README.md

metadata

tags:
  - text-classification
  - mcq
  - cnn
license: mit

CNN MCQ Model

A custom TextCNN architecture trained for MCQ answer prediction.

Architecture

• Embedding layer: vocab_size=30522, embed_dim=140
• 3 parallel Conv1d branches (kernel sizes 3, 4, 5), 100 filters each
• Fully connected output layer (300 -> 1)

Performance (validation)

• Accuracy / MAP@3 reported during training: MAP@3 = 0.98125
• Note: in the same experiment, a fine-tuned DistilBERT model scored higher (MAP@3 = 0.99375) and was selected as the primary/best model.

Usage

from modeling_cnn import TextCNN

model = TextCNN.from_pretrained("your-username/cnn-mcq-model")
model.eval()

# input_ids: tokenized input, shape (batch, seq_len)
logits = model(input_ids)

Files

• modeling_cnn.py — model architecture definition
• config.json — model hyperparameters
• model.safetensors — trained weights