锦中融合门户系统

import torch

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# 加载预训练模型和分词器

model_name = "bert-base-uncased"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 输入文本

text = "我想查询今天的课程表"

# 分词和编码

inputs = tokenizer(text, return_tensors="pt")

# 模型推理

with torch.no_grad():

outputs = model(**inputs)

# 获取预测结果

predictions = torch.softmax(outputs.logits, dim=1)

predicted_class = torch.argmax(predictions).item()

print("预测类别：", predicted_class)

from transformers import pipeline

# 加载生成模型

generator = pipeline("text-generation", model="gpt2")

# 输入提示

prompt = "今天有哪些课程？"

# 生成回答

response = generator(prompt, max_length=50, num_return_sequences=1)

print("生成的回答：", response[0]["generated_text"])

from flask import Flask, request, jsonify

from transformers import pipeline

app = Flask(__name__)

# 加载生成模型

generator = pipeline("text-generation", model="gpt2")

@app.route("/query", methods=["POST"])

def query():

data = request.json

question = data.get("question", "")

# 生成回答

response = generator(question, max_length=100, num_return_sequences=1)

return jsonify({"answer": response[0]["generated_text"]})

if __name__ == "__main__":

app.run(debug=True)