language: en
license: apache-2.0
datasets:
- ESGBERT/action_500
tags:
- ESG
- environmental
- 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
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{Schimanski23ESGBERT,
title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}},
author={Tobias Schimanski and Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold},
year={2023},
journal={Available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514},
}