{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-ebanlee--kobart-summary-v3","slug":"ebanlee--kobart-summary-v3","name":"kobart-summary-v3","type":"model","url":"https://huggingface.co/EbanLee/kobart-summary-v3","page_url":"https://unfragile.ai/ebanlee--kobart-summary-v3","categories":["text-writing"],"tags":["transformers","safetensors","bart","text2text-generation","summarization","ko","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-ebanlee--kobart-summary-v3__cap_0","uri":"capability://text.generation.language.korean.text.abstractive.summarization.with.bart.architecture","name":"korean text abstractive summarization with bart architecture","description":"Performs abstractive summarization on Korean text using a fine-tuned BART (Bidirectional Auto-Regressive Transformers) encoder-decoder architecture. The model encodes input Korean text through a bidirectional transformer encoder, then generates abstractive summaries token-by-token via an autoregressive decoder with cross-attention over encoded representations. Fine-tuned on Korean summarization datasets to learn domain-specific compression patterns and semantic preservation.","intents":["Automatically condense long Korean documents into concise summaries while preserving key information","Generate abstractive summaries for Korean news articles, reports, or user-generated content","Integrate Korean text summarization into content curation or document processing pipelines","Reduce token consumption in downstream NLP tasks by pre-summarizing Korean input texts"],"best_for":["Korean language content teams building document processing systems","Developers integrating summarization into Korean e-commerce, news, or publishing platforms","Teams needing lightweight, open-source Korean NLP without API dependencies","Researchers fine-tuning or evaluating Korean abstractive summarization models"],"limitations":["Abstractive generation may hallucinate or introduce factual errors not present in source text — requires human review for high-stakes applications","Performance degrades on very long documents (>1024 tokens) due to BART's context window constraints","No built-in handling of structured data, tables, or multi-modal content — text-only input","Fine-tuned on specific Korean datasets; performance on domain-specific jargon (medical, legal, technical) may be suboptimal","Inference latency ~2-5 seconds per document on CPU; GPU acceleration recommended for production throughput"],"requires":["Python 3.7+","transformers library (>=4.20.0)","torch or tensorflow backend (>=1.9.0)","4GB+ RAM for model loading (safetensors format reduces memory footprint)","Optional: GPU with CUDA 11.0+ for accelerated inference"],"input_types":["plain text (Korean)","UTF-8 encoded strings","text sequences up to ~1024 tokens (model's max context window)"],"output_types":["plain text (Korean)","UTF-8 encoded summary strings","variable length output (typically 20-30% of input length)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ebanlee--kobart-summary-v3__cap_1","uri":"capability://automation.workflow.batch.inference.with.huggingface.transformers.pipeline.api","name":"batch inference with huggingface transformers pipeline api","description":"Integrates with HuggingFace's Transformers pipeline abstraction to enable batch processing of multiple Korean texts through a single model instance. The pipeline handles tokenization, model inference, and post-processing (decoding) automatically, supporting batched inputs to amortize model loading overhead and maximize GPU utilization across multiple documents in a single forward pass.","intents":["Process multiple Korean documents in parallel batches to improve throughput and reduce per-document latency","Integrate the model into production inference services without manual tokenization/decoding logic","Leverage HuggingFace ecosystem tools (Inference API, Endpoints) for serverless or managed deployment"],"best_for":["Teams building batch processing pipelines for document summarization","Developers deploying to HuggingFace Inference Endpoints or compatible platforms","Production systems requiring standardized, abstracted inference interfaces"],"limitations":["Pipeline abstraction adds ~50-100ms overhead per batch due to tokenizer initialization and post-processing","Batch size is constrained by available GPU memory; no automatic batching strategy for heterogeneous input lengths","No streaming or incremental output support — entire summary must be generated before returning results"],"requires":["transformers library (>=4.20.0)","HuggingFace model card access (public model)","Optional: HuggingFace API token for private model access"],"input_types":["list of plain text strings (Korean)","single text string (auto-batched internally)"],"output_types":["list of summary dictionaries with 'summary_text' key","structured output with confidence scores (if model supports)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ebanlee--kobart-summary-v3__cap_2","uri":"capability://automation.workflow.safetensors.based.model.serialization.and.fast.loading","name":"safetensors-based model serialization and fast loading","description":"Model weights are serialized in safetensors format (a safer, faster alternative to PyTorch pickle format) enabling rapid model initialization with reduced memory fragmentation and built-in integrity checks. Safetensors uses memory-mapped file access, allowing lazy loading of weight tensors and eliminating the need to deserialize the entire model into memory before inference begins.","intents":["Reduce model loading time from 10-30 seconds (pickle) to <2 seconds (safetensors) in production environments","Minimize memory footprint during model initialization by lazy-loading weights on-demand","Ensure model integrity and prevent arbitrary code execution vulnerabilities inherent in pickle deserialization"],"best_for":["Production systems with strict latency requirements for model initialization","Containerized deployments (Docker, Kubernetes) where startup time impacts cold-start performance","Security-conscious teams requiring safe model distribution without pickle deserialization risks"],"limitations":["Safetensors support requires transformers library >=4.20.0; older versions fall back to pickle","Memory-mapped access may be slower than pre-loaded tensors for repeated inference on small models","No support for dynamic model architectures or custom weight initialization — requires static model definitions"],"requires":["transformers library (>=4.20.0)","safetensors library (>=0.3.0)","Python 3.7+"],"input_types":["safetensors binary files (.safetensors)"],"output_types":["loaded PyTorch model state dict in GPU/CPU memory"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ebanlee--kobart-summary-v3__cap_3","uri":"capability://data.processing.analysis.multi.language.tokenization.with.language.specific.preprocessing","name":"multi-language tokenization with language-specific preprocessing","description":"Integrates BART's multilingual tokenizer (based on BPE with Korean-specific vocabulary) to handle Korean text preprocessing, including character normalization, whitespace handling, and subword tokenization. The tokenizer converts raw Korean text into token IDs compatible with the BART encoder, preserving morphological and semantic information through learned BPE merges optimized for Korean morphology.","intents":["Automatically preprocess Korean text with proper handling of Hangul characters, spacing, and punctuation","Convert variable-length Korean documents into fixed-size token sequences for model inference","Preserve Korean linguistic structure (particles, verb conjugations) through subword tokenization"],"best_for":["Developers integrating Korean NLP without manual text preprocessing","Teams processing diverse Korean text sources (social media, news, formal documents) with varying formatting","Researchers analyzing tokenization behavior on Korean morphologically-rich text"],"limitations":["Tokenizer vocabulary is fixed at model training time; out-of-vocabulary Korean terms are split into subwords, potentially losing semantic meaning","No built-in handling of mixed-language text (Korean + English) — may tokenize English suboptimally","Maximum sequence length of 1024 tokens enforced; longer documents require truncation or sliding-window approaches","Whitespace normalization may lose formatting information (e.g., intentional spacing in poetry or structured text)"],"requires":["transformers library (>=4.20.0)","BART tokenizer configuration (auto-loaded from model card)"],"input_types":["raw Korean text strings (UTF-8)","text with mixed whitespace, punctuation, special characters"],"output_types":["token ID sequences (list of integers)","attention masks (binary tensors indicating valid tokens)","token type IDs (if applicable)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ebanlee--kobart-summary-v3__cap_4","uri":"capability://text.generation.language.encoder.decoder.attention.mechanism.for.context.aware.summary.generation","name":"encoder-decoder attention mechanism for context-aware summary generation","description":"Implements BART's cross-attention mechanism between the encoder (which processes input Korean text) and decoder (which generates summaries). During decoding, each generated token attends to the full encoded input representation, allowing the model to dynamically select relevant source text spans for each summary token. This enables abstractive compression while maintaining semantic fidelity to the source.","intents":["Generate summaries that faithfully represent key information from source text through attention-guided token selection","Understand which parts of the input document most influenced each summary sentence (interpretability)","Avoid hallucination by grounding summary generation in encoded source representations"],"best_for":["Applications requiring interpretable summarization with attention visualization","Teams building fact-checking or summary validation systems","Researchers studying abstractive summarization mechanisms and attention patterns"],"limitations":["Attention mechanism does not guarantee factual accuracy — model can still hallucinate despite attending to source text","Attention weights are learned heuristics, not explicit importance scores; high attention does not always correlate with summary relevance","Extracting and visualizing attention requires additional post-processing; not exposed in standard pipeline output","Attention computation adds ~15-20% overhead to inference latency compared to encoder-only models"],"requires":["transformers library with attention output support (>=4.20.0)","Optional: matplotlib or similar for attention visualization"],"input_types":["encoded input text representations (internal to model)"],"output_types":["attention weight matrices (batch_size, num_heads, target_seq_len, source_seq_len)","generated summary token sequences"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ebanlee--kobart-summary-v3__cap_5","uri":"capability://text.generation.language.autoregressive.decoding.with.beam.search.and.length.penalty","name":"autoregressive decoding with beam search and length penalty","description":"Generates summaries token-by-token using autoregressive decoding with beam search (exploring multiple hypothesis paths) and length penalty to balance summary brevity and completeness. The decoder maintains a beam of candidate summaries, scoring each based on log-probability and length-normalized penalties, selecting the highest-scoring complete sequence when an end-of-sequence token is generated.","intents":["Generate multiple candidate summaries and select the best one based on model confidence and length constraints","Control summary length through configurable length penalties to avoid excessively long or short outputs","Improve summary quality by exploring diverse decoding paths instead of greedy single-token selection"],"best_for":["Production systems requiring high-quality summaries with configurable length constraints","Applications where summary diversity or multiple candidate summaries are valuable","Teams fine-tuning decoding hyperparameters for domain-specific summarization quality"],"limitations":["Beam search increases inference latency by 3-5x compared to greedy decoding; beam_size=4 is typical trade-off","Length penalty is a heuristic hyperparameter requiring manual tuning per domain; no automatic optimization","Beam search does not guarantee globally optimal summary — only locally optimal within beam width","Decoding is sequential (cannot parallelize across tokens); GPU utilization is lower than batch inference"],"requires":["transformers library with beam search support (>=4.20.0)","GPU recommended for acceptable latency (beam_size>1)"],"input_types":["encoded input representations","decoding hyperparameters (beam_size, length_penalty, max_length)"],"output_types":["generated summary sequences (list of token IDs)","log-probability scores for each summary","optional: multiple candidate summaries (num_beams > 1)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library (>=4.20.0)","torch or tensorflow backend (>=1.9.0)","4GB+ RAM for model loading (safetensors format reduces memory footprint)","Optional: GPU with CUDA 11.0+ for accelerated inference","HuggingFace model card access (public model)","Optional: HuggingFace API token for private model access","safetensors library (>=0.3.0)","BART tokenizer configuration (auto-loaded from model card)","transformers library with attention output support (>=4.20.0)"],"failure_modes":["Abstractive generation may hallucinate or introduce factual errors not present in source text — requires human review for high-stakes applications","Performance degrades on very long documents (>1024 tokens) due to BART's context window constraints","No built-in handling of structured data, tables, or multi-modal content — text-only input","Fine-tuned on specific Korean datasets; performance on domain-specific jargon (medical, legal, technical) may be suboptimal","Inference latency ~2-5 seconds per document on CPU; GPU acceleration recommended for production throughput","Pipeline abstraction adds ~50-100ms overhead per batch due to tokenizer initialization and post-processing","Batch size is constrained by available GPU memory; no automatic batching strategy for heterogeneous input lengths","No streaming or incremental output support — entire summary must be generated before returning results","Safetensors support requires transformers library >=4.20.0; older versions fall back to pickle","Memory-mapped access may be slower than pre-loaded tensors for repeated inference on small models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.40974631099869147,"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":"2026-05-03T14:22:54.515Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":22900,"model_likes":22}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ebanlee--kobart-summary-v3","compare_url":"https://unfragile.ai/compare?artifact=ebanlee--kobart-summary-v3"}},"signature":"xPtaFiaIRigs0J4MqtQ6TlKf+N9gEmi2W1akhMe27Tknil1bpSa7rMxQmJ0er3AH9XIFRhAs9aIMtSli/IDuAA==","signedAt":"2026-06-22T05:59:13.280Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ebanlee--kobart-summary-v3","artifact":"https://unfragile.ai/ebanlee--kobart-summary-v3","verify":"https://unfragile.ai/api/v1/verify?slug=ebanlee--kobart-summary-v3","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"}}