Push model using huggingface_hub.

README.md

@@ -7,7 +7,7 @@ tags:
 pipeline_tag: text-classification
 ---
 
-# /var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmpk_iail3l/fhamborg/newsframes-aff
+# /var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmp20v_99_o/fhamborg/newsframes-aff
 
 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("/var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmpk_iail3l/fhamborg/newsframes-aff")
+model = SetFitModel.from_pretrained("/var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmp20v_99_o/fhamborg/newsframes-aff")
 # Run inference
 preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
 ```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "models/sentence-transformers-all-mpnet-base-v2-dataset_systematic_random_compare_with_absent.json-AFF_4-False-0.15-5e-06-10-3-0.7804744264931363/",
+  "_name_or_path": "models/sentence-transformers-all-mpnet-base-v2-dataset_systematic-random_compare_with-absent_radius-1.json-AFF_4-False-0.15-5e-06-20-4-0.8043882339240336-None-False/",
   "architectures": [
     "MPNetModel"
   ],

model_head.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9b8545b33e210f6339c6519741242ca227c41570d12b4f1a719736ddf68cbc27
+oid sha256:6dbafd6d691a17e239f450d5caaa557dc32247e3eeb1dfd1bca5f03999f29130
 size 25479

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4398568cf06a68bf8c5809f257f70b03d275e9f0886b3059fbd61d4bc87adfd2
+oid sha256:1579d5edbe6c6df9376e0f2a72741a2d28ae645bcdad8de769a2723880335c66
 size 438009257