{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-philschmid--bart-large-cnn-samsum","slug":"philschmid--bart-large-cnn-samsum","name":"bart-large-cnn-samsum","type":"model","url":"https://huggingface.co/philschmid/bart-large-cnn-samsum","page_url":"https://unfragile.ai/philschmid--bart-large-cnn-samsum","categories":["model-training"],"tags":["transformers","pytorch","bart","text2text-generation","sagemaker","summarization","en","dataset:samsum","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-philschmid--bart-large-cnn-samsum__cap_0","uri":"capability://text.generation.language.abstractive.summarization.with.bart.architecture","name":"abstractive-summarization-with-bart-architecture","description":"Generates abstractive summaries using BART (Bidirectional Auto-Regressive Transformers), a sequence-to-sequence model pre-trained on denoising objectives. The model encodes input text through a bidirectional transformer encoder, then decodes abstractive summaries via an autoregressive decoder with cross-attention to the encoder states. Fine-tuned on the SAMSum dataset (dialogue summarization), it learns to compress conversational text into concise summaries while preserving semantic meaning through learned token prediction rather than extractive copying.","intents":["I need to automatically summarize customer support conversations or meeting transcripts into brief action items","I want to reduce long-form dialogue text to key points for downstream processing or display","I need a model that generates abstractive summaries (not just extracting sentences) for dialogue-heavy content","I want to integrate summarization into a batch processing pipeline without managing model infrastructure"],"best_for":["teams building dialogue summarization features (customer service, meeting notes, chat logs)","developers prototyping NLP pipelines with pre-trained models on HuggingFace","organizations deploying to AWS SageMaker, Azure, or Hugging Face Inference Endpoints","projects requiring MIT-licensed, open-source models without commercial restrictions"],"limitations":["Input length capped at 1024 tokens (approximately 4000 characters); longer documents require chunking or truncation","Optimized for dialogue/conversational text (SAMSum dataset); performance degrades on technical documentation, code, or non-English text","Abstractive generation can hallucinate facts not present in source text; no built-in factuality verification","Inference latency ~500-1500ms per document on CPU; GPU acceleration required for production throughput","No fine-tuning utilities exposed; requires HuggingFace Transformers library for custom domain adaptation"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","HuggingFace Transformers library (transformers>=4.0.0)","4GB+ RAM for model loading (8GB+ recommended for batch processing)","Optional: CUDA 11.0+ for GPU acceleration"],"input_types":["plain text (dialogue, conversation, meeting transcript)","text with newline delimiters for speaker turns"],"output_types":["plain text (abstractive summary)","token-level attention weights (via model internals)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-philschmid--bart-large-cnn-samsum__cap_1","uri":"capability://automation.workflow.batch.inference.via.huggingface.pipeline.api","name":"batch-inference-via-huggingface-pipeline-api","description":"Exposes the model through HuggingFace's Pipeline abstraction, which handles tokenization, model loading, batching, and post-processing in a unified interface. The pipeline automatically manages device placement (CPU/GPU), handles variable-length inputs via dynamic padding, and supports batch processing with configurable batch sizes. Integrates seamlessly with HuggingFace Inference Endpoints and SageMaker for serverless or containerized deployment without custom inference code.","intents":["I want to run summarization on multiple documents in parallel without writing custom inference loops","I need to deploy this model to a managed inference service (SageMaker, HF Endpoints) with minimal configuration","I want automatic batching and device management (CPU/GPU) without manual optimization","I need to integrate summarization into a Python data pipeline with minimal boilerplate"],"best_for":["Python developers building ETL pipelines or batch processing jobs","teams deploying to AWS SageMaker or Hugging Face Inference Endpoints","rapid prototyping scenarios where inference code simplicity is prioritized over custom optimization","non-ML engineers integrating NLP into larger applications"],"limitations":["Pipeline abstraction adds ~50-100ms overhead per inference call due to tokenizer instantiation and post-processing","Batching is synchronous; no async/streaming support for real-time applications","No built-in caching of tokenized inputs; repeated summarization of identical texts re-tokenizes each time","Limited control over generation parameters (beam search width, length penalties) through pipeline API; requires direct model access for advanced tuning"],"requires":["Python 3.7+","transformers>=4.0.0","torch or tensorflow (depending on backend)","HuggingFace account for Inference Endpoints (optional, for managed deployment)"],"input_types":["list of strings (documents to summarize)","single string (for single-document inference)"],"output_types":["list of dictionaries with 'summary_text' key","structured JSON (when deployed via Inference Endpoints)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-philschmid--bart-large-cnn-samsum__cap_2","uri":"capability://text.generation.language.dialogue.optimized.token.generation.with.beam.search","name":"dialogue-optimized-token-generation-with-beam-search","description":"Generates summary tokens using beam search decoding (width configurable, typically 4-6 beams) rather than greedy decoding, exploring multiple hypothesis paths through the decoder to find higher-probability sequences. The model maintains dialogue context through cross-attention over the full input encoding, allowing it to track speaker turns and conversational flow. Generation stops via length penalties and end-of-sequence token prediction, producing summaries typically 30-50% shorter than input while preserving key dialogue points.","intents":["I need summaries that capture the most important dialogue points, not just the first-mentioned facts","I want to control summary length (e.g., 1-3 sentences) without manual post-processing","I need to summarize multi-speaker conversations while maintaining coherence across turns","I want better quality summaries than greedy decoding at the cost of slightly higher latency"],"best_for":["applications requiring high-quality abstractive summaries (customer support, meeting notes, legal transcripts)","scenarios where summary quality is prioritized over sub-100ms latency","teams building dialogue understanding systems that need semantic compression"],"limitations":["Beam search adds 3-5x latency vs greedy decoding (~1-2 seconds per document on CPU)","Beam width is fixed at model load time; cannot dynamically adjust per-request without reloading","No length constraints enforced at generation time; summaries may exceed target length if high-probability tokens extend beyond desired range","Dialogue-specific training (SAMSum) means performance degrades on non-conversational text (news, technical docs)","No built-in handling of speaker attribution in output; summaries are speaker-agnostic"],"requires":["Python 3.7+","transformers>=4.0.0 (with beam_search implementation)","PyTorch or TensorFlow backend","GPU recommended for <1 second latency per document"],"input_types":["dialogue text with speaker labels (e.g., 'Speaker A: ...' format)","plain conversational text without explicit speaker markers"],"output_types":["abstractive summary text (1-3 sentences typically)","token-level log probabilities (via model.generate with output_scores=True)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-philschmid--bart-large-cnn-samsum__cap_3","uri":"capability://automation.workflow.containerized.deployment.to.sagemaker.and.azure","name":"containerized-deployment-to-sagemaker-and-azure","description":"Model is packaged and compatible with AWS SageMaker inference containers and Azure ML endpoints, allowing one-click deployment without custom Docker image creation. SageMaker integration uses HuggingFace's pre-built inference containers (which include transformers, torch, and optimized inference code), while Azure compatibility enables deployment via Azure ML's model registry. Both platforms handle auto-scaling, request batching, and monitoring without manual infrastructure management.","intents":["I want to deploy this model to production without managing Docker, Kubernetes, or inference servers","I need auto-scaling summarization endpoints that handle variable traffic","I want to integrate summarization into an existing AWS SageMaker or Azure ML pipeline","I need monitoring and logging of inference requests without custom instrumentation"],"best_for":["AWS-native teams using SageMaker for model deployment and monitoring","Azure-first organizations with existing ML Ops infrastructure","teams without DevOps expertise who need managed inference","projects requiring compliance/audit logging (SageMaker CloudWatch integration)"],"limitations":["SageMaker deployment adds ~$0.50-2.00/hour for instance costs (ml.m5.xlarge baseline); GPU instances cost 5-10x more","Cold start latency ~30-60 seconds when scaling from zero; requires provisioned endpoints for <1 second response times","Azure ML deployment requires Azure subscription and familiarity with ML Ops; no free tier for inference endpoints","Model size (1.6GB) requires minimum instance types (ml.m5.xlarge or larger); cannot run on micro instances","No built-in A/B testing or canary deployment; requires manual endpoint configuration for gradual rollouts"],"requires":["AWS account with SageMaker permissions (or Azure subscription with ML Ops access)","IAM role with SageMaker:CreateModel, SageMaker:CreateEndpoint permissions","HuggingFace model card accessible (public or private with credentials)","Minimum instance type: ml.m5.xlarge (SageMaker) or Standard_D4s_v3 (Azure)"],"input_types":["JSON payload with 'inputs' key containing text to summarize","batch requests with multiple documents"],"output_types":["JSON response with 'summary_text' field","structured predictions with confidence scores (via SageMaker Batch Transform)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-philschmid--bart-large-cnn-samsum__cap_4","uri":"capability://data.processing.analysis.multi.language.tokenization.with.roberta.bpe","name":"multi-language-tokenization-with-roberta-bpe","description":"Uses RoBERTa's byte-pair encoding (BPE) tokenizer, which breaks input text into subword tokens via learned vocabulary merges. The tokenizer handles special characters, punctuation, and out-of-vocabulary words through subword fallback, enabling robust processing of noisy dialogue text (contractions, abbreviations, typos). Tokenization is deterministic and reversible, allowing exact reconstruction of input from token IDs via detokenization.","intents":["I need to handle noisy conversational text with contractions, abbreviations, and informal language","I want tokenization that gracefully handles out-of-vocabulary words without dropping them","I need to preprocess text for BART without manual cleaning or normalization","I want to understand token-level attention patterns for interpretability"],"best_for":["teams processing real-world dialogue with informal language, typos, and abbreviations","developers building interpretability tools that need token-to-text mapping","applications requiring deterministic tokenization for reproducibility"],"limitations":["BPE tokenization is language-agnostic but optimized for English; non-Latin scripts (Chinese, Arabic) may tokenize inefficiently with higher token counts","Vocabulary is fixed at 50,265 tokens; cannot add domain-specific tokens without retraining","Special tokens (e.g., [CLS], [SEP]) are hardcoded; custom special tokens require manual tokenizer modification","Detokenization is approximate; whitespace and punctuation may not perfectly match original input","No built-in handling of code, markup, or structured data; treats them as plain text"],"requires":["transformers>=4.0.0 (includes RoBERTa tokenizer)","Python 3.7+","No external dependencies beyond transformers"],"input_types":["raw text strings (dialogue, conversation, transcripts)","text with special characters, punctuation, contractions"],"output_types":["token IDs (list of integers)","attention masks (binary tensor indicating valid tokens)","token strings (for debugging/interpretability)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-philschmid--bart-large-cnn-samsum__cap_5","uri":"capability://planning.reasoning.sequence.to.sequence.attention.mechanism.for.context.preservation","name":"sequence-to-sequence-attention-mechanism-for-context-preservation","description":"Implements cross-attention between decoder and encoder states, allowing the decoder to attend to any position in the input sequence when generating each summary token. This mechanism preserves long-range dependencies in dialogue (e.g., referencing a fact mentioned 10 turns earlier) and enables the model to learn which input spans are most relevant to each summary token. Attention weights are interpretable, showing which input tokens influenced each output token.","intents":["I need to understand which parts of the input dialogue influenced each summary sentence","I want summaries that accurately reference facts from anywhere in the conversation, not just recent turns","I need to debug model behavior by visualizing attention patterns","I want to extract key dialogue spans that the model identified as important"],"best_for":["teams building interpretability tools for dialogue understanding","applications requiring explainability (legal, medical, compliance contexts)","developers debugging summarization quality issues","research projects analyzing dialogue structure and importance"],"limitations":["Attention visualization is post-hoc; does not guarantee that attention weights correspond to true causal influence","Cross-attention is computed for every decoder step; adds ~30% latency vs attention-free baselines","Attention weights are normalized probabilities; cannot directly extract 'importance scores' without additional calibration","Long input sequences (>1024 tokens) require truncation; attention cannot span beyond truncated context","No built-in mechanism to enforce attention to specific input spans; model learns attention patterns end-to-end"],"requires":["transformers>=4.0.0 (with attention output support)","PyTorch or TensorFlow with tensor manipulation capabilities","Visualization library (matplotlib, plotly) for attention heatmaps"],"input_types":["dialogue text (tokenized into 1-1024 tokens)"],"output_types":["attention weight matrices (shape: [num_decoder_layers, num_heads, summary_length, input_length])","aggregated attention heatmaps (for visualization)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-philschmid--bart-large-cnn-samsum__cap_6","uri":"capability://text.generation.language.length.constrained.generation.with.configurable.parameters","name":"length-constrained-generation-with-configurable-parameters","description":"Supports configurable generation parameters (max_length, min_length, length_penalty, early_stopping) that control summary length and generation behavior. The model uses length penalties during beam search to balance summary brevity with informativeness, preventing degenerate short summaries while avoiding excessively long outputs. Parameters can be set per-request, enabling dynamic control without model reloading.","intents":["I need summaries of a specific length (e.g., 1-2 sentences, 50-100 words)","I want to prevent the model from generating trivial summaries (e.g., 'I don't know')","I need to balance summary length across different input documents","I want to tune generation behavior without retraining the model"],"best_for":["applications with strict UI/UX constraints on summary length (e.g., mobile displays, chat interfaces)","systems requiring consistent summary length across heterogeneous inputs","teams experimenting with generation parameters to optimize quality/latency tradeoffs"],"limitations":["Length constraints are soft; model may exceed max_length if high-probability tokens extend beyond limit","min_length can force unnatural summaries if input is too short or simple","length_penalty is a hyperparameter requiring manual tuning; no automatic optimization","No token-level constraints (e.g., 'must include named entities'); only length-based control","Parameters are applied uniformly across all tokens; cannot enforce constraints on specific positions (e.g., 'first sentence must be <20 tokens')"],"requires":["transformers>=4.0.0","Python 3.7+","Understanding of generation parameter semantics (max_length, length_penalty, etc.)"],"input_types":["dialogue text (variable length)"],"output_types":["summary text (constrained length)","generation metadata (num_beams, length_penalty used)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","HuggingFace Transformers library (transformers>=4.0.0)","4GB+ RAM for model loading (8GB+ recommended for batch processing)","Optional: CUDA 11.0+ for GPU acceleration","transformers>=4.0.0","torch or tensorflow (depending on backend)","HuggingFace account for Inference Endpoints (optional, for managed deployment)","transformers>=4.0.0 (with beam_search implementation)","PyTorch or TensorFlow backend"],"failure_modes":["Input length capped at 1024 tokens (approximately 4000 characters); longer documents require chunking or truncation","Optimized for dialogue/conversational text (SAMSum dataset); performance degrades on technical documentation, code, or non-English text","Abstractive generation can hallucinate facts not present in source text; no built-in factuality verification","Inference latency ~500-1500ms per document on CPU; GPU acceleration required for production throughput","No fine-tuning utilities exposed; requires HuggingFace Transformers library for custom domain adaptation","Pipeline abstraction adds ~50-100ms overhead per inference call due to tokenizer instantiation and post-processing","Batching is synchronous; no async/streaming support for real-time applications","No built-in caching of tokenized inputs; repeated summarization of identical texts re-tokenizes each time","Limited control over generation parameters (beam search width, length penalties) through pipeline API; requires direct model access for advanced tuning","Beam search adds 3-5x latency vs greedy decoding (~1-2 seconds per document on CPU)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6327760055504634,"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":"2026-05-03T14:22:54.515Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":260012,"model_likes":267}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=philschmid--bart-large-cnn-samsum","compare_url":"https://unfragile.ai/compare?artifact=philschmid--bart-large-cnn-samsum"}},"signature":"Mtdq6DU6i3ofDtnmUqi4wPa6/FNHYewNS8iQ9u+0vcuCruMN2qXgwblFn2ooGiE5TJ7KNS0wYUzz1B+iM2a3DQ==","signedAt":"2026-06-21T08:50:41.980Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/philschmid--bart-large-cnn-samsum","artifact":"https://unfragile.ai/philschmid--bart-large-cnn-samsum","verify":"https://unfragile.ai/api/v1/verify?slug=philschmid--bart-large-cnn-samsum","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"}}