File size: 1,791 Bytes
da89f1c | 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 | import torch
import torch.nn as nn
from transformers import BertModel, BertConfig
class DocBERT(nn.Module):
"""
Document classification using BERT with improved architecture
based on Hedwig implementation patterns.
"""
def __init__(self, num_classes, bert_model_name='bert-base-uncased', dropout_prob=0.1):
super(DocBERT, self).__init__()
# Load pre-trained BERT model or config
self.bert = BertModel.from_pretrained(bert_model_name)
self.config = self.bert.config
# Dropout layer for regularization (helps prevent overfitting)
self.dropout = nn.Dropout(dropout_prob)
# Multiple classification heads approach (inspired by Hedwig)
self.hidden_size = self.config.hidden_size
self.classifier = nn.Linear(self.hidden_size, num_classes)
# Layer normalization before classification (helps stabilize training)
self.layer_norm = nn.LayerNorm(self.hidden_size)
def forward(self, input_ids, attention_mask=None, token_type_ids=None):
"""
Forward pass through the model
"""
# Get BERT outputs
outputs = self.bert(input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
# Get the [CLS] token representation (first token)
pooled_output = outputs.pooler_output
# Apply layer normalization
normalized_output = self.layer_norm(pooled_output)
# Apply dropout for regularization
dropped_output = self.dropout(normalized_output)
# Pass through the classifier
logits = self.classifier(dropped_output)
return logits |