You can build a basic Transformer text classifier in Keras by converting reviews to integer sequences, adding token and position embeddings, passing them through a Transformer block, and pooling the result into a two-class prediction. Keras’ IMDB example demonstrates this from-scratch approach; it is a learning example, not a recipe for fine-tuning a pretrained language model.
What the Keras Transformer classifier does
The official Keras text-classification example, by Apoorv Nandan, uses IMDB movie reviews to classify sentiment into two classes. Its model learns embeddings and attention weights from the tutorial dataset rather than starting from a pretrained language model.
At a high level, the model turns each review into a sequence of token IDs, represents each token and its position as vectors, applies self-attention and a feed-forward network, then aggregates the sequence and predicts a class.
Model architecture
- Token and position embeddings: The model embeds each token and adds an embedding for its position in the review. Position information matters because self-attention alone does not encode token order.
- Transformer block: Multi-head self-attention lets tokens incorporate information from other tokens. A feed-forward network further transforms the representation. Dropout, residual additions, and layer normalization are used in the block.
- Pooling and classifier: Global average pooling combines information across the sequence. Dense layers then produce a two-class softmax output.
This compact architecture is useful for learning how a Transformer classifier is assembled. It differs from transfer learning, where a model begins with pretrained language representations and is adapted to a classification task.
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Input preparation and tutorial settings
The example uses the IMDB dataset’s 25,000 training and 25,000 validation reviews. It limits the vocabulary to 20,000 words, keeps sequences up to 200 tokens long, and pads reviews so they can be processed in batches. These are tutorial choices, not universal settings: vocabulary size and sequence length affect what the model can represent and how much computation it requires.
For a raw-text workflow, Keras’ TextVectorization layer can standardize and split text, optionally form n-grams, and emit integer or dense encodings. You can let it learn a vocabulary by calling adapt(), or supply a vocabulary directly. Adapt it using training text only, then use the same preprocessing configuration for training and inference to avoid inconsistent inputs or validation leakage.
The TextVectorization documentation notes that the layer uses TensorFlow internally when used in a compiled model graph. If you use Keras with another backend, check that constraint and the current API behavior for your setup.
Build and train the classifier
The tutorial’s code defines a custom Transformer layer, adds token and positional embeddings, and connects the result to a pooled dense classifier. Its notebook imports standalone keras and keras.ops. The following outline captures the sequence of work; use the linked example for the complete, runnable implementation.
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- Load and prepare the IMDB data. Convert reviews to integer sequences using the tutorial’s 20,000-word vocabulary limit, truncate sequences to 200 tokens, and pad them to a common length.
- Define the Transformer layer. Combine multi-head attention and a feed-forward network with dropout, residual connections, and layer normalization.
- Add sequence embeddings. Embed token IDs and positions, then add the two representations before the Transformer block.
- Connect the classifier. Apply global average pooling across the sequence, followed by dense layers and a two-class softmax.
- Compile and fit. The example uses Adam, sparse categorical cross-entropy, accuracy, a batch size of 32, and two epochs.
In the tutorial page’s example run, validation accuracy is reported as 0.8444 after epoch one and 0.8745 after epoch two. The 0.8745 figure is Keras’ result for that tutorial run; it is not a guaranteed outcome, a general benchmark, or a controlled comparison with another method. Your result depends on the data, preprocessing, software versions, and training choices.
Check versions before adapting the code
The example code page was last modified on 2024-01-18. Keras APIs can evolve, so check the installed Keras version and current documentation rather than treating the notebook as a version guarantee. In particular, confirm the imports, layer arguments, and backend requirements in the environment where you plan to train and serve the model.
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When to consider a different Keras approach
Keras’ NLP examples index includes from-scratch Transformer, FNet, Switch Transformer, multi-label classification, and transfer-learning examples. KerasHub’s TextClassifier API wraps a backbone and preprocessor and supports loading presets.
Choose based on the problem rather than assuming one example is best:
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
- Task format: A two-class or single-label classifier differs from a multi-label task, where several categories may apply to one item.
- Pretrained weights: A from-scratch model is useful for understanding the components; a pretrained backbone may be a better starting point when transfer learning fits the task.
- Data, compute, and sequence length: These constrain the size and complexity of a model you can train and use.
- Goal: A compact educational implementation and a production baseline have different requirements for validation, serving, and maintenance.
The cited Keras pages describe available approaches but do not establish a controlled accuracy or efficiency ranking. Test candidates on the same task, data split, and evaluation procedure before choosing one.
Further reading
The Keras example points readers to Deep Learning with Python, Second Edition for relevant chapters on text classification and language models. The tutorial page does not establish current edition availability or retailer stock.
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