计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
text-and-tabular【效率与协作】Operate AutoTrain Advanced text, NLP, sentence-transformers, extractive QA, and tabular training workflows.
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Use this sub-skill for non-LLM text tasks, sentence-transformers, extractive QA, and tabular workflows.
autotrain text-classification --helpautotrain text-regression --helpautotrain token-classification --helpautotrain seq2seq --helpautotrain sentence-transformers --helpautotrain tabular --helpautotrain extractive-qa --helptext-classification, text-regression, token-classification, seq2seq, extractive-qa, sentence-transformers:pair, st:triplet, and tabularIf the request is LLM-specific, route to ../llm-training/; the data validator in this sub-skill still supports --task llm for local column checks.
| Family | Typical data shape | Notes |
|---|---|---|
| text classification/regression | text column + target/label column | Binary and multi-class classification resolve to the same text-classification trainer family. |
| token classification | token list column + tag list column | Values may be Python-list strings that AutoTrain parses with ast.literal_eval. |
| seq2seq | source text + target text | Use for summarization/translation-style data. |
| extractive QA | context text + question + answer dict/string | Answer values should be dict-like or parseable into dicts. |
| sentence-transformers | pair/triplet/QA rows | Trainer variants control whether target labels/scores are needed. |
| tabular | id column + one or more target columns | task distinguishes classification vs regression. |
inspect_cli.py helper.validate_config.py helper.scripts/validate_text_data.py.python skills/disco/autotrain-advanced/sub-skills/text-and-tabular/scripts/validate_text_data.py \
--task text-classification \
--text-column text \
--target-column label \
data.csv
The script performs local schema checks only. It does not import trainer code, upload data, or start training.
references/workflows.md — task/alias map and command/config patterns.references/data-formats.md — column schemas and validator examples.references/troubleshooting.md — column mapping, parser, Hub, and local dataset recovery.