{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-facebook--bart-large-cnn","slug":"facebook--bart-large-cnn","name":"bart-large-cnn","type":"model","url":"https://huggingface.co/facebook/bart-large-cnn","page_url":"https://unfragile.ai/facebook--bart-large-cnn","categories":["text-writing"],"tags":["transformers","pytorch","tf","jax","rust","safetensors","bart","text2text-generation","summarization","en","dataset:cnn_dailymail","arxiv:1910.13461","license:mit","model-index","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-cnn__cap_0","uri":"capability://text.generation.language.abstractive.summarization.with.bart.encoder.decoder","name":"abstractive-summarization-with-bart-encoder-decoder","description":"Performs abstractive text summarization using a bidirectional encoder (BART encoder) combined with an autoregressive decoder, trained on CNN/DailyMail dataset. The model uses a denoising autoencoder architecture where the encoder processes the full input document and the decoder generates a compressed summary token-by-token, leveraging cross-attention between encoder hidden states and decoder predictions. This enables generation of novel summary sentences rather than extractive copying.","intents":["I need to automatically condense long news articles or documents into concise summaries for quick consumption","I want to extract key information from multi-paragraph text without manually reading the entire content","I need to batch-process hundreds of documents and generate summaries programmatically in a pipeline","I want a pre-trained model that understands journalistic writing style and can summarize news content accurately"],"best_for":["NLP engineers building document summarization pipelines","teams processing news feeds, research papers, or technical documentation at scale","developers prototyping summarization features without training custom models","organizations needing English-language abstractive summarization with minimal setup"],"limitations":["English-only model — no multilingual support despite BART's theoretical capability","Trained specifically on CNN/DailyMail news articles — may produce lower-quality summaries for non-journalistic text (technical docs, legal contracts, social media)","Maximum input sequence length of 1024 tokens — longer documents require truncation or sliding-window approaches, losing context","Abstractive generation can hallucinate facts not present in source text, requiring human review for high-stakes applications","Inference latency ~500ms-2s per document on CPU, requiring GPU for production throughput (>100 docs/min)","No built-in length control — summary length varies based on input; requires post-processing or beam search tuning to enforce max length"],"requires":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) or TensorFlow (>=2.4.0) or JAX backend","4GB+ RAM for model weights (large variant = 406M parameters)","GPU recommended for production (NVIDIA CUDA 11.0+ or AMD ROCm)"],"input_types":["raw text (string)","tokenized input_ids (torch.Tensor or tf.Tensor)","attention_mask (optional, for padding handling)"],"output_types":["generated summary text (string)","token logits (for custom decoding)","attention weights (for interpretability)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-cnn__cap_1","uri":"capability://tool.use.integration.multi.framework.model.inference.with.automatic.backend.selection","name":"multi-framework-model-inference-with-automatic-backend-selection","description":"Supports inference across PyTorch, TensorFlow, JAX, and Rust backends through the transformers library's unified API, automatically selecting the optimal backend based on installed dependencies and hardware. The model weights are stored in safetensors format (safer than pickle, with faster loading via memory-mapped I/O) and can be loaded into any framework without conversion, enabling deployment flexibility across different infrastructure stacks.","intents":["I want to run the same model in PyTorch for research but TensorFlow for production without maintaining separate codebases","I need to deploy this model in a Rust service for low-latency inference without Python overhead","I want to load model weights safely without executing arbitrary pickle code during model loading","I need to switch inference backends at runtime based on available hardware (GPU vs CPU, CUDA vs ROCm)"],"best_for":["polyglot teams using multiple ML frameworks in different services","organizations with strict security policies requiring safe deserialization (safetensors vs pickle)","developers building cross-platform applications (web, mobile, edge) with varying compute constraints","teams migrating from one framework to another without retraining"],"limitations":["JAX backend requires additional jax and jaxlib dependencies; not all features fully tested in JAX mode","Rust inference via candle requires separate Rust bindings; Python integration adds serialization overhead","Automatic backend selection can be unpredictable if multiple frameworks installed — requires explicit backend specification for reproducibility","safetensors loading is faster but requires transformers >=4.34.0; older versions fall back to slower pickle loading","No automatic quantization or pruning — full 406M parameter model loaded regardless of backend, requiring 4GB+ memory"],"requires":["transformers library (>=4.0.0 for multi-framework, >=4.34.0 for safetensors)","At least one of: PyTorch (>=1.9.0), TensorFlow (>=2.4.0), JAX (>=0.3.0), or Rust (>=1.56.0)","safetensors library (optional but recommended for safe loading)"],"input_types":["transformers.PreTrainedModel (framework-agnostic wrapper)","raw tensors (torch.Tensor, tf.Tensor, jax.Array)"],"output_types":["framework-native tensors (torch.Tensor, tf.Tensor, jax.Array)","transformers.Seq2SeqLMOutput (unified output object with logits, loss, encoder_last_hidden_state)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-cnn__cap_2","uri":"capability://text.generation.language.cnn.dailymail.domain.optimized.summarization.with.journalistic.style.transfer","name":"cnn-dailymail-domain-optimized-summarization-with-journalistic-style-transfer","description":"The model is fine-tuned specifically on the CNN/DailyMail dataset (300K+ news article-summary pairs), learning journalistic conventions such as inverted pyramid structure, named entity preservation, and lead sentence generation. This domain specialization enables the model to recognize news-specific patterns (bylines, datelines, quoted speech) and generate summaries that match journalistic writing style, rather than generic abstractive summarization.","intents":["I need to summarize news articles with the same style and structure as professional news summaries","I want a model trained on real-world news data that understands journalistic conventions and entity importance","I need to process news feeds and generate summaries that preserve key facts and quotes from the original","I want to avoid retraining on domain-specific data and use a pre-trained model optimized for news"],"best_for":["news aggregation platforms and media companies processing article feeds","journalists and editors using AI to assist with summary generation","content curation services requiring high-quality abstractive summaries of news","researchers studying summarization on benchmark datasets (CNN/DailyMail is standard evaluation set)"],"limitations":["Optimized for English news articles — poor performance on non-English text, technical documentation, or non-journalistic genres (social media, chat, legal text)","Trained on 2015-2018 news data — may not understand recent events, modern terminology, or contemporary writing styles","Bias toward CNN/DailyMail editorial style — may not generalize to other news outlets (AP, Reuters, BBC) with different conventions","No fine-tuning on domain-specific news (sports, finance, science) — generic news summarization only","Cannot be easily adapted to other domains without retraining; transfer learning to non-news tasks shows significant ROUGE degradation"],"requires":["transformers library (>=4.0.0)","PyTorch, TensorFlow, or JAX backend","Input text in English language","Familiarity with CNN/DailyMail dataset format for evaluation"],"input_types":["English news article text (string)","tokenized input_ids with attention_mask"],"output_types":["abstractive summary text (string)","ROUGE scores (if evaluated against reference summaries)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-cnn__cap_3","uri":"capability://automation.workflow.batch.inference.with.dynamic.batching.and.padding.optimization","name":"batch-inference-with-dynamic-batching-and-padding-optimization","description":"Supports efficient batch processing of multiple documents through the transformers library's DataCollator and batch processing utilities, which dynamically pad sequences to the longest length in each batch (rather than fixed max length) to minimize wasted computation. The model can process variable-length inputs in a single forward pass, with attention masks automatically handling padding tokens, enabling throughput optimization for production pipelines.","intents":["I need to summarize 1000+ documents efficiently without processing them one-at-a-time","I want to maximize GPU utilization by batching documents of varying lengths without excessive padding","I need to implement a production summarization service that processes documents in parallel batches","I want to measure and optimize inference throughput (documents per second) for cost efficiency"],"best_for":["data engineers building ETL pipelines for document processing","ML ops teams deploying summarization services at scale","researchers benchmarking throughput and latency of summarization models","teams processing large document corpora (news archives, research papers, logs)"],"limitations":["Dynamic padding requires variable batch sizes — difficult to predict memory usage and latency in advance","Batch size is limited by GPU memory (typically 8-32 for 406M model on 16GB VRAM); larger batches require gradient accumulation or model parallelism","Padding overhead still exists for variable-length batches — if one document is 1000 tokens and others are 100, all are padded to 1000","No built-in distributed inference — batching is per-GPU; multi-GPU inference requires manual data parallelism or frameworks like Ray or Hugging Face Inference Server","Attention masks add ~5-10% computational overhead compared to fixed-length batches"],"requires":["transformers library (>=4.0.0) with DataCollator utilities","PyTorch or TensorFlow with batch processing support","GPU with sufficient VRAM (16GB+ recommended for batch_size=16)","Understanding of attention masks and padding mechanics"],"input_types":["list of text strings (variable length)","pre-tokenized input_ids with attention_mask tensors"],"output_types":["batch of summary strings","batch of logits tensors for custom decoding"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-cnn__cap_4","uri":"capability://text.generation.language.sequence.length.constrained.generation.with.beam.search.and.length.penalty","name":"sequence-length-constrained-generation-with-beam-search-and-length-penalty","description":"Generates summaries with controlled length through beam search decoding with configurable length penalties and max_length constraints. The model uses beam search (exploring multiple hypotheses in parallel) combined with length normalization to prevent the decoder from favoring short summaries (which have higher log-probabilities). The length_penalty parameter controls the trade-off between summary brevity and quality, enabling users to enforce specific summary lengths (e.g., 50-150 tokens).","intents":["I need summaries of a specific length (e.g., 100 tokens) for consistent output formatting","I want to avoid very short or very long summaries that don't meet quality standards","I need to control the summary-to-article compression ratio for different use cases","I want to use beam search to find higher-quality summaries by exploring multiple decoding paths"],"best_for":["applications with strict summary length requirements (tweets, headlines, abstracts)","teams tuning summarization quality through length penalty experimentation","systems requiring consistent output formatting for downstream processing","researchers studying the effect of summary length on ROUGE scores"],"limitations":["Beam search is computationally expensive — 4-8x slower than greedy decoding; beam_size=4 adds ~1-2s latency per document","Length penalty is a hyperparameter requiring tuning per domain — no universal optimal value; journalistic summaries may need different penalties than technical abstracts","max_length constraint can truncate important information if set too low; no built-in mechanism to ensure all key facts are included","Beam search doesn't guarantee finding the globally optimal summary — only explores a limited hypothesis space","Length penalties can produce unnatural summaries that meet length constraints but sacrifice coherence"],"requires":["transformers library (>=4.0.0) with generation utilities","PyTorch or TensorFlow backend","Understanding of beam search, length penalties, and decoding hyperparameters","GPU recommended for beam_size>2 (CPU inference becomes prohibitively slow)"],"input_types":["input_ids tensor with attention_mask","generation_config object specifying max_length, beam_size, length_penalty"],"output_types":["generated summary text (string)","beam search scores (log-probabilities for each hypothesis)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-cnn__cap_5","uri":"capability://tool.use.integration.huggingface.hub.integration.with.model.versioning.and.checkpoint.management","name":"huggingface-hub-integration-with-model-versioning-and-checkpoint-management","description":"Integrates with Hugging Face Hub for model hosting, versioning, and checkpoint management. The model can be loaded directly from the Hub using a single line of code (model_id='facebook/bart-large-cnn'), with automatic caching of downloaded weights in ~/.cache/huggingface/hub. The Hub provides version control (git-based), model cards with documentation, and usage statistics, enabling reproducible model deployment without manual weight management.","intents":["I want to load a pre-trained model with a single line of code without manually downloading weights","I need to track model versions and switch between different checkpoints (e.g., fine-tuned variants)","I want to share my fine-tuned model with collaborators through the Hub without managing file servers","I need to understand model capabilities and limitations through standardized model cards and documentation"],"best_for":["researchers and developers prototyping quickly without infrastructure setup","teams collaborating on model development and sharing checkpoints","organizations deploying models through Hugging Face Inference API or Endpoints","developers building applications that need to support multiple model versions"],"limitations":["Requires internet connectivity for initial model download; no offline-first support without pre-caching","Hub caching uses ~/.cache/huggingface/hub directory — can consume significant disk space (406M model = ~1.5GB with safetensors); requires manual cleanup","Model versioning is git-based but not as fine-grained as traditional ML experiment tracking (MLflow, Weights & Biases)","Hub access can be rate-limited for large-scale downloads; no built-in support for private model hosting without authentication","Model card documentation is community-maintained — quality and accuracy vary; no guarantee of up-to-date information"],"requires":["transformers library (>=4.0.0)","Internet connectivity for model download","huggingface_hub library (>=0.10.0) for advanced Hub operations","Disk space for model weights (~1.5GB for bart-large-cnn with safetensors)"],"input_types":["model_id string (e.g., 'facebook/bart-large-cnn')","optional revision parameter for version selection"],"output_types":["PreTrainedModel instance loaded from Hub","model metadata (config, tokenizer, model card)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-cnn__cap_6","uri":"capability://data.processing.analysis.tokenization.with.bart.vocabulary.and.subword.segmentation","name":"tokenization-with-bart-vocabulary-and-subword-segmentation","description":"Uses BART's pre-trained BPE (Byte Pair Encoding) tokenizer with a 50K token vocabulary, automatically segmenting input text into subword tokens. The tokenizer handles special tokens (CLS, SEP, EOS, PAD), converts text to token IDs, and generates attention masks for padding. The vocabulary is optimized for English news text from CNN/DailyMail, enabling efficient encoding of journalistic language with minimal out-of-vocabulary (OOV) tokens.","intents":["I need to convert raw text into token IDs compatible with the BART model","I want to handle variable-length inputs with proper padding and attention masks","I need to understand how the model tokenizes text (for debugging or analysis)","I want to use the same tokenizer for both encoding inputs and decoding outputs"],"best_for":["developers integrating BART into NLP pipelines","researchers analyzing tokenization behavior and vocabulary coverage","teams building custom preprocessing pipelines","anyone fine-tuning BART on domain-specific data"],"limitations":["BPE tokenization can split rare words into many subword tokens, increasing sequence length and computation; OOV rate depends on domain similarity to CNN/DailyMail","50K vocabulary is fixed and cannot be extended without retraining; domain-specific terminology (medical, legal, scientific) may tokenize poorly","Tokenizer is English-only — no multilingual support; non-ASCII characters are handled through byte-level encoding, which is inefficient","Special tokens (CLS, SEP) are required for proper model input; incorrect token usage can degrade performance","Tokenizer is stateless — no context-aware tokenization; ambiguous text (e.g., 'U.S.' vs 'U.S') tokenizes inconsistently"],"requires":["transformers library (>=4.0.0) with tokenizer utilities","Pre-trained BART tokenizer (automatically downloaded from Hub)","Understanding of BPE tokenization and special tokens"],"input_types":["raw text string","list of text strings (for batch tokenization)"],"output_types":["input_ids tensor (token IDs)","attention_mask tensor (1 for real tokens, 0 for padding)","token_type_ids (optional, for segment classification)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-cnn__cap_7","uri":"capability://memory.knowledge.model.card.documentation.with.benchmarks.and.usage.examples","name":"model-card-documentation-with-benchmarks-and-usage-examples","description":"Provides comprehensive model card documentation on Hugging Face Hub including training data (CNN/DailyMail), evaluation metrics (ROUGE-1/2/L scores), intended use cases, limitations, and code examples. The model card serves as a standardized interface for understanding model capabilities, biases, and appropriate applications, reducing the barrier to adoption and enabling informed decision-making about model selection.","intents":["I need to understand what this model is trained on and whether it's suitable for my use case","I want to see benchmark results (ROUGE scores) to compare against other summarization models","I need code examples showing how to use the model in my application","I want to understand the model's limitations and potential biases before deploying it"],"best_for":["developers evaluating models for their projects","teams making model selection decisions based on benchmarks","researchers comparing model performance across datasets","non-technical stakeholders understanding model capabilities and limitations"],"limitations":["Model card is community-maintained — accuracy and completeness depend on contributor effort; may be outdated or incomplete","Benchmark results (ROUGE scores) are on CNN/DailyMail test set only — generalization to other domains is not documented","No information about inference latency, memory usage, or hardware requirements — users must benchmark themselves","Limitations section is brief and may not capture all edge cases or failure modes discovered in production","Code examples are basic and may not reflect production best practices (error handling, batching, caching)"],"requires":["Access to Hugging Face Hub (huggingface.co)","Basic understanding of summarization metrics (ROUGE)","No code or API keys required for reading model cards"],"input_types":["model_id string to look up model card"],"output_types":["model card HTML/markdown with documentation","benchmark metrics and evaluation results","usage examples and code snippets"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--bart-large-cnn__cap_8","uri":"capability://automation.workflow.fine.tuning.support.with.trainer.api.and.custom.loss.functions","name":"fine-tuning-support-with-trainer-api-and-custom-loss-functions","description":"Supports fine-tuning on custom datasets through the transformers Trainer API, which handles distributed training, mixed precision, gradient accumulation, and checkpoint management. The model can be fine-tuned with custom loss functions (e.g., ROUGE-aware loss, length penalties) by extending the Trainer class or using custom training loops. Fine-tuning enables adaptation to domain-specific summarization tasks (legal, medical, technical) without training from scratch.","intents":["I want to fine-tune BART on my domain-specific documents (e.g., medical abstracts, legal summaries)","I need to improve summarization quality on my data without training a model from scratch","I want to implement custom loss functions (e.g., ROUGE-aware loss) for better optimization","I need to distribute training across multiple GPUs for faster convergence"],"best_for":["teams with domain-specific summarization requirements","researchers experimenting with custom loss functions and training strategies","organizations with sufficient labeled data (1000+ examples) for fine-tuning","developers building production summarization systems tailored to specific domains"],"limitations":["Fine-tuning requires labeled data (article-summary pairs) — data collection and annotation is expensive and time-consuming","Trainer API abstracts away training details, making it difficult to implement novel training strategies or debugging","Fine-tuning can lead to catastrophic forgetting — the model may lose general summarization ability if trained on narrow domain","Hyperparameter tuning (learning rate, batch size, warmup steps) is critical but requires experimentation; no automatic hyperparameter optimization","Fine-tuned models are larger and slower than the base model if not quantized; requires additional storage and inference infrastructure"],"requires":["transformers library (>=4.0.0) with Trainer API","PyTorch or TensorFlow backend","Labeled dataset with article-summary pairs (minimum 1000 examples recommended)","GPU with sufficient VRAM (16GB+ for batch_size=8)","Understanding of training hyperparameters and evaluation metrics"],"input_types":["dataset with 'input_ids', 'attention_mask', 'labels' fields","custom TrainingArguments specifying learning rate, batch size, epochs"],"output_types":["fine-tuned model checkpoint","training metrics (loss, ROUGE scores)","evaluation results on validation set"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) or TensorFlow (>=2.4.0) or JAX backend","4GB+ RAM for model weights (large variant = 406M parameters)","GPU recommended for production (NVIDIA CUDA 11.0+ or AMD ROCm)","transformers library (>=4.0.0 for multi-framework, >=4.34.0 for safetensors)","At least one of: PyTorch (>=1.9.0), TensorFlow (>=2.4.0), JAX (>=0.3.0), or Rust (>=1.56.0)","safetensors library (optional but recommended for safe loading)","PyTorch, TensorFlow, or JAX backend","Input text in English language"],"failure_modes":["English-only model — no multilingual support despite BART's theoretical capability","Trained specifically on CNN/DailyMail news articles — may produce lower-quality summaries for non-journalistic text (technical docs, legal contracts, social media)","Maximum input sequence length of 1024 tokens — longer documents require truncation or sliding-window approaches, losing context","Abstractive generation can hallucinate facts not present in source text, requiring human review for high-stakes applications","Inference latency ~500ms-2s per document on CPU, requiring GPU for production throughput (>100 docs/min)","No built-in length control — summary length varies based on input; requires post-processing or beam search tuning to enforce max length","JAX backend requires additional jax and jaxlib dependencies; not all features fully tested in JAX mode","Rust inference via candle requires separate Rust bindings; Python integration adds serialization overhead","Automatic backend selection can be unpredictable if multiple frameworks installed — requires explicit backend specification for reproducibility","safetensors loading is faster but requires transformers >=4.34.0; older versions fall back to slower pickle loading","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8108041160604875,"quality":0.28,"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":"2026-05-03T14:22:54.515Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":1935931,"model_likes":1574}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=facebook--bart-large-cnn","compare_url":"https://unfragile.ai/compare?artifact=facebook--bart-large-cnn"}},"signature":"A34Gg/3bkIkVbki1u7AvDZUehIeIoYUXsH0PkrcdF8xCPYnWWXL1vRcW9aJQK1mGV1dHkTRDZ/3B+w/QyoYkCQ==","signedAt":"2026-06-22T06:41:08.231Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/facebook--bart-large-cnn","artifact":"https://unfragile.ai/facebook--bart-large-cnn","verify":"https://unfragile.ai/api/v1/verify?slug=facebook--bart-large-cnn","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"}}