{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-sentence-transformers","slug":"pypi-sentence-transformers","name":"sentence-transformers","type":"repo","url":"https://pypi.org/project/sentence-transformers/","page_url":"https://unfragile.ai/pypi-sentence-transformers","categories":["rag-knowledge"],"tags":["Transformer","Networks","BERT","XLNet","sentence","embedding","PyTorch","NLP","deep","learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-sentence-transformers__cap_0","uri":"capability://data.processing.analysis.dense.embedding.generation.with.pooling.normalization","name":"dense-embedding-generation-with-pooling-normalization","description":"Generates fixed-dimensional dense embeddings from variable-length text using a modular nn.Sequential pipeline (Transformer → Pooling → Dense → Normalize). The SentenceTransformer class orchestrates transformer token outputs through configurable pooling strategies (mean, max, CLS token) and optional dense projection layers, producing normalized vectors optimized for semantic similarity search. Supports asymmetric query/document encoding via Router modules for specialized model variants.","intents":["I need to convert text into fixed-size vectors for semantic search","I want to encode queries and documents differently for better retrieval performance","I need to compute similarity scores between text pairs using embeddings"],"best_for":["RAG system builders implementing semantic search backends","Teams building vector databases with text-to-embedding pipelines","Developers optimizing retrieval-augmented generation with asymmetric encoders"],"limitations":["Pooling strategies (mean/max/CLS) are fixed at model load time — cannot dynamically switch pooling per inference","Dense projection layers add computational overhead (~10-15% latency) compared to raw transformer outputs","Normalization to unit vectors may reduce discriminative power for very similar documents in high-dimensional space","Router module for asymmetric encoding requires separate model training — cannot retrofit existing symmetric models"],"requires":["Python 3.8+","PyTorch 1.11+","Transformers library 4.34+","Pre-trained model from Hugging Face Hub or local checkpoint"],"input_types":["text (strings or list of strings)","sentences (variable length, up to model's max_seq_length)"],"output_types":["numpy arrays (embeddings, shape: [batch_size, embedding_dim])","torch tensors (optional, if convert_to_tensor=True)","similarity matrices (float32, shape: [n, m] for pairwise comparisons)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_1","uri":"capability://data.processing.analysis.cross.encoder.pairwise.reranking.with.joint.encoding","name":"cross-encoder-pairwise-reranking-with-joint-encoding","description":"Scores or ranks text pairs by jointly encoding both sentences through a single transformer, outputting similarity scores or classification labels. The CrossEncoder class wraps AutoModelForSequenceClassification, processing concatenated sentence pairs end-to-end rather than independently encoding them, achieving higher accuracy than bi-encoder similarity comparisons at the cost of O(n) inference time per document. Includes specialized rank() method for sorting document collections by relevance to a query.","intents":["I need to rerank search results from a dense retriever with higher accuracy","I want to score sentence pairs for semantic similarity with better precision than embedding-based methods","I need to sort documents by relevance to a query after initial retrieval"],"best_for":["RAG pipelines implementing two-stage retrieval (dense retriever + cross-encoder reranker)","Information retrieval teams optimizing ranking quality for search applications","Developers building question-answering systems requiring high-precision relevance scoring"],"limitations":["O(n) inference complexity — must score every candidate document individually, making it unsuitable for ranking millions of documents without batching/caching","Joint encoding requires concatenating both sentences, limiting to fixed max_seq_length (typically 512 tokens) — cannot score very long document pairs","Slower than bi-encoder similarity by 10-50x for large-scale ranking due to sequential transformer processing per pair","No built-in caching for repeated query scoring — requires manual implementation for multi-query scenarios"],"requires":["Python 3.8+","PyTorch 1.11+","Transformers library 4.34+","Pre-trained CrossEncoder model (e.g., cross-encoder/ms-marco-MiniLM-L-6-v2)"],"input_types":["sentence pairs (list of tuples: [(query, document), ...])","query + documents list (for rank() method)"],"output_types":["numpy arrays (scores, shape: [batch_size] or [batch_size, num_labels])","ranked documents (sorted by relevance score, descending)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_10","uri":"capability://automation.workflow.multi.dataset.training.with.batch.sampling.strategies","name":"multi-dataset-training-with-batch-sampling-strategies","description":"Trains models on multiple datasets simultaneously using configurable batch sampling strategies (round-robin, weighted sampling, sequential) to balance dataset contributions and prevent one dataset from dominating training. The Trainer system manages dataset loading, sampling, and loss aggregation across datasets, enabling multi-task learning and domain adaptation. Batch sampling strategies control how examples are selected from each dataset per training step, enabling flexible curriculum learning and data balancing.","intents":["I want to train on multiple datasets simultaneously for better generalization","I need to balance contributions from datasets of different sizes","I want to implement curriculum learning or multi-task learning strategies"],"best_for":["Teams training models on heterogeneous datasets from multiple domains","Researchers implementing multi-task learning for embedding models","Developers building domain-adaptive models that generalize across datasets"],"limitations":["Batch sampling strategy selection is non-obvious — wrong strategy can degrade performance or cause dataset imbalance","Multi-dataset training requires careful hyperparameter tuning (learning rate, batch size per dataset) — more complex than single-dataset training","Memory overhead increases with number of datasets — each dataset requires separate dataloader and sampling logic","No built-in support for dynamic dataset weighting — weights are fixed at training start, cannot adapt during training"],"requires":["Python 3.8+","Multiple labeled training datasets","Sufficient GPU memory for multiple dataloaders"],"input_types":["list of datasets (each with training examples)","batch sampling strategy (round-robin, weighted, sequential)","optional: dataset weights for weighted sampling"],"output_types":["trained model checkpoint","training metrics per dataset","combined loss across all datasets"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_11","uri":"capability://automation.workflow.automatic.model.card.generation.and.hub.integration","name":"automatic-model-card-generation-and-hub-integration","description":"Automatically generates model cards with training details, evaluation metrics, and usage instructions, and uploads trained models to Hugging Face Hub with version control and documentation. The model card system captures model architecture, training configuration, loss functions, and evaluation results, enabling reproducibility and community discovery. Hub integration enables seamless sharing, versioning, and collaborative model development with automatic README generation.","intents":["I want to share my fine-tuned model with the community with proper documentation","I need to track model versions and training configurations for reproducibility","I want to enable others to discover and use my models via Hugging Face Hub"],"best_for":["Researchers sharing models and enabling reproducibility","Teams managing multiple model versions and variants","Developers building model registries and model governance systems"],"limitations":["Model card generation is automatic but may require manual editing for completeness — not all important details are captured","Hub upload requires Hugging Face account and authentication — adds setup overhead","Large models (>2GB) may have upload bandwidth limitations — requires patience for large model uploads","No built-in support for private models on Hub — all uploaded models are public by default (unless account upgraded)"],"requires":["Python 3.8+","Hugging Face account","Hugging Face CLI authentication (huggingface-cli login)","Trained model checkpoint"],"input_types":["trained model","training configuration","evaluation metrics","optional: custom model card content"],"output_types":["model card (Markdown file with documentation)","uploaded model on Hugging Face Hub","model URL for sharing"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_12","uri":"capability://text.generation.language.prompt.engineering.and.instruction.tuning.support","name":"prompt-engineering-and-instruction-tuning-support","description":"Supports prompt engineering and instruction-tuning for embedding models by allowing custom prompts to be prepended to queries and documents during encoding. The library enables task-specific prompt templates (e.g., 'Represent this document for retrieval:') that guide the model to produce task-optimized embeddings. Instruction tuning improves performance on specific tasks by conditioning embeddings on task descriptions, enabling zero-shot transfer to new tasks.","intents":["I want to improve embedding quality by adding task-specific prompts","I need to adapt embeddings to specific retrieval tasks without retraining","I want to enable zero-shot transfer to new tasks via instruction tuning"],"best_for":["Teams optimizing embeddings for specific retrieval tasks","Researchers exploring instruction-tuning for embedding models","Developers implementing zero-shot transfer to new domains"],"limitations":["Prompt engineering is task-specific and requires manual tuning — no automatic prompt optimization","Prompts add computational overhead (longer sequences, slower encoding) — trade-off between quality and latency","Instruction tuning requires models trained with prompts — not compatible with models trained without prompts","Prompt effectiveness varies by model and task — no guarantee of improvement without validation"],"requires":["Python 3.8+","Model trained with prompt/instruction support","Task-specific prompt templates"],"input_types":["texts (strings)","optional: custom prompts (strings to prepend to texts)"],"output_types":["embeddings (with prompt-conditioned representations)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_2","uri":"capability://data.processing.analysis.sparse.embedding.generation.with.learned.token.weights","name":"sparse-embedding-generation-with-learned-token-weights","description":"Generates sparse embeddings (high-dimensional, mostly-zero vectors) by learning per-token importance weights through a SparseEncoder architecture, enabling efficient lexical-semantic hybrid search. Unlike dense embeddings, sparse vectors preserve interpretability (which tokens matter) and integrate seamlessly with traditional BM25 retrieval systems. The architecture learns to weight tokens based on semantic relevance rather than raw term frequency, improving recall on out-of-vocabulary terms.","intents":["I want to combine dense semantic search with sparse lexical search for hybrid retrieval","I need interpretable embeddings that show which tokens contribute to relevance scores","I want to improve recall on domain-specific or rare terminology without dense-only limitations"],"best_for":["Teams implementing hybrid search systems (dense + sparse) for production RAG","Information retrieval researchers optimizing recall-precision trade-offs","Developers building domain-specific search where interpretability matters (legal, medical, scientific)"],"limitations":["Sparse vectors require specialized indexing (e.g., Elasticsearch, Solr) — not compatible with standard vector databases optimized for dense embeddings","Token weight learning requires labeled relevance data — cannot train without query-document pairs with relevance judgments","Vocabulary is fixed at training time — out-of-vocabulary tokens receive zero weight, limiting generalization to unseen terminology","Sparse embeddings are typically 10-100x larger in memory than dense embeddings (though still compressible via sparsity)"],"requires":["Python 3.8+","PyTorch 1.11+","Transformers library 4.34+","Labeled training data with query-document relevance pairs","Sparse-compatible search backend (Elasticsearch, Solr, or custom inverted index)"],"input_types":["text (strings or list of strings)","labeled query-document pairs (for training)"],"output_types":["sparse vectors (scipy.sparse matrices or dense arrays with learned token weights)","token importance scores (which tokens contributed to embedding)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_3","uri":"capability://automation.workflow.model.fine.tuning.with.40.plus.loss.functions","name":"model-fine-tuning-with-40-plus-loss-functions","description":"Fine-tunes pre-trained sentence transformers using a Trainer system supporting 40+ specialized loss functions (ContrastiveLoss, TripletLoss, MultipleNegativesRankingLoss, CosineSimilarityLoss, etc.) tailored to different training objectives. The training pipeline handles dataset preparation, batch sampling strategies, and multi-dataset training, with automatic model card generation and Hub integration for sharing trained models. Loss functions are modular and composable, enabling custom training objectives for domain-specific tasks.","intents":["I need to fine-tune embeddings on domain-specific data to improve retrieval quality","I want to train asymmetric query/document encoders for specialized retrieval tasks","I need to combine multiple loss functions or datasets for multi-task learning"],"best_for":["ML teams optimizing embedding models for proprietary datasets or domains","Researchers experimenting with novel loss functions and training strategies","Developers building production RAG systems requiring domain-adapted embeddings"],"limitations":["Training requires labeled data (pairs, triplets, or relevance judgments) — unsupervised fine-tuning not supported","Loss function selection is critical and non-obvious — wrong choice can degrade performance; requires experimentation","Multi-dataset training requires careful sampling strategy to avoid one dataset dominating — batch sampling overhead adds complexity","Fine-tuned models may overfit to training domain — generalization to out-of-domain queries requires validation and regularization"],"requires":["Python 3.8+","PyTorch 1.11+","Transformers library 4.34+","Labeled training dataset (minimum 1000+ examples recommended)","GPU with 8GB+ VRAM (for batch sizes ≥32)"],"input_types":["training pairs (InputExample objects with texts and labels)","triplet data (anchor, positive, negative)","relevance-labeled query-document pairs"],"output_types":["fine-tuned model checkpoint (saved locally or to Hub)","training metrics (loss, validation scores)","model card (auto-generated documentation)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_4","uri":"capability://data.processing.analysis.model.evaluation.with.task.specific.evaluators","name":"model-evaluation-with-task-specific-evaluators","description":"Evaluates embedding and reranking models using task-specific evaluators (InformationRetrievalEvaluator, TripletEvaluator, BinaryAccuracyEvaluator, etc.) that compute standard IR metrics (NDCG, MAP, MRR, Recall@k) and classification metrics. Evaluators integrate with the Trainer system for automatic validation during training, supporting both dense and sparse model evaluation. Metrics are computed on held-out test sets and logged for model selection and hyperparameter tuning.","intents":["I need to measure embedding quality on retrieval tasks (NDCG, MAP, MRR)","I want to validate model performance during training and select the best checkpoint","I need to benchmark models against standard IR datasets (MS MARCO, Natural Questions, etc.)"],"best_for":["ML teams evaluating embedding models on standard benchmarks","Researchers comparing model architectures and loss functions","Developers validating fine-tuned models before production deployment"],"limitations":["Evaluators require labeled test data with relevance judgments — cannot evaluate without ground truth","Metrics are task-specific — NDCG@10 may not correlate with downstream application performance","Evaluation is computationally expensive for large test sets — O(n*m) for n queries and m documents","No built-in support for cross-lingual or multilingual evaluation metrics"],"requires":["Python 3.8+","PyTorch 1.11+","Labeled test dataset with queries and relevant documents","Pre-trained or fine-tuned model checkpoint"],"input_types":["queries (list of strings)","documents (list of strings)","relevance labels (binary or graded relevance scores)"],"output_types":["evaluation metrics (NDCG, MAP, MRR, Recall@k, accuracy, F1)","per-query scores (for analysis and debugging)","aggregated performance summary"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_5","uri":"capability://memory.knowledge.model.discovery.and.loading.from.hugging.face.hub","name":"model-discovery-and-loading-from-hugging-face-hub","description":"Loads pre-trained sentence transformer models directly from Hugging Face Hub (15,000+ models) with a single line of code, automatically downloading weights, tokenizers, and configuration. The library caches models locally and handles version management, supporting both dense (SentenceTransformer), cross-encoder (CrossEncoder), and sparse (SparseEncoder) architectures. Integration with Hub enables seamless model sharing, versioning, and community contributions.","intents":["I want to quickly load a pre-trained embedding model without manual configuration","I need to discover and compare different embedding models for my task","I want to share my fine-tuned model with the community via Hugging Face Hub"],"best_for":["Developers prototyping RAG systems quickly with pre-trained models","Teams evaluating multiple embedding models for benchmarking","Researchers sharing models and enabling reproducibility"],"limitations":["Model discovery requires browsing Hub manually or using filtering — no built-in recommendation system for task-specific model selection","Large models (>1GB) require significant download bandwidth and storage — no built-in model compression or quantization","Model compatibility depends on Transformers library version — older models may not load with newer library versions","No built-in version pinning — loading 'model-name' always fetches latest version, which may break backward compatibility"],"requires":["Python 3.8+","sentence-transformers library installed","Internet connection for initial model download","Hugging Face Hub account (optional, for private models)"],"input_types":["model identifier (string, e.g., 'all-MiniLM-L6-v2')","optional: model revision, cache directory, device"],"output_types":["loaded model object (SentenceTransformer, CrossEncoder, or SparseEncoder)","model configuration (tokenizer, architecture, pooling strategy)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_6","uri":"capability://automation.workflow.batch.inference.with.gpu.acceleration","name":"batch-inference-with-gpu-acceleration","description":"Processes multiple texts in batches through GPU-accelerated transformers, automatically managing batch size, device placement, and memory optimization. The encode() method supports configurable batch sizes, optional tensor conversion, and multi-GPU inference via DataParallel. Batching reduces per-sample latency by 5-10x compared to single-sample inference, with automatic memory management to prevent OOM errors on large batches.","intents":["I need to encode millions of documents efficiently for vector database indexing","I want to minimize latency for batch embedding requests in production","I need to process large datasets without running out of GPU memory"],"best_for":["Teams building vector database indexing pipelines for large corpora","Production RAG systems requiring high-throughput embedding generation","Researchers processing large-scale datasets for evaluation"],"limitations":["Batch size is a critical hyperparameter — too small reduces GPU utilization, too large causes OOM; requires manual tuning per GPU","Multi-GPU inference requires DataParallel setup — not automatically distributed across multiple GPUs without explicit configuration","Batching adds latency overhead for small batches (<32 samples) — single-sample inference may be faster","No built-in support for distributed inference across multiple machines — requires external orchestration (Ray, Spark, etc.)"],"requires":["Python 3.8+","PyTorch 1.11+","GPU with CUDA support (optional, but recommended for performance)","Sufficient GPU memory for batch size × max_seq_length × model_size"],"input_types":["list of texts (strings)","batch size (integer, default 32)","device specification (cpu, cuda, cuda:0, etc.)"],"output_types":["embeddings (numpy arrays or torch tensors)","shape: [num_texts, embedding_dim]"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_7","uri":"capability://data.processing.analysis.semantic.similarity.computation.with.multiple.metrics","name":"semantic-similarity-computation-with-multiple-metrics","description":"Computes pairwise semantic similarity between embeddings using multiple distance metrics (cosine similarity, Euclidean distance, dot product, Manhattan distance). The similarity() method efficiently computes similarity matrices for large embedding sets using vectorized operations, with optional normalization and threshold filtering. Supports both dense and sparse embeddings, enabling flexible similarity-based ranking and clustering.","intents":["I need to compute similarity scores between query and document embeddings for ranking","I want to find the most similar documents to a query from a large corpus","I need to cluster embeddings based on semantic similarity"],"best_for":["RAG systems computing query-document similarity for retrieval","Clustering and deduplication tasks based on semantic similarity","Recommendation systems ranking candidates by relevance"],"limitations":["Cosine similarity assumes normalized embeddings — non-normalized vectors produce incorrect scores","Computing full similarity matrix for n documents is O(n²) memory and time — infeasible for millions of documents without approximation","Similarity thresholds are task-dependent and require manual tuning — no automatic threshold selection","Sparse embeddings require different similarity computation than dense embeddings — not unified API"],"requires":["Pre-computed embeddings (numpy arrays or torch tensors)","Embeddings must be in same vector space (from same model)"],"input_types":["embeddings1 (numpy array or torch tensor, shape: [n, dim])","embeddings2 (numpy array or torch tensor, shape: [m, dim])"],"output_types":["similarity matrix (numpy array, shape: [n, m])","values in range [-1, 1] for cosine similarity or [0, inf] for Euclidean distance"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_8","uri":"capability://automation.workflow.model.export.to.onnx.and.openvino.backends","name":"model-export-to-onnx-and-openvino-backends","description":"Exports trained sentence transformer models to ONNX and OpenVINO formats for deployment on CPU-only or edge devices without PyTorch dependency. The export process converts transformer weights and pooling layers to ONNX intermediate representation, enabling inference optimization via quantization and pruning. OpenVINO export enables Intel hardware acceleration and reduced model size for embedded deployment.","intents":["I need to deploy embedding models on edge devices or CPU-only servers","I want to reduce model size and latency for production inference without GPU","I need to optimize models for specific hardware (Intel CPUs, mobile devices)"],"best_for":["Teams deploying embedding models on edge devices or IoT hardware","Production systems requiring CPU-only inference without GPU dependency","Developers optimizing model size and latency for mobile or embedded applications"],"limitations":["ONNX export requires manual configuration of input/output shapes — dynamic shapes not fully supported","Exported models lose some PyTorch-specific features (custom layers, dynamic control flow) — may require model simplification","OpenVINO optimization is Intel-specific — not portable to other hardware accelerators","Quantization during export may reduce model accuracy by 1-5% — requires validation on downstream tasks","No built-in support for exporting sparse encoders to ONNX — only dense and cross-encoder architectures"],"requires":["Python 3.8+","PyTorch 1.11+","ONNX library (for ONNX export)","OpenVINO toolkit (optional, for OpenVINO export)","Pre-trained or fine-tuned model checkpoint"],"input_types":["trained model (SentenceTransformer or CrossEncoder)","export format specification (onnx or openvino)","optional: quantization settings"],"output_types":["ONNX model file (.onnx)","OpenVINO IR files (.xml, .bin)","tokenizer configuration (for inference)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-sentence-transformers__cap_9","uri":"capability://data.processing.analysis.asymmetric.query.document.encoding.via.router.modules","name":"asymmetric-query-document-encoding-via-router-modules","description":"Implements asymmetric encoding where queries and documents are processed through different model paths using Router modules, enabling specialized optimization for query vs. document encoding. The Router selects between different transformer configurations or pooling strategies based on input type, allowing queries to use lightweight encoders while documents use heavier models. This architecture improves retrieval quality by optimizing for the asymmetric nature of search tasks (one query vs. many documents).","intents":["I want to optimize query and document encoding separately for better retrieval quality","I need to use lightweight models for queries while using heavier models for documents","I want to improve retrieval performance by leveraging asymmetric encoding"],"best_for":["RAG systems optimizing for asymmetric retrieval (one query vs. many documents)","Teams fine-tuning models specifically for query-document asymmetry","Production systems balancing query latency with document encoding quality"],"limitations":["Router modules require separate training — cannot retrofit existing symmetric models without retraining","Router configuration is model-specific — no standard way to convert symmetric to asymmetric models","Asymmetric encoding adds complexity to deployment — requires tracking which encoder path to use per input","Benefits depend on task — not all retrieval tasks benefit from asymmetric encoding (e.g., symmetric similarity tasks)"],"requires":["Python 3.8+","Pre-trained model with Router module support","Training data with query-document pairs (for fine-tuning asymmetric models)"],"input_types":["queries (text strings, encoded via query-specific path)","documents (text strings, encoded via document-specific path)"],"output_types":["query embeddings (optimized for query representation)","document embeddings (optimized for document representation)","both in same vector space for similarity computation"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.11+","Transformers library 4.34+","Pre-trained model from Hugging Face Hub or local checkpoint","Pre-trained CrossEncoder model (e.g., cross-encoder/ms-marco-MiniLM-L-6-v2)","Multiple labeled training datasets","Sufficient GPU memory for multiple dataloaders","Hugging Face account","Hugging Face CLI authentication (huggingface-cli login)","Trained model checkpoint"],"failure_modes":["Pooling strategies (mean/max/CLS) are fixed at model load time — cannot dynamically switch pooling per inference","Dense projection layers add computational overhead (~10-15% latency) compared to raw transformer outputs","Normalization to unit vectors may reduce discriminative power for very similar documents in high-dimensional space","Router module for asymmetric encoding requires separate model training — cannot retrofit existing symmetric models","O(n) inference complexity — must score every candidate document individually, making it unsuitable for ranking millions of documents without batching/caching","Joint encoding requires concatenating both sentences, limiting to fixed max_seq_length (typically 512 tokens) — cannot score very long document pairs","Slower than bi-encoder similarity by 10-50x for large-scale ranking due to sequential transformer processing per pair","No built-in caching for repeated query scoring — requires manual implementation for multi-query scenarios","Batch sampling strategy selection is non-obvious — wrong strategy can degrade performance or cause dataset imbalance","Multi-dataset training requires careful hyperparameter tuning (learning rate, batch size per dataset) — more complex than single-dataset training","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:17.402Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-sentence-transformers","compare_url":"https://unfragile.ai/compare?artifact=pypi-sentence-transformers"}},"signature":"KIuFbUdVQrbNpuGMeoj+//jQTIeWxTLlZLv6oUpxjGQkL8H76Ll50UNHKDnzH5g8uUohnypUPCvESiEl6IBNBg==","signedAt":"2026-06-20T11:26:20.782Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-sentence-transformers","artifact":"https://unfragile.ai/pypi-sentence-transformers","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-sentence-transformers","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"}}