--- language: - en tags: - Twitter - Climate Change license: mit --- # Model Card Climate-TwitterBERT-step-1 ## Overview: Using Covid-Twitter-BERT-v2 (https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) as the starting model, we continued domain-adaptive pre-training on a corpus of firm tweets between 2007 and 2020. The model was then fine-tuned on the downstream task to classify whether a given tweet is related to climate change topics. The model provides a label and probability score, indicating whether a given tweet is related to climate change topics (label = 'Climate') or not (label = 'Non-climate'). ## Performance metrics: Based on the test set, the model achieves the following results: • Loss: 0.0632 • F1-weighted: 0.9778 • F1: 0.9148 • Accuracy: 0.9775 • Precision: 0. 8841 • Recall: 0. 9477 ## Example usage: ```python from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification task_name = 'text-classification' model_name = 'Climate-TwitterBERT/ Climate-TwitterBERT-step1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) pipe = pipeline(task=task_name, model=model, tokenizer=tokenizer) tweet = "We are committed to significantly cutting our carbon emissions by 30% before 2030." result = pipe(tweet) # The 'result' variable will contain the classification output: 'Climate' or 'Non-climate'. ``` ## Citation: ```bibtex @article{fzz2025climatetwitter, title={Responding to Climate Change Crisis: Firms' Tradeoffs}, author={Fritsch, Felix and Zhang, Qi and Zheng, Xiang}, journal={Journal of Accounting Research}, year={2025}, doi={10.1111/1475-679X.12625} } ``` Fritsch, F., Zhang, Q., & Zheng, X. (2025). Responding to Climate Change Crisis: Firms' Tradeoffs. Journal of Accounting Research. https://doi.org/10.1111/1475-679X.12625 ## Framework versions • Transformers 4.28.1 • Pytorch 2.0.1+cu118 • Datasets 2.14.1 • Tokenizers 0.13.3