{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-cross-encoder--nli-deberta-v3-large","slug":"cross-encoder--nli-deberta-v3-large","name":"nli-deberta-v3-large","type":"model","url":"https://huggingface.co/cross-encoder/nli-deberta-v3-large","page_url":"https://unfragile.ai/cross-encoder--nli-deberta-v3-large","categories":["data-analysis"],"tags":["sentence-transformers","pytorch","onnx","safetensors","deberta-v2","text-classification","transformers","zero-shot-classification","en","dataset:nyu-mll/multi_nli","dataset:stanfordnlp/snli","base_model:microsoft/deberta-v3-large","base_model:quantized:microsoft/deberta-v3-large","license:apache-2.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-cross-encoder--nli-deberta-v3-large__cap_0","uri":"capability://data.processing.analysis.zero.shot.natural.language.inference.classification","name":"zero-shot natural language inference classification","description":"Classifies relationships between premise-hypothesis sentence pairs into entailment, contradiction, or neutral categories without task-specific fine-tuning. Uses DeBERTa v3-large's bidirectional transformer architecture trained on SNLI and MultiNLI datasets to compute probability distributions over the three NLI classes. The model accepts raw text pairs and outputs confidence scores for each relationship type, enabling downstream applications to infer semantic relationships without labeled examples.","intents":["determine if a hypothesis is entailed by, contradicted by, or neutral to a given premise","classify semantic relationships between sentence pairs for fact verification or claim validation","perform zero-shot text classification by reformulating categories as hypothesis statements","build fact-checking pipelines that assess whether claims are supported by source documents"],"best_for":["NLP engineers building fact-verification systems without domain-specific labeled data","teams implementing semantic similarity or entailment detection in search/retrieval pipelines","developers prototyping zero-shot classification tasks by converting labels to natural language hypotheses"],"limitations":["Optimized for English text only; performance degrades significantly on non-English or code-mixed inputs","Requires premise-hypothesis pairs as input; cannot directly classify single sentences without reformulation","Model size (435M parameters) requires ~1.7GB GPU memory; inference latency ~100-200ms per pair on CPU","Trained on news/Wikipedia-style text; may underperform on domain-specific language (medical, legal, technical jargon)","Cross-encoder architecture requires computing scores for each hypothesis separately; scales linearly with number of candidate classes"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","sentence-transformers library (>=2.2.0) or transformers library (>=4.0.0)","4GB+ RAM for inference; 8GB+ GPU VRAM recommended for batch processing","HuggingFace Hub access (model auto-downloads on first use, ~1.7GB)"],"input_types":["text (premise string)","text (hypothesis string)","list of premise-hypothesis pairs for batch inference"],"output_types":["structured data (dict with keys: 'entailment', 'neutral', 'contradiction' containing float confidence scores 0.0-1.0)","text (predicted label: 'entailment', 'neutral', or 'contradiction')","structured data (batch results as list of dicts with scores and labels)"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-large__cap_1","uri":"capability://data.processing.analysis.cross.encoder.semantic.pair.scoring.with.confidence.calibration","name":"cross-encoder semantic pair scoring with confidence calibration","description":"Computes normalized confidence scores for sentence pair relationships by processing both sentences jointly through a shared transformer encoder, then applying a classification head that outputs calibrated probability distributions. Unlike bi-encoders that embed sentences separately, this cross-encoder approach allows attention mechanisms to directly compare token-level interactions between premise and hypothesis, producing more reliable confidence estimates for downstream decision-making.","intents":["rank or score multiple hypothesis candidates against a single premise based on semantic relationship strength","obtain calibrated confidence scores suitable for thresholding or ranking in retrieval/ranking pipelines","compare semantic similarity between sentence pairs with explicit relationship type (not just similarity magnitude)","use model outputs directly in decision trees or rule-based systems that depend on entailment confidence"],"best_for":["ranking engineers building semantic re-rankers for search or QA systems","data scientists implementing confidence-aware classification pipelines with decision thresholds","teams building fact-checking or claim validation systems requiring interpretable confidence scores"],"limitations":["Cross-encoder design requires separate forward pass per hypothesis; cannot leverage batch processing as efficiently as bi-encoders for large candidate sets (N hypotheses = N forward passes)","Confidence calibration is dataset-dependent; scores may not generalize perfectly to domains far from SNLI/MultiNLI distribution","No built-in uncertainty quantification; single-point estimates without confidence intervals","Requires both premise and hypothesis at inference time; cannot pre-compute embeddings for hypothesis candidates"],"requires":["Python 3.7+","sentence-transformers library (>=2.2.0) or transformers library (>=4.0.0)","PyTorch 1.9+ or TensorFlow 2.4+","GPU with 6GB+ VRAM for batch inference; CPU inference viable but slow (~200-500ms per pair)"],"input_types":["text (premise string, max ~512 tokens)","text (hypothesis string, max ~512 tokens)","list of tuples [(premise, hypothesis_1), (premise, hypothesis_2), ...] for batch scoring"],"output_types":["float (confidence score 0.0-1.0 for each class)","dict with keys 'entailment', 'neutral', 'contradiction' containing float scores","numpy array or tensor of shape (batch_size, 3) for batch inference"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-large__cap_2","uri":"capability://automation.workflow.multi.format.model.serialization.and.deployment.pytorch.onnx.safetensors","name":"multi-format model serialization and deployment (pytorch, onnx, safetensors)","description":"Supports loading and inference across multiple serialization formats (PyTorch native .pt, ONNX, SafeTensors) enabling deployment flexibility across different runtime environments. The model can be instantiated via sentence-transformers or transformers libraries, automatically handles format conversion, and supports both CPU and GPU inference with framework-agnostic ONNX export for edge deployment or non-Python environments.","intents":["deploy the model in production environments using ONNX runtime for language-agnostic inference (C++, Java, .NET, etc.)","load the model efficiently using SafeTensors format which provides faster deserialization and better memory safety than pickle-based PyTorch","integrate the model into existing PyTorch or TensorFlow pipelines without format conversion overhead","export the model for edge deployment or serverless environments with minimal dependencies"],"best_for":["MLOps engineers deploying models to production with format flexibility requirements","teams building polyglot inference services (Python backend + C++/Java services)","developers targeting edge devices or serverless platforms with strict dependency constraints"],"limitations":["ONNX export requires additional dependencies (onnx, onnxruntime) not included in base transformers package","SafeTensors format is newer; some older tools/frameworks may not support it natively","Format conversion (PyTorch → ONNX) may introduce minor numerical precision differences (~1e-6 relative error)","ONNX inference requires separate ONNX Runtime installation; no unified API across formats","Quantized variants require additional quantization tools (e.g., onnxruntime-tools) for optimization"],"requires":["Python 3.7+ for PyTorch/SafeTensors loading","PyTorch 1.9+ OR transformers library 4.0+","For ONNX: onnxruntime library (>=1.10.0) and onnx library (>=1.12.0)","For SafeTensors: safetensors library (>=0.3.0)","2GB+ disk space for model weights; 4GB+ RAM for loading"],"input_types":["HuggingFace Hub model identifier string","local file path to .pt, .onnx, or .safetensors weights","serialized model bytes from cloud storage (S3, GCS, etc.)"],"output_types":["PyTorch model object (torch.nn.Module)","ONNX InferenceSession object","transformers PreTrainedModel instance","inference results (same across all formats)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-large__cap_3","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.padding.and.efficient.tokenization","name":"batch inference with dynamic padding and efficient tokenization","description":"Processes multiple premise-hypothesis pairs in a single forward pass using dynamic padding (padding to max length in batch rather than fixed sequence length) and optimized tokenization via the transformers library's fast tokenizers. This reduces memory overhead and computation time compared to processing pairs sequentially, with automatic handling of variable-length inputs and GPU batching.","intents":["score hundreds or thousands of hypothesis candidates against a single premise efficiently in production","reduce per-pair inference latency by amortizing model loading and GPU overhead across batch","process document collections for fact-checking or semantic relationship extraction at scale","implement efficient ranking pipelines that score multiple candidates in parallel"],"best_for":["data engineers building batch processing pipelines for fact-checking or semantic analysis","ML engineers optimizing inference throughput for production ranking systems","researchers processing large corpora for NLI-based analysis or dataset creation"],"limitations":["Batch size is constrained by GPU memory; typical batch sizes 8-64 depending on GPU (A100: 128+, V100: 32-64, T4: 8-16)","Dynamic padding adds ~5-10ms overhead per batch for tokenization; not beneficial for very small batches (<4 pairs)","Memory usage scales linearly with batch size and max sequence length in batch; no automatic memory management","Requires manual batching logic; no built-in DataLoader integration (though compatible with PyTorch DataLoader)","Cross-encoder design means batch size doesn't reduce number of forward passes (unlike bi-encoders); linear scaling with hypothesis count"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","transformers library (>=4.0.0) with fast tokenizers","GPU with 6GB+ VRAM for batch_size >= 16; CPU inference viable for small batches","sentence-transformers library (>=2.2.0) for convenient batch API"],"input_types":["list of tuples: [(premise_1, hypothesis_1), (premise_2, hypothesis_2), ...]","pandas DataFrame with 'premise' and 'hypothesis' columns","generator/iterator of premise-hypothesis pairs for streaming inference"],"output_types":["numpy array of shape (batch_size, 3) with confidence scores","list of dicts, each containing {'entailment': float, 'neutral': float, 'contradiction': float}","pandas DataFrame with original inputs + score columns"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-large__cap_4","uri":"capability://data.processing.analysis.zero.shot.classification.via.hypothesis.reformulation","name":"zero-shot classification via hypothesis reformulation","description":"Enables zero-shot classification on arbitrary categories by reformulating class labels as natural language hypotheses and using the NLI model to score input text against each hypothesis. For example, classifying a document as 'sports', 'politics', or 'technology' is reformulated as three entailment classification tasks: 'This text is about sports', 'This text is about politics', etc. The model outputs entailment scores for each hypothesis, which are interpreted as class probabilities.","intents":["classify text into arbitrary categories without fine-tuning or labeled training data","perform multi-label classification by scoring text against multiple hypothesis statements","adapt classification tasks to new categories at inference time without retraining","implement few-shot classification by using example-based hypothesis generation"],"best_for":["product teams needing rapid classification capability without labeled data collection","researchers exploring zero-shot classification techniques on diverse datasets","developers building adaptive systems that classify into user-defined categories"],"limitations":["Classification quality depends heavily on hypothesis formulation; poorly worded hypotheses degrade accuracy significantly","Requires manual engineering of hypothesis templates for each classification task; no automatic template generation","Scales linearly with number of classes (N classes = N forward passes); impractical for 100+ class problems","Performance on out-of-domain categories may be poor if they differ significantly from NLI training data distribution","No built-in multi-label support; requires separate scoring and thresholding logic for multi-label tasks","Hypothesis length affects performance; very long or complex hypotheses may confuse the model"],"requires":["Python 3.7+","sentence-transformers library (>=2.2.0) or transformers library (>=4.0.0)","PyTorch 1.9+ or TensorFlow 2.4+","Manual hypothesis engineering (no automatic generation provided)"],"input_types":["text (document or sentence to classify)","list of category labels (strings) that are converted to hypotheses","custom hypothesis templates (e.g., 'This text is about {category}')"],"output_types":["dict mapping category labels to confidence scores (0.0-1.0)","predicted category label (highest entailment score)","ranked list of categories by confidence score"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","sentence-transformers library (>=2.2.0) or transformers library (>=4.0.0)","4GB+ RAM for inference; 8GB+ GPU VRAM recommended for batch processing","HuggingFace Hub access (model auto-downloads on first use, ~1.7GB)","GPU with 6GB+ VRAM for batch inference; CPU inference viable but slow (~200-500ms per pair)","Python 3.7+ for PyTorch/SafeTensors loading","PyTorch 1.9+ OR transformers library 4.0+","For ONNX: onnxruntime library (>=1.10.0) and onnx library (>=1.12.0)","For SafeTensors: safetensors library (>=0.3.0)"],"failure_modes":["Optimized for English text only; performance degrades significantly on non-English or code-mixed inputs","Requires premise-hypothesis pairs as input; cannot directly classify single sentences without reformulation","Model size (435M parameters) requires ~1.7GB GPU memory; inference latency ~100-200ms per pair on CPU","Trained on news/Wikipedia-style text; may underperform on domain-specific language (medical, legal, technical jargon)","Cross-encoder architecture requires computing scores for each hypothesis separately; scales linearly with number of candidate classes","Cross-encoder design requires separate forward pass per hypothesis; cannot leverage batch processing as efficiently as bi-encoders for large candidate sets (N hypotheses = N forward passes)","Confidence calibration is dataset-dependent; scores may not generalize perfectly to domains far from SNLI/MultiNLI distribution","No built-in uncertainty quantification; single-point estimates without confidence intervals","Requires both premise and hypothesis at inference time; cannot pre-compute embeddings for hypothesis candidates","ONNX export requires additional dependencies (onnx, onnxruntime) not included in base transformers package","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5098988220818559,"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":"2026-05-03T14:22:57.756Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":80926,"model_likes":39}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=cross-encoder--nli-deberta-v3-large","compare_url":"https://unfragile.ai/compare?artifact=cross-encoder--nli-deberta-v3-large"}},"signature":"T/fPDVVDzLDO52gJ0gVfqj4EB/NLLoe9Bsf2XEAPM/HkxbdDSIgmB8MNAhNXqqb7XLff19ycP2t7fv1EQgQjAw==","signedAt":"2026-06-21T19:42:08.373Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cross-encoder--nli-deberta-v3-large","artifact":"https://unfragile.ai/cross-encoder--nli-deberta-v3-large","verify":"https://unfragile.ai/api/v1/verify?slug=cross-encoder--nli-deberta-v3-large","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"}}