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@@ -40,9 +40,29 @@ When using this model, please consider citing it as follows:
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  **BibTeX:**
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- [More Information Needed]
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-
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- **APA:**
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-
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  **BibTeX:**
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+ ```bibtex
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+ @inproceedings{krahmer-2024-leaf,
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+ title = "{LEAF}: Predicting the Environmental Impact of Food Products based on their Name",
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+ author = "Krahmer, Bas",
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+ editor = "Stammbach, Dominik and
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+ Ni, Jingwei and
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+ Schimanski, Tobias and
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+ Dutia, Kalyan and
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+ Singh, Alok and
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+ Bingler, Julia and
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+ Christiaen, Christophe and
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+ Kushwaha, Neetu and
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+ Muccione, Veruska and
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+ A. Vaghefi, Saeid and
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+ Leippold, Markus",
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+ booktitle = "Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)",
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+ month = aug,
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+ year = "2024",
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+ address = "Bangkok, Thailand",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2024.climatenlp-1.10",
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+ pages = "133--142",
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+ abstract = "Although food consumption represents a sub- stantial global source of greenhouse gas emis- sions, assessing the environmental impact of off-the-shelf products remains challenging. Currently, this information is often unavailable, hindering informed consumer decisions when grocery shopping. The present work introduces a new set of models called LEAF, which stands for Linguistic Environmental Analysis of Food Products. LEAF models predict the life-cycle environmental impact of food products based on their name. It is shown that LEAF models can accurately predict the environmental im- pact based on just the product name in a multi- lingual setting, greatly outperforming zero-shot classification methods. Models of varying sizes and capabilities are released, along with the code and dataset to fully reproduce the study.",
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+ }
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+ ```
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