---

name: xlnet description: "Operate the legacy TensorFlow 1.x XLNet repository for model APIs, classification, SQuAD, RACE, and pretraining workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

XLNet repo skill

Use this skill when a Researcher task involves the original zihangdai/xlnet TensorFlow 1.x repository: released XLNet checkpoints, xlnet_config.json, spiece.model, source-script fine-tuning, SQuAD/RACE workflows, pretraining data, or direct XLNet graph APIs.

Before you act

1. Confirm the caller wants the original TensorFlow 1.x XLNet code, not modern Hugging Face transformers.XLNet* APIs.
2. Collect the model bundle paths: xlnet_config.json, spiece.model, and, for training, the TensorFlow checkpoint prefix.
3. Identify the workflow family and route to the focused sub-skill below.
4. Run scripts/check_xlnet_environment.py --help when you need a safe import/config diagnostic. It does not download data, open checkpoints, or launch training.
5. Read references/model-overview.md for released model artifacts, memory anchors, and hardware expectations.
6. Read references/troubleshooting.md for cross-cutting TensorFlow 1.x, protobuf, Abseil flag, checkpoint, and backend failures.
7. Read references/repo-provenance.md before deciding whether this skill is stale for a checkout.

Runtime prerequisites

• Legacy TensorFlow 1.x runtime. TensorFlow 2.x lacks several tf.contrib APIs used by the repository.
• sentencepiece, numpy, and task-specific metric packages such as scipy/scikit-learn for RACE metrics.
• An XLNet source checkout or runtime where source modules such as xlnet, modeling, run_classifier, run_squad, and run_race are importable.
• Released or fine-tuned model artifacts supplied by the user; this skill does not bundle checkpoints, tokenizer models, or datasets.
• GPU/TPU hardware only when the selected workflow requires actual training or large evaluation. Many checks and command builders are CPU-safe.

Minimal diagnostic from a runtime where XLNet source is importable:

python scripts/check_xlnet_environment.py --repo-root /path/to/xlnet --config /path/to/xlnet_config.json

If no checkout path is needed because the modules are already importable, omit --repo-root.

Route map

User task	Read next	Why
Use XLNetConfig, RunConfig, XLNetModel, tokenizer helpers, losses, checkpoint loading, or custom TensorFlow graph code.	sub-skills/model-api/SKILL.md	Covers verified signatures, tensor shapes, config validation, and runtime helpers.
Fine-tune/evaluate/predict GLUE MNLI, STS-B, IMDB, Yelp-5, or processor-backed classification/regression through run_classifier.py.	sub-skills/classification/SKILL.md	Covers task names, data layouts, train/eval/predict flags, and command generation.
Preprocess or fine-tune SQuAD 1.1/2.0, generate predictions, decode n-best answers, or handle no-answer thresholds.	sub-skills/squad-qa/SKILL.md	Covers SQuAD JSON, feature caches, GPU/TPU recipes, prediction outputs, and threshold metrics.
Run RACE multiple-choice reading-comprehension training/evaluation or adapt TPU RACE recipes.	sub-skills/race-reading-comprehension/SKILL.md	Covers RACE directory layout, high/middle filtering, four-candidate batching, and TPU command templates.
Prepare raw text or token-id corpora for pretraining, generate TFRecords, or build train_gpu.py/train.py pretraining commands.	sub-skills/data-pretraining/SKILL.md	Covers corpus format, SentencePiece assumptions, record_info files, and pretraining command builders.

Operating guardrails

• Do not import several task scripts in the same long-lived Python process; their Abseil flags collide. Inspect one CLI module per process.
• Keep data_dir, output_dir, model_dir, predict_dir, and init_checkpoint conceptually separate.
• Treat output_dir as a cache. Use a new cache or overwrite flag when sequence length, task data, tokenizer, or split changes.
• For multi-GPU task fine-tuning, num_core_per_host means number of GPUs and train_batch_size is per GPU.
• Prefer command-builder scripts in the sub-skills when assembling long commands. They print commands only and avoid accidental training, downloads, or GCS writes.
• Do not treat README result numbers as proof for a new run. They are context anchors; real results require matching data, checkpoints, hardware, and training time.

Bundled root files

• references/model-overview.md: released models, artifact contracts, memory table, and result anchors.
• references/troubleshooting.md: shared runtime, artifact, and backend troubleshooting.
• references/repo-provenance.md: source snapshot and refresh baseline.
• references/repo-routing-metadata.json: structured metadata for a future managed repo-skill import.
• scripts/check_xlnet_environment.py: safe runtime/config diagnostic helper.