在Jupyter Notebook中写问答模型uerroberta-base-chinese-extractive-qa的步骤

1. 安装Jupyter Notebook：如果尚未安装Jupyter Notebook，则可以使用以下命令来安装它：

pip install notebook

2. 启动Jupyter Notebook：在终端中运行以下命令来启动Jupyter Notebook：

jupyter notebook

3. 创建一个新的Jupyter Notebook：在Jupyter Notebook的主页中，点击右上角的"New"按钮，然后选择"Python 3"来创建一个新的Notebook。

4. 导入所需的库：在Notebook的第一个代码单元格中，导入所需的库：

!pip install uer
from uer.utils.config import load_hyperparam
from uer.utils.optimizers import *
from uer.utils.vocab import Vocab
from uer.model_builder import build_model
from uer.model_saver import save_model
from uer.opts import infer_opts
from uer.opts import infer_opts
from uer.targets import *
from uer.utils.constants import *
from uer.layers import *
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
import torch.nn.functional as F
from torch.optim import Adam
import json
import os
import argparse
import random

5. 加载模型和词汇表：在新的代码单元格中，加载预训练的模型和词汇表。

model_path = "uer/roberta-base-chinese-extractive-qa"
config_path = os.path.join(model_path, "config.json")
checkpoint_path = os.path.join(model_path, "model.bin")
vocab_path = os.path.join(model_path, "vocab.txt")

model_config = load_hyperparam(config_path)
model_config["vocab_size"] = len(vocab)

model = build_model(model_config)
model.load_state_dict(torch.load(checkpoint_path))
model.eval()

6. 定义预处理函数：在新的代码单元格中，定义用于对输入文本进行预处理的函数。

def preprocess(text):
    tokens = text.strip().split()
    token_ids = [vocab.get(t, vocab.get(UNK_TOKEN)) for t in tokens]
    seg_ids = [1] * len(token_ids)
    return token_ids, seg_ids

7. 定义问答函数：在新的代码单元格中，定义用于提取答案的函数。

def extract_answer(question, context):
    question_ids, question_seg_ids = preprocess(question)
    context_ids, context_seg_ids = preprocess(context)

    with torch.no_grad():
        question_tensor = torch.LongTensor([question_ids])
        question_segment_tensor = torch.LongTensor([question_seg_ids])
        context_tensor = torch.LongTensor([context_ids])
        context_segment_tensor = torch.LongTensor([context_seg_ids])

        question_tensor = question_tensor.to(device)
        question_segment_tensor = question_segment_tensor.to(device)
        context_tensor = context_tensor.to(device)
        context_segment_tensor = context_segment_tensor.to(device)

        logits = model(question_tensor, question_segment_tensor, context_tensor, context_segment_tensor)
        start_logits, end_logits = logits

        start_index = torch.argmax(start_logits)
        end_index = torch.argmax(end_logits)

        answer = context[start_index:end_index+1]

    return answer

8. 运行问答模型：在新的代码单元格中，调用问答函数并输入问题和上下文来提取答案。

question = "你的问题"
context = "你的上下文"

answer = extract_answer(question, context)
print(answer)

以上就是在Jupyter Notebook中使用uer/roberta-base-chinese-extractive-qa模型的步骤。请确保已经安装了相应的库和模型，并将模型文件路径和词汇表文件路径替换为正确的路径