Push model using huggingface_hub.

README.md

@@ -1,23 +1,21 @@
 ---
-language: multilingual
+base_model: mbrede/amc_setfit
 library_name: setfit
-license: mit
 metrics:
-- f1
+- accuracy
 pipeline_tag: text-classification
 tags:
 - setfit
 - sentence-transformers
 - text-classification
+- generated_from_setfit_trainer
 widget: []
 inference: true
-base_model:
-- intfloat/multilingual-e5-large
 ---
 
-# SetFit
+# SetFit with mbrede/amc_setfit
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A OneVsRestClassifier instance is used for classification.
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mbrede/amc_setfit](https://huggingface.co/mbrede/amc_setfit) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
 
 The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -28,13 +26,13 @@ The model has been trained using an efficient few-shot learning technique that i
 
 ### Model Description
 - **Model Type:** SetFit
-<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
+- **Sentence Transformer body:** [mbrede/amc_setfit](https://huggingface.co/mbrede/amc_setfit)
 - **Classification head:** a OneVsRestClassifier instance
 - **Maximum Sequence Length:** 512 tokens
 - **Number of Classes:** 4 classes
-- **Training Dataset:** [A database of coded PSE-text published by Schönbrodt et al. (2021)](http://dx.doi.org/10.23668/psycharchives.2738)
-- **Language:** multilingual
-- **License:** mit
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
 
 ### Model Sources
 
@@ -58,7 +56,7 @@ Then you can load this model and run inference.
 from setfit import SetFitModel
 
 # Download from the 🤗 Hub
-model = SetFitModel.from_pretrained("mbrede/amc_setfit")
+model = SetFitModel.from_pretrained("automatedMotiveCoder/setfit")
 # Run inference
 preds = model("I loved the spiderman movie!")
 ```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "mbrede/amc_setfit",
+  "_name_or_path": "automatedMotiveCoder/setfit",
   "architectures": [
     "XLMRobertaModel"
   ],

config_setfit.json

@@ -1,9 +1,9 @@
 {
+  "normalize_embeddings": false,
   "labels": [
     "ach",
     "aff",
-    "null",
-    "pow"
-  ],
-  "normalize_embeddings": false
+    "pow",
+    "null"
+  ]
 }

model_head.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a694fa966c23ebc1e5a721090a95f74ad61836c81f0feed10b14610b20ed26aa
+oid sha256:13e406ef1f1d9816c37240c218b0096fbca62a3b2610bb1ac85cc701954d8519
 size 35762