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
Initial release of InferenceGuard RoBERTa risk classifier
Browse files- config.json +16 -29
- model.safetensors +3 -0
config.json
CHANGED
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{
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"
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"output_hidden_states": false,
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"return_dict": true,
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"dtype": null,
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"chunk_size_feed_forward": 0,
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"is_encoder_decoder": false,
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1"
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},
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1
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},
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"problem_type": null,
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"_name_or_path": "",
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"base_model_name": "roberta-base",
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"num_heads": 4,
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"num_classes_per_head": 1,
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"hidden_size": 768,
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"classifier_dropout": 0.1,
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"temperature_values": [
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1.0,
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1.0,
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1.0,
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1.0
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],
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"attributes": [
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"age",
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"occupation",
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"education"
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],
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"model_type": "inferenceguard-roberta",
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"
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{
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"architectures": [
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"RobertaRiskClassifier"
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],
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"attributes": [
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"age",
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"occupation",
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"education"
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],
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"base_model_name": "roberta-base",
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"classifier_dropout": 0.1,
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"dtype": "float32",
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"hidden_size": 768,
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"model_type": "inferenceguard-roberta",
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"num_classes_per_head": 1,
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"num_heads": 4,
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"temperature_values": [
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1.0,
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1.0,
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1.0,
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1.0
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],
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"transformers_version": "5.20.2"
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}
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model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:40c4073349ff3827f09503349ec1be518a66bc234991af7fb39c46f3bf8255fd
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size 503339184
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