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The model encodes premise-hypothesis pairs through a transformer architecture with disentangled attention mechanisms, computing entailment/contradiction/neutral scores that map to custom labels. This enables dynamic category assignment at inference time without retraining.","intents":["classify user queries into intent categories without labeled training data","assign sentiment or emotion labels to text dynamically across different domains","perform topic classification or content moderation without domain-specific annotation","build multi-label classification systems that adapt to new categories on-the-fly"],"best_for":["NLP engineers building rapid prototyping systems for text classification","teams needing domain-agnostic content moderation without labeled datasets","developers implementing intent detection for conversational AI without task-specific training"],"limitations":["Zero-shot performance degrades with ambiguous or fine-grained category distinctions — typically 5-15% accuracy drop vs supervised baselines on specialized domains","Requires well-crafted category descriptions/prompts; poor label wording significantly impacts classification accuracy","No built-in confidence calibration — raw logits may not reflect true prediction confidence across diverse category sets","Inference latency ~150-300ms per sample on CPU, ~50-100ms on GPU due to full transformer forward pass"],"requires":["Python 3.7+","transformers library 4.20+","PyTorch 1.9+ or compatible backend","4GB+ VRAM for GPU inference (12GB+ recommended for batch processing)","HuggingFace model hub access or local model weights (~860MB)"],"input_types":["plain text (premise)","text labels/categories (hypothesis)","structured premise-hypothesis pairs"],"output_types":["classification scores (entailment probability per category)","predicted category label","confidence scores (0-1 range)"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sileod--deberta-v3-base-tasksource-nli__cap_1","uri":"capability://data.processing.analysis.multi.task.transfer.learning.via.extreme.mtl.pretraining","name":"multi-task transfer learning via extreme mtl pretraining","description":"Leverages extreme multi-task learning (extreme-MTL) pretraining across 1000+ NLI-related tasks from the TaskSource dataset collection. 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This dual-stream attention approach (768-dim hidden state, 12 attention heads) produces contextual embeddings that better capture semantic relationships while maintaining positional awareness, improving classification accuracy over standard transformer attention patterns.","intents":["generate high-quality contextual embeddings for downstream classification tasks","improve attention mechanism efficiency and interpretability through disentangled representations","encode text with better position-aware semantics for NLI-style reasoning"],"best_for":["NLP practitioners building text understanding systems requiring strong contextual representations","researchers studying attention mechanism design and interpretability","teams optimizing for accuracy-to-latency tradeoffs in production classification pipelines"],"limitations":["Disentangled attention adds ~10-15% computational overhead vs standard attention during inference","Requires 860MB model weights — larger than BERT-base (110MB), limiting deployment on edge devices","Maximum sequence length 512 tokens — longer documents require truncation or sliding window approaches","No built-in support for multi-lingual text despite base model architecture supporting it"],"requires":["Python 3.7+","transformers 4.20+","PyTorch 1.9+","2GB+ VRAM for inference"],"input_types":["tokenized text (via HuggingFace tokenizer)","raw text strings (auto-tokenized)"],"output_types":["contextual embeddings (768-dim vectors)","attention weights (12 heads × sequence_length × sequence_length)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sileod--deberta-v3-base-tasksource-nli__cap_3","uri":"capability://data.processing.analysis.premise.hypothesis.entailment.scoring.for.classification","name":"premise-hypothesis entailment scoring for classification","description":"Scores the entailment relationship between a premise (input text) and multiple hypotheses (category labels) by computing three logits: entailment, neutral, and contradiction. The model treats classification as an NLI problem where each category is formulated as a hypothesis (e.g., 'This text is about [category]'), and the entailment score indicates how likely the premise supports that hypothesis. Scores are normalized to probabilities for final category assignment.","intents":["convert arbitrary classification problems into NLI formulations for zero-shot inference","score multiple hypotheses against a single premise to enable multi-label classification","leverage entailment reasoning for more interpretable classification decisions"],"best_for":["developers implementing zero-shot classification without labeled training data","teams needing interpretable classification via explicit premise-hypothesis relationships","systems requiring flexible category definitions that can change at inference time"],"limitations":["Requires manual formulation of category hypotheses — poor hypothesis wording (e.g., vague or ambiguous labels) significantly degrades accuracy","Entailment scoring assumes binary relationships (entails/neutral/contradicts) which may not capture nuanced multi-way classification distinctions","Inference cost scales linearly with number of categories — classifying into 100 categories requires 100 forward passes","No built-in ranking or confidence calibration across multiple hypotheses"],"requires":["Python 3.7+","transformers 4.20+","well-defined category label set","understanding of NLI task formulation"],"input_types":["premise text (string)","hypothesis text (string or list of strings)"],"output_types":["entailment logits (3-dim: entailment, neutral, contradiction)","entailment probability (0-1)","predicted category (argmax over hypotheses)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sileod--deberta-v3-base-tasksource-nli__cap_4","uri":"capability://data.processing.analysis.batch.zero.shot.classification.with.dynamic.category.sets","name":"batch zero-shot classification with dynamic category sets","description":"Processes multiple text samples and category sets in batches, enabling efficient inference across diverse classification scenarios without retraining. The model accepts variable-length category lists per sample, dynamically constructs premise-hypothesis pairs, and returns per-sample classification scores. Batching is implemented via HuggingFace pipeline abstraction with automatic padding and attention masking.","intents":["classify large document collections into domain-specific categories without labeled data","implement dynamic category assignment where categories vary per sample or request","build scalable zero-shot classification services handling variable workloads"],"best_for":["production systems processing high-volume text classification requests","applications requiring flexible category definitions that change per request","teams needing efficient batch processing for cost optimization"],"limitations":["Batch size is limited by GPU VRAM — typical batch size 8-32 samples depending on sequence length and number of categories","Variable category counts per sample complicate batching — requires padding or dynamic batching strategies","Inference cost scales with O(num_samples × num_categories) — classifying 1000 samples into 50 categories requires 50,000 forward passes","No built-in caching of category embeddings — recomputes hypothesis encodings even for repeated categories across batches"],"requires":["Python 3.7+","transformers 4.20+","PyTorch 1.9+","4GB+ VRAM for batch inference"],"input_types":["list of text strings","list of category label lists (variable length per sample)"],"output_types":["batch classification scores (num_samples × num_categories)","predicted categories per sample","confidence scores per prediction"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sileod--deberta-v3-base-tasksource-nli__cap_5","uri":"capability://safety.moderation.rlhf.aligned.zero.shot.reasoning","name":"rlhf-aligned zero-shot reasoning","description":"Incorporates reinforcement learning from human feedback (RLHF) alignment during pretraining, improving the model's ability to reason about classification decisions in ways that align with human preferences. This alignment affects how the model scores entailment relationships, biasing it toward more human-interpretable and reliable classifications. The RLHF signal is embedded in the learned representations rather than exposed as explicit reasoning traces.","intents":["improve classification reliability by aligning model behavior with human preferences","reduce spurious correlations and improve robustness to adversarial inputs","build more trustworthy zero-shot classifiers for high-stakes applications"],"best_for":["teams deploying classifiers in high-stakes domains (content moderation, medical triage)","applications requiring robust performance against distribution shift and adversarial inputs","systems where classification errors have significant downstream consequences"],"limitations":["RLHF alignment is implicit in learned representations — no explicit reasoning traces or explanations for classifications","Alignment quality depends on RLHF training data quality and human annotator agreement — unknown biases may persist","No transparency into which human preferences are encoded or how they affect specific predictions","Alignment may reduce performance on edge cases or novel domains not well-represented in RLHF training data"],"requires":["Python 3.7+","transformers 4.20+","understanding of RLHF concepts"],"input_types":["premise text","hypothesis text"],"output_types":["RLHF-aligned entailment scores","classification probabilities"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","transformers library 4.20+","PyTorch 1.9+ or compatible backend","4GB+ VRAM for GPU inference (12GB+ recommended for batch processing)","HuggingFace model hub access or local model weights (~860MB)","transformers 4.20+","understanding of NLI task formulation","PyTorch 1.9+","2GB+ VRAM for inference","well-defined category label set"],"failure_modes":["Zero-shot performance degrades with ambiguous or fine-grained category distinctions — typically 5-15% accuracy drop vs supervised baselines on specialized domains","Requires well-crafted category descriptions/prompts; poor label wording significantly impacts classification accuracy","No built-in confidence calibration — raw logits may not reflect true prediction confidence across diverse category sets","Inference latency ~150-300ms per sample on CPU, ~50-100ms on GPU due to full transformer forward pass","Extreme MTL training introduces optimization complexity — model may underfit on highly specialized domains requiring domain-specific fine-tuning","Pretraining bias toward NLI-style tasks may reduce performance on non-classification tasks (e.g., structured extraction, ranking)","No transparency into which of the 1000+ tasks contribute most to specific predictions","Disentangled attention adds ~10-15% computational overhead vs standard attention during inference","Requires 860MB model weights — larger than BERT-base (110MB), limiting deployment on edge devices","Maximum sequence length 512 tokens — longer documents require truncation or sliding window approaches","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5625801223799651,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-04-22T08:08:24.267Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":117720,"model_likes":133}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sileod--deberta-v3-base-tasksource-nli","compare_url":"https://unfragile.ai/compare?artifact=sileod--deberta-v3-base-tasksource-nli"}},"signature":"hZBIv7Dm7YkJOB6FOeap0ebJ7YOLgtwGVcszzPodgQ/vQTX8AKThoNpVtz9tujPf3DVjbghwrTCHkBo0VIuWBg==","signedAt":"2026-06-20T18:48:22.758Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sileod--deberta-v3-base-tasksource-nli","artifact":"https://unfragile.ai/sileod--deberta-v3-base-tasksource-nli","verify":"https://unfragile.ai/api/v1/verify?slug=sileod--deberta-v3-base-tasksource-nli","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"}}