Description:
climateattention-10k classifies if a given sequence is related to climate topics. As a fine-tuned classifier based on climatebert/distilroberta-base-climate-f (Webersinke et al., 2021), it is using the following ClimaText dataset (Varini et al., 2020):
- AL-10Ks.tsv : 3000 (58 positives, 2942 negatives)
The training set is highly unbalanced. You might want to check the upscaling version: 'kruthof/climateattention-10k-upscaled'
How to use:
from transformers import AutoTokenizer, pipeline,RobertaForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("climatebert/distilroberta-base-climate-f")
climateattention = RobertaForSequenceClassification.from_pretrained('kruthof/climateattention-10k',num_labels=2)
ClimateAttention = pipeline("text-classification", model=climateattention, tokenizer=tokenizer)
ClimateAttention('Emissions have increased during the last several months')
>> [{'label': 'Yes', 'score': 0.9993829727172852}]
Performance:
Performance tested on the balanced ClimaText 10K test set, featuring 300 samples (67 positives, 233 negatives) (Varini et al., 2020)
| Accuracy | Precision | Recall | F1 |
|---|---|---|---|
| 0.9633 | 1 | 0.8358 | 0.9106 |
References:
Varini, F. S., Boyd-Graber, J., Ciaramita, M., & Leippold, M. (2020). ClimaText: A dataset for climate change topic detection. arXiv preprint arXiv:2012.00483.
Webersinke, N., Kraus, M., Bingler, J. A., & Leippold, M. (2021). Climatebert: A pretrained language model for climate-related text. arXiv preprint arXiv:2110.12010.
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