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ESG-BERT

nbroad

The applications of ESG-BERT can be expanded way beyond just text classification. It can be fine-tuned to perform various other downstream NLP tasks in the domain of Sustainable Investing. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.

Text analysis

Input Text→ Output Text

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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ESG-BERT

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