distilbert-base-q-cat

distilbert-base-q-cat

Model Description

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distilbert-base-q-cat is a lightweight, fine-tuned DistilBERT model designed for text classification, specifically focusing on categorizing questions into three distinct categories: fact, opinion, and hypothetical. The model was trained on a Quora dataset, leveraging keyword-based labeling and sentiment analysis to ensure high-quality categorization.

Features

Built on DistilBERT, ensuring faster inference and lower computational requirements compared to standard BERT.

Three Class Categories:

• Fact: Questions seeking factual or objective information.
• Opinion: Questions that elicit subjective views or opinions.
• Hypothetical: Questions exploring hypothetical scenarios or speculative ideas.

Pretrained and Fine-Tuned: Utilizes DistilBERT’s pretrained weights with additional fine-tuning on labeled data.

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Dataset

The model was trained using a custom dataset derived from Quora questions:

Data Preparation:

• Labeling involved keyword-based rules for fact and hypothetical questions.

• Sentiment analysis determined questions as opinion-based.

Dataset Size: ~50k samples, split into training, validation, and test sets.

Performance

The model achieves the following metrics on the validation set:

• Accuracy: 93.33%
• Precision: 93.41%
• Recall: 93.33%
• F1-Score: 93.32%

Installation

To use this model, install the required dependencies:

pip install transformers torch

Usage

Load Model and Tokenizer

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model and tokenizer
model_name = "distilbert-base-q-cat"
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3, ignore_mismatched_sizes=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Inference Example

def predict_question(question):
    inputs = tokenizer(question, return_tensors="pt", truncation=True, padding=True)
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(dim=-1).item()

    label_map = {0: "fact", 1: "opinion", 2: "hypothetical"}
    return label_map[predicted_class]

# Example usage
question = "What is artificial intelligence?"
print(predict_question(question))