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name: classification-models description: "Route classic TensorFlow 1.x text classification model selection, graph inspection, training, prediction, and artifact prerequisites for brightmart/text_classification." disable-model-invocation: true metadata: disco-role: operating license: MIT

Classification Models

Use this sub-skill when the task is to choose, inspect, train, restore, or adapt one of the repository's classic classification model families:

• fastTextB averaged embeddings for multi-label baselines.
• TextCNN convolution, max-pooling, and sigmoid multi-label classification.
• TextRNN, TextRCNN, and Hierarchical Attention Network variants.
• BERT fine-tuning/classification heads and online prediction patterns.
• Legacy TFLearn toy classification examples.

This repository is a legacy TensorFlow 1.x script collection, not an installable Python package. Assume Python 3.7-era compatibility work and TensorFlow 1.x/TFLearn semantics. Do not assume TensorFlow 2.x eager execution, tf.keras rewrites, or Python 3.13 compatibility.

Route by task

• For architecture choice, placeholders, tensor shapes, and safe graph-construction probes, read references/model-reference.md.
• For training, prediction, checkpoint, cache, and embedding prerequisites, read references/training-and-prediction.md.
• For TensorFlow 1.x, TFLearn, checkpoint, data-cache, and model-specific failure modes, read references/troubleshooting.md.
• To inspect small TensorFlow 1.x graphs without running full training, use scripts/inspect_tf1_model_shapes.py.

Boundaries

Use this sub-skill only for the classification families above. Route seq2seq, Transformer classification/generation, entity networks, and dynamic memory networks to sequence-and-memory-models. Route relation models, two-sentence models, boosting, hybrid CNN+RCNN, and ensemble prediction workflows to relation-and-ensemble-workflows. Route raw data cleaning, vocabulary construction, HDF5 generation, and label validation to data-preparation.

Safety posture

Full training usually requires external HDF5/pickle caches, optional word2vec binaries, checkpoint directories, and long runtimes. These artifacts are not bundled with the skill. Prefer tiny graph construction and shape inspection first; run training or prediction scripts only after data, vocabulary, label, checkpoint, and TensorFlow 1.x compatibility are confirmed.