Text Classification
Transformers
PyTorch
Safetensors
inferenceguard-roberta
privacy
inferential-privacy
roberta
multi-task-classification
risk-estimation
integrated-gradients
llm-firewall
Instructions to use AT707/InferenceGuard-Roberta-Risk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AT707/InferenceGuard-Roberta-Risk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AT707/InferenceGuard-Roberta-Risk")# pip install -U transformers accelerate # Load model directly from transformers import RobertaRiskClassifier model = RobertaRiskClassifier.from_pretrained("AT707/InferenceGuard-Roberta-Risk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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Used tool: manage_task
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````markdown
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---
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library_name: transformers
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tags:
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- **Integrated Gradients:** An axiomatic attribution method that calculates the path integral of gradients along the straight line from a baseline input to the actual input embeddings.
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- **Risk Bands:** Categorical classifications (LOW, MEDIUM, HIGH) used in user-facing applications to summarize numerical exposure scores into actionable thresholds.
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## More Information
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To deploy this model as part of the complete InferenceGuard pipeline or to set up live web monitoring, consult the documentation in README.md and the deployment recipes in docs/PRODUCTION_DEPLOYMENT.md.
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## Model Card Authors
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Engineered and evaluated by the InferenceGuard core development team.
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## Model Card Contact
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For questions, issues, or model weight contributions, open an issue in the project repository or contact the InferenceGuard development team.
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````
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---
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library_name: transformers
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tags:
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- **Integrated Gradients:** An axiomatic attribution method that calculates the path integral of gradients along the straight line from a baseline input to the actual input embeddings.
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- **Risk Bands:** Categorical classifications (LOW, MEDIUM, HIGH) used in user-facing applications to summarize numerical exposure scores into actionable thresholds.
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