Update README.md

README.md

@@ -14,8 +14,6 @@ It is a finetuned version of Phi3-3.8B on a high quality proprietary dataset con
 
 ### Model Description
 
-
-
 - **Developed by:** [https://www.SciPhi.ai](SciPhi.ai)
 
 ### Model Sources
@@ -32,7 +30,6 @@ It is a finetuned version of Phi3-3.8B on a high quality proprietary dataset con
 import json
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-
 def triplextract(model, tokenizer, text, entity_types, predicates):
 
     input_format = """
@@ -46,29 +43,21 @@ def triplextract(model, tokenizer, text, entity_types, predicates):
         {text}
         """
 
-    message = input_format.format(entity_types = json.dumps({"entity_types": entity_types}), predicates = json.dumps({"predicates": predicates}), text = text)
+    message = input_format.format(
+                entity_types = json.dumps({"entity_types": entity_types}),
+                predicates = json.dumps({"predicates": predicates}),
+                text = text)
 
     messages = [{'role': 'user', 'content': message}]
     input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt = True, return_tensors="pt").to("cuda")
     output = tokenizer.decode(model.generate(input_ids=input_ids, max_length=2048)[0], skip_special_tokens=True)
-    print(output)
     return output
 
-
-
+model = AutoModelForCausalLM.from_pretrained("sciphi/triplex", trust_remote_code=True).to('cuda').eval()
 tokenizer = AutoTokenizer.from_pretrained("sciphi/triplex", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("sciphi/triplex", trust_remote_code=True)
-
-
-model.to("cuda")
-
-model.eval()
-
 
 entity_types = [ "LOCATION", "POSITION", "DATE", "CITY", "COUNTRY", "NUMBER" ]
-
 predicates = [ "POPULATION", "AREA" ]
-
 text = """
 San Francisco,[24] officially the City and County of San Francisco, is a commercial, financial, and cultural center in Northern California.