Add SetFit model

README.md

@@ -7,7 +7,7 @@ tags:
 pipeline_tag: text-classification
 ---
 
-# C:\Users\LINUSL~1\AppData\Local\Temp\tmpejp_ke_t\xeonkai\setfit-articles-labels
+# C:\Users\LINUSL~1\AppData\Local\Temp\tmph5kuk9wb\xeonkai\setfit-articles-labels
 
 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -28,7 +28,7 @@ You can then run inference as follows:
 from setfit import SetFitModel
 
 # Download from Hub and run inference
-model = SetFitModel.from_pretrained("C:\Users\LINUSL~1\AppData\Local\Temp\tmpejp_ke_t\xeonkai\setfit-articles-labels")
+model = SetFitModel.from_pretrained("C:\Users\LINUSL~1\AppData\Local\Temp\tmph5kuk9wb\xeonkai\setfit-articles-labels")
 # Run inference
 preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
 ```

model_head.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:630a68fd7af4caffc741371d931ba0b074ebc581e94a8560407c165c01711ffa
-size 283294
+oid sha256:4c36d239a9abbe846b828a5663c00caf93564ed4207e2b0e2ad3f1d3aa39743f
+size 262865

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3dc9289aa4c75bb7dba8221105ef077673188ff34bd119af9a208425733dd30e
+oid sha256:4e07bbe21b782c28fef2086f2a225d8d41d0e852cf2ff881748b3ce7afc88907
 size 90888945