Push model using huggingface_hub.

README.md

@@ -7,7 +7,7 @@ tags:
 pipeline_tag: text-classification
 ---
 
-# /var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmpf2jea753/fhamborg/newsframes-cult
+# /var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmperkhwtkc/fhamborg/newsframes-cult
 
 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/tmpf2jea753/fhamborg/newsframes-cult")
+model = SetFitModel.from_pretrained("/var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmperkhwtkc/fhamborg/newsframes-cult")
 # 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-CULT_4-False-0.15-5e-06-20-4-0.5852604747148942/",
+ "_name_or_path": "models/sentence-transformers-all-mpnet-base-v2-dataset_systematic-random_compare_with-absent_radius-1.json-CULT_4-False-0.15-5e-06-15-5-0.6116108234770873-None-False/",
  "architectures": [
  "MPNetModel"
  ],

model_head.pkl

@@ -1,3 +1,3 @@
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+oid sha256:825c4374f6dcccd7582dbf348f8097ec3c50ee58d2c525a46e43674313d3d750
 size 25479

pytorch_model.bin

@@ -1,3 +1,3 @@
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 size 438009257