Where to add truncation=True for warning Truncation was not explicitely activated but max_length is provided a specific value, please use truncation=True to explicitely truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you may want to check this is the right behavior.
Don't know where to change the code for sentiment analysis. I just wanted to add truncation=True. I'm getting warning tokenizer started throwing this warning,

 **Truncation was not explicitly activated but max_length is provided with a specific value, please use truncation=True to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you may want to check this is the right behaviour.**

**I'm attaching the complete code below, can anyone suggest where to add truncation=True**

```
df = pd.read_csv("sentiment.csv")

df.columns = 'content', 'score'

sen = {'Negative': 0, 'Positive': 1}

df.score = [sen[item] for item in df.score]


def to_sentiment(rating):
    rating = int(rating)
    if rating == 0:
        return 0
    elif rating == 1:
        return 1
    else:
        pass


df['sentiment'] = df.score.apply(to_sentiment)

class_names = ['negative', 'positive']

tokenizer = BertTokenizer.from_pretrained(r'bert-pretrained-model')

sample_txt = 'When was I last outside? I am stuck at home for 2 weeks.'

tokens = tokenizer.tokenize(sample_txt)

token_ids = tokenizer.convert_tokens_to_ids(tokens)

encoding = tokenizer.encode_plus(
    sample_txt,
    max_length=32,
    add_special_tokens=True,  # Add '[CLS]' and '[SEP]'
    return_token_type_ids=False,
    pad_to_max_length=True,
    return_attention_mask=True,
    return_tensors='pt',  # Return PyTorch tensors
)
encoding.keys()

tokenizer.convert_ids_to_tokens(encoding['input_ids'][0])

token_lens = []
for txt in df.content:
    tokens = tokenizer.encode(txt, max_length=512)
    token_lens.append(len(tokens))

MAX_LEN = 160


class GPReviewDataset(Dataset):
    def __init__(self, reviews, targets, tokenizer, max_len):
        self.reviews = reviews
        self.targets = targets
        self.tokenizer = tokenizer
        self.max_len = max_len

    def __len__(self):
        return len(self.reviews)

    def __getitem__(self, item):
        review = str(self.reviews[item])
        target = self.targets[item]
        encoding = self.tokenizer.encode_plus(
            review,
            add_special_tokens=True,
            max_length=self.max_len,
            return_token_type_ids=False,
            pad_to_max_length=True,
            return_attention_mask=True,
            return_tensors='pt',
        )
        return {
            'review_text': review,
            'input_ids': encoding['input_ids'].flatten(),
            'attention_mask': encoding['attention_mask'].flatten(),
            'targets': torch.tensor(target, dtype=torch.long)
        }


df_train, df_test = train_test_split(
    df,
    test_size=0.1,
    random_state=RANDOM_SEED
)
df_val, df_test = train_test_split(
    df_test,
    test_size=0.5,
    random_state=RANDOM_SEED
)

def create_data_loader(df, tokenizer, max_len, batch_size):
    ds = GPReviewDataset(
        reviews=df.content.to_numpy(),
        targets=df.sentiment.to_numpy(),
        tokenizer=tokenizer,
        max_len=max_len
    )
    return DataLoader(
        ds,
        batch_size=batch_size,
        num_workers=0
    )


BATCH_SIZE = 16
train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)
val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)
test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)

data = next(iter(train_data_loader))
data.keys()

bert_model = BertModel.from_pretrained(r'bert-pretrained-model')

last_hidden_state, pooled_output = bert_model(
    input_ids=encoding['input_ids'],
    attention_mask=encoding['attention_mask'],
    return_dict=False
)

class SentimentClassifier(nn.Module):
    def __init__(self, n_classes):
        super(SentimentClassifier, self).__init__()
        self.bert = BertModel.from_pretrained(r'bert-pretrained-model', return_dict=False)
        self.drop = nn.Dropout(p=0.3)
        self.out = nn.Linear(self.bert.config.hidden_size, n_classes)

    def forward(self, input_ids, attention_mask):
        _, pooled_output = self.bert(
            input_ids=input_ids,
            attention_mask=attention_mask
        )
        output = self.drop(pooled_output)
        return self.out(output)


model = SentimentClassifier(len(class_names))
model = model.to(device)

input_ids = data['input_ids'].to(device)
attention_mask = data['attention_mask'].to(device)
print(input_ids.shape)  # batch size x seq length
print(attention_mask.shape)

model(input_ids, attention_mask)

EPOCHS = 10
optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)
total_steps = len(train_data_loader) * EPOCHS
scheduler = get_linear_schedule_with_warmup(
    optimizer,
    num_warmup_steps=0,
    num_training_steps=total_steps
)
loss_fn = nn.CrossEntropyLoss().to(device)


def train_epoch(
        model,
        data_loader,
        loss_fn,
        optimizer,
        device,
        scheduler,
        n_examples
):
    model = model.train()
    losses = []
    correct_predictions = 0
    for d in data_loader:
        input_ids = d["input_ids"].to(device)
        attention_mask = d["attention_mask"].to(device)
        targets = d["targets"].to(device)
        outputs = model(
            input_ids=input_ids,
            attention_mask=attention_mask
        )
        _, preds = torch.max(outputs, dim=1)
        loss = loss_fn(outputs, targets)
        correct_predictions += torch.sum(preds == targets)
        losses.append(loss.item())
        loss.backward()
        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()
        scheduler.step()
        optimizer.zero_grad()

    return correct_predictions.double() / n_examples, np.mean(losses)


def eval_model(model, data_loader, loss_fn, device, n_examples):
    model = model.eval()
    losses = []
    correct_predictions = 0
    with torch.no_grad():
        for d in data_loader:
            input_ids = d["input_ids"].to(device)
            attention_mask = d["attention_mask"].to(device)
            targets = d["targets"].to(device)
            outputs = model(
                input_ids=input_ids,
                attention_mask=attention_mask
            )
            _, preds = torch.max(outputs, dim=1)
            loss = loss_fn(outputs, targets)
            correct_predictions += torch.sum(preds == targets)
            losses.append(loss.item())

    return correct_predictions.double() / n_examples, np.mean(losses)


history = defaultdict(list)
best_accuracy = 0
for epoch in range(EPOCHS):
    print(f'Epoch {epoch + 1}/{EPOCHS}')
    print('-' * 10)
    train_acc, train_loss = train_epoch(
        model,
        train_data_loader,
        loss_fn,
        optimizer,
        device,
        scheduler,
        len(df_train)
    )
    print(f'Train loss {train_loss} accuracy {train_acc}')
    val_acc, val_loss = eval_model(
        model,
        val_data_loader,
        loss_fn,
        device,
        len(df_val)
    )
    print(f'Val   loss {val_loss} accuracy {val_acc}')
    print()
    history['train_acc'].append(train_acc)
    history['train_loss'].append(train_loss)
    history['val_acc'].append(val_acc)
    history['val_loss'].append(val_loss)
    if val_acc > best_accuracy:
        torch.save(model.state_dict(), 'best_model_state.bin')
        best_accuracy = val_acc


test_acc, _ = eval_model(
    model,
    test_data_loader,
    loss_fn,
    device,
    len(df_test)
)
test_acc.item()


def get_predictions(model, data_loader):
    model = model.eval()
    review_texts = []
    predictions = []
    prediction_probs = []
    real_values = []
    with torch.no_grad():
        for d in data_loader:
            texts = d["review_text"]
            input_ids = d["input_ids"].to(device)
            attention_mask = d["attention_mask"].to(device)
            targets = d["targets"].to(device)
            outputs = model(
                input_ids=input_ids,
                attention_mask=attention_mask
            )
            _, preds = torch.max(outputs, dim=1)
            review_texts.extend(texts)
            predictions.extend(preds)
            prediction_probs.extend(outputs)
            real_values.extend(targets)
    predictions = torch.stack(predictions).cpu()
    prediction_probs = torch.stack(prediction_probs).cpu()
    real_values = torch.stack(real_values).cpu()
    return review_texts, predictions, prediction_probs, real_values


y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(
    model,
    test_data_loader
)

idx = 2
review_text = y_review_texts[idx]
true_sentiment = y_test[idx]
pred_df = pd.DataFrame({
    'class_names': class_names,
    'values': y_pred_probs[idx]
})


print("\n".join(wrap(review_text)))
print()
print(f'True sentiment: {class_names[true_sentiment]}')
print("")


review_text = "My name is mark"

encoded_review = tokenizer.encode_plus(
    review_text,
    max_length=MAX_LEN,
    add_special_tokens=True,
    return_token_type_ids=False,
    pad_to_max_length=True,
    return_attention_mask=True,
    return_tensors='pt',
)

input_ids = encoded_review['input_ids'].to(device)
attention_mask = encoded_review['attention_mask'].to(device)
output = model(input_ids, attention_mask)
_, prediction = torch.max(output, dim=1)
print(f'Review text: {review_text}')
print(f'Sentiment  : {class_names[prediction]}
```
> encoding = tokenizer.encode_plus(
>     sample_txt,
>     max_length=32,
>     add_special_tokens=True,  # Add '[CLS]' and '[SEP]'
>     return_token_type_ids=False,
>     pad_to_max_length=True,
>     return_attention_mask=True,
>     return_tensors='pt',  # Return PyTorch tensors
> )

@nithinreddyy you need to add truncation=True here.<|||||>> > encoding = tokenizer.encode_plus(
> >     sample_txt,
> >     max_length=32,
> >     add_special_tokens=True,  # Add '[CLS]' and '[SEP]'
> >     return_token_type_ids=False,
> >     pad_to_max_length=True,
> >     return_attention_mask=True,
> >     return_tensors='pt',  # Return PyTorch tensors
> > )
> 
> @nithinreddyy you need to add truncation=True here.

Thank you will try.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.