yiyanghkust/finbert-esg

ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. FinBERT-ESG is a FinBERT model fine-tuned on 2,000 manually annotated sentences from firms' ESG reports and annual reports.

Input: A financial text.

Output: Environmental, Social, Governance or None.

How to use

You can use this model with Transformers pipeline for ESG classification.

# tested in transformers==4.18.0 
from transformers import BertTokenizer, BertForSequenceClassification, pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-esg',num_labels=4)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-esg')
nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer)
results = nlp('Rhonda has been volunteering for several years for a variety of charitable community programs.')
print(results) # [{'label': 'Social', 'score': 0.9906041026115417}]

Visit FinBERT.AI for more details on the recent development of FinBERT.

If you use the model in your academic work, please cite the following paper:

Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." Contemporary Accounting Research (2022).