{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-deepset--minilm-uncased-squad2","slug":"deepset--minilm-uncased-squad2","name":"minilm-uncased-squad2","type":"model","url":"https://huggingface.co/deepset/minilm-uncased-squad2","page_url":"https://unfragile.ai/deepset--minilm-uncased-squad2","categories":["model-training"],"tags":["transformers","pytorch","jax","safetensors","bert","question-answering","en","dataset:squad_v2","license:cc-by-4.0","model-index","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-deepset--minilm-uncased-squad2__cap_0","uri":"capability://search.retrieval.extractive.question.answering.on.document.passages","name":"extractive question-answering on document passages","description":"Performs span-based extractive QA by encoding questions and passages through a distilled BERT architecture (MiniLM), computing cross-attention between question and passage tokens, and predicting start/end token positions that mark the answer span. Uses a two-head classification approach (start logits, end logits) trained on SQuAD v2 data, enabling the model to identify when no answer exists in a passage.","intents":["extract direct answers from documents without generating new text","build search-augmented systems that cite source passages","implement reading comprehension pipelines for FAQ automation","create fact-checking systems that validate claims against reference texts"],"best_for":["teams building document retrieval + QA pipelines (RAG systems)","developers needing lightweight inference on CPU/edge devices","organizations requiring interpretable answers with passage citations"],"limitations":["Cannot generate answers not present in the input passage — only extracts existing spans","Performance degrades on passages longer than ~512 tokens due to BERT's context window","Struggles with multi-hop reasoning requiring information synthesis across multiple passages","No built-in handling of unanswerable questions — requires post-processing confidence thresholding"],"requires":["PyTorch 1.9+ or JAX/Flax runtime","Transformers library 4.0+","Input text must be pre-tokenized and formatted as (question, passage) pairs","GPU optional but recommended for batch inference >32 examples"],"input_types":["text (question string)","text (passage/document string)"],"output_types":["structured data (answer span with start/end token indices)","structured data (confidence scores for start/end positions)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--minilm-uncased-squad2__cap_1","uri":"capability://search.retrieval.passage.relevance.ranking.via.contextual.embeddings","name":"passage relevance ranking via contextual embeddings","description":"Encodes passages and questions into dense vector representations using the distilled transformer backbone, enabling semantic similarity computation for ranking candidate passages by relevance. The model learns to project questions and passages into a shared embedding space where relevant pairs have high cosine similarity, supporting efficient retrieval via approximate nearest neighbor search.","intents":["rank candidate passages by relevance to a question before QA extraction","build dense retrieval indexes for semantic search over document collections","implement passage filtering to reduce computational cost of QA inference","create similarity-based document clustering for knowledge organization"],"best_for":["teams implementing two-stage retrieval (dense ranking + extractive QA)","developers building semantic search without dedicated embedding models","organizations optimizing inference latency by pre-filtering irrelevant passages"],"limitations":["Embeddings are task-specific (trained on QA pairs) — may not generalize to non-QA similarity tasks","No explicit contrastive learning objective — embeddings less discriminative than models trained with triplet/contrastive losses","Requires encoding all candidate passages upfront — not suitable for real-time ad-hoc document collections","Fixed 384-dimensional embedding space — cannot be adapted without retraining"],"requires":["PyTorch 1.9+ or JAX runtime","Transformers library 4.0+","Vector similarity library (FAISS, Annoy, or Hnswlib) for efficient retrieval at scale","Batch processing recommended for encoding >1000 passages"],"input_types":["text (question string)","text (passage/document string)"],"output_types":["structured data (dense vector embeddings, 384-dim float32)","structured data (cosine similarity scores between 0-1)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--minilm-uncased-squad2__cap_2","uri":"capability://safety.moderation.unanswerable.question.detection.via.confidence.thresholding","name":"unanswerable question detection via confidence thresholding","description":"Detects questions that cannot be answered by a given passage by analyzing the probability distribution over start/end token positions. When the model's confidence in both start and end predictions falls below a learned threshold (typically derived from SQuAD v2 null answer examples), the system classifies the question as unanswerable, preventing spurious answer extraction.","intents":["prevent hallucinated answers when passages don't contain relevant information","implement graceful fallback behavior for out-of-distribution questions","measure QA system reliability by tracking unanswerable detection rates","filter low-confidence predictions in production pipelines"],"best_for":["production QA systems requiring high precision over recall","customer-facing applications where wrong answers are worse than no answer","teams building confidence-aware retrieval-augmented generation (RAG)"],"limitations":["Threshold selection is manual and dataset-dependent — no automatic calibration provided","SQuAD v2 unanswerable examples are synthetic (adversarial) — may not match real-world unanswerable questions","Confidence scores are not calibrated probabilities — raw logit differences don't map to true answer probability","Cannot distinguish between 'answer not in passage' and 'question is malformed' — both trigger unanswerable flag"],"requires":["PyTorch 1.9+ or JAX runtime","Transformers library 4.0+","Validation dataset to empirically determine confidence threshold","Post-processing logic to handle unanswerable predictions (e.g., fallback to retrieval)"],"input_types":["text (question string)","text (passage/document string)"],"output_types":["structured data (boolean: answerable/unanswerable)","structured data (confidence score for answerability)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--minilm-uncased-squad2__cap_3","uri":"capability://tool.use.integration.multi.format.model.serialization.and.deployment","name":"multi-format model serialization and deployment","description":"Supports loading and inference through multiple serialization formats (PyTorch, JAX/Flax, SafeTensors) and deployment targets (Hugging Face Inference API, Azure ML, local transformers pipeline), enabling flexible integration across different ML stacks and infrastructure. The model can be instantiated via transformers.AutoModel, converted to ONNX for edge deployment, or loaded directly from SafeTensors for faster initialization.","intents":["deploy the same model across PyTorch and JAX-based systems without retraining","integrate with cloud ML platforms (Azure, Hugging Face) without custom conversion","optimize inference latency by selecting the best serialization format per environment","version control models using SafeTensors' transparent, auditable format"],"best_for":["teams with heterogeneous ML infrastructure (PyTorch + JAX services)","organizations deploying to managed ML platforms (Azure ML, Hugging Face Endpoints)","developers optimizing cold-start latency in serverless environments"],"limitations":["SafeTensors format is newer — some legacy tools may not support it natively","JAX/Flax weights require separate conversion step from PyTorch — not automatic","Model size (~133MB) may exceed cold-start limits on some serverless platforms","No quantized variants provided — INT8/FP16 optimization requires manual conversion"],"requires":["PyTorch 1.9+ OR JAX 0.3+ (depending on target format)","Transformers library 4.0+","SafeTensors library 0.3+ for SafeTensors format","Cloud credentials (Azure, Hugging Face) for managed deployment"],"input_types":["model weights (PyTorch .bin, JAX .msgpack, SafeTensors .safetensors)"],"output_types":["loaded model object (torch.nn.Module, flax.linen.Module, or transformers.PreTrainedModel)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--minilm-uncased-squad2__cap_4","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.padding.and.token.level.attention","name":"batch inference with dynamic padding and token-level attention","description":"Processes multiple (question, passage) pairs in parallel using dynamic padding (padding to max length in batch, not fixed 512), token-level attention masks, and efficient batching to minimize wasted computation. The model computes attention only over non-padded tokens, reducing FLOPs and memory usage compared to fixed-size batching, while maintaining numerical equivalence with single-example inference.","intents":["process hundreds of QA examples efficiently in a single batch","minimize GPU memory usage by avoiding fixed-size padding overhead","implement high-throughput QA pipelines for document processing","optimize inference cost in cloud environments charged per GPU-hour"],"best_for":["teams processing large document collections (>10K passages) offline","developers building batch QA pipelines for data labeling or evaluation","organizations optimizing inference cost in cloud environments"],"limitations":["Dynamic padding adds ~5-10% overhead for variable-length batching logic","Batch size is limited by GPU memory — typical max batch size 32-64 on 8GB GPU","Attention computation is still O(n²) in sequence length — very long passages (>512 tokens) remain expensive","No gradient accumulation or mixed-precision training support — inference-only optimization"],"requires":["PyTorch 1.9+ or JAX runtime","Transformers library 4.0+ (handles dynamic padding automatically)","GPU with ≥4GB VRAM for batch size 32, ≥8GB for batch size 64","Batch processing framework (DataLoader, tf.data, or custom loop)"],"input_types":["structured data (list of (question, passage) tuples)","structured data (variable-length text sequences)"],"output_types":["structured data (batch of answer spans with start/end indices)","structured data (batch of confidence scores)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--minilm-uncased-squad2__cap_5","uri":"capability://memory.knowledge.cross.lingual.transfer.via.multilingual.pretraining.foundation","name":"cross-lingual transfer via multilingual pretraining foundation","description":"Although trained on English SQuAD v2, the model's MiniLM backbone was pretrained on multilingual data, enabling zero-shot transfer to non-English languages through fine-tuning or prompt-based adaptation. The shared token embeddings and attention patterns learned during multilingual pretraining provide a foundation for understanding questions and passages in other languages without retraining from scratch.","intents":["adapt the model to non-English QA tasks with minimal labeled data","build multilingual QA systems by fine-tuning on translated or native-language data","evaluate cross-lingual transfer performance to understand model generalization","reduce annotation cost by leveraging English training data for other languages"],"best_for":["teams building QA systems for non-English markets with limited labeled data","researchers studying cross-lingual transfer in distilled models","organizations with multilingual document collections needing QA coverage"],"limitations":["Zero-shot performance on non-English is significantly lower than English (typically 10-20% F1 drop)","Requires fine-tuning on target language data to achieve competitive performance — not true zero-shot","Language-specific tokenization differences may cause subword mismatch between English and target language","No explicit cross-lingual alignment training — transfer relies on implicit multilingual pretraining"],"requires":["PyTorch 1.9+ or JAX runtime","Transformers library 4.0+","Labeled QA dataset in target language (or machine-translated English SQuAD)","Fine-tuning infrastructure (GPU, training loop, evaluation metrics)"],"input_types":["text (question in target language)","text (passage in target language)"],"output_types":["structured data (answer span with start/end indices)","structured data (confidence scores)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or JAX/Flax runtime","Transformers library 4.0+","Input text must be pre-tokenized and formatted as (question, passage) pairs","GPU optional but recommended for batch inference >32 examples","PyTorch 1.9+ or JAX runtime","Vector similarity library (FAISS, Annoy, or Hnswlib) for efficient retrieval at scale","Batch processing recommended for encoding >1000 passages","Validation dataset to empirically determine confidence threshold","Post-processing logic to handle unanswerable predictions (e.g., fallback to retrieval)","PyTorch 1.9+ OR JAX 0.3+ (depending on target format)"],"failure_modes":["Cannot generate answers not present in the input passage — only extracts existing spans","Performance degrades on passages longer than ~512 tokens due to BERT's context window","Struggles with multi-hop reasoning requiring information synthesis across multiple passages","No built-in handling of unanswerable questions — requires post-processing confidence thresholding","Embeddings are task-specific (trained on QA pairs) — may not generalize to non-QA similarity tasks","No explicit contrastive learning objective — embeddings less discriminative than models trained with triplet/contrastive losses","Requires encoding all candidate passages upfront — not suitable for real-time ad-hoc document collections","Fixed 384-dimensional embedding space — cannot be adapted without retraining","Threshold selection is manual and dataset-dependent — no automatic calibration provided","SQuAD v2 unanswerable examples are synthetic (adversarial) — may not match real-world unanswerable questions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.47992505471031655,"quality":0.22,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:55.335Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":49594,"model_likes":47}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=deepset--minilm-uncased-squad2","compare_url":"https://unfragile.ai/compare?artifact=deepset--minilm-uncased-squad2"}},"signature":"id+uD0vgXX+G0hzWywHxSLSeugOI76qI616s8PozgWQ9lwZ/Duc6AS4TdLeJLt/2fp5l31PFv/zJYQ20L/GACA==","signedAt":"2026-06-21T16:14:27.343Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepset--minilm-uncased-squad2","artifact":"https://unfragile.ai/deepset--minilm-uncased-squad2","verify":"https://unfragile.ai/api/v1/verify?slug=deepset--minilm-uncased-squad2","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"}}