L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Données et recherche · SKILL.md

data-preparation

data-preparation

Prepares and inspects Multi30k and WMT-style BPE data for the attention-is-all-you-need-pytorch Transformer workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: data-preparation description: "Prepares and inspects Multi30k and WMT-style BPE data for the attention-is-all-you-need-pytorch Transformer workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Data Preparation

Use this sub-skill when a task asks how to preprocess translation data for this repo, inspect a saved preprocessing pickle, reason about the legacy torchtext schemas consumed by training or translation, or try the repository's BPE logic without downloading WMT corpora.

Route by task

  • Preprocess the default Multi30k German/English example: read references/workflows.md. It covers the spaCy model prerequisites, legacy torchtext version expectations, and the command shape for the default non-BPE path.
  • Inspect an existing *.pkl preprocessing artifact: run scripts/inspect_preprocessed_pickle.py and then read references/data-formats.md to map the reported schema to training/translation consumers.
  • Understand BPE support: read references/workflows.md and references/data-formats.md. The repository's BPE route is WIP and is suitable for training-data preparation experiments, not finished translation decoding.
  • Try BPE locally without WMT downloads: run scripts/bpe_tiny_demo.py. It is a safe, deterministic adaptation of the repository's learn_bpe/apply_bpe behavior for tiny local corpora.
  • Debug preprocessing failures: read references/troubleshooting.md before retrying downloads, changing dependency versions, or assuming a pickle is corrupt.

High-value facts

  • The default executable path calls the non-BPE main_wo_bpe flow. It creates a single pickle containing settings, source/target torchtext Field objects, and train/valid/test example lists.
  • The BPE flow is present but WIP. It requires switching the preprocessing entry point to the BPE main flow, downloads WMT-style archives, writes encoded *.src/*.trg files, and saves only a shared torchtext Field plus settings in the pickle.
  • Token constants are fixed: padding <blank>, unknown <unk>, BOS <s>, and EOS </s>. These must exist in the relevant field vocabularies before training or translation.
  • Custom -data_src/-data_trg files are rejected by the default non-BPE path; do not promise arbitrary local text preprocessing unless a new conversion path is intentionally implemented outside the stock command.

Bundled helpers

# Refuses to unpickle until the caller explicitly acknowledges pickle trust.
python sub-skills/data-preparation/scripts/inspect_preprocessed_pickle.py --help
python sub-skills/data-preparation/scripts/inspect_preprocessed_pickle.py --pickle m30k_deen_shr.pkl --trust-pickle

# Safe BPE demonstration with built-in tiny corpus; no downloads or repo imports.
python sub-skills/data-preparation/scripts/bpe_tiny_demo.py --help
python sub-skills/data-preparation/scripts/bpe_tiny_demo.py --symbols 12 --min-frequency 2

Both scripts are deterministic, runnable from arbitrary current directories, and avoid importing the original repository source. If a task needs model training, checkpoint handling, or translation output, route to the sibling training or translation sub-skill instead.

Skills enregistrées