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license: other
pipeline_tag: text-classification
library_name: setfit
base_model: sentence-transformers/all-MiniLM-L6-v2
tags:
- setfit
- sentence-transformers
- text-classification
- crp
- context-relay-protocol
- intent-classification
- speech-acts
datasets:
- banking77
- snips_built_in_intents
metrics:
- accuracy
model-index:
- name: crp-intent-setfit
results:
- task:
type: text-classification
name: Speech-act classification (4-class)
dataset:
type: banking77
name: CRP speech-act held-out mix
metrics:
- type: accuracy
value: 0.9340
name: Held-out accuracy (2,000 examples)
widget:
- text: "Please scan the repository for compliance issues."
- text: "What is the current deployment status?"
- text: "I believe the server is down."
- text: "This is frustrating and slow."
inference: true
---
# CRP Intent SetFit — speech-act classifier
Part of the [Context Relay Protocol (CRP)](https://crprotocol.io) ML-first
governance layer. Classifies a user turn into one of four CRP speech acts —
**request, question, assertion, expressive** — used by `crp/isa/intent.py`
(SPEC-051 Intent & Speech Acts) to drive routing and operation framing in
agentic pipelines.
Few-shot SetFit model: contrastive-tuned `all-MiniLM-L6-v2` body +
LogisticRegression head fit on the full training mix.
## Verified results (independent harness, 2026-07-28)
| Metric | Value |
|---|---|
| Held-out accuracy (2,000 unseen examples) | **0.9340** |
| F1 assertion / expressive / question / request | 0.980 / 1.000 / 0.880 / 0.933 |
| Production-style CRP prompts | **18/20** |
## Training data
Banking77 + SNIPS (intent names heuristically mapped to the four CRP speech
acts) + templated synthetic examples per class. 64-shot contrastive body
tuning, then the classifier head refit on the full mix.
## Usage
```python
from setfit import SetFitModel
model = SetFitModel.from_pretrained("AutoCyberAI/crp-intent-setfit")
model.predict(["Please scan the repository for compliance issues."])
# -> ['request']
```
In the CRP SDK this model is the default intent backend:
```bash
CRP_INTENT_MODEL=AutoCyberAI/crp-intent-setfit # default; no env needed
```
## Limitations
Four speech acts only; trained on English service/assistant phrasing. The
held-out score is in-distribution (same data mix); the production-prompt
score above is the out-of-distribution signal.
## License
Elastic License 2.0 — see the CRP repository for details.
|