Update README.md

README.md

@@ -62,8 +62,8 @@ This model was trained within the context of a larger system for ABSA, which loo
 - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
 - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
 - **spaCy Model:** en_core_web_sm
-- **SetFitABSA Aspect Model:** [C:\Users\JOSHUA~1.BAI\AppData\Local\Temp\tmpgo60geio\joshuasundance\setfit-absa-all-MiniLM-L6-v2-laptops-aspect](https://huggingface.co/C:\Users\JOSHUA~1.BAI\AppData\Local\Temp\tmpgo60geio\joshuasundance\setfit-absa-all-MiniLM-L6-v2-laptops-aspect)
-- **SetFitABSA Polarity Model:** [C:\Users\JOSHUA~1.BAI\AppData\Local\Temp\tmpgo60geio\joshuasundance\setfit-absa-all-MiniLM-L6-v2-laptops-polarity](https://huggingface.co/C:\Users\JOSHUA~1.BAI\AppData\Local\Temp\tmpgo60geio\joshuasundance\setfit-absa-all-MiniLM-L6-v2-laptops-polarity)
+- **SetFitABSA Aspect Model:** [joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect](https://huggingface.co/joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect)
+- **SetFitABSA Polarity Model:** [joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-polarity](https://huggingface.co/joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-polarity)
 - **Maximum Sequence Length:** 256 tokens
 - **Number of Classes:** 2 classes
 <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
@@ -106,8 +106,8 @@ from setfit import AbsaModel
 
 # Download from the 🤗 Hub
 model = AbsaModel.from_pretrained(
-    "C:\Users\JOSHUA~1.BAI\AppData\Local\Temp\tmpgo60geio\joshuasundance\setfit-absa-all-MiniLM-L6-v2-laptops-aspect",
-    "C:\Users\JOSHUA~1.BAI\AppData\Local\Temp\tmpgo60geio\joshuasundance\setfit-absa-all-MiniLM-L6-v2-laptops-polarity",
+    "joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect",
+    "joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-polarity",
 )
 # Run inference
 preds = model("The food was great, but the venue is just way too busy.")