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name: relation-and-ensemble-workflows description: "Operate legacy two-sentence relation, CNN+RCNN hybrid, boosting-weight, and ensemble-logit workflows for brightmart/text_classification." disable-model-invocation: true metadata: disco-role: operating license: MIT

Relation And Ensemble Workflows

Use this sub-skill when the task involves one of these legacy brightmart/text_classification workflow families:

• two-sentence relation classification, including concatenating the pair with an EOS token or feeding two separately padded CNN inputs;
• TextCNN plus RCNN hybrid variants where two branches produce compatible logits;
• validation-logit driven boosting weights for sparse-label classification;
• combining already exported model logits and selecting top-k labels.

This is an operating guide for a legacy TensorFlow 1.x script collection. Do not assume an installable Python package, TensorFlow 2.x eager execution, or Python 3.13 compatibility. Full source-style training/prediction usually requires external TSV data, HDF5/pickle caches, pretrained word2vec embeddings, and checkpoint directories that are not bundled here.

Start Here

1. For model/data-shape decisions, read references/workflows.md.
2. For failure triage, read references/troubleshooting.md.
3. To compute safe boosting label statistics from exported validation logits, use scripts/compute_boosting_label_weights.py.
4. To combine exported logits without the original checkpoints, use scripts/combine_logits_topk.py.

Route Elsewhere

• Single-text TextCNN, TextRNN, FastText, or HAN classifier selection belongs to classification-models.
• Raw file validation, tokenization, vocabulary building, and data-cache preparation belong to data-preparation.
• Dynamic-memory, entity-network, seq2seq, or memory-cell internals belong to sequence-and-memory-models.

Required Caller Facts

Before giving concrete run advice, establish:

• whether the input is a relation pair or a single sequence;
• whether relation pairs are one concatenated input_x with EOS or two inputs input_x/input_x2;
• the label-map direction and whether every model uses the same class index order;
• the shape of any exported logits (models x examples x classes preferred);
• whether the user is only post-processing logits or attempting full legacy TensorFlow 1.x checkpoint restoration.