{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-cross-encoder--nli-deberta-v3-base","slug":"cross-encoder--nli-deberta-v3-base","name":"nli-deberta-v3-base","type":"model","url":"https://huggingface.co/cross-encoder/nli-deberta-v3-base","page_url":"https://unfragile.ai/cross-encoder--nli-deberta-v3-base","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-base","base_model:quantized:microsoft/deberta-v3-base","license:apache-2.0","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-cross-encoder--nli-deberta-v3-base__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 pairs into entailment, contradiction, or neutral categories without task-specific fine-tuning. Uses a cross-encoder architecture where both texts are processed jointly through DeBERTa-v3-base's transformer layers, producing a 3-way classification logit output. The model was trained on SNLI and MultiNLI datasets using contrastive learning objectives, enabling it to generalize to unseen text pairs and domains without requiring labeled examples for new classification tasks.","intents":["Determine if a hypothesis is entailed by, contradicted by, or neutral to a given premise without labeled training data","Classify semantic relationships between sentence pairs for fact verification or claim validation","Build zero-shot text classification pipelines that adapt to new label sets without retraining","Score and rank text pairs by their logical relationship strength for ranking or filtering tasks"],"best_for":["NLP engineers building fact-checking or claim verification systems","Teams implementing zero-shot text classification without domain-specific labeled data","Developers creating semantic similarity or entailment scoring components for retrieval pipelines","Researchers prototyping NLI-based downstream tasks (question answering, semantic search)"],"limitations":["Cross-encoder architecture requires processing each premise-hypothesis pair independently, making it ~10-50x slower than bi-encoder alternatives for large-scale ranking tasks with many candidates","Trained primarily on English text (SNLI, MultiNLI); performance degrades significantly on non-English or domain-specific language (legal, medical, scientific)","Base model size (~278M parameters) requires GPU for reasonable inference latency; CPU inference ~500-1000ms per pair","No built-in confidence calibration; raw logits may not reflect true probability estimates across different input distributions","Assumes premise-hypothesis format; requires manual reformulation for other text pair tasks (similarity, paraphrase detection)"],"requires":["Python 3.7+","PyTorch 1.11+ or ONNX Runtime 1.13+","sentence-transformers library 2.2.0+ (for easy loading and inference)","4GB+ GPU VRAM for batch inference, or CPU with ~8GB RAM for single-pair inference","HuggingFace transformers library 4.20.0+ for direct model loading"],"input_types":["text (premise string)","text (hypothesis string)","structured pairs: {\"premise\": \"...\", \"hypothesis\": \"...\"}"],"output_types":["logits (3-dimensional float array: [entailment_score, neutral_score, contradiction_score])","class labels (0=entailment, 1=neutral, 2=contradiction)","confidence scores (softmax-normalized probabilities across 3 classes)"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-base__cap_1","uri":"capability://automation.workflow.multi.format.model.export.and.deployment","name":"multi-format model export and deployment","description":"Supports export to multiple inference frameworks (PyTorch, ONNX, SafeTensors) enabling deployment across diverse environments without retraining. The model can be loaded via sentence-transformers library for CPU/GPU inference, converted to ONNX format for edge devices and quantized inference, or exported as SafeTensors for secure model distribution. This multi-format support allows the same trained weights to be deployed in production systems (Azure, cloud APIs), edge devices, and research environments with minimal conversion overhead.","intents":["Export a trained NLI model to ONNX for deployment on edge devices or inference servers with strict latency requirements","Load the model in multiple frameworks (PyTorch, ONNX Runtime, transformers) without maintaining separate checkpoints","Quantize and compress the model for mobile or embedded deployment while preserving inference accuracy","Deploy the model to cloud platforms (Azure, AWS, GCP) with framework-agnostic serialization"],"best_for":["MLOps engineers deploying models across heterogeneous infrastructure (cloud, edge, on-premise)","Teams requiring model security and reproducibility via SafeTensors format","Developers building inference services that need framework flexibility (ONNX Runtime vs PyTorch)","Organizations optimizing for inference latency and model size on resource-constrained devices"],"limitations":["ONNX export may lose some PyTorch-specific optimizations; requires validation that quantized ONNX models maintain accuracy within acceptable thresholds","SafeTensors format is newer and less widely supported in legacy inference frameworks; requires updated dependencies","Model quantization (int8, fp16) requires additional conversion steps and may introduce 1-5% accuracy degradation depending on quantization method","No built-in versioning or model card metadata in exported formats; requires external tracking for model lineage"],"requires":["PyTorch 1.11+ for native model loading","ONNX Runtime 1.13+ for ONNX inference","safetensors library 0.3.0+ for SafeTensors format support","onnx and onnx-simplifier packages for ONNX conversion and optimization","Optional: quantization tools (TensorRT, TVM, or ONNX quantization tools) for model compression"],"input_types":["PyTorch checkpoint (.pt, .pth files)","HuggingFace model directory (config.json, pytorch_model.bin)","SafeTensors format (.safetensors files)"],"output_types":["ONNX model (.onnx file with protobuf graph definition)","SafeTensors checkpoint (.safetensors file)","Quantized ONNX (int8, fp16 variants)","PyTorch TorchScript (.pt file for C++ inference)"],"categories":["automation-workflow","model-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-base__cap_2","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.padding.and.attention.masking","name":"batch inference with dynamic padding and attention masking","description":"Processes multiple premise-hypothesis pairs simultaneously using efficient batching with dynamic padding and attention masking to minimize computational waste. The sentence-transformers integration handles tokenization, padding to the maximum sequence length within each batch (not a fixed global length), and generates attention masks that prevent the model from attending to padding tokens. This approach reduces memory usage and computation time compared to fixed-length padding, particularly for variable-length text pairs common in real-world NLI tasks.","intents":["Score hundreds or thousands of premise-hypothesis pairs in a single batch for efficient fact-checking pipelines","Minimize GPU memory usage and inference latency when processing variable-length text pairs","Implement efficient ranking of multiple hypotheses against a single premise","Build scalable NLI-based retrieval or filtering components that process large document collections"],"best_for":["Data scientists building batch inference pipelines for fact verification at scale","Teams implementing efficient semantic search or ranking systems with NLI scoring","Developers optimizing inference cost and latency in production systems","Researchers evaluating model performance across large benchmark datasets"],"limitations":["Batch size is limited by available GPU VRAM; typical batch sizes are 16-128 depending on sequence length and hardware (A100: 256-512, V100: 64-128, T4: 32-64)","Dynamic padding adds ~5-10ms overhead per batch for tokenization and mask generation; not negligible for very small batches (<4 pairs)","No built-in distributed inference; batching is single-GPU only; requires external frameworks (Ray, Hugging Face Accelerate) for multi-GPU scaling","Attention masking is applied at the transformer layer; no option to customize masking strategy for specialized use cases (e.g., hierarchical attention)"],"requires":["sentence-transformers 2.2.0+ with batch inference support","PyTorch 1.11+ with CUDA 11.6+ for GPU batching (or CPU fallback with significant latency)","Sufficient GPU VRAM: 4GB minimum for batch_size=16, 8GB+ recommended for batch_size=64+","Optional: Hugging Face Accelerate for distributed multi-GPU batching"],"input_types":["list of premise strings","list of hypothesis strings","list of dictionaries: [{\"premise\": \"...\", \"hypothesis\": \"...\"}, ...]"],"output_types":["batch logits (shape: [batch_size, 3])","batch class labels (shape: [batch_size])","batch confidence scores (shape: [batch_size, 3])"],"categories":["data-processing-analysis","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-base__cap_3","uri":"capability://planning.reasoning.cross.lingual.and.domain.transfer.via.zero.shot.generalization","name":"cross-lingual and domain transfer via zero-shot generalization","description":"Generalizes NLI classification to unseen domains and languages without fine-tuning by leveraging learned entailment patterns from SNLI and MultiNLI training data. The model learns abstract semantic relationships (logical entailment, contradiction, neutrality) that transfer across domains (news, social media, scientific text) and partially to non-English languages through multilingual word embeddings in the underlying DeBERTa architecture. This zero-shot transfer enables deployment to new domains and languages without collecting labeled data or retraining, though with degraded performance compared to in-domain models.","intents":["Apply an English-trained NLI model to new domains (medical, legal, scientific) without domain-specific fine-tuning","Evaluate whether NLI patterns learned from SNLI generalize to user-specific text classification tasks","Prototype multilingual fact-checking systems using a single English-trained model as a baseline","Assess domain transfer performance to decide whether fine-tuning is necessary for a specific use case"],"best_for":["Researchers studying domain adaptation and transfer learning in NLI tasks","Teams prototyping fact-checking systems for new domains before investing in labeled data collection","Developers building multilingual NLI systems with limited non-English training data","Organizations evaluating whether zero-shot NLI is sufficient for their use case before fine-tuning"],"limitations":["Performance degrades 5-15% on out-of-domain text (medical, legal, scientific) compared to in-domain fine-tuned models; requires evaluation on target domain","Multilingual transfer is limited; the model is primarily English-trained and does not support non-English input directly; cross-lingual transfer via translation introduces additional latency and error","No built-in domain adaptation or few-shot learning; requires external fine-tuning frameworks (Hugging Face Trainer, PyTorch Lightning) to improve performance on new domains","Transfer performance is unpredictable across domains; requires benchmark evaluation on target domain before production deployment"],"requires":["Evaluation dataset from target domain to assess transfer performance (minimum 100-500 labeled examples)","Optional: machine translation model (e.g., Helsinki-NLP/opus-mt-*) for cross-lingual transfer","PyTorch 1.11+ and sentence-transformers 2.2.0+ for inference","Optional: Hugging Face Trainer or PyTorch Lightning for fine-tuning on target domain if zero-shot performance is insufficient"],"input_types":["text (premise in target domain or language)","text (hypothesis in target domain or language)"],"output_types":["logits (3-dimensional float array)","class labels (0=entailment, 1=neutral, 2=contradiction)","confidence scores (softmax-normalized probabilities)"],"categories":["planning-reasoning","transfer-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-base__cap_4","uri":"capability://search.retrieval.semantic.entailment.scoring.for.ranking.and.retrieval","name":"semantic entailment scoring for ranking and retrieval","description":"Produces calibrated entailment scores (logits or probabilities) for premise-hypothesis pairs that can be used to rank, filter, or score text pairs in retrieval and ranking pipelines. The model outputs a 3-way classification (entailment, neutral, contradiction) with associated confidence scores; these can be aggregated into a single entailment score by taking the entailment logit or probability, enabling ranking of multiple hypotheses by their likelihood of being entailed by a premise. This capability enables integration into semantic search, question answering, and information retrieval systems where entailment strength is a relevance signal.","intents":["Rank multiple candidate answers or documents by their entailment to a user query or question","Filter irrelevant or contradictory documents from a retrieval result set based on entailment scores","Score semantic similarity between text pairs using entailment as a proxy for relevance","Implement fact-checking by scoring whether retrieved documents entail or contradict a claim"],"best_for":["Information retrieval engineers building semantic search systems with entailment-based ranking","QA system developers scoring candidate answers by their entailment to questions","Fact-checking teams ranking evidence documents by their support for or contradiction of claims","NLP researchers evaluating entailment as a relevance signal in retrieval tasks"],"limitations":["Entailment scores are not calibrated across different input distributions; raw logits may not be directly comparable between different premise-hypothesis pairs or domains","Ranking with entailment scores is slower than embedding-based retrieval (bi-encoders) because each pair requires a forward pass; unsuitable for real-time ranking of thousands of candidates without caching or approximation","No built-in confidence estimation or uncertainty quantification; logits may not reflect true probability of entailment, especially for out-of-domain text","Entailment is a coarse-grained relevance signal; does not capture partial relevance, semantic similarity, or topical relevance that may be important for some retrieval tasks"],"requires":["sentence-transformers 2.2.0+ for easy scoring interface","PyTorch 1.11+ or ONNX Runtime 1.13+ for inference","GPU recommended for batch scoring of large candidate sets (CPU inference ~500-1000ms per pair)","Optional: caching layer (Redis, in-memory dict) for scoring repeated premise-hypothesis pairs"],"input_types":["premise string (query, question, or claim)","hypothesis string (candidate answer, document, or evidence)","batch of premise-hypothesis pairs"],"output_types":["entailment logit (single float value)","entailment probability (float in [0, 1])","3-way logits (array of 3 floats: [entailment, neutral, contradiction])","ranked list of candidates sorted by entailment score"],"categories":["search-retrieval","ranking"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.11+ or ONNX Runtime 1.13+","sentence-transformers library 2.2.0+ (for easy loading and inference)","4GB+ GPU VRAM for batch inference, or CPU with ~8GB RAM for single-pair inference","HuggingFace transformers library 4.20.0+ for direct model loading","PyTorch 1.11+ for native model loading","ONNX Runtime 1.13+ for ONNX inference","safetensors library 0.3.0+ for SafeTensors format support","onnx and onnx-simplifier packages for ONNX conversion and optimization","Optional: quantization tools (TensorRT, TVM, or ONNX quantization tools) for model compression"],"failure_modes":["Cross-encoder architecture requires processing each premise-hypothesis pair independently, making it ~10-50x slower than bi-encoder alternatives for large-scale ranking tasks with many candidates","Trained primarily on English text (SNLI, MultiNLI); performance degrades significantly on non-English or domain-specific language (legal, medical, scientific)","Base model size (~278M parameters) requires GPU for reasonable inference latency; CPU inference ~500-1000ms per pair","No built-in confidence calibration; raw logits may not reflect true probability estimates across different input distributions","Assumes premise-hypothesis format; requires manual reformulation for other text pair tasks (similarity, paraphrase detection)","ONNX export may lose some PyTorch-specific optimizations; requires validation that quantized ONNX models maintain accuracy within acceptable thresholds","SafeTensors format is newer and less widely supported in legacy inference frameworks; requires updated dependencies","Model quantization (int8, fp16) requires additional conversion steps and may introduce 1-5% accuracy degradation depending on quantization method","No built-in versioning or model card metadata in exported formats; requires external tracking for model lineage","Batch size is limited by available GPU VRAM; typical batch sizes are 16-128 depending on sequence length and hardware (A100: 256-512, V100: 64-128, T4: 32-64)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5698715576797431,"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":187439,"model_likes":42}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=cross-encoder--nli-deberta-v3-base","compare_url":"https://unfragile.ai/compare?artifact=cross-encoder--nli-deberta-v3-base"}},"signature":"aayglBkmIQz0S/bwB4DklXfVuHbuLO8Bh216UYYxMt4o+Tyq9rORFaEIvbhZxtLW4bYv73puU7EWwKzFDC4jAg==","signedAt":"2026-06-21T02:22:37.838Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cross-encoder--nli-deberta-v3-base","artifact":"https://unfragile.ai/cross-encoder--nli-deberta-v3-base","verify":"https://unfragile.ai/api/v1/verify?slug=cross-encoder--nli-deberta-v3-base","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"}}