{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-deepset--xlm-roberta-large-squad2","slug":"deepset--xlm-roberta-large-squad2","name":"xlm-roberta-large-squad2","type":"model","url":"https://huggingface.co/deepset/xlm-roberta-large-squad2","page_url":"https://unfragile.ai/deepset--xlm-roberta-large-squad2","categories":["model-training"],"tags":["transformers","pytorch","safetensors","xlm-roberta","question-answering","multilingual","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--xlm-roberta-large-squad2__cap_0","uri":"capability://search.retrieval.multilingual.extractive.question.answering.with.span.prediction","name":"multilingual extractive question-answering with span prediction","description":"Performs extractive QA by encoding question-context pairs through XLM-RoBERTa's 24-layer transformer architecture, then predicting start/end token positions via a linear classification head trained on SQuAD v2. The model uses cross-lingual transfer to handle 100+ languages without language-specific fine-tuning, leveraging shared multilingual embeddings learned from 2.5TB of CommonCrawl text across 100 languages.","intents":["Extract answers from multilingual documents when the answer text appears verbatim in the source material","Build QA systems that work across multiple languages without maintaining separate models per language","Retrieve specific factual information from long-form text in non-English languages","Implement search result summarization that identifies relevant spans from retrieved documents"],"best_for":["multilingual SaaS platforms needing unified QA across 50+ languages","research teams building cross-lingual information retrieval systems","teams deploying QA in low-resource languages leveraging cross-lingual transfer"],"limitations":["Extractive-only: cannot generate answers not present in source text; fails on paraphrasing or reasoning questions","SQuAD v2 training includes unanswerable questions but performance degrades on out-of-domain contexts with no valid answer","Context window limited to ~512 tokens; longer documents require sliding window or chunking strategies","Cross-lingual transfer quality varies by language pair; performance drops significantly for low-resource languages (e.g., Swahili, Tagalog) vs high-resource ones (English, Spanish, Chinese)","No built-in confidence calibration; raw logit differences don't reliably indicate answer correctness across languages"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","Minimum 2GB GPU VRAM for inference (batch size 1); 8GB+ for batch processing","Input text must be pre-tokenized or passed as raw strings to HuggingFace pipeline API"],"input_types":["text (question as string)","text (context/passage as string)","structured JSON with 'question' and 'context' fields"],"output_types":["structured JSON with 'answer' (string), 'start' (token index), 'end' (token index), 'score' (float 0-1)"],"categories":["search-retrieval","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--xlm-roberta-large-squad2__cap_1","uri":"capability://search.retrieval.cross.lingual.zero.shot.question.answering.transfer","name":"cross-lingual zero-shot question-answering transfer","description":"Leverages XLM-RoBERTa's multilingual embedding space trained on 100+ languages to answer questions in languages not seen during SQuAD v2 fine-tuning. The model maps question and context tokens into a shared semantic space where English training signals transfer to unseen languages through aligned subword representations and cross-lingual word embeddings.","intents":["Answer questions in low-resource languages (e.g., Swahili, Vietnamese) without collecting language-specific training data","Build QA systems that automatically support new languages as they're added to the platform","Reduce data labeling costs by training once on English SQuAD and deploying across 100 languages"],"best_for":["global platforms serving 50+ language markets with limited per-language annotation budgets","research projects studying cross-lingual transfer learning and multilingual NLP","startups needing rapid multilingual feature rollout without language-specific ML engineering"],"limitations":["Performance degrades for language pairs linguistically distant from English (e.g., Basque, Finnish show 15-25% F1 drop vs English)","Requires context and question in the same language; mixed-language inputs not supported","Subword tokenization misalignment for languages with non-Latin scripts can reduce span prediction accuracy","No explicit handling of language-specific morphology; inflected forms may not align with training data"],"requires":["Transformers library 4.0+ with XLM-RoBERTa tokenizer","Input text must be in UTF-8 encoding","Language must use Latin, Cyrillic, Arabic, CJK, or other scripts in XLM-RoBERTa's 250K shared vocabulary"],"input_types":["text (question in any of 100+ supported languages)","text (context in matching language)"],"output_types":["structured JSON with answer span, confidence score, and token indices"],"categories":["search-retrieval","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--xlm-roberta-large-squad2__cap_2","uri":"capability://safety.moderation.adversarial.unanswerable.question.detection","name":"adversarial unanswerable question detection","description":"Trained on SQuAD v2's adversarial examples where human annotators wrote plausible but unanswerable questions, the model learns to distinguish answerable vs unanswerable queries through a special [CLS] token classification head. When the model's confidence for any span falls below a learned threshold, it outputs a null prediction indicating no valid answer exists in the context.","intents":["Detect when user questions cannot be answered from available documents and trigger fallback behaviors (e.g., web search, escalation)","Reduce hallucination by refusing to extract answers when context doesn't support them","Improve QA system reliability by filtering out low-confidence predictions before presenting to users"],"best_for":["production QA systems where false answers are worse than no answer (e.g., legal, medical, financial domains)","chatbots needing to gracefully handle out-of-scope questions","information retrieval pipelines requiring high precision over recall"],"limitations":["Unanswerable detection is probabilistic; threshold tuning required per use case (no universal optimal threshold)","Adversarial examples in SQuAD v2 are English-specific; unanswerable detection quality varies across languages","Model may incorrectly flag answerable questions as unanswerable if context is paraphrased vs training data style","No explanation for why a question is deemed unanswerable; only binary decision provided"],"requires":["Transformers library 4.0+ with pipeline API or manual logit extraction","Post-processing logic to interpret null predictions and implement fallback behavior"],"input_types":["text (question and context pair)"],"output_types":["structured JSON with 'answer' (null or string), 'is_answerable' (boolean), 'confidence' (float)"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--xlm-roberta-large-squad2__cap_3","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.batching.and.gpu.acceleration","name":"batch inference with dynamic batching and gpu acceleration","description":"Supports efficient batch processing of multiple QA pairs through HuggingFace's pipeline API with automatic padding, attention mask generation, and GPU batching. The model uses mixed-precision inference (FP16) to reduce memory footprint by 50% while maintaining accuracy, enabling batch sizes of 32-64 on 8GB GPUs vs batch size 1 with FP32.","intents":["Process thousands of QA pairs in parallel for batch document analysis or dataset annotation","Reduce per-query inference latency by amortizing model loading and GPU setup across multiple inputs","Scale QA inference to handle production traffic spikes without proportional hardware scaling"],"best_for":["data processing pipelines analyzing large document collections","batch annotation systems for ML training data generation","high-throughput QA APIs serving 100+ requests per second"],"limitations":["Batch size limited by GPU VRAM; OOM errors if batch exceeds available memory","Dynamic batching adds ~50-100ms overhead per batch for padding and tensor allocation","FP16 inference may introduce numerical instability for edge cases (rare but possible)","No built-in request queuing or load balancing; requires external orchestration for distributed inference"],"requires":["GPU with CUDA 11.0+ or CPU (much slower)","Transformers library 4.0+ with torch.cuda support","Minimum 2GB VRAM for batch size 1, 8GB+ for batch size 32"],"input_types":["list of JSON objects with 'question' and 'context' fields","CSV or JSONL with question/context columns"],"output_types":["list of JSON objects with answer, score, and span indices","JSONL with one result per line"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--xlm-roberta-large-squad2__cap_4","uri":"capability://search.retrieval.token.level.span.extraction.with.confidence.scoring","name":"token-level span extraction with confidence scoring","description":"Predicts answer spans by computing logit scores for each token's probability of being the answer start and end position. The model outputs raw logits that are converted to probabilities via softmax, with the final answer confidence computed as the product of start and end token probabilities, enabling ranking of multiple candidate answers.","intents":["Extract exact answer text from documents with confidence scores for ranking","Identify multiple candidate answers and rank them by model confidence","Debug model predictions by inspecting token-level scores and attention patterns"],"best_for":["QA systems requiring confidence-ranked answer candidates for re-ranking or filtering","research projects analyzing model behavior and failure modes","applications needing interpretability of which tokens the model considered as answer boundaries"],"limitations":["Confidence scores are not calibrated; raw logit products don't reflect true answer correctness probability","Span extraction is greedy; doesn't consider overlapping or nested spans","Token indices depend on tokenizer; converting back to character offsets requires careful alignment","No ranking of multiple valid answers; only top-1 span returned by default"],"requires":["Access to model logits (requires manual forward pass, not just pipeline API)","Tokenizer for converting token indices back to text spans","Post-processing logic to handle special tokens and subword merging"],"input_types":["text (question and context)"],"output_types":["structured JSON with start_logits (list of floats), end_logits (list of floats), answer (string), confidence (float)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--xlm-roberta-large-squad2__cap_5","uri":"capability://search.retrieval.multilingual.document.retrieval.and.ranking.integration","name":"multilingual document retrieval and ranking integration","description":"Designed to integrate with retrieval pipelines where a dense retriever (e.g., DPR, ColBERT) returns top-k candidate passages, and this model re-ranks and extracts answers from those passages. The model's multilingual capabilities enable end-to-end retrieval-augmented QA across 100+ languages without separate retrieval models per language.","intents":["Build retrieval-augmented QA systems that retrieve multilingual documents and extract answers in one pipeline","Rank retrieved passages by answer extractability before presenting to users","Implement dense retrieval + extractive QA without language-specific engineering"],"best_for":["large-scale document QA systems (Wikipedia, knowledge bases, enterprise intranets)","multilingual search engines needing answer extraction from retrieved results","research systems studying retrieval-augmented generation"],"limitations":["Requires pre-retrieved passages; no built-in retrieval component (must integrate with separate dense retriever)","Performance depends heavily on retrieval quality; poor retrieval = poor QA regardless of extraction model","No re-ranking of passages; processes top-k in order and returns first answer found","Context window of 512 tokens limits passage length; longer documents require chunking"],"requires":["Dense retriever (e.g., DPR, ColBERT, BM25) to provide candidate passages","Passage-level metadata (e.g., document ID, source URL) for result attribution","Integration framework (e.g., Haystack, LangChain) to orchestrate retrieval + QA"],"input_types":["question (string)","list of passages (strings) from retriever"],"output_types":["structured JSON with answer, source passage, confidence, and document metadata"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--xlm-roberta-large-squad2__cap_6","uri":"capability://code.generation.editing.fine.tuning.on.custom.qa.datasets","name":"fine-tuning on custom qa datasets","description":"Model weights are available for fine-tuning on domain-specific QA datasets using standard PyTorch/HuggingFace training loops. The model's XLM-RoBERTa backbone can be unfrozen to adapt to specialized vocabularies and answer patterns, with transfer learning from SQuAD v2 pretraining providing strong initialization.","intents":["Adapt the model to domain-specific QA tasks (e.g., medical, legal, technical documentation) with custom training data","Improve performance on languages underrepresented in SQuAD v2 by fine-tuning on language-specific datasets","Build proprietary QA models by fine-tuning on internal documents and questions"],"best_for":["enterprises with domain-specific QA requirements and labeled training data (100+ examples)","research teams studying domain adaptation and transfer learning","teams building proprietary QA models for competitive advantage"],"limitations":["Requires labeled QA dataset with question-context-answer triples; annotation cost is significant","Fine-tuning on small datasets (<100 examples) risks overfitting; requires careful regularization","No built-in curriculum learning or hard example mining; requires manual data curation","Fine-tuned models lose multilingual generalization if trained only on single language","Computational cost: ~2-4 hours on single GPU for 10k examples"],"requires":["PyTorch 1.9+ and Transformers 4.0+","GPU with 8GB+ VRAM for training","Labeled QA dataset in SQuAD format (JSON with question, context, answer_start, text fields)","Training code (can use HuggingFace Trainer or custom loop)"],"input_types":["JSON dataset in SQuAD format","CSV with question, context, answer columns"],"output_types":["fine-tuned model weights (PyTorch .pt or SafeTensors format)","training metrics (loss, F1, EM scores)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepset--xlm-roberta-large-squad2__cap_7","uri":"capability://automation.workflow.deployment.to.cloud.endpoints.azure.aws.huggingface.inference.api","name":"deployment to cloud endpoints (azure, aws, huggingface inference api)","description":"Model is compatible with HuggingFace Inference API, Azure ML endpoints, and AWS SageMaker for serverless or managed inference. Deployment handles model loading, batching, and auto-scaling transparently, with support for both CPU and GPU inference backends.","intents":["Deploy QA model as a REST API without managing infrastructure or GPU servers","Scale inference automatically based on traffic without manual capacity planning","Integrate QA into existing cloud ML platforms (Azure, AWS) with minimal engineering"],"best_for":["startups and small teams without ML infrastructure expertise","enterprises using Azure or AWS as primary cloud provider","applications with variable traffic patterns requiring auto-scaling"],"limitations":["HuggingFace Inference API has rate limits (varies by tier); not suitable for extremely high-throughput use cases","Cloud deployment adds network latency (~100-500ms) vs local inference","Pricing scales with inference volume; cost can exceed self-hosted for high-traffic applications","Limited customization of inference parameters (batch size, precision) on managed endpoints","Cold start latency (~5-10s) on serverless platforms when model not in memory"],"requires":["HuggingFace account or Azure/AWS credentials","API key for authentication","HTTP client library for calling endpoints"],"input_types":["JSON with question and context fields (via HTTP POST)"],"output_types":["JSON with answer, score, and span indices (via HTTP response)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","Minimum 2GB GPU VRAM for inference (batch size 1); 8GB+ for batch processing","Input text must be pre-tokenized or passed as raw strings to HuggingFace pipeline API","Transformers library 4.0+ with XLM-RoBERTa tokenizer","Input text must be in UTF-8 encoding","Language must use Latin, Cyrillic, Arabic, CJK, or other scripts in XLM-RoBERTa's 250K shared vocabulary","Transformers library 4.0+ with pipeline API or manual logit extraction","Post-processing logic to interpret null predictions and implement fallback behavior","GPU with CUDA 11.0+ or CPU (much slower)"],"failure_modes":["Extractive-only: cannot generate answers not present in source text; fails on paraphrasing or reasoning questions","SQuAD v2 training includes unanswerable questions but performance degrades on out-of-domain contexts with no valid answer","Context window limited to ~512 tokens; longer documents require sliding window or chunking strategies","Cross-lingual transfer quality varies by language pair; performance drops significantly for low-resource languages (e.g., Swahili, Tagalog) vs high-resource ones (English, Spanish, Chinese)","No built-in confidence calibration; raw logit differences don't reliably indicate answer correctness across languages","Performance degrades for language pairs linguistically distant from English (e.g., Basque, Finnish show 15-25% F1 drop vs English)","Requires context and question in the same language; mixed-language inputs not supported","Subword tokenization misalignment for languages with non-Latin scripts can reduce span prediction accuracy","No explicit handling of language-specific morphology; inflected forms may not align with training data","Unanswerable detection is probabilistic; threshold tuning required per use case (no universal optimal threshold)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5480053309632243,"quality":0.26,"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":124380,"model_likes":57}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=deepset--xlm-roberta-large-squad2","compare_url":"https://unfragile.ai/compare?artifact=deepset--xlm-roberta-large-squad2"}},"signature":"WtLjL+s8uC1JbR2ouSy3bocC9FVMGgY0hAgpl5je+tU05gJ8TTrEBETdetUV4/TPbVWDkqR5oZbGJnwWvwf7Cg==","signedAt":"2026-06-20T16:06:42.801Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepset--xlm-roberta-large-squad2","artifact":"https://unfragile.ai/deepset--xlm-roberta-large-squad2","verify":"https://unfragile.ai/api/v1/verify?slug=deepset--xlm-roberta-large-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"}}