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app.py

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+import gradio as gr
+
+from components.about import create_about
+from components.detail import create_detail
+from components.submit import create_submit
+from components.top import create_top
+from components.data import create_data
+from components.result import create_result
+
+with gr.Blocks(title="Ango Leaderboard") as app:
+    top_components = create_top()
+    with gr.Tab("Result"):
+        create_result(top_components)
+    with gr.Tab("Details"):
+        create_detail(top_components)
+    with gr.Tab("Data"):
+        create_data(top_components)
+    with gr.Tab("Submit"):
+        create_submit()
+    with gr.Tab("About"):
+        create_about()
+
+app.queue()
+app.launch(server_name="127.0.0.1", server_port=8080, ssl_verify=False)

assets/color.py

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+color_dict = {
+    '言语理解与表达': '#B22222',
+    '数量关系': '#CC6600',
+    '判断推理': '#CC9900',
+    '资料分析': '#228B22',
+    '常识判断': '#0077BE',
+    '未分类': '#9400D3'
+}

assets/constant.py

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+DELIMITER = "|"

assets/content.py

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+TITLE = """<h1 align="center" id="space-title">🧊🌊ANGO Leaderboard</h1>"""
+INTRODUCTION_TEXT = """
+
+ANGO is <b>A</b> <b>N</b>ovel <b>G</b>eneration-<b>O</b>riented Chinese LLM evaluation benchmark.
+
+We introduces the format of single-question multiple-keypoints dataset for the first time, which include 171 keypoints accumulated in 4 hierarchical levels and 9 difficulty categories.
+
+
+The data were exclusively obtained from the Administrative Proficiency Test,
+ which serves as a significant component of the Chinese civil service examination.
+
+
+We will apply a seasonal system for the leaderboard, updating them every two months. 
+The corresponding test dataset will be announced at the beginning of each season, 
+and some questions will be eliminated at the end of the season.
+
+
+Read more details in "About" page!
+"""
+KEYPOINT_TEXT = """
+Because single question may contains more than one keypoint, so the total number of keypoint count is higher than question count
+"""
+TEST_SET_TEXT = """
+The test set comprises a total of 1768 records.
+
+Among these records, there are 988 distinct combinations of Keypoints, which indicates the provision of an additional few-shot examples amounting to 988 * 5.
+
+The test set encompasses all 171 Keypoint categories.
+
+For further information, please refer to the "About" page.
+"""
+TEST_SCRIPT_TEXT = """
+<br> 
+The evaluation script requires three mandatory arguments, while the others should remain unchanged.
+
+--model_path: specifies the location where the model parameters are saved.
+--dataset_path: indicates the directory where the ANGO test set data is stored.
+
+--save_path: denotes the path where the evaluation results will be saved.
+
+You can modify the specific functions to adapt them to your model.
+
+<br> 
+Upon completion of the evaluation, the script will generate three files:
+
+acc_result: This file contains the predicted results for each record, along with statistical data at the question level.
+
+category_result: This file provides statistical data at the Keypoint level.
+
+difficulty_result: This file includes statistical data categorized by difficulty level.
+"""
+SUBMIT_TEXT = """
+Need More Resource
+"""
+
+ABOUT_HTML = """
+<h1>What is ANGO</h1>
+<p>We introduce a novel Chinese LLM benchmark dataset called ANGO, aiming to provide more in-depth guidance for model training and evaluation. We introduce the format of a single-question multiple-keypoints dataset for the first time, which will provide the most complete description for each question, enabling the test results to comprehensively showcase the model's performance from multiple perspectives. Based on the single-question multiple-keypoints format, we design a more detailed and refined model capability classification system - the Keypoint Tree, which reflects the relationships between different keypoints. It includes a total of 171 specific model capabilities accumulated in 4 hierarchical levels. With the help of the KeyPoint Tree, the performance of models on multiple levels of capabilities can be quickly measured, and corresponding adjustments can be made. ANGO also involves two new question attributes: human accuracy and human error-prone options. Based on human accuracy, we propose a more detailed difficulty classification compared to previous benchmarks. By combining the human accuracy of the question itself, the human accuracy of the involved key points, and the actual score of the question, all questions are divided into 9 difficulty levels, providing a quantifiable reference for evaluating models of different difficulty.</p>
+
+<p>In addition to the innovative data, we propose a complete set of verification processes tailored for ANGO, which can provide fairer results compared to the current leaderboards. This includes conducting multiple experiments with option shuffling to mitigate the issue of data leakage, designing test set sampling strategies that fully utilize the characteristics of ANGO, and implementing elimination mechanisms for high-accuracy questions. Based on these, we establish a dynamic updating system for the test set, resembling a seasonal system. Thanks to these methods, ANGO can continually update the test results, ensuring the fairness and effectiveness of the leaderboard. By preserving the test results from multiple seasons, it can provide researchers with an overview of the current trends in optimizing models within the community.</p>
+
+<h1 id="space-title">Data Source</h1>
+<p>The data utilized in our study were exclusively obtained from the Administrative Proficiency Test, which serves as a significant component of the Chinese civil service examination.</p>
+<p>The Administrative Proficiency Test is entirely composed of multiple-choice questions and aims to evaluate the abilities and skills necessary for practical administrative work. This test covers a wide range of knowledge areas, including Expression& Comprehension , Data Analysis, Quantitative Relations, Judgement&Inference, and Common Knowledge. As a comprehensive assessment tool, it requires candidates to respond to a series of questions related to administrative work within a limited timeframe. These questions may involve policy formulation, problem-solving, personnel and resource management, as well as handling emergency situations. By formulating these questions, it facilitates the evaluation of candidates' analytical thinking, Judgement&Inference, problem-solving abilities, and language proficiency.</p>
+<p>The nature of the Administrative Proficiency Test necessitates candidates to tackle complex questions within a specified timeframe, making it an ideal testing environment for assessing the language capabilities of language models. Language models typically demonstrate excellent performance in generating and comprehending text, and this test provides concrete and intricate contexts that simulate real-world language communication and decision-making processes. By employing language models to answer these questions, we can evaluate their understanding of complex problems, Judgement&Inference abilities, as well as the accuracy and fluency of their language expressions.</p>
+<p>Furthermore, the Administrative Proficiency Test encompasses a broad coverage and diversity. It includes questions and scenarios from various administrative domains, such as government administration, social affairs, and economic development. This diversity aids in evaluating the language processing abilities of language models across different fields, thereby providing a more comprehensive understanding of their potential strengths and limitations in practical applications. Moreover, it offers valuable insights for future model improvements and applications.</p>
+<p>ANGO's data covers all 34 provinces in China and includes three different types of examinations conducted between 2008 and 2023, including formal and mock exams.</p>
+<h1 id="space-title">Data Processing</h1>
+<p>In order to enhance the quality of our data, we employed a simple yet efficient preprocessing approach.</p>
+<h4>Duplicate Removal</h4>
+<p>Given that mock exams often include previous exam questions, our data contained numerous duplicates. To address this issue, we employed a straightforward strategy of removing duplicates based on the record ID obtained from the data source. As a result of this step, the size of our data was reduced to 88,799 instances.</p>
+
+<h4>Image Removal</h4>
+<p>The data consisted of two types of images: formula pictures and other types (such as images containing graphics). However, since our primary focus was on Chinese Natural Language Processing (NLP) evaluation rather than the multi-modal domain, we opted to remove all records containing pure images. This resulted in the removal of 17,650 records.</p>
+
+<h4>Formula Replacement</h4>
+<p>As mentioned earlier, our data still contained formula pictures, and we recognized the importance of including formulae to ensure diversity in our data. To address this, we extracted 8,144 unique formula images from a pool of 34,062 LaTeX formulas derived from 5,574 questions. These images were then processed using a Formula OCR (Optical Character Recognition) model, followed by manual verification to ensure formula accuracy. Ultimately, we obtained a clean data consisting of 71,149 instances.</p>
+<h1 id="space-title">Data Format</h1>
+
+<ul>
+  <li><strong>Question:</strong> The content of the question.</li>
+  <li><strong>Material:</strong> Some questions require additional information from a given material.</li>
+  <li><strong>Type:</strong> The classification of the question, encompassing single-choice and multiple-choice formats.</li>
+  <li><strong>Options:</strong> The candidate answers, presented in a line-separated format.</li>
+  <li><strong>Choice:</strong> The correct answer to the question.</li>
+  <li><strong>Keypoints:</strong> All the keypoints involved in the question.</li>
+  <li><strong>Human Accuracy:</strong> The accuracy of humans on this question.</li>
+  <li><strong>Human Count:</strong> The number of times this question has been completed by humans.</li>
+  <li><strong>Most Wrong:</strong> The option that humans are most likely to choose incorrectly.</li>
+  <li><strong>Difficulty:</strong> The level of difficulty of the question, given by our standard.</li>
+  <li><strong>Solution:</strong> A concise explanation of the methodology to arrive at the correct answer.</li>
+  <li><strong>Source:</strong> The original index and examination source of the question.</li>
+  <li><strong>Formulas:</strong> The count of formulas present in the material, question, and options.</li>
+</ul>
+
+<p>Here is an example record:</p>
+
+<div style="border: 1px solid black; padding: 10px;">
+  <p>
+    <strong>Question:</strong> Forward: Backward<br>
+    <strong>Material:</strong> Please select the option that best resembles the relationship between the given words or phrases in the question stem.<br>
+    <strong>Type:</strong> Single Choice<br>
+    <strong>Options:</strong><br>
+    A. Urge: Advise<br>
+    B. Ocean: Land<br>
+    C. Vibration: Quiet<br>
+    D. Extend: Compress<br>
+    <strong>Choice:</strong> D<br>
+    <strong>Difficulty:</strong> 4<br>
+    <strong>KeyPoints:</strong> Semantic Relationship - Antonym<br>
+    <strong>Human Accuracy:</strong> 79.564999<br>
+    <strong>Human Count:</strong> 183494<br>
+    <strong>Most Wrong:</strong> C<br>
+    <strong>Solution:</strong> Step 1: Determine the logical relationship between the words in the question stem. The two words in the question stem are antonyms. Step 2: Determine the logical relationship between the options. The option that has the same logical relationship as the question stem is option D. Option A is a synonym relationship, option B is a parallel relationship, and in option C, the antonym of "quiet" should be "noisy" instead of "vibration". Therefore, the correct answer is D.<br>
+    <strong>Source:</strong> 2011 Jiangsu Province Civil Service Recruitment Examination 'Administrative Aptitude Test' (Category A), Question 41<br>
+    <strong>Formulas:</strong> 0
+  </p>
+</div>
+
+<h1 id="space-title">Evaluation(Not Implement Yet)</h1>
+<p>To mitigate the impact of data leakage during model pretraining on benchmark evaluations, we have employed multiple benchmark evaluation tricks to enhance fairness and real-time performance of the benchmarks.</p>
+
+<h4>Confusion of Options Order</h4>
+<p>Sometimes, a model's correct answer to a specific question may not be due to mastering a certain ability or understanding the question, but rather because it has recognized patterns of token order in the training data. By shuffling the order of options in multiple-choice questions and making multiple predictions with the correct answer placed in different options, we can average the results to reduce the model's reliance on character order.</p>
+
+<h4>Season For Dynamic Evaluation</h4>
+<p>Thanks to sampling strategies optimized for ANGO, we can periodically sample the test set and update the leaderboard. This prevents certain institutions or individuals from maliciously hacking ANGO to inflate the model's performance. However, due to the limited number of questions in some key areas, dynamic iteration may not be feasible for all questions.</p>
+
+<h4>Question Elimination Mechanism</h4>
+<p>In addition to the aforementioned dynamic updating of season, a new question elimination mechanism has been proposed. This mechanism calculates the average accuracy of each question across all models for each iteration. Questions with accuracies exceeding a threshold are temporarily removed by ANGO to ensure reliable discrimination among questions in ANGO.</p>
+"""

assets/difficulty_distribution.json

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assets/evaluation.py

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+import os
+import json
+import re
+import argparse
+import torch
+import gc
+from tqdm import tqdm
+
+from transformers import AutoModelForCausalLM, AutoModel, AutoTokenizer
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '1'
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Validation')
+    parser.add_argument('--model_path', dest="model_path")
+    parser.add_argument('--dataset_path', dest="dataset_path")
+    parser.add_argument('--save_path', dest='save_path')
+    parser.add_argument('--max_length', dest="max_length", default=2000)
+    parser.add_argument('--max_new_tokens', dest="max_new_tokens", default=48)
+    parser.add_argument('--input_template', dest="input_template",
+                        default="材料：{material}\n问题：{question}\n{options}\n答案：{response}")
+    parser.add_argument('--query_template', dest="query_template", default="问题：{question}\n{options}\n答案：{response}")
+    parser.add_argument('--system_prompt', dest="system_prompt",
+                        default="你是一名考生，请回答问题的正确选项，比如C。如果有多个正确选项，请按顺序回答所有正确选项，比如ABD。")
+    parser.add_argument('--level_delimiter', dest="level_delimiter", default="|")
+    args = parser.parse_args()
+    return args
+
+
+class Validator:
+    def __init__(self, args):
+        self.load_model(args.model_path)
+        self.load_dataset(args.dataset_path)
+        self.save_dir = os.path.join(args.save_path, os.path.split(model_path)[-1])
+        if not os.path.exists(self.save_dir):
+            os.makedirs(self.save_dir)
+
+        self.max_length = args.max_length
+        self.max_new_tokens = args.max_new_tokens
+        self.input_template = args.input_template
+        self.query_template = args.query_template
+        self.system_prompt = args.system_prompt
+        self.level_delimiter = args.level_delimiter
+
+    def load_model(self, model_path):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+        self.model = AutoModelForCausalLM.from_pretrained(model_path, device_map={"": 0}, trust_remote_code=True)
+        self.model.eval()
+
+    def load_dataset(self, dataset_path):
+        self.dataset = json.load(open(dataset_path, encoding="utf-8"))
+
+    def format_prompt(self, material, question, options, response):
+        if material:
+            return self.input_template.format(material=material, question=question, options=options,
+                                              response=response).strip()
+        return self.query_template.format(question=question, options=options, response=response).strip()
+
+    def build_prompt(self, item):
+        query_prompt = self.format_prompt(item['material'], item['question'], item['options'], "")
+        history_prompts = []
+        for sub in item['history']:
+            history_prompts.append(self.format_prompt(sub['material'], sub['question'], sub['options'], sub['choice']))
+
+            final_prompt = self.system_prompt + "\n" + "\n".join(history_prompts) + "\n" + query_prompt
+
+            if len(self.tokenizer.tokenize(final_prompt)) > self.max_length:
+                history_prompts.pop()
+                break
+        return self.system_prompt + "\n" + "\n".join(history_prompts) + "\n" + query_prompt
+
+    def get_predict(self, prompt):
+        gen_kwargs = {"do_sample": False, "max_new_tokens": self.max_new_tokens}
+        inputs = self.tokenizer([prompt], return_tensors="pt")
+        inputs = inputs.to(self.model.device)
+        with torch.no_grad():
+            outputs = self.model.generate(**inputs, return_dict_in_generate=True, **gen_kwargs)
+        predict = self.tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)[len(prompt):]
+        return predict
+
+    def extract_answer(self, results):
+        predict_result = {"acc": 0, "wrong_value": 0, "human_acc": 0, "hit": 0, "total": 0, "wrong_hit": 0,
+                          "wrong_total": 0, 'detail': []}
+        for result in results:
+            answer = result['item']['choice']
+            most_wrong = result['item']['most_wrong']
+            human_acc = result['item']['human_acc'] * 0.01
+
+            predict = ""
+            for e in re.split(r'[、,.，和\s]\s*', result['predict']):
+                if not e.isascii():
+                    break
+                predict += e
+            result['predict'] = predict
+            predict_result['detail'].append(
+                {"answer": answer, "most_wrong": most_wrong, "predict": predict, "human_acc": human_acc})
+            predict_result['hit'] += 1 if answer == predict else 0
+            predict_result['wrong_hit'] += 1 if most_wrong == predict else 0
+            predict_result['wrong_value'] += 1 - human_acc if most_wrong == predict else 0
+            predict_result['human_acc'] += human_acc
+            predict_result['total'] += 1
+        predict_result['acc'] = predict_result['hit'] / predict_result['total']
+        predict_result['wrong_total'] = predict_result['total'] - predict_result['hit']
+        predict_result['wrong_value'] = predict_result['wrong_value'] / predict_result['wrong_total']
+        predict_result['human_acc'] = predict_result['human_acc'] / len(results)
+
+        json.dump(predict_result, open(os.path.join(self.save_dir, "acc_result.json"), "w", encoding="utf-8"),
+                  ensure_ascii=False)
+
+    def category_summary(self, results):
+        category_result = {"总计": {"hit": 0, "all": 0, "difficulty": {}, "human_acc": 0}}
+        for result in results:
+            hit = 1 if result['item']['choice'] == result['predict'] else 0
+            categories_list = result['item']['categories']
+            difficulty = result['item']['difficulty']
+            human_acc = result['item']['human_acc']
+            for categories in categories_list:
+                if difficulty not in category_result["总计"]["difficulty"]:
+                    category_result["总计"]["difficulty"][difficulty] = {"hit": 0, "all": 0}
+                category_result["总计"]["difficulty"][difficulty]['hit'] += hit
+                category_result["总计"]["difficulty"][difficulty]['all'] += 1
+                category_result["总计"]['hit'] += hit
+                category_result["总计"]['all'] += 1
+                category_result["总计"]['human_acc'] += human_acc
+                category_subset = []
+                for category in categories:
+                    category_subset.append(category)
+                    category_name = self.level_delimiter.join(category_subset)
+                    if not category_name:
+                        category_name = "未分类"
+                    if category_name not in category_result:
+                        category_result[category_name] = {"hit": 0, "all": 0, "difficulty": {}, "human_acc": 0}
+                    if difficulty not in category_result[category_name]["difficulty"]:
+                        category_result[category_name]["difficulty"][difficulty] = {"hit": 0, "all": 0}
+                    category_result[category_name]["difficulty"][difficulty]['hit'] += hit
+                    category_result[category_name]["difficulty"][difficulty]['all'] += 1
+                    category_result[category_name]['hit'] += hit
+                    category_result[category_name]['all'] += 1
+                    category_result[category_name]['human_acc'] += human_acc
+
+        for k, v in category_result.items():
+            v['acc'] = v['hit'] / v['all']
+            v['human_acc'] = v['human_acc'] / v['all']
+            for d, sub_v in v['difficulty'].items():
+                sub_v['acc'] = sub_v['hit'] / sub_v['all']
+        json.dump(category_result, open(os.path.join(self.save_dir, "category_result.json"), "w", encoding="utf-8"),
+                  ensure_ascii=False)
+
+    def difficulty_summary(self, results):
+        difficulty_result = {"总计": {"hit": 0, "all": 0}}
+        for result in results:
+            hit = 1 if result['item']['choice'] == result['predict'] else 0
+            difficulty = result['item']['difficulty']
+            if difficulty not in difficulty_result:
+                difficulty_result[difficulty] = {"hit": 0, "all": 0}
+            difficulty_result[difficulty]['hit'] += hit
+            difficulty_result[difficulty]['all'] += 1
+            difficulty_result["总计"]['hit'] += hit
+            difficulty_result["总计"]['all'] += 1
+
+        for k in difficulty_result:
+            difficulty_result[k]['acc'] = difficulty_result[k]['hit'] / difficulty_result[k]['all']
+
+        json.dump(difficulty_result, open(os.path.join(self.save_dir, "difficulty_result.json"), "w", encoding="utf-8"),
+                  ensure_ascii=False)
+
+    def __call__(self):
+        results = []
+        for item in tqdm(self.dataset):
+            prompt = self.build_prompt(item)
+            predict = self.get_predict(prompt)
+            results.append({"item": item, "predict": predict})
+            gc.collect()
+
+        json.dump(results, open(os.path.join(self.save_dir, "raw.json"), "w", encoding="utf-8"), ensure_ascii=False)
+        self.extract_answer(results)
+        self.category_summary(results)
+        self.difficulty_summary(results)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    Validator(args)()

assets/keypoint_distribution.json

@@ -0,0 +1 @@
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assets/path.py

@@ -0,0 +1,4 @@
+SEASON = {
+    "latest": "202309",
+    "2023-09": "202309"
+}

components/about.py

@@ -0,0 +1,7 @@
+import gradio as gr
+
+from assets.content import ABOUT_HTML
+
+
+def create_about():
+    gr.HTML(ABOUT_HTML)

components/data.py

@@ -0,0 +1,80 @@
+import os
+
+import gradio as gr
+import pandas as pd
+import plotly
+import plotly.graph_objects as go
+
+from assets.color import color_dict
+from assets.path import SEASON
+
+
+def read_testset(season):
+    return pd.read_json(os.path.join("results", SEASON[season], "test_dataset.json"))
+
+
+def build_keypoint_plot(dataset):
+    keypoint_set = {}
+    for i, categories in enumerate(dataset['categories']):
+        for category in categories:
+            parent = ""
+            for keypoint in category:
+                if not keypoint:
+                    keypoint = "未分类"
+                if keypoint not in keypoint_set:
+                    keypoint_set[keypoint] = {"value": 0}
+                keypoint_set[keypoint]['value'] += 1
+                keypoint_set[keypoint]['parent'] = parent
+                keypoint_set[keypoint]['color'] = category[0] if category[0] else "未分类"
+                parent = keypoint
+
+    labels, parents, values, colors = [], [], [], []
+    for k, v in keypoint_set.items():
+        labels.append(k)
+        parents.append(v['parent'])
+        values.append(v['value'])
+        colors.append(color_dict[v['color']])
+
+    fig = go.Figure(go.Sunburst(
+        labels=labels,
+        parents=parents,
+        values=values,
+        branchvalues="total",
+        insidetextorientation='radial',
+        marker={"colors": colors}
+    ))
+    return fig
+
+
+def build_difficulty_plot(dataset):
+    xs = sorted(dataset['difficulty'].unique())
+    ys = [len(dataset[dataset['difficulty'] == x]) for x in xs]
+
+    fig = go.Figure([go.Bar(x=xs, y=ys, marker={"color": ys, "colorscale": "Viridis",
+                                                "colorbar": {"title": "Total"}})])
+    fig.update_layout(yaxis=dict(type='log'))
+    return fig
+
+
+def build_plot(season):
+    dataset = pd.read_json(os.path.join("results", SEASON[season], "test_dataset.json"))
+    return build_keypoint_plot(dataset), build_difficulty_plot(dataset)
+
+
+def create_data(top_components):
+    with gr.Tab("All data"):
+        with gr.Row():
+            all_keypoint_plot = gr.Plot(
+                plotly.io.from_json(open("assets/keypoint_distribution.json", encoding="utf-8").read()),
+                label="Keypoint Distribution")
+            all_difficulty_plot = gr.Plot(
+                plotly.io.from_json(open("assets/difficulty_distribution.json", encoding="utf-8").read()),
+                label="Difficulty Distribution")
+    with gr.Tab("Test Data"):
+        with gr.Row():
+            k_fig, d_fig = build_plot("latest")
+            test_keypoint_plot = gr.Plot(k_fig, label="Keypoint Distribution")
+            test_difficulty_plot = gr.Plot(d_fig, label="Difficulty Distribution")
+
+    return {"all_keypoint": all_keypoint_plot, "all_difficulty": all_difficulty_plot,
+            "test_keypoint": test_keypoint_plot, "test_difficulty": test_difficulty_plot}

components/detail.py

@@ -0,0 +1,85 @@
+import json
+import os
+
+import gradio as gr
+import plotly.graph_objects as go
+
+from assets.constant import DELIMITER
+from assets.path import SEASON
+
+DEEPEST = 4
+
+
+def build_plot(category_result, columns):
+    k_x, k_y, k_text, k_color = [], [], [], []
+    d_xy = {}
+    for c in columns:
+        k_x.append(c.split(DELIMITER)[-1])
+        result = category_result.get(c)
+        k_y.append(round(result.get("acc"), 4))
+        sub_count = sum([1 for k in category_result if k.startswith(c)]) - 1
+        k_text.append(
+            f'hit:{result.get("hit")} sub_count:{sub_count}')
+        for d, v in result['difficulty'].items():
+            if d not in d_xy:
+                d_xy[d] = {"hit": 0, "all": 0}
+            d_xy[d]['hit'] += v['hit']
+            d_xy[d]['all'] += v['all']
+        k_color.append(result.get("all"))
+    d_x = sorted(d_xy, reverse=True)
+    d_y, d_text, d_color = [], [], []
+    for d in d_x:
+        v = d_xy[d]
+        d_y.append(v['hit'] / v['all'])
+        d_text.append(f'hit/total:{v["hit"]}/{v["all"]}')
+        d_color.append(v['all'])
+
+    k_fig = go.Figure([go.Bar(x=k_x, y=k_y, hovertext=k_text, marker={"color": k_color, "colorscale": "Viridis",
+                                                                      "colorbar": {"title": "Total"}})])
+    k_fig.update_layout(yaxis=dict(range=[0, 1]))
+    d_fig = go.Figure([go.Bar(x=d_x, y=d_y, hovertext=d_text,
+                              marker={"color": d_color, "colorscale": "Cividis", "colorbar": {"title": "Total"}})])
+    d_fig.update_layout(yaxis=dict(range=[0, 1]))
+    return k_fig, d_fig, k_x
+
+
+def create_detail(top_components):
+    models = os.listdir(os.path.join("results", SEASON["latest"], "details"))
+    model_dropdown = gr.Dropdown(choices=models, label="Select Model")
+
+    category_result = gr.State()
+    with gr.Row():
+        keypoint_dropdowns = [gr.Dropdown([], visible=False, label=f"Level{i + 1}") for i in range(DEEPEST)]
+    keypoint_plot = gr.Plot(label="Keypoint Acc")
+    difficulty_plot = gr.Plot(label="Difficulty Acc")
+
+    for i in range(DEEPEST):
+        keypoint_dropdown = keypoint_dropdowns[i]
+
+        def keypoint_dropdown_func(x, *args):
+            keypoints = DELIMITER.join(args)
+            columns = [k for k in x if k.startswith(keypoints) and k.count(DELIMITER) == len(args)]
+            sub = True
+            if not columns:
+                columns = [keypoints]
+                sub = False
+            k_fig, d_fig, choices = build_plot(x, columns)
+            updates = list(args) + [gr.update(choices=choices, visible=sub)] + [
+                gr.update(choices=[], visible=False)] * (DEEPEST - len(args) - 1)
+            return gr.update(value=k_fig), gr.update(value=d_fig), *updates
+
+        keypoint_dropdown.input(keypoint_dropdown_func, [category_result, *keypoint_dropdowns[:i + 1]],
+                                [keypoint_plot, difficulty_plot, *keypoint_dropdowns])
+
+    def model_dropdown_func(x):
+        dir = os.path.join("results", SEASON["latest"], "details", x)
+        new_category_result = json.load(open(os.path.join(dir, "category_result-all.json"), encoding="utf-8"))
+        columns = sorted([k for k in new_category_result if k.count(DELIMITER) == 0],
+                         key=lambda c: new_category_result[c]['all'], reverse=True)
+        k_fig, d_fig, choices = build_plot(new_category_result, columns)
+        return new_category_result, gr.update(value=k_fig), gr.update(value=d_fig), gr.update(choices=choices,
+                                                                                              visible=True), *[
+            gr.update(value=None, visible=False) for _ in range(DEEPEST - 1)]
+
+    model_dropdown.change(model_dropdown_func, model_dropdown, [category_result, keypoint_plot, difficulty_plot,
+                                                                *keypoint_dropdowns])

components/result.py

@@ -0,0 +1,57 @@
+import json
+import os
+
+import gradio as gr
+import pandas as pd
+
+from assets.constant import DELIMITER
+from assets.content import KEYPOINT_TEXT
+from assets.path import SEASON
+
+
+def build_question(season):
+    dir = os.path.join("results", SEASON[season], "details")
+    rows = []
+    for model in os.listdir(dir):
+        acc_result = json.load(open(os.path.join(dir, model, "acc_result.json"), encoding="utf-8"))
+        rows.append(
+            [model, round(acc_result['acc'], 4), round(acc_result['human_acc'], 4), round(acc_result['wrong_value'], 4),
+             acc_result['hit'],acc_result['wrong_hit'], acc_result['wrong_total'], acc_result['total']])
+    return pd.DataFrame(rows, columns=["Model", "Acc", "Human Acc", "Wrong Value", "Hit", "Wrong Hit", "Wrong Total",
+                                       "Total"]).sort_values("Acc", ascending=False)
+
+
+def build_keypoint(season):
+    dir = os.path.join("results", SEASON[season], "details")
+    rows, columns, final_columns = [], [], []
+    for model in os.listdir(dir):
+        category_result = json.load(open(os.path.join(dir, model, "category_result-all.json"), encoding="utf-8"))
+        if not columns:
+            columns = sorted([k for k in category_result if not k.count(DELIMITER)],
+                             key=lambda x: category_result[x]['all'], reverse=True)
+            final_columns = [f"{c}:{category_result.get(c).get('all')}" for c in columns]
+        rows.append([model] + [round(category_result.get(c).get("acc"), 4) for c in columns])
+    return pd.DataFrame(rows, columns=["Model"] + final_columns).sort_values(final_columns[0], ascending=False)
+
+
+def build_difficulty(season):
+    dir = os.path.join("results", SEASON[season], "details")
+    rows, columns, final_columns = [], [], []
+    for model in os.listdir(dir):
+        difficulty_result = json.load(open(os.path.join(dir, model, "difficulty_result.json"), encoding="utf-8"))
+        if not columns:
+            columns = sorted(difficulty_result, reverse=True)
+            final_columns = [f"{c}:{difficulty_result.get(c).get('all')}" for c in columns]
+        rows.append([model] + [round(difficulty_result.get(c).get("acc"), 4) for c in columns])
+
+    return pd.DataFrame(rows, columns=["Model"] + final_columns).sort_values(final_columns[0], ascending=False)
+
+
+def create_result(top_components):
+    with gr.Tab("Question Level"):
+        question_df = gr.DataFrame(build_question("latest"), label="Acc Result")
+    with gr.Tab("Keypoint Level"):
+        gr.Markdown(KEYPOINT_TEXT)
+        keypoint_df = gr.DataFrame(build_keypoint("latest"), label="Keypoint Level1 Result")
+    with gr.Tab("Difficulty Level"):
+        difficulty_df = gr.DataFrame(build_difficulty("latest"), label="Difficulty Result")

components/submit.py

@@ -0,0 +1,15 @@
+import os
+
+import gradio as gr
+
+from assets.content import SUBMIT_TEXT, TEST_SCRIPT_TEXT, TEST_SET_TEXT
+from assets.path import SEASON
+
+
+def create_submit():
+    test_box = gr.Markdown(value=TEST_SET_TEXT, scale=4)
+    test_file = gr.File(value=os.path.join("results", SEASON["latest"], "test_dataset.json"),
+                        label="Test Set", scale=1)
+    script_box = gr.Markdown(value=TEST_SCRIPT_TEXT, scale=4)
+    script_button = gr.File(value=os.path.join("assets/evaluation.py"), label="Test Set", scale=1)
+    gr.Markdown(SUBMIT_TEXT)

components/top.py

@@ -0,0 +1,13 @@
+import gradio as gr
+
+from assets.content import TITLE, INTRODUCTION_TEXT
+from assets.path import SEASON
+
+
+def create_top():
+    gr.HTML(TITLE)
+    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+    with gr.Row():
+        season_dropdown = gr.Dropdown(choices=list(SEASON), value="latest", label="Season Select")
+        language_dropdown = gr.Dropdown(choices=['en', 'zh'], value='en', label='Language Select')
+    return {"season": season_dropdown, "language": language_dropdown}

requirements.txt

@@ -0,0 +1,2 @@
+plotly
+pandas

results/202309/details/Atom-7B/category_result-all.json

@@ -0,0 +1 @@
+{"总计": {"hit": 1329, "all": 3862, "difficulty": {"3": {"hit": 227, "all": 546, "acc": 0.4157509157509158}, "4": {"hit": 439, "all": 1078, "acc": 0.4072356215213358}, "5": {"hit": 351, "all": 1102, "acc": 0.31851179673321234}, "6": {"hit": 195, "all": 647, "acc": 0.30139103554868624}, "7": {"hit": 115, "all": 485, "acc": 0.23711340206185566}, "2": {"hit": 2, "all": 4, "acc": 0.5}}, "human_acc": 62.54492157609842, "acc": 0.3441222164681512}, "判断推理": {"hit": 115, "all": 350, "difficulty": {"3": {"hit": 20, "all": 41, "acc": 0.4878048780487805}, "4": {"hit": 31, "all": 77, "acc": 0.4025974025974026}, "5": {"hit": 52, "all": 155, "acc": 0.33548387096774196}, "6": {"hit": 10, "all": 55, "acc": 0.18181818181818182}, "7": {"hit": 2, "all": 22, "acc": 0.09090909090909091}}, "human_acc": 67.0821947146088, "acc": 0.32857142857142857}, "判断推理|类比推理": {"hit": 29, "all": 96, "difficulty": {"3": {"hit": 9, "all": 12, "acc": 0.75}, "4": {"hit": 5, "all": 20, "acc": 0.25}, "5": {"hit": 10, "all": 39, "acc": 0.2564102564102564}, "6": {"hit": 4, "all": 22, "acc": 0.18181818181818182}, "7": {"hit": 1, "all": 3, "acc": 0.3333333333333333}}, "human_acc": 66.82436428905626, "acc": 0.3020833333333333}, "判断推理|类比推理|逻辑关系": {"hit": 18, "all": 52, "difficulty": {"3": {"hit": 7, "all": 10, "acc": 0.7}, "4": {"hit": 3, "all": 11, "acc": 0.2727272727272727}, "5": {"hit": 4, "all": 15, "acc": 0.26666666666666666}, "6": {"hit": 3, "all": 13, "acc": 0.23076923076923078}, "7": {"hit": 1, "all": 3, "acc": 0.3333333333333333}}, "human_acc": 66.80522117180384, "acc": 0.34615384615384615}, "判断推理|类比推理|逻辑关系|逻辑关系-对应关系": {"hit": 4, "all": 10, "difficulty": {"3": {"hit": 1, "all": 1, "acc": 1.0}, "4": {"hit": 1, "all": 5, "acc": 0.2}, "6": {"hit": 1, "all": 3, "acc": 0.3333333333333333}, "5": {"hit": 1, "all": 1, "acc": 1.0}}, "human_acc": 67.17893055848, "acc": 0.4}, "判断推理|类比推理|逻辑关系|逻辑关系-并列关系": {"hit": 5, "all": 11, "difficulty": {"4": {"hit": 1, "all": 3, "acc": 0.3333333333333333}, "6": {"hit": 1, "all": 3, "acc": 0.3333333333333333}, "5": {"hit": 2, "all": 4, "acc": 0.5}, "3": {"hit": 1, "all": 1, "acc": 1.0}}, "human_acc": 70.78792280516363, "acc": 0.45454545454545453}, "判断推理|类比推理|语义关系": {"hit": 7, "all": 30, "difficulty": {"5": {"hit": 5, "all": 18, "acc": 0.2777777777777778}, "4": {"hit": 2, "all": 7, "acc": 0.2857142857142857}, "6": {"hit": 0, "all": 5, "acc": 0.0}}, "human_acc": 67.00170566224, "acc": 0.23333333333333334}, "判断推理|类比推理|语义关系|语义关系-近义关系": {"hit": 3, "all": 10, "difficulty": {"5": {"hit": 3, "all": 6, "acc": 0.5}, "4": {"hit": 0, "all": 3, "acc": 0.0}, "6": {"hit": 0, "all": 1, "acc": 0.0}}, "human_acc": 69.32486766705999, "acc": 0.3}, "判断推理|类比推理|语义关系|语义关系-反义关系": {"hit": 3, "all": 10, "difficulty": {"5": {"hit": 2, "all": 8, "acc": 0.25}, "4": {"hit": 1, "all": 1, "acc": 1.0}, "6": {"hit": 0, "all": 1, "acc": 0.0}}, "human_acc": 74.48564823635999, "acc": 0.3}, "判断推理|类比推理|拆分思维": {"hit": 4, "all": 14, "difficulty": {"4": {"hit": 0, "all": 2, "acc": 0.0}, "6": {"hit": 1, "all": 4, "acc": 0.25}, "5": {"hit": 1, "all": 6, "acc": 0.16666666666666666}, "3": {"hit": 2, "all": 2, "acc": 1.0}}, "human_acc": 66.51545006774286, "acc": 0.2857142857142857}, "判断推理|类比推理|语义关系|语义-其他": {"hit": 1, "all": 10, "difficulty": {"5": {"hit": 0, "all": 4, "acc": 0.0}, "6": {"hit": 0, "all": 3, "acc": 0.0}, "4": {"hit": 1, "all": 3, "acc": 0.3333333333333333}}, "human_acc": 57.19460108329999, "acc": 0.1}, "未分类": {"hit": 3, "all": 10, "difficulty": {"6": {"hit": 0, "all": 1, "acc": 0.0}, "3": {"hit": 0, "all": 1, "acc": 0.0}, "4": {"hit": 2, "all": 4, "acc": 0.5}, "5": {"hit": 1, "all": 2, "acc": 0.5}, "7": {"hit": 0, "all": 2, "acc": 0.0}}, "human_acc": 56.33420313286, "acc": 0.3}, "判断推理|类比推理|逻辑关系|逻辑关系-包容关系": {"hit": 3, "all": 11, "difficulty": {"5": {"hit": 1, "all": 4, "acc": 0.25}, "6": {"hit": 0, "all": 3, "acc": 0.0}, "3": {"hit": 2, "all": 3, "acc": 0.6666666666666666}, "7": {"hit": 0, "all": 1, "acc": 0.0}}, "human_acc": 69.688079058, "acc": 0.2727272727272727}, "常识判断": {"hit": 69, "all": 226, "difficulty": {"5": {"hit": 29, "all": 87, "acc": 0.3333333333333333}, "7": {"hit": 10, "all": 48, "acc": 0.20833333333333334}, "6": {"hit": 16, "all": 50, "acc": 0.32}, "4": {"hit": 14, "all": 38, "acc": 0.3684210526315789}, "3": {"hit": 0, "all": 3, "acc": 0.0}}, "human_acc": 46.34274676514825, "acc": 0.3053097345132743}, "常识判断|人文常识": {"hit": 14, "all": 42, "difficulty": {"5": {"hit": 8, "all": 20, "acc": 0.4}, "7": {"hit": 1, "all": 2, "acc": 0.5}, "6": {"hit": 2, "all": 12, "acc": 0.16666666666666666}, "4": {"hit": 3, "all": 8, "acc": 0.375}}, "human_acc": 53.66391099976427, "acc": 0.3333333333333333}, "常识判断|人文常识|文化常识": {"hit": 2, "all": 10, "difficulty": {"5": {"hit": 1, "all": 5, "acc": 0.2}, "6": {"hit": 1, "all": 4, "acc": 0.25}, "4": {"hit": 0, "all": 1, "acc": 0.0}}, "human_acc": 50.96110414555001, "acc": 0.2}, "常识判断|人文常识|文学常识": {"hit": 5, "all": 10, "difficulty": {"7": {"hit": 1, "all": 1, "acc": 1.0}, "5": {"hit": 3, "all": 4, "acc": 0.75}, "4": {"hit": 0, "all": 2, "acc": 0.0}, "6": {"hit": 1, "all": 3, "acc": 0.3333333333333333}}, "human_acc": 58.752282007969995, "acc": 0.5}, "常识判断|人文常识|中国历史": {"hit": 2, "all": 10, "difficulty": {"6": {"hit": 0, "all": 2, "acc": 0.0}, "4": {"hit": 1, "all": 2, "acc": 0.5}, "5": {"hit": 1, "all": 5, "acc": 0.2}, "7": {"hit": 0, "all": 1, "acc": 0.0}}, "human_acc": 48.81674459668, "acc": 0.2}, "常识判断|科技常识": {"hit": 15, "all": 50, "difficulty": {"7": {"hit": 3, "all": 10, "acc": 0.3}, "5": {"hit": 4, "all": 17, "acc": 0.23529411764705882}, "6": {"hit": 5, "all": 14, "acc": 0.35714285714285715}, "4": {"hit": 3, "all": 9, "acc": 0.3333333333333333}}, "human_acc": 51.632498263487996, "acc": 0.3}, "常识判断|科技常识|科技理论与成就": {"hit": 2, "all": 10, "difficulty": {"7": {"hit": 0, "all": 3, "acc": 0.0}, "5": {"hit": 2, "all": 4, "acc": 0.5}, "4": {"hit": 0, "all": 2, "acc": 0.0}, "6": {"hit": 0, "all": 1, "acc": 0.0}}, "human_acc": 51.63379060368, "acc": 0.2}, "常识判断|地理国情": {"hit": 3, "all": 21, "difficulty": {"5": {"hit": 1, "all": 4, "acc": 0.25}, "7": {"hit": 0, "all": 9, "acc": 0.0}, "4": {"hit": 2, "all": 6, "acc": 0.3333333333333333}, "6": {"hit": 0, "all": 2, "acc": 0.0}}, "human_acc": 43.98006307271429, "acc": 0.14285714285714285}, "常识判断|地理国情|自然常识": {"hit": 0, "all": 10, "difficulty": {"5": {"hit": 0, "all": 1, "acc": 0.0}, "7": {"hit": 0, "all": 6, "acc": 0.0}, "4": {"hit": 0, "all": 1, "acc": 0.0}, "6": {"hit": 0, "all": 2, "acc": 0.0}}, "human_acc": 34.48786343879, "acc": 0.0}, "常识判断|科技常识|化学常识": {"hit": 2, "all": 10, "difficulty": {"5": {"hit": 0, "all": 3, "acc": 0.0}, "4": {"hit": 0, "all": 1, "acc": 0.0}, "7": {"hit": 1, "all": 2, "acc": 0.5}, "6": {"hit": 1, "all": 4, "acc": 0.25}}, "human_acc": 49.661417476859995, "acc": 0.2}, "常识判断|科技常识|生物常识": {"hit": 6, "all": 10, "difficulty": {"5": {"hit": 1, "all": 3, "acc": 0.3333333333333333}, "6": {"hit": 1, "all": 2, "acc": 0.5}, "7": {"hit": 2, "all": 3, "acc": 0.6666666666666666}, "4": {"hit": 2, "all": 2, "acc": 1.0}}, 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results/202309/details/Atom-7B/difficulty_result.json

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results/202309/details/Baichuan-13B/category_result-all.json

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results/202309/details/Baichuan-13B/difficulty_result.json

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results/202309/details/Baichuan-7B/category_result-all.json

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results/202309/details/Baichuan-7B/difficulty_result.json

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results/202309/details/Baichuan2-13B/category_result-all.json

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results/202309/details/Baichuan2-13B/difficulty_result.json

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results/202309/details/Baichuan2-7B/category_result-all.json

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results/202309/details/Baichuan2-7B/difficulty_result.json

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results/202309/details/ChatGLM-6B/category_result-all.json

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results/202309/details/ChatGLM-6B/difficulty_result.json

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results/202309/details/ChatGLM2-6B/category_result-all.json

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results/202309/details/ChatGLM2-6B/difficulty_result.json

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results/202309/details/ChineseLlama-13B/category_result-all.json

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results/202309/details/ChineseLlama-13B/difficulty_result.json

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+{"总计": {"hit": 490, "all": 1768, "acc": 0.27714932126696834}, "3": {"hit": 64, "all": 193, "acc": 0.3316062176165803}, "4": {"hit": 151, "all": 470, "acc": 0.32127659574468087}, "5": {"hit": 144, "all": 561, "acc": 0.25668449197860965}, "6": {"hit": 84, "all": 306, "acc": 0.27450980392156865}, "7": {"hit": 46, "all": 236, "acc": 0.19491525423728814}, "2": {"hit": 1, "all": 2, "acc": 0.5}}