learn-abc
/

banking77-intent-classifier

@@ -3,186 +3,194 @@ library_name: transformers
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 tags: []
 ---
+**Repo:** `learn-abc/banking77-intent-classifier`
+# Banking77 Intent Classifier (10-Intent)
+## Overview
+This model is a **fine-tuned BERT-based intent classifier** designed for **banking and financial customer queries**.
+It is trained by **mapping the original 77 Banking77 intents into a smaller, production-friendly set of custom intents**, making it suitable for real-world conversational systems where simpler intent routing is required.
+The model performs **single-label text classification** and is intended to be used as an **intent detection component**, not as a conversational or generative model.
+---
+## Model Details
+* **Base model:** `bert-base-uncased`
+* **Task:** Text Classification (Intent Classification)
+* **Architecture:** `BertForSequenceClassification`
+* **Languages:** English (robust to informal and conversational phrasing)
+* **Max sequence length:** 64 tokens
+* **Output:** One intent label with confidence score
+---
+## Custom Intent Schema
+The original **77 Banking77 intents** were **mapped and consolidated** into the following **12 production intents**:
+* `ACCOUNT_INFO`
+* `ATM_SUPPORT`
+* `CARD_ISSUE`
+* `CARD_MANAGEMENT`
+* `CARD_REPLACEMENT`
+* `CHECK_BALANCE`
+* `EDIT_PERSONAL_DETAILS`
+* `FAILED_TRANSFER`
+* `FEES`
+* `LOST_OR_STOLEN_CARD`
+* `MINI_STATEMENT`
+* `FALLBACK`
+Any user query that does not clearly belong to one of the supported categories is mapped to **FALLBACK**.
+This design simplifies downstream business logic while retaining strong intent separation.
+---
+## Training Data
+* **Primary dataset:** [PolyAI Banking77](https://huggingface.co/datasets/PolyAI/banking77)
+* **Original training samples:** 10,003
+* **Test samples:** 3,080
+* **After intent mapping and augmentation:**
+  * **Training samples:** 19,846
+  * **Includes:** 280 explicitly added `FALLBACK` examples
+### Training Intent Distribution (Post-Mapping)
+| Intent                | Samples |
+| --------------------- | ------- |
+| ACCOUNT_INFO          | 1,983   |
+| MINI_STATEMENT        | 1,809   |
+| FEES                  | 1,490   |
+| FAILED_TRANSFER       | 1,045   |
+| CARD_MANAGEMENT       | 1,026   |
+| CARD_REPLACEMENT      | 749     |
+| ATM_SUPPORT           | 743     |
+| CARD_ISSUE            | 456     |
+| CHECK_BALANCE         | 352     |
+| LOST_OR_STOLEN_CARD   | 229     |
+| EDIT_PERSONAL_DETAILS | 121     |
+| FALLBACK              | 280     |
+Class imbalance was handled using **class weighting** during training.
+---
+## Training Configuration
+```text
+Base Model: bert-base-uncased
+Epochs: 5
+Batch Size: 32
+Learning Rate: 5e-5
+Max Sequence Length: 64
+Optimizer: AdamW
+Loss: Cross-Entropy (with class weights)
+```
+---
+## Evaluation Results
+Final evaluation on the Banking77 test set:
+* **Accuracy:** 96.04%
+* **F1 (Micro):** 0.960
+* **F1 (Macro):** 0.956
+These results indicate strong overall performance with good balance across both high-frequency and low-frequency intents.
+---
+## Usage
+### Load the model
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model_id = "learn-abc/banking77-intent-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+def predict_intent(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=64)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=-1)
+        pred_id = probs.argmax(dim=-1).item()
+        confidence = probs[0][pred_id].item()
+    return model.config.id2label[pred_id], confidence
+# Example usage:
+if __name__ == "__main__":
+    test_texts = [
+        "What is my account balance?",
+        "Show me my last 10 transactions.",
+        "I want to update my address.",
+        "How do I apply for a loan?"
+    ]
+    for text in test_texts:
+        intent, confidence = predict_intent(text)
+        print(f"Input: {text}\nPredicted Intent: {intent} (Confidence: {confidence:.2f})\n")
+```
+---
+## Intended Use
+This model is suitable for:
+* Banking chatbots
+* Voice assistant intent routing
+* Customer support automation
+* FAQ classification systems
+It is designed to be used **together with business rules**, confirmation flows, and fallback handling.
+---
+## Limitations and Safety Notes
+* The model **does not perform authentication or authorization**
+* It **must not directly trigger financial actions**
+* High-risk intents (e.g. lost or stolen card) should always require explicit user confirmation
+* Predictions should be validated with confidence thresholds and fallback logic
+This model is **not a replacement for human review** in sensitive workflows.
+---
+## Notes on Model Warnings
+During training, warnings related to missing or unexpected keys were observed.
+These are expected when fine-tuning a pre-trained BERT checkpoint for a downstream classification task and **do not impact inference correctness**.
+---
+## Citation
+If you use this model, please cite:
+* Devlin et al., *BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding*
+* PolyAI Banking77 Dataset
+---
+## Maintainer
+Developed and fine-tuned for production-oriented banking intent classification.
+---
 [More Information Needed]
 [More Information Needed]
+## Model Card Authors
+* **Author:** [Abhishek Singh](https://github.com/SinghIsWriting/)
+* **LinkedIn:** [My LinkedIn Profile](https://www.linkedin.com/in/abhishek-singh-bba2662a9)
+* **Portfolio:** [Abhishek Singh Portfolio](https://portfolio-abhishek-singh-nine.vercel.app/)
 ## Model Card Contact