{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-cahya--t5-base-indonesian-summarization-cased","slug":"cahya--t5-base-indonesian-summarization-cased","name":"t5-base-indonesian-summarization-cased","type":"model","url":"https://huggingface.co/cahya/t5-base-indonesian-summarization-cased","page_url":"https://unfragile.ai/cahya--t5-base-indonesian-summarization-cased","categories":["model-training"],"tags":["transformers","pytorch","tf","jax","t5","text2text-generation","pipeline:summarization","summarization","id","dataset:id_liputan6","text-generation-inference","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-cahya--t5-base-indonesian-summarization-cased__cap_0","uri":"capability://text.generation.language.indonesian.language.abstractive.text.summarization.with.t5.architecture","name":"indonesian-language abstractive text summarization with t5 architecture","description":"Performs abstractive summarization on Indonesian text using a T5-base transformer model (220M parameters) fine-tuned on the ID_Liputan6 dataset. The model operates via encoder-decoder attention mechanisms, encoding source text into contextual representations and decoding abstractive summaries token-by-token. Supports multiple framework backends (PyTorch, TensorFlow, JAX) through HuggingFace transformers library, enabling framework-agnostic deployment and inference optimization.","intents":["Automatically generate concise Indonesian news summaries from full articles without manual extraction","Reduce Indonesian document length by 60-80% while preserving key information for rapid content consumption","Build Indonesian content curation pipelines that require semantic compression rather than extractive truncation","Integrate abstractive summarization into Indonesian news aggregation or content management systems"],"best_for":["Indonesian news organizations and media platforms processing high-volume content","Developers building Indonesian language NLP pipelines requiring semantic compression","Teams deploying multilingual summarization systems with Indonesian language support","Researchers working on low-resource language summarization benchmarks"],"limitations":["Model trained exclusively on Indonesian news domain (ID_Liputan6) — performance degrades significantly on non-news Indonesian text (technical documentation, social media, academic papers)","T5-base architecture has ~220M parameters — requires 1-2GB GPU memory for inference, unsuitable for edge devices or extreme latency constraints (<100ms)","No built-in handling of very long documents (>512 tokens) — requires external chunking/sliding window strategies that may lose cross-chunk context","Abstractive generation can hallucinate facts not present in source text — requires human review for high-stakes applications (legal, medical)","No multilingual capability — strictly Indonesian input; cannot handle code-switched or mixed-language text"],"requires":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) OR TensorFlow (>=2.4.0) OR JAX (>=0.2.0)","2GB+ available GPU memory (VRAM) for batch inference, or CPU-only mode with 10-20x latency penalty","HuggingFace Hub access (internet connection for model download on first use)"],"input_types":["text/plain (Indonesian language)","UTF-8 encoded strings","Sequences up to 512 tokens (approximately 2000-3000 characters)"],"output_types":["text/plain (Indonesian language)","Generated token sequences (variable length, typically 20-150 tokens)","Attention weights (optional, for interpretability)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cahya--t5-base-indonesian-summarization-cased__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":"Provides unified inference interface across PyTorch, TensorFlow, and JAX backends through HuggingFace transformers abstraction layer. The model automatically selects the optimal framework based on system availability and user preference, handling framework-specific optimizations (torch.jit compilation, TF graph mode, JAX JIT tracing) transparently. Supports both eager execution and graph-based inference modes for latency/throughput trade-offs.","intents":["Deploy the same model across heterogeneous infrastructure (PyTorch on-prem, TensorFlow on GCP, JAX on TPU clusters) without code changes","Optimize inference performance by selecting the best-performing backend for specific hardware (GPU type, TPU, CPU)","Reduce vendor lock-in by maintaining framework portability across PyTorch, TensorFlow, and JAX ecosystems","Benchmark framework performance differences on the same model without reimplementation"],"best_for":["ML teams managing multi-cloud or hybrid infrastructure with different framework preferences","Researchers comparing framework performance on identical model architectures","Organizations migrating between PyTorch and TensorFlow without retraining","Deployment engineers optimizing for specific hardware (TPU, GPU, CPU) with framework flexibility"],"limitations":["Framework conversion adds ~5-10% model size overhead due to format compatibility layers","JAX backend requires explicit device placement configuration — not automatic like PyTorch/TensorFlow","Performance characteristics vary significantly across frameworks (PyTorch typically 10-20% faster on NVIDIA GPUs, TensorFlow optimized for TPUs, JAX best for research/custom kernels)","No automatic mixed-precision (AMP) synchronization across frameworks — requires manual per-framework configuration","Serialization format conversions (safetensors ↔ PyTorch ↔ TensorFlow) can introduce numerical precision drift (float32 → float16 quantization)"],"requires":["At least one of: PyTorch (>=1.9.0), TensorFlow (>=2.4.0), or JAX (>=0.2.0)","transformers library (>=4.0.0) with framework-specific extras installed","Framework-specific CUDA/cuDNN or TPU drivers if using GPU/TPU acceleration"],"input_types":["text/plain","torch.Tensor, tf.Tensor, or jax.Array depending on selected backend"],"output_types":["Framework-native tensors (torch.Tensor, tf.Tensor, jax.Array)","NumPy arrays (via .numpy() conversion)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cahya--t5-base-indonesian-summarization-cased__cap_2","uri":"capability://automation.workflow.huggingface.inference.endpoints.compatible.deployment","name":"huggingface inference endpoints compatible deployment","description":"Model is optimized for HuggingFace Inference Endpoints platform, supporting serverless API deployment with automatic scaling, batching, and hardware selection. Includes pre-configured inference pipeline definitions that enable one-click deployment to managed endpoints with built-in monitoring, versioning, and A/B testing capabilities. Supports both synchronous REST API calls and asynchronous batch processing through the Endpoints infrastructure.","intents":["Deploy Indonesian summarization as a managed REST API without managing infrastructure or containers","Scale inference from 0 to thousands of requests/second automatically without manual capacity planning","Integrate summarization into production applications via simple HTTP POST requests with built-in rate limiting and authentication","Monitor model performance, latency, and cost in real-time through HuggingFace dashboard"],"best_for":["Startups and small teams without DevOps resources for model deployment infrastructure","Teams requiring rapid API deployment for proof-of-concepts or MVPs","Organizations needing managed model versioning and rollback capabilities","Developers building applications that need summarization as a microservice"],"limitations":["Inference Endpoints pricing is ~2-5x higher than self-hosted GPU instances for sustained high-volume traffic (>1000 req/min)","Cold start latency of 2-5 seconds on first request after scale-down (serverless penalty)","Request payload limited to ~10MB per API call — requires external chunking for very large documents","No custom inference code execution — limited to standard transformers pipeline operations","Data residency constraints — HuggingFace Endpoints default to US region, may not meet GDPR/data sovereignty requirements for Indonesian organizations"],"requires":["HuggingFace account with Inference Endpoints subscription (paid tier)","API token for authentication","HTTP client library (requests, curl, etc.)","Network connectivity to HuggingFace API endpoints"],"input_types":["JSON payload with 'inputs' field containing Indonesian text","text/plain in request body"],"output_types":["JSON response with 'summary_text' field","HTTP status codes (200, 400, 429, 500)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cahya--t5-base-indonesian-summarization-cased__cap_3","uri":"capability://text.generation.language.cased.token.handling.for.indonesian.morphology.preservation","name":"cased token handling for indonesian morphology preservation","description":"Model preserves Indonesian character casing and diacritical marks (e.g., 'é', 'ñ') through cased tokenization rather than lowercasing all input, enabling better handling of proper nouns, acronyms, and borrowed words common in Indonesian news. The tokenizer maintains case information in token embeddings, improving summarization quality for named entities and domain-specific terminology that rely on case distinctions.","intents":["Preserve proper nouns and organization names in generated summaries (e.g., 'PT Telkom' vs 'pt telkom')","Maintain acronym capitalization in summaries (e.g., 'COVID-19', 'DPR', 'TNI')","Improve summarization of Indonesian text with borrowed words and foreign names that depend on casing","Generate more readable and contextually appropriate summaries for news articles with mixed-case content"],"best_for":["News organizations requiring accurate entity preservation in automated summaries","Content systems where proper noun capitalization affects readability and professionalism","Indonesian language processing pipelines where case sensitivity improves downstream NLP tasks"],"limitations":["Cased tokenization increases vocabulary size by ~15-20% compared to uncased models, slightly increasing memory footprint","Model performance depends on consistent casing in training data (ID_Liputan6) — may struggle with all-caps or unusual casing patterns","No special handling for Indonesian-specific morphology (affixes, reduplication) — casing preservation is orthogonal to morphological analysis","Casing information is lost if input text is pre-lowercased before tokenization"],"requires":["Input text with preserved original casing","UTF-8 encoding support for Indonesian diacritical marks"],"input_types":["text/plain with original casing preserved"],"output_types":["text/plain with casing preserved in generated summary"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cahya--t5-base-indonesian-summarization-cased__cap_4","uri":"capability://text.generation.language.id.liputan6.dataset.optimized.summarization.with.domain.specific.patterns","name":"id_liputan6 dataset-optimized summarization with domain-specific patterns","description":"Model is fine-tuned on the ID_Liputan6 dataset (Indonesian news articles with human-written summaries), learning domain-specific summarization patterns including news lead structure, inverted pyramid style, and journalistic conventions. The fine-tuning process optimized for news-specific metrics (ROUGE scores on news summaries) rather than generic text summarization, resulting in summaries that follow news writing conventions and prioritize key information as journalists do.","intents":["Generate news summaries that follow journalistic conventions (lead paragraph, key facts first) rather than generic abstractive summaries","Summarize Indonesian news articles with domain-appropriate compression ratios and information prioritization","Build news aggregation systems that produce summaries consistent with editorial standards","Evaluate summarization quality on Indonesian news using ROUGE metrics calibrated to news domain"],"best_for":["Indonesian news organizations and media platforms","News aggregation and content curation services","Researchers studying summarization on non-English, low-resource language datasets","Teams building Indonesian-language content management systems with news focus"],"limitations":["Model is heavily optimized for news domain — performance degrades significantly on non-news Indonesian text (technical docs, social media, academic papers, product descriptions)","ID_Liputan6 dataset contains only news articles from specific Indonesian news sources — may not generalize to news from other sources with different writing styles or editorial standards","Fine-tuning on news domain may introduce news-specific biases (e.g., emphasis on sensational or conflict-driven narratives) into summaries","No domain adaptation or transfer learning capabilities — cannot be easily fine-tuned on custom domains without retraining from scratch","ROUGE metrics used for training may not correlate perfectly with human judgment of summary quality"],"requires":["Indonesian news text as input","Understanding of news domain conventions for evaluation"],"input_types":["Indonesian news articles (text/plain)"],"output_types":["News-style summaries (text/plain) following journalistic conventions"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) OR TensorFlow (>=2.4.0) OR JAX (>=0.2.0)","2GB+ available GPU memory (VRAM) for batch inference, or CPU-only mode with 10-20x latency penalty","HuggingFace Hub access (internet connection for model download on first use)","At least one of: PyTorch (>=1.9.0), TensorFlow (>=2.4.0), or JAX (>=0.2.0)","transformers library (>=4.0.0) with framework-specific extras installed","Framework-specific CUDA/cuDNN or TPU drivers if using GPU/TPU acceleration","HuggingFace account with Inference Endpoints subscription (paid tier)","API token for authentication"],"failure_modes":["Model trained exclusively on Indonesian news domain (ID_Liputan6) — performance degrades significantly on non-news Indonesian text (technical documentation, social media, academic papers)","T5-base architecture has ~220M parameters — requires 1-2GB GPU memory for inference, unsuitable for edge devices or extreme latency constraints (<100ms)","No built-in handling of very long documents (>512 tokens) — requires external chunking/sliding window strategies that may lose cross-chunk context","Abstractive generation can hallucinate facts not present in source text — requires human review for high-stakes applications (legal, medical)","No multilingual capability — strictly Indonesian input; cannot handle code-switched or mixed-language text","Framework conversion adds ~5-10% model size overhead due to format compatibility layers","JAX backend requires explicit device placement configuration — not automatic like PyTorch/TensorFlow","Performance characteristics vary significantly across frameworks (PyTorch typically 10-20% faster on NVIDIA GPUs, TensorFlow optimized for TPUs, JAX best for research/custom kernels)","No automatic mixed-precision (AMP) synchronization across frameworks — requires manual per-framework configuration","Serialization format conversions (safetensors ↔ PyTorch ↔ TensorFlow) can introduce numerical precision drift (float32 → float16 quantization)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.33039845671161433,"quality":0.35,"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":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:54.515Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":10971,"model_likes":6}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=cahya--t5-base-indonesian-summarization-cased","compare_url":"https://unfragile.ai/compare?artifact=cahya--t5-base-indonesian-summarization-cased"}},"signature":"s6zrhBf4tpYijonlwJvwcW4692xjGfJt27oxNsgiO4PtcxL8n734iaH1wyXjHB1t2zqBERT/7ygWIDNyeJVWDQ==","signedAt":"2026-06-21T19:47:28.347Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cahya--t5-base-indonesian-summarization-cased","artifact":"https://unfragile.ai/cahya--t5-base-indonesian-summarization-cased","verify":"https://unfragile.ai/api/v1/verify?slug=cahya--t5-base-indonesian-summarization-cased","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"}}