Distilroberta Base Climate Specificity

🚀 Model Card for distilroberta-base-climate-specificity

This is a fine - tuned language model that classifies climate - related paragraphs into specific and non - specific ones, offering high - precision climate text analysis.

🚀 Quick Start

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_specificity"
model_name = "climatebert/distilroberta-base-climate-specificity"

# 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)

✨ Features

This is the fine - tuned ClimateBERT language model with a classification head for classifying climate - related paragraphs into specific and non - specific paragraphs.

Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta - base - climate - specificity model is fine - tuned on our climatebert/climate_specificity dataset.

⚠️ Important Note

This model is trained on paragraphs. It may not perform well on sentences.

📚 Documentation

Citation Information

@techreport{bingler2023cheaptalk,
    title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk},
    author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
    type={Working paper},
    institution={Available at SSRN 3998435},
    year={2023}
}

📄 License

This model is licensed under the Apache 2.0 license.