Push model using huggingface_hub.

README.md

@@ -7,7 +7,7 @@ tags:
 pipeline_tag: text-classification
 ---
 
-# /var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmpu5azod0n/fhamborg/newsframes-gov
+# /var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmpltc1u5xt/fhamborg/newsframes-gov
 
 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/tmpu5azod0n/fhamborg/newsframes-gov")
+model = SetFitModel.from_pretrained("/var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmpltc1u5xt/fhamborg/newsframes-gov")
 # 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-GOV_4-False-0.15-5e-06-10-3-0.5013648539675772/",
+  "_name_or_path": "models/sentence-transformers-all-mpnet-base-v2-dataset_systematic-random_compare_with-absent_radius-1.json-GOV_4-False-0.15-5e-06-15-4-0.5163019500462747-None-False/",
   "architectures": [
     "MPNetModel"
   ],

model_head.pkl

@@ -1,3 +1,3 @@
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 size 25479

pytorch_model.bin

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