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The model employs a Swin Transformer vision encoder to extract spatial features from document images, then decodes them into LaTeX-compatible Markdown using a transformer decoder trained on arXiv papers. This enables preservation of mathematical equations, tables, and hierarchical document structure in machine-readable format.","intents":["I need to extract text and equations from a PDF paper image and convert it to editable Markdown","I want to digitize scanned academic documents while preserving mathematical notation and formatting","I need to build a document processing pipeline that converts paper images to structured text for downstream NLP tasks","I want to create searchable text from scientific paper images without manual OCR correction"],"best_for":["researchers and academics digitizing paper archives","teams building document processing pipelines for scientific literature","developers creating knowledge extraction systems from academic PDFs","organizations automating paper-to-digital workflows at scale"],"limitations":["Optimized for scientific/academic documents; performance degrades on non-technical or handwritten content","Requires high-quality document images (300+ DPI recommended); low-resolution or heavily skewed images produce degraded output","No native support for multi-page PDF processing; requires per-page image extraction before model inference","Output Markdown may require post-processing for complex table structures or non-standard equation formatting","Inference latency ~2-5 seconds per page on CPU; GPU acceleration recommended for batch processing","Model size ~340M parameters; requires ~1.2GB VRAM for inference"],"requires":["Python 3.8+","PyTorch 1.9+ or compatible framework","Transformers library 4.25+","PIL/Pillow for image preprocessing","GPU with 2GB+ VRAM recommended (CPU inference possible but slow)","Input images in JPEG, PNG, or PDF format"],"input_types":["image (JPEG, PNG, TIFF, WebP)","document page image (300+ DPI recommended)","PDF page rendered as image"],"output_types":["text (Markdown format)","structured text with LaTeX equations","UTF-8 encoded Markdown with math notation"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--nougat-base__cap_1","uri":"capability://automation.workflow.batch.document.image.processing.with.transformers","name":"batch-document-image-processing-with-transformers","description":"Enables efficient batch processing of multiple document images through the Hugging Face Transformers library's pipeline abstraction, supporting dynamic batching and automatic device placement (CPU/GPU). The model integrates with the standard transformers.pipeline() interface, allowing developers to load the model once and process multiple images with automatic tensor batching, memory management, and optional GPU acceleration without manual CUDA code.","intents":["I want to process 1000+ document images efficiently without writing custom batching logic","I need to deploy this model in production with automatic GPU/CPU fallback","I want to integrate document-to-text conversion into an existing Transformers-based NLP pipeline","I need to process documents with automatic memory management and batch size optimization"],"best_for":["ML engineers building production document processing services","teams using Hugging Face Transformers as their standard framework","developers needing quick integration without custom model loading code","organizations processing document batches with variable image sizes"],"limitations":["Batch processing requires images of similar dimensions for optimal efficiency; highly variable image sizes may reduce throughput","Pipeline abstraction adds ~50-100ms overhead per batch compared to raw model inference","No built-in support for distributed inference across multiple GPUs or machines; requires external orchestration","Memory usage scales linearly with batch size; large batches on limited VRAM require manual batch size tuning","No native async/streaming support; blocking inference calls may bottleneck high-throughput applications"],"requires":["Python 3.8+","transformers library 4.25+","torch 1.9+","CUDA 11.0+ for GPU acceleration (optional but recommended)","Sufficient VRAM for batch size (2GB minimum for batch_size=1, scales linearly)"],"input_types":["PIL Image objects","image file paths (JPEG, PNG, TIFF)","numpy arrays (H×W×3 format)","batches of images as lists"],"output_types":["list of strings (Markdown text per image)","batch results with metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--nougat-base__cap_2","uri":"capability://image.visual.equation.aware.text.extraction.with.latex.preservation","name":"equation-aware-text-extraction-with-latex-preservation","description":"Extracts text from scientific document images while preserving mathematical equations in LaTeX format, using a decoder trained on arXiv papers where equations are annotated with their source LaTeX. The model learns to recognize equation regions in images and generate corresponding LaTeX code rather than attempting to OCR equations as plain text, enabling downstream tools to render or parse equations correctly.","intents":["I need to extract equations from paper images as LaTeX code, not as garbled text","I want to build a system that preserves mathematical notation when digitizing scientific papers","I need equation-aware text extraction for a math-focused search or indexing system","I want to convert paper images to a format where equations are machine-parseable"],"best_for":["researchers building math-aware document search systems","teams digitizing scientific literature with equation preservation","developers creating LaTeX-to-PDF pipelines from scanned papers","organizations indexing mathematical content from academic papers"],"limitations":["Equation accuracy depends on image quality; blurry or low-contrast equations may produce invalid LaTeX","Complex multi-line equations or equation arrays may be split incorrectly across output","Inline vs. display equation distinction may not always be preserved in output formatting","Rare or specialized mathematical notation not well-represented in arXiv training data may be misrecognized","No support for equation-specific post-processing; invalid LaTeX output requires manual correction"],"requires":["Python 3.8+","PyTorch 1.9+","Transformers 4.25+","High-quality document images (150+ DPI minimum for readable equations)","LaTeX renderer for validation (optional, for testing output)"],"input_types":["image (document page with equations)","scanned paper image","PDF page rendered as image"],"output_types":["Markdown text with embedded LaTeX equations","structured text with equation boundaries marked","UTF-8 text with $...$ or $$...$$ delimited equations"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--nougat-base__cap_3","uri":"capability://image.visual.vision.encoder.decoder.architecture.inference","name":"vision-encoder-decoder-architecture-inference","description":"Implements a modular vision-encoder-decoder architecture where a Swin Transformer encoder extracts hierarchical visual features from document images, and a transformer decoder generates Markdown text token-by-token. The encoder processes images at multiple scales (4×, 8×, 16×, 32×) to capture both fine details and document structure, while the decoder uses cross-attention to align generated text with visual features, enabling structured output generation.","intents":["I want to understand how the model processes document images at different scales","I need to extract intermediate visual features for custom downstream tasks","I want to implement similar encoder-decoder architectures for other document types","I need to debug or visualize what visual features the model extracts from documents"],"best_for":["researchers studying vision-language model architectures","developers implementing custom encoder-decoder models","teams needing to extract intermediate representations for transfer learning","engineers debugging model behavior on specific document types"],"limitations":["Encoder is frozen (not fine-tunable in base model); full model fine-tuning requires significant computational resources","Hierarchical feature extraction adds computational overhead; inference slower than single-scale approaches","Cross-attention mechanism requires full image context in memory; cannot process arbitrarily large images","No built-in support for extracting intermediate layer activations; requires custom forward hooks","Architecture assumes document-like input; performance degrades on non-document images"],"requires":["Python 3.8+","PyTorch 1.9+ with autograd support","Transformers 4.25+","Understanding of transformer architecture and attention mechanisms","GPU recommended for reasonable inference speed (CPU inference ~5-10s per image)"],"input_types":["image tensor (3×H×W, normalized)","PIL Image objects","numpy arrays"],"output_types":["encoder hidden states (hierarchical features)","decoder logits (token probabilities)","attention weights (cross-attention visualizations)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--nougat-base__cap_4","uri":"capability://safety.moderation.safetensors.format.model.loading.with.security","name":"safetensors-format-model-loading-with-security","description":"Loads model weights from Hugging Face Hub using the safetensors format, which provides secure deserialization without arbitrary code execution risks. The model is distributed as safetensors files instead of pickle, preventing malicious code injection during model loading. Integration with transformers library enables automatic format detection and loading without explicit format specification.","intents":["I want to load this model securely without risk of code injection from untrusted sources","I need to verify model integrity before loading in a production environment","I want to use a model format that doesn't require pickle deserialization","I need to load models in restricted environments where arbitrary code execution is disabled"],"best_for":["security-conscious teams deploying models in production","organizations with strict code execution policies","developers building model serving infrastructure","teams handling sensitive data requiring secure model loading"],"limitations":["Safetensors format is newer; some older tools may not support it natively","No built-in signature verification; relies on HTTPS and Hugging Face Hub security","Model weights are still downloaded from internet; requires network access and bandwidth","No local caching mechanism beyond transformers' default cache directory","Safetensors format adds minimal overhead but still requires disk I/O for loading"],"requires":["Python 3.8+","transformers 4.25+ (with safetensors support)","safetensors library 0.3.0+","Internet connection for initial model download","~1.2GB disk space for model weights"],"input_types":["model identifier string (facebook/nougat-base)","local path to safetensors files"],"output_types":["loaded model object (PreTrainedModel)","model configuration"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--nougat-base__cap_5","uri":"capability://tool.use.integration.huggingface.hub.integration.with.model.caching","name":"huggingface-hub-integration-with-model-caching","description":"Integrates with Hugging Face Hub for automatic model discovery, downloading, and caching. The model is hosted on Hub with versioning support, allowing developers to specify model revisions and automatically cache downloaded weights locally. Integration with transformers library enables one-line model loading with automatic Hub authentication, version management, and cache directory configuration.","intents":["I want to load the latest version of this model without manually managing downloads","I need to pin a specific model version for reproducibility across environments","I want to cache model weights locally to avoid repeated downloads","I need to integrate this model into a system that uses Hugging Face Hub for model management"],"best_for":["teams using Hugging Face Hub as their model registry","developers building reproducible ML pipelines","organizations with limited bandwidth needing efficient caching","teams managing multiple model versions for A/B testing"],"limitations":["Requires internet connection for initial model download; no offline-first support","Cache directory must have sufficient disk space (~1.2GB for this model)","Hub API rate limits may apply for high-frequency model downloads","No built-in support for private model repositories without authentication token","Cache invalidation requires manual deletion; no automatic cleanup of old versions"],"requires":["Python 3.8+","transformers 4.25+","huggingface-hub library 0.13+","Internet connection for model download","~1.2GB free disk space in cache directory","Optional: Hugging Face API token for private models"],"input_types":["model identifier (facebook/nougat-base)","revision string (main, v1.0, commit hash)"],"output_types":["cached model weights path","loaded model object","model metadata and configuration"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--nougat-base__cap_6","uri":"capability://image.visual.multi.language.document.support.with.arxiv.training","name":"multi-language-document-support-with-arxiv-training","description":"Trained on arXiv papers spanning multiple languages and scientific domains, enabling the model to handle documents in English, Chinese, Japanese, and other languages common in academic publishing. The decoder learns language-specific tokenization and formatting conventions through exposure to diverse arXiv papers, supporting multilingual Markdown output with proper character encoding.","intents":["I need to process scientific papers in languages other than English","I want to digitize multilingual academic documents while preserving formatting","I need a document-to-text model that works across international research papers","I want to build a multilingual document processing pipeline for academic content"],"best_for":["international research teams processing papers in multiple languages","organizations digitizing global academic archives","developers building multilingual document search systems","teams supporting non-English scientific communities"],"limitations":["Performance varies by language; English-dominant training data may bias output toward English","Right-to-left languages (Arabic, Hebrew) may not be fully supported","Language detection is implicit; model may mix languages in output if input contains multiple languages","Character encoding issues may occur with rare Unicode characters or special symbols","No explicit language specification; model infers language from document content"],"requires":["Python 3.8+ with UTF-8 encoding support","PyTorch 1.9+","Transformers 4.25+","Proper locale/encoding configuration for non-ASCII characters","Input images with clear text in supported languages"],"input_types":["document images in English, Chinese, Japanese, or other arXiv-represented languages","multilingual scientific papers","academic documents with mixed language content"],"output_types":["Markdown text in detected language","UTF-8 encoded output with proper character representation","multilingual text with equations preserved"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.9+ or compatible framework","Transformers library 4.25+","PIL/Pillow for image preprocessing","GPU with 2GB+ VRAM recommended (CPU inference possible but slow)","Input images in JPEG, PNG, or PDF format","torch 1.9+","CUDA 11.0+ for GPU acceleration (optional but recommended)","Sufficient VRAM for batch size (2GB minimum for batch_size=1, scales linearly)","PyTorch 1.9+"],"failure_modes":["Optimized for scientific/academic documents; performance degrades on non-technical or handwritten content","Requires high-quality document images (300+ DPI recommended); low-resolution or heavily skewed images produce degraded output","No native support for multi-page PDF processing; requires per-page image extraction before model inference","Output Markdown may require post-processing for complex table structures or non-standard equation formatting","Inference latency ~2-5 seconds per page on CPU; GPU acceleration recommended for batch processing","Model size ~340M parameters; requires ~1.2GB VRAM for inference","Batch processing requires images of similar dimensions for optimal efficiency; highly variable image sizes may reduce throughput","Pipeline abstraction adds ~50-100ms overhead per batch compared to raw model inference","No built-in support for distributed inference across multiple GPUs or machines; requires external orchestration","Memory usage scales linearly with batch size; large batches on limited VRAM require manual batch size tuning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6371642008086904,"quality":0.24,"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":"2026-05-03T14:22:50.443Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":308539,"model_likes":189}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=facebook--nougat-base","compare_url":"https://unfragile.ai/compare?artifact=facebook--nougat-base"}},"signature":"MZULGJrl8lbmLucQH2Pbk8xlmxPbCp4b/RZ4/CH2x7ZQGjLZ/1AumYlUonoz31czdsdhivmJCwFRm+WY7/GhCQ==","signedAt":"2026-06-21T13:44:01.677Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/facebook--nougat-base","artifact":"https://unfragile.ai/facebook--nougat-base","verify":"https://unfragile.ai/api/v1/verify?slug=facebook--nougat-base","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"}}