{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-daekeun-ml--koelectra-small-v3-nsmc","slug":"daekeun-ml--koelectra-small-v3-nsmc","name":"koelectra-small-v3-nsmc","type":"model","url":"https://huggingface.co/daekeun-ml/koelectra-small-v3-nsmc","page_url":"https://unfragile.ai/daekeun-ml--koelectra-small-v3-nsmc","categories":["model-training"],"tags":["transformers","pytorch","safetensors","electra","text-classification","classification","ko","dataset:nsmc","license:mit","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-daekeun-ml--koelectra-small-v3-nsmc__cap_0","uri":"capability://data.processing.analysis.korean.sentiment.classification.with.electra.based.fine.tuning","name":"korean sentiment classification with electra-based fine-tuning","description":"Performs binary sentiment classification (positive/negative) on Korean text using a small ELECTRA discriminator model fine-tuned on the NSMC (Naver Sentiment Movie Comments) dataset. The model leverages ELECTRA's replaced-token detection pretraining approach combined with task-specific fine-tuning on 200K Korean movie reviews, enabling efficient sentiment inference with 23.5M parameters. Inference runs locally via PyTorch/Hugging Face Transformers without requiring API calls, supporting batch processing and custom confidence thresholds.","intents":["Classify Korean customer reviews or social media comments as positive or negative sentiment","Build a Korean sentiment analysis pipeline for product feedback or brand monitoring","Fine-tune or adapt this model for domain-specific Korean sentiment tasks (e.g., restaurant reviews, app ratings)","Deploy lightweight sentiment classification in resource-constrained environments (mobile, edge devices)","Evaluate sentiment distribution across large Korean text corpora for market research"],"best_for":["Korean NLP teams building sentiment analysis systems without cloud API dependencies","Startups needing low-latency, on-device sentiment classification for Korean content","Researchers benchmarking Korean text classification models against ELECTRA baselines","Companies processing sensitive Korean customer data requiring on-premise inference"],"limitations":["Binary classification only (positive/negative) — no neutral/multi-class support or confidence-weighted gradations","Trained exclusively on movie review domain (NSMC) — may have domain shift when applied to non-review Korean text (e.g., news, technical docs, social media slang)","Small model size (23.5M params) trades off accuracy for speed — likely lower F1 than larger BERT-base or KoBERT models on out-of-domain data","No built-in handling of sarcasm, negation scope, or context-dependent sentiment in Korean (e.g., '별로' as subtle negative)","Requires Korean text preprocessing (tokenization, normalization) — no automatic handling of typos, abbreviations, or internet slang common in Korean social media"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","Hugging Face Transformers library 4.0+","~500MB disk space for model weights (safetensors format)","GPU optional but recommended for batch inference >100 samples"],"input_types":["raw Korean text strings (UTF-8 encoded)","pre-tokenized Korean text (space-separated tokens)","batch lists of Korean text samples"],"output_types":["binary class labels (0=negative, 1=positive)","logits (raw model scores before softmax)","probability distributions (softmax-normalized confidence scores per class)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-daekeun-ml--koelectra-small-v3-nsmc__cap_1","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.padding.and.token.optimization","name":"batch inference with dynamic padding and token optimization","description":"Processes multiple Korean text samples in parallel batches using Hugging Face Transformers' DataCollator with dynamic padding, which pads sequences to the longest sample in each batch rather than a fixed max length. This reduces computational waste and memory overhead when processing variable-length Korean text. Supports configurable batch sizes and automatic device placement (CPU/GPU), enabling efficient throughput for production inference pipelines without manual padding logic.","intents":["Process 100s-1000s of Korean reviews in a single batch job without manual padding overhead","Optimize GPU memory usage when classifying variable-length Korean text (short comments vs long reviews)","Build a production sentiment analysis API that batches incoming Korean text requests for throughput efficiency","Reduce inference latency per sample by amortizing model loading and GPU transfer costs across batches"],"best_for":["Backend engineers building batch processing pipelines for Korean sentiment analysis","Data teams running nightly jobs to classify large Korean text corpora","ML ops teams deploying inference services with SLA requirements for throughput"],"limitations":["Dynamic padding requires variable batch sizes — incompatible with strict fixed-shape tensor requirements (e.g., ONNX export with fixed input shapes)","Batch processing introduces latency variance — single-sample inference may be slower than dedicated optimized models due to framework overhead","No built-in batching across multiple GPU devices — single-GPU bottleneck for very large datasets (>1M samples)","Memory usage scales with longest sequence in batch — pathological cases (one very long review in batch of short ones) negate padding benefits"],"requires":["Hugging Face Transformers 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Sufficient RAM for batch size × max_sequence_length × hidden_dim (e.g., batch_size=32, seq_len=512 requires ~2GB GPU memory)"],"input_types":["list of Korean text strings (variable length)","pandas DataFrame with text column","generator/iterator of text samples"],"output_types":["numpy arrays of logits (batch_size × num_classes)","pandas DataFrame with predictions and confidence scores","generator of predictions (streaming mode)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-daekeun-ml--koelectra-small-v3-nsmc__cap_2","uri":"capability://memory.knowledge.hugging.face.hub.model.versioning.and.safetensors.format.loading","name":"hugging face hub model versioning and safetensors format loading","description":"Loads model weights from Hugging Face Hub using safetensors format (a secure, fast serialization standard) instead of pickle, with automatic version management and caching. The model is stored as a public repository with git-based versioning, allowing reproducible downloads of specific commits/tags. Safetensors format enables faster deserialization (~10x vs pickle) and eliminates arbitrary code execution risks during weight loading, making it suitable for production and untrusted environments.","intents":["Download and cache the exact version of koelectra-small-v3-nsmc model weights for reproducible inference","Integrate the model into production systems with security guarantees (no pickle deserialization vulnerabilities)","Track model evolution and revert to previous versions if newer checkpoints degrade performance","Share the model across teams with version pinning to ensure consistent results"],"best_for":["Production ML teams requiring secure, reproducible model loading without pickle vulnerabilities","Organizations with strict security policies prohibiting arbitrary code execution during model loading","Researchers needing version-controlled model snapshots for reproducibility"],"limitations":["Requires internet connectivity for initial download — no offline-first workflow without pre-caching","Hub caching uses ~/.cache/huggingface/hub directory — can consume significant disk space if multiple large models are cached","Safetensors format is newer — some older tools/frameworks may not support it natively (requires fallback to PyTorch format)","Version pinning requires explicit commit hash or tag — default behavior always fetches latest, risking silent model updates"],"requires":["Internet connection for initial model download","Hugging Face Transformers 4.0+","safetensors library (auto-installed with transformers)","~500MB free disk space in ~/.cache/huggingface/hub","Optional: Hugging Face API token for private model access (not needed for public models)"],"input_types":["model identifier string ('daekeun-ml/koelectra-small-v3-nsmc')","optional revision parameter (commit hash, branch, or tag)"],"output_types":["PyTorch model object (PreTrainedModel)","tokenizer object (PreTrainedTokenizer)","config object (PretrainedConfig)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-daekeun-ml--koelectra-small-v3-nsmc__cap_3","uri":"capability://data.processing.analysis.tokenization.with.korean.morphological.awareness","name":"tokenization with korean morphological awareness","description":"Tokenizes Korean text using ELECTRA's pretrained WordPiece tokenizer, which was trained on Korean corpora and includes morphological awareness for Korean-specific linguistic patterns (e.g., particles, verb conjugations, compound words). The tokenizer handles Korean-specific edge cases like spacing conventions, Hangul decomposition, and subword segmentation optimized for Korean morphology. Supports both encoding (text → token IDs) and decoding (token IDs → text) with configurable special tokens and truncation strategies.","intents":["Convert raw Korean text into token IDs compatible with the koelectra model for inference","Handle Korean-specific tokenization challenges (spacing, particles, compound words) without manual preprocessing","Decode model predictions back to human-readable Korean text for interpretability","Customize tokenization behavior (max length, truncation strategy, special tokens) for domain-specific Korean text"],"best_for":["Korean NLP engineers building end-to-end pipelines without custom tokenization logic","Teams processing diverse Korean text (formal documents, social media, reviews) with morphological awareness","Researchers comparing tokenization strategies across Korean models"],"limitations":["WordPiece tokenization may split Korean words into many subword tokens — less interpretable than morphological analysis tools (e.g., Mecab, Okt)","Tokenizer vocabulary is fixed at 30K tokens — cannot add new domain-specific Korean terms without retraining","No built-in handling of Korean abbreviations or internet slang (e.g., 'ㅇㅈ' for '인정') — requires preprocessing","Tokenization is deterministic but not reversible for all cases — decoding may not perfectly reconstruct original spacing/punctuation"],"requires":["Hugging Face Transformers 4.0+","Python 3.7+","sentencepiece library (auto-installed with transformers)"],"input_types":["raw Korean text strings (UTF-8)","lists of Korean text samples","token IDs (for decoding)"],"output_types":["token IDs (list of integers)","attention masks (binary mask for padding tokens)","token type IDs (for multi-sequence inputs)","decoded text (string)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-daekeun-ml--koelectra-small-v3-nsmc__cap_4","uri":"capability://code.generation.editing.transfer.learning.and.fine.tuning.foundation.for.korean.text.tasks","name":"transfer learning and fine-tuning foundation for korean text tasks","description":"Provides a pretrained ELECTRA discriminator checkpoint that can be fine-tuned for downstream Korean text classification tasks beyond sentiment analysis. The model's learned representations capture Korean linguistic patterns from pretraining, enabling efficient transfer learning with minimal labeled data. Supports standard fine-tuning workflows (adding task-specific head, freezing/unfreezing layers, learning rate scheduling) via Hugging Face Transformers' Trainer API or custom PyTorch training loops.","intents":["Fine-tune this model on custom Korean text classification datasets (e.g., toxicity detection, topic classification, intent recognition)","Leverage pretrained Korean representations to reduce labeled data requirements for new Korean NLP tasks","Adapt the model to domain-specific Korean text (e.g., medical, legal, technical documents) with task-specific fine-tuning","Benchmark transfer learning performance across different Korean text classification tasks"],"best_for":["Korean NLP teams with limited labeled data for custom classification tasks","Researchers studying transfer learning effectiveness for Korean language models","Companies building multiple Korean text classification models (toxicity, intent, topic) from a shared pretrained base"],"limitations":["Fine-tuning requires labeled data — no zero-shot or few-shot capabilities without additional techniques (e.g., prompt-based learning)","Small model size (23.5M params) limits capacity for complex Korean linguistic phenomena — may underperform on nuanced tasks (e.g., sarcasm, context-dependent meaning)","Fine-tuning on small datasets (<1K samples) risks overfitting — requires careful regularization and validation strategies","No built-in domain adaptation techniques — domain shift from NSMC (movie reviews) to other Korean text requires explicit handling"],"requires":["Hugging Face Transformers 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Labeled Korean text dataset (minimum 100-500 samples for meaningful fine-tuning)","GPU recommended for fine-tuning (CPU training is slow for >10K samples)","Optional: Weights & Biases or MLflow for experiment tracking"],"input_types":["labeled Korean text dataset (CSV, JSON, or Hugging Face Dataset format)","task-specific labels (binary, multi-class, or multi-label)"],"output_types":["fine-tuned model checkpoint (PyTorch or safetensors format)","training metrics (loss, accuracy, F1, etc.)","predictions on new Korean text"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-daekeun-ml--koelectra-small-v3-nsmc__cap_5","uri":"capability://data.processing.analysis.confidence.scoring.and.probability.calibration.for.sentiment.predictions","name":"confidence scoring and probability calibration for sentiment predictions","description":"Outputs softmax-normalized probability distributions over sentiment classes (positive/negative), enabling confidence-based filtering and decision-making. The model produces logits that are converted to probabilities via softmax, allowing downstream systems to reject low-confidence predictions or apply different handling strategies based on confidence thresholds. Supports both hard predictions (argmax class) and soft predictions (probability distributions) for flexible integration into decision pipelines.","intents":["Filter out low-confidence sentiment predictions to reduce false positives in production systems","Apply different business logic based on prediction confidence (e.g., auto-approve high-confidence, escalate medium-confidence to human review)","Measure model uncertainty and identify ambiguous Korean text that requires human annotation","Calibrate confidence thresholds based on business metrics (precision/recall tradeoff)"],"best_for":["Production systems requiring confidence-based filtering to manage false positive rates","Teams building human-in-the-loop workflows where confidence scores drive escalation decisions","Data annotation pipelines that prioritize uncertain predictions for labeling"],"limitations":["Softmax probabilities are not calibrated — confidence scores may not reflect true prediction accuracy (e.g., model may be 90% confident but only 70% correct)","No built-in calibration techniques — requires post-hoc calibration (temperature scaling, Platt scaling) if well-calibrated probabilities are critical","Confidence is per-sample only — no uncertainty quantification across the full dataset or model-level confidence estimates","Binary classification limits confidence interpretation — no way to distinguish between 'confident negative' vs 'uncertain between negative and positive'"],"requires":["Model inference output (logits or probabilities)","Optional: scikit-learn or other calibration library for post-hoc calibration"],"input_types":["model logits (raw scores before softmax)","Korean text samples"],"output_types":["probability distributions (softmax-normalized scores, sum to 1.0)","confidence scores (max probability across classes)","hard predictions (argmax class label)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","Hugging Face Transformers library 4.0+","~500MB disk space for model weights (safetensors format)","GPU optional but recommended for batch inference >100 samples","Hugging Face Transformers 4.0+","Sufficient RAM for batch size × max_sequence_length × hidden_dim (e.g., batch_size=32, seq_len=512 requires ~2GB GPU memory)","Internet connection for initial model download","safetensors library (auto-installed with transformers)","~500MB free disk space in ~/.cache/huggingface/hub"],"failure_modes":["Binary classification only (positive/negative) — no neutral/multi-class support or confidence-weighted gradations","Trained exclusively on movie review domain (NSMC) — may have domain shift when applied to non-review Korean text (e.g., news, technical 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