NLP Toolkit

Verseagility is a Python-based toolkit for your custom natural language processing task, allowing you to bring your own data. It is a central component of the Microsoft Services Knowledge Mining offering.

See the wiki for detailed documentation how to get started with the toolkit.

Supported Use Cases

Binary, multi-class & multi-label classification
Named entity recognition
Question answering
Text summarization

Live Demo

The live demo of models resulting from Verseagility is hosted at MTC Germany:

https://aka.ms/nlp-demo

Repository Structure

├── /assets            <- Version controlled assets, such as stopword lists. Max size 
│                         per file: 10 MB. Training data should
│                         be stored in local data directory, outside of repository or within gitignore. 
│
├── /demo              <- Demo environment that can be deployed as is, or customized. 
│
├── /deploy            <- Scripts used for deploying training or test service  
│   ├── training.py    <- Deploy your training to a remote compute instance, via AML
│   │   
│   ├── hyperdrive.py  <- Deploy hyperparemeter sweep on a remote compute instance, via AML
│   │
│   └── service.py     <- Deploy a service (endpoint) to ACI or AKS, via AML
│
├── /docs              <- Detailed documentation.
│
├── /notebook          <- Jupyter notebooks. Naming convention is <[Task]-[Short Description]>,
│                         for example: 'Data - Exploration.ipynb'
│
├── /pipeline          <- Document processing pipeline components, including document cracker. 
│
├── /project           <- Project configuration files, detailing the tasks to be completed.
│
├── /scraper           <- Website scraper used to fetch sample data. 
│                         Can be reused for similarly structured forum websites.
│
├── /src               <- Source code for use in this project.
│   ├── infer.py       <- Inference file, for scoring the model
│   │   
│   ├── data.py        <- Use case agnostic utils file, for data management incl upload/download
│   │
│   └── helper.py      <- Use case agnostic utils file, with common functions incl secret handling
│
├── /tests              <- Unit tests (using pytest)
│
├── README.md          <- The top-level README for developers using this project.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment.
│                         Can be generated using `pip freeze > requirements.txt`
│
└── config.ini         <- Configuration and secrets used while developing locally
                          Secrets in production should be stored in the Azure KeyVault

Naming

Assets

<project name>(-<task>)-<step>(-<environment>)

where step in [source, train, deploy], for data assets.
where task is an int, referring to the parameters, for models.

Acknowledgements

Verseagility is built in part using the following:

Transformers by HuggingFace
FARM by deepset ai
spaCy by Explosion ai
flair by Zalando Research
gensim

Maintainers:

To-Dos

The following section contains a list of possible new features or enhancements. Feel free to contribute.

Classification

multi label support
integrate handling for larger documents vs short documents
integrate explicit handling for unbalanced datasets
ONNX support

NER

improve duplicate handling

Question Answering

apply advanced IR methods

Summarization

(IP) full test of integration

Deployment

deploy service to Azure Function (without AzureML)
setup GitHub actions

Notebooks Templates

(IP) review model results (auto generate after each training step)
review model bias (auto generate after each training step)
(IP) available models benchmark (incl AutoML)

Tests

unit tests (pytest)

Please note:

For training data corpora larger than 10.000 documents, we recommend to upload them chunk-wise to the BLOB-storage, otherwise it might come to bottlenecks in the document processor function

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

msshada/verseagility