We lifted 4 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| taisti/tasteset | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| check_if_entity_correctly_began | Ran | taisti/tasteset/experiments/BERT.py code served (permissive licence) · get_code("c191d39ec838e425") |
| token_to_entity_predictions | Ran | taisti/tasteset/experiments/BERT.py code served (permissive licence) · get_code("6c664fea7abbb58f") |
| tokenize_and_align_labels | Ran | taisti/tasteset/experiments/BERT.py code served (permissive licence) · get_code("3507ec55b62e1468") |
| bio_to_biluo | Not yet run | taisti/tasteset/src/utils.py code served (permissive licence) · get_code("e2b9b4fc2b45e61c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Food Computing is currently a fast-growing field of research. Natural language processing (NLP) is also increasingly essential in this field, especially for recognising food entities. However, there are still only a few well-defined tasks that serve as benchmarks for solutions in this area. We introduce a new dataset -- called \textit{TASTEset} -- to bridge this gap. In this dataset, Named Entity Recognition (NER) models are expected to find or infer various types of entities helpful in processing recipes, e.g.~food products, quantities and their units, names of cooking processes, physical quality of ingredients, their purpose, taste. The dataset consists of 700 recipes with more than 13,000 entities to extract. We provide a few state-of-the-art baselines of named entity recognition models, which show that our dataset poses a solid challenge to existing models. The best model achieved, on average, 0.95 $F_1$ score, depending on the entity type -- from 0.781 to 0.982. We share the dataset and the task to encourage progress on more in-depth and complex information extraction from recipes.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2204.07775")
get_code_for_paper("2204.07775")
have("2204.07775")
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