| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - text-classification |
| - banking |
| - intent-detection |
| - transformers |
| library_name: transformers |
| pipeline_tag: text-classification |
| model_type: bert |
| metrics: |
| - accuracy |
| - recall |
| - precision |
| base_model: |
| - google-bert/bert-base-uncased |
| --- |
| |
| # Question Classification Model for Bank Queries |
|
|
| This model is fine-tuned specifically for banking-related queries to classify whether a user intends to perform a **transaction** or not. |
|
|
| ## 🧠 Use Case |
|
|
| Given a text input (a user question or statement), the model returns: |
| - `"True"`: if the query is a **question** |
| - `"False"`: otherwise |
|
|
| --- |
|
|
| ## 🔧 How to Use |
|
|
| You can use this model directly with the Hugging Face `transformers` pipeline: |
|
|
| ```python |
| from transformers import pipeline |
| |
| hf_model = "pankaj1881/question-classification" |
| |
| classifier = pipeline("text-classification", model=hf_model) |
| |
| query = "I want to transfer 500 dollars to my friend" |
| result = classifier(query) |
| |
| print(result) |
| # Output example: [{'label': 'False', 'score': 0.8767889142036438}] i.e it's not a question. |
