metadata

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:

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.