{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-nguyenvulebinh--vi-mrc-large","slug":"nguyenvulebinh--vi-mrc-large","name":"vi-mrc-large","type":"model","url":"https://huggingface.co/nguyenvulebinh/vi-mrc-large","page_url":"https://unfragile.ai/nguyenvulebinh--vi-mrc-large","categories":["research-search"],"tags":["transformers","pytorch","roberta","question-answering","vi","vn","en","dataset:squad","license:cc-by-nc-4.0","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-nguyenvulebinh--vi-mrc-large__cap_0","uri":"capability://search.retrieval.vietnamese.extractive.question.answering.with.span.prediction","name":"vietnamese extractive question-answering with span prediction","description":"Performs extractive QA by fine-tuned RoBERTa-large encoder that predicts start and end token positions within a passage to extract answer spans. Uses transformer-based sequence classification with token-level logits to identify answer boundaries, trained on Vietnamese SQuAD-format datasets with cross-lingual transfer from English pre-training. Architecture leverages masked language modeling representations to contextualize Vietnamese text and identify semantically relevant answer spans without generating new text.","intents":["Extract factual answers from Vietnamese documents given natural language questions","Build Vietnamese QA systems for customer support, FAQ automation, or document search","Integrate extractive QA into Vietnamese NLP pipelines without training from scratch","Benchmark Vietnamese language understanding on span-selection tasks"],"best_for":["Vietnamese NLP teams building production QA systems","Researchers evaluating Vietnamese language model capabilities","Developers integrating QA into Vietnamese-language applications","Teams migrating from English-only QA to multilingual support"],"limitations":["Extractive-only: cannot generate answers not present in source text, limiting paraphrase or reasoning-based QA","Context window limited to ~512 tokens (RoBERTa max sequence length), requiring passage truncation for long documents","No cross-lingual zero-shot transfer to other languages; requires language-specific fine-tuning","Performance degrades on out-of-domain Vietnamese text not similar to SQuAD training distribution","No built-in handling of multi-hop reasoning or questions requiring information synthesis across passages"],"requires":["PyTorch 1.9+","Transformers library 4.0+","HuggingFace Hub access for model download","GPU with 4GB+ VRAM for inference (CPU inference possible but slow)","Vietnamese text input with UTF-8 encoding"],"input_types":["text (Vietnamese passage)","text (Vietnamese question)"],"output_types":["structured data (answer span with start/end token indices)","text (extracted answer substring)","numeric (confidence scores for start/end positions)"],"categories":["search-retrieval","nlp-vietnamese"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nguyenvulebinh--vi-mrc-large__cap_1","uri":"capability://memory.knowledge.cross.lingual.transfer.learning.for.vietnamese.question.answering","name":"cross-lingual transfer learning for vietnamese question-answering","description":"Leverages RoBERTa-large's multilingual pre-training (trained on 100+ languages including Vietnamese and English) to transfer knowledge from English SQuAD fine-tuning to Vietnamese QA tasks. The model architecture preserves language-agnostic contextual representations learned during pre-training, allowing the token classification head to generalize across Vietnamese and English without explicit cross-lingual alignment. Fine-tuning on Vietnamese SQuAD data adapts the shared encoder representations while maintaining transfer benefits from English QA patterns.","intents":["Apply English QA knowledge to Vietnamese tasks with minimal Vietnamese training data","Reduce Vietnamese QA fine-tuning data requirements by leveraging English SQuAD scale","Build QA systems for low-resource Vietnamese dialects or domains using cross-lingual transfer","Evaluate multilingual representation quality in transformer encoders"],"best_for":["Teams with limited Vietnamese QA training data seeking to leverage English resources","Researchers studying cross-lingual transfer in transformer models","Organizations building QA for multiple languages with shared infrastructure","Low-resource Vietnamese NLP projects"],"limitations":["Cross-lingual transfer effectiveness depends on linguistic similarity; distant language pairs show degraded performance","Requires careful hyperparameter tuning to balance English pre-training knowledge with Vietnamese fine-tuning","No explicit alignment mechanism between Vietnamese and English token spaces; relies on implicit multilingual representations","Transfer benefits plateau with large Vietnamese training datasets, making it less valuable for high-resource scenarios"],"requires":["PyTorch 1.9+","Transformers library 4.0+","Vietnamese SQuAD or equivalent annotated QA dataset for fine-tuning","GPU with 8GB+ VRAM for efficient fine-tuning"],"input_types":["text (Vietnamese passage)","text (Vietnamese question)"],"output_types":["structured data (answer span with confidence)","text (extracted answer)"],"categories":["memory-knowledge","nlp-vietnamese"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nguyenvulebinh--vi-mrc-large__cap_2","uri":"capability://data.processing.analysis.squad.format.dataset.fine.tuning.and.evaluation","name":"squad-format dataset fine-tuning and evaluation","description":"Supports standard SQuAD format input/output (JSON with passages, questions, answers with character offsets) for both training and evaluation. The model integrates with HuggingFace Datasets library to load SQuAD-compatible data, compute exact-match and F1 metrics during training, and enable reproducible benchmarking. Fine-tuning pipeline handles tokenization, token-to-character offset mapping, and loss computation for span prediction without requiring custom data loaders.","intents":["Fine-tune the model on custom Vietnamese QA datasets in standard SQuAD format","Evaluate model performance using standard QA metrics (EM, F1) for reproducible benchmarking","Integrate with existing SQuAD-based training pipelines and evaluation frameworks","Compare Vietnamese QA performance across models using standardized metrics"],"best_for":["Researchers benchmarking Vietnamese QA models","Teams with SQuAD-format Vietnamese datasets seeking to fine-tune","Organizations standardizing on SQuAD format for QA evaluation","Developers building reproducible QA evaluation pipelines"],"limitations":["SQuAD format assumes single-span answers; multi-span or yes/no questions require preprocessing","Exact-match metric penalizes paraphrased answers even if semantically correct","F1 metric based on token overlap, not semantic similarity, leading to misleading scores for synonymous answers","Character offset mapping can fail on inconsistent whitespace or encoding issues in source data"],"requires":["HuggingFace Datasets library 2.0+","SQuAD-format JSON files with 'passages', 'questions', 'answers' fields","Python 3.7+","Transformers library 4.0+"],"input_types":["structured data (SQuAD JSON format)","text (passage and question text)"],"output_types":["numeric (EM and F1 scores)","structured data (predictions with answer spans)","text (model predictions)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nguyenvulebinh--vi-mrc-large__cap_3","uri":"capability://safety.moderation.token.level.confidence.scoring.for.answer.span.prediction","name":"token-level confidence scoring for answer span prediction","description":"Outputs logit scores for start and end token positions, enabling confidence-based answer filtering and ranking. The model computes softmax probabilities over all tokens in the passage for both start and end positions, allowing downstream systems to rank candidate answers by joint probability (start_prob × end_prob) or filter low-confidence predictions. This enables uncertainty quantification and selective answer suppression in production systems.","intents":["Filter low-confidence QA predictions to reduce hallucination in production systems","Rank multiple candidate answers by confidence for user-facing applications","Detect out-of-domain questions where the model is uncertain","Implement confidence-based fallback strategies (e.g., escalate to human review)"],"best_for":["Production QA systems requiring confidence-based filtering","Teams building user-facing QA applications with quality thresholds","Systems implementing human-in-the-loop QA with confidence-based escalation","Researchers studying model uncertainty in extractive QA"],"limitations":["Confidence scores reflect model calibration, not answer correctness; miscalibrated models may output high confidence for wrong answers","No built-in confidence threshold tuning; requires manual calibration on validation data","Joint probability (start × end) assumes independence between start/end predictions, which may not hold","Confidence scores do not account for passage relevance; irrelevant passages may yield high-confidence wrong answers"],"requires":["PyTorch 1.9+","Transformers library 4.0+","Ability to extract logits from model output (not just argmax predictions)"],"input_types":["text (passage and question)"],"output_types":["numeric (start/end logits and softmax probabilities)","numeric (joint confidence score)","structured data (answer with confidence metadata)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nguyenvulebinh--vi-mrc-large__cap_4","uri":"capability://automation.workflow.batch.inference.with.passage.question.pair.processing","name":"batch inference with passage-question pair processing","description":"Supports efficient batch processing of multiple passage-question pairs through HuggingFace Transformers pipeline API, which handles tokenization, batching, and output aggregation. The model processes variable-length passages and questions by padding to max sequence length within each batch, enabling GPU-accelerated inference across multiple examples. Batch size can be tuned for memory/latency tradeoffs on different hardware.","intents":["Process large volumes of QA requests efficiently in batch mode","Reduce per-request latency by amortizing model loading and GPU setup costs","Build scalable QA services handling thousands of questions against document collections","Optimize GPU utilization for cost-effective inference on cloud infrastructure"],"best_for":["Teams building batch QA processing pipelines for document analysis","Organizations processing large QA datasets offline","Cloud-based QA services optimizing for throughput and cost","Researchers evaluating model performance on large benchmarks"],"limitations":["Batch processing introduces latency compared to single-request inference; not suitable for real-time low-latency applications","Memory usage scales with batch size and max sequence length; large batches may require GPU with 16GB+ VRAM","Padding to max sequence length in batch wastes computation on short passages; dynamic batching not natively supported","No built-in distributed inference; requires manual sharding across multiple GPUs or machines"],"requires":["PyTorch 1.9+","Transformers library 4.0+","GPU with 4GB+ VRAM (8GB+ recommended for large batches)","Sufficient system memory for batch data loading"],"input_types":["structured data (list of passage-question pairs)"],"output_types":["structured data (batch predictions with answer spans and scores)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nguyenvulebinh--vi-mrc-large__cap_5","uri":"capability://automation.workflow.azure.deployment.and.cloud.inference.endpoints","name":"azure deployment and cloud inference endpoints","description":"Model is compatible with Azure ML endpoints for serverless inference deployment, enabling pay-per-use QA without managing infrastructure. Azure integration handles model versioning, auto-scaling based on request volume, and REST API exposure. The model can be deployed as a managed endpoint with configurable compute resources (CPU/GPU), enabling cost-optimized inference for variable traffic patterns.","intents":["Deploy Vietnamese QA model to production without managing servers","Scale QA inference automatically based on request volume","Expose QA capability via REST API for web/mobile applications","Monitor and version QA model deployments in cloud infrastructure"],"best_for":["Teams using Azure cloud infrastructure","Organizations seeking serverless QA deployment","Startups avoiding infrastructure management overhead","Enterprises requiring managed model versioning and monitoring"],"limitations":["Azure-specific deployment; requires Azure account and familiarity with Azure ML","Cold start latency on serverless endpoints (1-5 seconds) unsuitable for real-time applications","Pricing based on compute hours and API calls; high-volume inference may be more expensive than self-hosted","Limited customization of inference pipeline compared to self-hosted deployment","Vendor lock-in to Azure ecosystem; migration to other clouds requires redeployment"],"requires":["Azure account with ML workspace","Azure ML SDK or CLI for deployment","Model downloaded from HuggingFace Hub","REST API client for inference requests"],"input_types":["JSON (passage and question text)"],"output_types":["JSON (answer span with confidence scores)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+","Transformers library 4.0+","HuggingFace Hub access for model download","GPU with 4GB+ VRAM for inference (CPU inference possible but slow)","Vietnamese text input with UTF-8 encoding","Vietnamese SQuAD or equivalent annotated QA dataset for fine-tuning","GPU with 8GB+ VRAM for efficient fine-tuning","HuggingFace Datasets library 2.0+","SQuAD-format JSON files with 'passages', 'questions', 'answers' fields","Python 3.7+"],"failure_modes":["Extractive-only: cannot generate answers not present in source text, limiting paraphrase or reasoning-based QA","Context window limited to ~512 tokens (RoBERTa max sequence length), requiring passage truncation for long documents","No cross-lingual zero-shot transfer to other languages; requires language-specific fine-tuning","Performance degrades on out-of-domain Vietnamese text not similar to SQuAD training distribution","No built-in handling of multi-hop reasoning or questions requiring information synthesis across passages","Cross-lingual transfer effectiveness depends on linguistic similarity; distant language pairs show degraded performance","Requires careful hyperparameter tuning to balance English pre-training knowledge with Vietnamese fine-tuning","No explicit alignment mechanism between Vietnamese and English token spaces; relies on implicit multilingual representations","Transfer benefits plateau with large Vietnamese training datasets, making it less valuable for high-resource scenarios","SQuAD format assumes single-span answers; multi-span or yes/no questions require preprocessing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.490480746178973,"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":109840,"model_likes":6}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nguyenvulebinh--vi-mrc-large","compare_url":"https://unfragile.ai/compare?artifact=nguyenvulebinh--vi-mrc-large"}},"signature":"lHCrXeIfJkxItu3sb3xYvEYmLY4V3HWHP5c+0rwP5CcoE7+Y2Agqsa3aOtZdH5vWfWlrE9TnC4v7mJIokbJIAw==","signedAt":"2026-06-19T21:31:59.248Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nguyenvulebinh--vi-mrc-large","artifact":"https://unfragile.ai/nguyenvulebinh--vi-mrc-large","verify":"https://unfragile.ai/api/v1/verify?slug=nguyenvulebinh--vi-mrc-large","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"}}