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pv056-task2-bert – AI Model by maennyn | AlphaNeural AI
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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.
