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  - **Developed by:** Team CodeBlooded
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- - **Funded by [optional]:** EpiUse & University of Pretoria
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  - **Model type:** DistilBertForSequenceClassification
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  - **Language(s) (NLP):** English
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  ### Primary use case
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- * **Task:** Automated categorization of banking and credit card transaction descriptions
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  * **Users:** Personal finance apps, budgeting tools, fintech platforms
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  ### Out-of-scope use cases
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  ## Training Data
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- * **Source:** Kaggle `personal_transactions.csv` dataset (\~XX,XXX entries)
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  * **Mapping:** Original vendor-level categories mapped into an internal schema of \~M high-level categories (`data/categories.json`).
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  * **Feedback augmentation:** User-corrected labels from `feedback_corrected.json` are appended to the training set for continuous improvement.
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  * **Metric:** Macro F1-score
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  * **Results:**
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- * Macro F1 on test set: **0.XX**
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  ---
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  - **Developed by:** Team CodeBlooded
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+ - **Funded by:** EpiUse & University of Pretoria
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  - **Model type:** DistilBertForSequenceClassification
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  - **Language(s) (NLP):** English
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  ### Primary use case
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+ * **Task:** Automated categorization of banking and credit card transaction descriptions for South Afrucan banks
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  * **Users:** Personal finance apps, budgeting tools, fintech platforms
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  ### Out-of-scope use cases
 
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  ## Training Data
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+ * **Source:** Kaggle `personal_transactions.csv` dataset
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  * **Mapping:** Original vendor-level categories mapped into an internal schema of \~M high-level categories (`data/categories.json`).
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  * **Feedback augmentation:** User-corrected labels from `feedback_corrected.json` are appended to the training set for continuous improvement.
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  * **Metric:** Macro F1-score
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  * **Results:**
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+ * Macro F1 on test set: **0.XX** (not yet measured)
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