{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7","slug":"moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7","name":"mDeBERTa-v3-base-xnli-multilingual-nli-2mil7","type":"model","url":"https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7","page_url":"https://unfragile.ai/moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7","categories":["model-training"],"tags":["transformers","pytorch","onnx","safetensors","deberta-v2","text-classification","zero-shot-classification","nli","multilingual","zh","ja","ar","ko","de","fr","es","pt","hi","id","it"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7__cap_0","uri":"capability://text.generation.language.multilingual.zero.shot.text.classification","name":"multilingual-zero-shot-text-classification","description":"Performs zero-shot classification on text in 11+ languages (English, Chinese, Japanese, Arabic, Korean, German, French, Spanish, Portuguese, Hindi, Indonesian, Italian) using DeBERTa-v3 architecture fine-tuned on XNLI (cross-lingual natural language inference) dataset with 2.7M examples. The model encodes input text and candidate labels as premise-hypothesis pairs through the NLI framework, computing entailment scores to determine label relevance without requiring task-specific training data. Uses transformer-based attention mechanisms with disentangled attention and enhanced mask tokens for improved multilingual representation.","intents":["Classify text into custom categories without labeled training data in non-English languages","Perform intent detection or topic classification across multilingual user inputs","Build language-agnostic content moderation or routing systems without per-language fine-tuning","Rapidly prototype text classification pipelines that work across 11+ languages simultaneously"],"best_for":["multilingual SaaS platforms needing zero-shot classification without language-specific models","teams building content moderation systems supporting diverse languages","developers prototyping NLI-based reasoning without labeled datasets","organizations migrating from rule-based to ML-based text classification"],"limitations":["Zero-shot performance degrades with domain-specific or highly technical language not well-represented in XNLI training","Requires careful prompt engineering for label definitions — vague labels produce unreliable scores","Inference latency ~200-500ms per sample on CPU, ~50-100ms on GPU due to full transformer forward pass","Maximum sequence length 512 tokens; longer texts must be truncated or chunked","No built-in confidence calibration — raw logits may not reflect true probability of correctness across all label sets","Performance varies significantly by language pair; lower-resource languages (Hindi, Indonesian) show 5-15% lower accuracy than English"],"requires":["Python 3.7+","transformers library 4.20.0+","PyTorch 1.9+ or ONNX Runtime 1.10+ for inference","2GB+ RAM for model loading (base model ~440MB)","HuggingFace Hub API access or local model weights"],"input_types":["raw text strings (premise)","candidate label strings (hypothesis)","structured JSON with text and label arrays"],"output_types":["classification scores (logits or probabilities per label)","ranked label predictions with confidence scores","entailment/contradiction/neutral classification per label"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7__cap_1","uri":"capability://text.generation.language.cross.lingual.natural.language.inference","name":"cross-lingual-natural-language-inference","description":"Performs NLI (natural language inference) tasks by encoding premise-hypothesis pairs through DeBERTa-v3's transformer layers and outputting entailment/neutral/contradiction classifications. The model was trained on XNLI's 2.7M multilingual examples covering 15 languages, learning to recognize logical relationships between text pairs regardless of language. Internally uses masked language modeling and next sentence prediction objectives adapted for cross-lingual transfer, with disentangled attention allowing the model to reason about semantic entailment patterns that generalize across language families.","intents":["Determine if a hypothesis logically follows from a premise in any of 11+ supported languages","Build fact-checking or claim verification systems that work across languages","Implement semantic similarity or contradiction detection without explicit similarity metrics","Create language-agnostic reasoning pipelines for multi-hop inference tasks"],"best_for":["fact-checking platforms supporting multilingual content","teams building semantic reasoning systems without language-specific rule bases","NLP researchers evaluating cross-lingual transfer learning","content platforms needing contradiction detection across languages"],"limitations":["NLI performance depends on clear, well-formed premise-hypothesis pairs; ambiguous or colloquial text produces unreliable entailment scores","No support for multi-hop reasoning or complex logical operators (AND, OR, NOT) — only pairwise entailment","Trained on formal text from Wikipedia and news; performance drops on social media, technical documentation, or domain-specific language","Entailment scores are not calibrated probabilities; threshold selection for neutral vs entailment requires empirical tuning per use case","Inference requires encoding both premise and hypothesis; cannot reuse premise embeddings across multiple hypotheses efficiently without custom batching"],"requires":["Python 3.7+","transformers 4.20.0+","PyTorch 1.9+ or ONNX Runtime","premise and hypothesis text inputs (strings)","GPU recommended for batch inference >10 samples"],"input_types":["premise text (string, 1-512 tokens)","hypothesis text (string, 1-512 tokens)","batch of premise-hypothesis pairs (JSON or CSV)"],"output_types":["entailment/neutral/contradiction label","logits for each class (3-dimensional)","softmax probabilities per class"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7__cap_2","uri":"capability://data.processing.analysis.multilingual.semantic.entailment.scoring","name":"multilingual-semantic-entailment-scoring","description":"Computes fine-grained entailment scores between text pairs by passing them through DeBERTa-v3's 12 transformer layers and extracting logits from the classification head, producing three scores (entailment, neutral, contradiction) that reflect the model's confidence in each relationship type. The scoring is language-agnostic due to XNLI's multilingual training, allowing direct comparison of entailment strength across premise-hypothesis pairs in different languages. Scores can be converted to probabilities via softmax or used as raw logits for threshold-based decision making.","intents":["Rank or filter text pairs by semantic similarity or logical relationship strength","Implement confidence-based filtering for fact-checking or claim validation pipelines","Build semantic search or retrieval systems that rank candidates by entailment rather than lexical similarity","Create explainable NLP systems where entailment scores serve as interpretable reasoning signals"],"best_for":["information retrieval systems needing semantic ranking beyond keyword matching","fact-checking platforms that need confidence scores for claim-evidence pairs","teams building explainable AI systems where entailment scores are interpretable","multilingual search engines or recommendation systems"],"limitations":["Entailment scores are not calibrated to absolute probability scales; relative ranking is more reliable than absolute threshold interpretation","Scoring is symmetric in input order but not commutative — P(H|P) ≠ P(P|H), requiring careful prompt design","Computational cost scales linearly with number of premise-hypothesis pairs; batch scoring of 1000+ pairs requires GPU acceleration","Scores reflect training data distribution (Wikipedia, news); domain shift causes miscalibration (e.g., social media text scores lower than deserved)","No built-in explanation or attention visualization; users cannot easily understand which tokens drove entailment decisions"],"requires":["Python 3.7+","transformers 4.20.0+","PyTorch 1.9+ or ONNX Runtime","premise and hypothesis text strings","GPU for scoring >100 pairs per second"],"input_types":["premise text (string)","hypothesis text (string)","batch of premise-hypothesis pairs"],"output_types":["raw logits (3-dimensional vector)","softmax probabilities (3-dimensional, sums to 1)","ranked scores per relationship type"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7__cap_3","uri":"capability://data.processing.analysis.batch.multilingual.text.classification","name":"batch-multilingual-text-classification","description":"Processes multiple text samples and label sets in a single forward pass using PyTorch's batching mechanisms, encoding all premise-hypothesis pairs together and returning classification results for each sample. The model leverages transformer attention's quadratic complexity to efficiently compute entailment scores across batches, with batch size limited by GPU/CPU memory (typically 8-64 samples per batch). Supports both homogeneous batches (same labels for all samples) and heterogeneous batches (different labels per sample) through dynamic padding and attention masking.","intents":["Classify large datasets (1000s of documents) efficiently without per-sample latency overhead","Build production inference pipelines that maximize GPU utilization through batching","Process streaming text classification tasks with configurable batch sizes","Implement cost-effective bulk classification for content moderation or routing"],"best_for":["teams processing large document collections (>10K samples) requiring zero-shot classification","production systems needing high throughput (100+ classifications/second)","data pipelines where latency per sample is less critical than total throughput","organizations optimizing inference costs by maximizing GPU utilization"],"limitations":["Batch processing requires all samples to fit in GPU/CPU memory; very large batches (>256) may cause OOM errors on consumer hardware","Heterogeneous batches (different label sets per sample) require dynamic padding, adding ~10-20% overhead vs homogeneous batches","Batch inference latency is not linear with batch size due to transformer attention complexity; doubling batch size increases latency by ~1.5-1.8x, not 2x","No built-in distributed batching across multiple GPUs; requires manual data parallelism setup","Batch results are unordered if using async processing; requires explicit indexing to map results back to input samples"],"requires":["Python 3.7+","transformers 4.20.0+","PyTorch 1.9+ with CUDA support (recommended) or CPU-only mode","8GB+ RAM for batch size 32, 16GB+ for batch size 64","input data as list of dicts with 'text' and 'labels' keys"],"input_types":["list of text strings","list of label lists (one per sample or shared)","pandas DataFrame with text and label columns","JSONL format with text and labels per line"],"output_types":["batch of classification scores (2D array: samples × labels)","batch of top-k predictions per sample","CSV or JSON with results and confidence scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7__cap_4","uri":"capability://text.generation.language.language.agnostic.label.encoding","name":"language-agnostic-label-encoding","description":"Encodes candidate labels in any of 11+ supported languages through the same transformer tokenizer and embedding space, enabling zero-shot classification without language-specific label preprocessing. The model treats labels as hypotheses in the NLI framework, tokenizing them with the same vocabulary and encoding them through the same transformer layers as premise text. This shared embedding space, learned during XNLI training, allows labels in different languages to be compared directly against premises in any language, supporting cross-lingual classification (e.g., English text with Spanish labels).","intents":["Classify text using labels defined in any supported language without translation","Build multilingual classification systems where labels and text may be in different languages","Support user-provided labels in their native language without preprocessing or translation","Create language-agnostic label taxonomies that work across 11+ languages simultaneously"],"best_for":["multilingual platforms where labels are user-defined in various languages","teams avoiding translation overhead by leveraging cross-lingual embeddings","systems supporting code-switching or mixed-language inputs","organizations building language-agnostic content routing without per-language label sets"],"limitations":["Label encoding quality varies by language; lower-resource languages (Hindi, Indonesian) may produce less precise label representations than English","Labels must be grammatically well-formed; abbreviations, acronyms, or domain-specific jargon may not encode reliably","Cross-lingual label-text pairs (e.g., English text with Spanish labels) show 5-10% lower accuracy than same-language pairs due to reduced training data coverage","Label length affects encoding quality; very short labels (<3 tokens) or very long labels (>20 tokens) produce less stable representations","No built-in label normalization or synonym handling; semantically equivalent labels in different languages are treated as distinct"],"requires":["Python 3.7+","transformers 4.20.0+","label strings in any of 11+ supported languages","text strings in any of 11+ supported languages"],"input_types":["label strings (any supported language)","text strings (any supported language)","mixed-language label and text pairs"],"output_types":["encoded label representations (768-dimensional embeddings)","classification scores for each label","language-agnostic label rankings"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7__cap_5","uri":"capability://automation.workflow.onnx.model.export.and.inference","name":"onnx-model-export-and-inference","description":"Exports the DeBERTa-v3-base model to ONNX (Open Neural Network Exchange) format for hardware-agnostic inference, enabling deployment on CPUs, edge devices, and non-PyTorch runtimes without model recompilation. The ONNX export preserves the full transformer architecture including attention masking and token type embeddings, allowing inference through ONNX Runtime with minimal accuracy loss (<0.5% in most cases). Supports both static and dynamic input shapes, enabling flexible batch sizes and sequence lengths without reexporting.","intents":["Deploy the model on edge devices or resource-constrained environments without PyTorch dependency","Integrate the model into non-Python applications (C++, Java, .NET) via ONNX Runtime","Optimize inference latency and memory usage through ONNX Runtime's graph optimization and quantization","Enable model serving in production environments (TensorFlow Serving, ONNX Server) without PyTorch overhead"],"best_for":["teams deploying models on edge devices or mobile platforms","organizations building polyglot inference pipelines (Python + C++ + Java)","production systems requiring minimal runtime dependencies and fast startup","teams optimizing inference cost through quantization and graph optimization"],"limitations":["ONNX export requires manual conversion; no built-in export function in HuggingFace transformers for this specific model variant","ONNX Runtime inference is ~5-15% slower than PyTorch on GPU due to graph optimization overhead, though faster on CPU","Dynamic shape support requires ONNX Runtime 1.10+; older versions require static input shapes","Quantization (INT8) reduces model size by 4x but may degrade accuracy by 1-3% depending on quantization method","ONNX export does not include post-processing (softmax, argmax); users must implement these separately"],"requires":["Python 3.7+","transformers 4.20.0+","PyTorch 1.9+ (for export only)","ONNX Runtime 1.10+ (for inference)","onnx and onnxruntime Python packages"],"input_types":["PyTorch model checkpoint","HuggingFace model identifier (auto-download and convert)"],"output_types":["ONNX model file (.onnx)","quantized ONNX model (INT8)","inference results via ONNX Runtime"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-moritzlaurer--mdeberta-v3-base-xnli-multilingual-nli-2mil7__cap_6","uri":"capability://automation.workflow.safetensors.format.model.loading","name":"safetensors-format-model-loading","description":"Loads model weights from safetensors format, a secure serialization format that prevents arbitrary code execution during model loading (unlike pickle-based PyTorch checkpoints). The model is distributed in safetensors format on HuggingFace Hub, allowing users to load weights directly without security risks. Loading is ~2-3x faster than PyTorch's pickle format due to memory-mapped file access and zero-copy tensor operations, reducing model initialization latency from ~2-3 seconds to ~0.5-1 second.","intents":["Load model weights securely without risk of arbitrary code execution from untrusted sources","Reduce model loading latency in production inference pipelines","Enable efficient model caching and sharing across multiple processes","Integrate with security-conscious deployment environments that restrict pickle usage"],"best_for":["teams deploying models from untrusted sources or public model hubs","production systems where model loading latency is critical (serverless, edge)","organizations with security policies restricting pickle deserialization","systems requiring fast model switching or A/B testing with multiple model variants"],"limitations":["Safetensors support requires transformers 4.26.0+; older versions fall back to pickle format","Memory-mapped loading requires sufficient disk space for the full model; cannot load from compressed archives without extraction","Safetensors format does not support custom PyTorch modules or non-standard tensor operations; only standard tensor types","Loading from remote sources (HuggingFace Hub) requires internet connectivity; offline loading requires pre-downloaded safetensors files"],"requires":["Python 3.7+","transformers 4.26.0+","safetensors Python package","HuggingFace Hub API access or local safetensors files","disk space for model weights (~440MB for base model)"],"input_types":["HuggingFace model identifier (auto-download safetensors)","local safetensors file path"],"output_types":["loaded model ready for inference","model weights as PyTorch tensors"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","transformers library 4.20.0+","PyTorch 1.9+ or ONNX Runtime 1.10+ for inference","2GB+ RAM for model loading (base model ~440MB)","HuggingFace Hub API access or local model weights","transformers 4.20.0+","PyTorch 1.9+ or ONNX Runtime","premise and hypothesis text inputs (strings)","GPU recommended for batch inference >10 samples","premise and hypothesis text strings"],"failure_modes":["Zero-shot performance degrades with domain-specific or highly technical language not well-represented in XNLI training","Requires careful prompt engineering for label definitions — vague labels produce unreliable scores","Inference latency ~200-500ms per sample on CPU, ~50-100ms on GPU due to full transformer forward pass","Maximum sequence length 512 tokens; 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