<jupyter_start><jupyter_text><jupyter_code>def generate_bigrams(x):
    n_grams = set(zip(*[x[i:] for i in range(2)]))
    for n_gram in n_grams:
        x.append(' '.join(n_gram))
    return x
generate_bigrams(['This', 'film', 'is', 'terrible'])
import torch
from torchtext import data
from torchtext import datasets

SEED = 1234

torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True

TEXT = data.Field(tokenize = 'spacy', preprocessing = generate_bigrams)
LABEL = data.LabelField(dtype = torch.float)
import random

train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)

train_data, valid_data = train_data.split(random_state = random.seed(SEED))
MAX_VOCAB_SIZE = 25_000

TEXT.build_vocab(train_data, 
                 max_size = MAX_VOCAB_SIZE, 
                 vectors = "glove.6B.100d", 
                 unk_init = torch.Tensor.normal_)

LABEL.build_vocab(train_data)
BATCH_SIZE = 64

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(
    (train_data, valid_data, test_data), 
    batch_size = BATCH_SIZE, 
    device = device)
import torch.nn as nn
import torch.nn.functional as F

class FastText(nn.Module):
    def __init__(self, vocab_size, embedding_dim, output_dim, pad_idx):
        
        super().__init__()
        
        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)
        
        self.fc = nn.Linear(embedding_dim, output_dim)
        
    def forward(self, text):
        
        #text = [sent len, batch size]
        
        embedded = self.embedding(text)
                
        #embedded = [sent len, batch size, emb dim]
        
        embedded = embedded.permute(1, 0, 2)
        
        #embedded = [batch size, sent len, emb dim]
        
        pooled = F.avg_pool2d(embedded, (embedded.shape[1], 1)).squeeze(1) 
        
        #pooled = [batch size, embedding_dim]
                
        return self.fc(pooled)
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
OUTPUT_DIM = 1
PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]

model = FastText(INPUT_DIM, EMBEDDING_DIM, OUTPUT_DIM, PAD_IDX)
def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

print(f'The model has {count_parameters(model):,} trainable parameters')
pretrained_embeddings = TEXT.vocab.vectors

model.embedding.weight.data.copy_(pretrained_embeddings)
UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]

model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM)
model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)
import torch.optim as optim

optimizer = optim.Adam(model.parameters())
criterion = nn.BCEWithLogitsLoss()

model = model.to(device)
criterion = criterion.to(device)
def binary_accuracy(preds, y):
    """
    Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8
    """

    #round predictions to the closest integer
    rounded_preds = torch.round(torch.sigmoid(preds))
    correct = (rounded_preds == y).float() #convert into float for division 
    acc = correct.sum() / len(correct)
    return acc
def train(model, iterator, optimizer, criterion):
    
    epoch_loss = 0
    epoch_acc = 0
    
    model.train()
    
    for batch in iterator:
        
        optimizer.zero_grad()
        
        predictions = model(batch.text).squeeze(1)
        
        loss = criterion(predictions, batch.label)
        
        acc = binary_accuracy(predictions, batch.label)
        
        loss.backward()
        
        optimizer.step()
        
        epoch_loss += loss.item()
        epoch_acc += acc.item()
        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)
def evaluate(model, iterator, criterion):
    
    epoch_loss = 0
    epoch_acc = 0
    
    model.eval()
    
    with torch.no_grad():
    
        for batch in iterator:

            predictions = model(batch.text).squeeze(1)
            
            loss = criterion(predictions, batch.label)
            
            acc = binary_accuracy(predictions, batch.label)

            epoch_loss += loss.item()
            epoch_acc += acc.item()
        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)
import time

def epoch_time(start_time, end_time):
    elapsed_time = end_time - start_time
    elapsed_mins = int(elapsed_time / 60)
    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
    return elapsed_mins, elapsed_secs
N_EPOCHS = 5

best_valid_loss = float('inf')

for epoch in range(N_EPOCHS):

    start_time = time.time()
    
    train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
    valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
    
    end_time = time.time()

    epoch_mins, epoch_secs = epoch_time(start_time, end_time)
    
    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'tut3-model.pt')
    
    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')
    print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
    print(f'\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')
model.load_state_dict(torch.load('tut3-model.pt'))

test_loss, test_acc = evaluate(model, test_iterator, criterion)

print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')
import spacy
nlp = spacy.load('en')

def predict_sentiment(model, sentence):
    model.eval()
    tokenized = generate_bigrams([tok.text for tok in nlp.tokenizer(sentence)])
    indexed = [TEXT.vocab.stoi[t] for t in tokenized]
    tensor = torch.LongTensor(indexed).to(device)
    tensor = tensor.unsqueeze(1)
    prediction = torch.sigmoid(model(tensor))
    return prediction.item()
predict_sentiment(model, "This film is terrible")
predict_sentiment(model, "This film is great")
predict_sentiment(model, "This film is worst")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11343833780160859, 'fraction_numerical': 0.01089142091152815, 'mean_word_length': 3.096774193548387, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '441441', 'license': 'Apache-2.0', 'n_tokens_mistral': 2124, 'n_tokens_neox': 2044, 'n_words': 425, 'path': 'S9_NeuralEmbedding/3_NLP.ipynb', 'repo_name': 'EVA4-RS-Group/Phase2', 'size': '5968'}