
# 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:
```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:

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.

------------------------------
https://kruthof.github.io

{"license": "mit", "pipeline_tag": "token-classification"}
transformers pytorch roberta text-classification token-classification license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #token-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 

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):


* URL : 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:
===========


Performance:
============


Performance tested on the balanced ClimaText 10K test set, featuring 300 samples (67 positives, 233 negatives) (Varini et al., 2020)



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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URL



TAGS
#transformers #pytorch #roberta #text-classification #token-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #roberta #text-classification #token-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
