vera-ai-nlp-generating

name	vera-ai-nlp-generating
description	Server-side extension that completes the full analysis pipeline for NLP text classification after vera-ai-nlp-reviewing has run. Adds SVM, Random Forest, LightGBM classifiers with TF-IDF and optional extra features, subgroup analysis by metadata or text properties, deep learning models (GRU, TextCNN, ALBERT with optional tabular fusion and hyperparameter search), cross-method comparison with unified feature importance on a 0-100 scale, and manuscript-ready methods.md and results.md. Applies output variation and code style diversity for natural, non-repetitive output. Open-source skill. Triggered after vera-ai-nlp-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.
argument-hint	["testing-output-dir-or-dataset"]
allowed-tools	Read, Bash, Write, Edit, Grep, Glob

NLP Text Classification --- Full Analysis & Manuscript Generation

Table of Contents

• Scope Boundary
• Workflow
• Additional Inputs
• Output Structure
• Key References (read before generation)
• Reporting Standards
• Method Status
• Configuration Defaults
• Why These Defaults
• GPU Availability
• Minimal Smoke Test
• Cross-Skill Interface

Open-source skill. Read reference/specs/output-variation-protocol.md before every generation --- apply all variation layers for natural, diverse output.

Scope Boundary

Use this skill when:

• vera-ai-nlp-reviewing has already established a credible baseline and the user wants a fuller model battery.
• The task is supervised text classification, optionally with a small set of numeric side features.

Do not use this skill when:

• The task is generation, retrieval, summarization, sequence labeling, or prompt-only classification.
• The desired method is not in the shipped model list below.
• The available hardware cannot support anything beyond CPU smoke testing and the user expects transformer-scale benchmarking.

Workflow

Continues from where vera-ai-nlp-reviewing stopped (PART 0-2 done).

Step	Responsibility	Executor	Document	Input	Output
Additional ML models	Run Additional Models	Main Agent	workflow/step04-run-additional-models.md	Prior step output	PART 3 code + prose
Subgroup	Analyze Subgroups	Main Agent	workflow/step05-analyze-subgroups.md	Prior step output	PART 4 code + prose
Deep learning	Fit Advanced Models	Main Agent	workflow/step06-fit-advanced-models.md	Prior step output	PART 5 code + prose
Comparison	Compare Models	Main Agent	workflow/step07-compare-models.md	Prior step output	PART 6 code + prose
Manuscript	Generate Manuscript	Main Agent	workflow/step08-generate-manuscript.md	Prior step output	methods.md + results.md

Additional Inputs

Collect if not already provided:

• Target discipline (for reporting conventions)
• Target journal or style (ACL, EMNLP, NeurIPS, etc.)
• Research question / hypothesis
• Subgroup variable or text property for stratification

Output Structure

output/
├── methods.md
├── results.md
├── tables/             ← Markdown + CSV per table
├── figures/            ← PNGs, 300 DPI
├── references.bib
└── code.py             ← Style-varied

Key References (read before generation)

File	Purpose
reference/specs/output-variation-protocol.md	Output quality variation layers
reference/specs/code-style-variation.md	Seven-dimension code style diversity
reference/patterns/sentence-bank.md	4-6 phrasings per result type
reference/rules/reporting-standards.md	Hard rules for ML/DL reporting

Reporting Standards

Same as vera-ai-nlp-reviewing, plus:

• All models: report F1 (weighted) and AUC (macro) with bootstrapped 95% CIs
• Deep learning: report training epochs, best epoch, learning rate, batch size
• ALBERT: report pre-trained model name, whether base was frozen
• Feature importance: unified 0-100 scale across ML and DL models
• Model comparison: frame as convergent findings, not horse race
• Tree-based with small N: frame as "exploratory"; never claim generalizability

Method Status

Status	Methods
Implemented in this skill	SVM, Random Forest, LightGBM, GRU, TextCNN, ALBERT
Implemented optional variants	Text + extra-feature fusion where the corresponding *_extra.py module exists
Not shipped in the open-source build	BERT, RoBERTa, DeBERTa, SetFit, prompt-only classifiers, few-shot API-based methods

Configuration Defaults

Pipeline constants live in config/default.json. Read it before generation. Adds to the testing-skill config:

• ml_models.* — SVM/RF/LightGBM hyperparameter grids
• deep_models.* — GRU/TextCNN/ALBERT hyperparameter grids, epochs, batch size, learning rate
• gpu.{required_for, fallback_models} — e.g., ALBERT requires GPU; falls back to GRU+TextCNN if unavailable (see GPU Availability section below)
• feature_importance.scale (0, 100) — unified scale across model families
• subgroup.{min_size, interaction_alpha} — subgroup-analysis gating

To override: create config/local.json.

Why These Defaults

• ALBERT is the shipped transformer because it is materially lighter than full BERT-style baselines while still exercising a modern contextual encoder path.
• GRU + TextCNN are the CPU fallback because they preserve two complementary inductive biases, sequential recurrence and local n-gram filters, without pretending a transformer run happened.
• Tree-based models on sparse text remain marked "exploratory" at small N because they are useful robustness checks, not generally the most defensible primary NLP model.

GPU Availability

Check at workflow step 06 (advanced models) start. Implementation pattern:

import torch
GPU_AVAILABLE = torch.cuda.is_available()
MODELS_TO_FIT = ["GRU", "TextCNN"] + (["ALBERT"] if GPU_AVAILABLE else [])
if not GPU_AVAILABLE:
    print("NOTE: CUDA not available; falling back to GRU + TextCNN. ALBERT skipped.")

Do NOT silently proceed without a model — the fallback must always include at least GRU + TextCNN on CPU.

Minimal Smoke Test

• Smoke-test prompt: "Continue from vera-ai-nlp-reviewing on a 4-class 20 Newsgroups subset. Fit the additional ML models and the CPU-safe deep models, then produce the manuscript-ready artifacts."
• CPU pass condition: SVM, Random Forest, LightGBM, GRU, and TextCNN run; ALBERT may be skipped if CUDA is unavailable, but the fallback decision must be logged.
• Expected artifacts: methods.md, results.md, tables/, figures/, references.bib, and one consolidated code.py.

Cross-Skill Interface

Method Unit Contract:
├── code_python      → .py script (style-varied)
├── methods_md       → methods.md (varied structure)
├── results_md       → results.md (varied phrasing)
├── tables/          → Markdown + CSV
├── figures/         → PNGs 300 DPI (varied layout)
├── references_bib   → .bib with cited references
└── comparison       → cross-method narrative (in results.md)