Model Card for Model ID
This model is used to classify the user-intent for the Danswer project, visit https://github.com/danswer-ai/danswer.
Model Details
Multiclass classifier on top of distilbert-base-uncased
Model Description
Classifies user intent of queries into categories including: 0: Keyword Search 1: Semantic Search 2: Direct Question Answering
- Developed by: [DanswerAI]
- License: [MIT]
- Finetuned from model [optional]: [distilbert-base-uncased]
Model Sources [optional]
- Repository: [https://github.com/danswer-ai/danswer]
- Demo [optional]: [Upcoming!]
Uses
This model is intended to be used in the Danswer Question-Answering System
Bias, Risks, and Limitations
This model has a very small dataset maintained by DanswerAI. If interested, reach out to danswer.dev@gmail.com.
Recommendations
This model is intended to be used in the Danswer (QA System)
How to Get Started with the Model
from transformers import AutoTokenizer
from transformers import TFDistilBertForSequenceClassification
import tensorflow as tf
model = TFDistilBertForSequenceClassification.from_pretrained("danswer/intent-model")
tokenizer = AutoTokenizer.from_pretrained("danswer/intent-model")
class_semantic_mapping = {
0: "Keyword Search",
1: "Semantic Search",
2: "Question Answer"
}
# Get user input
user_query = "How do I set up Danswer to run on my local environment?"
# 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)]}")