Update README.md

README.md

@@ -35,8 +35,13 @@ model = SentenceTransformer('GiliGold/Knesset-multi-e5-large')
 embedding_vector = model.encode(sentence)
 
 # Load the valence model
+#Option 1: Download files from https://huggingface.co/GiliGold/VAD_binomial_regression_models/tree/main)
 with open("valence_model.pkl", "rb") as file:
     valence_model = pickle.load(file)
+#Option 2: Load from Hugging Face hub
+from huggingface_hub import hf_hub_download
+repo_id = "GiliGold/VAD_binomial_regression_models"
+valence_model = hf_hub_download(repo_id=repo_id, filename="valence_model.pkl")
 
 # Assume `embedding_vector` is the vector obtained from the Knesset-multi model
 valence_score = valence_model.predict([embedding_vector])