{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-monologg--koelectra-base-v3-finetuned-korquad","slug":"monologg--koelectra-base-v3-finetuned-korquad","name":"koelectra-base-v3-finetuned-korquad","type":"finetune","url":"https://huggingface.co/monologg/koelectra-base-v3-finetuned-korquad","page_url":"https://unfragile.ai/monologg--koelectra-base-v3-finetuned-korquad","categories":["model-training"],"tags":["transformers","pytorch","safetensors","electra","question-answering","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-monologg--koelectra-base-v3-finetuned-korquad__cap_0","uri":"capability://search.retrieval.extractive.question.answering.on.korean.text","name":"extractive question-answering on korean text","description":"Performs span-based extractive QA on Korean language documents using a fine-tuned ELECTRA encoder that identifies start and end token positions corresponding to answer spans. The model uses bidirectional transformer attention over the concatenated question-document pair to compute logits for each token position, enabling it to locate answers within provided context without generating text. Fine-tuned on KorQuAD dataset (Korean SQuAD equivalent) with 60,407 training examples, achieving 84.3% exact match and 92.2% F1 on the test set.","intents":["Extract answers to factual questions from Korean documents or paragraphs","Build Korean language search systems that return specific answer spans rather than ranked documents","Create customer support chatbots that locate answers in Korean knowledge bases","Implement reading comprehension evaluation systems for Korean text"],"best_for":["Korean NLP teams building QA systems for customer support, documentation, or knowledge bases","Researchers evaluating Korean language understanding models","Companies serving Korean-speaking markets needing on-device QA inference"],"limitations":["Extractive-only: cannot generate answers not present in the provided context","Context length limited to ~512 tokens due to ELECTRA-base architecture, constraining document size","Performance degrades on questions requiring multi-hop reasoning across distant document sections","No out-of-context answer capability — if answer is not in provided text, model will still extract a span (potentially incorrect)","Trained exclusively on KorQuAD; performance on other Korean QA datasets or domains may vary significantly"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","Hugging Face transformers library 4.0+","GPU with 2GB+ VRAM for inference (CPU inference supported but slower)","Korean text input (UTF-8 encoded)"],"input_types":["text (Korean language question string)","text (Korean language context/document passage)"],"output_types":["structured data (start token index, end token index, answer span text, confidence scores)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-monologg--koelectra-base-v3-finetuned-korquad__cap_1","uri":"capability://data.processing.analysis.token.level.confidence.scoring.for.answer.spans","name":"token-level confidence scoring for answer spans","description":"Computes softmax-normalized probability distributions over token positions for both answer start and end locations, enabling confidence quantification for extracted spans. The model outputs logit scores for each token in the input sequence, which are converted to probabilities indicating the likelihood that each position marks the answer boundary. This allows downstream systems to rank multiple candidate answers or filter low-confidence extractions.","intents":["Rank multiple potential answer spans by confidence to surface the most likely correct answer","Filter out low-confidence predictions to reduce hallucinated or incorrect answer extractions","Implement confidence-based thresholding in production systems to route uncertain queries to human review","Analyze model uncertainty patterns across different question types or document domains"],"best_for":["Production QA systems requiring confidence filtering to maintain answer quality","Teams building human-in-the-loop workflows where low-confidence predictions escalate to review","Researchers studying model calibration and uncertainty in Korean NLP tasks"],"limitations":["Confidence scores reflect model uncertainty, not ground-truth correctness — miscalibrated model may assign high confidence to incorrect answers","No built-in mechanism to distinguish between 'answer not in context' and 'low-confidence extraction' — both produce low scores","Softmax normalization is local to each position; joint probability of start-end pairs is not explicitly modeled"],"requires":["Python 3.7+","PyTorch or TensorFlow with transformers library","Access to raw logit outputs from model (not just post-processed answer text)"],"input_types":["text (Korean question and context)"],"output_types":["structured data (start position probability distribution, end position probability distribution, span confidence score)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-monologg--koelectra-base-v3-finetuned-korquad__cap_2","uri":"capability://automation.workflow.batch.inference.on.multiple.question.context.pairs","name":"batch inference on multiple question-context pairs","description":"Supports efficient processing of multiple QA examples in a single forward pass through batching, leveraging PyTorch/TensorFlow's vectorized operations to amortize transformer computation across multiple sequences. The model accepts batched input tensors with padding and attention masks, enabling throughput optimization for scenarios like evaluating entire datasets or processing queued user queries. Compatible with Hugging Face Inference Endpoints for serverless batch processing.","intents":["Evaluate model performance across entire test datasets without sequential inference loops","Process queued customer questions in batch to maximize GPU utilization and reduce per-query latency","Build data pipelines that annotate large document collections with QA pairs","Run periodic batch inference jobs for knowledge base indexing or answer pre-computation"],"best_for":["Teams with high-volume QA workloads (100+ queries per minute) needing throughput optimization","Researchers benchmarking on KorQuAD or similar datasets","Companies using Hugging Face Inference Endpoints for serverless inference"],"limitations":["Batch size limited by GPU memory; typical max batch size 32-64 for base model on 8GB VRAM","Padding overhead increases computation for variable-length inputs; optimal for homogeneous batch sizes","No built-in dynamic batching — requires manual batching logic in application code"],"requires":["PyTorch or TensorFlow with batch processing support","GPU with sufficient VRAM for batch size (2GB+ for batch_size=1, scales linearly)","Hugging Face transformers library with DataLoader or equivalent batching utility"],"input_types":["text (multiple Korean question-context pairs)"],"output_types":["structured data (batched start/end logits, batched answer spans with scores)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-monologg--koelectra-base-v3-finetuned-korquad__cap_3","uri":"capability://data.processing.analysis.multilingual.tokenization.with.korean.morphological.awareness","name":"multilingual tokenization with korean morphological awareness","description":"Uses WordPiece tokenization with a Korean-specific vocabulary built during ELECTRA pretraining, enabling proper handling of Korean morphological features like agglutination, compound words, and particles. The tokenizer segments Korean text into subword units that align with linguistic boundaries, improving model understanding of Korean grammar compared to generic multilingual tokenizers. Vocabulary includes 21,000 Korean tokens plus shared multilingual tokens.","intents":["Correctly tokenize Korean text with complex morphology (compound words, verb conjugations, particles) for accurate model input","Preserve Korean linguistic structure during tokenization to improve downstream QA accuracy","Handle mixed Korean-English text (common in technical documentation) with appropriate subword segmentation"],"best_for":["Korean NLP pipelines requiring linguistically-aware tokenization","Teams working with Korean technical or domain-specific text with mixed language content","Researchers studying tokenization effects on Korean language understanding"],"limitations":["Vocabulary is fixed to 21,000 Korean tokens; out-of-vocabulary Korean words are split into subword units, potentially losing semantic information","Tokenizer is not trainable — cannot adapt to domain-specific terminology without retraining the entire model","No explicit handling of Korean punctuation or special characters beyond standard Unicode normalization"],"requires":["Hugging Face transformers library with KoELECTRA tokenizer","Korean text input in UTF-8 encoding","Python 3.7+"],"input_types":["text (Korean language strings, optionally mixed with English)"],"output_types":["structured data (token IDs, token strings, attention masks, token type IDs)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-monologg--koelectra-base-v3-finetuned-korquad__cap_4","uri":"capability://planning.reasoning.transfer.learning.from.electra.pretraining.to.downstream.qa.task","name":"transfer learning from electra pretraining to downstream qa task","description":"Leverages weights from ELECTRA-base pretraining (trained on Korean corpora with replaced-token detection objective) as initialization for the QA fine-tuning task, enabling rapid convergence and improved generalization with limited labeled data. The model reuses the pretrained transformer encoder and adds a lightweight QA head (two linear layers for start/end token classification) that is trained on KorQuAD. This transfer learning approach reduces training time and data requirements compared to training from scratch.","intents":["Fine-tune the model on custom Korean QA datasets using the pretrained weights as a strong initialization","Adapt the model to domain-specific QA tasks (legal documents, medical records, technical manuals) with limited labeled examples","Understand how ELECTRA pretraining transfers to downstream Korean NLP tasks"],"best_for":["Teams with domain-specific Korean QA data (100-1000 examples) wanting to fine-tune without training from scratch","Researchers studying transfer learning in Korean NLP","Companies needing to adapt QA models to proprietary Korean documents"],"limitations":["Fine-tuning requires GPU and training infrastructure; not suitable for resource-constrained environments","Pretrained weights are frozen in the released model — no easy way to continue pretraining on new Korean corpora","Transfer learning effectiveness depends on similarity between KorQuAD and target domain; performance may degrade on out-of-domain questions"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","GPU with 4GB+ VRAM for fine-tuning","Labeled Korean QA dataset in SQuAD format","Hugging Face transformers library with Trainer API"],"input_types":["structured data (SQuAD-format JSON with questions, contexts, answers)"],"output_types":["model weights (fine-tuned checkpoint)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-monologg--koelectra-base-v3-finetuned-korquad__cap_5","uri":"capability://tool.use.integration.inference.via.hugging.face.inference.endpoints.serverless.deployment","name":"inference via hugging face inference endpoints (serverless deployment)","description":"Model is compatible with Hugging Face Inference Endpoints, a managed serverless inference service that handles model loading, GPU allocation, request queuing, and auto-scaling without requiring custom infrastructure. Users submit HTTP requests with question and context, and the service returns answer predictions with confidence scores. The endpoint automatically manages batching, caching, and multi-GPU distribution for high-throughput scenarios.","intents":["Deploy the QA model to production without managing GPU infrastructure or containerization","Scale inference from 0 to thousands of requests per minute with automatic load balancing","Integrate Korean QA into web applications or APIs with simple HTTP requests","Monitor inference performance and costs through Hugging Face dashboards"],"best_for":["Startups and small teams without DevOps infrastructure for model deployment","Applications with variable traffic patterns requiring auto-scaling","Teams prioritizing time-to-market over infrastructure customization"],"limitations":["Inference latency includes network round-trip time (typically 100-500ms) compared to local inference (10-50ms)","Pricing scales with inference volume; high-volume applications may be more cost-effective with self-hosted infrastructure","Limited customization of inference pipeline; cannot inject custom preprocessing or postprocessing logic","Dependent on Hugging Face service availability and uptime SLAs"],"requires":["Hugging Face account with API token","HTTP client library (requests, curl, etc.)","Network connectivity to Hugging Face API endpoints","Billing setup for paid inference tier (free tier has rate limits)"],"input_types":["JSON (question and context fields)"],"output_types":["JSON (answer, start/end positions, confidence scores)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","Hugging Face transformers library 4.0+","GPU with 2GB+ VRAM for inference (CPU inference supported but slower)","Korean text input (UTF-8 encoded)","PyTorch or TensorFlow with transformers library","Access to raw logit outputs from model (not just post-processed answer text)","PyTorch or TensorFlow with batch processing support","GPU with sufficient VRAM for batch size (2GB+ for batch_size=1, scales linearly)","Hugging Face transformers library with DataLoader or equivalent batching utility"],"failure_modes":["Extractive-only: cannot generate answers not present in the provided context","Context length limited to ~512 tokens due to ELECTRA-base architecture, constraining document size","Performance degrades on questions requiring multi-hop reasoning across distant document sections","No out-of-context answer capability — if answer is not in provided text, model will still extract a span (potentially incorrect)","Trained exclusively on KorQuAD; performance on other Korean QA datasets or domains may vary significantly","Confidence scores reflect model uncertainty, not ground-truth correctness — miscalibrated model may assign high confidence to incorrect answers","No built-in mechanism to distinguish between 'answer not in context' and 'low-confidence extraction' — both produce low scores","Softmax normalization is local to each position; joint probability of start-end pairs is not explicitly modeled","Batch size limited by GPU memory; typical max batch size 32-64 for base model on 8GB VRAM","Padding overhead increases computation for variable-length inputs; optimal for homogeneous batch sizes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.466937866772287,"quality":0.37,"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":78274,"model_likes":6}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=monologg--koelectra-base-v3-finetuned-korquad","compare_url":"https://unfragile.ai/compare?artifact=monologg--koelectra-base-v3-finetuned-korquad"}},"signature":"+jIXrFCLOhy6io8RXU35pmcwE/MXNKsW3s7RI8wTXchS+ONya2GBbcYvlvPj2qH6Yk5zRVSp+yT/g1OUUd/cCw==","signedAt":"2026-06-21T04:39:45.222Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/monologg--koelectra-base-v3-finetuned-korquad","artifact":"https://unfragile.ai/monologg--koelectra-base-v3-finetuned-korquad","verify":"https://unfragile.ai/api/v1/verify?slug=monologg--koelectra-base-v3-finetuned-korquad","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"}}