New mteb/api subpackage exposes the leaderboard data as a FastAPI
service backed by ResultCache + the existing polars summary builders.
Routes mirror the SvelteKit frontend's data needs: benchmark menu,
benchmark detail, and prerendered summary tables. CORS origins,
preload, and cache locations come from settings.

Dockerfile clones mteb@api, installs .[api], and serves uvicorn on
:7860 as UID 1000 — drop-in for a Hugging Face Space.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

`RUN git clone` always produces the same layer hash because the command
string never changes, so HF Spaces was rebuilding the image on top of a
stale checkout — the cors_origins NoDecode fix never made it into the
running container. Pull the latest commit SHA from GitHub via ADD just
before the clone; ADD invalidates the layer whenever the response body
changes, which forces a fresh clone per push.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

# Conflicts:
#	mteb/benchmarks/benchmark.py

The api module needed only this one-line helper from
mteb.leaderboard.app, but importing it pulled in gradio, pandas, and
cachetools — none of which belong in the [api] extra. Promoting it to
a property on ResultCache lets every consumer (api, leaderboard,
bench script) reach the path without dragging the Gradio stack into
the API container.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Drops the cold-start cost of cloning the GitHub results repo on first
request by pulling the same data from huggingface.co/datasets/mteb/results
during image build. Goes into the default huggingface_hub cache under
HF_HOME so callers reach it via the standard hub APIs. The download is
guarded with `|| true` so it stays non-fatal while the dataset is still
being populated upstream — the API just falls back to the GitHub clone
on first request.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

The results-repo sync now pushes one HF dataset config per benchmark
(plus a ``default`` config holding every result, deduped). Rewires the
API consumer to match:

* ``_load_from_hub`` enumerates configs and ``load_dataset(name=cfg,
  split='train')`` each. A failure on one config no longer poisons the
  whole load.
* ``_load_per_benchmark_frames`` collapses to two paths — hub or cold
  rebuild — and returns a ``(per_benchmark, all_results)`` tuple
  instead of the ``_LoadedFrames`` dataclass. The two named wrappers
  (``get_all_benchmark_frames`` / ``get_all_results_df``) go away;
  callers destructure inline.
* Hub-supplied ``default`` config short-circuits the per-benchmark
  concat for the unified view.

Other follow-ups:

* ``BenchmarkResults`` gains ``load_leaderboard_frame`` and
  ``split_leaderboard_frame`` so loading the raw combined frame can be
  decoupled from splitting it. The new
  ``_split_by_benchmark_tasks`` filters via an inner join on
  ``(task_name, split, subset)`` tuples — off-spec subsets/splits no
  longer leak through to ``_create_summary_table``'s
  ``group_by(model_name, task_name).mean()``.
* ``MTEB_API_CACHE_REPO`` moves to ``Settings`` alongside
  ``cors_origins`` / ``preload``; consumers go through
  ``settings.cache_repo()``.
* /robots.txt added to silence Space probes.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Adds the MVEB (Massive Video Embedding Benchmark) benchmark objects to
main so the leaderboard and get_benchmark() can resolve them. The
underlying tasks are already on main; this adds only the curated
benchmark groupings and their registration.

- benchmarks.py: MVEB (23 tasks), MVEB(text, video) (19), MVEB(video)
  (9), MVEB(beta, extended) (184, alias MVEB(extended)).
- benchmarks/__init__.py: import + __all__ registration.
- _leaderboard_menu.py: new "Video" group under General Purpose.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

# Conflicts:
#	pyproject.toml

# Conflicts:
#	pyproject.toml