feat: multi-model multi-label classify-warc output#31
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`classify-warc` now scores each record against any number of fasttext models in one pass, and any number of labels per model, instead of one label from one model. `--model-repo`/`--model-file`/`--labels` are parallel-list flags; `*` (the default for `--labels`) expands to every label of that model via `model.get_labels()`. Output columns are `score_<label>` for a single model and `score_m<idx>_<label>` for multiple, so two models can share a label name (e.g. both GneissWeb classifiers emit `__label__cc`) without colliding. Also: `--overwrite` to replace an existing output instead of failing; `--resume-from-output` validates the prior CSV's header is byte-equal to the new run's schema (with a structured added/removed diff on mismatch); per-label stats land in the sidecar as `score.<column>.*`.
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classify-warcnow scores each record against any number of fasttext models in one pass, and any number of labels per model, instead of one label from one model.--model-repo/--model-file/--labelsare parallel-list flags;*(the default for--labels) expands to every label of that model viamodel.get_labels(). Output columns arescore_<label>for a single model andscore_m<idx>_<label>for multiple, so two models can share a label name (e.g. both GneissWeb classifiers emit__label__cc) without colliding.Also:
--overwriteto replace an existing output instead of failing;--resume-from-outputvalidates the prior CSV's header is byte-equal to the new run's schema (with a structured added/removed diff on mismatch); per-label stats land in the sidecar asscore.<column>.*.