license: apache-2.0
datasets:
- ClimatePolicyRadar/national-climate-targets
language:
- en
pipeline_tag: text-classification
tags:
- climate
widget:
- text: >-
The Net Zero Strategy, published in October 2021, was the first document
of its kind for a major economy. It set out the government’s vision for a
market-led, technology-driven transition to decarbonise the UK economy and
reach net zero by 2050.
inference:
parameters:
function_to_apply: sigmoid
National Climate Targets Classifier - Climate Policy Radar
A multi-label text-classifier trained on the National Climate Targets dataset by Climate Policy Radar.
Using the climatebert/distilroberta-base-climate-f model as a starting point, this classifier is trained on the ClimatePolicyRadar/national-climate-targets dataset to predict Net Zero ("NZT") , "Reduction" and "Other" targets in a multi-label setting. The training data is an expert annotated subset of national laws, policies and UNFCCC submissions.
For more information on the annotation methodology and classifier training see our paper.
Getting started
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "ClimatePolicyRadar/national-climate-targets"
example = "The Net Zero Strategy, published in October 2021, was the first "\
"document of its kind for a major economy. It set out the government’s "\
"vision for a market-led, technology-driven transition to decarbonise "\
"the UK economy and reach net zero by 2050."
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# using sigmoid because the model is multi-label
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, function_to_apply="sigmoid")
pipe(example, padding=True, truncation=True)
>>> [{'label': 'NZT', 'score': 0.9142044186592102}]
Licence
Our classifier is licensed as Apache 2.0.
Please read our Terms of Use, including any specific terms relevant to commercial use. Contact [email protected] with any questions.
Links
Citation
@misc{juhasz2024identifying,
title={Identifying Climate Targets in National Laws and Policies using Machine Learning},
author={Matyas Juhasz and Tina Marchand and Roshan Melwani and Kalyan Dutia and Sarah Goodenough and Harrison Pim and Henry Franks},
year={2024},
eprint={2404.02822},
archivePrefix={arXiv},
primaryClass={cs.CY}
}
Authors & Contact
Climate Policy Radar team: Matyas Juhasz, Tina Marchand, Roshan Melwani, Kalyan Dutia, Sarah Goodenough, Harrison Pim, and Henry Franks.