An out-of-the-box long-text NLP framework.
Author: vortezwohl
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}
}-
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
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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)] -
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) -
Import dependencies
from deeplotx import LoRA
Assumed that the
modelhas been loadedmodel = ... # Maybe an LLM or some other deep neural network models lora_model = LoRA.apply_to(model, target_modules=['q_proj'], rank=16, alpha=32, dropout_rate=.05)
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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])
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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
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from deeplotx import ( FeedForward, MultiHeadFeedForward, LinearRegression, LogisticRegression, SoftmaxRegression, RecursiveSequential, LongContextRecursiveSequential, RoPE, LoRA, 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)