{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-moritzlaurer--mdeberta-v3-base-mnli-xnli","slug":"moritzlaurer--mdeberta-v3-base-mnli-xnli","name":"mDeBERTa-v3-base-mnli-xnli","type":"model","url":"https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli","page_url":"https://unfragile.ai/moritzlaurer--mdeberta-v3-base-mnli-xnli","categories":["model-training"],"tags":["transformers","pytorch","onnx","safetensors","deberta-v2","text-classification","zero-shot-classification","nli","multilingual","en","ar","bg","de","el","es","fr","hi","ru","sw","th"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-moritzlaurer--mdeberta-v3-base-mnli-xnli__cap_0","uri":"capability://data.processing.analysis.multilingual.zero.shot.text.classification.via.natural.language.inference","name":"multilingual zero-shot text classification via natural language inference","description":"Performs zero-shot classification by reformulating classification tasks as natural language inference (NLI) problems. The model encodes input text and candidate labels as premise-hypothesis pairs, computing entailment probabilities to determine label relevance without task-specific fine-tuning. Uses DeBERTa-v3's disentangled attention mechanism with cross-lingual transfer learned from MNLI and XNLI datasets, enabling classification across 11+ languages without language-specific retraining.","intents":["classify text into arbitrary categories without labeled training data","perform multilingual sentiment analysis, topic detection, or intent classification in production without retraining","rapidly prototype text classification pipelines for low-resource languages using English-trained models","build dynamic classification systems where label sets change at inference time without model updates"],"best_for":["NLP teams building multilingual content moderation or routing systems","developers prototyping text classification without labeled datasets","production systems requiring dynamic label sets (e.g., user-defined categories)","low-resource language applications leveraging cross-lingual transfer"],"limitations":["Zero-shot performance degrades with domain-specific vocabulary or highly specialized label sets; fine-tuning on task-specific data typically improves accuracy by 5-15%","Computational cost scales linearly with number of candidate labels (N labels = N forward passes); 100+ labels becomes expensive","Cross-lingual transfer quality varies by language pair; performance on underrepresented languages (e.g., Swahili, Thai) is lower than on high-resource languages (English, French)","No built-in confidence calibration; raw entailment scores require manual thresholding for reliable rejection of low-confidence predictions","Requires careful prompt engineering for label descriptions; generic labels ('positive', 'negative') underperform descriptive ones ('expresses satisfaction', 'expresses frustration')"],"requires":["Python 3.7+","transformers library 4.20.0+","PyTorch 1.9+ or TensorFlow 2.4+","4GB+ GPU VRAM for batch inference (CPU inference possible but ~10x slower)","HuggingFace Hub access or local model weights (~840MB disk space)"],"input_types":["raw text strings (sentences, paragraphs, documents)","pre-tokenized text with token IDs","variable-length sequences (up to 512 tokens)"],"output_types":["classification scores (logits) per label","normalized probabilities (softmax over entailment/contradiction/neutral)","predicted label with confidence score","ranking of all candidate labels by relevance"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-mnli-xnli__cap_1","uri":"capability://data.processing.analysis.cross.lingual.natural.language.inference.with.entailment.scoring","name":"cross-lingual natural language inference with entailment scoring","description":"Scores the relationship between premise and hypothesis text pairs across 11 languages by computing three-way classification (entailment, neutral, contradiction) using transformer-based sequence pair encoding. The model processes concatenated premise-hypothesis inputs through DeBERTa-v3-base's 12 layers with 768 hidden dimensions, outputting normalized probabilities for each relationship type. Trained on MNLI (English) and XNLI (multilingual) datasets, enabling zero-shot cross-lingual inference without language-specific fine-tuning.","intents":["determine semantic relationships between text pairs for fact verification or claim validation","build multilingual semantic similarity or contradiction detection systems","extract structured relationships from unstructured text by framing as entailment problems","implement cross-lingual question-answering by scoring answer relevance to questions"],"best_for":["fact-checking platforms requiring multilingual entailment scoring","semantic search systems that need relationship-aware ranking","teams building multilingual question-answering or reading comprehension systems","content moderation systems detecting contradictory or misleading claims"],"limitations":["Entailment scoring is sensitive to premise-hypothesis order; swapping order can change scores by 5-20%","Performance on very long sequences (>256 tokens) degrades due to 512-token context window; requires truncation or hierarchical approaches","Neutral class is often underspecified in training data, leading to lower precision on neutral predictions compared to entailment/contradiction","No built-in handling of negation or modal operators; 'not X' may not reliably contradict 'X'","Cross-lingual performance asymmetric; English-to-other-language transfer stronger than other-language-to-English"],"requires":["Python 3.7+","transformers 4.20.0+","PyTorch 1.9+ or TensorFlow 2.4+","2GB+ GPU VRAM for inference","Input text in supported languages: English, Arabic, Bulgarian, German, Greek, Spanish, French, Hindi, Russian, Swahili, Thai"],"input_types":["premise-hypothesis text pairs (two separate strings)","pre-tokenized token ID pairs","variable-length sequences (up to 512 tokens combined)"],"output_types":["three-class logits (entailment, neutral, contradiction)","normalized probabilities for each class","entailment score (typically softmax probability of entailment class)","predicted relationship class with confidence"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-mnli-xnli__cap_2","uri":"capability://data.processing.analysis.dynamic.label.agnostic.text.categorization.without.retraining","name":"dynamic label-agnostic text categorization without retraining","description":"Enables runtime definition of arbitrary classification labels by leveraging NLI reformulation, allowing label sets to change between inference calls without model retraining or fine-tuning. The model treats each candidate label as a hypothesis and computes entailment probability with the input text as premise, enabling open-ended categorization. Supports both single-label and multi-label scenarios by adjusting probability aggregation (argmax vs threshold-based).","intents":["build content routing systems where categories are user-defined or change frequently","implement dynamic tagging systems for documents without pre-defined label vocabularies","create adaptive content moderation that responds to emerging categories in real-time","prototype classification tasks rapidly by testing different label sets without retraining"],"best_for":["SaaS platforms where each customer defines their own classification categories","content platforms with evolving or user-generated label sets","rapid prototyping teams iterating on classification schemas","production systems requiring label set updates without model redeployment"],"limitations":["Inference latency scales linearly with label count; 100 labels = ~100x slower than single-label classification","Label descriptions significantly impact accuracy; poorly worded labels reduce performance by 10-30%","No learned label embeddings; each label is treated independently, missing potential label relationships or hierarchies","Multi-label scenarios require manual threshold tuning; no automatic threshold optimization","Batch processing with variable label sets requires custom batching logic (cannot use standard DataLoader)"],"requires":["Python 3.7+","transformers 4.20.0+","PyTorch 1.9+ or TensorFlow 2.4+","2GB+ GPU VRAM","Application logic to manage dynamic label sets and threshold tuning"],"input_types":["text to classify (string)","list of candidate labels (variable-length list of strings)","optional label descriptions for improved accuracy"],"output_types":["per-label entailment scores","ranked label list with confidence scores","binary predictions (label applies or not) for multi-label scenarios","top-K labels for open-ended categorization"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-mnli-xnli__cap_3","uri":"capability://data.processing.analysis.multilingual.semantic.understanding.with.11.language.support","name":"multilingual semantic understanding with 11-language support","description":"Encodes text semantics across 11 languages (English, Arabic, Bulgarian, German, Greek, Spanish, French, Hindi, Russian, Swahili, Thai) using a shared transformer representation space learned from MNLI and XNLI multilingual training data. The model's disentangled attention mechanism learns language-agnostic content representations while maintaining position information, enabling cross-lingual transfer without language-specific parameters or adapters.","intents":["build multilingual NLP pipelines that work across languages without language-specific models","perform zero-shot classification or entailment scoring in languages with no task-specific training data","create multilingual content moderation or routing systems with a single model","enable cross-lingual semantic search or similarity matching"],"best_for":["global platforms serving users in multiple languages","teams with limited resources for language-specific model development","applications requiring consistent behavior across language boundaries","low-resource language communities where task-specific training data is scarce"],"limitations":["Performance varies significantly by language; high-resource languages (English, French, Spanish) achieve 85-90% accuracy while low-resource languages (Swahili, Thai) achieve 70-80%","Cross-lingual transfer is asymmetric; English-to-other transfer stronger than other-language-to-English","Language mixing (code-switching) not explicitly supported; mixed-language inputs may degrade performance","Tokenization assumes language-specific tokenizers; requires proper language detection for optimal tokenization","No language-specific fine-tuning; task-specific adaptation requires retraining on all target languages simultaneously"],"requires":["Python 3.7+","transformers 4.20.0+","PyTorch 1.9+ or TensorFlow 2.4+","2GB+ GPU VRAM","Input text in one of 11 supported languages"],"input_types":["text in any of 11 supported languages","pre-tokenized token IDs","variable-length sequences (up to 512 tokens)"],"output_types":["language-agnostic semantic representations (768-dim embeddings)","classification or entailment scores","cross-lingual similarity scores"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-mnli-xnli__cap_4","uri":"capability://data.processing.analysis.efficient.inference.via.deberta.v3.architecture.with.disentangled.attention","name":"efficient inference via deberta-v3 architecture with disentangled attention","description":"Implements DeBERTa-v3-base architecture (12 layers, 768 hidden dimensions, 86M parameters) with disentangled attention mechanism that separates content and position representations, reducing computational complexity compared to standard multi-head attention. The model uses ONNX and SafeTensors export formats for optimized inference across CPU, GPU, and edge devices, with native support for quantization and distillation.","intents":["deploy text classification at scale with reduced latency and memory footprint","run inference on resource-constrained devices (mobile, edge) via ONNX quantization","optimize inference cost in production by reducing model size and computation","integrate with inference frameworks (ONNX Runtime, TensorRT) for hardware acceleration"],"best_for":["production teams optimizing inference cost and latency","edge deployment scenarios with memory or compute constraints","high-throughput systems requiring sub-100ms latency per request","teams using ONNX Runtime or TensorRT for inference optimization"],"limitations":["DeBERTa-v3-base is larger than DistilBERT (66M params) or MobileBERT (25M params); not suitable for extreme resource constraints","ONNX export requires careful handling of attention masks and token type IDs; some frameworks may not support all attention variants","Quantization (INT8) can reduce accuracy by 1-3% depending on task; requires validation on target task","Inference optimization frameworks (TensorRT, ONNX Runtime) have framework-specific quirks; not all optimizations work across all backends","No built-in batching optimization; requires custom batching logic for high-throughput scenarios"],"requires":["Python 3.7+","transformers 4.20.0+","PyTorch 1.9+ or TensorFlow 2.4+ (for ONNX export)","ONNX Runtime 1.10+ (for ONNX inference)","2GB+ GPU VRAM or 4GB+ CPU RAM"],"input_types":["text strings","pre-tokenized token IDs","ONNX-compatible tensor formats"],"output_types":["classification logits","normalized probabilities","ONNX-compatible tensor outputs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","transformers library 4.20.0+","PyTorch 1.9+ or TensorFlow 2.4+","4GB+ GPU VRAM for batch inference (CPU inference possible but ~10x slower)","HuggingFace Hub access or local model weights (~840MB disk space)","transformers 4.20.0+","2GB+ GPU VRAM for inference","Input text in supported languages: English, Arabic, Bulgarian, German, Greek, Spanish, French, Hindi, Russian, Swahili, Thai","2GB+ GPU VRAM","Application logic to manage dynamic label sets and threshold tuning"],"failure_modes":["Zero-shot performance degrades with domain-specific vocabulary or highly specialized label sets; 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