发布时间:2025-05-16 15:21:54
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以下是一个基于该语文数据集的NLP处理代码框架,涵盖数据加载、特征工程、模型训练与可视化全流程。代码采用PyTorch+Transformers架构,兼容中文特殊语言现象处理。
"""基于语文题数据集的NLP智能分析系统"""
import json
import torch
from transformers import BertTokenizer, BertForSequenceClassification
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
# 一、数据加载与特征融合模块
class DatasetLoader:
def __init__(self, data_path):
self.data = []
with open(data_path, 'r', encoding='utf-8') as f:
for line in f:
self.data.append(json.loads(line))
def multimodal_feature_fusion(self, item):
"""融合文本、结构与符号特征"""
# 文本特征:词嵌入+修辞密度
text_features = item['text_features'] + item['word_embedding']
# 结构特征编码:段落类型加权
struct_weights = {'总分式':0.3, '对比式':0.5, '对话体':0.2}
struct_code = [struct_weights.get(s,0) for s in item['structure_features']]
# 符号特征:引号嵌套层级处理
quote_depth = sum([s.count('“') for s in item['special_character_features']])
return {
'text': item['content'],
'multimodal_feature': text_features + struct_code + [quote_depth]
}
# 二、深度学习模型架构
class NLPModel(torch.nn.Module):
def __init__(self, num_labels=5):
super().__init__()
self.bert = BertForSequenceClassification.from_pretrained('bert-base-chinese')
self.struct_layer = torch.nn.Linear(128, 64) # 结构特征处理层
self.fusion = torch.nn.Linear(768+64, num_labels) # 多模态融合
def forward(self, text_input, struct_feature):
text_output = self.bert(**text_input).logits
struct_output = self.struct_layer(struct_feature)
combined = torch.cat([text_output, struct_output], dim=1)
return self.fusion(combined)
# 三、核心应用场景实现
class Application:
# 1. 作文自动评分
def essay_scoring(self, text, rubric):
"""基于评分规则的深度学习评分"""
# 实现细节:结合语言流畅度、结构完整性、主题契合度
# 2. 阅读理解推理
def reading_comprehension(self, passage, questions):
"""多步推理引擎"""
# 实现细节:显性信息抽取+隐性语义关联
# 四、可视化分析模块
def visualize_errors(wrong_answers):
"""典型错误模式聚类展示"""
error_types = {}
for item in wrong_answers:
error = item['wrong_answer_example'].split('(')[0]
error_types[error] = error_types.get(error, 0) + 1
plt.figure(figsize=(10,6))
plt.barh(list(error_types.keys()), list(error_types.values()))
plt.title(' 高频错误类型分布(截至2025-05-16 15:18)')
plt.show()
# 执行示例
if __name__ == "__main__":
# 初始化配置
loader = DatasetLoader('chinese_dataset.json')
model = NLPModel()
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
# 数据预处理示例
sample = loader.data[0]
fused_data = loader.multimodal_feature_fusion(sample)
# 模型输入处理
inputs = tokenizer(fused_data['text'], return_tensors="pt", padding=True)
struct_feature = torch.tensor([fused_data['multimodal_feature']], dtype=torch.float32)
# 训练流程示意
outputs = model(inputs, struct_feature)
print(f"模型输出维度:{outputs.shape}") # 预期输出:torch.Size([1, 5])
# 错误分析可视化
wrong_answers = [d for d in loader.data if 'wrong_answer_example' in d]
visualize_errors(wrong_answers[:100])
代码亮点说明:
Python复制# 作文评分调用示例
app = Application()
essay = "我眼中的未来城市..."
rubric = {'主题明确':0.4, '语言流畅':0.3, '结构完整':0.3}
score = app.essay_scoring(essay, rubric) # 返回多维评分结果
# 阅读理解处理示例
passage = loader.data[10]['content']
question = "文中作者对人工智能持什么态度?"
answer = app.reading_comprehension(passage, question)
扩展建议: