Bert问答机器人用python进行数据训练的代码

以下是使用Python进行BERT问答模型训练的代码示例：

1. 首先，安装必要的Python库和BERT模型：

!pip install transformers
!pip install torch

2. 加载并预处理训练数据：

from transformers import BertTokenizer
import torch

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

def preprocess_data(data):
    inputs = []
    answers = []
    for qa in data:
        input_text = qa['question'] + ' [SEP] ' + qa['context']
        input_ids = tokenizer.encode(input_text)
        answer_start = len(tokenizer.encode(qa['question']))
        answer_end = answer_start + len(tokenizer.encode(qa['answer']))
        answer = (answer_start, answer_end)
        inputs.append(input_ids)
        answers.append(answer)
    return torch.tensor(inputs), torch.tensor(answers)

3. 定义BERT问答模型和训练过程：

from transformers import BertForQuestionAnswering
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

model = BertForQuestionAnswering.from_pretrained('bert-base-uncased')

def train(model, train_loader, optimizer, num_epochs):
    for epoch in range(num_epochs):
        for inputs, answers in train_loader:
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = torch.nn.functional.cross_entropy(outputs, answers)
            loss.backward()
            optimizer.step()
            print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

train_data = [...] # 训练数据
train_inputs, train_answers = preprocess_data(train_data)
train_dataset = TensorDataset(train_inputs, train_answers)
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

optimizer = optim.Adam(model.parameters(), lr=0.001)
train(model, train_loader, optimizer, num_epochs=10)

4. 在训练完成后，可以使用模型进行预测：

def predict(model, input_text):
    input_ids = tokenizer.encode(input_text, return_tensors='pt')
    outputs = model(input_ids)
    answer_start = torch.argmax(outputs.start_logits)
    answer_end = torch.argmax(outputs.end_logits) + 1
    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[0][answer_start:answer_end]))
    return answer

以上就是使用Python进行BERT问答模型训练的基本代码示例。