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All Projects → fastnlp → Fastnlp

fastnlp / Fastnlp

Licence: apache-2.0
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

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deep-learningnatural-language-processingtext-classificationtext-processingnlp-librarychinese-nlpnlp-parsing

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fastNLP

Build Status codecov Pypi Hex.pm Documentation Status

fastNLP是一款面向自然语言处理（NLP）的轻量级框架，目标是快速实现NLP任务以及构建复杂模型。

fastNLP具有如下的特性：

  • 统一的Tabular式数据容器，简化数据预处理过程；
  • 内置多种数据集的Loader和Pipe，省去预处理代码;
  • 各种方便的NLP工具，例如Embedding加载（包括ELMo和BERT）、中间数据cache等;
  • 部分数据集与预训练模型的自动下载；
  • 提供多种神经网络组件以及复现模型（涵盖中文分词、命名实体识别、句法分析、文本分类、文本匹配、指代消解、摘要等任务）;
  • Trainer提供多种内置Callback函数，方便实验记录、异常捕获等。

安装指南

fastNLP 依赖以下包:

  • numpy>=1.14.2
  • torch>=1.0.0
  • tqdm>=4.28.1
  • nltk>=3.4.1
  • requests
  • spacy
  • prettytable>=0.7.2

其中torch的安装可能与操作系统及 CUDA 的版本相关，请参见 PyTorch 官网 。 在依赖包安装完成后，您可以在命令行执行如下指令完成安装

pip install fastNLP
python -m spacy download en

fastNLP教程

中文文档、 教程

快速入门

详细使用教程

扩展教程

内置组件

大部分用于的 NLP 任务神经网络都可以看做由词嵌入（embeddings）和两种模块：编码器（encoder）、解码器（decoder）组成。

以文本分类任务为例，下图展示了一个BiLSTM+Attention实现文本分类器的模型流程图：

fastNLP 在 embeddings 模块中内置了几种不同的embedding：静态embedding（GloVe、word2vec）、上下文相关embedding （ELMo、BERT）、字符embedding（基于CNN或者LSTM的CharEmbedding）

与此同时，fastNLP 在 modules 模块中内置了两种模块的诸多组件，可以帮助用户快速搭建自己所需的网络。 两种模块的功能和常见组件如下:

类型 功能 例子
encoder 将输入编码为具有具有表示能力的向量 Embedding, RNN, CNN, Transformer, ...
decoder 将具有某种表示意义的向量解码为需要的输出形式 MLP, CRF, ...

项目结构

fastNLP的大致工作流程如上图所示，而项目结构如下：

fastNLP 开源的自然语言处理库
fastNLP.core 实现了核心功能，包括数据处理组件、训练器、测试器等
fastNLP.models 实现了一些完整的神经网络模型
fastNLP.modules 实现了用于搭建神经网络模型的诸多组件
fastNLP.embeddings 实现了将序列index转为向量序列的功能，包括读取预训练embedding等
fastNLP.io 实现了读写功能，包括数据读入与预处理，模型读写，数据与模型自动下载等

In memory of @FengZiYjun. May his soul rest in peace. We will miss you very very much!

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