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name: classification description: "Construct and adapt XLNet run_classifier.py classification and regression workflows for processor-backed tasks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

XLNet classification/regression sub-skill

Use this sub-skill when the task is to fine-tune, evaluate, or generate predictions with XLNet's processor-backed run_classifier.py workflow.

Best-fit tasks

• GLUE MNLI matched and mismatched classification with task_name=mnli_matched or task_name=mnli_mismatched.
• GLUE STS-B sentence-pair regression with task_name=sts-b and --is_regression=True.
• IMDB binary sentiment classification with task_name=imdb.
• Yelp-5 review classification with task_name=yelp5.
• Similar classification/regression jobs after reshaping data to one of the supported processor layouts or adding a new processor in the caller's working copy.

Route elsewhere

• SQuAD span question answering belongs in ../squad-qa/.
• RACE multiple-choice reading comprehension belongs in ../race-reading-comprehension/.
• Direct XLNet graph/model API work, new losses, custom classifier heads, tokenizer internals, or checkpoint surgery belongs in ../model-api/.

Operating procedure

1. Identify the task processor and raw data layout in references/data-formats.md. Do not assume arbitrary TSV columns are configurable: the built-in processors hard-code filenames, columns, and labels.

2. Separate the runtime paths before generating a command:

   • data_dir: raw GLUE/IMDB/Yelp data.
   • output_dir: generated TFRecord cache.
   • model_dir: fine-tuned checkpoints and TensorFlow event files.
   • init_checkpoint: released or already fine-tuned checkpoint used only to initialize variables, normally during training.
   • predict_dir: prediction TSV/logit JSON destination for --do_predict=True.

3. Use the bundled safe command generator, not a source notebook or shell snippet, to produce a command template:

   python scripts/build_classifier_command.py --help
   

   The generator prints a python run_classifier.py ... command and never downloads data, launches training, or opens checkpoints.

4. Apply the task-specific workflow guidance in references/workflows.md. For STS-B, keep regression mode enabled in every train/eval/predict command.

5. Check CLI semantics in references/cli-reference.md, especially GPU/TPU flags, per-GPU train_batch_size, eval_all_ckpt, and prediction outputs.

6. Before running an expensive job, review references/troubleshooting.md for stale TFRecord caches, missing model artifacts, bad dataset layout, GPU memory limits, TPU/GCS path mismatches, and multi-GPU eval pitfalls.

High-signal guardrails

• Supported task_name values in this sub-skill are exactly mnli_matched, mnli_mismatched, sts-b, imdb, and yelp5.
• sts-b is regression. Always pass --is_regression=True; evaluation sorts by eval_pearsonr instead of eval_accuracy.
• Multi-GPU training uses TensorFlow MirroredStrategy; in this codebase, train_batch_size is per GPU, not global.
• Evaluate on one GPU unless the caller has intentionally audited sharding. The original workflow separates multi-GPU training from single-GPU evaluation to avoid incorrect metrics.
• Keep model_dir separate from the pretrained init_checkpoint directory. Eval-all-checkpoints should scan fine-tuned checkpoints in model_dir, not the released model directory.
• output_dir is a preprocessing cache. Change it or pass --overwrite_data=True when changing task data, sequence length, SentencePiece model, or regression/classification mode.