Athena- Intent

Classifies intent of the query for Athena

Architecture

distilbert-base-uncased backbone, finetuned over a multiclass classification problem

Description

Classifies user intent of queries into the following classes: 0: Keyword Search 1: Semantic Search 2: Direct Question Answering

Uses

This model is intended to be used in Athena for performing QA on enterprise document stores.

Bias, Risks, and Limitations

Dataset was generated using ChatGPT (gpt-3.5-turbo). It consists of 5000 English sentences and the nature of their intent, annotated manually.

Usage

from transformers import AutoTokenizer
from transformers import TFDistilBertForSequenceClassification
import tensorflow as tf

model = TFDistilBertForSequenceClassification.from_pretrained("sourcerersupreme/athena-intent")
tokenizer = AutoTokenizer.from_pretrained("sourcerersupreme/athena-intent")

class_semantic_mapping = {
        0: "Keyword",
        1: "Semantic",
        2: "QA"
    }

# Get user input
user_query = "What is a CDP?"

# Encode the user input
inputs = tokenizer(user_query, return_tensors="tf", truncation=True, padding=True)

# Get model predictions
predictions = model(inputs)[0]

# Get predicted class
predicted_class = tf.math.argmax(predictions, axis=-1)

print(f"Predicted class: {class_semantic_mapping[int(predicted_class)]}")