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 | |
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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)
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