Update README.md

README.md

@@ -7,4 +7,71 @@ metrics:
 base_model:
 - google-bert/bert-base-uncased
 pipeline_tag: text-classification
----
+---
+
+# Fine-Tuned BERT for Transaction Categorization
+
+This is a fine-tuned [BERT model](https://huggingface.co/transformers/model_doc/bert.html) specifically trained to categorize financial transactions into predefined categories. The model was trained on a dataset of English transaction descriptions to classify them into categories like "Groceries," "Transport," "Entertainment," and more.
+
+## Model Details
+
+- **Base Model**: [bert-base-uncased](https://huggingface.co/bert-base-uncased).
+- **Fine-Tuning Task**: Transaction Categorization (multi-class classification).
+- **Languages**: English.
+
+### Example Categories
+The model classifies transactions into categories such as:
+```python
+CATEGORIES = {
+
+0: "Utilities",
+1: "Health",
+2: "Dining",
+3: "Travel",
+4: "Education",
+5: "Subscription",
+6: "Family",
+7: "Food",
+8: "Festivals",
+9: "Culture",
+10: "Apparel",
+11: "Transportation",
+12: "Investment",
+13: "Shopping",
+14: "Groceries",
+15: "Documents",
+16: "Grooming",
+17: "Entertainment",
+18: "Social Life",
+19: "Beauty",
+20: "Rent",
+21: "Money transfer",
+22: "Salary",
+23: "Tourism",
+24: "Household",
+}
+```
+---
+
+## How to Use the Model
+
+To use this model, you can load it directly with Hugging Face's `transformers` library:
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+
+# Load the model
+model_name = "kuro-08/bert-transaction-categorization"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+
+# Sample transaction description
+transaction = "Transaction: Payment at Starbucks for coffee - Type: income/expense"
+inputs = tokenizer(transaction, return_tensors="pt", truncation=True, padding=True)
+
+# Predict the category
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_category = logits.argmax(-1).item()
+
+print(f"Predicted category: {predicted_category}")