Transformers documentation

Text classification

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Text classification

Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, which assigns a label like 🙂 positive, 🙁 negative, or 😐 neutral to a sequence of text.

This guide will show you how to:

  1. Finetune DistilBERT on the IMDb dataset to determine whether a movie review is positive or negative.
  2. Use your finetuned model for inference.

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate accelerate

We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

>>> from huggingface_hub import notebook_login

>>> notebook_login()

Load IMDb dataset

Start by loading the IMDb dataset from the 🤗 Datasets library:

>>> from datasets import load_dataset

>>> imdb = load_dataset("imdb")

Then take a look at an example:

>>> imdb["test"][0]
{
    "label": 0,
    "text": "I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn't match the background, and painfully one-dimensional characters cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It's really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \"Gene Roddenberry's Earth...\" otherwise people would not continue watching. Roddenberry's ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again.",
}

There are two fields in this dataset:

  • text: the movie review text.
  • label: a value that is either 0 for a negative review or 1 for a positive review.

Preprocess

The next step is to load a DistilBERT tokenizer to preprocess the text field:

>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")

Create a preprocessing function to tokenize text and truncate sequences to be no longer than DistilBERT’s maximum input length:

>>> def preprocess_function(examples):
...     return tokenizer(examples["text"], truncation=True)

To apply the preprocessing function over the entire dataset, use 🤗 Datasets map function. You can speed up map by setting batched=True to process multiple elements of the dataset at once:

tokenized_imdb = imdb.map(preprocess_function, batched=True)

Now create a batch of examples using DataCollatorWithPadding. It’s more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.

Pytorch
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>>> from transformers import DataCollatorWithPadding

>>> data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
TensorFlow
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>>> from transformers import DataCollatorWithPadding

>>> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")

Evaluate

Including a metric during training is often helpful for evaluating your model’s performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

>>> import evaluate

>>> accuracy = evaluate.load("accuracy")

Then create a function that passes your predictions and labels to compute to calculate the accuracy:

>>> import numpy as np


>>> def compute_metrics(eval_pred):
...     predictions, labels = eval_pred
...     predictions = np.argmax(predictions, axis=1)
...     return accuracy.compute(predictions=predictions, references=labels)

Your compute_metrics function is ready to go now, and you’ll return to it when you setup your training.

Train

Before you start training your model, create a map of the expected ids to their labels with id2label and label2id:

>>> id2label = {0: "NEGATIVE", 1: "POSITIVE"}
>>> label2id = {"NEGATIVE": 0, "POSITIVE": 1}
Pytorch
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If you aren’t familiar with finetuning a model with the Trainer, take a look at the basic tutorial here!

You’re ready to start training your model now! Load DistilBERT with AutoModelForSequenceClassification along with the number of expected labels, and the label mappings:

>>> from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer

>>> model = AutoModelForSequenceClassification.from_pretrained(
...     "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
... )

At this point, only three steps remain:

  1. Define your training hyperparameters in TrainingArguments. The only required parameter is output_dir which specifies where to save your model. You’ll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the Trainer will evaluate the accuracy and save the training checkpoint.
  2. Pass the training arguments to Trainer along with the model, dataset, tokenizer, data collator, and compute_metrics function.
  3. Call train() to finetune your model.
>>> training_args = TrainingArguments(
...     output_dir="my_awesome_model",
...     learning_rate=2e-5,
...     per_device_train_batch_size=16,
...     per_device_eval_batch_size=16,
...     num_train_epochs=2,
...     weight_decay=0.01,
...     evaluation_strategy="epoch",
...     save_strategy="epoch",
...     load_best_model_at_end=True,
...     push_to_hub=True,
... )

>>> trainer = Trainer(
...     model=model,
...     args=training_args,
...     train_dataset=tokenized_imdb["train"],
...     eval_dataset=tokenized_imdb["test"],
...     tokenizer=tokenizer,
...     data_collator=data_collator,
...     compute_metrics=compute_metrics,
... )

>>> trainer.train()

Trainer applies dynamic padding by default when you pass tokenizer to it. In this case, you don’t need to specify a data collator explicitly.

Once training is completed, share your model to the Hub with the push_to_hub() method so everyone can use your model:

>>> trainer.push_to_hub()
TensorFlow
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If you aren’t familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
>>> from transformers import create_optimizer
>>> import tensorflow as tf

>>> batch_size = 16
>>> num_epochs = 5
>>> batches_per_epoch = len(tokenized_imdb["train"]) // batch_size
>>> total_train_steps = int(batches_per_epoch * num_epochs)
>>> optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)

Then you can load DistilBERT with TFAutoModelForSequenceClassification along with the number of expected labels, and the label mappings:

>>> from transformers import TFAutoModelForSequenceClassification

>>> model = TFAutoModelForSequenceClassification.from_pretrained(
...     "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
... )

Convert your datasets to the tf.data.Dataset format with prepare_tf_dataset():

>>> tf_train_set = model.prepare_tf_dataset(
...     tokenized_imdb["train"],
...     shuffle=True,
...     batch_size=16,
...     collate_fn=data_collator,
... )

>>> tf_validation_set = model.prepare_tf_dataset(
...     tokenized_imdb["test"],
...     shuffle=False,
...     batch_size=16,
...     collate_fn=data_collator,
... )

Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don’t need to specify one unless you want to:

>>> import tensorflow as tf

>>> model.compile(optimizer=optimizer)  # No loss argument!

The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks.

Pass your compute_metrics function to KerasMetricCallback:

>>> from transformers.keras_callbacks import KerasMetricCallback

>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)

Specify where to push your model and tokenizer in the PushToHubCallback:

>>> from transformers.keras_callbacks import PushToHubCallback

>>> push_to_hub_callback = PushToHubCallback(
...     output_dir="my_awesome_model",
...     tokenizer=tokenizer,
... )

Then bundle your callbacks together:

>>> callbacks = [metric_callback, push_to_hub_callback]

Finally, you’re ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:

>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for text classification, take a look at the corresponding PyTorch notebook or TensorFlow notebook.

Inference

Great, now that you’ve finetuned a model, you can use it for inference!

Grab some text you’d like to run inference on:

>>> text = "This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three."

The simplest way to try out your finetuned model for inference is to use it in a pipeline(). Instantiate a pipeline for sentiment analysis with your model, and pass your text to it:

>>> from transformers import pipeline

>>> classifier = pipeline("sentiment-analysis", model="stevhliu/my_awesome_model")
>>> classifier(text)
[{'label': 'POSITIVE', 'score': 0.9994940757751465}]

You can also manually replicate the results of the pipeline if you’d like:

Pytorch
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Tokenize the text and return PyTorch tensors:

>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_model")
>>> inputs = tokenizer(text, return_tensors="pt")

Pass your inputs to the model and return the logits:

>>> from transformers import AutoModelForSequenceClassification

>>> model = AutoModelForSequenceClassification.from_pretrained("stevhliu/my_awesome_model")
>>> with torch.no_grad():
...     logits = model(**inputs).logits

Get the class with the highest probability, and use the model’s id2label mapping to convert it to a text label:

>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'POSITIVE'
TensorFlow
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Tokenize the text and return TensorFlow tensors:

>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_model")
>>> inputs = tokenizer(text, return_tensors="tf")

Pass your inputs to the model and return the logits:

>>> from transformers import TFAutoModelForSequenceClassification

>>> model = TFAutoModelForSequenceClassification.from_pretrained("stevhliu/my_awesome_model")
>>> logits = model(**inputs).logits

Get the class with the highest probability, and use the model’s id2label mapping to convert it to a text label:

>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
>>> model.config.id2label[predicted_class_id]
'POSITIVE'