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"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space.""" |
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import ast |
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import argparse |
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import pickle |
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import gradio as gr |
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import numpy as np |
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notebook_url = "https://colab.research.google.com/drive/1RAWb22-PFNI-X1gPVzc927SGUdfr6nsR?usp=sharing" |
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basic_component_values = [None] * 6 |
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leader_component_values = [None] * 5 |
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def make_leaderboard_md(elo_results): |
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leaderboard_md = f""" |
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# Leaderboard |
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| [Vote](https://chat.lmsys.org/?arena) | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) | |
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🏆 This leaderboard is based on the following three benchmarks. |
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- [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) - a crowdsourced, randomized battle platform. We use 70K+ user votes to compute Elo ratings. |
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- [MT-Bench](https://arxiv.org/abs/2306.05685) - a set of challenging multi-turn questions. We use GPT-4 to grade the model responses. |
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- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a model's multitask accuracy on 57 tasks. |
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💻 Code: The Arena Elo ratings are computed by this [notebook]({notebook_url}). The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). The MMLU scores are computed by [InstructEval](https://github.com/declare-lab/instruct-eval) and [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub). Higher values are better for all benchmarks. Empty cells mean not available. |
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""" |
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return leaderboard_md |
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def make_leaderboard_md_live(elo_results): |
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leaderboard_md = f""" |
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# Leaderboard |
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Last updated: {elo_results["last_updated_datetime"]} |
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{elo_results["leaderboard_table"]} |
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""" |
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return leaderboard_md |
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def update_elo_components(max_num_files, elo_results_file): |
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log_files = get_log_files(max_num_files) |
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if elo_results_file is None: |
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battles = clean_battle_data(log_files) |
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elo_results = report_elo_analysis_results(battles) |
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leader_component_values[0] = make_leaderboard_md_live(elo_results) |
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leader_component_values[1] = elo_results["win_fraction_heatmap"] |
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leader_component_values[2] = elo_results["battle_count_heatmap"] |
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leader_component_values[3] = elo_results["bootstrap_elo_rating"] |
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leader_component_values[4] = elo_results["average_win_rate_bar"] |
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basic_stats = report_basic_stats(log_files) |
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md0 = f"Last updated: {basic_stats['last_updated_datetime']}" |
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md1 = "### Action Histogram\n" |
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md1 += basic_stats["action_hist_md"] + "\n" |
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md2 = "### Anony. Vote Histogram\n" |
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md2 += basic_stats["anony_vote_hist_md"] + "\n" |
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md3 = "### Model Call Histogram\n" |
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md3 += basic_stats["model_hist_md"] + "\n" |
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md4 = "### Model Call (Last 24 Hours)\n" |
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md4 += basic_stats["num_chats_last_24_hours"] + "\n" |
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basic_component_values[0] = md0 |
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basic_component_values[1] = basic_stats["chat_dates_bar"] |
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basic_component_values[2] = md1 |
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basic_component_values[3] = md2 |
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basic_component_values[4] = md3 |
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basic_component_values[5] = md4 |
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def update_worker(max_num_files, interval, elo_results_file): |
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while True: |
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tic = time.time() |
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update_elo_components(max_num_files, elo_results_file) |
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durtaion = time.time() - tic |
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print(f"update duration: {durtaion:.2f} s") |
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time.sleep(max(interval - durtaion, 0)) |
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def load_demo(url_params, request: gr.Request): |
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logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}") |
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return basic_component_values + leader_component_values |
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def model_hyperlink(model_name, link): |
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return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>' |
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def load_leaderboard_table_csv(filename, add_hyperlink=True): |
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lines = open(filename).readlines() |
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heads = [v.strip() for v in lines[0].split(",")] |
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rows = [] |
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for i in range(1, len(lines)): |
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row = [v.strip() for v in lines[i].split(",")] |
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for j in range(len(heads)): |
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item = {} |
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for h, v in zip(heads, row): |
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if h == "Arena Elo rating": |
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if v != "-": |
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v = int(ast.literal_eval(v)) |
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else: |
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v = np.nan |
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elif h == "MMLU": |
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if v != "-": |
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v = round(ast.literal_eval(v) * 100, 1) |
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else: |
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v = np.nan |
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elif h == "MT-bench (win rate %)": |
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if v != "-": |
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v = round(ast.literal_eval(v[:-1]), 1) |
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else: |
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v = np.nan |
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elif h == "MT-bench (score)": |
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if v != "-": |
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v = round(ast.literal_eval(v), 2) |
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else: |
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v = np.nan |
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item[h] = v |
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if add_hyperlink: |
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item["Model"] = model_hyperlink(item["Model"], item["Link"]) |
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rows.append(item) |
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return rows |
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def build_basic_stats_tab(): |
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empty = "Loading ..." |
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basic_component_values[:] = [empty, None, empty, empty, empty, empty] |
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md0 = gr.Markdown(empty) |
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gr.Markdown("#### Figure 1: Number of model calls and votes") |
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plot_1 = gr.Plot(show_label=False) |
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with gr.Row(): |
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with gr.Column(): |
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md1 = gr.Markdown(empty) |
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with gr.Column(): |
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md2 = gr.Markdown(empty) |
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with gr.Row(): |
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with gr.Column(): |
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md3 = gr.Markdown(empty) |
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with gr.Column(): |
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md4 = gr.Markdown(empty) |
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return [md0, plot_1, md1, md2, md3, md4] |
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def build_leaderboard_tab(elo_results_file, leaderboard_table_file): |
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if elo_results_file is None: |
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md = "Loading ..." |
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p1 = p2 = p3 = p4 = None |
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else: |
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with open(elo_results_file, "rb") as fin: |
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elo_results = pickle.load(fin) |
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md = make_leaderboard_md(elo_results) |
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p1 = elo_results["win_fraction_heatmap"] |
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p2 = elo_results["battle_count_heatmap"] |
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p3 = elo_results["bootstrap_elo_rating"] |
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p4 = elo_results["average_win_rate_bar"] |
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md_1 = gr.Markdown(md, elem_id="leaderboard_markdown") |
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if leaderboard_table_file: |
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data = load_leaderboard_table_csv(leaderboard_table_file) |
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headers = [ |
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"Model", |
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"Arena Elo rating", |
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"MT-bench (score)", |
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"MMLU", |
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"License", |
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] |
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values = [] |
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for item in data: |
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row = [] |
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for key in headers: |
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value = item[key] |
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row.append(value) |
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values.append(row) |
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values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9) |
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headers[1] = "⭐ " + headers[1] |
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headers[2] = "📈 " + headers[2] |
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gr.Dataframe( |
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headers=headers, |
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datatype=["markdown", "number", "number", "number", "str"], |
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value=values, |
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elem_id="leaderboard_dataframe", |
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) |
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gr.Markdown( |
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"If you want to see more models, please help us [add them](https://github.com/lm-sys/FastChat/blob/main/docs/arena.md#how-to-add-a-new-model)." |
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) |
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else: |
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pass |
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gr.Markdown( |
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f"""## More Statistics for Chatbot Arena\n |
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We added some additional figures to show more statistics. The code for generating them is also included in this [notebook]({notebook_url}). |
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Please note that you may see different orders from different ranking methods. This is expected for models that perform similarly, as demonstrated by the confidence interval in the bootstrap figure. Going forward, we prefer the classical Elo calculation because of its scalability and interpretability. You can find more discussions in this blog [post](https://lmsys.org/blog/2023-05-03-arena/). |
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""" |
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) |
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leader_component_values[:] = [md, p1, p2, p3, p4] |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown( |
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"#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles" |
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) |
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plot_1 = gr.Plot(p1, show_label=False) |
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with gr.Column(): |
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gr.Markdown( |
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"#### Figure 2: Battle Count for Each Combination of Models (without Ties)" |
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) |
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plot_2 = gr.Plot(p2, show_label=False) |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown( |
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"#### Figure 3: Bootstrap of Elo Estimates (1000 Rounds of Random Sampling)" |
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) |
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plot_3 = gr.Plot(p3, show_label=False) |
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with gr.Column(): |
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gr.Markdown( |
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"#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)" |
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) |
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plot_4 = gr.Plot(p4, show_label=False) |
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return [md_1, plot_1, plot_2, plot_3, plot_4] |
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block_css = """ |
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#notice_markdown { |
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font-size: 104% |
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} |
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#notice_markdown th { |
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display: none; |
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} |
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#notice_markdown td { |
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padding-top: 6px; |
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padding-bottom: 6px; |
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} |
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#leaderboard_markdown { |
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font-size: 104% |
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} |
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#leaderboard_markdown td { |
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padding-top: 6px; |
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padding-bottom: 6px; |
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} |
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#leaderboard_dataframe td { |
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line-height: 0.1em; |
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} |
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""" |
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def build_demo(elo_results_file, leaderboard_table_file): |
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text_size = gr.themes.sizes.text_lg |
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with gr.Blocks( |
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title="Chatbot Arena Leaderboard", |
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theme=gr.themes.Base(text_size=text_size), |
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css=block_css, |
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) as demo: |
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leader_components = build_leaderboard_tab( |
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elo_results_file, leaderboard_table_file |
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) |
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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("--share", action="store_true") |
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args = parser.parse_args() |
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demo = build_demo("elo_results_20230905.pkl", "leaderboard_table_20230905.csv") |
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demo.launch(share=args.share) |
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