%global _empty_manifest_terminate_build 0
Name:		python-detext
Version:	3.2.0
Release:	1
Summary:	please add a summary manually as the author left a blank one
License:	BSD-2-CLAUSE
URL:		https://pypi.org/project/detext/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/be/b5/7830e3f839fe0de22356c5e59982dc03706588ccd63e32162a903252ff1b/detext-3.2.0.tar.gz
BuildArch:	noarch


%description
**DeText** is a <b>_De_</b>ep **_Text_** understanding framework for NLP related ranking, classification, 
and language generation tasks.  It leverages semantic matching using deep neural networks to 
understand member intents in search and recommender systems. 
As a general NLP framework, DeText can be applied to many tasks, including search & recommendation ranking, 
multi-class classification and query understanding tasks.
More details can be found in the [LinkedIn Engineering blog post](https://engineering.linkedin.com/blog/2020/open-sourcing-detext).
## Highlight
* Natural language understanding powered by state-of-the-art deep neural networks
  * automatic feature extraction with deep models
  * end-to-end training
  * interaction modeling between ranking sources and targets
* A general framework with great flexibility
  * customizable model architectures
  * multiple text encoder support
  * multiple data input types support
  * various optimization choices
  * standard training flow control
* Easy-to-use
  * Configuration based modeling (e.g., all configurations through command line)
## General Model Architecture
DeText supports a general model architecture that contains following components:
* **Word embedding layer**.  It converts the sequence of words into a d by n matrix.
* **CNN/BERT/LSTM for text encoding layer**.  It takes into the word embedding matrix as input, and maps the text data into a fixed length embedding.
* **Interaction layer**.  It generates deep features based on the text embeddings.  Options include concatenation, cosine similarity, etc.
* **Wide & Deep Feature Processing**.  We combine the traditional features with the interaction features (deep features) in a wide & deep fashion.
* **MLP layer**. The MLP layer is to combine wide features and deep features. 
All parameters are jointly updated to optimize the training objective.
![](detext_model_architecture.png) 
### Model Configurables
DeText offers great flexibility for clients to build customized networks for their own use cases:
* **LTR/classification layer**: in-house LTR loss implementation, or tf-ranking LTR loss, multi-class classification support.
* **MLP layer**: customizable number of layers and number of dimensions.
* **Interaction layer**: support Cosine Similarity, Hadamard Product, and Concatenation.
* **Text embedding layer**: support CNN, BERT, LSTM with customized parameters on filters, layers, dimensions, etc.
* **Continuous feature normalization**: element-wise rescaling, value normalization.
* **Categorical feature processing**: modeled as entity embedding.
All these can be customized via hyper-parameters in the DeText template. Note that tf-ranking is 
supported in the DeText framework, i.e., users can choose the LTR loss and metrics defined in DeText.
## User Guide
### Dev environment set up
1. Create your virtualenv (Python version >= 3.7)
    ```shell script
    VENV_DIR = <your venv dir>
    python3 -m venv $VENV_DIR  # Make sure your python version >= 3.7
    source $VENV_DIR/bin/activate  # Enter the virtual environment
    ```
1. Upgrade pip and setuptools version
    ```shell script
    pip3 install -U pip
    pip3 install -U setuptools
    ```
1. Run setup for DeText:
    ```shell script
    pip install . -e
    ```
1. Verify environment setup through pytest. If all tests pass, the environment is correctly set up
    ```shell script
    pytest 
    ```
1. Refer to the training manual ([TRAINING.md](user_guide/TRAINING.md)) to find information about customizing the model:
    * Training data format and preparation
    * Key parameters to customize and train DeText models
    * Detailed information about all DeText training parameters for full customization
1. Train a model using DeText (e.g., [run_detext.sh](test/resources/run_detext.sh))
### Tutorial
If you would like a simple try out of the library, you can refer to the following notebooks for tutorial
* [text_classification_demo.ipynb](user_guide/notebooks/text_classification_demo.ipynb)
    This notebook shows how to use DeText to train a multi-class text classification model on a public query intent 
    classification dataset. Detailed instructions on data preparation, model training, model inference are included.
* [autocompletion.ipynb](user_guide/notebooks/autocompletion.ipynb)
    This notebook shows how to use DeText to train a text ranking model on a public query auto completion dataset.
    Detailed steps on data preparation, model training, model inference examples are included.
## **Citation**
Please cite DeText in your publications if it helps your research:
```
@manual{guo-liu20,
  author    = {Weiwei Guo and
               Xiaowei Liu and
               Sida Wang and 
               Huiji Gao and
               Bo Long},
  title     = {DeText: A Deep NLP Framework for Intelligent Text Understanding},
  url       = {https://engineering.linkedin.com/blog/2020/open-sourcing-detext},
  year      = {2020}
}
@inproceedings{guo-gao19,
  author    = {Weiwei Guo and
               Huiji Gao and
               Jun Shi and 
               Bo Long},
  title     = {Deep Natural Language Processing for Search Systems},
  booktitle = {ACM SIGIR 2019},
  year      = {2019}
}
@inproceedings{guo-gao19,
  author    = {Weiwei Guo and
               Huiji Gao and
               Jun Shi and 
               Bo Long and 
               Liang Zhang and
               Bee-Chung Chen and
               Deepak Agarwal},
  title     = {Deep Natural Language Processing for Search and Recommender Systems},
  booktitle = {ACM SIGKDD 2019},
  year      = {2019}
}
@inproceedings{guo-liu20,
  author    = {Weiwei Guo and
               Xiaowei Liu and
               Sida Wang and 
               Huiji Gao and
               Ananth Sankar and 
               Zimeng Yang and 
               Qi Guo and 
               Liang Zhang and
               Bo Long and 
               Bee-Chung Chen and 
               Deepak Agarwal},
  title     = {DeText: A Deep Text Ranking Framework with BERT},
  booktitle = {ACM CIKM 2020},
  year      = {2020}
}
@inproceedings{jia-long20,
  author    = {Jun Jia and
               Bo Long and
               Huiji Gao and 
               Weiwei Guo and 
               Jun Shi and
               Xiaowei Liu and
               Mingzhou Zhou and
               Zhoutong Fu and
               Sida Wang and
               Sandeep Kumar Jha},
  title     = {Deep Learning for Search and Recommender Systems in Practice},
  booktitle = {ACM SIGKDD 2020},
  year      = {2020}
}
@inproceedings{wang-guo20,
  author    = {Sida Wang and
               Weiwei Guo and
               Huiji Gao and
               Bo Long},
  title     = {Efficient Neural Query Auto Completion},
  booktitle = {ACM CIKM 2020},
  year      = {2020}
}
@inproceedings{liu-guo20,
  author    = {Xiaowei Liu and
               Weiwei Guo and
               Huiji Gao and
               Bo Long},
  title     = {Deep Search Query Intent Understanding},
  booktitle = {arXiv:2008.06759},
  year      = {2020}
}
```

%package -n python3-detext
Summary:	please add a summary manually as the author left a blank one
Provides:	python-detext
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-detext
**DeText** is a <b>_De_</b>ep **_Text_** understanding framework for NLP related ranking, classification, 
and language generation tasks.  It leverages semantic matching using deep neural networks to 
understand member intents in search and recommender systems. 
As a general NLP framework, DeText can be applied to many tasks, including search & recommendation ranking, 
multi-class classification and query understanding tasks.
More details can be found in the [LinkedIn Engineering blog post](https://engineering.linkedin.com/blog/2020/open-sourcing-detext).
## Highlight
* Natural language understanding powered by state-of-the-art deep neural networks
  * automatic feature extraction with deep models
  * end-to-end training
  * interaction modeling between ranking sources and targets
* A general framework with great flexibility
  * customizable model architectures
  * multiple text encoder support
  * multiple data input types support
  * various optimization choices
  * standard training flow control
* Easy-to-use
  * Configuration based modeling (e.g., all configurations through command line)
## General Model Architecture
DeText supports a general model architecture that contains following components:
* **Word embedding layer**.  It converts the sequence of words into a d by n matrix.
* **CNN/BERT/LSTM for text encoding layer**.  It takes into the word embedding matrix as input, and maps the text data into a fixed length embedding.
* **Interaction layer**.  It generates deep features based on the text embeddings.  Options include concatenation, cosine similarity, etc.
* **Wide & Deep Feature Processing**.  We combine the traditional features with the interaction features (deep features) in a wide & deep fashion.
* **MLP layer**. The MLP layer is to combine wide features and deep features. 
All parameters are jointly updated to optimize the training objective.
![](detext_model_architecture.png) 
### Model Configurables
DeText offers great flexibility for clients to build customized networks for their own use cases:
* **LTR/classification layer**: in-house LTR loss implementation, or tf-ranking LTR loss, multi-class classification support.
* **MLP layer**: customizable number of layers and number of dimensions.
* **Interaction layer**: support Cosine Similarity, Hadamard Product, and Concatenation.
* **Text embedding layer**: support CNN, BERT, LSTM with customized parameters on filters, layers, dimensions, etc.
* **Continuous feature normalization**: element-wise rescaling, value normalization.
* **Categorical feature processing**: modeled as entity embedding.
All these can be customized via hyper-parameters in the DeText template. Note that tf-ranking is 
supported in the DeText framework, i.e., users can choose the LTR loss and metrics defined in DeText.
## User Guide
### Dev environment set up
1. Create your virtualenv (Python version >= 3.7)
    ```shell script
    VENV_DIR = <your venv dir>
    python3 -m venv $VENV_DIR  # Make sure your python version >= 3.7
    source $VENV_DIR/bin/activate  # Enter the virtual environment
    ```
1. Upgrade pip and setuptools version
    ```shell script
    pip3 install -U pip
    pip3 install -U setuptools
    ```
1. Run setup for DeText:
    ```shell script
    pip install . -e
    ```
1. Verify environment setup through pytest. If all tests pass, the environment is correctly set up
    ```shell script
    pytest 
    ```
1. Refer to the training manual ([TRAINING.md](user_guide/TRAINING.md)) to find information about customizing the model:
    * Training data format and preparation
    * Key parameters to customize and train DeText models
    * Detailed information about all DeText training parameters for full customization
1. Train a model using DeText (e.g., [run_detext.sh](test/resources/run_detext.sh))
### Tutorial
If you would like a simple try out of the library, you can refer to the following notebooks for tutorial
* [text_classification_demo.ipynb](user_guide/notebooks/text_classification_demo.ipynb)
    This notebook shows how to use DeText to train a multi-class text classification model on a public query intent 
    classification dataset. Detailed instructions on data preparation, model training, model inference are included.
* [autocompletion.ipynb](user_guide/notebooks/autocompletion.ipynb)
    This notebook shows how to use DeText to train a text ranking model on a public query auto completion dataset.
    Detailed steps on data preparation, model training, model inference examples are included.
## **Citation**
Please cite DeText in your publications if it helps your research:
```
@manual{guo-liu20,
  author    = {Weiwei Guo and
               Xiaowei Liu and
               Sida Wang and 
               Huiji Gao and
               Bo Long},
  title     = {DeText: A Deep NLP Framework for Intelligent Text Understanding},
  url       = {https://engineering.linkedin.com/blog/2020/open-sourcing-detext},
  year      = {2020}
}
@inproceedings{guo-gao19,
  author    = {Weiwei Guo and
               Huiji Gao and
               Jun Shi and 
               Bo Long},
  title     = {Deep Natural Language Processing for Search Systems},
  booktitle = {ACM SIGIR 2019},
  year      = {2019}
}
@inproceedings{guo-gao19,
  author    = {Weiwei Guo and
               Huiji Gao and
               Jun Shi and 
               Bo Long and 
               Liang Zhang and
               Bee-Chung Chen and
               Deepak Agarwal},
  title     = {Deep Natural Language Processing for Search and Recommender Systems},
  booktitle = {ACM SIGKDD 2019},
  year      = {2019}
}
@inproceedings{guo-liu20,
  author    = {Weiwei Guo and
               Xiaowei Liu and
               Sida Wang and 
               Huiji Gao and
               Ananth Sankar and 
               Zimeng Yang and 
               Qi Guo and 
               Liang Zhang and
               Bo Long and 
               Bee-Chung Chen and 
               Deepak Agarwal},
  title     = {DeText: A Deep Text Ranking Framework with BERT},
  booktitle = {ACM CIKM 2020},
  year      = {2020}
}
@inproceedings{jia-long20,
  author    = {Jun Jia and
               Bo Long and
               Huiji Gao and 
               Weiwei Guo and 
               Jun Shi and
               Xiaowei Liu and
               Mingzhou Zhou and
               Zhoutong Fu and
               Sida Wang and
               Sandeep Kumar Jha},
  title     = {Deep Learning for Search and Recommender Systems in Practice},
  booktitle = {ACM SIGKDD 2020},
  year      = {2020}
}
@inproceedings{wang-guo20,
  author    = {Sida Wang and
               Weiwei Guo and
               Huiji Gao and
               Bo Long},
  title     = {Efficient Neural Query Auto Completion},
  booktitle = {ACM CIKM 2020},
  year      = {2020}
}
@inproceedings{liu-guo20,
  author    = {Xiaowei Liu and
               Weiwei Guo and
               Huiji Gao and
               Bo Long},
  title     = {Deep Search Query Intent Understanding},
  booktitle = {arXiv:2008.06759},
  year      = {2020}
}
```

%package help
Summary:	Development documents and examples for detext
Provides:	python3-detext-doc
%description help
**DeText** is a <b>_De_</b>ep **_Text_** understanding framework for NLP related ranking, classification, 
and language generation tasks.  It leverages semantic matching using deep neural networks to 
understand member intents in search and recommender systems. 
As a general NLP framework, DeText can be applied to many tasks, including search & recommendation ranking, 
multi-class classification and query understanding tasks.
More details can be found in the [LinkedIn Engineering blog post](https://engineering.linkedin.com/blog/2020/open-sourcing-detext).
## Highlight
* Natural language understanding powered by state-of-the-art deep neural networks
  * automatic feature extraction with deep models
  * end-to-end training
  * interaction modeling between ranking sources and targets
* A general framework with great flexibility
  * customizable model architectures
  * multiple text encoder support
  * multiple data input types support
  * various optimization choices
  * standard training flow control
* Easy-to-use
  * Configuration based modeling (e.g., all configurations through command line)
## General Model Architecture
DeText supports a general model architecture that contains following components:
* **Word embedding layer**.  It converts the sequence of words into a d by n matrix.
* **CNN/BERT/LSTM for text encoding layer**.  It takes into the word embedding matrix as input, and maps the text data into a fixed length embedding.
* **Interaction layer**.  It generates deep features based on the text embeddings.  Options include concatenation, cosine similarity, etc.
* **Wide & Deep Feature Processing**.  We combine the traditional features with the interaction features (deep features) in a wide & deep fashion.
* **MLP layer**. The MLP layer is to combine wide features and deep features. 
All parameters are jointly updated to optimize the training objective.
![](detext_model_architecture.png) 
### Model Configurables
DeText offers great flexibility for clients to build customized networks for their own use cases:
* **LTR/classification layer**: in-house LTR loss implementation, or tf-ranking LTR loss, multi-class classification support.
* **MLP layer**: customizable number of layers and number of dimensions.
* **Interaction layer**: support Cosine Similarity, Hadamard Product, and Concatenation.
* **Text embedding layer**: support CNN, BERT, LSTM with customized parameters on filters, layers, dimensions, etc.
* **Continuous feature normalization**: element-wise rescaling, value normalization.
* **Categorical feature processing**: modeled as entity embedding.
All these can be customized via hyper-parameters in the DeText template. Note that tf-ranking is 
supported in the DeText framework, i.e., users can choose the LTR loss and metrics defined in DeText.
## User Guide
### Dev environment set up
1. Create your virtualenv (Python version >= 3.7)
    ```shell script
    VENV_DIR = <your venv dir>
    python3 -m venv $VENV_DIR  # Make sure your python version >= 3.7
    source $VENV_DIR/bin/activate  # Enter the virtual environment
    ```
1. Upgrade pip and setuptools version
    ```shell script
    pip3 install -U pip
    pip3 install -U setuptools
    ```
1. Run setup for DeText:
    ```shell script
    pip install . -e
    ```
1. Verify environment setup through pytest. If all tests pass, the environment is correctly set up
    ```shell script
    pytest 
    ```
1. Refer to the training manual ([TRAINING.md](user_guide/TRAINING.md)) to find information about customizing the model:
    * Training data format and preparation
    * Key parameters to customize and train DeText models
    * Detailed information about all DeText training parameters for full customization
1. Train a model using DeText (e.g., [run_detext.sh](test/resources/run_detext.sh))
### Tutorial
If you would like a simple try out of the library, you can refer to the following notebooks for tutorial
* [text_classification_demo.ipynb](user_guide/notebooks/text_classification_demo.ipynb)
    This notebook shows how to use DeText to train a multi-class text classification model on a public query intent 
    classification dataset. Detailed instructions on data preparation, model training, model inference are included.
* [autocompletion.ipynb](user_guide/notebooks/autocompletion.ipynb)
    This notebook shows how to use DeText to train a text ranking model on a public query auto completion dataset.
    Detailed steps on data preparation, model training, model inference examples are included.
## **Citation**
Please cite DeText in your publications if it helps your research:
```
@manual{guo-liu20,
  author    = {Weiwei Guo and
               Xiaowei Liu and
               Sida Wang and 
               Huiji Gao and
               Bo Long},
  title     = {DeText: A Deep NLP Framework for Intelligent Text Understanding},
  url       = {https://engineering.linkedin.com/blog/2020/open-sourcing-detext},
  year      = {2020}
}
@inproceedings{guo-gao19,
  author    = {Weiwei Guo and
               Huiji Gao and
               Jun Shi and 
               Bo Long},
  title     = {Deep Natural Language Processing for Search Systems},
  booktitle = {ACM SIGIR 2019},
  year      = {2019}
}
@inproceedings{guo-gao19,
  author    = {Weiwei Guo and
               Huiji Gao and
               Jun Shi and 
               Bo Long and 
               Liang Zhang and
               Bee-Chung Chen and
               Deepak Agarwal},
  title     = {Deep Natural Language Processing for Search and Recommender Systems},
  booktitle = {ACM SIGKDD 2019},
  year      = {2019}
}
@inproceedings{guo-liu20,
  author    = {Weiwei Guo and
               Xiaowei Liu and
               Sida Wang and 
               Huiji Gao and
               Ananth Sankar and 
               Zimeng Yang and 
               Qi Guo and 
               Liang Zhang and
               Bo Long and 
               Bee-Chung Chen and 
               Deepak Agarwal},
  title     = {DeText: A Deep Text Ranking Framework with BERT},
  booktitle = {ACM CIKM 2020},
  year      = {2020}
}
@inproceedings{jia-long20,
  author    = {Jun Jia and
               Bo Long and
               Huiji Gao and 
               Weiwei Guo and 
               Jun Shi and
               Xiaowei Liu and
               Mingzhou Zhou and
               Zhoutong Fu and
               Sida Wang and
               Sandeep Kumar Jha},
  title     = {Deep Learning for Search and Recommender Systems in Practice},
  booktitle = {ACM SIGKDD 2020},
  year      = {2020}
}
@inproceedings{wang-guo20,
  author    = {Sida Wang and
               Weiwei Guo and
               Huiji Gao and
               Bo Long},
  title     = {Efficient Neural Query Auto Completion},
  booktitle = {ACM CIKM 2020},
  year      = {2020}
}
@inproceedings{liu-guo20,
  author    = {Xiaowei Liu and
               Weiwei Guo and
               Huiji Gao and
               Bo Long},
  title     = {Deep Search Query Intent Understanding},
  booktitle = {arXiv:2008.06759},
  year      = {2020}
}
```

%prep
%autosetup -n detext-3.2.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-detext -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 3.2.0-1
- Package Spec generated