HowToSD-ComfyUI-Pt-Wrapper/docs/training_rnn_for_classification.md

Training RNN for Text Classification

You can use ComfyUI-Pt-Wrapper to train an RNN for text classification, such as sentiment analysis on a movie review dataset.

You can find the workflow at examples/workflows/rnn_classification.json. You can also use GRU workflow at examples/workflows/gru_classification.json.

The flow consists of the following groups of nodes:

• Loading data
• Setting up the model, loss, optimizer, and scheduler
• Training

Loading Data

Specify a Hugging Face text dataset using Ptf Hf Glove Dataset to load the text data, and encode each word using GloVe embeddings.
Then use Pt Data Loader to batch the data and feed it into the training node.

Setting Up Optimizer and Scheduler

This workflow uses Pto Adam as the optimizer and Pto Lr Scheduler Reduce On Plateau as the scheduler, but you can experiment with other optimizers and schedulers included in this extension.

Loss

Use Ptn BCE with Logits Loss, which includes a built-in sigmoid activation, allowing you to pass raw logits directly from the model.

Model

Use an RNN model with a linear head (Ptn RNN Linear).
Set the input dimension to match the GloVe embedding size (e.g., 100 in this case).

Training

Use Pt Train RNN Model to configure training parameters.
One important parameter is use_valid_token_mean:

• When True, it averages outputs of all non-zero tokens.
• When False, it uses only the last token's output, which may be weak if there's heavy padding.

If your loss does not decrease, check this flag first.
You can also enable classification_metrics to print validation accuracy to the console.

Expected Accuracy

Using this workflow, you should get around 83% validation accuracy on the Hugging Face version of the IMDB dataset, which is a solid result for an RNN. The GRU workflow achieves approximately 87% validation accuracy.