mirror of
https://github.com/lilakk/BLEUBERI.git
synced 2026-04-19 12:58:12 +00:00
420 lines
12 KiB
Python
420 lines
12 KiB
Python
"""
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Usage:
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python3 qa_browser.py --share
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"""
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import argparse
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from collections import defaultdict
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import re
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import gradio as gr
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from fastchat.llm_judge.common import (
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load_questions,
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load_model_answers,
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load_single_model_judgments,
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load_pairwise_model_judgments,
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resolve_single_judgment_dict,
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resolve_pairwise_judgment_dict,
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get_single_judge_explanation,
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get_pairwise_judge_explanation,
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)
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questions = []
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model_answers = {}
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model_judgments_normal_single = {}
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model_judgments_math_single = {}
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model_judgments_normal_pairwise = {}
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model_judgments_math_pairwise = {}
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question_selector_map = {}
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category_selector_map = defaultdict(list)
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def display_question(category_selector, request: gr.Request):
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choices = category_selector_map[category_selector]
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return gr.Dropdown(
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value=choices[0],
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choices=choices,
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)
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def display_pairwise_answer(
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question_selector, model_selector1, model_selector2, request: gr.Request
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):
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q = question_selector_map[question_selector]
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qid = q["question_id"]
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ans1 = model_answers[model_selector1][qid]
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ans2 = model_answers[model_selector2][qid]
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chat_mds = pairwise_to_gradio_chat_mds(q, ans1, ans2)
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gamekey = (qid, model_selector1, model_selector2)
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judgment_dict = resolve_pairwise_judgment_dict(
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q,
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model_judgments_normal_pairwise,
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model_judgments_math_pairwise,
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multi_turn=False,
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)
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explanation = (
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"##### Model Judgment (first turn)\n"
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+ get_pairwise_judge_explanation(gamekey, judgment_dict)
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)
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judgment_dict_turn2 = resolve_pairwise_judgment_dict(
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q,
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model_judgments_normal_pairwise,
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model_judgments_math_pairwise,
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multi_turn=True,
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)
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explanation_turn2 = (
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"##### Model Judgment (second turn)\n"
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+ get_pairwise_judge_explanation(gamekey, judgment_dict_turn2)
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)
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return chat_mds + [explanation] + [explanation_turn2]
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def display_single_answer(question_selector, model_selector1, request: gr.Request):
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q = question_selector_map[question_selector]
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qid = q["question_id"]
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ans1 = model_answers[model_selector1][qid]
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chat_mds = single_to_gradio_chat_mds(q, ans1)
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gamekey = (qid, model_selector1)
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judgment_dict = resolve_single_judgment_dict(
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q, model_judgments_normal_single, model_judgments_math_single, multi_turn=False
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)
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explanation = "##### Model Judgment (first turn)\n" + get_single_judge_explanation(
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gamekey, judgment_dict
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)
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judgment_dict_turn2 = resolve_single_judgment_dict(
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q, model_judgments_normal_single, model_judgments_math_single, multi_turn=True
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)
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explanation_turn2 = (
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"##### Model Judgment (second turn)\n"
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+ get_single_judge_explanation(gamekey, judgment_dict_turn2)
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)
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return chat_mds + [explanation] + [explanation_turn2]
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newline_pattern1 = re.compile("\n\n(\d+\. )")
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newline_pattern2 = re.compile("\n\n(- )")
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def post_process_answer(x):
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"""Fix Markdown rendering problems."""
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x = x.replace("\u2022", "- ")
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x = re.sub(newline_pattern1, "\n\g<1>", x)
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x = re.sub(newline_pattern2, "\n\g<1>", x)
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return x
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def pairwise_to_gradio_chat_mds(question, ans_a, ans_b, turn=None):
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end = len(question["turns"]) if turn is None else turn + 1
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mds = ["", "", "", "", "", "", ""]
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for i in range(end):
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base = i * 3
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if i == 0:
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mds[base + 0] = "##### User\n" + question["turns"][i]
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else:
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mds[base + 0] = "##### User's follow-up question \n" + question["turns"][i]
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mds[base + 1] = "##### Assistant A\n" + post_process_answer(
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ans_a["choices"][0]["turns"][i].strip()
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)
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mds[base + 2] = "##### Assistant B\n" + post_process_answer(
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ans_b["choices"][0]["turns"][i].strip()
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)
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ref = question.get("reference", ["", ""])
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ref_md = ""
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if turn is None:
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if ref[0] != "" or ref[1] != "":
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mds[6] = f"##### Reference Solution\nQ1. {ref[0]}\nQ2. {ref[1]}"
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else:
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x = ref[turn] if turn < len(ref) else ""
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if x:
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mds[6] = f"##### Reference Solution\n{ref[turn]}"
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else:
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mds[6] = ""
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return mds
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def single_to_gradio_chat_mds(question, ans, turn=None):
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end = len(question["turns"]) if turn is None else turn + 1
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mds = ["", "", "", "", ""]
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for i in range(end):
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base = i * 2
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if i == 0:
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mds[base + 0] = "##### User\n" + question["turns"][i]
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else:
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mds[base + 0] = "##### User's follow-up question \n" + question["turns"][i]
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mds[base + 1] = "##### Assistant A\n" + post_process_answer(
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ans["choices"][0]["turns"][i].strip()
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)
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ref = question.get("reference", ["", ""])
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ref_md = ""
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if turn is None:
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if ref[0] != "" or ref[1] != "":
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mds[4] = f"##### Reference Solution\nQ1. {ref[0]}\nQ2. {ref[1]}"
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else:
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x = ref[turn] if turn < len(ref) else ""
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if x:
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mds[4] = f"##### Reference Solution\n{ref[turn]}"
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else:
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mds[4] = ""
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return mds
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def build_question_selector_map():
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global question_selector_map, category_selector_map
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# Build question selector map
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for q in questions:
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preview = f"{q['question_id']}: " + q["turns"][0][:128] + "..."
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question_selector_map[preview] = q
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category_selector_map[q["category"]].append(preview)
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def build_pairwise_browser_tab():
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global question_selector_map, category_selector_map
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models = list(model_answers.keys())
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num_sides = 2
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num_turns = 2
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side_names = ["A", "B"]
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question_selector_choices = list(question_selector_map.keys())
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category_selector_choices = list(category_selector_map.keys())
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# Selectors
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with gr.Row():
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with gr.Column(scale=1, min_width=200):
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category_selector = gr.Dropdown(
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choices=category_selector_choices, label="Category", container=False
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)
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with gr.Column(scale=100):
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question_selector = gr.Dropdown(
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choices=question_selector_choices, label="Question", container=False
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)
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model_selectors = [None] * num_sides
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with gr.Row():
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for i in range(num_sides):
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with gr.Column():
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if i == 0:
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value = models[0]
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else:
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value = "gpt-3.5-turbo"
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model_selectors[i] = gr.Dropdown(
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choices=models,
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value=value,
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label=f"Model {side_names[i]}",
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container=False,
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)
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# Conversation
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chat_mds = []
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for i in range(num_turns):
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chat_mds.append(gr.Markdown(elem_id=f"user_question_{i+1}"))
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with gr.Row():
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for j in range(num_sides):
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with gr.Column(scale=100):
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chat_mds.append(gr.Markdown())
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if j == 0:
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with gr.Column(scale=1, min_width=8):
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gr.Markdown()
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reference = gr.Markdown(elem_id=f"reference")
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chat_mds.append(reference)
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model_explanation = gr.Markdown(elem_id="model_explanation")
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model_explanation2 = gr.Markdown(elem_id="model_explanation")
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# Callbacks
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category_selector.change(display_question, [category_selector], [question_selector])
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question_selector.change(
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display_pairwise_answer,
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[question_selector] + model_selectors,
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chat_mds + [model_explanation] + [model_explanation2],
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)
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for i in range(num_sides):
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model_selectors[i].change(
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display_pairwise_answer,
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[question_selector] + model_selectors,
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chat_mds + [model_explanation] + [model_explanation2],
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)
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return (category_selector,)
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def build_single_answer_browser_tab():
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global question_selector_map, category_selector_map
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models = list(model_answers.keys())
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num_sides = 1
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num_turns = 2
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side_names = ["A"]
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question_selector_choices = list(question_selector_map.keys())
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category_selector_choices = list(category_selector_map.keys())
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# Selectors
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with gr.Row():
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with gr.Column(scale=1, min_width=200):
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category_selector = gr.Dropdown(
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choices=category_selector_choices, label="Category", container=False
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)
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with gr.Column(scale=100):
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question_selector = gr.Dropdown(
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choices=question_selector_choices, label="Question", container=False
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)
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model_selectors = [None] * num_sides
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with gr.Row():
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for i in range(num_sides):
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with gr.Column():
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model_selectors[i] = gr.Dropdown(
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choices=models,
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value=models[i] if len(models) > i else "",
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label=f"Model {side_names[i]}",
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container=False,
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)
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# Conversation
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chat_mds = []
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for i in range(num_turns):
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chat_mds.append(gr.Markdown(elem_id=f"user_question_{i+1}"))
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with gr.Row():
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for j in range(num_sides):
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with gr.Column(scale=100):
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chat_mds.append(gr.Markdown())
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if j == 0:
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with gr.Column(scale=1, min_width=8):
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gr.Markdown()
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reference = gr.Markdown(elem_id=f"reference")
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chat_mds.append(reference)
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model_explanation = gr.Markdown(elem_id="model_explanation")
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model_explanation2 = gr.Markdown(elem_id="model_explanation")
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# Callbacks
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category_selector.change(display_question, [category_selector], [question_selector])
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question_selector.change(
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display_single_answer,
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[question_selector] + model_selectors,
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chat_mds + [model_explanation] + [model_explanation2],
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)
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for i in range(num_sides):
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model_selectors[i].change(
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display_single_answer,
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[question_selector] + model_selectors,
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chat_mds + [model_explanation] + [model_explanation2],
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)
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return (category_selector,)
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block_css = """
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#user_question_1 {
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background-color: #DEEBF7;
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}
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#user_question_2 {
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background-color: #E2F0D9;
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}
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#reference {
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background-color: #FFF2CC;
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}
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#model_explanation {
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background-color: #FBE5D6;
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}
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"""
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def load_demo():
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dropdown_update = gr.Dropdown.update(value=list(category_selector_map.keys())[0])
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return dropdown_update, dropdown_update
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def build_demo():
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build_question_selector_map()
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with gr.Blocks(
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title="MT-Bench Browser",
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theme=gr.themes.Base(text_size=gr.themes.sizes.text_lg),
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css=block_css,
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) as demo:
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gr.Markdown(
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"""
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# MT-Bench Browser
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The code to generate answers and judgments is at [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge).
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"""
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)
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with gr.Tab("Single Answer Grading"):
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(category_selector,) = build_single_answer_browser_tab()
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with gr.Tab("Pairwise Comparison"):
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(category_selector2,) = build_pairwise_browser_tab()
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demo.load(load_demo, [], [category_selector, category_selector2])
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return demo
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--host", type=str, default="0.0.0.0")
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parser.add_argument("--port", type=int)
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parser.add_argument("--share", action="store_true")
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parser.add_argument("--bench-name", type=str, default="mt_bench")
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args = parser.parse_args()
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print(args)
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question_file = f"data/{args.bench_name}/question.jsonl"
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answer_dir = f"data/{args.bench_name}/model_answer"
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pairwise_model_judgment_file = (
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f"data/{args.bench_name}/model_judgment/gpt-4_pair.jsonl"
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)
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single_model_judgment_file = (
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f"data/{args.bench_name}/model_judgment/gpt-4_single.jsonl"
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)
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# Load questions
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questions = load_questions(question_file, None, None)
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# Load answers
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model_answers = load_model_answers(answer_dir)
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# Load model judgments
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model_judgments_normal_single = (
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model_judgments_math_single
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) = load_single_model_judgments(single_model_judgment_file)
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model_judgments_normal_pairwise = (
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model_judgments_math_pairwise
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) = load_pairwise_model_judgments(pairwise_model_judgment_file)
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demo = build_demo()
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demo.queue(
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default_concurrency_limit=10, status_update_rate=10, api_open=False
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).launch(
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server_name=args.host, server_port=args.port, share=args.share, max_threads=200
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)
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