2.21.9

2.21.9 (2026-09-29)

Documentation

• docs: Add blogs (#5521)

• add blogs

• fix eof

• format

• try to update lock

• remove todo comment (28aea48)

Fix

• fix: repair NanoVDR document encoding path (#5552)

• fix: repair NanoVDR document encoding path

nanovdr/NanoVDR-S-Multi fails at document-encoding time with an
ImportError, and has done since beee210 (#4699). Two bugs, both past
_load_teacher():

1. _load_teacher imports _build_qwen3_vl_for_embedding_class from
   qwen3_vl_embedding_models, but #4699 deleted that factory when it
   moved Qwen3VLEmbeddingWrapper from AbsEncoder to
   InstructSentenceTransformerModel. Recovered it verbatim from
   beee2102^ and vendored it here: it was private, and this wrapper is
   its only remaining consumer, so re-adding it to that module would
   partly undo #4699. Only additions are the type annotations current
   lint requires; Cache/Qwen3VLConfig stay under TYPE_CHECKING so
   the module remains importable without torch (#5463).

2. _encode_queries passed convert_to_numpy=False while leaving
   convert_to_tensor at its default, which sentence-transformers
   documents as returning list[Tensor]. mteb._convert_to_tensor then
   raises "only one element tensors can be converted to Python scalars".
   Now convert_to_tensor=True, plus .cpu() to match
   _encode_documents -- cos_sim does no device harmonisation, so a
   CUDA query matrix against a CPU corpus matrix would fail.

The import is lazy and only runs when encoding documents, so
mteb.get_model() and query encoding both worked and the breakage went
unnoticed for ~3.5 months.

Verified end to end on VidoreTabfquadRetrieval: the teacher loads, the
corpus encodes, and the run completes past the similarity step that
previously raised.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

• refactor: drop the vendored Qwen3VL wrapper

The vendored _build_qwen3_vl_for_embedding_class turns out to be
redundant. Qwen3VLModel is already the LM-head-less trunk and its
forward returns Qwen3VLModelOutputWithPast.last_hidden_state, which is
all the pooling in _encode_documents needs. Its
_checkpoint_conversion_mapping = {} override was dead weight too: that
attribute is not set on Qwen3VLPreTrainedModel, Qwen3VLModel, or
Qwen3VLForConditionalGeneration in current transformers.

The one thing the wrapper did provide was resetting rope_deltas per
forward -- Qwen3VLModel.forward reads self.rope_deltas but never
resets it -- so that moves to the call site.

Verified against the previous commit:

• from_pretrained loads 625/625 weights with no init warnings, so the
  checkpoint keys match Qwen3VLModel directly
• same input gives a bitwise identical last_hidden_state (max abs
  diff 0.0)
• MockAny2AnyRetrievalT2I gives identical values for all 149 metrics

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

---

Co-authored-by: Claude Opus 5 <noreply@anthropic.com> (75b4800)

Refactor

• refactor: move DS1000Retrieval to v2 dataset format (#5542)

Point DS1000Retrieval at the re-uploaded mteb/DS1000Retrieval dataset
and drop the custom load_data, same as #5495.

Part of #3424. (94eec4e)

Unknown

• fix bugs in docs (#5535)

• fix bugs in docs

• merged other task types

• fix links related to get_tasks (d98f640)

• Revert "dataset: add MM-BRIGHT retrieval tasks" (#5531)

Revert "dataset: add MM-BRIGHT retrieval tasks (#5168)"

This reverts commit 5d3212e. (c7fc65d)