[P] How can you use GridSearchCV to find the optimal vocab size and embedding dimension for an LSTM?

I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data..

​

x = df['tweet_text'] y = df['potentially_harmful'] from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.2, random_state=0) x_train= x_train.to_numpy().reshape(-1, 1) y_train= y_train.to_numpy().reshape(-1, 1) x_test = x_test.to_numpy().reshape(-1, 1) y_test = y_test.to_numpy().reshape(-1,1) 

​

And then I tried to create a model that I could …

embedding gridsearchcv lstm machinelearning