retrieval-study / create_json_data.py
elibrowne
Question data for E5 and ColBERT online and formatted
e65ac7c
import json
import csv
with open("question_data.csv", "r") as f:
reader = csv.reader(f)
questions = []
for row in reader:
questions.append(row)
questions = questions[2:] # cut off top two (labels, passage #s)
# qid,prompt,question,a,b,c,d,answer,gold_passage,top10_colbert,,,,,,,,,,generation_colbert,top10_e5,,,,,,,,,,generation_e5,gold_passage_generation
# 0 1 2 3 4 5 6 7 8 9-18 19 20-29 30 31
# See example.json for how these files will be ported
full_question_dict = {} # stores all "id":q_data pairs
for entry in questions:
# Create individual question data
q_data = {}
if not entry[1] == "":
entry[2] = entry[1] + " " + entry[2]
q_data["question"] = entry[2]
q_data["answers"] = entry[3:7] # inclusive of (3, 6) -> A, B, C, D
answer_map = {"A": 0, "B": 1, "C": 2, "D": 3}
q_data["correct_answer_index"] = answer_map[entry[7]] # entry[7] = "A" -> index = 0
q_data["top10_colbert"] = entry[9:19] # inclusive of (9-18) -> 10 retrievals
q_data["generation_colbert"] = entry[19]
q_data["top10_e5"] = entry[20:30] # inclusive of (20-29) -> 10 retrievals
q_data["generation_e5"] = entry[30]
q_data["top10_contains_gold_passage"] = False # this is always the case b/c of programming. Does not reflect reality
q_data["gold_passage"] = entry[8]
q_data["gold_passage_generation"] = entry[31]
# Add to full question dictionary
full_question_dict[entry[0]] = q_data # entry[0] is qid
with open("question_data.json", "w") as f:
json.dump(full_question_dict, f)