Add escalation-detection

README.md

@@ -16,17 +16,15 @@ You can install it with `pip install adaptive-classifier`.
 ## Model Details
 
 - Base Model: answerdotai/ModernBERT-base
-- Number of Classes: 4
-- Total Examples: 320
+- Number of Classes: 2
+- Total Examples: 162
 - Embedding Dimension: 768
 
 ## Class Distribution
 
 ```
-escalate_immediately: 80 examples (25.0%)
-escalate_soon: 80 examples (25.0%)
-manager_review: 80 examples (25.0%)
-no_escalation: 80 examples (25.0%)
+normal: 81 examples (50.0%)
+urgent: 81 examples (50.0%)
 ```
 
 ## Usage
@@ -50,7 +48,7 @@ classifier.add_examples(texts, labels)
 
 ## Training Details
 
-- Training Steps: 10
+- Training Steps: 6
 - Examples per Class: See distribution above
 - Prototype Memory: Active
 - Neural Adaptation: Active

config.json

@@ -32,17 +32,13 @@
   },
   "embedding_dim": 768,
   "id_to_label": {
-    "0": "manager_review",
-    "1": "no_escalation",
-    "2": "escalate_soon",
-    "3": "escalate_immediately"
+    "0": "urgent",
+    "1": "normal"
   },
   "label_to_id": {
-    "escalate_immediately": 3,
-    "escalate_soon": 2,
-    "manager_review": 0,
-    "no_escalation": 1
+    "normal": 1,
+    "urgent": 0
   },
   "model_name": "answerdotai/ModernBERT-base",
-  "train_steps": 10
+  "train_steps": 6
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:41230e71745d997fb7e5bf03c26e2bc4475dd27ed1ec988bc25aaa8585b9d571
-size 3562928
+oid sha256:0ef7a1a9ce25a269b56a66835d253b8c7068b4bec1f2d6c21ae152f3975d8378
+size 3553504