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name: text-classification description: "Routes legacy TensorFlow 1.x text-classification workflows for brightmart/text_classification, including raw data preparation, classic baselines, seq2seq and memory models, relation workflows, and ensemble post-processing." disable-model-invocation: true metadata: disco-role: operating license: MIT

text-classification

Use this repo skill for the legacy brightmart/text_classification checkout: a TensorFlow 1.x / TFLearn-era collection of classic text-classification models, Zhihu-style data utilities, seq2seq label generation, memory-network variants, relation workflows, and logit post-processing helpers.

This repository is not an installable Python package. When you need runtime inspection, use a Python 3.7-era TensorFlow 1.x/TFLearn environment and the bundled smoke scripts; do not assume TensorFlow 2.x, eager execution, or Python 3.13 compatibility.

Start here

1. Read references/repo-provenance.md if you need to compare this skill against another checkout or decide whether it is stale.
2. Read references/model-overview.md for the high-level model-family map.
3. Read references/troubleshooting.md for cross-cutting TensorFlow 1.x, cache, checkpoint, and import pitfalls.
4. Use scripts/check_legacy_text_classification_env.py when you want a safe environment/import smoke check.

Route map

Task signal	Read this sub-skill	Why
Raw text lines, __label__ parsing, vocabulary/label dictionaries, n-grams, cache keys, TSV validation, label formatting	sub-skills/data-preparation/SKILL.md	Validates and normalizes the repo's Zhihu-style data formats before a model run.
fastTextB, TextCNN, TextRNN, TextRCNN, Hierarchical Attention Network, BERT, toy TFLearn examples, graph-shape inspection	sub-skills/classification-models/SKILL.md	Covers the classic flat classifier families and their checkpoints, shapes, and losses.
Seq2seq with attention, Transformer seq2seq/classifier, EntityNetwork, Dynamic Memory Network, _GO/_END/_PAD label generation	sub-skills/sequence-and-memory-models/SKILL.md	Covers sequence-generation and memory-network style workflows.
Two-sentence relation classification, TextCNN+RCNN hybrids, boosting label weights, ensemble logit fusion	sub-skills/relation-and-ensemble-workflows/SKILL.md	Covers paired-input workflows and post-processing of exported logits.

Shared references

• references/model-overview.md — concise model-family selection guide for the repo.
• references/troubleshooting.md — cross-cutting failure patterns that affect more than one sub-skill.
• references/repo-provenance.md — commit, dirty-state, and evidence-path snapshot for staleness checks.
• references/repo-routing-metadata.json — structured metadata consumed by the repo-skills router during import.

Shared scripts

• scripts/check_legacy_text_classification_env.py — safe import/environment smoke check for the legacy TensorFlow 1.x stack and common helper modules.

Operating rules

• Prefer the closest sub-skill instead of trying to solve every workflow from the root.
• Treat README performance numbers as historical context, not a reproduction guarantee.
• Expect external HDF5 caches, pickle vocabularies, pretrained word2vec binaries, checkpoint directories, and long runs for full training/prediction.
• Use data-preparation first whenever raw line formats, cache keys, or label encodings are unclear; many downstream failures start there.
• Use classification-models for the main flat classifiers; use sequence-and-memory-models only when label generation or memory context is part of the contract.
• Use relation-and-ensemble-workflows for two-input relation tasks, hybrid CNN+RCNN variants, boosting, and logit fusion instead of trying to adapt a flat classifier ad hoc.

When to cross-check

• If a task mentions a specific model family, read that sub-skill's references before making graph or data claims.
• If a task mentions checkpoint restore, label-map mismatch, or fixed batch sizes, read the appropriate troubleshooting page before rerunning anything.
• If you are considering a refresh for another checkout, compare the current repository state to references/repo-provenance.md first.