CodeBlooded-capstone
/

fin-classifier

@@ -1,199 +1,117 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- finance
 ---
+- **Developed by:** Team CodeBlooded
+- **Funded by [optional]:** EpiUse & University of Pretoria
+- **Model type:** DistilBertForSequenceClassification
+- **Language(s) (NLP):** English
+# fin-classifier
+## Overview
+**Repository:** CodeBlooded-capstone/fin-classifier
+A DistilBERT-based text classification model for categorizing financial transaction descriptions into one of N predefined categories.
+---
+## Model Details
+* **Model type:** `DistilBertForSequenceClassification`
+* **Version:** v1.0 (initial release)
+* **Hugging Face repo:** [https://huggingface.co/CodeBlooded-capstone/fin-classifier](https://huggingface.co/CodeBlooded-capstone/fin-classifier)
+* **Authors:** Your Team Name
+---
+## Intended Use
+### Primary use case
+* **Task:** Automated categorization of banking and credit card transaction descriptions
+* **Users:** Personal finance apps, budgeting tools, fintech platforms
+### Out-of-scope use cases
+* Legal or compliance decisions
+* Any use requiring 100% classification accuracy or safety guarantees
+---
+## Training Data
+* **Source:** Kaggle `personal_transactions.csv` dataset (\~XX,XXX entries)
+* **Mapping:** Original vendor-level categories mapped into an internal schema of \~M high-level categories (`data/categories.json`).
+* **Feedback augmentation:** User-corrected labels from `feedback_corrected.json` are appended to the training set for continuous improvement.
+---
 ## Evaluation
+* **Split:** 90% train / 10% test split (seed=42) from the training corpus
+* **Metric:** Macro F1-score
+* **Results:**
+  * Macro F1 on test set: **0.XX**
+---
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+tokenizer = AutoTokenizer.from_pretrained("CodeBlooded-capstone/fin-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("CodeBlooded-capstone/fin-classifier")
+classifier = pipeline(
+    "text-classification",
+    model=model,
+    tokenizer=tokenizer,
+    return_all_scores=False
+)
+example = "STARBUCKS STORE 1234"
+print(classifier(example))  # {'label': 'Food & Dining', 'score': 0.95}
+```
+---
+## Limitations & Bias
+* Performance varies by category: categories with fewer examples may see lower F1.
+* The model reflects biases present in the original Kaggle dataset (e.g., over/under-representation of certain merchants).
+* Should not be used as a sole source for financial decision-making.
+---
+## Maintenance & Continuous Learning
+* New user feedback corrections are stored in `model/feedback_corrected.json` and incorporated during retraining.
+* Checkpoints are saved to `results/` and versioned on Hugging Face.
+---
+## License
+Apache 2.0
+---
+## Citation
+```
+@misc{fin-classifier2025,
+  author = {CodeBlooded},
+  title = {fin-classifier: A DistilBERT-based Transaction Categorization Model},
+  year = {2025},
+  howpublished = {\url{https://huggingface.co/CodeBlooded-capstone/fin-classifier}}
+}
+```
+---
+*This model card was generated on 2025-07-12.*