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Unlike bi-encoder approaches that embed query and passage separately, this model processes them together to capture fine-grained interaction patterns, producing a single relevance score per pair that reflects semantic and lexical alignment.","intents":["I need to improve search result quality by reranking initial retrieval results from BM25 or dense retrievers","I want to filter low-relevance passages from a large corpus before feeding them to an LLM to reduce context noise","I need to rank candidate answers by relevance to a user query in a QA pipeline"],"best_for":["RAG pipeline builders optimizing retrieval quality without retraining","search teams implementing two-stage ranking (dense retrieval + reranking)","multilingual applications requiring English and Chinese relevance scoring"],"limitations":["Cross-encoder inference is O(n) in number of passages — requires scoring each query-passage pair individually, making it slower than bi-encoder retrieval for large-scale ranking","No built-in batching optimization — requires manual batch processing to avoid memory exhaustion on GPU","Fixed maximum sequence length (512 tokens) — truncates long passages, losing tail context","English and Chinese only — no support for other languages despite XLM-RoBERTa's multilingual capability"],"requires":["Python 3.7+","PyTorch 1.11+ or ONNX Runtime 1.14+","4GB+ GPU VRAM for batch inference (batch_size=32), or CPU inference with 8GB RAM","sentence-transformers library 2.2.0+ for model loading and inference utilities"],"input_types":["text (query string, 1-512 tokens)","text (passage string, 1-512 tokens)","structured pairs: {\"query\": \"...\", \"passage\": \"...\"}"],"output_types":["float (relevance score, typically 0-1 range after sigmoid)","ranked list of passages with scores","structured JSON with passage IDs and relevance scores"],"categories":["search-retrieval","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-base__cap_1","uri":"capability://search.retrieval.multilingual.relevance.scoring.with.xlm.roberta.backbone","name":"multilingual relevance scoring with xlm-roberta backbone","description":"Scores relevance across English and Chinese text pairs using XLM-RoBERTa's shared multilingual embedding space, enabling zero-shot cross-lingual ranking where a query in one language can score passages in another. The model leverages XLM-RoBERTa's 100-language pretraining to generalize relevance patterns across linguistic boundaries without language-specific fine-tuning.","intents":["I need to rank Chinese search results by relevance to English queries in a cross-lingual search system","I want a single reranker model that handles both English and Chinese without maintaining separate models","I need to score relevance in mixed-language documents or multilingual corpora"],"best_for":["teams building cross-lingual search or QA systems for Asian markets","multilingual RAG systems serving English-speaking users querying Chinese knowledge bases","companies reducing model complexity by consolidating language-specific rerankers"],"limitations":["Cross-lingual performance degrades compared to monolingual scoring — typically 2-4 points lower NDCG when ranking Chinese passages by English queries","No explicit language detection — requires external language identification to optimize prompt engineering or query expansion","Trained primarily on English-Chinese pairs — performance on other language combinations is untested and likely poor"],"requires":["Python 3.7+","sentence-transformers 2.2.0+","PyTorch 1.11+ or ONNX Runtime","UTF-8 text encoding support"],"input_types":["text (English query or passage)","text (Chinese query or passage in simplified or traditional characters)","mixed-language text pairs"],"output_types":["float (relevance score)","ranked list with language metadata"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-base__cap_2","uri":"capability://data.processing.analysis.onnx.based.inference.with.hardware.acceleration","name":"onnx-based inference with hardware acceleration","description":"Exports the cross-encoder model to ONNX format for optimized inference across CPUs, GPUs, and specialized accelerators (TPUs, NPUs) without PyTorch runtime dependency. ONNX Runtime applies graph-level optimizations (operator fusion, quantization, memory pooling) and enables deployment on edge devices or serverless functions with minimal latency overhead compared to native PyTorch inference.","intents":["I want to deploy the reranker in a serverless function (AWS Lambda, Google Cloud Functions) with minimal cold-start overhead","I need to run reranking on edge devices or mobile clients with limited memory and compute","I want to optimize inference latency and throughput for high-volume production ranking"],"best_for":["production teams deploying reranking in latency-sensitive pipelines (target <50ms per query)","edge AI teams running inference on resource-constrained devices","serverless/FaaS platforms requiring minimal runtime footprint"],"limitations":["ONNX export requires manual conversion — not all PyTorch operations are ONNX-compatible, limiting future model updates","Quantization (INT8) can reduce accuracy by 1-2 points NDCG depending on calibration dataset","ONNX Runtime version compatibility — older ONNX Runtime versions may not support all optimizations, requiring version pinning","No dynamic shape support in some ONNX Runtime versions — requires fixed batch sizes or padding"],"requires":["ONNX Runtime 1.14+","Python 3.7+ (or C++ for native ONNX Runtime)","Pre-converted ONNX model file (available on HuggingFace Hub)","Optional: CUDA 11.0+ for GPU acceleration, or TensorRT for NVIDIA GPUs"],"input_types":["ONNX-compatible tensor format (numpy arrays, PyTorch tensors converted to numpy)","tokenized input IDs and attention masks (shape: [batch_size, sequence_length])"],"output_types":["numpy array (relevance scores, shape: [batch_size, 1])","float32 or float16 depending on quantization"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-base__cap_3","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.padding.and.memory.optimization","name":"batch inference with dynamic padding and memory optimization","description":"Processes multiple query-passage pairs in parallel using dynamic padding (padding to longest sequence in batch rather than fixed max length) and gradient checkpointing to reduce memory footprint. The sentence-transformers integration automatically handles batching, tokenization, and output aggregation, allowing efficient scoring of thousands of passages per query without manual memory management.","intents":["I need to rerank 1000+ passages per query efficiently without running out of GPU memory","I want to maximize throughput in a batch reranking job (e.g., offline ranking of a document corpus)","I need to balance latency and throughput for a production ranking service"],"best_for":["batch processing pipelines ranking large document collections (100K+ passages)","production search systems with SLA requirements for throughput (queries/second)","teams optimizing GPU utilization in multi-tenant inference clusters"],"limitations":["Dynamic padding adds ~5-10ms overhead per batch due to shape computation and padding operations","Batch size is limited by GPU VRAM — typical max batch_size=128 on 16GB GPU, requiring multiple passes for large-scale ranking","No built-in distributed batching — requires manual sharding across multiple GPUs or machines","Padding efficiency degrades with highly variable sequence lengths (e.g., mixing 10-token and 500-token passages)"],"requires":["sentence-transformers 2.2.0+","PyTorch 1.11+","GPU with 8GB+ VRAM (or CPU with 16GB+ RAM for batch_size=32)","Optional: distributed inference framework (Ray, Hugging Face Inference Server) for multi-GPU batching"],"input_types":["list of dicts: [{\"query\": \"...\", \"passage\": \"...\"}, ...]","list of tuples: [(query_str, passage_str), ...]","pandas DataFrame with 'query' and 'passage' columns"],"output_types":["numpy array (scores, shape: [num_pairs])","list of floats","pandas Series with scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-base__cap_4","uri":"capability://safety.moderation.safetensors.format.support.for.secure.model.loading","name":"safetensors format support for secure model loading","description":"Loads model weights from safetensors format (a safer alternative to pickle-based PyTorch .pt files) that prevents arbitrary code execution during deserialization. The safetensors format is language-agnostic and enables fast, memory-mapped loading of large models without materializing the entire weight tensor in memory during load time.","intents":["I want to load the model safely without risk of code injection from untrusted model files","I need to load the model quickly in a resource-constrained environment using memory mapping","I want to ensure model integrity and reproducibility across different hardware and software versions"],"best_for":["security-conscious teams deploying models from untrusted sources or public repositories","resource-constrained environments (edge devices, serverless) requiring fast model loading","teams requiring model provenance and integrity verification"],"limitations":["safetensors support requires sentence-transformers 2.2.0+ — older versions fall back to PyTorch format","No built-in signature verification — safetensors prevents code execution but doesn't verify model authenticity","Memory mapping only works on systems with sufficient virtual address space — may fail on 32-bit systems"],"requires":["sentence-transformers 2.2.0+","safetensors library 0.3.0+","Python 3.7+"],"input_types":["safetensors file (.safetensors extension)","HuggingFace model ID (auto-downloads safetensors variant if available)"],"output_types":["loaded PyTorch model state dict","ready-to-use sentence-transformers CrossEncoder object"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-base__cap_5","uri":"capability://data.processing.analysis.mteb.benchmark.evaluation.and.model.comparison","name":"mteb benchmark evaluation and model comparison","description":"Model is evaluated on MTEB (Massive Text Embedding Benchmark) reranking tasks, providing standardized performance metrics (NDCG@10, MAP, MRR) across diverse domains and languages. MTEB evaluation enables direct comparison with other rerankers and tracking of model performance improvements across versions using a shared evaluation framework.","intents":["I want to compare this reranker's performance against other models using standardized benchmarks","I need to validate that the model meets minimum performance thresholds for my use case before deployment","I want to track model performance improvements and regressions across versions"],"best_for":["teams evaluating reranker options and comparing against baselines","researchers benchmarking new ranking approaches","production teams establishing performance SLAs and monitoring model drift"],"limitations":["MTEB benchmarks may not reflect domain-specific performance — a model strong on MTEB may underperform on proprietary datasets","Benchmark results are static snapshots — don't capture real-world performance on live traffic or evolving query distributions","No fine-tuning guidance — MTEB results don't indicate how to adapt the model for specific domains"],"requires":["MTEB library 1.0+ for running evaluations","Python 3.7+","Internet connection to download benchmark datasets"],"input_types":["MTEB task definitions (queries, corpus, relevance judgments)"],"output_types":["NDCG@10, MAP, MRR scores per task","aggregated scores across tasks","comparison tables vs other models"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-base__cap_6","uri":"capability://tool.use.integration.text.embeddings.inference.server.integration","name":"text-embeddings-inference server integration","description":"Compatible with text-embeddings-inference (TEI) server, a high-performance inference server optimized for embedding and reranking models. TEI provides REST/gRPC APIs, automatic batching, dynamic padding, and GPU optimization without requiring custom inference code, enabling production deployment with minimal infrastructure setup.","intents":["I want to deploy the reranker as a scalable microservice with REST API without writing custom inference code","I need automatic request batching and GPU optimization for high-throughput ranking","I want to run the reranker in a containerized environment (Docker, Kubernetes) with minimal configuration"],"best_for":["teams deploying reranking as a microservice in Kubernetes or Docker environments","production systems requiring high throughput (1000+ requests/second) with automatic batching","teams wanting to avoid custom inference server implementation and maintenance"],"limitations":["TEI server adds network latency (typically 5-20ms per request) compared to in-process inference","Requires separate server process and resource allocation — adds operational complexity vs embedded inference","TEI is Rust-based and may have different numerical behavior than PyTorch on edge cases","No built-in authentication or rate limiting — requires external API gateway for production security"],"requires":["text-embeddings-inference server (Docker image available)","Docker or Kubernetes for deployment","GPU with 8GB+ VRAM (or CPU mode with performance degradation)","HTTP client library for REST API calls"],"input_types":["JSON POST request: {\"inputs\": [{\"query\": \"...\", \"passage\": \"...\"}]}","gRPC protobuf messages"],"output_types":["JSON response: {\"scores\": [0.95, 0.42, ...]}","gRPC response with scores"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-base__cap_7","uri":"capability://automation.workflow.azure.endpoints.deployment.compatibility","name":"azure endpoints deployment compatibility","description":"Model is compatible with Azure Machine Learning endpoints, enabling one-click deployment to Azure's managed inference infrastructure. Azure integration provides automatic scaling, monitoring, and integration with Azure's ML ecosystem without custom deployment code.","intents":["I want to deploy the reranker to Azure ML endpoints for managed inference and auto-scaling","I need to integrate the reranker into an Azure ML pipeline or batch inference job","I want monitoring and logging through Azure's native observability tools"],"best_for":["teams already invested in Azure ML ecosystem","enterprises requiring managed inference with SLA guarantees","teams wanting to avoid infrastructure management for model serving"],"limitations":["Azure-specific deployment — not portable to other cloud providers without re-deployment","Azure pricing applies — managed inference is more expensive than self-hosted alternatives","Limited customization of inference runtime — Azure controls underlying infrastructure","Requires Azure account and ML workspace setup"],"requires":["Azure subscription with ML workspace","Azure CLI or Python SDK","Model registered in Azure ML model registry"],"input_types":["JSON payload compatible with Azure ML endpoints"],"output_types":["JSON response from Azure ML endpoint"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-baai--bge-reranker-base__cap_8","uri":"capability://memory.knowledge.model.index.metadata.and.discoverability","name":"model-index metadata and discoverability","description":"Includes model-index metadata (model card, training details, evaluation results) on HuggingFace Hub, enabling automated discovery, comparison, and integration with tools that consume model metadata. Model-index enables programmatic access to model capabilities, training data, and performance metrics for automated model selection and evaluation.","intents":["I want to programmatically discover and compare reranker models based on performance metrics and training data","I need to validate model provenance and training methodology before deployment","I want to integrate model metadata into automated model selection pipelines"],"best_for":["teams building automated model selection systems","researchers comparing models across multiple dimensions","tools and frameworks that consume model metadata for integration"],"limitations":["Model-index is optional metadata — not all models include complete metadata","Metadata accuracy depends on model authors — no validation or auditing of claims","Metadata may become stale as models are updated"],"requires":["HuggingFace Hub access","Model card parser (huggingface_hub library)"],"input_types":["model ID (e.g., 'BAAI/bge-reranker-base')"],"output_types":["structured metadata (JSON)","model card content (markdown)","evaluation results and metrics"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.11+ or ONNX Runtime 1.14+","4GB+ GPU VRAM for batch inference (batch_size=32), or CPU inference with 8GB RAM","sentence-transformers library 2.2.0+ for model loading and inference utilities","sentence-transformers 2.2.0+","PyTorch 1.11+ or ONNX Runtime","UTF-8 text encoding support","ONNX Runtime 1.14+","Python 3.7+ (or C++ for native ONNX Runtime)","Pre-converted ONNX model file (available on HuggingFace Hub)"],"failure_modes":["Cross-encoder inference is O(n) in number of passages — requires scoring each query-passage pair individually, making it slower than bi-encoder retrieval for large-scale ranking","No built-in batching optimization — requires manual batch processing to avoid memory exhaustion on GPU","Fixed maximum sequence length (512 tokens) — truncates long passages, losing tail context","English and Chinese only — no support for other languages despite XLM-RoBERTa's multilingual capability","Cross-lingual performance degrades compared to monolingual scoring — typically 2-4 points lower NDCG when ranking Chinese passages by English queries","No explicit language detection — requires external language identification to optimize prompt engineering or query expansion","Trained primarily on English-Chinese pairs — performance on other language combinations is untested and likely poor","ONNX export requires manual conversion — not all PyTorch operations are ONNX-compatible, limiting future model updates","Quantization (INT8) can reduce accuracy by 1-2 points NDCG depending on calibration dataset","ONNX Runtime version compatibility — older ONNX Runtime versions may not support all optimizations, requiring version pinning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8021829144207501,"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":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:23:00.976Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":3106509,"model_likes":233}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=baai--bge-reranker-base","compare_url":"https://unfragile.ai/compare?artifact=baai--bge-reranker-base"}},"signature":"ms8IrgMi2K6IzY1FzyHsFd7HnjGCU+th4Z/1qjHrpugpaY73GsOi2nmrwUGqE+ueouGYuPG8S2FrZ1UhyBq/DA==","signedAt":"2026-06-20T03:04:44.998Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/baai--bge-reranker-base","artifact":"https://unfragile.ai/baai--bge-reranker-base","verify":"https://unfragile.ai/api/v1/verify?slug=baai--bge-reranker-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"}}