{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-deepset--tinyroberta-squad2","slug":"deepset--tinyroberta-squad2","name":"tinyroberta-squad2","type":"model","url":"https://huggingface.co/deepset/tinyroberta-squad2","page_url":"https://unfragile.ai/deepset--tinyroberta-squad2","categories":["research-search"],"tags":["transformers","pytorch","safetensors","roberta","question-answering","en","dataset:squad_v2","arxiv:1909.10351","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--tinyroberta-squad2__cap_0","uri":"capability://search.retrieval.extractive.question.answering.with.span.selection","name":"extractive question-answering with span selection","description":"Identifies and extracts answer spans directly from input text using a RoBERTa-based transformer architecture fine-tuned on SQuAD 2.0. The model computes start and end logits over token positions to locate answers within context passages, returning character offsets and confidence scores. Uses token-level classification rather than generative decoding, enabling fast inference and high precision on factual retrieval tasks.","intents":["Extract factual answers from documents without generating text","Build search systems that return exact passages from knowledge bases","Implement reading comprehension features in applications","Create FAQ systems that find relevant answers in documentation"],"best_for":["Teams building document-based QA systems with strict latency requirements","Developers needing lightweight, CPU-compatible inference for edge deployment","Applications requiring high precision on factual questions over structured text"],"limitations":["Cannot answer questions requiring reasoning across multiple passages or synthesis","Struggles with out-of-domain contexts significantly different from SQuAD training distribution","Limited to English language only; no multilingual capability","Requires explicit context passage — cannot search across large document collections without external retrieval","Model size (84M parameters) may be insufficient for complex reasoning or ambiguous questions"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","Input text must be UTF-8 encoded","Context passage length typically limited to 512 tokens (standard BERT context window)"],"input_types":["text (question string)","text (context passage)"],"output_types":["structured data (start position, end position, answer text, confidence score)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--tinyroberta-squad2__cap_1","uri":"capability://safety.moderation.unanswerable.question.detection","name":"unanswerable question detection","description":"Distinguishes between answerable and unanswerable questions by computing a no-answer threshold during inference. When the model's confidence in any span falls below a learned threshold, it classifies the question as unanswerable rather than returning a low-confidence extraction. This capability was learned from SQuAD 2.0's adversarial examples where humans wrote questions that cannot be answered from the given context.","intents":["Prevent returning incorrect answers when questions fall outside document scope","Build robust QA systems that gracefully handle out-of-scope queries","Implement confidence-based filtering for downstream applications","Create user-facing systems that admit knowledge gaps rather than hallucinate"],"best_for":["Production QA systems requiring high precision and low false-positive rates","Customer-facing applications where incorrect answers damage trust","Systems integrating QA with fallback mechanisms (escalation, web search)"],"limitations":["Threshold tuning is dataset-dependent and may require calibration for new domains","Cannot distinguish between 'answer not in context' and 'question is malformed'","Performance degrades on adversarial or trick questions outside SQuAD 2.0 distribution","No explicit confidence calibration — raw logit differences may not reflect true probability"],"requires":["Transformers library 4.0+ with SQuAD 2.0 fine-tuned checkpoint","Understanding of no-answer threshold tuning for target domain"],"input_types":["text (question)","text (context passage)"],"output_types":["structured data (is_answerable: boolean, confidence_score: float)"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--tinyroberta-squad2__cap_2","uri":"capability://memory.knowledge.token.level.embedding.and.representation.learning","name":"token-level embedding and representation learning","description":"Generates contextualized token embeddings using RoBERTa's masked language model pre-training, where each token's representation is computed by stacking transformer layers that attend to surrounding context. Fine-tuning on SQuAD 2.0 adapts these representations to emphasize features relevant to answer span boundaries. Embeddings can be extracted from intermediate layers for downstream tasks like semantic similarity or clustering.","intents":["Extract semantic representations of text for similarity matching or clustering","Build custom downstream models using frozen or fine-tuned embeddings","Analyze what linguistic features the model learned for QA","Implement transfer learning by reusing learned representations"],"best_for":["Researchers analyzing transformer representations and attention patterns","Teams building multi-task systems that share encoder representations","Applications requiring semantic similarity beyond exact span matching"],"limitations":["Embeddings are context-dependent — same word has different vectors in different sentences","Dimensionality (768 for RoBERTa-base) may be excessive for simple similarity tasks","No built-in normalization or scaling — cosine similarity requires manual L2 normalization","Embeddings are optimized for QA task, not general semantic similarity"],"requires":["PyTorch or TensorFlow with transformers library","GPU memory for batch processing (embeddings require full forward pass)"],"input_types":["text (tokenized or raw)"],"output_types":["numerical arrays (768-dimensional float vectors per token)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--tinyroberta-squad2__cap_3","uri":"capability://automation.workflow.batch.inference.with.variable.length.context.handling","name":"batch inference with variable-length context handling","description":"Processes multiple question-context pairs simultaneously through padding and attention masking, automatically handling variable-length inputs by padding shorter sequences to the longest in the batch and masking padded positions. Supports both PyTorch and TensorFlow inference backends with optimized memory allocation and computation graphs. Inference can run on CPU or GPU with automatic device selection.","intents":["Process multiple QA requests in parallel for throughput optimization","Build API endpoints that batch incoming requests for efficiency","Implement efficient evaluation pipelines for benchmarking","Deploy models with minimal latency per request through batching"],"best_for":["High-throughput API services handling multiple concurrent QA requests","Batch evaluation and benchmarking workflows","Resource-constrained environments where batching amortizes overhead"],"limitations":["Batch size is limited by available GPU memory (typically 8-32 for 12GB VRAM)","Padding to longest sequence in batch increases computation for shorter examples","No dynamic batching — batch size must be fixed at inference time","Attention masking adds ~5-10% computational overhead vs fixed-length inputs"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library with batch processing utilities","GPU with sufficient VRAM for batch size (minimum 4GB for batch_size=8)"],"input_types":["list of text pairs (questions and contexts)"],"output_types":["structured data (batch of answer spans with scores)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--tinyroberta-squad2__cap_4","uri":"capability://automation.workflow.model.quantization.and.compression.compatibility","name":"model quantization and compression compatibility","description":"Model weights are stored in safetensors format and are compatible with quantization frameworks (ONNX, TensorRT, bitsandbytes) that reduce model size and inference latency. The architecture supports 8-bit and 16-bit quantization without significant accuracy loss, enabling deployment on edge devices and mobile platforms. Quantized versions can achieve 4-8x speedup with <2% accuracy degradation on SQuAD benchmarks.","intents":["Deploy QA models on edge devices with limited compute (mobile, IoT)","Reduce model serving costs by decreasing memory footprint and latency","Implement on-device inference for privacy-sensitive applications","Optimize inference for real-time applications with strict latency budgets"],"best_for":["Mobile and edge deployment scenarios","Cost-sensitive cloud deployments with high request volume","Privacy-critical applications requiring on-device inference"],"limitations":["Quantization requires separate conversion step and testing for target framework","8-bit quantization may reduce accuracy on adversarial or out-of-domain examples","No official quantized checkpoints provided — requires manual conversion","Quantization benefits vary by hardware (GPU quantization less beneficial than CPU)"],"requires":["Quantization framework (ONNX Runtime, TensorRT, or bitsandbytes)","Target hardware specifications for optimal quantization strategy","Validation dataset to verify accuracy after quantization"],"input_types":["model weights (safetensors format)"],"output_types":["quantized model (int8 or float16)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--tinyroberta-squad2__cap_5","uri":"capability://tool.use.integration.huggingface.model.hub.integration.and.versioning","name":"huggingface model hub integration and versioning","description":"Model is versioned and distributed through HuggingFace Model Hub with automatic version tracking, commit history, and model card documentation. Integrates with transformers library's AutoModel API for one-line loading without manual weight downloading. Supports model variants, configuration overrides, and revision pinning for reproducible deployments. Includes safetensors weights, PyTorch checkpoints, and TensorFlow SavedModel formats.","intents":["Load and use the model with minimal setup code","Pin specific model versions for reproducible research and production","Access model documentation, training details, and benchmark results","Integrate with HuggingFace ecosystem tools (Hugging Face Inference API, Spaces)"],"best_for":["Researchers and developers using HuggingFace ecosystem","Teams requiring version control and reproducibility for ML models","Projects leveraging HuggingFace Inference API or Spaces for deployment"],"limitations":["Requires internet connectivity to download model on first use","Model hub availability depends on HuggingFace infrastructure uptime","No built-in caching strategy — requires manual cache management for offline use","Model card documentation is community-maintained and may be incomplete"],"requires":["transformers library 4.0+","Internet connectivity for initial model download","HuggingFace account optional (required for private model access)"],"input_types":["model identifier string (deepset/tinyroberta-squad2)"],"output_types":["loaded model object (AutoModelForQuestionAnswering)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--tinyroberta-squad2__cap_6","uri":"capability://tool.use.integration.multi.framework.model.export.and.inference","name":"multi-framework model export and inference","description":"Model weights are available in multiple formats (PyTorch, TensorFlow, safetensors) enabling deployment across different inference frameworks and hardware. Supports conversion to ONNX for cross-platform inference, TensorRT for NVIDIA GPU optimization, and CoreML for Apple device deployment. Framework-agnostic architecture allows switching backends without retraining or model modification.","intents":["Deploy the same model across heterogeneous infrastructure (GPU, CPU, TPU, mobile)","Optimize inference for specific hardware without model retraining","Migrate between frameworks (PyTorch to TensorFlow) without losing accuracy","Build polyglot inference systems supporting multiple backends"],"best_for":["Teams with heterogeneous deployment environments","Organizations migrating between ML frameworks","Multi-platform applications requiring consistent model behavior"],"limitations":["Format conversion requires manual steps and validation for accuracy preservation","Some framework-specific optimizations may not transfer across formats","ONNX and TensorRT exports require separate conversion tooling","Numerical precision differences between frameworks may cause minor accuracy variance"],"requires":["Source framework (PyTorch or TensorFlow) for conversion","Target framework libraries (ONNX Runtime, TensorRT, CoreML tools)","Validation dataset to verify accuracy after conversion"],"input_types":["model weights (PyTorch, TensorFlow, or safetensors)"],"output_types":["model in target format (ONNX, TensorRT, CoreML, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--tinyroberta-squad2__cap_7","uri":"capability://data.processing.analysis.squad.2.0.benchmark.evaluation.and.metric.computation","name":"squad 2.0 benchmark evaluation and metric computation","description":"Model is trained and evaluated on SQuAD 2.0 benchmark with standard metrics (Exact Match, F1 score) computed over predicted answer spans. Supports evaluation against official SQuAD 2.0 test set with published results (EM: 76.8%, F1: 84.6% on dev set). Enables reproducible benchmarking and comparison against other QA models using standardized evaluation protocols.","intents":["Evaluate QA model performance using industry-standard metrics","Compare this model against other QA systems on common benchmarks","Reproduce published results for research validation","Establish baseline performance for domain-specific fine-tuning"],"best_for":["Researchers comparing QA models on standardized benchmarks","Teams establishing baseline performance before domain-specific fine-tuning","Projects requiring reproducible evaluation against published results"],"limitations":["SQuAD 2.0 metrics (EM, F1) may not reflect performance on other domains","Benchmark performance does not guarantee real-world accuracy on production data","Model may overfit to SQuAD 2.0 distribution and underperform on out-of-domain contexts","Published metrics are on development set; test set results require official submission"],"requires":["SQuAD 2.0 dataset (publicly available)","Official evaluation script for metric computation","Transformers library with tokenizer for preprocessing"],"input_types":["predictions (answer spans with positions)","ground truth (SQuAD 2.0 annotations)"],"output_types":["structured data (Exact Match %, F1 score %)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--tinyroberta-squad2__cap_8","uri":"capability://code.generation.editing.fine.tuning.and.transfer.learning.capability","name":"fine-tuning and transfer learning capability","description":"Model architecture and weights support supervised fine-tuning on custom QA datasets using standard transformer training loops. Enables transfer learning by initializing with SQuAD 2.0-pretrained weights and adapting to domain-specific data. Supports parameter-efficient fine-tuning methods (LoRA, adapter layers) for reducing training cost. Compatible with standard training frameworks (Hugging Face Trainer, PyTorch Lightning).","intents":["Adapt the model to domain-specific QA tasks with limited labeled data","Fine-tune on proprietary datasets without starting from scratch","Implement parameter-efficient fine-tuning to reduce training cost","Build specialized QA systems for specific industries or knowledge domains"],"best_for":["Teams with domain-specific QA datasets (legal, medical, technical documentation)","Projects with limited compute budgets requiring efficient fine-tuning","Applications requiring customization beyond general-purpose QA"],"limitations":["Fine-tuning requires labeled QA data in target domain (typically 1000+ examples for good results)","Risk of catastrophic forgetting if fine-tuning data is too small or domain-specific","Hyperparameter tuning is dataset-dependent and may require experimentation","Fine-tuned models may not generalize to out-of-domain questions"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library with Trainer API","Labeled QA dataset in target domain","GPU with sufficient VRAM (minimum 8GB for batch_size=16)"],"input_types":["question-context-answer triplets (text)","answer span positions (integers)"],"output_types":["fine-tuned model weights"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--tinyroberta-squad2__cap_9","uri":"capability://automation.workflow.inference.latency.optimization.for.real.time.applications","name":"inference latency optimization for real-time applications","description":"Model size (84M parameters) and architecture enable sub-100ms inference latency on modern GPUs and CPUs, suitable for real-time QA applications. Supports inference optimization techniques including layer fusion, mixed precision (FP16), and attention optimization. Inference time is dominated by forward pass through 12 transformer layers with 768-dimensional hidden states, enabling predictable latency scaling with batch size.","intents":["Build real-time QA systems with sub-100ms response times","Deploy QA features in latency-sensitive applications (chat, search)","Optimize inference cost by reducing compute time per request","Implement interactive QA systems with responsive user experience"],"best_for":["Real-time QA APIs and chat systems","Interactive applications requiring sub-100ms response times","Cost-sensitive deployments where latency directly impacts infrastructure costs"],"limitations":["Latency varies significantly with context length (512 tokens max) and batch size","CPU inference is 10-20x slower than GPU, limiting real-time capability on CPU-only systems","Latency includes tokenization and post-processing overhead (~10-20ms)","No built-in request queuing or load balancing — requires external orchestration"],"requires":["GPU (NVIDIA, AMD, or Apple) for sub-100ms latency; CPU inference requires optimization","Inference framework optimized for latency (TensorRT, ONNX Runtime)","Monitoring and profiling tools to measure actual latency in production"],"input_types":["text (question and context)"],"output_types":["structured data (answer span with score)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","Input text must be UTF-8 encoded","Context passage length typically limited to 512 tokens (standard BERT context window)","Transformers library 4.0+ with SQuAD 2.0 fine-tuned checkpoint","Understanding of no-answer threshold tuning for target domain","PyTorch or TensorFlow with transformers library","GPU memory for batch processing (embeddings require full forward pass)","Transformers library with batch processing utilities","GPU with sufficient VRAM for batch size (minimum 4GB for batch_size=8)"],"failure_modes":["Cannot answer questions requiring reasoning across multiple passages or synthesis","Struggles with out-of-domain contexts significantly different from SQuAD training distribution","Limited to English language only; no multilingual capability","Requires explicit context passage — cannot search across large document collections without external retrieval","Model size (84M parameters) may be insufficient for complex reasoning or ambiguous questions","Threshold tuning is dataset-dependent and may require calibration for new domains","Cannot distinguish between 'answer not in context' and 'question is malformed'","Performance degrades on adversarial or trick questions outside SQuAD 2.0 distribution","No explicit confidence calibration — raw logit differences may not reflect true probability","Embeddings are context-dependent — same word has different vectors in different sentences","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5737978777614979,"quality":0.3,"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":145572,"model_likes":113}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=deepset--tinyroberta-squad2","compare_url":"https://unfragile.ai/compare?artifact=deepset--tinyroberta-squad2"}},"signature":"Y7a5UJGEtiT30UwvgM0qKozQEURwgoxIqY1cIepf4gLJ1jvtYqAAZsOwDCxRtfmCEUM8YzNrP4hsuubDrdThAg==","signedAt":"2026-06-22T00:56:45.755Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepset--tinyroberta-squad2","artifact":"https://unfragile.ai/deepset--tinyroberta-squad2","verify":"https://unfragile.ai/api/v1/verify?slug=deepset--tinyroberta-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"}}