ESG-BERT

Maintainer: nbroad

Total Score

55

Last updated 5/28/2024

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

ESG-BERT is a domain-specific BERT language model developed by Mukut Mukherjee, Charan Pothireddi, and Parabole.ai for text mining in sustainable investing. It is a fine-tuned version of the parent BERT language model.

The model was shared by the maintainer nbroad on the HuggingFace platform. ESG-BERT is a cased language model trained on text data relevant to environmental, social, and governance (ESG) investing.

Model Inputs and Outputs

Inputs

  • Text: The model takes in text data as input, such as news articles, research reports, or other documents related to sustainable investing.

Outputs

  • Text classification: The primary output of ESG-BERT is text classification, where the model can categorize input text into relevant ESG-related topics or themes.

Capabilities

ESG-BERT is designed to excel at text mining tasks in the domain of sustainable investing. It can be used to automatically analyze large volumes of text data to identify relevant ESG-related information, trends, and insights. For example, the model could be used to classify news articles by their ESG focus, extract key ESG-related concepts and entities from investment research reports, or gauge sentiment around ESG issues in social media posts.

What Can I Use It For?

The applications of ESG-BERT extend beyond just text classification. The model can be fine-tuned to perform various other downstream NLP tasks in the domain of sustainable investing, such as:

  • ESG-focused topic modeling: Identify the main themes and topics discussed in large ESG-related text corpora.
  • ESG risk assessment: Automatically assess the ESG-related risks and exposures discussed in corporate disclosures or news reports.
  • Sustainable investment research: Extract relevant ESG data and insights from research reports to inform investment decisions.
  • ESG compliance monitoring: Automate the monitoring of ESG regulatory and disclosure requirements across industries.

Things to Try

One interesting aspect of ESG-BERT is its domain-specific training, which allows it to better understand and contextualize the language used in sustainable investing. Compared to a more general language model, ESG-BERT may be better equipped to handle ESG-related jargon, identify subtle nuances in ESG discussions, and pick up on relevant ESG themes and concepts.

Researchers and practitioners in the sustainable investing space could experiment with fine-tuning ESG-BERT on their own ESG-related text data to further enhance its performance on domain-specific tasks. The model's strong base in BERT combined with its specialized training could make it a powerful tool for extracting valuable insights from the growing body of ESG-focused information.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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