Update README.md

README.md

@@ -35,13 +35,16 @@ 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)
+#Option 1: Manually 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
+
+#Option 2: Download using 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")
+model_v_path  = hf_hub_download(repo_id=repo_id, filename="valence_model.pkl")
+with open(model_v_path, "rb") as f:
+    valence_model = pickle.load(f)
 
 # Assume `embedding_vector` is the vector obtained from the Knesset-multi model
 valence_score = valence_model.predict([embedding_vector])