Text Classification
Transformers
Safetensors
English
deberta-v2
moderation
safety
privacy
research
text-embeddings-inference
Instructions to use krazyjakee/gandalf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krazyjakee/gandalf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="krazyjakee/gandalf")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("krazyjakee/gandalf") model = AutoModelForSequenceClassification.from_pretrained("krazyjakee/gandalf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download evaluation.json from krazyjakee/gandalf: direct link, hf CLI and curl.
- Browser
- Download file 35.7 kB
-
https://huggingface.co/krazyjakee/gandalf/resolve/main/evaluation.json
- Command line
-
hf download hf://krazyjakee/gandalf/evaluation.json
-
curl -L -o evaluation.json https://huggingface.co/krazyjakee/gandalf/resolve/main/evaluation.json
35.7 kB
| { | |
| "release": "Gandalf 0.0.1", | |
| "model_weights_sha256": "916370da013a428a8ed3e00791049d12b3b3fd5836969d8b0492bc5de372d5c8", | |
| "all_rows_gate": { | |
| "definition": { | |
| "recall_at_least": 0.9, | |
| "benign_false_positive_rate_at_most": 0.01, | |
| "confidence": 0.95 | |
| }, | |
| "split": "gate_all", | |
| "calibration_split": "calibration", | |
| "passes": false, | |
| "presets_passing": [], | |
| "presets": [ | |
| { | |
| "preset": "identity.hate", | |
| "passes": false, | |
| "thresholds": { | |
| "action_threshold": 0.7420825064182281, | |
| "review_threshold": 0.4827668070793152, | |
| "selection": "action: lowest calibration threshold within the benign limit; review: 95% calibration recall", | |
| "note": null | |
| }, | |
| "recall": { | |
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| "rate": 0.7943262411347518, | |
| "lower_bound": 0.7202325037685993 | |
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| "rate": 0.011376564277588168, | |
| "upper_bound": 0.020814502705574688 | |
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| "rate": 0.05785123966942149, | |
| "upper_bound": 0.11461150735579681 | |
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| "rate": 0.003875968992248062, | |
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| } | |
| }, | |
| "auroc_vs": { | |
| "safe": 0.9857107125279372, | |
| "sibling_unsafe": 0.9548678272082527, | |
| "other_unsafe": 0.9928253339930727 | |
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| }, | |
| { | |
| "preset": "solicitation.sensitive_info", | |
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| "thresholds": { | |
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| "review_threshold": 0.1611337810754776, | |
| "selection": "action: lowest calibration threshold within the benign limit; review: 95% calibration recall", | |
| "note": "review threshold reached the action threshold; the review band is empty" | |
| }, | |
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| "selection": "action: lowest calibration threshold within the benign limit; review: 95% calibration recall", | |
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| { | |
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| ] | |
| }, | |
| "dynahate": { | |
| "status": "external check on human-labelled DynaHate test rows; threshold frozen by the preset gate", | |
| "test_sha256": "0eaa0bca8d509686c74ceb7fedc58a508a2614bd9509503d4b0c95afa8c7af7b", | |
| "rows": 4118, | |
| "threshold": 0.7420825064182281, | |
| "recall_hate": { | |
| "hits": 1507, | |
| "count": 2267, | |
| "rate": 0.6647551830613145, | |
| "lower_bound": 0.6450578039324775, | |
| "upper_bound": 0.6838951269985043 | |
| }, | |
| "flag_rate_nothate": { | |
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| "rate": 0.1836844948676391, | |
| "lower_bound": 0.1667049324987952, | |
| "upper_bound": 0.20197431213319922 | |
| }, | |
| "auroc_hate_vs_nothate": 0.8238435953145417 | |
| }, | |
| "limitations": [ | |
| "Source category tags are not all exact human policy judgements.", | |
| "Teacher-filtered results are not independent teacher comparisons.", | |
| "Generated diagnostic examples are not human acceptance evidence.", | |
| "No physical-phone performance or deployment-compliance claim." | |
| ] | |
| } | |