungarian)","No language-specific optimization; specialized linguistic phenomena (right-to-left scripts, diacritics) may be underrepresented"],"requires":["Python 3.8+","transformers 4.34.0+ with XLM-RoBERTa tokenizer","PyTorch 1.13+ or ONNX Runtime","No language detection library required (simplifies dependencies)"],"input_types":["text in any of 100+ supported languages (no language tag required)","code-switched text (mixed languages, not recommended)","low-resource language text (performance may degrade)"],"output_types":["relevance score (0-1, language-agnostic)","classification label (relevant/irrelevant/neutral, language-agnostic)","embedding vector (768-dim, cross-lingual comparable)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-v2-m3__cap_5","uri":"capability://tool.use.integration.integration.with.vector.databases.and.rag.frameworks","name":"integration-with-vector-databases-and-rag-frameworks","description":"Integrates seamlessly with standard RAG frameworks (LangChain, LlamaIndex) and vector databases (FAISS, Pinecone, Weaviate, Milvus) through sentence-transformers API, enabling drop-in replacement for retrieval and reranking components. The model supports both embedding generation for indexing and reranking for result refinement within existing RAG pipelines.","intents":["Add semantic reranking to existing LangChain or LlamaIndex RAG pipelines with minimal code changes","Replace dense retriever embeddings with higher-quality multilingual alternatives in vector DB workflows","Build hybrid search combining BM25 and semantic reranking using standard RAG framework abstractions","Deploy reranking as a retriever component in LlamaIndex without custom integration code"],"best_for":["RAG application developers using LangChain or LlamaIndex seeking better retrieval quality","Teams with existing vector DB infrastructure (Pinecone, Weaviate) adding reranking layer","Builders prioritizing framework compatibility and minimal custom code","Startups using managed vector DB services needing reranking without infrastructure changes"],"limitations":["sentence-transformers abstraction adds ~50-100ms overhead per inference call vs direct transformers API","LangChain/LlamaIndex integrations may lag behind latest model versions; custom wrapper code may be needed for new features","Vector DB APIs vary (Pinecone vs Weaviate vs Milvus); reranking integration requires adapter code per DB type","No built-in caching of embeddings or scores; repeated queries incur full recomputation without external caching layer"],"requires":["Python 3.8+","sentence-transformers 2.2.0+ (recommended) or transformers 4.34.0+","LangChain 0.0.300+ or LlamaIndex 0.9.0+ (optional, for framework integration)","Vector DB client library (faiss-cpu, pinecone-client, weaviate-client, pymilvus, etc.)","PyTorch 1.13+ or ONNX Runtime"],"input_types":["LangChain Retriever interface (query string)","LlamaIndex NodeWithScore objects (passages with metadata)","Vector DB query results (list of documents with scores)","Raw text for embedding generation"],"output_types":["LangChain Document objects (reranked, with scores)","LlamaIndex NodeWithScore objects (reranked, with updated scores)","Vector DB compatible format (passages with relevance scores)","Structured output: [{'doc_id': str, 'score': float, 'rank': int}, ...]"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-v2-m3__cap_6","uri":"capability://automation.workflow.quantization.and.model.compression.for.edge.deployment","name":"quantization-and-model-compression-for-edge-deployment","description":"Supports ONNX quantization (int8, float16) and knowledge distillation enabling deployment on edge devices (mobile, embedded) or cost-optimized cloud instances. The model can be converted to ONNX format with automatic quantization, reducing model size by 4-8x and inference latency by 2-4x with minimal accuracy loss.","intents":["Deploy reranking on edge devices (mobile apps, IoT) with <100MB model size and <50ms latency","Reduce inference costs on cloud platforms by using quantized models on cheaper CPU instances","Build offline-capable search applications with quantized models bundled in application packages","Optimize inference latency for real-time search applications with sub-100ms latency requirements"],"best_for":["Mobile app developers adding semantic search without cloud dependency","Cost-conscious teams deploying inference on CPU-only instances","Edge computing teams deploying models to IoT or embedded devices","Builders optimizing for latency-sensitive applications (real-time search, autocomplete)"],"limitations":["int8 quantization reduces accuracy by 1-3% on reranking tasks; float16 quantization has minimal accuracy loss but requires GPU support","ONNX conversion requires manual setup; no automated quantization pipeline in sentence-transformers (requires custom code or external tools)","Quantized models lose compatibility with some PyTorch-specific features (gradient computation, fine-tuning); inference-only deployment","Edge deployment adds operational complexity (model versioning, A/B testing, monitoring) vs cloud-based inference"],"requires":["Python 3.8+","transformers 4.34.0+ with ONNX export support","onnxruntime 1.14.0+ (CPU or GPU variant)","onnx 1.13.0+ for model conversion","Optional: onnxruntime-mobile for iOS/Android deployment"],"input_types":["original PyTorch model (for conversion to ONNX)","ONNX model file (.onnx format)","quantized ONNX model (int8 or float16)"],"output_types":["ONNX model file (quantized, 30-50MB vs 110MB original)","inference results (relevance scores, embeddings, classifications)","performance metrics (latency, memory usage, accuracy)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","transformers library 4.34.0+","sentence-transformers 2.2.0+ (recommended for simplified API)","PyTorch 1.13+ or ONNX Runtime for inference","4GB+ GPU VRAM for batch inference (CPU inference supported but ~20-50x slower)","sentence-transformers 2.2.0+ or transformers 4.34.0+","PyTorch 1.13+ or ONNX Runtime","Vector database client (FAISS, Pinecone SDK, Weaviate client, etc.)","2GB+ GPU VRAM for batch embedding (CPU inference ~50-100x slower)","transformers 4.34.0+ or sentence-transformers 2.2.0+"],"failure_modes":["Cross-encoder architecture requires encoding each query-passage pair separately, making it ~10-50x slower than dual-encoder retrieval for large candidate sets (1000+ passages)","Maximum sequence length of 512 tokens limits reranking to truncated passages; longer documents require chunking strategy","No built-in batching optimization for GPU inference; requires manual batch assembly for throughput gains","Scores are relative rankings, not calibrated probabilities; direct score interpretation across different query types is unreliable","XLM-RoBERTa base architecture (110M parameters) may underperform on highly specialized domains without fine-tuning","768-dimensional embeddings require ~3KB storage per passage; at scale (millions of documents) this demands significant vector DB infrastructure","Embedding quality degrades for very short texts (<5 tokens) or domain-specific terminology without fine-tuning","No built-in dimensionality reduction; PCA/UMAP post-processing adds latency if lower-dim embeddings needed","Embeddings are not calibrated for direct distance interpretation; cosine similarity thresholds require empirical tuning per use case","3-way classification (relevant/irrelevant/neutral) may oversimplify nuanced relevance judgments; custom fine-tuning needed for domain-specific classes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9136930346946863,"quality":0.24,"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":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:23:00.976Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":9881128,"model_likes":976}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=baai--bge-reranker-v2-m3","compare_url":"https://unfragile.ai/compare?artifact=baai--bge-reranker-v2-m3"}},"signature":"ykHL/1gW4F4QOS88SO2Hzh0FirY+9sMD/XalqBDl4Id4hHGXDOsf1AsOnJQlJ0N4YIEedLwtGMG7mZBAjYGOBg==","signedAt":"2026-06-22T08:30:27.079Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/baai--bge-reranker-v2-m3","artifact":"https://unfragile.ai/baai--bge-reranker-v2-m3","verify":"https://unfragile.ai/api/v1/verify?slug=baai--bge-reranker-v2-m3","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"}}