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name: model-api description: "Use XLNet TensorFlow graph APIs, config objects, tokenization helpers, checkpoints, losses, optimizers, and runtime helpers directly." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model API

Use this sub-skill for direct XLNet model work: graph construction, config inspection, tokenization, checkpoint initialization, optimizer setup, and diagnosis of API/runtime failures.

Route Here

• Inspect or create XLNetConfig, RunConfig, and XLNetModel.
• Decide whether a custom head should consume get_pooled_out() or get_sequence_output().
• Use modeling.transformer_xl, modeling.lm_loss, or modeling.summarize_sequence for custom graph work.
• Attach function_builder losses for classification, regression, SQuAD-style QA, or RACE-style multiple choice.
• Set up checkpoint loading, training ops, TPU config, or multi-GPU gradient handling.
• Normalize text and turn it into SentencePiece pieces or ids with prepro_utils.

Reroute Elsewhere

• Downstream task CLIs belong in the task-specific sub-skills for classification, SQuAD QA, or RACE.
• Corpus TFRecord generation and pretraining loops belong in data-pretraining.
• If your task is mostly command assembly rather than graph/API work, do not stay in model-api.

Fast Workflow

1. Run scripts/inspect_xlnet_config.py <xlnet_config.json> first. The config must define n_layer, d_model, n_head, d_head, d_inner, ff_activation, untie_r, and n_token.
2. Load XLNetConfig and RunConfig from the JSON or flags. Keep input_ids, seg_ids, and input_mask in [len, bsz] order when calling XLNetModel.
3. Pick the right output:
   • get_pooled_out() for a single vector per example.
   • get_sequence_output() for token-level or span-level heads.
   • get_new_memory() when you need streaming memory.
   • get_embedding_table() when you need tied embeddings or LM projection.
4. Build task heads with the shared initializer from get_initializer() and then apply classification_loss, regression_loss, get_qa_outputs, get_race_loss, or lm_loss as appropriate.
5. For runtime wiring, call model_utils.init_from_checkpoint() before the first session run and model_utils.get_train_op() after the loss is defined. Use gpu_utils only when you need manual tower placement or gradient averaging.
6. Preprocess raw text with prepro_utils.preprocess_text() before encode_pieces() or encode_ids(), and make sure the SentencePiece model from the release zip is available.

References

• references/api-reference.md
• references/checkpoints-and-runtime.md
• references/troubleshooting.md
• scripts/inspect_xlnet_config.py