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name: text-and-sequence description: "Routes TensorLayer NLP, word-embedding, PTB, and seq2seq workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Text and Sequence

Use this sub-skill for TensorLayer NLP helpers, word-embedding utilities, PTB iteration, text generation, and seq2seq models. This is the route for text workflows that rely on TensorLayer's sequence APIs.

Typical requests

• Build or inspect a text vocabulary.
• Generate skip-gram batches for word embedding.
• Work with PTB or text-generation examples.
• Instantiate Seq2seq or Seq2seqLuongAttention.
• Sample from a probability vector or map words to ids.

Read first

• references/nlp-reference.md for the text and sequence API surface.
• references/workflows.md for tiny vocabulary, skip-gram, PTB, and seq2seq patterns.
• references/troubleshooting.md for vocabulary, sequence-shape, and dataset-download failures.

Bundled check

• scripts/smoke_text.py exercises sentence processing, vocabulary building, skip-gram batches, PTB iteration, probability sampling, and tiny seq2seq construction.

Boundaries

Include here:

• tensorlayer.nlp
• tensorlayer.models.seq2seq
• tensorlayer.models.seq2seq_with_attention
• PTB and text-generation workflows
• word embedding and sampling helpers

Exclude or route elsewhere:

• generic data preprocessing and TFRecord helpers -> data-and-utilities
• core layer/model architecture -> core-modeling
• training loops and CLI help -> training-and-cli
• vision or RL workflows -> vision-and-apps / reinforcement-learning

Fast path

1. Identify whether the request is about tokenization, vocabulary, sampling, or sequence modeling.
2. Prefer a tiny synthetic corpus before any downloaded dataset.
3. Use the smoke script to validate the basic text utilities and model constructors.