README.md

---
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