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metadata

license: mit
datasets:
  - atulgupta002/banking_customer_service_query_intent
language:
  - en
base_model:
  - google-bert/bert-base-uncased
pipeline_tag: text-classification
tags:
  - finance
  - banking
  - intent
  - classification
  - customer
  - service
  - BERT

Banking Customer Service Intent Classifier

This model is designed to classify customer service queries into different intents, based on the type of inquiry made by the customer. It was fine-tuned on a synthetic dataset of realistic banking customer service interactions and can classify the following intents:

transaction_query
password_reset
loan_inquiry
fraud_report
credit_card_application
balance_inquiry

Dataset

Link: https://huggingface.co/datasets/atulgupta002/banking_customer_service_query_intent

Model Overview

The model is a fine-tuned BERT-based architecture that classifies text inputs into one of the six specified intents. It leverages the transformers library by Hugging Face for tokenization and model loading.

Intended Use

This model is suitable for deployment in applications that require automatic classification of customer service queries, such as:

Chatbots
Virtual assistants
Automatic re-routing incoming emails,calls, and texts

It can be used to classify various types of banking queries, such as requests for account balance, loan inquiries, or fraud reports.

Installation

To install the necessary dependencies, use the following:

pip install transformers torch

Inference

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

labels = [
    'transaction_query',
    'password_reset',
    'loan_inquiry',
    'fraud_report',
    'credi_card_application',
    'balance_inquiry'
]
label2id = {label: idx for idx, label in enumerate(labels)}
id2label = {idx: label for label, idx in label2id.items()}

# Load the pre-trained model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("atulgupta002/banking_customer_service_query_intent_classifier")
tokenizer = AutoTokenizer.from_pretrained("atulgupta002/banking_customer_service_query_intent_classifier")

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predicted_class_id = logits.argmax().item()

    return id2label[predicted_class_id]

query = "I want to apply for a new credit card"
print(predict(query))

atulgupta002
/

banking_customer_service_query_intent_classifier