{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-openai-community--roberta-base-openai-detector","slug":"openai-community--roberta-base-openai-detector","name":"roberta-base-openai-detector","type":"model","url":"https://huggingface.co/openai-community/roberta-base-openai-detector","page_url":"https://unfragile.ai/openai-community--roberta-base-openai-detector","categories":["data-analysis"],"tags":["transformers","pytorch","tf","jax","safetensors","roberta","text-classification","exbert","en","dataset:bookcorpus","dataset:wikipedia","arxiv:1904.09751","arxiv:1910.09700","arxiv:1908.09203","license:mit","text-embeddings-inference","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-openai-community--roberta-base-openai-detector__cap_0","uri":"capability://data.processing.analysis.binary.classification.of.ai.generated.text","name":"binary-classification-of-ai-generated-text","description":"Classifies input text as either human-written or AI-generated (specifically OpenAI model outputs) using a fine-tuned RoBERTa-base transformer backbone. The model was trained on a dataset of human text from BookCorpus and Wikipedia paired with text generated by GPT-2, enabling it to detect statistical and linguistic patterns characteristic of neural language model outputs. It outputs logits for both classes, allowing threshold-based confidence tuning for different detection sensitivity requirements.","intents":["detect whether a given text passage was generated by an AI language model or written by a human","identify AI-generated content in academic submissions, user-generated content platforms, or content moderation workflows","measure the proportion of AI-generated text in a corpus or document collection","implement content authenticity verification in applications requiring human authorship attestation"],"best_for":["content moderation teams filtering AI-generated spam or synthetic content","academic integrity platforms detecting AI-assisted essay writing","social media platforms identifying bot-generated posts","researchers studying AI detection robustness and adversarial examples"],"limitations":["trained primarily on GPT-2 outputs; detection accuracy degrades significantly on text from newer models (GPT-3.5, GPT-4, Claude) due to distribution shift","no built-in handling of mixed human-AI text or iteratively edited content","performance drops on non-English text despite English-only training data","vulnerable to adversarial attacks like paraphrasing, style transfer, or deliberate obfuscation","binary classification only — cannot identify which specific model generated the text or provide confidence calibration across different domains"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ or JAX runtime","transformers library 4.0+","minimum 512MB GPU memory or CPU with ~350MB RAM for inference","input text preprocessed to <= 512 tokens (RoBERTa's context window)"],"input_types":["raw text (string)","tokenized sequences (token IDs with attention masks)"],"output_types":["logits (2-dimensional: [human_score, ai_score])","probability distribution (softmax-normalized)","binary classification label (0=human, 1=AI-generated)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai-community--roberta-base-openai-detector__cap_1","uri":"capability://tool.use.integration.multi.framework.model.inference.with.format.conversion","name":"multi-framework-model-inference-with-format-conversion","description":"Supports inference across PyTorch, TensorFlow, and JAX backends through the HuggingFace transformers library's unified interface, with automatic model weight conversion via safetensors format. The model weights are stored in safetensors (a safer, faster serialization format than pickle) and automatically loaded into the target framework's runtime, eliminating manual format conversion. This enables deployment flexibility across different infrastructure stacks without retraining or maintaining separate model checkpoints.","intents":["deploy the same model across heterogeneous infrastructure (PyTorch servers, TensorFlow serving, JAX-based inference engines)","integrate the detector into existing ML pipelines built on different frameworks without model conversion overhead","run inference on edge devices or specialized hardware that supports only specific frameworks","ensure reproducibility and security by using safetensors instead of pickle-based model serialization"],"best_for":["ML teams with mixed-framework infrastructure (some services in PyTorch, others in TensorFlow)","organizations deploying to cloud platforms with framework-specific optimizations (TensorFlow on Google Cloud, PyTorch on AWS)","security-conscious teams avoiding pickle deserialization vulnerabilities","edge deployment scenarios where framework choice is constrained by hardware or runtime availability"],"limitations":["framework conversion adds ~50-200ms latency on first load (weights must be deserialized and converted to target framework format)","JAX backend requires additional jax and jaxlib dependencies not included in base transformers install","no automatic quantization or pruning across frameworks — model size remains constant (~350MB) regardless of target backend","TensorFlow eager execution mode may be slower than graph mode for batch inference; requires manual tf.function wrapping for optimization"],"requires":["transformers library 4.20+ (safetensors support)","PyTorch 1.9+ OR TensorFlow 2.4+ OR JAX 0.3.0+","safetensors library 0.3.0+","~1GB disk space for model weights"],"input_types":["raw text (auto-tokenized by transformers pipeline)","pre-tokenized sequences (token IDs, attention masks, token type IDs)"],"output_types":["framework-native tensors (torch.Tensor, tf.Tensor, jnp.ndarray)","numpy arrays (via .numpy() conversion)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai-community--roberta-base-openai-detector__cap_2","uri":"capability://tool.use.integration.huggingface.endpoints.compatible.deployment","name":"huggingface-endpoints-compatible-deployment","description":"Model is compatible with HuggingFace Inference Endpoints, enabling serverless deployment without managing containers or infrastructure. The model metadata and task definition (text-classification) are registered in HuggingFace's model hub, allowing one-click deployment to managed endpoints with automatic scaling, batching, and monitoring. Requests are routed through HuggingFace's inference API, which handles tokenization, model loading, and response formatting transparently.","intents":["deploy the detector as a REST API without writing deployment code or managing servers","scale inference automatically based on request volume without manual infrastructure provisioning","integrate the detector into applications via simple HTTP requests to a managed endpoint","monitor inference latency, throughput, and cost through HuggingFace's dashboard"],"best_for":["startups and small teams without DevOps infrastructure","rapid prototyping and MVP development requiring quick deployment","applications with variable traffic patterns benefiting from auto-scaling","teams preferring managed services over self-hosted inference servers"],"limitations":["inference latency includes network round-trip time (~50-200ms depending on geographic location and load)","cold-start latency on first request after deployment (~2-5 seconds as model is loaded into memory)","pricing is per-inference-call; high-volume applications may be more cost-effective with self-hosted inference","no local caching or batch processing optimization — each request is processed independently","API rate limits apply; burst traffic may be throttled","data is transmitted to HuggingFace servers; not suitable for applications with strict data residency requirements"],"requires":["HuggingFace account with API token","HTTP client library (requests, curl, etc.)","network connectivity to huggingface.co","optional: HuggingFace Inference Endpoints subscription for custom endpoints"],"input_types":["raw text (sent as JSON payload in HTTP request)"],"output_types":["JSON response with classification scores and labels"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai-community--roberta-base-openai-detector__cap_3","uri":"capability://tool.use.integration.region.specific.deployment.with.azure.integration","name":"region-specific-deployment-with-azure-integration","description":"Model is deployable to Azure cloud infrastructure with region-specific endpoint configuration, enabling compliance with data residency and latency requirements. Azure integration is handled through HuggingFace's model hub metadata (region:us tag) and Azure's native model registry, allowing deployment to Azure ML endpoints with automatic scaling and monitoring. This enables organizations to keep inference workloads within specific geographic regions for regulatory compliance (GDPR, HIPAA, etc.).","intents":["deploy the detector to Azure infrastructure for organizations already invested in Azure ecosystem","ensure inference happens within specific geographic regions for data residency compliance","integrate with Azure ML pipelines and monitoring tools","leverage Azure's auto-scaling and load balancing for production workloads"],"best_for":["enterprises using Azure as primary cloud provider","organizations with GDPR, HIPAA, or other data residency requirements","teams building ML pipelines within Azure ML ecosystem","applications requiring low-latency inference in specific geographic regions"],"limitations":["Azure-specific deployment requires Azure ML workspace setup and configuration","pricing follows Azure's compute pricing model; may be more expensive than HuggingFace Endpoints for low-volume use","requires Azure credentials and IAM permissions; adds operational complexity vs HuggingFace-only deployment","model updates must be re-deployed to Azure endpoints; no automatic syncing with HuggingFace hub","limited to Azure regions where the model is registered (currently US region only based on metadata)"],"requires":["Azure subscription with ML workspace","Azure CLI or SDK (azure-ai-ml package)","appropriate IAM permissions for model deployment","network connectivity to Azure endpoints"],"input_types":["raw text (via Azure ML endpoint API)"],"output_types":["JSON response with classification scores"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-openai-community--roberta-base-openai-detector__cap_4","uri":"capability://automation.workflow.text.embeddings.inference.optimization","name":"text-embeddings-inference-optimization","description":"Model is compatible with HuggingFace's Text Embeddings Inference (TEI) server, a high-performance inference engine optimized for transformer-based text classification and embedding models. TEI provides SIMD vectorization, dynamic batching, and memory-efficient inference through Rust-based implementation, reducing latency by 3-5x compared to standard PyTorch inference. The model can be deployed as a TEI container, automatically benefiting from these optimizations without code changes.","intents":["run inference with significantly lower latency and higher throughput than standard PyTorch servers","deploy the detector in resource-constrained environments (edge devices, cost-optimized cloud instances)","batch multiple classification requests efficiently without manual batching logic","reduce inference costs by improving hardware utilization through optimized inference"],"best_for":["high-throughput production systems requiring sub-100ms latency","edge deployment scenarios with limited compute resources","cost-sensitive applications processing large volumes of text","teams building real-time content moderation pipelines"],"limitations":["TEI is Rust-based and requires Docker or container runtime; adds deployment complexity vs Python-only solutions","dynamic batching introduces variable latency (p50 vs p99 latency may differ significantly); not suitable for strict SLA requirements","limited to inference-only; 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