Question 1

How do I use this CNN text classification code with my own dataset?

Accepted Answer

You'll need to edit the data loading functions in train.py and eval.py to preprocess your text into the expected format, such as tokenizing and padding sequences. The code assumes a specific structure, so adapt it for your CSV or text files accordingly.

Question 2

What accuracy can I expect from this model on standard benchmarks?

Accepted Answer

Based on the original paper, it achieves around 80-85% on datasets like MR or SST, but this varies with hyperparameters. It's outperformed by newer models like transformers, so treat it as a baseline rather than a state-of-the-art solution.

Question 3

CNN vs LSTM for text classification: which is better with this implementation?

Accepted Answer

This implementation focuses on CNNs, which are faster and good at capturing local text patterns like n-grams. LSTMs might excel with long-range dependencies, but CNNs often offer a good trade-off for sentence classification with less computational cost.

Question 4

How to update this code for TensorFlow 2.0?

Accepted Answer

You'd need to refactor it to use TensorFlow 2.x APIs, such as replacing tf.Session with eager execution, updating variable scopes, and adjusting optimizer calls. This is a non-trivial task requiring familiarity with TensorFlow migration guides.

Question 5

Is this project still maintained or actively developed?

Accepted Answer

No, it appears archived or inactive, with the last commits likely from years ago. It's best used as a learning resource rather than for ongoing projects, as community support and updates are minimal.

Question 6

Can I use pre-trained word embeddings like GloVe with this model?

Accepted Answer

Yes, but you'll need to modify the embedding layer in the code to load pre-trained vectors. The current implementation uses random embeddings, so integrate external files by adjusting the TensorFlow variables accordingly.

Question 7

What are the default filter sizes 3,4,5 and why are they chosen?

Accepted Answer

These filter sizes correspond to capturing n-grams of length 3, 4, and 5 words, based on the original paper where multiple sizes help detect varying phrase patterns in sentences. You can change them via command-line arguments for experimentation.

Sentence Classification with CNN

What is Sentence Classification with CNN?

Overview

Use Cases

Best For

Related Projects

Found a gem we're missing?

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions