| --- |
| license: mit |
| pipeline_tag: token-classification |
| --- |
| |
| # Description: |
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| 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): |
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| * AL-10Ks.tsv : 3000 (58 positives, 2942 negatives) |
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| The training set is highly unbalanced. You might want to check the upscaling version: 'kruthof/climateattention-10k-upscaled' |
|
|
| # How to use: |
| ```python |
| 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: |
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| Performance tested on the balanced ClimaText 10K test set, featuring 300 samples (67 positives, 233 negatives) (Varini et al., 2020) |
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| |Accuracy| Precision | Recall | F1 | |
| |----|-----|-----|-----| |
| | 0.9633 | 1 | 0.8358 | 0.9106 | |
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| # References: |
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| Varini, F. S., Boyd-Graber, J., Ciaramita, M., & Leippold, M. (2020). |
| ClimaText: A dataset for climate change topic detection. arXiv preprint arXiv:2012.00483. |
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| 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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| ------------------------------ |
| https://kruthof.github.io |
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