{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-cross-encoder--nli-deberta-v3-small","slug":"cross-encoder--nli-deberta-v3-small","name":"nli-deberta-v3-small","type":"model","url":"https://huggingface.co/cross-encoder/nli-deberta-v3-small","page_url":"https://unfragile.ai/cross-encoder--nli-deberta-v3-small","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-small","base_model:quantized:microsoft/deberta-v3-small","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-small__cap_0","uri":"capability://data.processing.analysis.zero.shot.natural.language.inference.classification","name":"zero-shot natural language inference classification","description":"Classifies relationships between sentence pairs (premise-hypothesis) into entailment, contradiction, or neutral categories without task-specific fine-tuning. Uses a cross-encoder architecture where both sentences are jointly encoded through DeBERTa-v3-small's transformer layers with attention mechanisms that model bidirectional dependencies, then passed through a classification head trained on SNLI and MultiNLI datasets. The model outputs probability scores across three NLI labels, enabling downstream zero-shot classification by mapping arbitrary text labels to entailment relationships.","intents":["Determine if a hypothesis logically follows from a given premise without retraining","Classify text into custom categories by framing them as entailment problems","Build zero-shot text classifiers that generalize to unseen label sets","Detect semantic contradictions or agreements between document pairs"],"best_for":["ML engineers building zero-shot classification pipelines without labeled data","NLP practitioners needing lightweight inference for entailment tasks","Teams deploying edge models requiring <100MB footprint with CPU inference"],"limitations":["Cross-encoder architecture requires O(n²) comparisons for n candidate labels, making it slower than bi-encoder approaches for large label sets (>50 labels)","Trained exclusively on English NLI datasets; performance degrades significantly on non-English text or domain-specific terminology","Fixed sequence length of 512 tokens; longer documents must be truncated or chunked, losing context","Probability calibration assumes balanced class distribution; performs poorly on highly imbalanced label sets without post-hoc calibration"],"requires":["Python 3.7+","PyTorch 1.9+ or ONNX Runtime 1.10+","sentence-transformers library 2.0+","transformers library 4.8+","4GB RAM minimum for inference, 8GB+ for batch processing"],"input_types":["text (premise string)","text (hypothesis string)","structured pairs: {\"premise\": \"...\", \"hypothesis\": \"...\"}"],"output_types":["structured data: {\"entailment\": 0.92, \"neutral\": 0.05, \"contradiction\": 0.03}","text labels: [\"entailment\", \"neutral\", \"contradiction\"]","numeric scores: [0.92, 0.05, 0.03]"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-small__cap_1","uri":"capability://automation.workflow.multi.format.model.export.and.deployment","name":"multi-format model export and deployment","description":"Provides pre-converted model weights in PyTorch, ONNX, and SafeTensors formats, enabling deployment across heterogeneous inference stacks without custom conversion pipelines. The model is distributed through HuggingFace Hub with automatic format detection, allowing frameworks like sentence-transformers to load the optimal format for the target runtime (CPU via ONNX, GPU via PyTorch, or quantized inference via SafeTensors). This eliminates format conversion bottlenecks and enables seamless integration with Azure, edge devices, and containerized services.","intents":["Deploy the same model to CPU servers, GPU clusters, and edge devices without retraining or conversion","Integrate with ONNX Runtime for cross-platform inference optimization","Load quantized model variants for memory-constrained environments","Avoid custom model conversion scripts and associated technical debt"],"best_for":["DevOps teams managing multi-platform ML deployments","Edge ML engineers targeting mobile, IoT, or embedded systems","Organizations standardizing on ONNX for inference optimization"],"limitations":["ONNX export may lose some dynamic behavior from PyTorch (e.g., custom ops); requires validation on target hardware","SafeTensors format is newer; some legacy inference frameworks lack native support","No automatic quantization; INT8 or FP16 variants must be manually generated or sourced separately","Format selection is automatic but not always optimal; manual tuning may be needed for latency-critical applications"],"requires":["HuggingFace transformers 4.8+","sentence-transformers 2.0+ for automatic format selection","ONNX Runtime 1.10+ (for ONNX inference)","PyTorch 1.9+ (for PyTorch format)","safetensors library 0.2+ (for SafeTensors format)"],"input_types":["model identifier: 'cross-encoder/nli-deberta-v3-small'","HuggingFace Hub URL","local filesystem path"],"output_types":["PyTorch model checkpoint (.pt, .pth)","ONNX model graph (.onnx)","SafeTensors binary (.safetensors)","model config (config.json, tokenizer files)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-small__cap_2","uri":"capability://data.processing.analysis.sentence.pair.entailment.scoring.with.probability.calibration","name":"sentence-pair entailment scoring with probability calibration","description":"Computes calibrated probability distributions over NLI labels for arbitrary sentence pairs by passing joint embeddings through a softmax classification head. The model outputs three normalized probabilities (entailment, neutral, contradiction) that sum to 1.0, trained via cross-entropy loss on SNLI and MultiNLI corpora. Calibration is implicit through the training objective, allowing downstream applications to use raw probabilities for ranking, thresholding, or confidence-based filtering without additional post-hoc calibration.","intents":["Rank candidate answers by their entailment probability relative to a question","Filter low-confidence predictions using probability thresholds","Build confidence-aware NLI pipelines that reject ambiguous cases","Aggregate multiple premise-hypothesis pairs with weighted averaging of probabilities"],"best_for":["QA systems that need to rank candidate answers by semantic relevance","Fact-checking pipelines requiring confidence scores for evidence assessment","Retrieval-augmented generation systems filtering retrieved documents by entailment"],"limitations":["Probability calibration assumes balanced class distribution in training data; real-world label distributions may cause miscalibration (e.g., entailment may be overconfident)","No uncertainty quantification beyond softmax probabilities; cannot distinguish between 'model is unsure' vs 'model is confidently wrong'","Probabilities are not comparable across different premise-hypothesis pairs; cannot use raw scores for cross-pair ranking without normalization","Softmax temperature is fixed; no built-in mechanism to adjust confidence without retraining"],"requires":["sentence-transformers 2.0+","PyTorch 1.9+ or ONNX Runtime 1.10+","tokenizer compatible with DeBERTa (included in model package)","512 token context window per sentence pair"],"input_types":["text tuple: (premise: str, hypothesis: str)","batch of tuples: List[Tuple[str, str]]","structured dict: {\"premise\": \"...\", \"hypothesis\": \"...\"}"],"output_types":["probability vector: [0.92, 0.05, 0.03]","labeled scores: {\"entailment\": 0.92, \"neutral\": 0.05, \"contradiction\": 0.03}","argmax label: \"entailment\""],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-small__cap_3","uri":"capability://automation.workflow.batch.inference.with.dynamic.padding.and.attention.masking","name":"batch inference with dynamic padding and attention masking","description":"Processes multiple sentence pairs in parallel using dynamic padding (padding only to the longest sequence in the batch) and attention masking to prevent the model from attending to padding tokens. The sentence-transformers library automatically batches inputs, applies tokenization with attention masks, and passes padded tensors through the transformer layers with masked self-attention. This approach reduces memory overhead compared to fixed-size padding and enables efficient GPU utilization for variable-length inputs.","intents":["Process thousands of premise-hypothesis pairs efficiently without OOM errors","Parallelize inference across multiple GPUs or TPUs","Minimize latency for real-time classification pipelines by batching requests","Reduce memory footprint by avoiding fixed-size padding overhead"],"best_for":["Production systems processing high-volume classification requests (100+ pairs/sec)","Batch processing pipelines for offline evaluation or dataset annotation","Resource-constrained environments (edge devices, shared GPU clusters)"],"limitations":["Dynamic padding requires recomputation of attention masks per batch; overhead becomes significant for very small batches (<4 samples)","Batch size must fit in GPU memory; no automatic gradient checkpointing for memory optimization","Attention masking is applied at the token level; no support for hierarchical masking or custom attention patterns","Batch processing introduces latency variance; p99 latency may be 2-3x higher than p50 due to variable sequence lengths"],"requires":["sentence-transformers 2.0+","PyTorch 1.9+ with CUDA 11.0+ (for GPU batching)","batch_size parameter tuned to available GPU memory (typically 32-256 for 8GB VRAM)","tokenizer with attention mask support"],"input_types":["list of tuples: List[Tuple[str, str]]","pandas DataFrame with 'premise' and 'hypothesis' columns","generator yielding batches of pairs"],"output_types":["numpy array: shape (batch_size, 3)","list of dicts: [{\"entailment\": 0.92, ...}, ...]","pandas Series with probability vectors"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-small__cap_4","uri":"capability://text.generation.language.cross.lingual.transfer.via.multilingual.pretraining","name":"cross-lingual transfer via multilingual pretraining","description":"Leverages DeBERTa-v3-small's multilingual pretraining on 100+ languages to enable limited zero-shot transfer to non-English text, though with degraded performance. The model's transformer layers learned language-agnostic representations during pretraining on masked language modeling and next-sentence prediction across diverse languages. However, the NLI classification head was fine-tuned exclusively on English SNLI/MultiNLI data, creating a mismatch between multilingual representations and English-specific decision boundaries.","intents":["Classify NLI relationships in non-English languages without retraining","Build multilingual zero-shot classifiers by mapping labels to entailment in other languages","Detect cross-lingual semantic relationships (e.g., French premise vs English hypothesis)"],"best_for":["Prototyping multilingual NLI systems before collecting language-specific training data","Low-resource language applications where fine-tuning data is unavailable"],"limitations":["Performance drops 15-30% on non-English text compared to English due to English-only fine-tuning of the classification head","No explicit cross-lingual alignment; mixing languages in premise-hypothesis pairs produces unreliable results","Multilingual transfer is implicit and uncontrolled; no mechanism to specify source/target language pairs","Tokenization may be suboptimal for languages with different morphology (e.g., agglutinative languages like Turkish)"],"requires":["sentence-transformers 2.0+","DeBERTa tokenizer supporting 100+ languages","understanding that performance will degrade for non-English inputs"],"input_types":["text in any of 100+ languages supported by DeBERTa","mixed-language pairs (e.g., French premise + English hypothesis)"],"output_types":["probability vector: [0.75, 0.15, 0.10] (lower confidence than English)","labeled scores with degraded calibration"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cross-encoder--nli-deberta-v3-small__cap_5","uri":"capability://search.retrieval.semantic.similarity.ranking.via.entailment.scores","name":"semantic similarity ranking via entailment scores","description":"Repurposes NLI classification scores for semantic similarity ranking by treating entailment probability as a proxy for semantic relatedness. When comparing a query against multiple candidates, the model scores each candidate as a hypothesis against the query as a premise, producing entailment probabilities that correlate with semantic similarity. This approach differs from traditional bi-encoder similarity (cosine distance in embedding space) by modeling directional relationships and capturing logical dependencies.","intents":["Rank search results or retrieved documents by semantic relevance to a query","Find the most semantically similar candidate from a set without computing pairwise embeddings","Build semantic search systems that understand logical relationships beyond surface similarity"],"best_for":["Information retrieval systems where relevance is defined by logical entailment rather than lexical overlap","QA systems ranking candidate answers by semantic fit","Recommendation systems filtering candidates by semantic coherence"],"limitations":["Entailment is directional (A→B ≠ B→A); ranking requires choosing which text is premise vs hypothesis, affecting results","Entailment probability is not equivalent to similarity; two unrelated texts may have low entailment but also low similarity","O(n) forward passes required for n candidates; slower than bi-encoder approaches using precomputed embeddings","No built-in ranking aggregation; combining multiple premise-hypothesis pairs requires manual score normalization"],"requires":["sentence-transformers 2.0+","understanding of directional nature of entailment","computational budget for O(n) inference passes per query"],"input_types":["query text (premise)","list of candidate texts (hypotheses)","structured ranking request: {\"query\": \"...\", \"candidates\": [...]}"],"output_types":["ranked list: [(candidate_1, 0.92), (candidate_2, 0.75), ...]","similarity scores: {candidate_1: 0.92, candidate_2: 0.75}","top-k results with scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or ONNX Runtime 1.10+","sentence-transformers library 2.0+","transformers library 4.8+","4GB RAM minimum for inference, 8GB+ for batch processing","HuggingFace transformers 4.8+","sentence-transformers 2.0+ for automatic format selection","ONNX Runtime 1.10+ (for ONNX inference)","PyTorch 1.9+ (for PyTorch format)","safetensors library 0.2+ (for SafeTensors format)"],"failure_modes":["Cross-encoder architecture requires O(n²) comparisons for n candidate labels, making it slower than bi-encoder approaches for large label sets (>50 labels)","Trained exclusively on English NLI datasets; performance degrades significantly on non-English text or domain-specific terminology","Fixed sequence length of 512 tokens; longer documents must be truncated or chunked, losing context","Probability calibration assumes balanced class distribution; performs poorly on highly imbalanced label sets without post-hoc calibration","ONNX export may lose some dynamic behavior from PyTorch (e.g., custom ops); requires validation on target hardware","SafeTensors format is newer; some legacy inference frameworks lack native support","No automatic quantization; INT8 or FP16 variants must be manually generated or sourced separately","Format selection is automatic but not always optimal; manual tuning may be needed for latency-critical applications","Probability calibration assumes balanced class distribution in training data; real-world label distributions may cause miscalibration (e.g., entailment may be overconfident)","No uncertainty quantification beyond softmax probabilities; cannot distinguish between 'model is unsure' vs 'model is confidently wrong'","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5654135490589941,"quality":0.37,"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":247798,"model_likes":14}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=cross-encoder--nli-deberta-v3-small","compare_url":"https://unfragile.ai/compare?artifact=cross-encoder--nli-deberta-v3-small"}},"signature":"0rpSBKNoId649B/wPyY8wjkI0rQZjB/0YIOWWtsdlEz+ZILYkgaljanZO3vnfkuPierx7lCuKXp0Ke56XqehAA==","signedAt":"2026-06-21T01:12:14.938Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cross-encoder--nli-deberta-v3-small","artifact":"https://unfragile.ai/cross-encoder--nli-deberta-v3-small","verify":"https://unfragile.ai/api/v1/verify?slug=cross-encoder--nli-deberta-v3-small","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"}}