File size: 7,335 Bytes
b26a28d 7313e9a b26a28d |
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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 |
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torch import nn
from transformers import RobertaModel
from faknow.model.layers.layer import TextCNNLayer
from faknow.model.model import AbstractModel
import pandas as pd
class _MLP(nn.Module):
def __init__(self,
input_dim: int,
embed_dims: List[int],
dropout_rate: float,
output_layer=True):
super().__init__()
layers = list()
for embed_dim in embed_dims:
layers.append(nn.Linear(input_dim, embed_dim))
layers.append(nn.BatchNorm1d(embed_dim))
layers.append(nn.ReLU())
layers.append(nn.Dropout(p=dropout_rate))
input_dim = embed_dim
if output_layer:
layers.append(torch.nn.Linear(input_dim, 1))
self.mlp = torch.nn.Sequential(*layers)
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x (Tensor): shared feature from domain and text, shape=(batch_size, embed_dim)
"""
return self.mlp(x)
class _MaskAttentionLayer(torch.nn.Module):
"""
Compute attention layer
"""
def __init__(self, input_size: int):
super(_MaskAttentionLayer, self).__init__()
self.attention_layer = torch.nn.Linear(input_size, 1)
def forward(self,
inputs: Tensor,
mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
weights = self.attention_layer(inputs).view(-1, inputs.size(1))
if mask is not None:
weights = weights.masked_fill(mask == 0, float("-inf"))
weights = torch.softmax(weights, dim=-1).unsqueeze(1)
outputs = torch.matmul(weights, inputs).squeeze(1)
return outputs, weights
class MDFEND(AbstractModel):
r"""
MDFEND: Multi-domain Fake News Detection, CIKM 2021
paper: https://dl.acm.org/doi/10.1145/3459637.3482139
code: https://github.com/kennqiang/MDFEND-Weibo21
"""
def __init__(self,
pre_trained_bert_name: str,
domain_num: int,
mlp_dims: Optional[List[int]] = None,
dropout_rate=0.2,
expert_num=5):
"""
Args:
pre_trained_bert_name (str): the name or local path of pre-trained bert model
domain_num (int): total number of all domains
mlp_dims (List[int]): a list of the dimensions in MLP layer, if None, [384] will be taken as default, default=384
dropout_rate (float): rate of Dropout layer, default=0.2
expert_num (int): number of experts also called TextCNNLayer, default=5
"""
super(MDFEND, self).__init__()
self.domain_num = domain_num
self.expert_num = expert_num
self.bert = RobertaModel.from_pretrained(
pre_trained_bert_name).requires_grad_(False)
self.embedding_size = self.bert.config.hidden_size
self.loss_func = nn.BCELoss()
if mlp_dims is None:
mlp_dims = [384]
filter_num = 64
filter_sizes = [1, 2, 3, 5, 10]
experts = [
TextCNNLayer(self.embedding_size, filter_num, filter_sizes)
for _ in range(self.expert_num)
]
self.experts = nn.ModuleList(experts)
self.gate = nn.Sequential(
nn.Linear(self.embedding_size * 2, mlp_dims[-1]), nn.ReLU(),
nn.Linear(mlp_dims[-1], self.expert_num), nn.Softmax(dim=1))
self.attention = _MaskAttentionLayer(self.embedding_size)
self.domain_embedder = nn.Embedding(num_embeddings=self.domain_num,
embedding_dim=self.embedding_size)
self.classifier = _MLP(320, mlp_dims, dropout_rate)
def forward(self, token_id: Tensor, mask: Tensor,
domain: Tensor) -> Tensor:
"""
Args:
token_id (Tensor): token ids from bert tokenizer, shape=(batch_size, max_len)
mask (Tensor): mask from bert tokenizer, shape=(batch_size, max_len)
domain (Tensor): domain id, shape=(batch_size,)
Returns:
FloatTensor: the prediction of being fake, shape=(batch_size,)
"""
text_embedding = self.bert(token_id,
attention_mask=mask).last_hidden_state
attention_feature, _ = self.attention(text_embedding, mask)
domain_embedding = self.domain_embedder(domain.view(-1, 1)).squeeze(1)
gate_input = torch.cat([domain_embedding, attention_feature], dim=-1)
gate_output = self.gate(gate_input)
shared_feature = 0
for i in range(self.expert_num):
expert_feature = self.experts[i](text_embedding)
shared_feature += (expert_feature * gate_output[:, i].unsqueeze(1))
label_pred = self.classifier(shared_feature)
return torch.sigmoid(label_pred.squeeze(1))
def calculate_loss(self, data) -> Tensor:
"""
calculate loss via BCELoss
Args:
data (dict): batch data dict
Returns:
loss (Tensor): loss value
"""
token_ids = data['text']['token_id']
masks = data['text']['mask']
domains = data['domain']
labels = data['label']
output = self.forward(token_ids, masks, domains)
return self.loss_func(output, labels.float())
def predict(self, data_without_label) -> Tensor:
"""
predict the probability of being fake news
Args:
data_without_label (Dict[str, Any]): batch data dict
Returns:
Tensor: one-hot probability, shape=(batch_size, 2)
"""
token_ids = data_without_label['text']['token_id']
masks = data_without_label['text']['mask']
domains = data_without_label['domain']
output_prob = self.forward(token_ids, masks,domains)
return output_prob
from faknow.data.dataset.text import TextDataset
from faknow.data.process.text_process import TokenizerFromPreTrained
from faknow.evaluate.evaluator import Evaluator
import torch
from torch.utils.data import DataLoader
testing_path = "data/test_data.json"
df = pd.read_json(testing_path)
df.head()
df =df[:100]
df["label"] = int(0)
df.head()
print(len(df))
new_testing_json_path = "data/testing.json"
df.to_json(new_testing_json_path, orient='records')
MODEL_SAVE_PATH = "models/last-epoch-model-2024-03-08-15_34_03_6.pth"
max_len, bert = 160 , 'sinhala-nlp/sinbert-sold-si'
tokenizer = TokenizerFromPreTrained(max_len, bert)
# dataset
batch_size = 100
testing_set = TextDataset(new_testing_json_path, ['text'], tokenizer)
testing_loader = DataLoader(testing_set, batch_size, shuffle=False)
# prepare model
domain_num = 3
model = MDFEND(bert, domain_num , expert_num=18 , mlp_dims = [5080 ,4020, 3010, 2024 ,1012 ,606 , 400])
model.load_state_dict(torch.load(f=MODEL_SAVE_PATH, map_location=torch.device('cpu')))
outputs = []
for batch_data in testing_loader:
outputs.append(model.predict(batch_data))
outputs
# 1 ====> offensive
# 0 ====> not offensive
label = []
for output in outputs:
for out in output:
output_prob = out.item()
if output_prob >= 0.5:
label.append(1)
else:
label.append(0)
label
|