Model Card for GovernanceBERT-governance
Model Description
Based on this paper, this is the GovernanceBERT-governance language model. A language model that is trained to better classify governance texts in the ESG domain.
Using the GovernanceBERT-base model as a starting point, the GovernanceBERT-governance Language Model is additionally fine-trained on a 2k governance dataset to detect governance 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/GovernanceBERT-governance"
model_name = "ESGBERT/GovernanceBERT-governance"
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("An ethical code has been issued to all Group employees.", padding=True, truncation=True))
More details can be found in the paper
@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},
}
- Downloads last month
- 442
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.