Instructions to use ShawnyPapa77/cpeg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShawnyPapa77/cpeg with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ShawnyPapa77/cpeg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,417 Bytes
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"thresholds": {
"lo": 0.05784,
"hi": 0.736825,
"rule": "lo = 1st percentile of harmful p, hi = 95th percentile of safe p, on confident half B of the head's fit pool"
},
"routing": "p >= hi -> block; p < lo -> pass; otherwise delegate",
"piece_calibration": {
"a": 0.6507,
"b": 0.0792,
"form": "sigmoid(a * logit(s) + b)"
},
"piece_hot_threshold": 0.26094,
"substance_hot_threshold": 0.5,
"head": {
"model": "sklearn HistGradientBoostingClassifier(max_iter=200, learning_rate=0.1, max_leaf_nodes=15, random_state=42)",
"fit_rows": "confident rows (y <= 0.2 or y >= 0.8) of half A",
"features": [
"core",
"req_max",
"req_mean",
"mat_max",
"n_req",
"n_mat",
"n_frame",
"has_frame",
"mixed",
"has_decoded",
"has_joined",
"core_is_whole",
"pair_max",
"deframed_max",
"clause_max",
"all_max",
"top2",
"hot",
"frame_max",
"s_core",
"s_max",
"s_top2",
"s_mean",
"s_hot",
"s_frame_max"
]
},
"head_fit_features": "gate1_head_fit.csv.gz",
"classifiers": {
"piece_classifier": {
"encoder": "microsoft/deberta-v3-large",
"max_tokens": 256,
"truncation": "head + tail",
"pooling": "[CLS]"
},
"substance_classifier": {
"encoder": "microsoft/deberta-v3-base",
"max_tokens": 256,
"truncation": "head + tail",
"pooling": "[CLS]"
}
}
} |