deeplotx 0.8.1

Easy-2-use long text NLP toolkit.

Deep Long Text Learning Kit

Author: 吴子豪

开箱即用的长文本语义建模框架

安装

• 使用 pip

  pip install -U deeplotx
  

• 使用 uv (推荐)

  uv add -U deeplotx
  

• 从 github 安装最新特性

  pip install -U git+https://github.com/vortezwohl/DeepLoTX.git
  

核心功能

• 长文本嵌入

  • 基于通用 BERT 的长文本嵌入 (最大支持长度, 无限长, 通过 max_length 定义)

    from deeplotx import LongTextEncoder
    
    # 最大文本长度为 2048 个 tokens, 块大小为 512 个 tokens, 块间重叠部分为 64 个 tokens.
    encoder = LongTextEncoder(
        max_length=2048,
        chunk_size=512,
        overlapping=64
    )
    # 对 "我是吴子豪, 这是一个测试文本." 计算嵌入, 并展平.
    encoder.encode('我是吴子豪, 这是一个测试文本.', flatten=True, use_cache=True)
    

    输出:

    tensor([ 0.5163,  0.2497,  0.5896,  ..., -0.9815, -0.3095,  0.4232])
    

  • 基于 Longformer 的长文本嵌入 (最大支持长度 4096 个 tokens)

    from deeplotx import LongformerEncoder
    
    encoder = LongformerEncoder()
    encoder.encode('我是吴子豪, 这是一个测试文本.')
    

• 相似性计算

  • 基于向量的相似性

    import deeplotx.similarity as sim
    
    vector_0, vector_1 = [1, 2, 3, 4], [4, 3, 2, 1]
    # 欧几里得距离
    distance_0 = sim.euclidean_similarity(vector_0, vector_1)
    print(distance_0)
    # 余弦距离
    distance_1 = sim.cosine_similarity(vector_0, vector_1)
    print(distance_1)
    # 切比雪夫距离
    distance_2 = sim.chebyshev_similarity(vector_0, vector_1)
    print(distance_2)
    

    输出:

    4.47213595499958
    0.33333333333333337
    3
    

  • 基于集合的相似性

    import deeplotx.similarity as sim
    
    set_0, set_1 = {1, 2, 3, 4}, {4, 5, 6, 7}
    # 杰卡德距离
    distance_0 = sim.jaccard_similarity(set_0, set_1)
    print(distance_0)
    # Ochiai 距离
    distance_1 = sim.ochiai_similarity(set_0, set_1)
    print(distance_1)
    # Dice 系数
    distance_2 = sim.dice_coefficient(set_0, set_1)
    print(distance_2)
    # Overlap 系数
    distance_3 = sim.overlap_coefficient(set_0, set_1)
    print(distance_3)
    

    输出:

    0.1428571428572653
    0.2500000000001875
    0.25000000000009376
    0.2500000000001875
    

  • 基于概率分布的相似性

    import deeplotx.similarity as sim
    
    dist_0, dist_1 = [0.3, 0.2, 0.1, 0.4], [0.2, 0.1, 0.3, 0.4]
    # 交叉熵
    distance_0 = sim.cross_entropy(dist_0, dist_1)
    print(distance_0)
    # KL 散度
    distance_1 = sim.kl_divergence(dist_0, dist_1)
    print(distance_1)
    # JS 散度
    distance_2 = sim.js_divergence(dist_0, dist_1)
    print(distance_2)
    # Hellinger 距离
    distance_3 = sim.hellinger_distance(dist_0, dist_1)
    print(distance_3)
    

    输出:

    0.3575654913778237
    0.15040773967762736
    0.03969123741566945
    0.20105866986400994
    

• 预定义深度神经网络

  from deeplotx import (
      BaseNeuralNetwork,  # 深度神经网络基类
      FeedForward,  # 前馈神经网络
      LinearRegression,  # 线性回归
      LogisticRegression,  # 逻辑回归 / 二分类 / 多标签分类
      SoftmaxRegression,  # Softmax 回归 / 多分类
      RecursiveSequential,  # 序列模型 / 循环神经网络
      LongContextRecursiveSequential,  # 长上下文序列模型 / 自注意力融合循环神经网络
      SelfAttention,  # 自注意力模块
      AutoRegression,  # 自回归模型 / 循环神经网络
      LongContextAutoRegression  # 长上下文自回归模型 / 自注意力融合循环神经网络
  )
  

  基础网络结构:

  from typing_extensions import override
  
  import torch
  from torch import nn
  
  from deeplotx.nn.base_neural_network import BaseNeuralNetwork
  
  
  class FeedForwardUnit(BaseNeuralNetwork):
      def __init__(self, feature_dim: int, expansion_factor: int | float = 2,
                  bias: bool = True, dropout_rate: float = 0.05, model_name: str | None = None,
                  device: str | None = None, dtype: torch.dtype | None = None):
          super().__init__(in_features=feature_dim, out_features=feature_dim, model_name=model_name, device=device, dtype=dtype)
          self._dropout_rate = dropout_rate
          self.fc1 = nn.Linear(feature_dim, int(feature_dim * expansion_factor), bias=bias,
                              device=self.device, dtype=self.dtype)
          self.fc2 = nn.Linear(int(feature_dim * expansion_factor), feature_dim, bias=bias,
                              device=self.device, dtype=self.dtype)
          self.parametric_relu_1 = nn.PReLU(num_parameters=1, init=5e-3,
                                          device=self.device, dtype=self.dtype)
          self.layer_norm = nn.LayerNorm(normalized_shape=self.fc1.in_features, eps=1e-9,
                                      device=self.device, dtype=self.dtype)
  
      @override
      def forward(self, x: torch.Tensor) -> torch.Tensor:
          x = self.ensure_device_and_dtype(x, device=self.device, dtype=self.dtype)
          residual = x
          x = self.layer_norm(x)
          x = self.fc1(x)
          x = self.parametric_relu_1(x)
          if self._dropout_rate > .0:
              x = torch.dropout(x, p=self._dropout_rate, train=self.training)
          return self.fc2(x) + residual
  
  
  class FeedForward(BaseNeuralNetwork):
      def __init__(self, feature_dim: int, num_layers: int = 1, expansion_factor: int | float = 2,
                  bias: bool = True, dropout_rate: float = 0.05, model_name: str | None = None,
                  device: str | None = None, dtype: torch.dtype | None = None):
          if num_layers < 1:
              raise ValueError('num_layers cannot be less than 1.')
          super().__init__(in_features=feature_dim, out_features=feature_dim, model_name=model_name, device=device, dtype=dtype)
          self.ffn_layers = nn.ModuleList([FeedForwardUnit(feature_dim=feature_dim,
                                                          expansion_factor=expansion_factor, bias=bias,
                                                          dropout_rate=dropout_rate,
                                                          device=self.device, dtype=self.dtype)] * num_layers)
  
      @override
      def forward(self, x: torch.Tensor) -> torch.Tensor:
          x = self.ensure_device_and_dtype(x, device=self.device, dtype=self.dtype)
          for ffn in self.ffn_layers:
              x = ffn(x)
          return x
  

  自注意力模块:

  from typing_extensions import override
  
  import torch
  
  from deeplotx.nn.base_neural_network import BaseNeuralNetwork
  from deeplotx.nn.feed_forward import FeedForward
  
  
  class SelfAttention(BaseNeuralNetwork):
      def __init__(self, feature_dim: int, bias: bool = True, proj_layers: int = 1,
                  proj_expansion_factor: int | float = 1.5, dropout_rate: float = 0.02,
                  model_name: str | None = None, device: str | None = None, dtype: torch.dtype | None = None):
          super().__init__(in_features=feature_dim, out_features=feature_dim, model_name=model_name,
                          device=device, dtype=dtype)
          self._feature_dim = feature_dim
          self.q_proj = FeedForward(feature_dim=self._feature_dim, num_layers=proj_layers,
                                  expansion_factor=proj_expansion_factor,
                                  bias=bias, dropout_rate=dropout_rate, device=self.device, dtype=self.dtype)
          self.k_proj = FeedForward(feature_dim=self._feature_dim, num_layers=proj_layers,
                                  expansion_factor=proj_expansion_factor,
                                  bias=bias, dropout_rate=dropout_rate, device=self.device, dtype=self.dtype)
          self.v_proj = FeedForward(feature_dim=self._feature_dim, num_layers=proj_layers,
                                  expansion_factor=proj_expansion_factor,
                                  bias=bias, dropout_rate=dropout_rate, device=self.device, dtype=self.dtype)
  
      def _attention(self, x: torch.Tensor, mask: torch.Tensor | None = None) -> torch.Tensor:
          q, k = self.q_proj(x), self.k_proj(x)
          attn = torch.matmul(q, k.transpose(-2, -1))
          attn = attn / (self._feature_dim ** 0.5)
          attn = attn.masked_fill(mask == 0, -1e9) if mask is not None else attn
          return torch.softmax(attn, dim=-1)
  
      @override
      def forward(self, x: torch.Tensor, mask: torch.Tensor | None = None) -> torch.Tensor:
          x = self.ensure_device_and_dtype(x, device=self.device, dtype=self.dtype)
          if mask is not None:
              mask = self.ensure_device_and_dtype(mask, device=self.device, dtype=self.dtype)
          v = self.v_proj(x)
          return torch.matmul(self._attention(x, mask), v)
  

• 使用预定义训练器实现文本二分类任务

  from deeplotx import TextBinaryClassifierTrainer, LongTextEncoder
  from deeplotx.util import get_files, read_file
  
  # 定义向量编码策略 (默认使用 bert-base-uncased 作为嵌入模型)
  long_text_encoder = LongTextEncoder(
      max_length=2048,  # 最大文本大小, 超出截断
      chunk_size=448,  # 块大小 (按 Token 计)
      overlapping=32,  # 块间重叠大小 (按 Token 计)
      cache_capacity=512  # 缓存大小
  )
  
  trainer = TextBinaryClassifierTrainer(
      long_text_encoder=long_text_encoder,
      batch_size=2,
      train_ratio=0.9  # 训练集和验证集比例
  )
  
  # 读取数据
  pos_data_path = 'path/to/pos_dir'
  neg_data_path = 'path/to/neg_dir'
  pos_data = [read_file(x) for x in get_files(pos_data_path)]
  neg_data = [read_file(x) for x in get_files(neg_data_path)]
  
  # 开始训练
  model = trainer.train(pos_data, neg_data, 
                        num_epochs=36, learning_rate=2e-5,  # 设置训练轮数和学习率
                        balancing_dataset=True,  # 是否平衡数据集
                        alpha=1e-4, rho=.2,  # 设置 elastic net 正则化的超参数 alpha 和 rho
                        hidden_dim=256, recursive_layers=2)  # 设置循环神经网络的结构
  
  # 保存模型权重
  model.save(model_name='test_model', model_dir='model')
  
  # 加载已保存的模型
  model = model.load(model_name='test_model', model_dir='model')
  
  # 使用训练好的模型进行预测
  model.predict(long_text_encoder.encode('这是一个测试文本.', flatten=False))