{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-facebook--bart-large-mnli","slug":"facebook--bart-large-mnli","name":"bart-large-mnli","type":"model","url":"https://huggingface.co/facebook/bart-large-mnli","page_url":"https://unfragile.ai/facebook--bart-large-mnli","categories":["data-analysis"],"tags":["transformers","pytorch","jax","rust","safetensors","bart","text-classification","zero-shot-classification","dataset:multi_nli","arxiv:1910.13461","arxiv:1909.00161","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-facebook--bart-large-mnli__cap_0","uri":"capability://data.processing.analysis.zero.shot.text.classification.via.natural.language.inference","name":"zero-shot text classification via natural language inference","description":"Classifies arbitrary text into user-defined categories without task-specific fine-tuning by reformulating classification as an entailment problem. The model takes a premise (input text) and generates entailment scores against multiple hypothesis templates (e.g., 'This text is about [category]'), then ranks categories by entailment confidence. Uses BART's seq2seq architecture with cross-attention over encoder-decoder layers to reason about semantic relationships between text and category descriptions.","intents":["classify documents into custom categories without labeled training data","perform multi-label or multi-class categorization on new domains at inference time","rapidly prototype text classification pipelines without annotation overhead","assign intent labels to user queries in conversational systems without retraining"],"best_for":["teams building rapid-iteration NLP systems with evolving category schemas","developers prototyping intent detection or topic classification without labeled datasets","production systems requiring domain-agnostic text categorization across multiple use cases"],"limitations":["entailment-based approach adds ~2-3x inference latency vs task-specific classifiers due to per-category hypothesis generation and scoring","performance degrades with vague or overlapping category descriptions; requires careful prompt engineering of hypothesis templates","no built-in support for hierarchical or structured category taxonomies; flat category lists only","context window limited to 1024 tokens; longer documents must be truncated or chunked externally","entailment reasoning can be brittle with adversarial or out-of-distribution text; no confidence calibration guarantees"],"requires":["PyTorch 1.9+ or JAX/Flax for inference","transformers library 4.6.0+","minimum 3GB GPU VRAM for fp32 inference (1.5GB with fp16 quantization)","HuggingFace Hub API access or local model weights (~1.6GB disk)"],"input_types":["raw text strings (documents, sentences, queries)","text with optional metadata (author, timestamp, source)"],"output_types":["classification scores (logits or probabilities per category)","ranked category predictions with confidence scores","entailment scores (contradiction, neutral, entailment) for each hypothesis"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-mnli__cap_1","uri":"capability://data.processing.analysis.multi.label.classification.with.soft.probability.scores","name":"multi-label classification with soft probability scores","description":"Extends zero-shot classification to support multiple simultaneous category assignments per input by computing independent entailment scores for each category and applying configurable thresholds or softmax normalization. The model generates separate entailment hypotheses for each label (e.g., 'This text is about sports', 'This text is about politics') and scores them independently, allowing overlapping predictions. Supports both threshold-based hard assignments and probability-based soft scores for downstream ranking or filtering.","intents":["assign multiple topic tags to documents or content items","detect multiple intents or entities in a single user utterance","perform hierarchical or multi-faceted text categorization","generate confidence-weighted predictions for downstream ranking systems"],"best_for":["content platforms requiring multi-tag annotation without manual labeling","conversational AI systems handling utterances with multiple intents","information retrieval systems needing faceted document classification"],"limitations":["no explicit modeling of label dependencies or correlations; treats each category independently","threshold selection requires manual tuning per domain; no automatic calibration","computational cost scales linearly with number of categories (N hypotheses = N forward passes)","probability scores are not calibrated across categories; direct comparison between labels unreliable"],"requires":["PyTorch 1.9+ or JAX/Flax","transformers library 4.6.0+","custom post-processing logic for threshold selection or probability aggregation"],"input_types":["text strings","text with category lists (for hypothesis template generation)"],"output_types":["multi-label binary predictions (per-category 0/1 assignments)","soft probability scores (0.0-1.0 per category)","ranked label lists with confidence scores"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-mnli__cap_2","uri":"capability://data.processing.analysis.cross.lingual.transfer.via.multilingual.entailment.reasoning","name":"cross-lingual transfer via multilingual entailment reasoning","description":"Applies zero-shot classification to non-English text by leveraging BART's implicit multilingual understanding developed during Multi-NLI pre-training on English data. The model accepts text and category descriptions in languages beyond English (Spanish, French, German, etc.) and performs entailment reasoning across language boundaries through shared semantic space learned during pre-training. No explicit translation or language-specific fine-tuning required; performance depends on target language similarity to English and category description clarity.","intents":["classify documents in non-English languages without language-specific models","build multilingual content moderation or intent detection systems","extend zero-shot classification to low-resource languages without retraining"],"best_for":["teams supporting multiple languages with limited per-language labeled data","global platforms requiring consistent classification across language variants","low-resource language scenarios where language-specific models unavailable"],"limitations":["performance significantly degrades for languages distant from English (e.g., Chinese, Arabic, Japanese); no explicit cross-lingual alignment","category descriptions in non-English languages may be misinterpreted if semantically distant from English training data","no language detection or automatic hypothesis template translation; requires manual per-language prompt engineering","entailment reasoning less reliable for morphologically complex or agglutinative languages"],"requires":["PyTorch 1.9+","transformers library 4.6.0+","manual category description translation or multilingual prompt templates"],"input_types":["text in non-English languages","category descriptions in target language"],"output_types":["classification scores and predictions in target language context"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-mnli__cap_3","uri":"capability://data.processing.analysis.entailment.score.interpretation.and.confidence.ranking","name":"entailment score interpretation and confidence ranking","description":"Produces three-way entailment judgments (entailment, neutral, contradiction) for each category hypothesis and converts these scores into interpretable confidence rankings. The model outputs logits across the entailment label space and applies softmax normalization to generate probabilities, with entailment probability serving as the primary confidence signal. Supports extracting intermediate attention weights and hidden states for interpretability analysis of which input tokens influenced category predictions.","intents":["rank candidate categories by confidence for downstream filtering or re-ranking","identify when model is uncertain and defer to human review or fallback systems","debug classification failures by examining which input spans influenced predictions","calibrate decision thresholds based on entailment score distributions"],"best_for":["production systems requiring confidence-based filtering or rejection sampling","interpretability-focused applications needing explanation of predictions","quality assurance pipelines identifying low-confidence predictions for review"],"limitations":["entailment scores not calibrated across different input lengths or category description styles; direct comparison unreliable","neutral class often overused by model; entailment vs. neutral distinction sometimes ambiguous","attention weights reflect model internals but don't guarantee faithful explanations of reasoning","confidence scores don't account for out-of-distribution inputs; high confidence possible on adversarial examples"],"requires":["PyTorch 1.9+ with gradient computation enabled for attention extraction","transformers library 4.6.0+ with output_attentions=True flag"],"input_types":["text strings","category descriptions"],"output_types":["entailment logits (3 values per category: entailment, neutral, contradiction)","softmax probabilities (0.0-1.0 per entailment class)","attention weight matrices (batch_size × num_heads × seq_len × seq_len)","hidden state representations for custom downstream analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-mnli__cap_4","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.batching.and.memory.optimization","name":"batch inference with dynamic batching and memory optimization","description":"Processes multiple texts and category sets in parallel through PyTorch/JAX batching with automatic padding and attention mask generation. Supports variable-length inputs within a batch through dynamic padding (pad to max length in batch rather than fixed size) and optional gradient checkpointing to reduce peak memory usage during inference. Integrates with HuggingFace transformers' pipeline API for automatic tokenization, batching, and output post-processing with configurable batch sizes and device placement (CPU/GPU).","intents":["classify large document collections efficiently without sequential processing","deploy classification on resource-constrained hardware (mobile, edge devices)","optimize throughput for high-volume inference serving (API endpoints, batch jobs)"],"best_for":["batch processing pipelines (nightly jobs, data lake classification)","inference serving requiring high throughput and low latency","edge deployment on devices with limited GPU memory"],"limitations":["dynamic batching adds ~5-10% overhead vs. static batch sizes due to padding computation","memory usage still scales with batch size × sequence length; very large batches require gradient checkpointing (adds ~20% latency)","no built-in distributed inference; multi-GPU scaling requires manual data parallelism setup","tokenization overhead becomes significant for very short texts; minimum effective batch size ~8-16"],"requires":["PyTorch 1.9+ or JAX/Flax","transformers library 4.6.0+","GPU with 3GB+ VRAM for fp32 (1.5GB for fp16 quantization)","optional: CUDA 11.0+ for GPU acceleration"],"input_types":["lists of text strings","lists of category lists"],"output_types":["batched classification scores","batched predictions with confidence scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-mnli__cap_5","uri":"capability://data.processing.analysis.quantized.inference.for.reduced.latency.and.memory.footprint","name":"quantized inference for reduced latency and memory footprint","description":"Supports inference with reduced-precision weights (fp16, int8, int4) through PyTorch's native quantization, ONNX Runtime quantization, or third-party frameworks (bitsandbytes, AutoGPTQ). Converts 1.6GB fp32 weights to ~800MB (fp16) or ~400MB (int8) with minimal accuracy loss, enabling deployment on memory-constrained devices. Quantization applied post-training without fine-tuning; inference speed improves 1.5-3x depending on hardware support (GPU tensor cores, CPU VNNI instructions).","intents":["deploy model on edge devices or mobile with limited memory (< 2GB)","reduce inference latency for real-time classification (< 100ms per request)","lower cloud inference costs through reduced GPU memory requirements and faster batch processing"],"best_for":["edge deployment (mobile, IoT, embedded systems)","cost-sensitive cloud inference (smaller GPU instances)","latency-critical applications (real-time chatbots, content moderation)"],"limitations":["int8 quantization may reduce accuracy by 1-3% on challenging categories; int4 can degrade further","quantization benefits depend on hardware support; CPU inference may see minimal speedup without VNNI","no automatic quantization; requires manual conversion and validation per target hardware","quantized models not compatible with gradient-based fine-tuning; requires full-precision base model for adaptation"],"requires":["PyTorch 1.9+ with quantization support","optional: bitsandbytes for int8 quantization, AutoGPTQ for int4","optional: ONNX Runtime for cross-platform quantized inference","GPU with tensor core support (NVIDIA A100/V100, AMD MI250) for optimal speedup"],"input_types":["text strings"],"output_types":["classification scores (same format as full-precision model)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-mnli__cap_6","uri":"capability://planning.reasoning.hypothesis.template.customization.and.prompt.engineering","name":"hypothesis template customization and prompt engineering","description":"Allows users to define custom hypothesis templates that reformulate category descriptions into natural language statements for entailment scoring. Instead of default 'This text is about [category]', users can specify domain-specific templates like 'The sentiment of this review is [category]' or 'This document discusses [category] in detail'. Templates are applied per-category and support variable substitution; model scores entailment of custom hypotheses against input text. Template quality directly impacts classification accuracy; poorly-worded templates degrade performance.","intents":["adapt zero-shot classification to domain-specific terminology and phrasing","improve accuracy for specialized categories through careful prompt engineering","control how categories are interpreted by the model through explicit hypothesis wording"],"best_for":["domain experts fine-tuning classification for specialized use cases","teams iterating on category definitions and hypothesis phrasing","applications requiring consistent interpretation of ambiguous category names"],"limitations":["no automatic template optimization; requires manual experimentation and validation","template quality highly subjective; no principled way to select best phrasing","longer or more complex templates increase inference latency (more tokens to process)","template effectiveness varies by language and domain; no transfer of templates across domains"],"requires":["manual template definition per category","understanding of natural language phrasing and domain terminology"],"input_types":["text strings","custom hypothesis templates (natural language strings with [category] placeholders)"],"output_types":["classification scores using custom templates"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-mnli__cap_7","uri":"capability://tool.use.integration.integration.with.huggingface.hub.and.model.versioning","name":"integration with huggingface hub and model versioning","description":"Provides seamless integration with HuggingFace Model Hub for model discovery, versioning, and distributed caching. Supports automatic model download and caching with version pinning (e.g., 'facebook/bart-large-mnli@revision=main'), enabling reproducible inference across environments. Integrates with HuggingFace's safetensors format for faster model loading and improved security (no arbitrary code execution during deserialization). Supports model cards with documentation, usage examples, and license information.","intents":["discover and load pre-trained models without manual weight management","ensure reproducible inference by pinning specific model versions","share models and configurations across teams through Hub integration"],"best_for":["teams using HuggingFace ecosystem (transformers, datasets, accelerate)","reproducible research and production deployments requiring version control","open-source projects sharing models and configurations"],"limitations":["requires internet connectivity for initial model download; no offline-first support","model caching directory can grow large (1.6GB per model); requires manual cleanup","Hub API rate limits may throttle concurrent model downloads","no built-in model update notifications; manual version checking required"],"requires":["HuggingFace transformers library 4.6.0+","internet connectivity for model download","optional: HuggingFace Hub API token for private models"],"input_types":["model identifiers (e.g., 'facebook/bart-large-mnli')","revision/branch names for version pinning"],"output_types":["loaded model and tokenizer objects"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-mnli__cap_8","uri":"capability://code.generation.editing.fine.tuning.and.domain.adaptation.with.task.specific.data","name":"fine-tuning and domain adaptation with task-specific data","description":"Enables supervised fine-tuning on labeled classification data to adapt the model to specific domains or improve performance on custom categories. Fine-tuning updates BART's decoder and cross-attention layers while optionally freezing encoder weights to preserve zero-shot capability. Supports both standard supervised learning (labeled examples) and few-shot adaptation (5-10 examples per category). Fine-tuning typically requires 100-1000 labeled examples per category for meaningful improvement; training time ~1-4 hours on single GPU.","intents":["improve accuracy on domain-specific categories with labeled training data","adapt model to new classification tasks while retaining zero-shot capability","perform few-shot learning with minimal labeled examples"],"best_for":["teams with labeled datasets for specific domains (e.g., medical, legal, financial)","iterative development where zero-shot baseline is refined with human feedback","few-shot scenarios with 5-50 labeled examples per category"],"limitations":["fine-tuning on small datasets (< 100 examples) risks overfitting; requires careful regularization","fine-tuned models lose generalization to unseen categories; zero-shot capability degrades","no automatic hyperparameter tuning; requires manual learning rate, batch size, and epoch selection","fine-tuning requires GPU; CPU-only training impractical (> 24 hours)"],"requires":["PyTorch 1.9+ with training support","transformers library 4.6.0+","GPU with 8GB+ VRAM for fine-tuning","labeled training data (100+ examples recommended)"],"input_types":["text strings with category labels","optional: validation data for early stopping"],"output_types":["fine-tuned model weights","training metrics (loss, accuracy, F1)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-mnli__cap_9","uri":"capability://tool.use.integration.api.endpoint.deployment.and.serving.infrastructure","name":"api endpoint deployment and serving infrastructure","description":"Supports deployment as REST API endpoints through HuggingFace Inference API, Azure ML, AWS SageMaker, or self-hosted solutions (FastAPI, Flask, TorchServe). Model can be served with automatic batching, request queuing, and horizontal scaling across multiple GPU instances. Inference API provides standardized request/response format with support for streaming outputs and async processing. Deployment handles tokenization, model inference, and output post-processing transparently.","intents":["expose classification as HTTP API for web applications and microservices","scale inference across multiple GPU instances for high-throughput serving","integrate classification into existing ML pipelines and data processing workflows"],"best_for":["production systems requiring API-based classification","teams deploying models on cloud platforms (Azure, AWS, GCP)","high-volume inference scenarios requiring auto-scaling"],"limitations":["API latency overhead (~50-200ms) vs. local inference due to network round-trip","cloud deployment costs scale with inference volume; no free tier for high-traffic applications","API rate limits and quotas may throttle bursty traffic","cold start latency (model loading) can be 10-30 seconds on serverless platforms"],"requires":["HuggingFace Inference API account or cloud platform (Azure, AWS, GCP)","optional: self-hosted infrastructure (Kubernetes, Docker, GPU instances)","API authentication and rate limiting setup"],"input_types":["HTTP POST requests with JSON payload (text, categories)"],"output_types":["JSON responses with classification scores and predictions"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or JAX/Flax for inference","transformers library 4.6.0+","minimum 3GB GPU VRAM for fp32 inference (1.5GB with fp16 quantization)","HuggingFace Hub API access or local model weights (~1.6GB disk)","PyTorch 1.9+ or JAX/Flax","custom post-processing logic for threshold selection or probability aggregation","PyTorch 1.9+","manual category description translation or multilingual prompt templates","PyTorch 1.9+ with gradient computation enabled for attention extraction","transformers library 4.6.0+ with output_attentions=True flag"],"failure_modes":["entailment-based approach adds ~2-3x inference latency vs task-specific classifiers due to per-category hypothesis generation and scoring","performance degrades with vague or overlapping category descriptions; requires careful prompt engineering of hypothesis templates","no built-in support for hierarchical or structured category taxonomies; flat category lists only","context window limited to 1024 tokens; longer documents must be truncated or chunked externally","entailment reasoning can be brittle with adversarial or out-of-distribution text; no confidence calibration guarantees","no explicit modeling of label dependencies or correlations; treats each category independently","threshold selection requires manual tuning per domain; no automatic calibration","computational cost scales linearly with number of categories (N hypotheses = N forward passes)","probability scores are not calibrated across categories; direct comparison between labels unreliable","performance significantly degrades for languages distant from English (e.g., Chinese, Arabic, Japanese); no explicit cross-lingual alignment","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8326322517754232,"quality":0.3,"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":2655180,"model_likes":1565}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=facebook--bart-large-mnli","compare_url":"https://unfragile.ai/compare?artifact=facebook--bart-large-mnli"}},"signature":"NA6TgNf/swF3qUnUthviL6NBW/jd3yKQ5giHrzVYJNLTaBTdv3agsZW+Dk9aaRA0VyrSS7EkaWqeY6ro0jIQCQ==","signedAt":"2026-06-20T22:39:00.598Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/facebook--bart-large-mnli","artifact":"https://unfragile.ai/facebook--bart-large-mnli","verify":"https://unfragile.ai/api/v1/verify?slug=facebook--bart-large-mnli","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"}}