Thanks for the data sample.
It looks like you are dealing with some kind of indices, is that correct?
If that’s the case, I would recommend to use an nn.Embedding layer to map these indices to some dense floating point representation.
Here is a small code modification to your model:

class reviewClassifier(nn.Module):
    def __init__(self):
        super(reviewClassifier, self).__init__()
        self.emb = nn.Embedding(914, 6)
        self.fc1 = nn.Linear(in_features=20*6, out_features=64) 
        self.fc2 = nn.Linear(in_features=64, out_features=32)
        self.fc3 = nn.Linear(in_features=32, out_features=1)
    def forward(self, x):
        x = self.emb(x)
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        y_pred = torch.sigmoid(x)
        return y_pred

Also you would have to pass your data as torch.LongTensors now.
If the values should represent some other features, you might want to normalize the data before feeding it to the model (e.g. such that mean=0, stddev=1).