deeplotx 0.9.8a0

An out-of-the-box long-text NLP framework.

Deep Long Text Learning

An out-of-the-box long-text NLP framework.

Author: vortezwohl

Citation

If you are incorporating the DeepLoTX framework into your research, please remember to properly cite it to acknowledge its contribution to your work.

Если вы интегрируете фреймворк DeepLoTX в своё исследование, пожалуйста, не забудьте правильно сослаться на него, указывая его вклад в вашу работу.

もしあなたが研究に DeepLoTX フレームワークを組み入れているなら、その貢献を認めるために適切に引用することを忘れないでください.

如果您正在將 DeepLoTX 框架整合到您的研究中，請務必正確引用它，以聲明它對您工作的貢獻.

@software{Wu_DeepLoTX_2025,
author = {Wu, Zihao},
license = {GPL-3.0},
month = aug,
title = {{DeepLoTX}},
url = {https://github.com/vortezwohl/DeepLoTX},
version = {0.9.5},
year = {2025}
}

Installation

• With pip

  pip install -U deeplotx
  

• With uv (recommended)

  uv add -U deeplotx
  

• Get the latest features from GitHub

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

Quick start

• Named entity recognition

  Multilingual is supported.

  Gender recognition is supported.

  Import dependencies

  from deeplotx import BertNER
  
  ner = BertNER()
  

  ner('你好, 我的名字是吴子豪, 来自福建福州.')
  

  stdout:

  [NamedPerson(text='吴子豪', type='PER', base_probability=0.9995428418719051, gender=<Gender.Male: 'male'>, gender_probability=0.9970703125),
  NamedEntity(text='福建', type='LOC', base_probability=0.9986373782157898),
  NamedEntity(text='福州', type='LOC', base_probability=0.9993632435798645)]
  

  ner("Hi, i'm Vortez Wohl, author of DeeploTX.")
  

  stdout:

  [NamedPerson(text='Vortez Wohl', type='PER', base_probability=0.9991965342072855, gender=<Gender.Male: 'male'>, gender_probability=0.87255859375)]
  

• Gender recognition

  Multilingual is supported.

  Integrated from Name2Gender

  Import dependencies

  from deeplotx import Name2Gender
  
  n2g = Name2Gender()
  

  Recognize gender of "Elon Musk":

  n2g('Elon Musk')
  

  stdout:

  <Gender.Male: 'male'>
  

  Recognize gender of "Anne Hathaway":

  n2g('Anne Hathaway')
  

  stdout:

  <Gender.Female: 'female'>
  

  Recognize gender of "吴彦祖":

  n2g('吴彦祖', return_probability=True)
  

  stdout:

  (<Gender.Male: 'male'>, 1.0)
  

• Long text embedding

  • BERT based long text embedding

    from deeplotx import LongTextEncoder
    
    encoder = LongTextEncoder(
        chunk_size=448,
        overlapping=32
    )
    encoder.encode('我是吴子豪, 这是一个测试文本.', flatten=False)
    

    stdout:

    tensor([ 2.2316e-01,  2.0300e-01,  ...,  1.5578e-01, -6.6735e-02])
    

  • Longformer based long text embedding

    from deeplotx import LongformerEncoder
    
    encoder = LongformerEncoder()
    encoder.encode('Thank you for using DeepLoTX.')
    

    stdout:

    tensor([-2.7490e-02,  6.6503e-02, ..., -6.5937e-02,  6.7802e-03])
    

• Similarities calculation

  • Vector based

    import deeplotx.similarity as sim
    
    vector_0, vector_1 = [1, 2, 3, 4], [4, 3, 2, 1]
    distance_0 = sim.euclidean_similarity(vector_0, vector_1)
    print(distance_0)
    distance_1 = sim.cosine_similarity(vector_0, vector_1)
    print(distance_1)
    distance_2 = sim.chebyshev_similarity(vector_0, vector_1)
    print(distance_2)
    

    stdout:

    4.47213595499958
    0.33333333333333337
    3
    

  • Set based

    import deeplotx.similarity as sim
    
    set_0, set_1 = {1, 2, 3, 4}, {4, 5, 6, 7}
    distance_0 = sim.jaccard_similarity(set_0, set_1)
    print(distance_0)
    distance_1 = sim.ochiai_similarity(set_0, set_1)
    print(distance_1)
    distance_2 = sim.dice_coefficient(set_0, set_1)
    print(distance_2)
    distance_3 = sim.overlap_coefficient(set_0, set_1)
    print(distance_3)
    

    stdout:

    0.1428571428572653
    0.2500000000001875
    0.25000000000009376
    0.2500000000001875
    

  • Distribution based

    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]
    distance_0 = sim.cross_entropy(dist_0, dist_1)
    print(distance_0)
    distance_1 = sim.kl_divergence(dist_0, dist_1)
    print(distance_1)
    distance_2 = sim.js_divergence(dist_0, dist_1)
    print(distance_2)
    distance_3 = sim.hellinger_distance(dist_0, dist_1)
    print(distance_3)
    

    stdout:

    0.3575654913778237
    0.15040773967762736
    0.03969123741566945
    0.20105866986400994
    

• Pre-defined neural networks

  from deeplotx import (
      FeedForward, 
      MultiHeadFeedForward, 
      LinearRegression, 
      LogisticRegression, 
      SoftmaxRegression, 
      RecursiveSequential, 
      LongContextRecursiveSequential, 
      RoPE, 
      Attention, 
      MultiHeadAttention, 
      RoFormerEncoder, 
      AutoRegression, 
      LongContextAutoRegression 
  )
  

  The fundamental FFN (MLPs):

  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.up_proj = nn.Linear(in_features=feature_dim, out_features=int(feature_dim * expansion_factor),
                                  bias=bias, device=self.device, dtype=self.dtype)
          self.down_proj = nn.Linear(in_features=int(feature_dim * expansion_factor), out_features=feature_dim,
                                  bias=bias, device=self.device, dtype=self.dtype)
          self.parametric_relu = nn.PReLU(num_parameters=1, init=5e-3,
                                          device=self.device, dtype=self.dtype)
          self.layer_norm = nn.LayerNorm(normalized_shape=self.up_proj.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.up_proj(x)
          x = self.parametric_relu(x)
          if self._dropout_rate > .0:
              x = torch.dropout(x, p=self._dropout_rate, train=self.training)
          return self.down_proj(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) for _ in range(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
  

  Attention:

  from typing_extensions import override
  
  import torch
  
  from deeplotx.nn.base_neural_network import BaseNeuralNetwork
  from deeplotx.nn.feed_forward import FeedForward
  from deeplotx.nn.rope import RoPE, DEFAULT_THETA
  
  
  class Attention(BaseNeuralNetwork):
      def __init__(self, feature_dim: int, bias: bool = True, positional: 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,
                  **kwargs):
          super().__init__(in_features=feature_dim, out_features=feature_dim, model_name=model_name,
                          device=device, dtype=dtype)
          self._positional = positional
          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)
          if self._positional:
              self.rope = RoPE(feature_dim=self._feature_dim, theta=kwargs.get('theta', DEFAULT_THETA),
                              device=self.device, dtype=self.dtype)
  
      def _attention(self, x: torch.Tensor, y: torch.Tensor, mask: torch.Tensor | None = None) -> torch.Tensor:
          q, k = self.q_proj(x), self.k_proj(y)
          if self._positional:
              q, k = self.rope(q), self.rope(k)
          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, dtype=self.dtype, dim=-1)
  
      @override
      def forward(self, x: torch.Tensor, y: torch.Tensor | None = None, mask: torch.Tensor | None = None) -> torch.Tensor:
          x = self.ensure_device_and_dtype(x, device=self.device, dtype=self.dtype)
          y = x if y is None else self.ensure_device_and_dtype(y, 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(y)
          return torch.matmul(self._attention(x, y, mask), v)
  

• Text binary classification task with predefined trainer

  from deeplotx import TextBinaryClassifierTrainer, LongTextEncoder
  from deeplotx.util import get_files, read_file
  
  long_text_encoder = LongTextEncoder(
      max_length=2048, 
      chunk_size=448, 
      overlapping=32, 
      cache_capacity=512 
  )
  trainer = TextBinaryClassifierTrainer(
      long_text_encoder=long_text_encoder,
      batch_size=2,
      train_ratio=0.9 
  )
  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)]
  model = trainer.train(pos_data, neg_data, 
                      num_epochs=36, learning_rate=2e-5, 
                      balancing_dataset=True, alpha=1e-4, 
                      rho=.2, encoder_layers=2, 
                      attn_heads=8, 
                      recursive_layers=2) 
  model.save(model_name='test_model', model_dir='model')
  model = model.load(model_name='test_model', model_dir='model')
  model.predict(long_text_encoder.encode('这是一个测试文本.', flatten=False))