| --- |
| license: mit |
| language: |
| - en |
| base_model: |
| - google-bert/bert-base-uncased |
| pipeline_tag: text-classification |
| tags: |
| - multilabel-classification |
| - food-safety |
| - product-category |
| - hazard-category |
| - bert |
| - data-augmentation |
| - optuna |
| - interpretability |
| - low-resource |
| - imbalance-handling |
| model_type: bert |
| task: |
| name: SemEval 2025 Task 9: The Food Hazard Detection Challenge - Multilabel Text Classification |
| type: text-classification |
| link: https://food-hazard-detection-semeval-2025.github.io/ |
| dataset: |
| - custom |
| training: |
| input_features: ["title", "text"] |
| label_names: ["product-category", "hazard-category", "product", "hazard"] |
| augmentation: |
| methods: |
| - lexical: [synonym-replacement, random-swap, word-deletion] |
| - embedding: [contextual-substitution, insertion] |
| - llm: [gpt-4-paraphrasing] |
| strategy: "quantile-based underrepresented class boosting (q=0.99)" |
| optimizer: AdamW |
| scheduler: cosine_with_restarts |
| hyperparameter_search: optuna |
| evaluation: |
| metrics: [f1-score] |
| limitations: |
| - Augmentation focused on titles only; text augmentation could further help. |
|
|
