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--- |
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library_name: transformers |
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tags: |
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- food |
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- environment |
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- NLP |
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- Eco-Score |
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- products |
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- multilingual |
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- BERT |
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- classification |
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- Open Food Facts |
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- climate |
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license: mit |
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datasets: |
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- baskra/LEAF |
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--- |
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# LEAF: Predicting the Environmental Impact of Food Products based on their Name |
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The `leaf-base` model is |
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a [`distiluse-base-multilingual-cased-v2`](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) |
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model fine-tuned on the [LEAF dataset](https://huggingface.co/datasets/baskra/LEAF). |
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To load the model, use the following code: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("baskra/leaf-base") |
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model = AutoModel.from_pretrained("baskra/leaf-base", trust_remote_code=True) |
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model(**tokenizer("Nutella", return_tensors="pt")) |
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# {'logits': tensor([[-11.9081, ...]]), 'class_idx': tensor([1553]), 'ef_score': tensor([0.0129]), 'class': ['Chocolate spread with hazelnuts']} |
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``` |
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## Citation |
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When using this model, please consider citing it as follows: |
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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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