kgourgou/bert-base-uncased-QA-classification

An experiment into classifying whether a pair of (question, answer) is valid. This is not a very good model at this point, but eventually such a model could help with RAG. For a stronger model, check this one by vectara.

Input must be formatted as

question: {your query}? answer: {your possible answer}

The output probabilities are for

  1. class 0 = the answer string couldn't be an answer to the question and
  2. class 1 = the answer string could be an answer to the question.

"Could be" should be interpreted as a type match, e.g., if the question requires the answer to be a person or a number or a date.

Examples:

  • "question: What number comes after five? answer: four" → this should be class 1 as the answer is a number (even if it's not the right number).
  • "question: Which person is associated with Kanye West? answer: a tree" → this should be class 0 as a tree is not a person.

Base model details

The base model is bert-base-uncased. For this experiment, I only use the "squad" dataset after preprocessing it to bring it to the required format.

Downloads last month
14
Safetensors
Model size
109M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train kgourgou/bert-base-uncased-QA-classification