PyTorch-Ignite PyTorch-Ignite

Transformers for Text Classification with IMDb Reviews

In this tutorial we will fine tune a model from the Transformers library for text classification using PyTorch-Ignite. We will be following the Fine-tuning a pretrained model tutorial for preprocessing text and defining the model, optimizer and dataloaders.

Then we are going to use Ignite for:

  • Training and evaluating the model
  • Computing metrics
  • Setting up experiments and monitoring the model

According to the tutorial, we will use the IMDb Movie Reviews Dataset to classify a review as either positive or negative.

Required Dependencies

!pip install pytorch-ignite transformers datasets

Before we dive in, we will seed everything using manual_seed.

from ignite.utils import manual_seed


Basic Setup

Next we will follow the tutorial and load up our dataset and tokenizer to preprocess the data.

Data Preprocessing

from datasets import load_dataset

raw_datasets = load_dataset("imdb")
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenized_datasets =, batched=True)

We move towards the end of the tutorial for PyTorch specific instructions. Here we are extracting a larger subset of our original datasets. We also don’t need to provide a seed since we seeded everything at the beginning.

tokenized_datasets = tokenized_datasets.remove_columns(["text"])
tokenized_datasets = tokenized_datasets.rename_column("label", "labels")

small_train_dataset = tokenized_datasets["train"].shuffle().select(range(5000))
small_eval_dataset = tokenized_datasets["test"].shuffle().select(range(5000))


from import DataLoader

train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)


from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)


from transformers import AdamW

optimizer = AdamW(model.parameters(), lr=5e-5)

LR Scheduler

We will use the built-in Ignite alternative of linear scheduler which is PiecewiseLinear. We will also increase the number of epochs.

from ignite.contrib.handlers import PiecewiseLinear

num_epochs = 10
num_training_steps = num_epochs * len(train_dataloader)

milestones_values = [
        (0, 5e-5),
        (num_training_steps, 0.0),
lr_scheduler = PiecewiseLinear(
        optimizer, param_name="lr", milestones_values=milestones_values

Set Device

import torch

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

Create Trainer

Ignite’s Engine allows users to define a process_function to process a given batch of data. This function is applied to all the batches of the dataset. This is a general class that can be applied to train and validate models. A process_function has two parameters engine and batch.

The code for processing a batch of training data in the tutorial is as follows:

for batch in train_dataloader:
    batch = {k: for k, v in batch.items()}
    outputs = model(**batch)
    loss = outputs.loss


Therefore we will define a process_function (called train_step below) to do the above tasks:

  • Set model in train mode.
  • Move items of the batch to device.
  • Perform forward pass and generate output.
  • Extract loss.
  • Perform backward pass using loss to calculate gradients for the model parameters.
  • Optimize model parameters using gradients and optimizer.

Finally, we choose to return the loss so we can utilize it for further processing.

You will also notice that we do not update the lr_scheduler and progress_bar in train_step. This is because Ignite automatically takes care of it as we will see later in this tutorial.

def train_step(engine, batch):  
    batch = {k: for k, v in batch.items()}
    outputs = model(**batch)
    loss = outputs.loss


    return loss

And then we create a model trainer by attaching the train_step to the training engine. Later, we will use trainer for looping over the training dataset for num_epochs.

from ignite.engine import Engine

trainer = Engine(train_step)

The lr_scheduler we defined previously was a handler.

Handlers can be any type of function (lambda functions, class methods, etc.). On top of that, Ignite provides several built-in handlers to reduce redundant code. We attach these handlers to engine which is triggered at a specific event. These events can be anything like the start of an iteration or the end of an epoch. Here is a complete list of built-in events.

Therefore, we will attach the lr_scheduler (handler) to the trainer (engine) via add_event_handler() so it can be triggered at Events.ITERATION_STARTED (start of an iteration) automatically.

from ignite.engine import Events

trainer.add_event_handler(Events.ITERATION_STARTED, lr_scheduler)

This is the reason we did not include lr_scheduler.step() in train_step().

Progress Bar

Next we create an instance of Ignite’s ProgessBar() and attach it to the trainer to replace progress_bar.update(1).

from ignite.contrib.handlers import ProgressBar

pbar = ProgressBar()

We can either, simply track the progress:


Or also track the output of trainer (or train_step):

pbar.attach(trainer, output_transform=lambda x: {'loss': x})

Create Evaluator

Similar to the training process_function, we setup a function to evaluate a single batch of train/validation/test data.

for batch in eval_dataloader:
    batch = {k: for k, v in batch.items()}
    with torch.no_grad():
        outputs = model(**batch)

    logits = outputs.logits
    predictions = torch.argmax(logits, dim=-1)
    metric.add_batch(predictions=predictions, references=batch["labels"])

Here is what evaluate_step() below does:

  • Sets model in eval mode.
  • Move items of the batch to device.
  • With torch.no_grad(), no gradients are calculated for any succeding steps.
  • Perform a forward pass on the model to calculate outputs from batch
  • Get the real predictions from logits (probability of positive and negative classes).

Finally, we return the predictions and the actual labels so that we can compute the metrics.

You will notice that we did not compute the metrics in evaluate_step(). This is because Ignite provides built-in metrics which we can later attach to the engine.

Note: Ignite suggests attaching metrics to evaluators and not trainers because during the training the model parameters are constantly changing and it is best to evaluate model on a stationary model. This information is important as there is a difference in the functions for training and evaluating. Training returns a single scalar loss. Evaluating returns y_pred and y as that output is used to calculate metrics per batch for the entire dataset.

All metrics in Ignite require y_pred and y as outputs of the function attached to the Engine.

def evaluate_step(engine, batch):

    batch = {k: for k, v in batch.items()}
    with torch.no_grad():
        outputs = model(**batch)

    logits = outputs.logits
    predictions = torch.argmax(logits, dim=-1)

    return {'y_pred': predictions, 'y': batch["labels"]}

Below we create two engines, a training evaluator and a validation evaluator. train_evaluator and validation_evaluator use the same function but they serve different purposes as we will see later in this tutorial.

train_evaluator = Engine(evaluate_step)
validation_evaluator = Engine(evaluate_step)

Attach Metrics

The 🤗 tutorial defines one metric, accuracy, to be used for evaluation:

metric= load_metric("accuracy")

We can easily attach Ignite’s built-in Accuracy() metric to to train_evaluator and validation_evaluator. We also need to specify the metric name (accuracy below). Internally, it will use y_pred and y to compute the accuracy.

from ignite.metrics import Accuracy

Accuracy().attach(train_evaluator, 'accuracy')
Accuracy().attach(validation_evaluator, 'accuracy')

Log Metrics

Now we will define custom handlers (functions) and attach them to various Events of the training process.

The functions below both achieve similar tasks. They print the results of the evaluator run on a dataset. log_training_results() does this on the training evaluator and train dataset, while log_validation_results() on the validation evaluator and validation dataset. Another difference is how these functions are attached in the trainer engine.

The first method involves using a decorator, the syntax is simple - @ trainer.on(Events.EPOCH_COMPLETED), means that the decorated function will be attached to the trainer and called at the end of each epoch.

The second method involves using the add_event_handler method of trainer - trainer.add_event_handler(Events.EPOCH_COMPLETED, custom_function). This achieves the same result as the above.

def log_training_results(engine):
    metrics = train_evaluator.state.metrics
    avg_accuracy = metrics['accuracy']
    print(f"Training Results - Epoch: {engine.state.epoch}  Avg accuracy: {avg_accuracy:.3f}")
def log_validation_results(engine):
    metrics = validation_evaluator.state.metrics
    avg_accuracy = metrics['accuracy']
    print(f"Validation Results - Epoch: {engine.state.epoch}  Avg accuracy: {avg_accuracy:.3f}")

trainer.add_event_handler(Events.EPOCH_COMPLETED, log_validation_results)

Early Stopping

Now we’ll setup a EarlyStopping handler for the training process. EarlyStopping requires a score_function that allows the user to define whatever criteria to stop training. In this case, if the loss of the validation set does not decrease in 2 epochs (patience), the training process will stop early.

from ignite.handlers import EarlyStopping

def score_function(engine):
    val_accuracy = engine.state.metrics['accuracy']
    return val_accuracy

handler = EarlyStopping(patience=2, score_function=score_function, trainer=trainer)
validation_evaluator.add_event_handler(Events.COMPLETED, handler)

Model Checkpoint

Lastly, we want to save the best model weights. So we will use Ignite’s ModelCheckpoint handler to checkpoint models at the end of each epoch. This will create a models directory and save the 2 best models (n_saved) with the prefix bert-base-cased.

from ignite.handlers import ModelCheckpoint

checkpointer = ModelCheckpoint(dirname='models', filename_prefix='bert-base-cased', n_saved=2, create_dir=True)
trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpointer, {'model': model})

Begin Training!

Next, we’ll run the trainer for 10 epochs and monitor the results. Below we can see that ProgessBar prints the loss per iteration, and prints the results of training and validation as we specified in our custom function., max_epochs=num_epochs)

That’s it! We have successfully trained and evaluated a Transformer for Text Classification.