used native setfit head

README.md

@@ -95,7 +95,8 @@ classifier.to(device);
 mentions = ["working class people", "highly-educated professionals", "people without a stable job"]
 
 # Get predictions
-predictions = classifier.predict(mentions)
+with torch.no_grad():
+    predictions = classifier.predict(mentions)
 print(predictions)
 
 # Map predictions to labels
@@ -124,7 +125,8 @@ model = SentenceTransformer(model_name, device=device)
 mentions = ["working class people", "highly-educated professionals", "people without a stable job"]
 
 # Compute mention embeddings
-embeddings = model.encode(mentions)
+with torch.no_grad():
+    embeddings = model.encode(mentions)
 ````
 
 ## Training Details

config_setfit.json

@@ -1,4 +1,5 @@
 {
+  "normalize_embeddings": true,
   "labels": [
     "economic__class_membership",
     "economic__ecology_of_group",
@@ -6,6 +7,5 @@
     "economic__employment_status",
     "economic__income_wealth_economic_status",
     "economic__occupation_profession"
-  ],
-  "normalize_embeddings": true
+  ]
 }

model_head.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:19295f225826c8065cfa39829867a649b182ca7eefe816870d436fbc161ac4b7
-size 19966
+oid sha256:022006403a97ce3c6eb9c994cfb2c789aedf7b15a5da2978868f0b9afa439697
+size 19962