How to develop custom MLP in PyTorch, using own text dataset

Question

I have a basic understanding of MLP's and neural networks but I am completely lost on how to start when trying to implement it in code.

I am trying to develop a multilayer perceptron model to determine whether two sentences are paraphrases of each other. I have my own training, validation, and test data files/dataframes and have many questions about how to implement a model using PyTorch. I scoured the internet, trying to find a tutorial that I could follow along but failed, every tutorial makes use of MNIST or other image databases, and not custom datasets involving only text.

Here is a half-baked attempt at me trying to start the preprocessing portion of the model, I am not sure if this is the right way to start:

import pandas as pd

class ParaphraseDataSet(Dataset):
    
    def __init__(self, path):
        columns = ['id', 's1', 's2', 'gold label']
        df = pd.read_csv(path, sep = '\t+', names = columns, engine='python')
        
        self.X = df.values[:, :-1]
        self.y = df['gold label'].values
        self.y = self.y.astype(int)
        self.y = self.y.reshape((len(self.y), 1))
    
    def __len__(self):
        return len(self.X)

Here is a photo of the current training dataframe, the gold label is the target, and I have 6-7 features I want to implement (not shown). Questions:

• How do I start building the model after preprocessing my data? How do I define the features to my MLP model? How do I load this type of data into the dataloaders?
• Are the features perceptrons? (6 features = 6 perceptrons in the first layer?)
• Are there any good online tutorials where an MLP model is developed to classify text?

Thanks, sorry if this seems like a lot.