| Summary: | This article takes a step in the direction of adapting existing Natural Language Processing (NLP) models to diverse and heterogeneous settings of Environmental Due Diligence (EDD). The approach we followed was to enrich the vocabulary of deep learning models with more data from environmental domain by collecting the data from open-source regulatory documents provided by Environmental Protection Agency (EPA) [1]. We used active learning and data augmentation methods to resolve the imbalanced classes and fine-tuned DistilBERT on EDD data to develop environmental due diligence model which is hosted as an inference Application Programming Interface (API) on Hugging Face Hub. This model was packaged to predict EDD classes, determine relevancy and ranking, and allows users to fine tune the model to more EDD classes. This package, EnvBert is hosted on Python Package Index (PyPI) repository [2]. We anticipate that the rich EDD dataset that we used to train the model and create a package would help the users contribute for a variety of NLP tasks on EDD textual data, especially for text classification purposes. We present the data in raw format; it has been open sourced and publicly available at https://data.mendeley.com/datasets/tx6vmd4g9p/4.
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