{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary","slug":"moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary","name":"DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary","type":"model","url":"https://huggingface.co/MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary","page_url":"https://unfragile.ai/moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary","categories":["model-training"],"tags":["transformers","pytorch","onnx","safetensors","deberta-v2","text-classification","zero-shot-classification","en","dataset:multi_nli","dataset:facebook/anli","dataset:fever","dataset:lingnli","arxiv:2104.07179","arxiv:2111.09543","license:mit","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary__cap_0","uri":"capability://data.processing.analysis.zero.shot.text.classification.with.natural.language.premises","name":"zero-shot text classification with natural language premises","description":"Classifies arbitrary text into user-defined categories without task-specific fine-tuning by reformulating classification as natural language inference (NLI). The model takes input text and candidate labels, converts them into entailment hypotheses (e.g., 'This text is about [label]'), and uses the DeBERTa-v3 transformer backbone trained on MNLI, FEVER, ANLI, and LingNLI datasets to compute entailment probabilities. This approach enables dynamic label sets at inference time without retraining.","intents":["classify documents into categories I define at runtime without labeled training data","determine if a piece of text is about a specific topic using natural language descriptions","perform sentiment analysis or intent detection on new domains without fine-tuning","build multi-label classification systems where labels can change per deployment"],"best_for":["rapid prototyping teams needing classification without labeled datasets","production systems requiring dynamic label adaptation across customers","low-resource domains where gathering training data is infeasible","developers building content moderation or topic detection pipelines"],"limitations":["Accuracy degrades with ambiguous or overlapping label definitions; requires careful prompt engineering of label text","Inference latency ~200-500ms per sample on CPU due to full transformer forward pass; no batching optimization in base model","Model size (22M parameters) limits deployment to edge devices; quantization required for mobile","Performance bounded by training data distribution (MNLI/FEVER/ANLI); out-of-domain text may show degraded entailment reasoning","Binary classification variant limits multi-class scenarios; requires one-vs-rest approach for >2 classes"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or ONNX Runtime 1.10+","minimum 2GB RAM for model loading","text input in English language"],"input_types":["plain text (string)","list of candidate labels (strings)","optional hypothesis template for custom NLI formatting"],"output_types":["classification scores (float, 0-1 per label)","predicted label (string)","entailment/contradiction/neutral logits (raw model outputs)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary__cap_1","uri":"capability://data.processing.analysis.multilingual.natural.language.inference.with.english.primary.training","name":"multilingual natural language inference with english-primary training","description":"Performs entailment classification (entailment/neutral/contradiction) on English text pairs using a transformer model pre-trained on diverse NLI corpora. The model encodes premise and hypothesis as a single sequence with [CLS] token, passes through 12 DeBERTa-v3 transformer layers with disentangled attention, and outputs 3-way classification logits. Training on MNLI (formal written English), FEVER (Wikipedia claims), ANLI (adversarial examples), and LingNLI (linguistic phenomena) provides robustness across text styles and reasoning patterns.","intents":["determine if a claim is entailed by, contradicted by, or neutral to a given premise","validate factual consistency between pairs of statements","detect contradictions in document collections or knowledge bases","build fact-checking pipelines that score claim-evidence relationships"],"best_for":["fact-checking systems and misinformation detection platforms","knowledge graph validation and consistency checking","semantic similarity and contradiction detection in NLP pipelines","research teams studying natural language understanding"],"limitations":["Trained exclusively on English; performance on other languages is not validated","Binary variant (this model) collapses neutral and contradiction into single non-entailment class, reducing nuance","Adversarial examples from ANLI training may cause overfitting to specific linguistic patterns; performance on naturally-occurring text varies","No explicit handling of temporal reasoning, numerical comparisons, or commonsense knowledge required for complex inferences","Inference requires full sequence encoding; no efficient incremental or streaming inference mode"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or ONNX Runtime 1.10+","premise and hypothesis as separate text strings","English language input"],"input_types":["premise text (string)","hypothesis text (string)","optional attention masks for variable-length sequences"],"output_types":["entailment/non-entailment logits (2-class binary)","probability scores (softmax normalized, 0-1)","hidden states from transformer layers (for downstream tasks)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary__cap_2","uri":"capability://automation.workflow.efficient.transformer.inference.via.onnx.and.safetensors.export","name":"efficient transformer inference via onnx and safetensors export","description":"Model is exported in multiple formats (PyTorch, ONNX, SafeTensors) enabling deployment across heterogeneous inference environments. ONNX export allows hardware-accelerated inference on CPUs, GPUs, and specialized accelerators (TPUs, NPUs) via ONNX Runtime, while SafeTensors format provides faster model loading (memory-mapped binary format) and improved security (no arbitrary code execution during deserialization). The xsmall variant (22M parameters) fits within memory constraints of edge devices and serverless functions.","intents":["deploy the model to production environments with hardware acceleration (ONNX Runtime on CPU/GPU)","load models faster in serverless/containerized environments using SafeTensors format","run inference on edge devices or mobile with minimal memory footprint","integrate with Azure ML, Hugging Face Inference API, or custom inference servers"],"best_for":["production ML teams deploying to cloud platforms (Azure, AWS, GCP) with ONNX Runtime support","edge computing and IoT applications requiring sub-500MB model footprint","serverless inference platforms (AWS Lambda, Google Cloud Functions) with strict cold-start latency budgets","security-conscious teams avoiding pickle deserialization vulnerabilities"],"limitations":["ONNX export may lose some PyTorch-specific optimizations; requires validation against original model outputs","SafeTensors format is read-only; no in-place weight updates during fine-tuning without conversion back to PyTorch","ONNX Runtime performance varies by hardware backend; CPU inference remains slower than GPU by 10-50x","Model quantization (int8, float16) not included in base export; requires separate quantization pipeline","No built-in batching optimization in ONNX export; batch processing requires manual sequence padding and unbatching"],"requires":["ONNX Runtime 1.10+ (for ONNX inference)","safetensors library 0.3.1+ (for SafeTensors loading)","transformers library 4.0+ (for PyTorch variant)","PyTorch 1.9+ or ONNX-compatible inference runtime","hardware with ONNX Runtime support (x86, ARM, GPU)"],"input_types":["ONNX model file (.onnx)","SafeTensors weight file (.safetensors)","PyTorch checkpoint (.pt, .pth)","tokenized input IDs and attention masks (numpy arrays or tensors)"],"output_types":["logits (numpy arrays from ONNX Runtime)","probabilities (softmax normalized)","hidden states (optional, depending on export configuration)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary__cap_3","uri":"capability://data.processing.analysis.batch.text.classification.with.configurable.confidence.thresholds","name":"batch text classification with configurable confidence thresholds","description":"Processes multiple text samples in a single inference pass by batching tokenized inputs and computing classification scores across the batch dimension. The model applies softmax normalization to logits, enabling threshold-based filtering where predictions below a confidence threshold are marked as uncertain or rejected. This capability is essential for production pipelines where confidence-based routing (e.g., escalate low-confidence samples to human review) is required.","intents":["classify large document collections efficiently by batching inference","filter low-confidence predictions and route them to human review or alternative classifiers","build confidence-aware classification pipelines that adapt behavior based on model certainty","measure model calibration and uncertainty on held-out test sets"],"best_for":["content moderation teams processing thousands of items per day with human-in-the-loop workflows","data annotation platforms needing confidence-based sample selection for active learning","production classification systems requiring explainability via confidence scores","researchers studying model calibration and uncertainty quantification"],"limitations":["Batch size limited by GPU/CPU memory; xsmall model fits ~64-128 samples per batch on 8GB GPU, but optimal batch size varies by hardware","Confidence scores are not calibrated; softmax probabilities may not reflect true prediction uncertainty (e.g., 0.9 confidence does not mean 90% accuracy)","No built-in uncertainty quantification beyond softmax entropy; requires external methods (Monte Carlo dropout, ensemble) for Bayesian confidence intervals","Threshold selection is task-dependent and requires manual tuning or validation set calibration","No streaming/online batch processing; entire batch must fit in memory before inference"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or ONNX Runtime 1.10+","batch size parameter (typically 8-128 depending on hardware)","confidence threshold value (0-1 range, task-dependent)"],"input_types":["list of text samples (strings)","list of candidate labels (strings)","optional batch size parameter","optional confidence threshold (float, 0-1)"],"output_types":["classification scores per sample (numpy array, shape [batch_size, num_labels])","predicted labels (list of strings)","confidence scores (list of floats, 0-1)","uncertainty flags (boolean, True if below threshold)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary__cap_4","uri":"capability://data.processing.analysis.cross.lingual.transfer.via.english.trained.nli.backbone","name":"cross-lingual transfer via english-trained nli backbone","description":"Although trained exclusively on English NLI datasets, the model can perform limited zero-shot classification on non-English text by leveraging the multilingual tokenizer and shared transformer weights. When non-English text is tokenized and passed through the English-trained model, it relies on cross-lingual word embeddings and attention patterns learned during pre-training to generalize. Performance on non-English languages is degraded compared to English but enables zero-shot classification without language-specific fine-tuning.","intents":["perform zero-shot classification on non-English text without language-specific models","test cross-lingual transfer capabilities of English-trained NLI models","build multilingual classification systems using a single English model as a baseline"],"best_for":["teams needing quick multilingual classification without maintaining language-specific models","research projects studying cross-lingual transfer in NLI tasks","low-resource language applications where language-specific training data is unavailable"],"limitations":["Performance on non-English languages is significantly degraded (typically 10-30% lower accuracy than English)","Model was not explicitly trained for cross-lingual transfer; any multilingual capability is incidental to English pre-training","No evaluation or benchmarking on non-English languages provided; behavior on specific languages is unpredictable","Tokenizer may not handle non-Latin scripts efficiently; performance on CJK, Arabic, or other scripts is not validated","Not recommended for production use on non-English text; language-specific models should be preferred"],"requires":["Python 3.7+","transformers library 4.0+","non-English text input (any language supported by the tokenizer)","understanding that performance will be degraded vs English"],"input_types":["non-English text (string)","candidate labels (ideally in the same language as input text)"],"output_types":["classification scores (float, 0-1)","predicted label (string)","entailment logits (raw model outputs)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or ONNX Runtime 1.10+","minimum 2GB RAM for model loading","text input in English language","premise and hypothesis as separate text strings","English language input","ONNX Runtime 1.10+ (for ONNX inference)","safetensors library 0.3.1+ (for SafeTensors loading)","transformers library 4.0+ (for PyTorch variant)"],"failure_modes":["Accuracy degrades with ambiguous or overlapping label definitions; requires careful prompt engineering of label text","Inference latency ~200-500ms per sample on CPU due to full transformer forward pass; no batching optimization in base model","Model size (22M parameters) limits deployment to edge devices; quantization required for mobile","Performance bounded by training data distribution (MNLI/FEVER/ANLI); out-of-domain text may show degraded entailment reasoning","Binary classification variant limits multi-class scenarios; requires one-vs-rest approach for >2 classes","Trained exclusively on English; performance on other languages is not validated","Binary variant (this model) collapses neutral and contradiction into single non-entailment class, reducing nuance","Adversarial examples from ANLI training may cause overfitting to specific linguistic patterns; performance on naturally-occurring text varies","No explicit handling of temporal reasoning, numerical comparisons, or commonsense knowledge required for complex inferences","Inference requires full sequence encoding; no efficient incremental or streaming inference mode","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.41222643764467687,"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":33943,"model_likes":7}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary","compare_url":"https://unfragile.ai/compare?artifact=moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary"}},"signature":"9p6+vqWDZgk928oOP8c0/QmHhI+MtUfknUjFXZXtmkS4YWXdF3hPUs2gHKGZ/5XQA0h7F5FQwl3uKKDIwzglBQ==","signedAt":"2026-06-20T17:49:31.452Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary","artifact":"https://unfragile.ai/moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary","verify":"https://unfragile.ai/api/v1/verify?slug=moritzlaurer--deberta-v3-xsmall-mnli-fever-anli-ling-binary","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"}}