{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-csarron--mobilebert-uncased-squad-v2","slug":"csarron--mobilebert-uncased-squad-v2","name":"mobilebert-uncased-squad-v2","type":"model","url":"https://huggingface.co/csarron/mobilebert-uncased-squad-v2","page_url":"https://unfragile.ai/csarron--mobilebert-uncased-squad-v2","categories":["model-training"],"tags":["transformers","pytorch","onnx","safetensors","mobilebert","question-answering","en","dataset:squad_v2","arxiv:2004.02984","license:mit","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-csarron--mobilebert-uncased-squad-v2__cap_0","uri":"capability://search.retrieval.extractive.question.answering.on.passages.with.span.prediction","name":"extractive question-answering on passages with span prediction","description":"Performs extractive QA by encoding question-passage pairs through a 24-layer MobileBERT transformer architecture, then predicting start and end token positions via dense classification heads. Uses SQuAD v2 fine-tuning which includes unanswerable questions, enabling the model to abstain when no valid answer exists in the passage. The model outputs logit scores for each token position, with post-processing to extract the highest-confidence span.","intents":["extract answers to questions from a given passage without generating new text","determine whether a question is answerable given a specific document context","build lightweight QA systems that run on mobile or edge devices with <25MB model size","integrate QA capabilities into document search or knowledge base retrieval pipelines"],"best_for":["mobile app developers building on-device QA features","teams deploying inference on resource-constrained environments (phones, IoT, edge servers)","document retrieval systems needing passage-level answer extraction","researchers benchmarking efficient transformer architectures against full-scale BERT"],"limitations":["extractive-only — cannot generate answers not present in the passage; fails on questions requiring reasoning across multiple sentences or paraphrasing","context window limited to ~512 tokens; passages longer than this must be chunked or truncated, losing information","performance degrades on out-of-domain text; trained exclusively on SQuAD v2 Wikipedia passages, may struggle with technical docs, medical text, or domain-specific jargon","no multi-hop reasoning — cannot answer questions requiring information synthesis across multiple passages","English-only; uncased tokenization means case-sensitive distinctions (e.g., 'US' vs 'us') are lost"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ (model available in both formats)","transformers library 4.0+","minimum 256MB RAM for inference (CPU) or 512MB VRAM (GPU)","input text must be pre-tokenized or use HuggingFace tokenizer; max sequence length 512 tokens"],"input_types":["text (question string)","text (passage/context string)","structured data (JSON with 'question' and 'context' fields)"],"output_types":["structured data (JSON with 'answer' text, 'start' and 'end' token indices, 'score' confidence float)","text (extracted answer span)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csarron--mobilebert-uncased-squad-v2__cap_1","uri":"capability://safety.moderation.unanswerable.question.detection.with.confidence.scoring","name":"unanswerable question detection with confidence scoring","description":"Leverages SQuAD v2 training which includes ~33% unanswerable questions to learn when to abstain from answering. The model predicts a special [CLS] token logit score alongside span predictions; when this score exceeds the span confidence, the model returns 'unanswerable' rather than forcing an incorrect extraction. This is implemented as a three-way classification: start position, end position, and 'no answer' token probability.","intents":["prevent hallucinated answers by detecting when a passage doesn't contain the answer to a question","build QA systems that gracefully fail rather than returning false information","measure confidence in extracted answers to filter low-confidence results in production pipelines","train models that learn when to say 'I don't know' rather than guessing"],"best_for":["production QA systems where false answers are costly (legal, medical, financial domains)","retrieval-augmented generation pipelines needing passage relevance filtering","teams building user-facing QA interfaces where confidence scores drive UI behavior (show answer vs 'not found')"],"limitations":["unanswerable detection is binary per passage — doesn't distinguish between 'answer not in this passage' and 'answer doesn't exist anywhere'","confidence scores are not calibrated probabilities; raw logit differences must be thresholded empirically per use case","performance on unanswerable questions varies by domain; SQuAD v2 unanswerable questions are adversarially written but may not match real-world 'no answer' patterns","no explanation of why a question is unanswerable; only returns a binary decision"],"requires":["transformers library 4.0+ with SQuAD v2 fine-tuned checkpoint","post-processing logic to compare [CLS] token score against span scores and apply threshold","empirical threshold tuning on validation set for your specific domain"],"input_types":["text (question string)","text (passage/context string)"],"output_types":["structured data (JSON with 'answer' or 'unanswerable' flag, 'confidence' float 0-1)"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csarron--mobilebert-uncased-squad-v2__cap_2","uri":"capability://automation.workflow.efficient.on.device.inference.with.onnx.and.quantization.support","name":"efficient on-device inference with onnx and quantization support","description":"Model is distributed in multiple optimized formats: PyTorch (.pt), ONNX (.onnx for cross-platform inference), and SafeTensors (.safetensors for secure deserialization). ONNX format enables hardware-accelerated inference on mobile (iOS/Android via ONNX Runtime), browsers (WebAssembly), and edge devices. The 25MB base model can be further quantized (INT8, FP16) reducing size to 6-12MB with <5% accuracy loss, enabling deployment on devices with <100MB storage.","intents":["deploy QA models directly on mobile phones without cloud API calls or network latency","run inference in browsers or edge servers using ONNX Runtime with hardware acceleration","reduce model size for on-device deployment through quantization while maintaining accuracy","build privacy-preserving QA systems where questions and passages never leave the device"],"best_for":["mobile app developers (iOS/Android) using ONNX Runtime or TensorFlow Lite","edge computing teams deploying on Raspberry Pi, Jetson, or similar constrained hardware","privacy-focused applications where inference must happen locally","teams building offline-first QA features without cloud dependency"],"limitations":["ONNX conversion requires manual testing; not all PyTorch operations are ONNX-compatible, though core QA operations are well-supported","quantization (INT8) introduces ~1-3% accuracy degradation; requires calibration on representative data","ONNX Runtime mobile binaries add ~15-20MB to app size, partially offsetting model size savings","no built-in batching optimization for ONNX; batch inference requires manual loop implementation","hardware acceleration (GPU/NPU) availability varies by device; fallback to CPU is slower"],"requires":["ONNX Runtime 1.10+ (mobile: 1.12+)","for iOS: ONNX Runtime CocoaPod or manual framework integration","for Android: ONNX Runtime AAR from Maven Central","for quantization: ONNX quantization tools (onnxruntime.quantization) or TensorRT","device storage: minimum 50MB free (model + runtime + app code)"],"input_types":["text (question and passage strings, tokenized to token IDs)"],"output_types":["structured data (start/end logits, no-answer logit as float arrays)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csarron--mobilebert-uncased-squad-v2__cap_3","uri":"capability://automation.workflow.batch.inference.with.dynamic.padding.and.token.level.attention","name":"batch inference with dynamic padding and token-level attention","description":"Supports batched inference through HuggingFace transformers pipeline API, which handles tokenization, padding, and attention mask generation automatically. Uses dynamic padding (pads to max length in batch, not fixed 512) to reduce computation. Attention mechanism is standard multi-head self-attention (12 heads in MobileBERT) with token-level masking to ignore padding tokens, enabling efficient processing of variable-length questions and passages.","intents":["process multiple question-passage pairs in parallel for throughput optimization","reduce per-sample latency by batching inference across multiple requests","handle variable-length inputs without wasting computation on padding","integrate into production serving systems (FastAPI, Flask) with batch request handling"],"best_for":["backend services processing multiple QA requests concurrently","batch processing pipelines (e.g., indexing documents with QA extraction)","teams optimizing inference cost by amortizing model load time across requests"],"limitations":["batch size is memory-constrained; typical GPU (8GB VRAM) supports batch size 32-64 before OOM","dynamic padding adds tokenization overhead; for fixed-length inputs, pre-padding may be faster","no built-in request queuing or load balancing; requires external orchestration (Ray, Kubernetes)","attention computation is O(n²) in sequence length; batching long passages (>400 tokens) becomes memory-intensive"],"requires":["transformers library 4.0+","sufficient GPU VRAM for batch size (estimate: 512MB per sample at batch size 32)","tokenizer (included in model repo) for preprocessing"],"input_types":["list of text tuples (question, passage)","structured data (JSON array with 'question' and 'context' fields)"],"output_types":["structured data (list of JSON objects with answer spans and scores)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csarron--mobilebert-uncased-squad-v2__cap_4","uri":"capability://code.generation.editing.knowledge.distillation.based.model.compression.for.transfer.learning","name":"knowledge distillation-based model compression for transfer learning","description":"MobileBERT was trained using knowledge distillation from BERT-large as the teacher model, transferring learned representations into a smaller student architecture. This enables fine-tuning on downstream tasks (like SQuAD v2) with minimal accuracy loss despite 4.3x parameter reduction. The distillation approach uses intermediate layer matching and attention transfer, not just final logit matching, preserving semantic understanding across layers.","intents":["fine-tune a pre-trained compressed model on custom QA datasets while maintaining accuracy","transfer knowledge from large models to small models for domain-specific QA tasks","build custom QA models for proprietary data without training from scratch","understand how knowledge distillation affects model behavior and accuracy trade-offs"],"best_for":["teams with domain-specific QA datasets wanting to fine-tune without training large models","researchers studying model compression and knowledge transfer","organizations building custom QA models for internal documents (legal, medical, technical)"],"limitations":["fine-tuning on small datasets (<1000 examples) may overfit; requires careful regularization and validation","distillation benefits are task-specific; performance on tasks very different from SQuAD v2 may degrade","no built-in tools for custom distillation; requires manual teacher model setup and training","knowledge distillation is a one-time process; the model cannot be further distilled without retraining"],"requires":["transformers library 4.0+","PyTorch 1.9+ for fine-tuning","custom QA dataset in SQuAD format (JSON with question, context, answer spans)","GPU with 8GB+ VRAM for fine-tuning (CPU training is impractical)"],"input_types":["structured data (SQuAD-format JSON: questions, passages, answer spans)"],"output_types":["model checkpoint (PyTorch .pt or ONNX .onnx format)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csarron--mobilebert-uncased-squad-v2__cap_5","uri":"capability://safety.moderation.multi.format.model.distribution.and.safe.deserialization","name":"multi-format model distribution and safe deserialization","description":"Model is distributed in three formats: PyTorch (.pt), ONNX (.onnx), and SafeTensors (.safetensors). SafeTensors is a newer format that avoids pickle deserialization vulnerabilities by using a simple binary format with explicit type information. This enables safe loading of untrusted model files without arbitrary code execution risk. All three formats are available from the HuggingFace Hub with automatic format detection.","intents":["load models safely without pickle deserialization vulnerabilities","choose the optimal format for your inference framework (PyTorch, ONNX, TensorFlow)","distribute models across teams without security concerns about malicious pickles","integrate with frameworks that require specific model formats (ONNX Runtime, TensorFlow Lite)"],"best_for":["security-conscious teams deploying models from untrusted sources","organizations with strict model governance requiring safe deserialization","cross-framework deployments needing format flexibility"],"limitations":["SafeTensors support requires transformers 4.26+; older versions fall back to PyTorch format","ONNX format requires separate conversion and testing; not all PyTorch operations are ONNX-compatible","format conversion adds storage overhead; all three formats must be stored separately (~75MB total for all formats)","no automatic format selection based on hardware; users must manually choose the appropriate format"],"requires":["transformers library 4.26+ for SafeTensors support","PyTorch 1.9+ for .pt format","ONNX Runtime 1.10+ for .onnx format"],"input_types":["model file (PyTorch .pt, ONNX .onnx, or SafeTensors .safetensors)"],"output_types":["loaded model object (PyTorch AutoModel, ONNX InferenceSession, etc.)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-csarron--mobilebert-uncased-squad-v2__cap_6","uri":"capability://automation.workflow.azure.deployment.and.cloud.inference.endpoints","name":"azure deployment and cloud inference endpoints","description":"Model is compatible with Azure ML inference endpoints, enabling serverless QA deployment with automatic scaling. Azure integration includes model registration, endpoint creation, and REST API exposure without manual infrastructure setup. The model can be deployed as a managed endpoint with auto-scaling based on request volume, with built-in monitoring and logging.","intents":["deploy QA models to Azure without managing infrastructure","expose QA as a REST API with automatic scaling and load balancing","integrate QA into Azure ML pipelines and workflows","monitor model performance and inference latency in production"],"best_for":["teams already using Azure ML or Azure cloud infrastructure","organizations needing managed inference without DevOps overhead","production QA services requiring auto-scaling and high availability"],"limitations":["Azure-specific; not portable to AWS, GCP, or on-premises without re-deployment","cold start latency for serverless endpoints can be 5-10 seconds on first request after idle period","pricing is per-inference + compute hours; high-volume QA may be more expensive than self-hosted","no built-in batch inference optimization; requires custom code for batch processing","model versioning and A/B testing require manual endpoint management"],"requires":["Azure subscription with ML workspace","Azure ML SDK (azureml-sdk) 1.30+","model registration in Azure ML model registry","compute cluster or managed endpoint for inference"],"input_types":["JSON (REST API request with 'question' and 'context' fields)"],"output_types":["JSON (REST API response with answer, score, and metadata)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or TensorFlow 2.4+ (model available in both formats)","transformers library 4.0+","minimum 256MB RAM for inference (CPU) or 512MB VRAM (GPU)","input text must be pre-tokenized or use HuggingFace tokenizer; max sequence length 512 tokens","transformers library 4.0+ with SQuAD v2 fine-tuned checkpoint","post-processing logic to compare [CLS] token score against span scores and apply threshold","empirical threshold tuning on validation set for your specific domain","ONNX Runtime 1.10+ (mobile: 1.12+)","for iOS: ONNX Runtime CocoaPod or manual framework integration","for Android: ONNX Runtime AAR from Maven Central"],"failure_modes":["extractive-only — cannot generate answers not present in the passage; fails on questions requiring reasoning across multiple sentences or paraphrasing","context window limited to ~512 tokens; passages longer than this must be chunked or truncated, losing information","performance degrades on out-of-domain text; trained exclusively on SQuAD v2 Wikipedia passages, may struggle with technical docs, medical text, or domain-specific jargon","no multi-hop reasoning — cannot answer questions requiring information synthesis across multiple passages","English-only; uncased tokenization means case-sensitive distinctions (e.g., 'US' vs 'us') are lost","unanswerable detection is binary per passage — doesn't distinguish between 'answer not in this passage' and 'answer doesn't exist anywhere'","confidence scores are not calibrated probabilities; raw logit differences must be thresholded empirically per use case","performance on unanswerable questions varies by domain; SQuAD v2 unanswerable questions are adversarially written but may not match real-world 'no answer' patterns","no explanation of why a question is unanswerable; only returns a binary decision","ONNX conversion requires manual testing; not all PyTorch operations are ONNX-compatible, though core QA operations are well-supported","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.41244220493119105,"quality":0.39,"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":32657,"model_likes":8}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=csarron--mobilebert-uncased-squad-v2","compare_url":"https://unfragile.ai/compare?artifact=csarron--mobilebert-uncased-squad-v2"}},"signature":"Fjpl0Lv2DsggFTi4CvxcP2bEjccDm7Rj9SKbhsmuS/nwIpCxz4AegivBSx8HHzt3ATGeTX0cRsKkWHD+rdfuBg==","signedAt":"2026-06-21T00:41:31.969Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/csarron--mobilebert-uncased-squad-v2","artifact":"https://unfragile.ai/csarron--mobilebert-uncased-squad-v2","verify":"https://unfragile.ai/api/v1/verify?slug=csarron--mobilebert-uncased-squad-v2","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"}}