Add citation

README.md

@@ -44,8 +44,28 @@ When using this model, please consider citing it as follows:
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
+```bibtex
+@inproceedings{krahmer-2024-leaf,
+    title = "{LEAF}: Predicting the Environmental Impact of Food Products based on their Name",
+    author = "Krahmer, Bas",
+    editor = "Stammbach, Dominik  and
+      Ni, Jingwei  and
+      Schimanski, Tobias  and
+      Dutia, Kalyan  and
+      Singh, Alok  and
+      Bingler, Julia  and
+      Christiaen, Christophe  and
+      Kushwaha, Neetu  and
+      Muccione, Veruska  and
+      A. Vaghefi, Saeid  and
+      Leippold, Markus",
+    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.climatenlp-1.10",
+    pages = "133--142",
+    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.",
+}
+```