detector_benchmark.detector.fast_detect_gpt

Classes

FastDetectGPT

Module Contents

class detector_benchmark.detector.fast_detect_gpt.FastDetectGPT(ref_model, scoring_model, ref_tokenizer, scoring_tokenizer, device)

Bases: detector_benchmark.detector.detector.Detector

ref_model

scoring_model

ref_tokenizer

scoring_tokenizer

device

get_samples(logits, labels) → torch.Tensor

Get the samples from the logits.

Parameters:

logits: torch.Tensor

The logits

labels: torch.Tensor

The labels

get_likelihood(logits, labels) → torch.Tensor

Get the likelihood from the logits.

Parameters:

logits: torch.Tensor

The logits

labels: torch.Tensor

The labels

get_sampling_discrepancy(logits_ref, logits_score, labels) → torch.Tensor

Get the sampling discrepancy from the logits.

Parameters:

logits_ref: torch.Tensor

The logits of the reference model

logits_score: torch.Tensor

The logits of the scoring model

labels: torch.Tensor

The labels

get_sampling_discrepancy_analytic(logits_ref, logits_score, labels) → torch.Tensor

Get the sampling discrepancy from the logits.

Parameters:

logits_ref: torch.Tensor

The logits of the reference model

logits_score: torch.Tensor

The logits of the scoring model

class ProbEstimatorFastDetectGPT(args=None, ref_path=None)

Probability estimator for the FastDetectGPT detector.

real_crits = []

fake_crits = []

crit_to_prob(crit) → float

Convert the criterion to probability.

Parameters:

crit: float

The criterion

Returns:

float

The probability

detect(texts: list[str], batch_size: int, detection_threshold: float = 0.5) → tuple[list[int], list[float], list[int]]

Detect the watermark in the texts.

Parameters:

texts: list[str]

The texts to detect the watermark in

batch_size: int

The batch size

detection_threshold: float

The detection threshold

Returns:

tuple[list[int], list[float], list[int]]

The predictions, the probabilities and the predictions at the threshold