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)
- 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.