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--- |
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license: mit |
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pipeline_tag: token-classification |
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--- |
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# Description: |
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climateattention-ctw classifies if a given sequence is related to climate topics. |
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As a fine-tuned classifier based on climatebert/distilroberta-base-climate-f (Webersinke et al., 2021), |
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it is using the following ClimaText dataset (Varini et al., 2020): |
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* Wiki-doc corpus, with 115847 samples (57922 positives, 57925 negatives) |
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For company disclosures or news articles you might want to check the 10k model: kruthof/climateattention-10k-upscaled |
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# How to use: |
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```python |
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from transformers import AutoTokenizer, pipeline,RobertaForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("climatebert/distilroberta-base-climate-f") |
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climateattention = RobertaForSequenceClassification.from_pretrained('kruthof/climateattention-ctw',num_labels=2) |
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ClimateAttention = pipeline("text-classification", model=climateattention, tokenizer=tokenizer) |
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ClimateAttention('Emissions have increased during the last several months') |
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>> [{'label': 'Yes', 'score': 0.9993829727172852}] |
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``` |
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# Performance: |
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Performance tested on the balanced ClimaText Wiki-doc test set, featuring 3826 samples (1913 positives, 1913 negatives) (Varini et al., 2020) |
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|Accuracy| Precision | Recall | F1 | |
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|----|-----|-----|-----| |
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| 0.8834 | 0.8717 | 0.8991 | 0.8852 | |
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``` |
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# References: |
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Varini, F. S., Boyd-Graber, J., Ciaramita, M., & Leippold, M. (2020). |
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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). |
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Climatebert: A pretrained language model for climate-related text. arXiv preprint arXiv:2110.12010. |
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------------------------------ |
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https://kruthof.github.io |
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