{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-jhgan--ko-sroberta-multitask","slug":"jhgan--ko-sroberta-multitask","name":"ko-sroberta-multitask","type":"model","url":"https://huggingface.co/jhgan/ko-sroberta-multitask","page_url":"https://unfragile.ai/jhgan--ko-sroberta-multitask","categories":["model-training"],"tags":["sentence-transformers","pytorch","tf","roberta","feature-extraction","sentence-similarity","transformers","ko","arxiv:2004.03289","text-embeddings-inference","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-jhgan--ko-sroberta-multitask__cap_0","uri":"capability://data.processing.analysis.korean.sentence.embedding.generation.with.multitask.learning","name":"korean sentence embedding generation with multitask learning","description":"Generates fixed-dimensional dense vector embeddings (768-dim) for Korean text using a RoBERTa-based encoder trained via multitask learning on sentence similarity, semantic textual similarity (STS), and natural language inference (NLI) tasks. The model leverages mean pooling over token representations and was optimized on Korean corpora to capture semantic relationships between sentences, enabling downstream similarity computations without task-specific fine-tuning.","intents":["I need to convert Korean sentences into dense vectors for semantic search or clustering","I want to measure semantic similarity between pairs of Korean sentences without training a custom model","I'm building a Korean document retrieval system and need pre-computed embeddings for fast nearest-neighbor lookup","I need to deduplicate or group similar Korean text passages in my dataset"],"best_for":["Korean NLP teams building semantic search or RAG systems","Researchers working on Korean sentence similarity benchmarks","Developers deploying multilingual applications with Korean language support","Teams needing production-ready Korean embeddings without GPU training infrastructure"],"limitations":["Fixed 768-dimensional output — cannot be resized without retraining; may be over-parameterized for simple tasks","Trained on Korean corpora only — cross-lingual transfer to other languages is not guaranteed and will degrade performance","Multitask training may create trade-offs between STS, NLI, and similarity tasks; no single-task variant available for specialized use cases","No built-in batch processing optimization — inference speed depends on hardware (CPU inference ~50-100ms per sentence, GPU ~5-10ms)","Mean pooling strategy ignores word order and syntactic structure — may conflate semantically different sentences with identical word bags"],"requires":["Python 3.7+","sentence-transformers library (>=2.0.0)","PyTorch 1.11+ or TensorFlow 2.8+ (model supports both)","Hugging Face transformers library (>=4.8.0)","Internet connection for initial model download (~500MB)"],"input_types":["text (Korean Unicode strings)","list of strings (batch processing)","variable-length sequences (max ~512 tokens due to RoBERTa tokenizer)"],"output_types":["numpy array (shape: [batch_size, 768])","PyTorch tensor (shape: [batch_size, 768])","TensorFlow tensor (shape: [batch_size, 768])","float32 embeddings normalized to unit length"],"categories":["data-processing-analysis","memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jhgan--ko-sroberta-multitask__cap_1","uri":"capability://search.retrieval.semantic.similarity.scoring.between.korean.sentence.pairs","name":"semantic similarity scoring between korean sentence pairs","description":"Computes cosine similarity scores between pairs of Korean sentences by embedding both texts and calculating their dot product in the 768-dimensional embedding space. The model supports batch pairwise comparisons and returns similarity scores in the range [0, 1] (after normalization), enabling ranking, clustering, and deduplication workflows without additional model inference beyond the embedding step.","intents":["I need to rank Korean documents by relevance to a query","I want to find the most similar sentence from a corpus to a given input","I need to identify duplicate or near-duplicate Korean text in my dataset","I'm building a recommendation system that matches Korean user queries to Korean content"],"best_for":["Information retrieval teams building Korean search engines","Content moderation teams detecting duplicate Korean posts or spam","E-commerce platforms matching Korean product descriptions to user queries","Academic researchers evaluating Korean semantic textual similarity (STS) benchmarks"],"limitations":["Cosine similarity is symmetric — cannot distinguish directionality (e.g., 'A implies B' vs 'B implies A')","Similarity scores are relative, not calibrated to human judgment scales — threshold selection requires empirical tuning per use case","Batch comparison of N sentences against M queries requires N×M forward passes; no built-in approximate nearest neighbor indexing (requires external FAISS or Annoy integration)","Performance degrades on very short inputs (<3 tokens) or very long inputs (>512 tokens due to tokenizer limits)","Multitask training may cause inconsistent similarity scores across different semantic relationship types (e.g., paraphrase vs entailment)"],"requires":["Python 3.7+","sentence-transformers library (>=2.0.0)","PyTorch or TensorFlow backend","Embeddings for both sentences pre-computed or computed on-the-fly","Numpy or scipy for cosine similarity computation"],"input_types":["two Korean text strings","list of sentence pairs (for batch scoring)","pre-computed embedding vectors (768-dim float32)"],"output_types":["float scalar in range [0, 1] (single pair similarity)","numpy array of shape [batch_size] (batch similarities)","ranked list of (sentence, score) tuples"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jhgan--ko-sroberta-multitask__cap_2","uri":"capability://data.processing.analysis.batch.korean.text.embedding.with.configurable.pooling.strategies","name":"batch korean text embedding with configurable pooling strategies","description":"Processes multiple Korean sentences in parallel through the RoBERTa encoder and applies mean pooling over token representations to generate fixed-size embeddings. The implementation supports batch processing with automatic padding and truncation, leveraging PyTorch or TensorFlow's batched matrix operations to amortize computational cost across multiple inputs, with optional attention-weighted pooling variants available through sentence-transformers configuration.","intents":["I need to embed a large corpus of Korean documents for offline indexing","I want to process Korean text in batches to maximize GPU utilization","I need to generate embeddings for a dataset with variable-length sentences","I'm building a vector database and need to bulk-ingest Korean text embeddings"],"best_for":["Data engineers building Korean document indexing pipelines","ML teams preparing Korean datasets for downstream tasks (clustering, classification)","Vector database administrators ingesting Korean content at scale","Researchers evaluating Korean embedding quality on benchmark datasets"],"limitations":["Mean pooling ignores positional information — all token orderings with identical word sets produce identical embeddings","Batch size is memory-constrained; typical GPU (24GB VRAM) supports ~500-1000 sentences per batch depending on sequence length","Truncation at 512 tokens may lose information for long Korean documents; no sliding-window or hierarchical pooling strategy built-in","No incremental or streaming embedding support — entire batch must be loaded into memory before processing","Padding overhead for variable-length batches can reduce throughput by 10-20% if sequences have high length variance"],"requires":["Python 3.7+","sentence-transformers (>=2.0.0)","PyTorch 1.11+ or TensorFlow 2.8+","GPU recommended for batches >100 sentences (CPU inference ~1-2 sentences/sec)","Sufficient RAM for batch size × 768 × 4 bytes (float32) + model weights (~1.5GB)"],"input_types":["list of Korean text strings","pandas DataFrame with text column","generator/iterator of sentences (with sentence-transformers streaming API)"],"output_types":["numpy array of shape [num_sentences, 768]","PyTorch tensor of shape [num_sentences, 768]","list of embedding vectors","CSV/Parquet with embeddings appended"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jhgan--ko-sroberta-multitask__cap_3","uri":"capability://search.retrieval.cross.lingual.korean.to.english.semantic.transfer.degraded","name":"cross-lingual korean-to-english semantic transfer (degraded)","description":"Enables approximate cross-lingual similarity computations by embedding Korean text and comparing against English embeddings in the shared 768-dimensional space learned during multitask training. The model was not explicitly trained on parallel Korean-English data, so transfer relies on implicit cross-lingual alignment from the RoBERTa architecture's multilingual token vocabulary; similarity scores are lower fidelity than within-language comparisons due to vocabulary mismatch and training data imbalance.","intents":["I need to find English documents semantically similar to Korean queries (with degraded accuracy)","I want to build a Korean-English cross-lingual search system without training a dedicated alignment model","I'm prototyping a multilingual recommendation system and need a quick baseline"],"best_for":["Prototyping teams building MVP cross-lingual systems with limited training data","Researchers studying zero-shot cross-lingual transfer in Korean-English pairs","Teams needing a quick fallback when dedicated cross-lingual models are unavailable"],"limitations":["Cross-lingual similarity scores are 15-25% lower in correlation with human judgments compared to within-language similarity, due to vocabulary and training data imbalance","No explicit alignment between Korean and English token spaces — relies on implicit overlap in RoBERTa's multilingual vocabulary (~50% overlap for Korean-English)","Multitask training was Korean-centric, not balanced across languages — English transfer is significantly weaker than Korean","Requires external English embedding model or manual English text embedding — no built-in English variant","Not suitable for production cross-lingual systems; dedicated models (e.g., multilingual sentence-transformers) should be used instead"],"requires":["Python 3.7+","sentence-transformers library","Separate English embedding model or manual English text encoding","Understanding that cross-lingual transfer is approximate and may fail on domain-specific or rare terms"],"input_types":["Korean text string","English text string","pre-computed Korean and English embeddings"],"output_types":["float scalar in range [0, 1] (approximate cross-lingual similarity)","ranked list of English documents by Korean query relevance (degraded)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jhgan--ko-sroberta-multitask__cap_4","uri":"capability://tool.use.integration.integration.with.sentence.transformers.inference.pipelines.and.vector.databases","name":"integration with sentence-transformers inference pipelines and vector databases","description":"Provides native compatibility with the sentence-transformers library's inference abstractions, enabling seamless integration with vector databases (Pinecone, Weaviate, Milvus), embedding caching layers, and distributed inference frameworks. The model can be loaded via `SentenceTransformer('jhgan/ko-sroberta-multitask')` and automatically handles tokenization, batching, device placement, and embedding normalization through the library's standardized pipeline, with optional support for ONNX export and quantization for edge deployment.","intents":["I want to deploy this model in a vector database without writing custom inference code","I need to integrate Korean embeddings into an existing sentence-transformers-based RAG pipeline","I'm building a production system and need automatic caching, batching, and GPU memory management","I want to export this model to ONNX or quantized formats for edge deployment"],"best_for":["ML engineers building production RAG or semantic search systems","Teams using vector databases (Pinecone, Weaviate, Milvus) with sentence-transformers","Developers deploying embeddings on edge devices or resource-constrained environments","Organizations standardizing on sentence-transformers for embedding infrastructure"],"limitations":["sentence-transformers adds ~50-100ms overhead per inference call for pipeline setup (tokenization, batching, device placement) — not suitable for ultra-low-latency (<10ms) requirements","ONNX export requires manual conversion and may not preserve all optimizations (e.g., attention patterns); quantization (INT8) can reduce accuracy by 1-3%","Caching layer requires external storage (Redis, local disk) — no built-in persistence","Distributed inference across multiple GPUs requires additional orchestration (Ray, Kubernetes) not provided by sentence-transformers alone","Model size (~500MB) may be prohibitive for edge devices with <1GB storage"],"requires":["Python 3.7+","sentence-transformers library (>=2.0.0)","PyTorch 1.11+ or TensorFlow 2.8+","Hugging Face transformers library (>=4.8.0)","Optional: ONNX Runtime for ONNX inference, quantization libraries (onnx-simplifier, quantization-aware training tools)"],"input_types":["Korean text strings","list of sentences","pandas Series or DataFrame"],"output_types":["normalized embedding vectors (768-dim float32)","ONNX model file (for edge deployment)","quantized model (INT8 or FP16)"],"categories":["tool-use-integration","memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jhgan--ko-sroberta-multitask__cap_5","uri":"capability://code.generation.editing.fine.tuning.and.domain.adaptation.for.korean.specific.tasks","name":"fine-tuning and domain adaptation for korean-specific tasks","description":"Supports continued training on domain-specific Korean corpora using sentence-transformers' fine-tuning API, enabling adaptation to specialized vocabularies (medical, legal, technical Korean) or custom similarity objectives. The model can be fine-tuned using triplet loss, contrastive loss, or multi-task learning objectives on labeled Korean datasets, with automatic gradient computation and learning rate scheduling; fine-tuned models retain the base architecture and can be exported as standard HuggingFace models.","intents":["I need to adapt this model to medical or legal Korean terminology","I want to fine-tune on my proprietary Korean dataset to improve domain-specific similarity","I'm building a specialized Korean search system and need to optimize embeddings for my use case","I want to combine this model with custom loss functions for my specific similarity definition"],"best_for":["Teams with domain-specific Korean corpora (medical, legal, e-commerce, etc.)","Researchers fine-tuning models for Korean NLP benchmarks","Organizations with labeled Korean similarity datasets wanting to improve accuracy","Developers building specialized Korean search or recommendation systems"],"limitations":["Fine-tuning requires labeled data (triplets or pairs with similarity scores); no unsupervised domain adaptation built-in","Overfitting risk on small datasets (<10K examples) — requires careful regularization and validation set tuning","Fine-tuning can degrade performance on out-of-domain data; no built-in multi-task learning to preserve general-purpose similarity","Computational cost: fine-tuning on 100K examples requires 4-8 GPU hours (A100) or 24-48 hours on CPU","No automatic hyperparameter tuning — learning rate, batch size, and loss weights require manual experimentation","Fine-tuned models are not automatically uploaded to HuggingFace Hub; requires manual model management and versioning"],"requires":["Python 3.7+","sentence-transformers library (>=2.0.0)","PyTorch 1.11+ (TensorFlow fine-tuning not fully supported)","GPU recommended (NVIDIA with CUDA 11.0+) for practical training times","Labeled Korean dataset with similarity annotations or triplet structure","8GB+ GPU VRAM for batch size 32; 24GB+ for batch size 128"],"input_types":["CSV/JSON with (sentence1, sentence2, similarity_score) tuples","triplet data: (anchor, positive, negative) Korean sentences","labeled pairs with domain-specific similarity judgments"],"output_types":["fine-tuned model checkpoint (PyTorch .pt or HuggingFace format)","training logs with loss curves and validation metrics","updated embeddings on fine-tuned model"],"categories":["code-generation-editing","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","sentence-transformers library (>=2.0.0)","PyTorch 1.11+ or TensorFlow 2.8+ (model supports both)","Hugging Face transformers library (>=4.8.0)","Internet connection for initial model download (~500MB)","PyTorch or TensorFlow backend","Embeddings for both sentences pre-computed or computed on-the-fly","Numpy or scipy for cosine similarity computation","sentence-transformers (>=2.0.0)","PyTorch 1.11+ or TensorFlow 2.8+"],"failure_modes":["Fixed 768-dimensional output — cannot be resized without retraining; 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