This model is a fine-tuned version of google-bert/bert-base-uncased with AutoTrain Advanced on a custom dataset annotated with question-type labels according to the taxonomy of interrogatives defined by Belnap & Steel (1976). It forms part of a broader ensemble of models that classify English-language questions into one of several interrogative categories, as described in the table below.

Interrogative Taxonomy Overview

Interrogative Type	Definition	Operationalisation
Hobson’s Choice	An interrogative that allows for no alternative responses beyond one predetermined option. These are often in the form of declarative or imperative statements.	Classified when the interrogative is declarative or imperative (1B = Yes) and/or has a presupposition (3A = Yes). And allows for no alternative responses (2C = 0).
Why Interrogatives	Interrogative with a single example and a pre-supposition.	Identified when the interrogative has a presupposition (3A = Yes) and offers only one alternative (2C = 1).
Whether Interrogatives	Interrogatives where the information being sought by the questioner is predefined among an explicit and finite list of alternatives. This includes questions that can be answered with yes/no.	Identified when the interrogative expects a yes/no answer (2A = Yes and 2C = 2) or lists a defined number of options (2B = Yes and 2C > 1, but not undefined).
Which Interrogatives	Interrogatives where the information being sought is part of a category (e.g., religion or tennis players) for which the options are possibly infinite and not explicitly specified.	Classified when the number of options is undefined (2C = Undefined) and it is an opinion or not a description (4A = Opinion or No).
What / How Interrogatives	Interrogative with an undefined range of possible answers, requesting a descriptive answer.	Classified when the interrogative has an undefined answer space (2C = Undefined) and requests a description (4A = Yes).
Not an Interrogative	Text that is not a question or not in interrogative form.	Prompts are not considered interrogatives when they neither requests an answer (1A = No) nor take a declarative/imperative form (1B = No).

Validation Metrics

loss: 0.09040748327970505

f1: 0.9759036144578314

precision: 0.9642857142857143

recall: 0.9878048780487805

auc: 0.9939024390243902

accuracy: 0.96

2. Intended Use

This model is intended for academic, research, and educational use.

3. How to Use

Input: Plain English text (trained on interrogatives that diverse participants asked Language Models)
Output: Predicted category label + confidence score
Training metrics: Available on the model’s Hugging Face page under the “Training Metrics” section (TensorBoard enabled)

Please find an example implementation in python below:

python

from transformers import pipeline

classifier = pipeline("text-classification", model="carowagner/classify-questions-1A")
classifier("How does this model work?")

4. Training Data Labelling Instructions

The annotators who labeled the fine-tuning dataset were given the following instructions for classification:

1A. Does this interrogative request an answer?

Answer YES if it requires an answer and NO if it does not require an answer. Statements like hello or hi are not considered to expect an answer.
If a statement is imperative, e.g., ‘legalise abortion’, then answer NO. If a statement is declarative, answer NO.
E.g., “I have to meet a director of a highschool for a substitute position. I feel a little anxious.”