用 TripletLoss 优化bert ranking

下面是 用 TripletLoss 优化bert ranking 的demo

 
import torch
from torch.utils.data import DataLoader, Dataset
from transformers import BertModel, BertTokenizer
from sklearn.metrics.pairwise import pairwise_distances
 
class TripletRankingDataset(Dataset):
    def __init__(self, queries, positive_docs, negative_docs, tokenizer, max_length):
        self.input_ids_q = []
        self.attention_masks_q = []
        self.input_ids_p = []
        self.attention_masks_p = []
        self.input_ids_n = []
        self.attention_masks_n = []
        
        for query, pos_doc, neg_doc in zip(queries, positive_docs, negative_docs):
            encoded_query = tokenizer.encode_plus(query, padding='max_length', truncation=True, max_length=max_length, return_tensors='pt')
            encoded_pos_doc = tokenizer.encode_plus(pos_doc, padding='max_length', truncation=True, max_length=max_length, return_tensors='pt')
            encoded_neg_doc = tokenizer.encode_plus(neg_doc, padding='max_length', truncation=True, max_length=max_length, return_tensors='pt')
            
            self.input_ids_q.append(encoded_query['input_ids'])
            self.attention_masks_q.append(encoded_query['attention_mask'])
            self.input_ids_p.append(encoded_pos_doc['input_ids'])
            self.attention_masks_p.append(encoded_pos_doc['attention_mask'])
            self.input_ids_n.append(encoded_neg_doc['input_ids'])
            self.attention_masks_n.append(encoded_neg_doc['attention_mask'])
        
        self.input_ids_q = torch.cat(self.input_ids_q, dim=0)
        self.attention_masks_q = torch.cat(self.attention_masks_q, dim=0)
        self.input_ids_p = torch.cat(self.input_ids_p, dim=0)
        self.attention_masks_p = torch.cat(self.attention_masks_p, dim=0)
        self.input_ids_n = torch.cat(self.input_ids_n, dim=0)
        self.attention_masks_n = torch.cat(self.attention_masks_n, dim=0)
        
    def __len__(self):
        return len(self.input_ids_q)
    
    def __getitem__(self, idx):
        input_ids_q = self.input_ids_q[idx]
        attention_mask_q = self.attention_masks_q[idx]
        input_ids_p = self.input_ids_p[idx]
        attention_mask_p = self.attention_masks_p[idx]
        input_ids_n = self.input_ids_n[idx]
        attention_mask_n = self.attention_masks_n[idx]
        return input_ids_q, attention_mask_q, input_ids_p, attention_mask_p, input_ids_n, attention_mask_n
 
class BERTTripletRankingModel(torch.nn.Module):
    def __init__(self, bert_model_name, hidden_size):
        super(BERTTripletRankingModel, self).__init__()
        self.bert = BertModel.from_pretrained(bert_model_name)
        self.dropout = torch.nn.Dropout(0.1)
        self.fc = torch.nn.Linear(hidden_size, 1)
        
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = self.dropout(outputs[1])
        logits = self.fc(pooled_output)
        return logits.squeeze()
 
def triplet_loss(anchor, positive, negative, margin):
    distance_positive = torch.nn.functional.pairwise_distance(anchor, positive)
    distance_negative = torch.nn.functional.pairwise_distance(anchor, negative)
    losses = torch.relu(distance_positive - distance_negative + margin)
    return torch.mean(losses)
 
# 初始化BERT模型和分词器
bert_model_name = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(bert_model_name)
 
# 示例输入数据
queries = ['I like cats', 'The sun is shining']
positive_docs = ['I like dogs', 'The weather is beautiful']
negative_docs = ['Snakes are dangerous', 'It is raining']
 
# 超参数
batch_size = 8
max_length = 128
learning_rate = 1e-5
num_epochs = 5
margin = 1.0
 
# 创建数据集和数据加载器
dataset = TripletRankingDataset(queries, positive_docs, negative_docs, tokenizer, max_length)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
 
# 初始化模型并加载预训练权重
model = BERTTripletRankingModel(bert_model_name, hidden_size=model.bert.config.hidden_size)
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 
# 训练模型
model.train()
 
for epoch in range(num_epochs):
    total_loss = 0
    
    for input_ids_q, attention_masks_q, input_ids_p, attention_masks_p, input_ids_n, attention_masks_n in dataloader:
        optimizer.zero_grad()
        
        embeddings_q = model(inputids_q, attention_masks_q)
        embeddings_p = model(input_ids_p, attention_masks_p)
        embeddings_n = model(input_ids_n, attention_masks_n)
        
        loss = triplet_loss(embeddings_q, embeddings_p, embeddings_n, margin)
        
        total_loss += loss.item()
        
        loss.backward()
        optimizer.step()
    
    print(f"Epoch {epoch+1}/{num_epochs} - Loss: {total_loss:.4f}")
 
# 推断模型
model.eval()
 
with torch.no_grad():
    embeddings = model.bert.embeddings.word_embeddings(dataset.input_ids_q)
    pairwise_distances = pairwise_distances(embeddings.numpy())
 
# 输出结果
for i, query in enumerate(queries):
    print(f"Query: {query}")
    print("Documents:")
    
    for j, doc in enumerate(positive_docs):
        doc_idx = pairwise_distances[0][i * len(positive_docs) + j]
        doc_dist = pairwise_distances[1][i * len(positive_docs) + j]
        
        print(f"Document index: {doc_idx}, Distance: {doc_dist:.4f}")
        print(f"Document: {doc}")
        print("")
 
    print("---------")