[CI only] Vectorize TFIDF.transform_many - #1
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Builds the document-term count matrix once and applies the tf / idf / L2 steps with scipy.sparse diagonal scaling, instead of computing the weights per document in Python. Output is unchanged (the existing parity tests, including the sklearn one, still pass), but it scales much better on large corpora: on 20 newsgroups (~11k docs) the count-matrix build lands roughly on par with sklearn's whole fit_transform.
The mypy pre-commit hook installed numpy unpinned, so CI pulled a newer numpy whose type stubs use the 3.12 'type' statement, which mypy rejects under --python-version=3.11 (the run aborts on the first such stub). Pin it to 2.4.3, the version already in uv.lock, matching the existing 'same as uv.lock' convention for ruff/mypy.
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Internal self-PR to run CI on the fork. Vectorizes transform_many + pins numpy for the mypy hook. Not for merge.