Spaces:
Running
Running
File size: 2,782 Bytes
5df855d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import torch
import nltk
from roberta_model_loader import roberta_model
from feature_ref_loader import feature_mgt_ref, feature_hwt_ref
from meta_train import net
from regression_model_loader import regression_model
from MMD import MMD_3_Sample_Test
from utils import DEVICE, FeatureExtractor, HWT, MGT
class RelativeTester:
def __init__(self):
print("Relative Tester init")
self.feature_extractor = FeatureExtractor(roberta_model, net)
def sents_split(self, text):
nltk.download("punkt", quiet=True)
nltk.download("punkt_tab", quiet=True)
sents = nltk.sent_tokenize(text)
return [sent for sent in sents if 5 < len(sent.split())]
def test(self, input_text, threshold=0.2, round=20):
print("Relative Tester test")
# Split the input text
sents = self.sents_split(input_text)
print("DEBUG: sents:", len(sents))
# Extract features
feature_for_sents = self.feature_extractor.process_sents(sents, False)
if len(feature_for_sents) <= 1:
# print("DEBUG: tooshort")
return "Too short to test! Please input more than 2 sentences."
# Cutoff the features
min_len = min(
len(feature_for_sents),
len(feature_hwt_ref),
len(feature_mgt_ref),
)
# Calculate MMD
h_u_list = []
p_value_list = []
t_list = []
for i in range(round):
feature_for_sents_sample = feature_for_sents[
torch.randperm(len(feature_for_sents))[:min_len]
]
feature_hwt_ref_sample = feature_hwt_ref[
torch.randperm(len(feature_hwt_ref))[:min_len]
]
feature_mgt_ref_sample = feature_mgt_ref[
torch.randperm(len(feature_mgt_ref))[:min_len]
]
h_u, p_value, t, *rest = MMD_3_Sample_Test(
net.net(feature_for_sents_sample),
net.net(feature_hwt_ref_sample),
net.net(feature_mgt_ref_sample),
feature_for_sents_sample.view(feature_for_sents_sample.shape[0], -1),
feature_hwt_ref_sample.view(feature_hwt_ref_sample.shape[0], -1),
feature_mgt_ref_sample.view(feature_mgt_ref_sample.shape[0], -1),
net.sigma,
net.sigma0_u,
net.ep,
0.05,
)
h_u_list.append(h_u)
p_value_list.append(p_value)
t_list.append(t)
power = sum(h_u_list) / len(h_u_list)
print("DEBUG: power:", power)
print("DEBUG: power list:", h_u_list)
# Return the result
return "Human" if power <= threshold else "AI"
relative_tester = RelativeTester()
|