add my data is csv file to this code how much spilt train and test and i need epoch contin caculate f$-$score plese : # Step $1$: Import Libraries

import numpy as np

from tensorflow.keras.datasets import imdb

from tensorflow.keras.preprocessing.sequence import pad$\_$sequences

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Embedding, LSTM$,$ Dense, Dropout

from tensorflow.keras.callbacks import EarlyStopping

# Step $2$: Load and Preprocess Data

vocab$\_$size $=10000$

maxlen $=200$

$($X$\_$train, y$\_$train$),($X$\_$test, y$\_$test$)=$ imdb.load$\_$data$($num$\_$words$=$vocab$\_$size$)$

X$\_$train $=$ pad$\_$sequences$($X$\_$train, maxlen$=$maxlen$)$

X$\_$test $=$ pad$\_$sequences$($X$\_$test, maxlen$=$maxlen$)$

# Step $3$: Build the Model

model $=$ Sequential$()$

model.add$($Embedding$($input$\_$dim$=$vocab$\_$size, output$\_$dim$=128,$ input$\_$length$=$maxlen$))$

model.add$($LSTM$($units$=64,$ dropout$=0\mathrm{.}2,$ recurrent$\_$dropout$=0\mathrm{.}2))$

model.add$($Dense$($units$=1,$ activation$=$'sigmoid'$))$

model.compile$($optimizer$=$'adam', loss$=$'binary$\_$crossentropy', metrics$=[\text{'}$accuracy$\text{'}])$

# Step $4$: Train the Model

early$\_$stopping $=$ EarlyStopping$($monitor$=$'val$\_$loss', patience$=3,$ restore$\_$best$\_$weights$=$True$)$

history $=$ model.fit$($X$\_$train, y$\_$train, batch$\_$size$=64,$ epochs$=10,$ validation$\_$data$=($X$\_$test, y$\_$test$),$ callbacks$=[$early$\_$stopping$])$

# Step $5$: Evaluate the Model

loss, accuracy $=$ model.evaluate$($X$\_$test, y$\_$test$)$

print$("$Test Loss:", loss$)$

print$("$Test Accuracy:", accuracy$)$