- Bump sentence-transformers lower bound 5.2.0 → 5.4.0. The new ranker /
  embedder paths (cross_encoder[0] subscript, sentence_transformers.
  sentence_transformer subpackage, get_embedding_dimension) all landed
  in 5.4.0; the previous floor would have ModuleNotFoundError'd /
  AttributeError'd anyone resolving 5.2.x–5.3.x.
- Constrain EmbedderFineTuningConfig.warmup_ratio to (0, 1). v5
  TrainingArguments interprets warmup_steps>=1 as a raw step count
  and <1 as a fraction, so a stray warmup_ratio=1.0 would silently
  produce one warmup step instead of full-training warmup.
- Refresh tests/test_deps.py synthetic metadata fixtures to v5
  version strings so the resolver tests exercise the version range
  we ship, not the v4 range we just left behind.
- Trim the v4→v5 narrating comments down to the WHY of the current
  code; per-line migration history belongs in the commit log.

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

Reviewer flagged that `gt=0` rejects the legal `warmup_ratio=0.0` config
(disable warmup). Relax to `ge=0`; `lt=1` is kept because that's the
v5 boundary where warmup_steps flips from ratio to raw step count.

Regenerate the published JSON schema so it reflects the constraint —
otherwise YAML authoring against the schema would pass schema
validation and fail at runtime.

Pushed back on the reviewer's claim that `warmup_steps=0.1` runs zero
warmup: transformers v5 typed `warmup_steps: float` and `get_warmup_steps`
branches on `>= 1`, not `> 0` — `0.1` takes the `math.ceil(N * 0.1)`
fraction branch (training_args.py:2089 in v5.12.1).

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

- _bert.py: coerce label2id/id2label keys to str. huggingface_hub 1.x
  StrictDataclassFieldValidationError rejects int-keyed label2id; the
  v5 AutoModelForSequenceClassification.from_pretrained pipeline now
  routes through that validator, so the previous {int: int} mapping
  raised on every BertScorer.fit (and cascaded into a fallback
  hf_hub_download call that the test guard caught as 'unpinned').
- ranker.py: cast cross_encoder[0] to Any for auto_model.classifier
  access (nn.Sequential.__getitem__ is typed Tensor | Module on v5);
  add arg-type ignores on CrossEncoder.predict(list[tuple[str,str]])
  calls — the v5 stub demands the much wider Sequence type but the
  list-of-pairs form is the documented call shape.
- Drop type: ignore comments mypy now reports as unused
  (AutoTokenizer.from_pretrained gained a typed stub in transformers
  v5; max_length matches TokenizerConfig.max_length cleanly).
- conftest.py: SentenceTransformer's constructor is typed Any on v5,
  so add no-any-return ignore at the fixture boundary.

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

A future refactor sees `{str(i): i}` as a no-op coercion and "simplifies"
back to `{i: i}`; mypy passes, then BertScorer.fit raises
StrictDataclassFieldValidationError at runtime. Comment makes the WHY
explicit at the call site, matching the WHY-only comment policy from
14f9576.

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

When PEFT is installed, transformers v5 calls find_adapter_config_file
on every AutoModelForSequenceClassification.from_pretrained. The
auto_factory only propagates `_commit_hash` (used for the cache
lookup) but NOT the outer `revision` to the fall-through
hf_hub_download. On a cold cache — i.e. our CI warm-cache job, which
populates model files but no negative marker for adapter_config.json —
that probe fires `hf_hub_download(repo_id, adapter_config.json,
revision=None)` and our test guard rightly flagged it as unpinned.

Pass `adapter_kwargs={"revision": revision}` so the adapter probe
inherits the pin. The first run still writes a `.no_exist` marker, but
all subsequent runs (and CI's pinned-only contract) stay clean.

Reproduces with: rm -rf ~/.cache/huggingface/hub/models--prajjwal1--bert-tiny/.no_exist
then pytest tests/pipeline/test_inference.py::test_inference_from_config[multiclass].

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

PEFT's get_peft_model_state_dict (save_and_load.py:380-384) runs an
embedding-resize sanity check on every Trainer.save_checkpoint by
calling model.config.__class__.from_pretrained(base_model_name_or_path)
with no revision. transformers fills in revision='main' as the default,
so the call hits hf_hub_download('prajjwal1/bert-tiny',
'config.json', revision='main') — unpinned, which our CI guard
correctly flags. On a cold cache (CI), this trips on every
LoRA/PTuning trial that runs through Trainer.

Clear base_model_name_or_path on the peft_config after get_peft_model
so the vocab check short-circuits at `if model_id is not None`. Our
dumper (PeftModelDumper / HFModelDumper) saves the base model
separately and the load path passes it explicitly, so the adapter
config doesn't need to remember it.

Reproduces with:
  rm -rf ~/.cache/huggingface/hub/models--prajjwal1--bert-tiny/.no_exist
  pytest tests/pipeline/test_inference.py::test_inference_from_config[multiclass]

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