Based on this paper, this is the EnvRoBERTa-environmental language model. A language model that is trained to better classify environmental texts in the ESG domain.

Note: We generally recommend choosing the EnvironmentalBERT-environmental model since it is quicker, less resource-intensive and only marginally worse in performance.

Using the EnvRoBERTa-base model as a starting point, the EnvRoBERTa-environmental Language Model is additionally fine-trained on a 2k environmental dataset to detect environmental 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:

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 
tokenizer_name = "ESGBERT/EnvRoBERTa-environmental"
model_name = "ESGBERT/EnvRoBERTa-environmental"
 
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("Scope 1 emissions are reported here on a like-for-like basis against the 2013 baseline and exclude emissions from additional vehicles used during repairs.", padding=True, truncation=True))

More details can be found in the paper

bibtex

@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},
}