Update README.md

README.md

@@ -18,9 +18,10 @@ tags:
 ## Summary
 
 A fine-tuned [Laya](https://huggingface.co/convaiinnovations/laya) decision model that classifies a newly
-opened GitHub issue as one of `bug`, `feature`, `question` from its title and body. It is the model behind the laya-triage
-GitHub Action, which auto-applies a label only when `answer_confidence` reaches a per-label threshold and
-otherwise escalates the issue to a human.
+opened GitHub issue as one of `bug`, `feature`, `question` from its title and body. It is the model behind the
+[laya-triage](https://github.com/Prasanna-KS-85/laya-triage) GitHub Action, which auto-applies a label only when `answer_confidence` reaches a
+per-label threshold and otherwise escalates the issue to a human. Source code, install instructions and the
+full evaluation write-up: https://github.com/Prasanna-KS-85/laya-triage.
 
 - Revision `76ece1fb0eb8b32bd5d8c509293c1692a2534805`: weights of training run R1 (`a704b3eadd185f1fa028576cfa50605b6eada4a4`) with the choice
   temperature refit on validation data (4.0188 → 2.6968); nothing else differs.
@@ -79,6 +80,32 @@ answer["choice"], answer["answer_confidence"], answer["probabilities"]
 
 Gate on `answer_confidence` (the calibrated probability of the chosen label), not on `confidence`.
 
+### With the laya-triage package
+
+The [laya-triage](https://github.com/Prasanna-KS-85/laya-triage) package wraps the same steps: it brings the frozen question and `preprocess()`, so
+nothing is retyped. Install it with `pip install "laya_triage @ git+https://github.com/Prasanna-KS-85/laya-triage@v1.0.0"` (on Linux, install
+the CPU-only torch wheel first; see the repository's docs/USING_THE_MODEL.md), then:
+
+```python
+"""Classify one issue with the Laya Triage model on CPU (fp32). Install: see docs/USING_THE_MODEL.md."""
+# The package brings the frozen question and the shared preprocess(): nothing is retyped.
+from laya_triage.classifier import Classifier
+from laya_triage.preprocess import preprocess
+
+REPO = "Prasanna85/laya-issue-triage"
+REVISION = "76ece1fb0eb8b32bd5d8c509293c1692a2534805"  # pinned: the revision the Action uses
+MAX_LEN = 1024  # must equal the training budget
+BUG_THRESHOLD = 0.6033  # the Action's default gate; feature and question are never auto-applied
+
+model = Classifier(REPO, REVISION, MAX_LEN)  # downloads once, then loads from the local cache
+state = preprocess("App closes when I open the export dialog",
+                   "Steps: open a notebook, choose File > Export. The app exits with KeyError: 'last_format'.")
+result = model.classify(state, issue_number=0)
+print(result.probabilities)  # {'bug': ..., 'feature': ..., 'question': ...}
+confident_bug = result.label == "bug" and result.answer_confidence >= BUG_THRESHOLD
+print("apply bug" if confident_bug else "escalate to a human")
+```
+
 ## Training data
 
 - [NLBSE'24 issue report classification](https://github.com/nlbse2024/issue-report-classification): issues
@@ -129,9 +156,10 @@ per-repository macro-F1 scores, as in the NLBSE'24 competition.
 
 - [c] Derived, not published. NLBSE'24 publishes per-repository, per-class P/R/F1 only; pooled values were
   recovered exactly from them, because every test repository has 100 issues per class.
-- **Protocol differences.** B3 trains one classifier per repository on that repository's official-train rows
-  (all of them, including the 4 that overlap test), uses raw text, and is copied rather than re-run.
-  M1 and B1 are one model across all 5 repositories, trained on 1,196 rows with model selection,
+- **Protocol differences.** B3 trains one classifier per repository, each on only that repository's
+  300 official-train rows (together the 5 classifiers use all 1,500 rows, including the
+  4 whose content also appears in test). The SetFit input is raw text; the RoBERTa and fastText
+  setups were not inspected. B3 is copied rather than re-run. M1 and B1 are one model across all 5 repositories, trained on 1,196 rows with model selection,
   temperature and thresholds fitted on the 300-issue validation split. B2 is the base checkpoint
   without fine-tuning. B3 publishes no probabilities, so it has no ECE.
 - Per class (M1), precision / recall: bug 0.8326 / 0.7760; feature 0.8317 / 0.8400; question 0.7467 / 0.7900.
@@ -199,6 +227,7 @@ switching the Action from dry-run to apply.
 - **Thresholds fitted on validation do not transfer exactly.** The `bug` threshold lost
   7.3 points of precision from validation to test.
 - **English only.** Trained and evaluated on English issues.
+- Exactly three classes (bug, feature, question); adding a class requires new labelled data, retraining and recalibrating.
 - **Latency was measured on a local CPU, not on a GitHub runner** (local Apple M4 Pro CPU, fp32, model preloaded):
   p50 178 ms and p95 436 ms per issue at 8 threads
   (test run); p50 288 ms and p95 690 ms at 2 threads (100 validation issues).