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
Download pytorch_model.bin from AT707/InferenceGuard-Roberta-Risk: direct link, hf CLI and curl.
- Browser
- Download file 503 MB
-
https://huggingface.co/AT707/InferenceGuard-Roberta-Risk/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AT707/InferenceGuard-Roberta-Risk/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AT707/InferenceGuard-Roberta-Risk/resolve/main/pytorch_model.bin
503 MB
- Xet hash:
- a57b8d18f9cfbf3fdc3766ef76a3fe41e2a3e02bc60dfa04f31e18cba4072540
- Size of remote file:
- 503 MB
- SHA256:
- 646169ebea08f8ecc435bd330d3f559b5353c05d05dca962ed4a4ae886b65f8d
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