{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-monologg--koelectra-small-v2-distilled-korquad-384","slug":"monologg--koelectra-small-v2-distilled-korquad-384","name":"koelectra-small-v2-distilled-korquad-384","type":"model","url":"https://huggingface.co/monologg/koelectra-small-v2-distilled-korquad-384","page_url":"https://unfragile.ai/monologg--koelectra-small-v2-distilled-korquad-384","categories":["model-training"],"tags":["transformers","pytorch","tflite","safetensors","electra","question-answering","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-monologg--koelectra-small-v2-distilled-korquad-384__cap_0","uri":"capability://data.processing.analysis.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 distilled ELECTRA transformer architecture fine-tuned on KorQuAD dataset. The model identifies and extracts the most probable answer span (start and end token positions) from a given passage that answers a natural language question, outputting confidence scores for both span boundaries. Uses token-level classification with softmax scoring over sequence length to pinpoint exact answer locations within context.","intents":["extract factual answers from Korean documents without generating new text","build Korean language search systems that return exact passages answering user queries","implement document-based QA for Korean customer support or FAQ systems","create reading comprehension evaluation systems for Korean educational content"],"best_for":["Korean NLP teams building production QA systems with strict latency requirements","developers deploying edge/mobile Korean language applications with limited compute","organizations needing lightweight Korean document retrieval without cloud dependencies"],"limitations":["Extractive-only — cannot generate answers not present in source text; fails on questions requiring reasoning or synthesis","384 token context window limits passage length to ~300 words; longer documents require chunking strategy","Fine-tuned exclusively on KorQuAD dataset; performance degrades on out-of-domain Korean text (medical, legal, technical jargon)","No multi-hop reasoning — cannot answer questions requiring information synthesis across multiple passages","Korean-only; zero cross-lingual transfer to other languages"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ runtime","transformers library 4.0+","minimum 512MB RAM for model weights (distilled variant)","input text must be valid UTF-8 encoded Korean"],"input_types":["text (Korean passage/context)","text (Korean question)"],"output_types":["structured data (start_logits array, end_logits array, answer_text string, confidence_score float)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-monologg--koelectra-small-v2-distilled-korquad-384__cap_1","uri":"capability://data.processing.analysis.distilled.transformer.inference.with.reduced.memory.footprint","name":"distilled transformer inference with reduced memory footprint","description":"Executes forward passes using a knowledge-distilled ELECTRA model with 40% parameter reduction compared to base ELECTRA, enabling deployment on resource-constrained devices. The distillation process transferred learned representations from a larger teacher model into this smaller student architecture, maintaining semantic understanding while reducing embedding dimensions and layer counts. Supports multiple inference backends (PyTorch, TensorFlow, TFLite) for flexible deployment across cloud, edge, and mobile environments.","intents":["deploy Korean QA models on mobile devices or embedded systems with <512MB memory","reduce inference latency for real-time Korean document search in production","minimize cloud compute costs by running inference locally instead of API calls","enable on-device Korean language processing without sending data to external servers"],"best_for":["mobile app developers building offline Korean language features","edge computing teams deploying models on IoT devices or Raspberry Pi","cost-sensitive teams running high-volume Korean QA inference"],"limitations":["Knowledge distillation introduces ~1-3% accuracy loss vs full ELECTRA-base on KorQuAD benchmark","TFLite quantization (if used) adds additional 2-5% accuracy degradation but enables sub-100ms inference","No dynamic batching optimization — single-sample inference only without custom ONNX conversion","Distillation tuned for KorQuAD domain; transfer to other Korean tasks may require retraining"],"requires":["PyTorch 1.9+ OR TensorFlow 2.4+ OR TFLite runtime","512MB+ RAM for model weights and inference buffers","transformers library 4.0+ for PyTorch/TensorFlow backends"],"input_types":["tensor (token_ids, attention_mask, token_type_ids)"],"output_types":["tensor (start_logits shape [batch_size, sequence_length], end_logits shape [batch_size, sequence_length])"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-monologg--koelectra-small-v2-distilled-korquad-384__cap_2","uri":"capability://data.processing.analysis.korean.specific.tokenization.with.subword.segmentation","name":"korean-specific tokenization with subword segmentation","description":"Applies Korean-optimized WordPiece tokenization that preserves morphological structure and handles Korean-specific Unicode ranges (Hangul syllables U+AC00-U+D7A3). The tokenizer uses a Korean-specific vocabulary learned during ELECTRA pre-training, enabling accurate segmentation of Korean compound words, particles, and verb conjugations that would be fragmented by generic multilingual tokenizers. Handles both modern Hangul and legacy Korean text encoding.","intents":["accurately tokenize Korean text preserving morphological boundaries for QA tasks","handle Korean particles and verb conjugations without excessive subword fragmentation","process Korean text with proper Unicode handling for Hangul syllables and combining marks"],"best_for":["Korean NLP pipelines requiring linguistically-aware tokenization","teams building Korean search systems where token boundaries affect retrieval quality"],"limitations":["Korean-only vocabulary — cannot tokenize mixed-language text (Korean+English) without fallback to subword splitting","Vocabulary size ~30K tokens optimized for KorQuAD domain; rare technical/medical Korean terms may tokenize into many subwords","No morphological analysis — tokenization is statistical, not rule-based; some compound words may split unexpectedly"],"requires":["transformers library 4.0+ with Korean tokenizer support","UTF-8 encoding for input text"],"input_types":["text (Korean string)"],"output_types":["structured data (token_ids list, tokens list, attention_mask list)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-monologg--koelectra-small-v2-distilled-korquad-384__cap_3","uri":"capability://automation.workflow.multi.backend.model.serialization.and.deployment","name":"multi-backend model serialization and deployment","description":"Provides model weights in multiple serialization formats (PyTorch safetensors, TensorFlow SavedModel, TFLite) enabling deployment across heterogeneous infrastructure without conversion overhead. The safetensors format enables secure, fast weight loading with built-in integrity checking; TensorFlow format supports graph optimization and quantization; TFLite enables mobile/edge deployment. A single model checkpoint can be loaded into any supported framework via the transformers library's unified interface.","intents":["deploy the same Korean QA model across PyTorch production servers and TensorFlow inference pipelines","convert model to TFLite for mobile deployment without manual format conversion","load model weights securely with integrity verification using safetensors format","optimize inference for specific hardware (CPU, GPU, TPU) using framework-specific backends"],"best_for":["teams with heterogeneous ML infrastructure (PyTorch + TensorFlow + mobile)","organizations requiring secure model distribution with integrity verification","developers deploying to Azure, AWS, or on-premise infrastructure with format flexibility"],"limitations":["Format conversion (PyTorch ↔ TensorFlow) requires transformers library; no native cross-framework compatibility","TFLite quantization requires separate conversion step; no automatic quantization in base model","Safetensors format is read-only for inference; training requires PyTorch native format","No ONNX export provided; ONNX deployment requires manual conversion"],"requires":["transformers library 4.0+ for unified loading interface","PyTorch 1.9+ OR TensorFlow 2.4+ depending on target backend","safetensors library for secure weight loading"],"input_types":["model checkpoint (safetensors, PyTorch, TensorFlow format)"],"output_types":["loaded model object (torch.nn.Module, tf.keras.Model, or TFLite interpreter)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-monologg--koelectra-small-v2-distilled-korquad-384__cap_4","uri":"capability://data.processing.analysis.span.based.answer.extraction.with.confidence.scoring","name":"span-based answer extraction with confidence scoring","description":"Predicts answer spans by computing logit scores for each token position as a potential answer start and end, then selects the span with highest combined probability. The model outputs two logit vectors (start_logits, end_logits) of length sequence_length; inference applies softmax to convert logits to probabilities and selects argmax for start/end positions. Confidence is computed as the product of start and end token probabilities, enabling ranking of multiple candidate answers or filtering low-confidence predictions.","intents":["extract exact answer spans from passages with confidence scores for ranking","filter low-confidence predictions to avoid returning incorrect answers","rank multiple candidate answers by confidence for multi-answer QA scenarios","identify answer boundaries precisely for highlighting in UI or downstream processing"],"best_for":["QA systems requiring exact answer locations for highlighting or citation","applications needing confidence filtering to maintain answer quality","search systems ranking multiple candidate answers by relevance confidence"],"limitations":["Span-based extraction cannot handle answers requiring paraphrasing or synthesis","Answer must be a contiguous span; cannot extract non-contiguous multi-word answers","Confidence scores are not calibrated probabilities; raw logit products may not reflect true correctness likelihood","No handling of unanswerable questions — always returns highest-scoring span even if answer is not in passage"],"requires":["model output logits (start_logits, end_logits tensors)","sequence_length ≤ 384 tokens"],"input_types":["tensor (start_logits shape [sequence_length], end_logits shape [sequence_length])"],"output_types":["structured data (answer_text string, start_position int, end_position int, confidence_score float)"],"categories":["data-processing-analysis"],"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+ runtime","transformers library 4.0+","minimum 512MB RAM for model weights (distilled variant)","input text must be valid UTF-8 encoded Korean","PyTorch 1.9+ OR TensorFlow 2.4+ OR TFLite runtime","512MB+ RAM for model weights and inference buffers","transformers library 4.0+ for PyTorch/TensorFlow backends","transformers library 4.0+ with Korean tokenizer support","UTF-8 encoding for input text","transformers library 4.0+ for unified loading interface"],"failure_modes":["Extractive-only — cannot generate answers not present in source text; fails on questions requiring reasoning or synthesis","384 token context window limits passage length to ~300 words; longer documents require chunking strategy","Fine-tuned exclusively on KorQuAD dataset; performance degrades on out-of-domain Korean text (medical, legal, technical jargon)","No multi-hop reasoning — cannot answer questions requiring information synthesis across multiple passages","Korean-only; zero cross-lingual transfer to other languages","Knowledge distillation introduces ~1-3% accuracy loss vs full ELECTRA-base on KorQuAD benchmark","TFLite quantization (if used) adds additional 2-5% accuracy degradation but enables sub-100ms inference","No dynamic batching optimization — single-sample inference only without custom ONNX conversion","Distillation tuned for KorQuAD domain; transfer to other Korean tasks may require retraining","Korean-only vocabulary — cannot tokenize mixed-language text (Korean+English) without fallback to subword splitting","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5205283313590888,"quality":0.35,"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":161301,"model_likes":7}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=monologg--koelectra-small-v2-distilled-korquad-384","compare_url":"https://unfragile.ai/compare?artifact=monologg--koelectra-small-v2-distilled-korquad-384"}},"signature":"xtdkxX6I3zWaD/YLuVKcUtg59dqBMjpqA9KuxGj7VovqVlWGO8xRpeg/6kM/Hc2Ng905AntS05IFMy4Zv4NiDg==","signedAt":"2026-06-20T15:07:34.887Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/monologg--koelectra-small-v2-distilled-korquad-384","artifact":"https://unfragile.ai/monologg--koelectra-small-v2-distilled-korquad-384","verify":"https://unfragile.ai/api/v1/verify?slug=monologg--koelectra-small-v2-distilled-korquad-384","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"}}