First version from tutorial

embedding_test.py

@@ -0,0 +1,40 @@
+import requests
+from retry import retry
+import pandas as pd
+
+model_id = "sentence-transformers/all-MiniLM-L6-v2"
+hf_token = "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"
+
+api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
+headers = {"Authorization": f"Bearer {hf_token}"}
+
+@retry(tries=3, delay=10)
+def query(texts):
+    response = requests.post(api_url, headers=headers, json={"inputs": texts})
+    result = response.json()
+    if isinstance(result, list):
+    	return result
+    elif list(result.keys())[0] == "error":
+    	raise RuntimeError("The model is currently loading, please re-run the query")
+    
+    
+texts = ["How do I get a replacement Medicare card?",
+        "What is the monthly premium for Medicare Part B?",
+        "How do I terminate my Medicare Part B (medical insurance)?",
+        "How do I sign up for Medicare?",
+        "Can I sign up for Medicare Part B if I am working and have health insurance through an employer?",
+        "How do I sign up for Medicare Part B if I already have Part A?",
+        "What are Medicare late enrollment penalties?",
+        "What is Medicare and who can get it?",
+        "How can I get help with my Medicare Part A and Part B premiums?",
+        "What are the different parts of Medicare?",
+        "Will my Medicare premiums be higher because of my higher income?",
+        "What is TRICARE ?",
+        "Should I sign up for Medicare Part B if I have Veterans' Benefits?"]
+def main():
+	output = query(texts)	
+	embeddings = pd.DataFrame(output)
+	embeddings.to_csv("embeddings.csv", index=False)
+
+if __name__ == '__main__':
+    main()