Steganografiya quyidagi sohalarda qo'llaniladi, lekin ular bilan cheklanmaydi

threshold = threshold_MAPE(upper_bound, lower_bound, number_labels)
mape_score = 0
for i, j in zip(outputs_original, outputs_suspect):
mape_score += abs(i - j) / i
mape_score = mape_score / len(outputs_suspect)
score = mape_score
# This comparison returns np.bool_
is_stolen = mape_score <= threshold

return {'is_stolen': is_stolen,
'score': score,
'threshold': threshold}


import logging
logging.basicConfig(level=logging.DEBUG)
logging.getLogger('PIL').setLevel(logging.WARNING)
logger = logging.getLogger('logger')

import copy
import warnings

import pandas as pd
import torch.nn as nn
from datasets import load_dataset
from mlmodelwatermarking.markface import Trainer as TrainerWM
from mlmodelwatermarking import TrainingWMArgs
from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
Trainer, TrainingArguments)

warnings.filterwarnings('ignore')


def tweet_analysis():
def tokenize_function(examples):
return tokenizer(
examples["tweet"],
padding="max_length",
truncation=True)

# Load data, model and tokenizer
raw_datasets = load_dataset("tweets_hate_speech_detection")
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
model = AutoModelForSequenceClassification.from_pretrained(
"bert-base-cased",
num_labels=2)
# Compute tokenized data
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
train_dataset = tokenized_datasets['train'].shuffle(seed=42) \
.select(range(1000))