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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 glob |
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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://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [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 90K+ 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 mostly computed by [InstructEval](https://github.com/declare-lab/instruct-eval). Higher values are better for all benchmarks. Empty cells mean not available. Last updated: October, 2023. |
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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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elem_id="leaderboard_markdown" |
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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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elem_id="leaderboard_markdown" |
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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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gr.Markdown(acknowledgment_md) |
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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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footer { |
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display:none !important |
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} |
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.image-container { |
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display: flex; |
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align-items: center; |
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padding: 1px; |
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} |
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.image-container img { |
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margin: 0 30px; |
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height: 20px; |
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max-height: 100%; |
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width: auto; |
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max-width: 20%; |
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} |
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""" |
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acknowledgment_md = """ |
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### Acknowledgment |
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<div class="image-container"> |
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<p> We thank <a href="https://www.kaggle.com/" target="_blank">Kaggle</a>, <a href="https://mbzuai.ac.ae/" target="_blank">MBZUAI</a>, <a href="https://www.anyscale.com/" target="_blank">AnyScale</a>, and <a href="https://huggingface.co/" target="_blank">HuggingFace</a> for their <a href="https://lmsys.org/donations/" target="_blank">sponsorship</a>. </p> |
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<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/7/7c/Kaggle_logo.png/400px-Kaggle_logo.png" alt="Image 1"> |
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<img src="https://mma.prnewswire.com/media/1227419/MBZUAI_Logo.jpg?p=facebookg" alt="Image 2"> |
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<img src="https://docs.anyscale.com/site-assets/logo.png" alt="Image 3"> |
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<img src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo-with-title.png" alt="Image 4"> |
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</div> |
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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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elo_result_files = glob.glob("elo_results_*.pkl") |
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elo_result_files.sort(key=lambda x: int(x[12:-4])) |
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elo_result_file = elo_result_files[-1] |
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leaderboard_table_files = glob.glob("leaderboard_table_*.csv") |
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leaderboard_table_files.sort(key=lambda x: int(x[18:-4])) |
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leaderboard_table_file = leaderboard_table_files[-1] |
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demo = build_demo(elo_result_file, leaderboard_table_file) |
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demo.launch(share=args.share) |
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