Update README.md

README.md

@@ -1,3 +1,47 @@
----
-license: mit
----
+---
+license: mit
+---
+# BioHopR
+
+[**Paper**]() | 
+
+## Description
+
+We introduce BioHopR, a novel benchmark designed to evaluate multi-hop, multi-answer reasoning in structured biomedical knowledge graphs.  
+Built from the comprehensive PrimeKG, BioHopR includes 1-hop and 2-hop reasoning tasks that reflect real-world biomedical complexities.
+
+## Prompt
+We used the below to get the response of the open source LLMs.
+```
+def generate_single(model, tokenizer, question):
+    q="You are an expert biomedical researcher.\n"+question+"\nJust give me the answer without any explanations.\nAnswer:\n"
+    inputs = tokenizer(q, return_tensors="pt", return_attention_mask=False).to(DEVICE)
+    response = model.generate(**inputs, 
+                         do_sample=False,
+                         temperature=0.0, 
+                         top_p=None,
+                         num_beams=1,
+                         no_repeat_ngram_size=3,
+                         eos_token_id=tokenizer.eos_token_id,  # End of sequence token
+                         pad_token_id=tokenizer.eos_token_id,  # Pad token
+                         max_new_tokens=32,
+                        )
+    output = tokenizer.decode(response.squeeze()[len(inputs['input_ids'][0]):], skip_special_tokens=True)
+    return output
+
+def generate_multi(model, tokenizer, question):
+    q="You are an expert biomedical researcher.\n"+question+"\nJust give me the answers without any explanations in a bullet-pointed list.\nAnswer:\n"
+    inputs = tokenizer(q, return_tensors="pt", return_attention_mask=False).to(DEVICE)
+    response = model.generate(**inputs, 
+                         do_sample=False,
+                         temperature=0.0, 
+                         top_p=None,
+                         num_beams=1,
+                         no_repeat_ngram_size=3,
+                         eos_token_id=tokenizer.eos_token_id,  # End of sequence token
+                         pad_token_id=tokenizer.eos_token_id,  # Pad token
+                         max_new_tokens=256,
+                        )
+    output = tokenizer.decode(response.squeeze()[len(inputs['input_ids'][0]):], skip_special_tokens=True)
+    return output
+```