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: 0.16687506437301636

f1_macro: 0.9044444444444445

f1_micro: 0.96

f1_weighted: 0.9579619047619048

precision_macro: 0.9844961240310077

precision_micro: 0.96

precision_weighted: 0.9618604651162791

recall_macro: 0.8452380952380952

recall_micro: 0.96

recall_weighted: 0.96

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-1B")
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:

1B. If NO to 1A: Is this a declarative/imperative interrogative?

Mark NA (not applicable) if the answer to 1A is YES.
Answer NO if the input is a simple noun phrase, such as “technology and society”.
Answer YES if it is a full statement, such as “Guns are too easy to buy in some countries.”