Update README.md

README.md

@@ -173,7 +173,7 @@ import numpy as np
 def make_reranker_input(t, q):
     return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}"
 
-def make_reranker_training_datum(context, question):
+def make_reranker_inference_conversation(context, question):
     system_message = "Given a query and a piece of text, output a score of 1-7 based on how related the query is to the text. 1 means least related and 7 is most related."
 
     return [
@@ -195,7 +195,7 @@ query_texts = [
     ("What is the square root of 999?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
 ]
 
-chats = [make_reranker_training_datum(c, q) for q, c in query_texts]
+chats = [make_reranker_inference_conversation(c, q) for q, c in query_texts]
 responses = llm.chat(chats, sampling_params)
 probs = np.array([[get_prob(r.outputs[0].logprobs[0], y) for y in idx_tokens] for r in responses])
 
@@ -223,7 +223,7 @@ import numpy as np
 def make_reranker_input(t, q):
     return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}"
 
-def make_reranker_training_datum(context, question):
+def make_reranker_inference_conversation(context, question):
     system_message = "Given a query and a piece of text, output a score of 1-7 based on how related the query is to the text. 1 means least related and 7 is most related."
 
     return [
@@ -250,7 +250,7 @@ query_texts = [
     ("What is the square root of 999?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
 ]
 
-chats = [make_reranker_training_datum(c, q) for q, c in query_texts]
+chats = [make_reranker_inference_conversation(c, q) for q, c in query_texts]
 responses = pipe(
     chats, 
     gen_config=GenerationConfig(temperature=1.0, logprobs=14, max_new_tokens=1, do_sample=True)