language:
- en
tags:
- Twitter
- Climate Change
license: mit
Model Card Climate-TwitterBERT-step-2
Overview:
Using Climate-TwitterBERT-step-1 (https://huggingface.co/Climate-TwitterBERT/Climate-TwitterBERT-step1) as the starting model, we fine-tuned on the downstream task to classify whether a given climate tweet belongs to hard/soft/promotion climate tweet.
The model provides a label and probability score, indicating whether a given tweet belongs to hard (label = 0), soft (label = 1), or promotion (label = 2).
Performance metrics:
Based on the test set, the model achieves the following results:
• Loss: 0.2613
• F1-weighted: 0.8008
• F1: 0.7798
• Accuracy: 0.8050
• Precision: 0.8034
• Recall: 0. 0.8050
Example usage:
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
task_name = 'text-classification'
model_name = 'Climate-TwitterBERT/ Climate-TwitterBERT-step2'
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: 0 = hard climate tweet, 1= soft climate tweet, and 2 = promotion 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