# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("ESGBERT/SocialBERT-base")
model = AutoModelForMaskedLM.from_pretrained("ESGBERT/SocialBERT-base")Quick Links
Model Card for SocialBERT-base
Model Description
Based on this paper, this is the SocialBERT-base language model. A language model that is trained to better understand social texts in the ESG domain.
Using the DistilRoBERTa model as a starting point, the SocialBERT-base Language Model is additionally pre-trained on a text corpus comprising social-related annual reports, sustainability reports, and corporate and general news.
More details can be found in the paper
@article{schimanski_ESGBERT_2024,
title = {Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication},
journal = {Finance Research Letters},
volume = {61},
pages = {104979},
year = {2024},
issn = {1544-6123},
doi = {https://doi.org/10.1016/j.frl.2024.104979},
url = {https://www.sciencedirect.com/science/article/pii/S1544612324000096},
author = {Tobias Schimanski and Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold},
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ESGBERT/SocialBERT-base")