| 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 | |
| ```python | |
| 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 | |