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.