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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.
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