To build text classification with a Transformer in Keras, tokenize each text into integer sequences, add token and position embeddings, pass the sequence through a Transformer block, pool its output, and train a classifier. Keras’ official example uses this approach for binary sentiment classification of IMDB movie reviews; it is a compact, from-scratch model rather than a recipe for fine-tuning a pretrained language model. The example is available at Keras’ text classification with Transformer tutorial.
What the Keras example builds
The model receives integer-encoded reviews. It combines an embedding for each token with an embedding for that token’s position in the sequence, then processes the result with a custom Transformer block. Global average pooling reduces the sequence to a single representation, which feeds dense layers and a two-class softmax output.
The Transformer block brings together multi-head self-attention and a feed-forward network. Dropout is applied, and residual connections and layer normalization help organize the block’s inputs and outputs. The example demonstrates how to assemble these parts as Keras layers; it does not load a pretrained text model.
How the tutorial prepares and trains the data
The tutorial uses the IMDB dataset, with 25,000 training examples and 25,000 validation examples. Its configuration caps the vocabulary at 20,000 words and keeps reviews to a maximum of 200 tokens before padding the sequences. These are choices for this demonstration, not recommended defaults for every dataset.
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It compiles the model with Adam, sparse categorical cross-entropy, and accuracy, then trains with batch size 32 for two epochs. Keras reports validation accuracy of 0.8444 after the first epoch and 0.8745 after the second in the tutorial’s example run. Those figures describe that run, on that dataset and configuration; they are not a performance guarantee or a controlled comparison with other models.
Using raw text with TextVectorization
If your input is raw text rather than pre-encoded sequences, Keras’ TextVectorization layer can standardize and split text, optionally generate n-grams, and produce integer or dense encodings. You can build its vocabulary from text using adapt() or supply a vocabulary directly.
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- Configure the layer’s standardization, splitting, output mode, and sequence length to match the task.
- When learning a vocabulary with
adapt(), use training text only. Using validation or test examples to build the vocabulary leaks information from those sets into preprocessing. - Apply the same preprocessing at training and inference time. You can place the layer before the model or include it in the model’s input pipeline.
The API documentation notes that TextVectorization uses TensorFlow internally when used in a compiled model graph. If you are using Keras with another backend, check that limitation against your intended setup.
Choose an approach for the task
A custom Transformer is useful when you want to understand or adapt the model’s components. It is not the only path in Keras’ NLP materials: the Keras NLP examples index also lists FNet, Switch Transformer, multi-label classification, and transfer-learning examples. KerasHub’s TextClassifier API wraps a backbone and preprocessor and supports loading presets.
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Choose based on the actual task and constraints rather than assuming one example is universally best:
- Label structure: The tutorial predicts one of two classes. A multi-label task, where a text can have several labels at once, needs an appropriate output and loss setup.
- Pretrained weights: Use a transfer-learning route when a suitable pretrained model is appropriate; the tutorial’s custom model does not fine-tune one.
- Sequence length and model size: Set these to fit the data and available compute instead of carrying over the tutorial’s limits unchanged.
- Goal: A from-scratch implementation can teach the architecture, while a pretrained or task-specific API may better suit a production baseline.
The cited examples identify available approaches but do not provide a controlled benchmark ranking their accuracy or efficiency for your dataset.
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Check Keras versions before adapting the code
The tutorial notebook imports standalone keras and keras.ops. Its code page was last modified on 2024-01-18, and current APIs can evolve. Check the version installed in your environment and the current Keras API before treating the example’s exact code as a version guarantee.
For additional study, the tutorial points readers to relevant chapters in Deep Learning with Python, Second Edition. Availability and edition details may vary, so verify them with the bookseller or library you use.
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