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 = 1) or not (label = 0).

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:

from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification

task_name = 'binary'
model_name = Climate-TwitterBERT/ Climate-TwitterBERT-step1'

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

pipe = pipeline(task=‘binary‘, 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: 0 = non-climate tweet, 1= climate tweet

Citation:

@article{fzz2023climatetwitter,
  title={Responding to Climate Change crisis - firms' tradeoffs},
  author={Fritsch, Felix and Zhang, Qi and Zheng, Xiang},
  journal={Working paper},
  year={2023},
  institution={University of Mannheim, the Chinese University of Hong Kong, and NHH Norwegian School of Economics},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4527255}
}

Fritsch, F., Zhang, Q., & Zheng, X. (2023). Responding to Climate Change crisis - firms' tradeoffs [Working paper]. University of Mannheim, the Chinese University of Hong Kong, and NHH Norwegian School of Economics.

Framework versions

• Transformers 4.28.1 • Pytorch 2.0.1+cu118 • Datasets 2.14.1 • Tokenizers 0.13.3