KingTechnician
/

osmosis-crossencoder-binary

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 ---
+tags:
+  - deberta-v3
+  - cross-encoder
+  - osmosis
+  - response-sufficiency
+  - binary-classification
+language: en
+license: mit
+base_model: MoritzLaurer/deberta-v3-base-zeroshot-v2.0
+datasets:
+  - KingTechnician/yahoo-answers-osmosis-labeled
+  - KingTechnician/triage-synthetic-data-v1
 ---
+# OSMoSIS Binary Cross-Encoder
+DeBERTa-v3 cross-encoder for binary response-sufficiency classification.
+Given `(objective, response)`, predicts ADDR (response addresses the objective)
+or NOADDR (response does not).
+## Intended use
+First stage of a cascaded pipeline. Confident binary predictions are used
+directly; low-confidence cases should route to an LLM judge for fine-grained
+classification.
+Trained on Sonnet-4.6-generated labels (flat prompt, echo-stripped responses),
+validated against 254 human-reviewed labels for deployment-grade evaluation.
+## Performance
+### Yahoo within-domain test
+Accuracy: **0.806**  |  Macro F1: **0.669**
+| Class | Precision | Recall | F1 | Support |
+|---|---|---|---|---|
+| ADDR | 0.857 | 0.909 | 0.882 | 798 |
+| NOADDR | 0.526 | 0.401 | 0.455 | 202 |
+### Triage synthetic held-out (architecture validation)
+Accuracy: **0.995**  |  Macro F1: **0.994**
+| Class | Precision | Recall | F1 | Support |
+|---|---|---|---|---|
+| ADDR | 1.000 | 0.987 | 0.993 | 150 |
+| NOADDR | 0.991 | 1.000 | 0.996 | 223 |
+### Human gold-standard held-out
+Accuracy: **0.776**  |  Macro F1: **0.680**
+| Class | Precision | Recall | F1 | Support |
+|---|---|---|---|---|
+| ADDR | 0.824 | 0.889 | 0.855 | 189 |
+| NOADDR | 0.580 | 0.446 | 0.504 | 65 |
+## Training
+- Base: `MoritzLaurer/deberta-v3-base-zeroshot-v2.0` (NLI-pretrained)
+- Data: Yahoo Answers (echo-stripped) + Triage synthetic, joint training
+- Best epoch: 3 (selected by val macro-F1)
+- Batch size: 16, max length: 512, LR: 2e-05
+- Class weights: [0.724, 1.615]
+- Early stopping: patience 3 on val macro-F1
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model = AutoModelForSequenceClassification.from_pretrained("KingTechnician/osmosis-crossencoder-binary")
+tokenizer = AutoTokenizer.from_pretrained("KingTechnician/osmosis-crossencoder-binary")
+inputs = tokenizer("What causes rain?",
+                   "Rain forms when water vapor condenses into droplets.",
+                   return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    logits = model(**inputs).logits
+    pred = logits.argmax(dim=-1).item()
+print(["ADDR", "NOADDR"][pred])
+```
+## Limitations
+- NOADDR class is heterogeneous (on-topic-but-not-answering, tangential, off-topic
+  all map to the same target). Sub-classification of NOADDR requires a stronger
+  model — see the cascade evaluation in the OSMoSIS repo.
+- Synthetic Triage results (near-ceiling) validate the architecture but are not
+  representative of open-domain difficulty. Use the human held-out number as the
+  realistic deployment estimate.