"Relax like a sloth, let DeText do the understanding for you"
DeText: A Deep Neural Text Understanding Framework
DeText is a Deep 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.
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:
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Word embedding layer. It converts the sequence of words into a d by n matrix.
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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.
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Interaction layer. It generates deep features based on the text embeddings. Options include concatenation, cosine similarity, etc.
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Wide & Deep Feature Processing. We combine the traditional features with the interaction features (deep features) in a wide & deep fashion.
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MLP layer. The MLP layer is to combine wide features and deep features.
All parameters are jointly updated to optimize the training objective.
Model Configurables
DeText offers great flexibility for clients to build customized networks for their own use cases:
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LTR/classification layer: in-house LTR loss implementation, or tf-ranking LTR loss, multi-class classification support.
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MLP layer: customizable number of layers and number of dimensions.
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Interaction layer: support Cosine Similarity, Hadamard Product, and Concatenation.
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Text embedding layer: support CNN, BERT, LSTM with customized parameters on filters, layers, dimensions, etc.
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Continuous feature normalization: element-wise rescaling, value normalization.
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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
- Create your virtualenv (Python version >= 3.7)
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
- Upgrade pip and setuptools version
pip3 install -U pip pip3 install -U setuptools
- Run setup for DeText:
pip install . -e - Verify environment setup through pytest. If all tests pass, the environment is correctly set up
pytest
- Refer to the training manual (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
- Train a model using DeText (e.g., run_detext.sh)
Tutorial
If you would like a simple try out of the library, you can refer to the following notebooks for tutorial
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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.
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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}
}