admin/frontend/controls.html

@@ -867,7 +867,7 @@ function classifierModeFieldsHtml(mode, data) {
       <div class="row g-2">
         <div class="col-md-6">
           <input class="form-control form-control-sm" id="classifier-encoder-model"
-                 placeholder="model (default: MoritzLaurer/deberta-v3-base-zeroshot-v2)"
+                 placeholder="model (default: facebook/bart-large-mnli)"
                  value="${escapeHtml(enc.model || '')}">
         </div>
         <div class="col-md-3">

config/config.yaml

@@ -707,7 +707,7 @@ classifier:
   # Read only when mode: local_encoder. Every field has a default, so
   # `encoder: {}` is enough to opt in.
   # encoder:
-  #   model: MoritzLaurer/deberta-v3-base-zeroshot-v2
+  #   model: facebook/bart-large-mnli
   #   device: cpu
   #   confidence_threshold: 0.5
 

docs/local-models.md

@@ -213,10 +213,24 @@ rarer each time, but never structurally impossible, because a chat-completion
 model can always in principle spend its budget thinking instead of
 answering. `classifier.mode: local_encoder` sidesteps the whole failure class
 instead of picking around it: a zero-shot NLI encoder
-(`classifier.encoder.model`, default `MoritzLaurer/deberta-v3-base-
-zeroshot-v2`) scores the task directly against `proficiency.categories` and
-returns a label plus a confidence — there is no generation step, so there is
-no trace to run away.
+(`classifier.encoder.model`, default `facebook/bart-large-mnli`) scores the
+task directly against `proficiency.categories` and returns a label plus a
+confidence — there is no generation step, so there is no trace to run away.
+(Was `MoritzLaurer/deberta-v3-base-zeroshot-v2` — smaller, ~184M vs ~407M
+params — until that repo started returning 401 on an unauthenticated GET of
+its own model page sometime after this project picked it, discovered live
+2026-09-06 when it took production down: the startup check correctly
+refused to boot rather than fail opaquely on the first request, but the
+service still crash-looped until the default was corrected.)
+
+`classifier.encoder.confidence_threshold` is a `[0.0, 1.0]` probability
+(`classify_zero_shot`'s own output), not a percent — the admin UI takes 0-100
+for a human to type and converts at the save boundary, but a config file
+edit or any other caller must use the raw probability. Config load now
+validates the range; a value like `80` used to be silently accepted and
+would make every real confidence score read as below-threshold, since none
+can exceed `1.0` (also caught live 2026-09-06, before a restart made it
+active).
 
 The trade is real, not free. It only produces `task_category` — no `task_tier`
 signal exists in a zero-shot label score, so tier falls back to

src/config.py

@@ -791,7 +791,15 @@ class LocalEncoderConfig(StrictModel):
     learn from without a new, separate opt-in data-capture feature.
     """
 
-    model: str = "MoritzLaurer/deberta-v3-base-zeroshot-v2"
+    # facebook/bart-large-mnli -- the reference model HuggingFace's own docs
+    # use for this exact pipeline. Was MoritzLaurer/deberta-v3-base-zeroshot-v2
+    # (smaller, ~184M vs ~407M params) until that repo started returning 401
+    # even on an unauthenticated GET of its model page -- gated or moved
+    # sometime after this project picked it. Caught live 2026-09-06: the
+    # startup check (ensure_available) correctly refused to boot rather than
+    # fail opaquely on the first request, but it still took production down
+    # until the default was fixed.
+    model: str = "facebook/bart-large-mnli"
     device: Literal["cpu", "cuda"] = "cpu"
     # Below this, the classification is treated as a FAILURE, not a low-
     # confidence answer -- the caller cascades exactly as it would for a

tests/test_classifier_modes_config.py

@@ -122,7 +122,7 @@ def test_local_encoder_with_empty_encoder_block_loads_with_defaults(raw):
     cfg["classifier"]["mode"] = "local_encoder"
     cfg["classifier"]["encoder"] = {}
     loaded = RouterConfig(**cfg)
-    assert loaded.classifier.encoder.model == "MoritzLaurer/deberta-v3-base-zeroshot-v2"
+    assert loaded.classifier.encoder.model == "facebook/bart-large-mnli"
     assert loaded.classifier.encoder.device == "cpu"
     assert loaded.classifier.encoder.confidence_threshold == 0.5