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import torch
from torch import nn
from torch.utils.data import Dataset
class SentencePairDataset(Dataset):
def __init__(self, sentence_pairs, labels, tokenizer, max_length):
self.sentence_pairs = sentence_pairs
self.labels = labels
self.tokenizer = tokenizer
self.max_length = max_length
def __len__(self):
return len(self.sentence_pairs)
def __getitem__(self, idx):
sentence, context = self.sentence_pairs[idx]
label = self.labels[idx]
# Calculate max lengths for each part
max_context_length = int(self.max_length * 0.8) # 80% for context
max_claim_length = int(self.max_length * 0.2) # 20% for claim
# Encode claim and context separately
encoding = self.tokenizer.encode_plus(
sentence,
text_pair=context,
add_special_tokens=True,
max_length=self.max_length,
padding="max_length",
return_attention_mask=True,
return_tensors="pt",
truncation=True,
truncation_strategy='longest_first' # Truncate the longer text first
)
return {
"input_ids": encoding["input_ids"].flatten(),
"attention_mask": encoding["attention_mask"].flatten(),
"label": torch.tensor(label, dtype=torch.long),
}
class MBERTClassifier(nn.Module):
def __init__(self, mbert, num_classes):
super(MBERTClassifier, self).__init__()
self.mbert = mbert
self.layer_norm = nn.LayerNorm(self.mbert.config.hidden_size)
self.dropout = nn.Dropout(0.2)
self.batch_norm = nn.BatchNorm1d(self.mbert.config.hidden_size)
self.linear = nn.LazyLinear(num_classes)
self.activation = nn.ELU()
def forward(self, input_ids, attention_mask):
_, pooled_output = self.mbert(input_ids=input_ids, attention_mask=attention_mask, return_dict=False)
norm_output = self.layer_norm(pooled_output)
batch_norm_output = self.batch_norm(norm_output)
logits = self.linear(batch_norm_output)
activated_output = self.activation(logits)
dropout_output = self.dropout(activated_output)
return dropout_output
def predict_proba(self, input_ids, attention_mask):
logits = self.forward(input_ids, attention_mask)
probabilities = torch.softmax(logits, dim=-1)
return probabilities |