EnvironmentalBERT-action

Model Card for EnvironmentalBERT-action

Model Description

As an extension to this paper, this is the EnvironmentalBERT-action language model. A language model that is trained to better classify action texts in the ESG domain.

Using the EnvironmentalBERT-base model as a starting point, the EnvironmentalBERT-action Language Model is additionally fine-trained on a dataset with 500 sentences to detect action text samples. The underlying dataset is comparatively small, so if you would like to contribute to it, feel free to reach out. :)

How to Get Started With the Model

See these tutorials on Medium for a guide on model usage, large-scale analysis, and fine-tuning.

It is highly recommended to first classify a sentence to be "environmental" or not with the EnvironmentalBERT-environmental model before classifying whether it is "action" or not. This intersection allows us to build a targeted insight into whether the sentence displays an "environmental action".

You can use the model with a pipeline for text classification:

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 
tokenizer_name = "ESGBERT/EnvironmentalBERT-action"
model_name = "ESGBERT/EnvironmentalBERT-action"
 
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 are actively working to reduce our CO2 emissions by planting trees in 25 countries.", padding=True, truncation=True))

More details to the base models can be found in this paper

While this dataset does not originate from the paper, it is a extension of it and the base models are described in it.

@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},
}