Model Card for environmental-claims

Model Description

The environmental-claims model is fine-tuned on the EnvironmentalClaims dataset by using the climatebert/distilroberta-base-climate-f model as pre-trained language model. The underlying methodology can be found in our research paper.

Climate Performance Model Card

environmental-claims
1. Is the resulting model publicly available?	Yes
2. How much time does the training of the final model take?	< 5 min
3. How much time did all experiments take (incl. hyperparameter search)?	60 hours
4. What was the power of GPU and CPU?	0.3 kW
5. At which geo location were the computations performed?	Switzerland
6. What was the energy mix at the geo location?	89 gCO2eq/kWh
7. How much CO2eq was emitted to train the final model?	2.2 g
8. How much CO2eq was emitted for all experiments?	1.6 kg
9. What is the average CO2eq emission for the inference of one sample?	0.0067 mg
10. Which positive environmental impact can be expected from this work?	This work can help detect and evaluate environmental claims and thus have a positive impact on the environment in the future.
11. Comments	-

Citation Information

@misc{stammbach2022environmentalclaims,
  title = {A Dataset for Detecting Real-World Environmental Claims},
  author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus},
  year = {2022},
  doi = {10.48550/ARXIV.2209.00507},
  url = {https://arxiv.org/abs/2209.00507},
  publisher = {arXiv},
}

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/environmental_claims"
model_name = "climatebert/environmental-claims"

# 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(model_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)