metadata

license: apache-2.0

Model Card for transition-physical

Model Description

This is the fine-tuned ClimateBERT language model with a classification head for detecting sentences that are either related to emission net zero or reduction targets. We use the climatebert/distilroberta-base-climate-f language model as a starting point and fine-tuned it on our human-annotated dataset.

Citation Information

@article{schimanski2023detecting,
  title={ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets},
  author={Tobias Schimanski and Julia Bingler and Camilla Hyslop and Mathias Kraus and Markus Leippold},
  year={2023}
}

How to Get Started With the Model

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

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
from transformers.pipelines.pt_utils import KeyDataset
import datasets
from tqdm.auto import tqdm
 
dataset_name = "climatebert/climate_detection"
tokenizer_name = “"climatebert/distilroberta-base-climate-f"
model_name = "climatebert/netzero-reduction"
 
# If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading
dataset = datasets.load_dataset(dataset_name, split="test")
 
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
 
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)
 
# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)):
   print(out)