metadata
language: en
license: apache-2.0
datasets:
- ESGBERT/social_2k
tags:
- ESG
- social
Model Card for SocialBERT-social
Model Description
Based on this paper, this is the SocialBERT-social language model. A language model that is trained to better classify social texts in the ESG domain.
Using the SocialBERT-base model as a starting point, the SocialBERT-social Language Model is additionally fine-trained on a 2k social dataset to detect social text samples.
How to Get Started With the Model
See these tutorials on Medium for a guide on model usage, large-scale analysis, and fine-tuning.
You can use the model with a pipeline for text classification:
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
tokenizer_name = "ESGBERT/SocialBERT-social"
model_name = "ESGBERT/SocialBERT-social"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) # set device=0 to use GPU
# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
print(pipe("We follow rigorous supplier checks to prevent slavery and ensure workers' rights.", padding=True, truncation=True))
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},
}