界面已本地化;技能介绍与说明文档按所选语言由 AI 自动翻译,代码、命令、路径和专业术语保留原文,以原作者文档为准。
探索技能
数据与研究 · SKILL.md

training

training

【数据与研究】Teach Donut fine-tuning, dataset layout, checkpoint handling, and evaluation.

独立 GitHub 快照 · 自动分类 · 以原作者文档为准

技能说明

GitHub

name: training description: "Teach Donut fine-tuning, dataset layout, checkpoint handling, and evaluation." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training

Use this sub-skill when the request is about Donut fine-tuning, dataset JSONL layout, validation metrics, checkpoint layout, or checkpoint evaluation.

Route here for

  • training or fine-tuning Donut on CORD, DocVQA, RVL-CDIP, TrainTicket, or a similar structured-document task
  • inspecting metadata.jsonl
  • understanding gt_parse versus gt_parses
  • comparing validation metrics or test scores
  • resuming a run from a saved checkpoint

Do not route here for

  • single-image inference or prompt construction details → ../inference/SKILL.md
  • the Gradio demo or app launch → ../inference/SKILL.md
  • SynthDoG dataset generation → ../synthdog/SKILL.md

Read first

  • references/workflows.md
  • references/configuration.md
  • references/troubleshooting.md

Fast rules

  • Training requires CUDA; the bundled scripts/train_donut.py preserves the source GPU, DDP, and 16-bit execution assumptions.
  • scripts/train_donut.py exposes only --config and --exp_version; all other overrides are passed through sconf.
  • metadata.jsonl stores file_name plus a JSON-encoded ground_truth string with either gt_parse or gt_parses.
  • val_metric is normalized edit distance, so lower is better.
  • test.py reports TED-based accuracy and F1, so higher is better.

Useful links inside this skill tree

  • Core API reference: ../../references/api-reference.md
  • Prompt-level prediction notes: ../inference/SKILL.md
  • Synthetic data creation: ../synthdog/SKILL.md

Bundled helpers

  • scripts/train_donut.py plus scripts/lightning_module.py
  • scripts/check_training_config.py
  • scripts/evaluate_dataset.py
  • references/configs/train_*.yaml

Default workflow

  1. Validate the config and local metadata with scripts/check_training_config.py.
  2. Train with the bundled scripts/train_donut.py or compare a checkpoint with scripts/evaluate_dataset.py.
  3. Use the tables in references/configuration.md and references/workflows.md before comparing runs.
我的收藏