inference.py

#load weights
path='/content/saved_weights.pt'
model.load_state_dict(torch.load(path));
model.eval();

#inference 
import spacy
nlp = spacy.load('en')

def predict(model, sentence):
    tokenized = [tok.text for tok in nlp.tokenizer(sentence)]  #tokenize the sentence 
    indexed = [TEXT.vocab.stoi[t] for t in tokenized]          #convert to integer sequence
    length = [len(indexed)]                                    #compute no. of words
    tensor = torch.LongTensor(indexed).to(device)              #convert to tensor
    tensor = tensor.unsqueeze(1).T                             #reshape in form of batch,no. of words
    length_tensor = torch.LongTensor(length)                   #convert to tensor
    prediction = model(tensor, length_tensor)                  #prediction 
    return prediction.item()