textClassifySMP2018 | 1

by chenguandan Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | textClassifySMP2018 Summary

textClassifySMP2018 is a Python library. textClassifySMP2018 has no bugs, it has no vulnerabilities and it has low support. However textClassifySMP2018 build file is not available. You can download it from GitHub.

1.数据训练数据：training.txt 验证数据：validataion.txt 格式为json 包括类别：'人类作者', '自动摘要', '机器作者', '机器翻译'. 3.程序 data_utils.py 数据预处理（字典构建、分词、转化为index） data_utils.py val/test 表示测试集、验证集的数据转换为训练相应的模型 train_topic_model.py 训练LDA主题模型，并且提取主题向量，存储在pickle文件中 hybridattmodel.py、hybridconvmodel.py等、conditionattmodel.py、conditiondensemamodel.py等训练相应的模型，hybrid*.py融合了字符序列、单词序列、部分特征、LDA主题向量、位置embedding condition*.py使用的条件模型，把y=f(x)转化为{0,1}=f(x,y_i)表示是否属于某个类别使用方法示例为：hybridattmodel.py pe，这里的pe表示使用position embedding conditiondualpathmode.py效果不好没有使用 charmodel.py、termmodel.py、attmodel.py、deepcnn.py只是用字符序列或者单词序列，效果不好，最终没有使用 trainembed.py 训练单词embedding trainembed.py 分别训练不同类别的单词embedding ensemble.py 读取不同的模型文件，集成预测结果，输出为result.csv fasttextmodel.py 调用fasttext包进行分类. hybridmodel.py 效果不好，最终没有使用。采用了少量特征（在data_utils.py中提取的），包括：  文本长度  英文、数字、其他字符数量  英文、数字、其他字符占总字符比例  是否包含URL.

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Support

textClassifySMP2018 has a low active ecosystem.

It has 4 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

textClassifySMP2018 has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of textClassifySMP2018 is current.

Quality

textClassifySMP2018 has no bugs reported.

Security

textClassifySMP2018 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

textClassifySMP2018 does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

textClassifySMP2018 releases are not available. You will need to build from source code and install.

textClassifySMP2018 has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed textClassifySMP2018 and discovered the below as its top functions. This is intended to give you an instant insight into textClassifySMP2018 implemented functionality, and help decide if they suit your requirements.

Predict all features
Fit the KFold
Loads weights from the model
Load weights from the model
Generate a value for a given label
Generate a value for a single - hot label
Predict vold
Predict for the model
Add a model to the model
Load condition stacking
Function to calculate the length of each character in sentences
Load a vector file
Convert a feature file to a list
Convert data from training data
Fit the kfold
Convert tfidf file
Train a hybrid model
Train condition conv model
Performs training of the model
Train training
Compute the prediction
Convert sent text to training
Convert a text file to a dictionary
Calculate updates for the optimizer
Predict2 feature
Calculates weights for the optimizer
Convert character from json file
Perform the feed - forward algorithm

Get all kandi verified functions for this library.

textClassifySMP2018 Key Features

No Key Features are available at this moment for textClassifySMP2018.

textClassifySMP2018 Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install textClassifySMP2018

You can download it from GitHub.
You can use textClassifySMP2018 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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