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The model processes input text through a 12-layer transformer encoder with attention mechanisms, producing 384-dimensional output vectors that capture semantic meaning suitable for similarity-based retrieval and clustering tasks. Embeddings are generated via mean pooling of the final transformer layer outputs, enabling efficient batch processing and downstream vector operations.","intents":["I need to convert documents and queries into vectors for semantic search without running a full LLM","I want to build a RAG system where I can index documents and retrieve relevant chunks by semantic similarity","I need to compute embeddings at scale for thousands of documents with minimal computational overhead","I want to cluster or classify text based on semantic similarity using vector distance metrics"],"best_for":["teams building RAG pipelines with English-language documents","developers implementing semantic search without full LLM inference costs","organizations needing lightweight embedding models deployable on CPU or edge devices","researchers benchmarking embedding quality on MTEB tasks"],"limitations":["English-only — no support for multilingual or non-English text; cross-lingual queries will have degraded performance","Fixed 384-dimensional output — cannot adjust embedding dimensionality without retraining or post-hoc projection","Context window limited to ~512 tokens — longer documents must be chunked, potentially losing cross-chunk semantic relationships","Mean pooling strategy may lose fine-grained positional information compared to CLS-token approaches in some domains","No built-in handling of domain-specific terminology — performance degrades on highly specialized jargon without fine-tuning"],"requires":["Python 3.8+","PyTorch 1.11+ or compatible ONNX runtime","transformers library 4.30+","sentence-transformers library 2.2+ (recommended for ease of use)","4GB+ RAM for inference (8GB+ recommended for batch processing)","Optional: CUDA 11.8+ for GPU acceleration"],"input_types":["plain text strings","text sequences up to ~512 tokens","batch lists of text documents"],"output_types":["dense float32 vectors (384 dimensions)","normalized vectors (L2 norm)","batch tensor outputs compatible with PyTorch/NumPy"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ibm-granite--granite-embedding-small-english-r2__cap_1","uri":"capability://search.retrieval.batch.semantic.similarity.computation","name":"batch-semantic-similarity-computation","description":"Computes pairwise cosine similarity scores between sets of text embeddings using vectorized operations, enabling efficient ranking and retrieval of semantically similar documents. The capability leverages PyTorch's matrix multiplication operations to compute similarity matrices in O(n*m) time, supporting both symmetric (document-to-document) and asymmetric (query-to-document) similarity calculations. Results are typically returned as dense similarity matrices or ranked lists of top-k similar items.","intents":["I need to find the top 10 most relevant documents for a user query from a corpus of 100k documents","I want to compute all-pairs similarity between documents to identify duplicates or near-duplicates","I need to rank search results by semantic relevance without re-embedding at query time"],"best_for":["RAG systems performing retrieval at inference time","document deduplication pipelines","semantic search engines with pre-indexed embeddings"],"limitations":["Memory complexity scales quadratically with corpus size — computing all-pairs similarity for 1M documents requires ~4TB of intermediate memory","No built-in approximate nearest neighbor (ANN) indexing — requires external FAISS, Annoy, or Milvus for sub-linear retrieval on large corpora","Cosine similarity assumes normalized vectors — unnormalized embeddings will produce incorrect scores","No ranking diversity — returns raw similarity scores without diversity-aware reranking"],"requires":["Pre-computed embeddings for all documents (from dense-vector-embedding-generation capability)","PyTorch or NumPy for matrix operations","Sufficient RAM to hold embedding matrix in memory (e.g., 100k docs × 384 dims × 4 bytes = ~150MB)"],"input_types":["embedding tensors (2D float arrays)","query embeddings (1D or 2D float arrays)"],"output_types":["similarity score matrices (2D float arrays)","ranked lists of (document_id, similarity_score) tuples","top-k indices and scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ibm-granite--granite-embedding-small-english-r2__cap_2","uri":"capability://data.processing.analysis.mteb.benchmark.compatible.evaluation","name":"mteb-benchmark-compatible-evaluation","description":"Model is pre-evaluated and compatible with the Massive Text Embedding Benchmark (MTEB) evaluation framework, enabling standardized assessment across 56+ diverse tasks including retrieval, clustering, semantic textual similarity, and classification. The model's performance is reported on MTEB leaderboard metrics, allowing direct comparison with other embedding models on standardized datasets. Integration with MTEB tooling enables reproducible evaluation and task-specific performance analysis without custom evaluation code.","intents":["I want to verify embedding quality on standard benchmarks before deploying to production","I need to compare this model's performance against other embedding models on identical tasks","I want to understand which task categories (retrieval, clustering, STS) this model excels at"],"best_for":["researchers evaluating embedding model quality","teams selecting embedding models for production RAG systems","organizations benchmarking embedding performance across multiple domains"],"limitations":["MTEB evaluation is English-only — no cross-lingual or multilingual task coverage","Benchmark results reflect average performance across diverse tasks — may not predict performance on domain-specific applications","MTEB tasks are primarily academic/general-domain — performance on specialized domains (medical, legal, code) may differ significantly","No real-time performance metrics — benchmark results are static snapshots, not live performance monitoring"],"requires":["mteb library (pip install mteb)","Internet connection to download benchmark datasets","Sufficient disk space for MTEB datasets (~50GB for full benchmark)","Python 3.8+"],"input_types":["MTEB task definitions","benchmark dataset samples"],"output_types":["MTEB leaderboard scores (NDCG@10, MRR, MAP, etc.)","task-specific metrics (precision, recall, F1)","performance comparison tables"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ibm-granite--granite-embedding-small-english-r2__cap_3","uri":"capability://automation.workflow.multi.framework.model.deployment","name":"multi-framework-model-deployment","description":"Model is distributed in multiple formats (PyTorch, SafeTensors, ONNX-compatible) and is compatible with multiple inference frameworks including Hugging Face Transformers, sentence-transformers, text-embeddings-inference (TEI), and cloud deployment platforms (Azure, AWS). This enables flexible deployment across different infrastructure stacks without model conversion, supporting CPU inference, GPU acceleration, and containerized endpoints. The SafeTensors format provides faster loading and improved security compared to pickle-based PyTorch checkpoints.","intents":["I want to deploy embeddings in a Docker container using text-embeddings-inference for low-latency serving","I need to run embeddings on Azure ML or AWS SageMaker without custom model conversion","I want to load the model in Python with sentence-transformers for quick prototyping","I need to serve embeddings via REST API with automatic batching and GPU support"],"best_for":["teams deploying embeddings to cloud platforms (Azure, AWS, GCP)","organizations needing multi-framework compatibility for different inference stacks","developers building containerized embedding services with TEI","researchers prototyping with sentence-transformers before production deployment"],"limitations":["SafeTensors format is newer — some older inference frameworks may not support it natively","ONNX conversion requires additional tooling and may have numerical precision differences vs PyTorch","Deployment-specific optimizations (quantization, pruning) are not included — require post-hoc optimization","No built-in model serving framework — requires external tools like vLLM, TEI, or FastAPI for production endpoints"],"requires":["For PyTorch: torch 1.11+, transformers 4.30+","For SafeTensors: safetensors library","For TEI deployment: Docker and text-embeddings-inference container","For cloud deployment: Azure ML SDK, AWS SageMaker SDK, or equivalent","Optional: ONNX Runtime for ONNX inference"],"input_types":["model weights in PyTorch format","model weights in SafeTensors format","model configuration files (config.json, tokenizer.json)"],"output_types":["loaded model objects (transformers.AutoModel, sentence_transformers.SentenceTransformer)","inference endpoints (REST API, gRPC)","containerized services (Docker images)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ibm-granite--granite-embedding-small-english-r2__cap_4","uri":"capability://data.processing.analysis.efficient.cpu.and.gpu.inference","name":"efficient-cpu-and-gpu-inference","description":"Model is optimized for both CPU and GPU inference through ModernBERT architecture design and sentence-transformers framework integration, supporting efficient batch processing with automatic device placement. The 50M parameter count and 384-dimensional output enable sub-100ms latency on modern CPUs and sub-10ms latency on GPUs, with linear scaling for batch sizes. Framework automatically handles mixed-precision inference (FP16 on GPUs) and gradient checkpointing for memory efficiency.","intents":["I need to embed documents on a CPU-only server without GPU infrastructure","I want to process embedding requests with <50ms latency per query on GPU","I need to batch embed 10k documents efficiently without running out of memory","I want to run embeddings on edge devices or mobile with minimal computational overhead"],"best_for":["resource-constrained environments (edge devices, serverless functions, shared hosting)","latency-sensitive applications requiring sub-100ms embedding latency","batch processing pipelines embedding millions of documents","organizations without dedicated GPU infrastructure"],"limitations":["CPU inference is 10-50x slower than GPU — not suitable for real-time, high-throughput scenarios without GPU","Batch size is limited by available RAM — CPU systems typically max out at batch size 32-64 before OOM","No built-in quantization — INT8 or INT4 quantization requires external tools (bitsandbytes, GPTQ) and may degrade quality","Mixed-precision (FP16) requires GPU support — CPU inference uses FP32 only, increasing memory footprint"],"requires":["Python 3.8+","PyTorch 1.11+ (CPU or GPU variant)","transformers 4.30+","For GPU: CUDA 11.8+ and compatible GPU (2GB+ VRAM)","For CPU: 4GB+ RAM minimum, 8GB+ recommended for batch processing"],"input_types":["text strings","batches of text (lists or tensors)"],"output_types":["embedding tensors (float32 or float16)","normalized embeddings (L2 norm)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ibm-granite--granite-embedding-small-english-r2__cap_5","uri":"capability://search.retrieval.semantic.text.similarity.scoring","name":"semantic-text-similarity-scoring","description":"Computes semantic similarity scores between pairs of text sequences by embedding both texts and computing cosine similarity of their vector representations. This enables fine-grained similarity measurement beyond keyword matching, capturing semantic relationships like paraphrases, synonyms, and conceptual similarity. Scores range from -1 to 1 (or 0 to 1 for normalized embeddings), with higher scores indicating greater semantic similarity.","intents":["I want to measure how similar two documents are semantically, not just by keyword overlap","I need to detect paraphrases or near-duplicate content in a document collection","I want to score the relevance of a search result to a user query on a continuous scale","I need to identify semantically equivalent text variations for content deduplication"],"best_for":["semantic textual similarity (STS) tasks and benchmarks","paraphrase detection systems","content deduplication pipelines","relevance scoring in search and recommendation systems"],"limitations":["Similarity is symmetric — no distinction between query-to-document vs document-to-query relationships","Cosine similarity is sensitive to vector magnitude — unnormalized embeddings produce incorrect scores","No threshold calibration — determining optimal similarity threshold for binary decisions requires task-specific tuning","Semantic similarity may not correlate with task-specific relevance — e.g., a document semantically similar to a query may not be relevant for fact-checking tasks"],"requires":["Two text inputs to compare","Embedding capability (dense-vector-embedding-generation-for-english-text)","Cosine similarity computation (batch-semantic-similarity-computation)"],"input_types":["text string pairs","pre-computed embedding pairs"],"output_types":["similarity scores (float, range -1 to 1 or 0 to 1)","similarity matrices for multiple comparisons"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.11+ or compatible ONNX runtime","transformers library 4.30+","sentence-transformers library 2.2+ (recommended for ease of use)","4GB+ RAM for inference (8GB+ recommended for batch processing)","Optional: CUDA 11.8+ for GPU acceleration","Pre-computed embeddings for all documents (from dense-vector-embedding-generation capability)","PyTorch or NumPy for matrix operations","Sufficient RAM to hold embedding matrix in memory (e.g., 100k docs × 384 dims × 4 bytes = ~150MB)","mteb library (pip install mteb)"],"failure_modes":["English-only — no support for multilingual or non-English text; cross-lingual queries will have degraded performance","Fixed 384-dimensional output — cannot adjust embedding dimensionality without retraining or post-hoc projection","Context window limited to ~512 tokens — longer documents must be chunked, potentially losing cross-chunk semantic relationships","Mean pooling strategy may lose fine-grained positional information compared to CLS-token approaches in some domains","No built-in handling of domain-specific terminology — performance degrades on highly specialized jargon without fine-tuning","Memory complexity scales quadratically with corpus size — computing all-pairs similarity for 1M documents requires ~4TB of intermediate memory","No built-in approximate nearest neighbor (ANN) indexing — requires external FAISS, Annoy, or Milvus for sub-linear retrieval on large corpora","Cosine similarity assumes normalized vectors — unnormalized embeddings will produce incorrect scores","No ranking diversity — returns raw similarity scores without diversity-aware reranking","MTEB evaluation is English-only — no cross-lingual or multilingual task coverage","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6970894102019358,"quality":0.37,"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":"2026-04-22T08:08:29.187Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":1015382,"model_likes":66}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ibm-granite--granite-embedding-small-english-r2","compare_url":"https://unfragile.ai/compare?artifact=ibm-granite--granite-embedding-small-english-r2"}},"signature":"47mcfZ671jliPy0HM1Z9DH4jnMRTiRqHMfhGP/0AjFX7rKfFHSCfrxn6D+iUx5GwQJ+YeyUdhNQOPS0gF0BzDw==","signedAt":"2026-06-19T21:12:45.721Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ibm-granite--granite-embedding-small-english-r2","artifact":"https://unfragile.ai/ibm-granite--granite-embedding-small-english-r2","verify":"https://unfragile.ai/api/v1/verify?slug=ibm-granite--granite-embedding-small-english-r2","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"}}